Journal of Statistical Research (JSR)

Journal of Statistical Research (JSR) is the official journal of the Institute of Statistical Research and Training since 1970. Since its inception, it has been an excellent means of transfer and communication of statistical knowledge across the globe. It publishes original research articles relating to the methodology and practice of statistics. All papers submitted to JSR go through a rigorous review process. The Journal is published twice a year, one in June and the other in December.

Call for Papers:  Special Issue on Longitudinal Data Analysis and Related Topics

JSR invites manuscripts for a s pecial issue in the broad areas of longitudinal data analysis and related topics. The manuscript submission deadline is December 31, 2023, and the anticipated publication date for this issue is June 2024 (Volume 58, Number 1). For more details, see the FLYER . 

JSR Vol 57 Number 1-2 (2023)

The list of articles can be found here .

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts

Statistics articles within Scientific Reports

Article 27 May 2024 | Open Access

Advanced stability analysis of a fractional delay differential system with stochastic phenomena using spectral collocation method

  • , Sami Ullah Khan
  •  &  Salman A. AlQahtani

Article 23 May 2024 | Open Access

Analysis of player speed and angle toward the ball in soccer

  • Álvaro Novillo
  • , Antonio Cordón-Carmona
  •  &  Javier M. Buldú

Article 15 May 2024 | Open Access

Modified correlated measurement errors model for estimation of population mean utilizing auxiliary information

  • Housila P. Singh
  •  &  Neha Garg

Article 14 May 2024 | Open Access

Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment

  • Doaa A. Abdel Hady
  • , Omar M. Mabrouk
  •  &  Tarek Abd El-Hafeez

Article 09 May 2024 | Open Access

Towards optimal model evaluation: enhancing active testing with actively improved estimators

  • JooChul Lee
  • , Likhitha Kolla
  •  &  Jinbo Chen

Article 08 May 2024 | Open Access

Research on proactive defense and dynamic repair of complex networks considering cascading effects

  • Zhuoying Shi
  • , Ying Wang
  •  &  Chaoqi Fu

Article 07 May 2024 | Open Access

A Markov network approach for reproducing purchase behaviours observed in convenience stores

  • Dan Johansson
  • , Hideki Takayasu
  •  &  Misako Takayasu

Article 06 May 2024 | Open Access

A Bayesian spatio-temporal model of COVID-19 spread in England

  • Xueqing Yin
  • , John M. Aiken
  •  &  Jonathan L. Bamber

Max-mixed EWMA control chart for joint monitoring of mean and variance: an application to yogurt packing process

  • Seher Malik
  • , Muhammad Hanif
  •  &  Jumanah Ahmed Darwish

Causal impact evaluation of occupational safety policies on firms’ default using machine learning uplift modelling

  • Berardino Barile
  • , Marco Forti
  •  &  Angelo Castaldo

Article 04 May 2024 | Open Access

Estimating neutrosophic finite median employing robust measures of the auxiliary variable

  • Saadia Masood
  • , Bareera Ibrar
  •  &  Zabihullah Movaheedi

Article 01 May 2024 | Open Access

Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space-time Markov switching model

  • Laís Picinini Freitas
  • , Dirk Douwes-Schultz
  •  &  Kate Zinszer

Article 29 April 2024 | Open Access

Exploring drivers of overnight stays and same-day visits in the tourism sector

  • Francesco Scotti
  • , Andrea Flori
  •  &  Giovanni Azzone

A support vector machine based drought index for regional drought analysis

  • Mohammed A Alshahrani
  • , Muhammad Laiq
  •  &  Muhammad Nabi

Article 25 April 2024 | Open Access

Joint Bayesian estimation of cell dependence and gene associations in spatially resolved transcriptomic data

  • Arhit Chakrabarti
  •  &  Bani K. Mallick

Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model

  • Benjamin Glemain
  • , Xavier de Lamballerie
  •  &  Fabrice Carrat

Article 24 April 2024 | Open Access

Applications of nature-inspired metaheuristic algorithms for tackling optimization problems across disciplines

  • Elvis Han Cui
  • , Zizhao Zhang
  •  &  Weng Kee Wong

Article 23 April 2024 | Open Access

Variable parameters memory-type control charts for simultaneous monitoring of the mean and variability of multivariate multiple linear regression profiles

  • Hamed Sabahno
  •  &  Marie Eriksson

Article 22 April 2024 | Open Access

Modeling health and well-being measures using ZIP code spatial neighborhood patterns

  • , Michael LaValley
  •  &  Shariq Mohammed

Article 20 April 2024 | Open Access

Sequence based model using deep neural network and hybrid features for identification of 5-hydroxymethylcytosine modification

  • Salman Khan
  • , Islam Uddin
  •  &  Dost Muhammad Khan

Article 19 April 2024 | Open Access

Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence

  • Thomas Louis
  • , François Lucia
  •  &  Roland Hustinx

Explainable prediction of node labels in multilayer networks: a case study of turnover prediction in organizations

  • László Gadár
  •  &  János Abonyi

Article 18 April 2024 | Open Access

The quasi-xgamma frailty model with survival analysis under heterogeneity problem, validation testing, and risk analysis for emergency care data

  • Hamami Loubna
  • , Hafida Goual
  •  &  Haitham M. Yousof

Memory type Bayesian adaptive max-EWMA control chart for weibull processes

  • Abdullah A. Zaagan
  • , Imad Khan
  •  &  Bakhtiyar Ahmad

Article 17 April 2024 | Open Access

Improved data quality and statistical power of trial-level event-related potentials with Bayesian random-shift Gaussian processes

  • Dustin Pluta
  • , Beniamino Hadj-Amar
  •  &  Marina Vannucci

Article 16 April 2024 | Open Access

Comparison and evaluation of overcoring and hydraulic fracturing stress measurements

  • , Meifeng Cai
  •  &  Mostafa Gorjian

Predictors of divorce and duration of marriage among first marriage women in Dejne administrative town

  • Nigusie Gashaye Shita
  •  &  Liknaw Bewket Zeleke

Article 12 April 2024 | Open Access

Determinants of multimodal fake review generation in China’s E-commerce platforms

  • Chunnian Liu
  •  &  Lan Yi

Article 11 April 2024 | Open Access

New ridge parameter estimators for the quasi-Poisson ridge regression model

  • Aamir Shahzad
  • , Muhammad Amin
  •  &  Muhammad Faisal

A bicoherence approach to analyze multi-dimensional cross-frequency coupling in EEG/MEG data

  • Alessio Basti
  • , Guido Nolte
  •  &  Laura Marzetti

Article 10 April 2024 | Open Access

Response times are affected by mispredictions in a stochastic game

  • Paulo Roberto Cabral-Passos
  • , Antonio Galves
  •  &  Claudia D. Vargas

The effect of city reputation on Chinese corporate risk-taking

  •  &  Haifeng Jiang

Article 06 April 2024 | Open Access

Improvement in variance estimation using transformed auxiliary variable under simple random sampling

  • , Syed Muhammad Asim
  •  &  Soofia Iftikhar

Article 28 March 2024 | Open Access

Fatty liver classification via risk controlled neural networks trained on grouped ultrasound image data

  • Tso-Jung Yen
  • , Chih-Ting Yang
  •  &  Hsin-Chou Yang

Article 27 March 2024 | Open Access

A new unit distribution: properties, estimation, and regression analysis

  • Kadir Karakaya
  • , C. S. Rajitha
  •  &  Ahmed M. Gemeay

Article 26 March 2024 | Open Access

GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides

  • Jaskaran Singh
  • , Narendra N. Khanna
  •  &  Jasjit S. Suri

On topological indices and entropy measures of beryllonitrene network via logarithmic regression model

  • , Muhammad Kamran Siddiqui
  •  &  Fikre Bogale Petros

Article 22 March 2024 | Open Access

Measuring the similarity of charts in graphical statistics

  • Krzysztof Górnisiewicz
  • , Zbigniew Palka
  •  &  Waldemar Ratajczak

Article 21 March 2024 | Open Access

Risk prediction and interaction analysis using polygenic risk score of type 2 diabetes in a Korean population

  • Minsun Song
  • , Soo Heon Kwak
  •  &  Jihyun Kim

A longitudinal causal graph analysis investigating modifiable risk factors and obesity in a European cohort of children and adolescents

  • Ronja Foraita
  • , Janine Witte
  •  &  Vanessa Didelez

Article 19 March 2024 | Open Access

A novel group decision making method based on CoCoSo and interval-valued Q-rung orthopair fuzzy sets

  • , Hongwu Qin
  •  &  Xiuqin Ma

Impact of using virtual avatars in educational videos on user experience

  • Ruyuan Zhang
  •  &  Qun Wu

A generalisation of the method of regression calibration and comparison with Bayesian and frequentist model averaging methods

  • Mark P. Little
  • , Nobuyuki Hamada
  •  &  Lydia B. Zablotska

Article 18 March 2024 | Open Access

Monitoring gamma type-I censored data using an exponentially weighted moving average control chart based on deep learning networks

  • Pei-Hsi Lee
  •  &  Shih-Lung Liao

Article 15 March 2024 | Open Access

Statistical detection of selfish mining in proof-of-work blockchain systems

  • Sheng-Nan Li
  • , Carlo Campajola
  •  &  Claudio J. Tessone

Article 13 March 2024 | Open Access

Evaluation metrics and statistical tests for machine learning

  • Oona Rainio
  • , Jarmo Teuho
  •  &  Riku Klén

PARSEG: a computationally efficient approach for statistical validation of botanical seeds’ images

  • Luca Frigau
  • , Claudio Conversano
  •  &  Jaromír Antoch

Application of analysis of variance to determine important features of signals for diagnostic classifiers of displacement pumps

  • Jarosław Konieczny
  • , Waldemar Łatas
  •  &  Jerzy Stojek

Article 12 March 2024 | Open Access

Prediction and detection of side effects severity following COVID-19 and influenza vaccinations: utilizing smartwatches and smartphones

  • , Margaret L. Brandeau
  •  &  Dan Yamin

Article 08 March 2024 | Open Access

Evaluating the lifetime performance index of omega distribution based on progressive type-II censored samples

  • N. M. Kilany
  •  &  Lobna H. El-Refai

Advertisement

Browse broader subjects

  • Mathematics and computing

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

statistical research journal

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Statistical methods used in the public health literature and implications for training of public health professionals

* E-mail: [email protected]

Affiliation School of Public Health, Georgia State University, Atlanta, GA, United States of America

Affiliation Department of Sociology, Georgia State University, Atlanta, GA, United States of America

Affiliations School of Public Health, Georgia State University, Atlanta, GA, United States of America, Center for Surveillance, Epidemiology and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, GA, United States of America

Affiliation Center for Surveillance, Epidemiology and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, GA, United States of America

  • Matthew J. Hayat, 
  • Amanda Powell, 
  • Tessa Johnson, 
  • Betsy L. Cadwell

PLOS

  • Published: June 7, 2017
  • https://doi.org/10.1371/journal.pone.0179032
  • Reader Comments

Table 1

Statistical literacy and knowledge is needed to read and understand the public health literature. The purpose of this study was to quantify basic and advanced statistical methods used in public health research. We randomly sampled 216 published articles from seven top tier general public health journals. Studies were reviewed by two readers and a standardized data collection form completed for each article. Data were analyzed with descriptive statistics and frequency distributions. Results were summarized for statistical methods used in the literature, including descriptive and inferential statistics, modeling, advanced statistical techniques, and statistical software used. Approximately 81.9% of articles reported an observational study design and 93.1% of articles were substantively focused. Descriptive statistics in table or graphical form were reported in more than 95% of the articles, and statistical inference reported in more than 76% of the studies reviewed. These results reveal the types of statistical methods currently used in the public health literature. Although this study did not obtain information on what should be taught, information on statistical methods being used is useful for curriculum development in graduate health sciences education, as well as making informed decisions about continuing education for public health professionals.

Citation: Hayat MJ, Powell A, Johnson T, Cadwell BL (2017) Statistical methods used in the public health literature and implications for training of public health professionals. PLoS ONE 12(6): e0179032. https://doi.org/10.1371/journal.pone.0179032

Editor: C. Mary Schooling, Hunter College, UNITED STATES

Received: November 25, 2016; Accepted: May 23, 2017; Published: June 7, 2017

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Public health practice relies on the peer reviewed public health literature for current research and findings that support an evidence basis for effective practice. Studies have shown that statistical literacy and knowledge are needed for understanding published research [ 1 ]. The rapid growth and widespread availability in computing power and user-friendly statistical software packages in recent decades has led to the use of more advanced statistical methods and analyses being used and reported in the health literature [ 2 ]. However, statistical training in public health may not have kept up with the modern data explosion and statistical complexities increasingly being applied in health studies and reported in scientific publications. A comprehensive understanding of statistical concepts and methods is essential for understanding current research and developing effective public health practice.

Biostatistics education is a core requirement in all graduate degree public health programs accredited by the Association of Schools and Programs of Public Health (ASPPH) in the United States [ 3 ]. One of the core curriculum competencies in biostatistics education for the master of public health (MPH) degree is to develop skill and knowledge to critically evaluate the application, presentation, and interpretation of statistical analyses in public health studies [ 3 , 4 , 5 ]. Although this is a desired outcome of training, there are no known recent studies that quantify the types of statistical methods used in the public health literature. Information on methods used is needed to make informed decisions about curriculum development, continuing education, and training of public health professionals.

The purpose of this work is to quantify the use of basic and advanced statistical methods in the general public health literature. A critical question of interest is “What statistical concepts and methods do public health professionals need to know to read and understand the literature?” Our study provides the needed evidence basis for beginning to answer this question.

The data collection form used in this study was created by the study authors and designed to gather information on statistical methods described in each randomly selected article. The form was rigorously developed and tested prior to use on the study sample. Our data collection form consisted of a closed-coding system for quantifying statistical methods reported by the authors of each published paper. We developed the form through a process of four pilot studies in which three reviewers read and cataloged the statistical methods reported in random samples of articles from our selected journals for the 2014 publication year. The results presented in the following tables in this article are framed to correspond to the data collection form. In reviewing each article, the selection of any variable on the review form indicated that variable had been explicitly or implicitly reported within the text of the paper. The final form included domains with specific items in each for article type, study design, sampling technique, summary statistics, reporting of statistical inference, statistical tests, statistical models, reporting of missing data, causal inference, and statistical software.

We aimed to obtain a list of influential general public health journals from which to sample articles. We sampled articles from seven top tier public health journals using the following method. Journals were selected based on a multi-faceted process. First, to gauge a general familiarity with general public health journals, we conducted an online internet search using the term “most influential public health journals.” From this, we compiled a master list of fourteen journals appearing on three or more lists identified from our online search. Next, one of the authors informally surveyed three experienced public health faculty members for suggestions of reputable public health journals. None were added, as all journals suggested were on our list. We next checked that all journals were recognized and included in PubMed. “PubMed comprises more than 26 million citations for biomedical literature from MEDLINE, life science journals, and online books.” ( pubmed.com )” We next examined impact factors, deciding in advance to only include journals with exceptional impact factors. The cutoff was set at 3.0. After eliminating journals that were deemed more medically-focused and those that were specific to public health topics (e.g., policy, environmental health), we had seven remaining journals. Table 1 displays the 5-year impact factors for the 7 selected journals. The lowest impact factor was 4.245 for the European Journal of Epidemiology which we agreed was acceptable. We considered this set of 7 journals to comprise a representative sample of the top-tier general public health literature.

thumbnail

  • PPT PowerPoint slide
  • PNG larger image
  • TIFF original image

https://doi.org/10.1371/journal.pone.0179032.t001

Sample size determination

The goal of this study was to quantify the types and frequencies of use of statistical methods in the public health literature. For purposes of determining the number of articles to be sampled to adequately accomplish this, we considered statistical methods that were rarely used. Thus, we concluded that if we calculated the sample size needed to detect rarely used methods, we would have a sufficient sample size to also cover the other more frequently occurring methods. Some advanced statistical techniques (such as nonlinear regression) were reported in only 1 of 42 articles in our pilot work. We therefore used ≈2.4% (= 1/42) as our estimate for a rarely used method to determine sample size with a precision estimation approach. Detection of a proportion of occurrence of 0.024 for an infrequently occurring statistical method and a given precision (interval width) of 0.05 resulted in a needed sample size of 188 articles. This is a reasonable precision within which we can be confident in our detection of rarely used statistical methods in the public health literature. We equated the notion of ‘attrition’ in our study to inappropriate articles that we agreed should be excluded from review (e.g., qualitative studies, editorials, etc.). In our pilot work, we had 5 articles appearing in the Research section of the journal that we deemed unsuitable for review. This equated to an ‘attrition’ rate of ≈12% (5/42). Assuming an attrition rate of 12%, we estimated a target sample size of 211 articles. Since we had 4 reviewers, for purposes of rounding, we decided to sample a total of 216 articles

The journals selected were American Journal of Public Health , American Journal of Preventive Medicine , International Journal of Epidemiology , European Journal of Epidemiology , Epidemiology , American Journal of Epidemiology , and Bulletin of the World Health Organization . All research based articles published in 2013 in these journals were eligible for review. Table 1 displays the names of our selected study journals, descriptions of the sections from which articles were sampled, and the number of eligible and sampled articles. There were a total of 1,023 research articles published in total across all seven study journals.

Data collection and analysis

We randomly sampled with probability proportional to the number of articles contributed by each journal [ S1 File ]. The 216 articles that comprised the study sample were then randomly allocated to four article groupings of 54 articles each. Each of the four reviewers was randomly assigned to review two of these 54-article groups (for a total of 108 articles per reviewer) and paired with one other reviewer for each article group. Review pairs consisted of one senior author (BC, MH) and one junior author (AP, TJ), such that both senior authors worked with both junior authors but not with one another and vice versa. Reviewers read and cataloged each article individually and a final consensus was reached in review pairs. Each pair met to review their assigned articles in three waves (wave 1 = 15 articles, wave 2 = 19 articles, and wave 3 = 20 articles), and the ordering of review and discussion between pairs was randomly ordered to mitigate learning and other group interaction effects on data collection. All four reviewers met as a large group periodically throughout the review process to discuss flagged articles and to ensure procedural consistency. Criteria for flagging articles included articles questionable for inclusion in our study (e.g., qualitative studies, program evaluation, study design overview reports).

Data entry was conducted using EpiInfo7 [ 6 ] and data analysis performed with the SAS Software System (SAS Institute, Cary NC). The online database created in EpiInfo7 was designed to match the paper form used throughout the review process for ease of data entry and efficiency. Master copies of the paper forms drafted during each pair reviewer meeting were collected and hand-entered by one of the reviewers. Upon completion of data entry, 10% of the records were randomly sampled, and a second reviewer cross-checked the entered records with the master copies. Percent agreement was near 100%, indicating a high confidence with the accuracy of the data entry process.

Data analysis consisted of frequency distributions for all study variables.

A total of 216 articles were reviewed. Table 2 displays the frequency of reported study types, as well as occurrence of descriptive and inferential statistics. The majority of articles were substantively focused (93.1%, n = 201) and reported an observational study design (81.9%, n = 177). Descriptive statistics (91.7%, n = 198) and tables (95.4%, n = 206) were reported in the vast majority of articles. Visual displays of data in the form of charts, figures, or graphs, were reported in 61.6% (n = 133) of the articles. The odds ratio was the most commonly reported epidemiological statistic (40.7%, n = 88). P-values and confidence intervals were the most commonly reported results from the use of inferential statistics, appearing in 72.2% (n = 156) and 76.4% (n = 165) articles, respectively. The reporting of more than one level of significance, indicated by a hierarchy of ‘*’ symbols (e.g., p< 0.10*, p< 0.05**, p< 0.01***), was used in 18.1% (n = 39) of the studies. Adjustments for multiple testing were only reported in 5.1% (n = 11) of the studies reviewed. The Pearson’s Chi-Square or Fisher’s Exact statistical test were used in 25.9% (n = 56) of the reviewed articles.

thumbnail

https://doi.org/10.1371/journal.pone.0179032.t002

Frequency of reported use of statistical models in the public health literature are reported in Table 3 . We classified all types of logistic regression analyses (including binomial, ordinal, and multinomial) that assumed independent observations into a single category labeled simply as “Logistic Regression.” This was the most commonly reported statistical modeling technique used in the articles reviewed (38.4%, n = 83). Linear regression and Cox Proportional Hazards Regression were reported in 19.4% (n = 42) and 15.3% (n = 33) articles, respectively.

thumbnail

https://doi.org/10.1371/journal.pone.0179032.t003

Advanced statistical models that accommodate an independence assumption violation required of classical statistical methods are displayed in Table 3 as dependent statistical models. The general linear mixed model, which assumes a normal distribution, was reported in 6.9% (n = 15) articles, and the generalized linear mixed model, which includes an extension of logistic and Poisson regression models to allow for dependent data, were reported 10.2% (n = 22) of the time. Complex statistical modeling techniques, including structural equation modeling and latent variable models, were reported in less than 5% of the study sample. Missing data was handled most often with casewise deletion (30.6%, n = 66). Multiple imputation was only used in 5.6% (n = 12) of the studies reviewed.

Statistical software packages cited in the reviewed articles is described in Table 4 . The most common statistical software package cited as used by study authors was the SAS Software System. STATA was the second most commonly used software package (25.5%, n = 54). R was used in (8.3% n = 18) of the studies.

thumbnail

https://doi.org/10.1371/journal.pone.0179032.t004

In order to properly and adequately train public health professionals to access scientific publications, it is essential to, at a minimum, be teaching statistical methods actually used and reported in top tier public health journals. Classical statistical frameworks, including hypothesis testing, confidence intervals, and statistical models, are essential and need to be taught in order for a student to read and comprehend what is being published. Our study results show that descriptive statistics were reported in a tabular or graphical format in more than 95% of the articles reviewed. Somewhat surprisingly, when statistical techniques were used, classical statistical modeling techniques were infrequently used, with logistic regression as the most commonly reported type of model applied in the articles reviewed. While these study data only quantify the methods used in the literature, based on its frequent use we advocate for logistic regression to be included in biostatistics education for graduate public health students. It is not specifically mentioned in the current ASPPH competency guidelines for MPH students [ 4 ].

Less than half of the studies reviewed mentioned anything about missing data. It is extremely unlikely that missing data is not encountered in the majority of public health research. This lack of reporting about missing data, including attrition, non-response, and dropouts, may reflect a need for journal submission guidelines to require mention of missing data, including its frequency, and how it was addressed in the statistical analysis. About a third of the studies reported using casewise deletion, a relatively outdated and biased approach for analyzing missing data. Missing data is a well-recognized challenge with human subject research. Modern methods for handling missing data (e.g., multiple imputation) were rarely used. This indicates several possible needs. On one hand, in order for newly developing public health professionals to read and understand the limitations of inadequately handling missing data in a statistical analysis, biostatistics education needs to include training on this topic. And on the other, public health professionals may benefit from an introduction to modern methods for handling missing data in a short course or continuing education workshop.

About 18% of studies reported significance testing results with a notation of some variation of the following format: *p < .05, **p < .01, ***p < .001. A result with two asterisks is mistakenly interpreted as more significant than a result with one asterisk [ 7 ]. The level of significance in a scientific investigation, also known as alpha (α), is a fixed quantity determined before observing the data. In fact, all that is meaningful is whether or not the p-value is less than alpha. Use of the asterisks notation indicates a possible misunderstanding of p-values and the classical null hypothesis significance testing process used in determining statistical significance [ 8 ]. The relatively high frequency of this problematic reporting could be avoided with education and training on appropriate statistical reporting of inferential statistics.

Statistical software is needed to analyze data. SAS and STATA were the two most commonly used packages reported. Exposure to one or both of these packages may be beneficial. Online training courses in statistical methods and statistical software have grown in popularity and may be an option for many working professionals seeking additional training in a format that is manageable with a full time position.

About 82% of studies were observational and less than 6% experimental. As the modern data age continues to evolve, with the increasing use of administrative and other large data sources, it seems plausible to expect more observational data not originally intended for research to become available and used in public health research. Avoiding misuse and ensuring scientific validity of health-related findings from such sources depends on well-educated and trained public health professionals. Although experimental studies remain as the gold standard for enabling causal inference, only a handful were reported. And while there are statistical methods that make causal inference with observational data possible, these approaches were scarcely used in our study sample.

When statistical techniques were used, the vast majority of statistical methods seen in our sample were classical statistical techniques commonly taught in a first or second course in introductory and intermediate statistics. Classical statistics is based on normal theory and rooted in the general linear model (GLM), a framework that includes the three t-tests, linear regression, and ANOVA. The GLM paradigm assumes independence between observations. When this assumption is violated, as is the case with repeated measures data, more advanced statistical techniques are needed to account for the data dependencies that arise. Advanced statistical modeling techniques, including mixed and marginal models, are such methods. However, these techniques, as well as complex statistical modeling techniques such as structural equation modeling and factor analysis, were rarely applied and reported.

The scarce reporting of advanced methods could be an indication that these methods are not of importance or relevance in public health studies. However, since training in these methods has only become available in more recent years, we postulate this may be due to the historic lack of education and training availability on these topics. Many of the advanced statistical techniques rarely observed in our study are methods that were not available in mainstream statistical software ten to twenty years ago. For example, seasoned researchers may not have been exposed to modernized statistical modeling techniques which now available and appropriate for analyzing dependent or multilevel data [ 9 ].

Education in modernized statistical methods, including advanced modeling and computationally intensive statistical techniques, is necessary for staying current and implementing new advanced and methods. In addition to solid training in classical statistics, we suggest that graduate public health programs may also benefit from providing advanced biostatistics education and training opportunities to their students. Statistical software and computing power now enables researchers to readily access and make use of advanced statistical methods. Public health professionals may benefit greatly from continuing education training opportunities that provide a structured foray into such methods, coupled with statistical software training to show how to apply these methods to real world data.

Limitations

Reporting of a statistical method does not necessarily mean its use was appropriate or correct. We did not evaluate the appropriateness or correctness of application. The work presented here is limited to an assessment of statistical methods currently used in the general public health literature. Methods applied in research studies may not be adequate, correct, or appropriate. Previous work estimates that up to 80% of published research is wasted due to poor methods [ 10 ]. Our work did not assess these aspects, and instead focused on quantifying which methods were used.

It is also important to note that the language used by authors to describe some statistical methods varied. For example, classical linear regression was referred to in many ways, including fixed-effects regression, linear regression, least-squares regression, and general linear model. In a few cases, the description of statistical methods used was unclear and necessitated group discussion to come to a consensus. Finally, our study is limited to studies accepted for publication. It would be interesting to assess any possible publication bias resulting from statistical methods used in accepted as compared to rejected manuscripts. Since articles were selected only from 2013, the cross-sectional nature of this study limits an ability to consider how the use of statistical methods has changed over time.

Conclusions

Statistics knowledge is essential for reading and understanding public health research. Review of a random sample of publications from top tier general public health journals showed descriptive statistics and tabular results were reported in more than 95% of the articles. About three quarters of the articles reviewed reported inferential statistics (e.g., p-value, confidence interval). In addition, classic and advanced statistical models were reported in more than a third of the publications. A working knowledge of descriptive and inferential statistics is essential to comprehend, evaluate, and interpret the results for most research studies. Graduate training for public health students and continuing education in biostatistics education for public health professionals are essential for acquiring and maintaining statistics knowledge, as well as continuing to develop new skills as more complex methods are increasingly used and reported.

There is a noticeable lack of an evidence basis to make curricula decisions about biostatistics education. Biostatistics competencies in graduate public health education include developing and cultivating a student’s ability to read and understand the public health scientific literature. However, little is known about the methods used in the literature. The work presented here may be useful to curriculum committees deciding on course and content offerings.

Supporting information

S1 file. this is the study data in an excel file format..

https://doi.org/10.1371/journal.pone.0179032.s001

Author Contributions

  • Conceptualization: MJH AP TJ BC.
  • Data curation: MJH AP TJ BC.
  • Formal analysis: MJH.
  • Investigation: MJH AP TJ BC.
  • Methodology: MJH AP TJ BC.
  • Project administration: MJH AP TJ BC.
  • Resources: MJH AP TJ BC.
  • Software: MJH AP TJ BC.
  • Supervision: MJH AP TJ BC.
  • Validation: MJH AP TJ BC.
  • Visualization: MJH.
  • Writing – original draft: MJH AP TJ BC.
  • Writing – review & editing: MJH AP TJ BC.
  • View Article
  • Google Scholar
  • PubMed/NCBI
  • 3. ASPPH Education Committee. 2006. Master’s Degree in Public Health Core Competency Development Project, Version 2.31, downloaded 8/25/2016 at http://www.aspph.org/educate/models/mph-competency-model .
  • 4. Council on Education for Public Health (2016). Accreditation Criteria: Schools of Public Health and Public Health Programs. Silver Spring, MD: Author. https://ceph.org/assets/2016.Criteria.pdf
  • 6. Dean AG, Arner TG, Sunki GG, Friedman R, Lantinga M, Sangam S, Zubieta JC, Sullivan KM, Brendel KA, Gao Z, Fontaine N, Shu M, Fuller G, Smith DC, Nitschke DA, and Fagan RF. Epi Info™, a database and statistics program for public health professionals. CDC, Atlanta, GA, USA, 2011.

Login

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

  • Advanced Search
  • Journal List
  • Indian J Anaesth
  • v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

INTRODUCTION

Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g001.jpg

Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.

STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g002.jpg

Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g003.jpg

where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g004.jpg

where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g005.jpg

where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g006.jpg

where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g007.jpg

where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g008.jpg

Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g009.jpg

Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g010.jpg

Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g011.jpg

If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g012.jpg

PARAMETRIC AND NON-PARAMETRIC TESTS

Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

  • The assumption of normality which specifies that the means of the sample group are normally distributed
  • The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g013.jpg

where X = sample mean, u = population mean and SE = standard error of mean

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g014.jpg

where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

  • To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g015.jpg

where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g016.jpg

where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g017.jpg

Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

An external file that holds a picture, illustration, etc.
Object name is IJA-60-662-g018.jpg

A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.

SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

  • StatPages.net – provides links to a number of online power calculators
  • G-Power – provides a downloadable power analysis program that runs under DOS
  • Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
  • SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

  • Search Menu
  • Sign in through your institution
  • Advance Articles
  • Author Guidelines
  • Submission Site
  • Open Access Policy
  • Self-Archiving Policy
  • Why publish with Series A?
  • About the Journal of the Royal Statistical Society Series A: Statistics in Society
  • About The Royal Statistical Society
  • Editorial Board
  • Advertising & Corporate Services
  • Journals on Oxford Academic
  • Books on Oxford Academic

Principles of Statistical Analyses: Learning from Randomized Experiments

ORCID logo

  • Article contents
  • Figures & tables
  • Supplementary Data

Egor Bronnikov, Principles of Statistical Analyses: Learning from Randomized Experiments, Journal of the Royal Statistical Society Series A: Statistics in Society , 2024;, qnae052, https://doi.org/10.1093/jrsssa/qnae052

  • Permissions Icon Permissions

Aiming to provide a concise and thorough guide for readers with (some) mathematical experience, Principles of Statistical Analyses achieves this exceptionally well.

Part I builds the foundation for statistical inference by introducing the axioms of probability theory, presenting discrete, continuous, and multivariate distributions, and covering concentration inequalities and limit theorems as well as touches on stochastic processes. Part II focuses on practical issues of sampling and data collection. Although the author discusses these topics concisely, he successfully covers key aspects of experimental design and observational studies. The most substantial section of the book—in terms of both content and depth—is Part III, which introduces elements of statistical inference. It begins with statistical models, estimators and their properties, and tests, before delving into topics such as (one and multiple) proportions, (one, multiple, and multiple paired) numerical samples, correlational analysis, multiple testing, and, finally, regression analysis.

The clear advantages of Principles of Statistical Analyses are its conciseness, mathematical rigour, and a large number of problems (about 700). However, although knowledge of measure theory is (almost) unnecessary for understanding the book's material, the reader should be prepared to use knowledge of calculus and real analysis.

Email alerts

Citing articles via.

  • Recommend to Your Librarian
  • Advertising & Corporate Services
  • Journals Career Network
  • Email Alerts

Affiliations

  • Online ISSN 1467-985X
  • Print ISSN 0964-1998
  • Copyright © 2024 Royal Statistical Society
  • About Oxford Academic
  • Publish journals with us
  • University press partners
  • What we publish
  • New features  
  • Open access
  • Institutional account management
  • Rights and permissions
  • Get help with access
  • Accessibility
  • Advertising
  • Media enquiries
  • Oxford University Press
  • Oxford Languages
  • University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

  • Copyright © 2024 Oxford University Press
  • Cookie settings
  • Cookie policy
  • Privacy policy
  • Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

  • Download PDF
  • Share X Facebook Email LinkedIn
  • Permissions

TREND Reporting Guidelines for Nonrandomized/Quasi-Experimental Study Designs

  • 1 Department of Surgery and Perioperative Care, Dell Medical School, University of Texas, Austin
  • 2 Department of Emergency Medicine, Denver Health Medical Center, University of Colorado School of Medicine, Denver
  • 3 Department of Epidemiology, Colorado School of Public Health, Aurora
  • 4 Statistical Editor, JAMA Surgery
  • 5 Department of Surgery, University of Michigan Health System, Ann Arbor
  • 6 Section Editor, JAMA Surgery
  • Editorial Effective Use of Reporting Guidelines to Improve the Quality of Surgical Research Benjamin S. Brooke, MD, PhD; Amir A. Ghaferi, MD, MSc; Melina R. Kibbe, MD JAMA Surgery
  • Guide to Statistics and Methods SQUIRE Reporting Guidelines for Quality Improvement Studies Rachel R. Kelz, MD, MSCE, MBA; Todd A. Schwartz, DrPH; Elliott R. Haut, MD, PhD JAMA Surgery
  • Guide to Statistics and Methods STROBE Reporting Guidelines for Observational Studies Amir A. Ghaferi, MD, MS; Todd A. Schwartz, DrPH; Timothy M. Pawlik, MD, MPH, PhD JAMA Surgery
  • Guide to Statistics and Methods CHEERS Reporting Guidelines for Economic Evaluations Oluwadamilola M. Fayanju, MD, MA, MPHS; Jason S. Haukoos, MD, MSc; Jennifer F. Tseng, MD, MPH JAMA Surgery
  • Guide to Statistics and Methods TRIPOD Reporting Guidelines for Diagnostic and Prognostic Studies Rachel E. Patzer, PhD, MPH; Amy H. Kaji, MD, PhD; Yuman Fong, MD JAMA Surgery
  • Guide to Statistics and Methods ISPOR Reporting Guidelines for Comparative Effectiveness Research Nader N. Massarweh, MD, MPH; Jason S. Haukoos, MD, MSc; Amir A. Ghaferi, MD, MS JAMA Surgery
  • Guide to Statistics and Methods PRISMA Reporting Guidelines for Meta-analyses and Systematic Reviews Shipra Arya, MD, SM; Amy H. Kaji, MD, PhD; Marja A. Boermeester, MD, PhD JAMA Surgery
  • Guide to Statistics and Methods AAPOR Reporting Guidelines for Survey Studies Susan C. Pitt, MD, MPHS; Todd A. Schwartz, DrPH; Danny Chu, MD JAMA Surgery
  • Guide to Statistics and Methods MOOSE Reporting Guidelines for Meta-analyses of Observational Studies Benjamin S. Brooke, MD, PhD; Todd A. Schwartz, DrPH, MS; Timothy M. Pawlik, MD, MPH, PhD JAMA Surgery
  • Guide to Statistics and Methods The CONSORT Framework Ryan P. Merkow, MD, MS; Amy H. Kaji, MD, PhD; Kamal M. F. Itani, MD JAMA Surgery
  • Guide to Statistics and Methods SRQR and COREQ Reporting Guidelines for Qualitative Studies Lesly A. Dossett, MD, MPH; Amy H. Kaji, MD, PhD; Amalia Cochran, MD JAMA Surgery

The Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) guidelines were first published in 2004, in response to the perceived value and effect of the Consolidated Standards of Reporting Trials (CONSORT) guidelines that had been introduced a decade earlier. 1 The initial development of these guidelines was spearheaded by the US Centers for Disease Control and Prevention HIV/AIDS Prevention Research Synthesis team. The initial interest was specifically in standardization of the reporting of behavioral interventions in HIV/AIDS (eg, interventions to improve adherence to antiretroviral therapy or to increase frequency of partner testing), but the group broadened its interest to include all evaluations of interventions using nonrandomized designs. The guideline authors, comprising researchers, policy makers, and journal editors, developed a reporting framework that was based on the CONSORT guidelines but adapted for evaluation of interventions other than randomized trials.

  • Editorial Effective Use of Reporting Guidelines to Improve the Quality of Surgical Research JAMA Surgery

Read More About

Haynes AB , Haukoos JS , Dimick JB. TREND Reporting Guidelines for Nonrandomized/Quasi-Experimental Study Designs. JAMA Surg. 2021;156(9):879–880. doi:10.1001/jamasurg.2021.0552

Manage citations:

© 2024

Artificial Intelligence Resource Center

Surgery in JAMA : Read the Latest

Browse and subscribe to JAMA Network podcasts!

Others Also Liked

Select your interests.

Customize your JAMA Network experience by selecting one or more topics from the list below.

  • Academic Medicine
  • Acid Base, Electrolytes, Fluids
  • Allergy and Clinical Immunology
  • American Indian or Alaska Natives
  • Anesthesiology
  • Anticoagulation
  • Art and Images in Psychiatry
  • Artificial Intelligence
  • Assisted Reproduction
  • Bleeding and Transfusion
  • Caring for the Critically Ill Patient
  • Challenges in Clinical Electrocardiography
  • Climate and Health
  • Climate Change
  • Clinical Challenge
  • Clinical Decision Support
  • Clinical Implications of Basic Neuroscience
  • Clinical Pharmacy and Pharmacology
  • Complementary and Alternative Medicine
  • Consensus Statements
  • Coronavirus (COVID-19)
  • Critical Care Medicine
  • Cultural Competency
  • Dental Medicine
  • Dermatology
  • Diabetes and Endocrinology
  • Diagnostic Test Interpretation
  • Drug Development
  • Electronic Health Records
  • Emergency Medicine
  • End of Life, Hospice, Palliative Care
  • Environmental Health
  • Equity, Diversity, and Inclusion
  • Facial Plastic Surgery
  • Gastroenterology and Hepatology
  • Genetics and Genomics
  • Genomics and Precision Health
  • Global Health
  • Guide to Statistics and Methods
  • Hair Disorders
  • Health Care Delivery Models
  • Health Care Economics, Insurance, Payment
  • Health Care Quality
  • Health Care Reform
  • Health Care Safety
  • Health Care Workforce
  • Health Disparities
  • Health Inequities
  • Health Policy
  • Health Systems Science
  • History of Medicine
  • Hypertension
  • Images in Neurology
  • Implementation Science
  • Infectious Diseases
  • Innovations in Health Care Delivery
  • JAMA Infographic
  • Law and Medicine
  • Leading Change
  • Less is More
  • LGBTQIA Medicine
  • Lifestyle Behaviors
  • Medical Coding
  • Medical Devices and Equipment
  • Medical Education
  • Medical Education and Training
  • Medical Journals and Publishing
  • Mobile Health and Telemedicine
  • Narrative Medicine
  • Neuroscience and Psychiatry
  • Notable Notes
  • Nutrition, Obesity, Exercise
  • Obstetrics and Gynecology
  • Occupational Health
  • Ophthalmology
  • Orthopedics
  • Otolaryngology
  • Pain Medicine
  • Palliative Care
  • Pathology and Laboratory Medicine
  • Patient Care
  • Patient Information
  • Performance Improvement
  • Performance Measures
  • Perioperative Care and Consultation
  • Pharmacoeconomics
  • Pharmacoepidemiology
  • Pharmacogenetics
  • Pharmacy and Clinical Pharmacology
  • Physical Medicine and Rehabilitation
  • Physical Therapy
  • Physician Leadership
  • Population Health
  • Primary Care
  • Professional Well-being
  • Professionalism
  • Psychiatry and Behavioral Health
  • Public Health
  • Pulmonary Medicine
  • Regulatory Agencies
  • Reproductive Health
  • Research, Methods, Statistics
  • Resuscitation
  • Rheumatology
  • Risk Management
  • Scientific Discovery and the Future of Medicine
  • Shared Decision Making and Communication
  • Sleep Medicine
  • Sports Medicine
  • Stem Cell Transplantation
  • Substance Use and Addiction Medicine
  • Surgical Innovation
  • Surgical Pearls
  • Teachable Moment
  • Technology and Finance
  • The Art of JAMA
  • The Arts and Medicine
  • The Rational Clinical Examination
  • Tobacco and e-Cigarettes
  • Translational Medicine
  • Trauma and Injury
  • Treatment Adherence
  • Ultrasonography
  • Users' Guide to the Medical Literature
  • Vaccination
  • Venous Thromboembolism
  • Veterans Health
  • Women's Health
  • Workflow and Process
  • Wound Care, Infection, Healing
  • Register for email alerts with links to free full-text articles
  • Access PDFs of free articles
  • Manage your interests
  • Save searches and receive search alerts

Unleashing economic potential: decoding the FDI-economic growth nexus in G-15 economies amidst unique host country traits

  • Published: 27 May 2024

Cite this article

statistical research journal

  • Aastha Bajaj   ORCID: orcid.org/0000-0003-4336-7120 1 &
  • Lakshmi Bhooshetty 1  

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

This study examined the impacts of Foreign Direct Investment (FDI) on economic growth across top the five G-15 countries over a period of 33 years, while considering the influence of key host country traits, namely macroeconomic stability, financial development, human capital, and trade openness. The selection of these variables was firmly supported by both theoretical foundations and empirical studies that highlight their significant role in shaping the FDI–growth interconnection. Panel data derived from World Bank Indicators, spanning the period from 1989 to 2021, were analyzed using a feasible generalized least squares method (FGLS), a rigorous approach, including descriptive statistics, correlation analysis, cross-sectional dependence tests, unit root tests, and multiple regression models. By exploring the interconnection between FDI and the characteristics of the host country, this study sheds light on how these factors collectively contributed to economic growth in the G-15 economies. Descriptive statistics indicated a favorable trend in economic growth, with an average of 3.470 and a standard deviation of 4.289. Correlation analysis revealed significant positive relationships between Economic Growth and Gross Capital Formation, Human Capital, and Liquid Liabilities. Conversely, FDI, Inflation, and Trade Openness displayed insignificant positive correlations with Economic Growth. The findings also demonstrated that favorable host country traits magnified the impact of FDI on economic growth. Specifically, increased Financial Development, Human Capital, and Trade Openness enhanced the positive effects of FDI on economic growth. However, Inflation had a dampening effect on the growth factor. Policymakers in G-15 countries should give precedence to developing strong financial markets, promoting trade liberalization, and investing in human capital to optimize the advantages of FDI. This research addresses a critical gap in the literature as limited empirical work has been conducted on the FDI–growth relationships specific to the G-15 economies, which hold substantial influence in the global investment landscape and showcase remarkable economic growth. By employing rigorous panel data methodology and a long-term dataset, we provides original insights into the interaction between FDI and host country characteristics, contributing to the existing body of knowledge.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Data availability

Broad money (% of GDP) | Data. (n.d.). https://data.worldbank.org/indicator/FM.LBL.BMNY.GD.ZS . FDI, net inflows (% of GDP) | Data. (n.d.). https://data.worldbank.org/indicator/BX.KLT.DINV.WD.GD.ZS . GDP growth (annual %) | Data. (n.d.). https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG . Gross Capital Formation (% of GDP) | Data. (n.d.). https://data.worldbank.org/indicator/NE.GDI.TOTL.ZS . Inflation, consumer prices (annual %) | Data. (n.d.). https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG . School enrollment, secondary (% gross) | Data. (n.d.). https://data.worldbank.org/indicator/SE.SEC.ENRR . Trade (% of GDP) | Data. (n.d.). https://data.worldbank.org/indicator/NE.TRD.GNFS.ZS . Labour force, total | Data. (n.d.). https://data.worldbank.org/indicator/SL.TLF.TOTL.IN .

Adeniyi FO (2020) Impact of foreign direct investment and inflation on economic growth of five randomly selected Countries in Africa. J Econ Int Finance 12(2):65–73

Article   Google Scholar  

Aggarwal V, Karwasra N (2023) A bibliometric analysis on trade openness and economic growth: current dynamics and future direction. Compet Rev Int Bus J. https://doi.org/10.1108/CR-11-2022-0177

Agudze K, Ibhagui O (2021) Inflation and FDI in industrialized and developing economies. Int Rev Appl Econ 35(5):749–764

Google Scholar  

Amin A, Liu Y, Yu J, Chandio AA, Rasool SF, Luo J, Zaman S (2020) How does energy poverty affect economic development? A panel data analysis of South Asian countries. Environ Sci Pollut Res 27(25):31623–31635. https://doi.org/10.1007/s11356-020-09173-6

Appiah M, Gyamfi BA, Adebayo TS, Bekun FV (2023) Do financial development, foreign direct investment, and economic growth enhance industrial development? Fresh evidence from Sub-Sahara African countries. Port Econ J 22(2):203–227. https://doi.org/10.1007/s10258-022-00207-0

Asafo-Adjei E, Owusu Junior P, Adam AM, Arthur CL, Boateng E, Ankomah K (2023) Asymmetric relationships among financial sector development, corruption, foreign direct investment, and economic growth in sub-Saharan Africa. Cogent Econ Finance. https://doi.org/10.1080/23322039.2023.2182454

Baharumshah AZ, Almasaied SW (2009) Foreign direct investment and economic growth in Malaysia: Interactions with human capital and financial deepening. Emerg Mark Financ Trade 45(1):90–102

Bajrami H, Zeqiri N (2019) Theories of foreign direct investment (FDI) and the significance of human capital. Int J Bus Manage. https://doi.org/10.20472/BM.2019.7.1.002

Baltagi BH, Hashem Pesaran M (2007) Heterogeneity and cross section dependence in panel data models: theory and applications introduction. J App Econom 22(2):229–232

Bekhet HA, Al-Smadi RW (2014) Determining the causality relationships among FDI determinants: evidence from Jordan. Int J Sustain Econ 6(3):261–274

Bibi S, Ahmad ST, Rashid H (2014) Impact of trade openness, FDI, exchange rate and inflation on economic growth: a case study of Pakistan. Int J Account Financ Rep 4(2):236

Borensztein E, De Gregorio J, Lee J-W (1998) How does foreign direct investment affect economic growth? J Int Econ 45(1):115–135. https://doi.org/10.1016/S0022-1996(97)00033-0

Bostan I, Toma C, Aevoae G, Robu I-B, Mardiros DN, Topliceanu ȘC (2023) Effects of internal and external factors on economic growth in emerging economies: evidence from CEE countries. East Eur Econ 61(1):66–85. https://doi.org/10.1080/00128775.2022.2109489

Broad money (% of GDP) | Data. (n.d.). https://data.worldbank.org/indicator/FM.LBL.BMNY.GD.ZS

Burlea-Schiopoiu A, Brostescu S, Popescu L (2023) The impact of foreign direct investment on the economic development of emerging countries of the European Union. Int J Financ Econ 28(2):2148–2177. https://doi.org/10.1002/ijfe.2530

Carkovic M, Levine RE (2002) Does foreign direct investment accelerate economic growth? SSRN Electron J. https://doi.org/10.2139/ssrn.314924

Ciftci C, Durusu-Ciftci D (2022) Economic freedom, foreign direct investment, and economic growth: the role of sub-components of freedom. J Int Trade Econ Dev 31(2):233–254. https://doi.org/10.1080/09638199.2021.1962392

Dankyi AB, Abban OJ, Yusheng K, Coulibaly TP (2022) Human capital, foreign direct investment, and economic growth: evidence from ECOWAS in a decomposed income level panel. Environ Chall 9:100602. https://doi.org/10.1016/j.envc.2022.100602

Fadhil MA, Almsafir MK (2015) The role of FDI inflows in economic growth in Malaysia (time series: 1975–2010). Proced Econ Finance 23:1558–1566

Foreign direct investment, net inflows (% of GDP) | Data . (n.d.). https://data.worldbank.org/indicator/BX.KLT.DINV.WD.GD.ZS

Gao T (2005) Foreign direct investment and growth under economic integration. J Int Econom (print) 67(1):157–174. https://doi.org/10.1016/j.jinteco.2004.11.003

GDP growth (annual %) | Data . (n.d.). https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG

Gross capital formation (% of GDP) | Data . (n.d.). https://data.worldbank.org/indicator/NE.GDI.TOTL.ZS

Hansen H, Rand J (2006) On the causal links between FDI and growth in developing countries. World Econ 29(1):21–41. https://doi.org/10.1111/j.1467-9701.2006.00756.x

Hassan HM (2022) The importance of trade openness and inflation for attracting Foreign Direct Investment in GCC Countries. Available at SSRN 4315836 .

Hermes N, Lensink R (2003) Foreign direct investment, financial development and economic growth. J Dev Stud 40(1):142–163. https://doi.org/10.1080/00220380412331293707

Hoechle D (2007) Robust standard errors for panel regressions with cross-sectional dependence. Stand Genom Sci 7(3):281–312

Hossain MK, Hossain MS (2023) Causal interaction between foreign direct investment inflows and china’s economic growth. Sustainability 15(10):7994. https://doi.org/10.3390/su15107994

Hurlin C, Mignon V (2007). Second generation panel unit root tests

Inflation, consumer prices (annual %) | Data . (n.d.). https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG

Jie H, Zaman S, Zaman QU, Shah AH, Lou J (2023) A pathway to a sustainable future: Investigating the contribution of technological innovations, clean energy, and Women’s empowerment in mitigating global environmental challenges. J Clean Prod 421:138499. https://doi.org/10.1016/j.jclepro.2023.138499

Joo BA, Shawl S, Makina D (2022) The interaction between FDI host country characteristics and economic growth A new panel evidence from BRICS. J Econ Dev. https://doi.org/10.1108/JED-03-2021-0035

Joshua U, Rotimi ME, Sarkodie SA (2020) Global FDI inflow and its implication across economic income groups. J Risk Financial Manage 13(11):291. https://doi.org/10.3390/jrfm13110291

Kar S (2013) Exploring the causal link between FDI and human capital development in India. Decision 40(1):3–13

Khan I, Xue J, Zaman S, Mehmood Z (2023) Nexus between FDI, Economic growth, industrialization, and employment opportunities: empirical evidence from Pakistan. J Knowl Econ 14(3):3153–3175. https://doi.org/10.1007/s13132-022-01006-w

Kulu E, Mensah S, Sena PM (2021) Effects of foreign direct investment on economic growth in Ghana: the role of institutions. J Econom Dev 20(1):23–34

Kumari R, Shabbir MS, Saleem S, Yahya Khan G, Abbasi BA, Lopez LB (2023) An empirical analysis among foreign direct investment, trade openness and economic growth: evidence from the Indian economy. South Asian J Business Stud 12(1):127–149. https://doi.org/10.1108/SAJBS-06-2020-0199

Labor force, total | Data . (n.d.). https://data.worldbank.org/indicator/SL.TLF.TOTL.IN

Leitão NC, Dos Santos Parente CC, Balsalobre-Lorente D, Cantos Cantos JM (2022) Revisiting the effects of energy, population, foreign direct investment, and economic growth in Visegrad countries under the EKC scheme. Environ Sci Pollut Res 30(6):15102–15114. https://doi.org/10.1007/s11356-022-23188-1

Liargovas PG, Skandalis KS (2012) Foreign direct investment and trade openness: the case of developing economies. Soc Indic Res 106(2):323–331

Linh HTD, Duong NT, Hien HT (2023) The relationship among exports, foreign direct investment, and economic growth in Vietnam - A VAR approach. VNU Univ Econ Bus 3(2):11. https://doi.org/10.57110/vnujeb.v3i2.164

Mamun A, Kabir MHMI (2023) The remittance, foreign direct investment, export, and economic growth in bangladesh: a time series analysis. Arab Econ Bus J. https://doi.org/10.38039/2214-4625.1022

Mustafa AMM (2019) The relationship between foreign direct investment and inflation: econometric analysis and forecasts in the case of Sri Lanka. J Polit Law 12(2):44. https://doi.org/10.5539/jpl.v12n2p44

Nguyen CDT (2022) Impact of international trade cooperation and distribution on foreign direct investment: evidence from Vietnam. J Distrib Sci 20(4):77–83

Noorbakhsh F, Paloni A, Youssef A (2001) Human capital and FDI inflows to developing countries: new empirical evidence. World Dev 29(9):1593–1610. https://doi.org/10.1016/S0305-750X(01)00054-7

Ntamwiza JMV, Masengesho F (2022) Impact of gross capital formation and foreign direct investment on economic growth in Rwanda (1990–2017). Curr Urban Stud 10(01):1–13. https://doi.org/10.4236/cus.2022.101001

Olorogun L, Salami M, Victor Bekun F (2020) Revisiting the Nexus between FDI, financial development and economic growth: Empirical evidence from Nigeria. J Public Affairs. https://doi.org/10.1002/pa.2561

Osei MJ, Kim J (2020) Foreign direct investment and economic growth: is more financial development better? Econ Model 93:154–161. https://doi.org/10.1016/j.econmod.2020.07.009

Pham HT, Gan C, Hu B (2022) Causality between financial development and foreign direct investment in Asian developing countries. J Risk Financ Manage 15(5):195

Rahman P, Zhang Z, Musa M (2023) Do technological innovation, foreign investment, trade and human capital have a symmetric effect on economic growth? Novel dynamic ARDL simulation study on Bangladesh. Econ Chang Restruct 56(2):1327–1366. https://doi.org/10.1007/s10644-022-09478-1

Rao DT, Sethi N, Dash DP, Bhujabal P (2023) Foreign aid, FDI and economic growth in South-East Asia and South Asia. Glob Bus Rev 24(1):31–47. https://doi.org/10.1177/0972150919890957

Rasool SF, Zaman S, Jehan N, Chin T, Khan S, Zaman Q (2022) Investigating the role of the tech industry, renewable energy, and urbanization in sustainable environment: policy directions in the context of developing economies. Technol Forecast Soc Chang 183:121935. https://doi.org/10.1016/j.techfore.2022.121935

Ridha MR, Budi Parwanto N (2020) The effect of foreign direct investment, human development and macroeconomic condition on economic growth: evidence from Indonesia. J Indones Appl Econ 8(2):46–54. https://doi.org/10.21776/ub.jiae.2020.008.02.5

School enrollment, secondary (% gross) | Data . (n.d.). https://data.worldbank.org/indicator/SE.SEC.ENRR

Shinwari R, Zakeria I, Usman M, Sadiq M (2023) Revisiting the relationship between FDI, natural resources, and economic growth in Afghanistan: does political (in) stability matter? J Knowl Econ. https://doi.org/10.1007/s13132-023-01264-2

Singh S, Arya V, Yadav MP, Power GJ (2023) Does financial development improve economic growth? The role of asymmetrical relationships. Glob Financ J 56:100831. https://doi.org/10.1016/j.gfj.2023.100831

Tiwari AK, Mutascu M (2011) Economic growth and FDI in Asia: a panel-data approach. Economic Analysis and Policy 41(2):173–187

Trade (% of GDP) | Data . (n.d.). https://data.worldbank.org/indicator/NE.TRD.GNFS.ZS

Tran VNA, Huynh CM (2022). The impact of foreign direct investment on financial development in Asian countries

Tsaurai K (2018) Investigating the impact of inflation on foreign direct investment in Southern Africa. Acta Univ Danub Econ 14(4):597–611

Yimer A (2023) The effects of FDI on economic growth in Africa. J Int Trade Econ Dev 32(1):2–36. https://doi.org/10.1080/09638199.2022.2079709

Zaman S, Wang Z, Zaman Q (2021) Exploring the relationship between remittances received, education expenditures, energy use, income, poverty, and economic growth: fresh empirical evidence in the context of selected remittances receiving countries. Environ Sci Pollut Res 28(14):17865–17877. https://doi.org/10.1007/s11356-020-11943-1

Zaman Q, Wang Z, Zaman S, Rasool SF (2021) Investigating the nexus between education expenditure, female employers, renewable energy consumption and CO2 emission: evidence from China. J Clean Prod 312:127824. https://doi.org/10.1016/j.jclepro.2021.127824

Zaman S, Zaman Q, Zhang L, Wang Z, Jehan N (2022) Interaction between agricultural production, female employment, renewable energy, and environmental quality: policy directions in context of developing economies. Renew Energy 186:288–298. https://doi.org/10.1016/j.renene.2021.12.131

Zaman Q, Zhao Y, Zaman S, Shah AH (2023) Examining the symmetrical effect of traditional energy resources, industrial production, and poverty lessening on ecological sustainability: Policy track in the milieu of five neighboring Asian economies. Resour Polic 83:103606. https://doi.org/10.1016/j.resourpol.2023.103606

Download references

Author information

Authors and affiliations.

Department of Commerce, CHRIST (Deemed to be University), Bengaluru, India

Aastha Bajaj & Lakshmi Bhooshetty

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Aastha Bajaj .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Bajaj, A., Bhooshetty, L. Unleashing economic potential: decoding the FDI-economic growth nexus in G-15 economies amidst unique host country traits. Asia-Pac J Reg Sci (2024). https://doi.org/10.1007/s41685-024-00340-y

Download citation

Received : 04 September 2023

Accepted : 16 May 2024

Published : 27 May 2024

DOI : https://doi.org/10.1007/s41685-024-00340-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Foreign direct investment (FDI)
  • Economic growth
  • Host country traits
  • FDI–growth relationship
  • Feasible generalized least squares (FGLS)
  • Panel data analysis
  • G-15 economies
  • Find a journal
  • Publish with us
  • Track your research
  • - Google Chrome

Intended for healthcare professionals

  • Access provided by Google Indexer
  • My email alerts
  • BMA member login
  • Username * Password * Forgot your log in details? Need to activate BMA Member Log In Log in via OpenAthens Log in via your institution

Home

Search form

  • Advanced search
  • Search responses
  • Search blogs
  • Effect of the HPV...

Effect of the HPV vaccination programme on incidence of cervical cancer and grade 3 cervical intraepithelial neoplasia by socioeconomic deprivation in England: population based observational study

Linked editorial.

HPV vaccine: the key to eliminating cervical cancer inequities

  • Related content
  • Peer review
  • Milena Falcaro , senior statistician 1 ,
  • Kate Soldan , scientist and epidemiologist 2 ,
  • Busani Ndlela , cancer information analyst 3 ,
  • Peter Sasieni , professor of cancer epidemiology 1
  • 1 Centre for Cancer Screening, Prevention and Early Diagnosis, Wolfson Institute of Population Health, Queen Mary University of London, London EC1M 6BQ, UK
  • 2 Blood Safety, Hepatitis, Sexually Transmitted Infections and HIV Division, UK Health Security Agency (UKHSA), London, UK
  • 3 National Disease Registration Service (NDRS), NHS England, London, UK
  • Correspondence to: P Sasieni p.sasieni{at}qmul.ac.uk (or @petersasieni on X)
  • Accepted 27 March 2024

Objectives To replicate previous analyses on the effectiveness of the English human papillomavirus (HPV) vaccination programme on incidence of cervical cancer and grade 3 cervical intraepithelial neoplasia (CIN3) using 12 additional months of follow-up, and to investigate effectiveness across levels of socioeconomic deprivation.

Design Observational study.

Setting England, UK.

Participants Women aged 20-64 years resident in England between January 2006 and June 2020 including 29 968 with a diagnosis of cervical cancer and 335 228 with a diagnosis of CIN3. In England, HPV vaccination was introduced nationally in 2008 and was offered routinely to girls aged 12-13 years, with catch-up campaigns during 2008-10 targeting older teenagers aged <19 years.

Main outcome measures Incidence of invasive cervical cancer and CIN3.

Results In England, 29 968 women aged 20-64 years received a diagnosis of cervical cancer and 335 228 a diagnosis of CIN3 between 1 January 2006 and 30 June 2020. In the birth cohort of women offered vaccination routinely at age 12-13 years, adjusted age standardised incidence rates of cervical cancer and CIN3 in the additional 12 months of follow-up (1 July 2019 to 30 June 2020) were, respectively, 83.9% (95% confidence interval (CI) 63.8% to 92.8%) and 94.3% (92.6% to 95.7%) lower than in the reference cohort of women who were never offered HPV vaccination. By mid-2020, HPV vaccination had prevented an estimated 687 (95% CI 556 to 819) cervical cancers and 23 192 (22 163 to 24 220) CIN3s. The highest rates remained among women living in the most deprived areas, but the HPV vaccination programme had a large effect in all five levels of deprivation. In women offered catch-up vaccination, CIN3 rates decreased more in those from the least deprived areas than from the most deprived areas (reductions of 40.6% v 29.6% and 72.8% v 67.7% for women offered vaccination at age 16-18 and 14-16, respectively). The strong downward gradient in cervical cancer incidence from high to low deprivation in the reference unvaccinated group was no longer present among those offered the vaccine.

Conclusions The high effectiveness of the national HPV vaccination programme previously seen in England continued during the additional 12 months of follow-up. HPV vaccination was associated with a substantially reduced incidence of cervical cancer and CIN3 across all five deprivation groups, especially in women offered routine vaccination.

Introduction

Human papillomavirus (HPV) comprises a family of viruses, a subset of which are responsible for virtually all cervical and some anogenital and oropharyngeal cancers. 1 More than 100 countries worldwide have introduced prophylactic HPV vaccination as part of routine immunisation schedules. 2 One important outcome yet to be reported is whether vaccination has reduced or increased the inequalities seen for cervical disease in the UK and elsewhere.

In England, the national HPV vaccination programme started in 2008 using the bivalent Cervarix vaccine to prevent infections due to HPV types 16 and 18, which are estimated to cause around 80% of all cervical cancers in the UK. 3 Vaccination was offered routinely to 12-13 year old (school year 8) girls and as part of a catch-up campaign to those aged <19 years. 4 In September 2012 the programme switched to the quadrivalent vaccine (Gardasil), which additionally protects against HPV types 6 and 11 (responsible for genital warts), and in 2019 the programme was extended to 12-13 year old boys. Those who are eligible but not vaccinated can receive the vaccine free of charge from their general practitioner until their 25th birthday. 5

The introduction and implementation of HPV immunisation in this way means that noticeable discontinuities exist in the proportion of women vaccinated by date of birth, enabling a rigorous evaluation of the effectiveness of the programme. 6 For example, women born in August 1990 are unlikely to have received HPV vaccination, whereas among those born in the year from 1 September 1990 nearly 70% have received at least one dose of the vaccine.

Findings on the early effect of national HPV vaccination programmes have been encouraging. A wealth of real world evidence for the effect of vaccination on HPV prevalence exists 7 8 9 10 11 and evidence is growing for its effectiveness in reducing high grade cervical intraepithelial neoplasia (CIN) 12 13 14 15 and cervical cancer in vaccinated women. 14 16 17 18 19 For instance, we found that in England rates of grade 3 CIN (CIN3) and of cervical cancer were greatly reduced among those who were offered HPV vaccination, and that the magnitude of the reduction was greatest in the cohorts with the highest uptake and younger age at vaccination. 14 We estimated that by mid-2019 the immunisation programme had prevented cervical cancer in nearly 450 women and CIN3 in around 17 000 women.

Along with preventing ill health, a key aim of the NHS is to reduce health inequalities. 20 To this end, we investigated whether the effect of immunisation against HPV has resulted in a reduction in inequalities in cervical disease or a widening. Concern has been expressed that if the uptake of HPV vaccination is lower in those at greatest risk of cervical cancer, as has been seen in the US, 21 this could accentuate health inequalities. One study found that the introduction of HPV immunisation in England might initially have increased inequities in HPV related cancer incidence among ethnic minority groups because of the differential effect of herd protection in subpopulations with dissimilar vaccination coverage. 22 Previous studies have suggested that white people have a higher awareness of HPV and acceptance of the immunisation 23 and that vaccination uptake is lower in women from ethnic minority groups and more deprived areas. 24 Using data on HPV vaccination coverage by local area, however, a study found little variation by deprivation score in women offered routine vaccination (83% v 86% for most and least deprived areas, respectively) and only a small negative correlation between deprivation and vaccine uptake in those offered catch-up vaccination (47% v 53% for most and least deprived areas, respectively). 25 A full understanding of the effect of HPV vaccination across different socioeconomic groups is complicated by the poor uptake of cervical screening observed among younger women in the most deprived areas, leading to lower rates of screen detected cervical cancer and CIN3 at age 25 years compared with women in less deprived areas. 26 27

We replicated results from an analysis of population based cancer registry data to evaluate if the high vaccination effectiveness seen previously continued during an additional year of follow-up. The combined data were also used to investigate the effect of the vaccination programme by socioeconomic deprivation.

To represent socioeconomic deprivation, we used the index of multiple deprivation, a small area measure based on several domains of deprivation, such as income, employment, and health. The index is determined by using a standard statistical geographical unit, called lower super output area, which divides England into small areas of similar sized populations (on average about 1500 residents, or 650 households). 28 The lower super output areas are then ranked from the most to the least deprived and divided into five equal groups. The first and fifth groups correspond to the 20% most deprived and 20% least deprived lower super output areas in England, respectively.

We retrieved the records of all women aged 20-64 years resident in England with a diagnosis of invasive cervical cancer (ICD-10 (international classification of diseases, 10th revision) code C53) or CIN3 (ICD-10 code D06) between 1 January 2006 and 30 June 2020. These records are stored in the database managed by NHS England’s National Disease Registration Service, 29 and for each patient included information on index of multiple deprivation derived from the patient’s home postcode at the time of diagnosis. To convert these counts into rates, we used mid-year estimates of the female population for England by single year of age, calendar year (January 2006 to June 2020), and index of multiple deprivation (five groups). These estimates were retrieved from multiple tables publicly available on the website of the UK’s Office for National Statistics (ONS). 30 The supplementary material provides more details about the index of multiple deprivation versions used by the National Disease Registration Service and ONS, along with information on how we derived the population estimates required in our statistical analysis.

Statistical analysis

We separately analysed incidence rates of cervical cancer and CIN3 by using extensions of our previously described age-period-cohort Poisson model. 14 31 32 Data on women with cancer or CIN3 were aggregated by single month of age, calendar time (period), and date of birth (cohort). We derived the corresponding population risk time by subdividing the mid-year ONS population estimates into one month intervals for age, period, and cohort. For the analysis of the effectiveness by deprivation, we further split both the data on women with cancer or CIN3 and the population estimates by deprivation group (fifths). We then used the population risk time as the denominator for calculating rates (formally, the subdivided population estimates were log transformed and included in the Poisson regression model as an offset). Confidence intervals were computed using robust standard errors. 33 34

The code for the analysis was written and tested on synthetic data (extending the Simulacrum dataset) 35 by a statistician (MF) at King’s College London and then run on the real dataset by an analyst (BN) at the National Disease Registration Service.

We started by considering a core model where we included the main effects for age, period, and birth cohort, along with selected age by cohort and age by period interactions (see supplementary table S1). The interaction terms were included to account for variations in screening policy and historical events that affected cervical cancer rates. Specifically, we defined seven birth cohorts to capture differences in the age at first invitation to screening and the school years in which HPV vaccination was offered (see table 1 ). We added terms for seasonality and for events that may have affected registrations for cervical cancer and CIN3, such as the covid-19 lockdown, the “Jade Goody effect,” 36 37 and the 2019 cervical screening awareness campaign. In our previous paper, 14 we used several similar regression models to study the sensitivity of results to the precise way in which we adjusted for potential confounding factors. Because we found that the estimates of the cohort specific incidence rate ratios changed little across the various models, here we report on only a single model adjustment for confounders.

Characteristics of the birth cohorts

  • View inline

Using the core model described, we investigated if the high effectiveness of the HPV immunisation programme reported previously 14 continued during an additional 12 months of follow-up. To do this we split the main effect of each cohort offered vaccination into two subgroup effects depending on whether the data related to the periods 1 January 2006 to 30 June 2019 or 1 July 2019 to 30 June 2020; this approach corresponded to adding three cohort by period interaction terms.

To evaluate the impact of socioeconomic deprivation on incidences of cervical cancer and CIN3, we extended the core model by adding main effects for deprivation and deprivation by cohort interactions. Specifically, we allowed the effect of each deprivation level to vary between unvaccinated women (cohorts 1-4) and those offered vaccination (cohorts 5-7), but we assumed it was otherwise constant within these two groups. We did not include further interactions between deprivation and other covariates as they were not of primary interest in this analysis. Using the fitted Poisson regression models, we made “what if” predictions by changing the value of one or more predictors and by leaving the others as observed. In this way it was possible to compare what happened (factual scenario) with what would have happened under an alternative (counterfactual) scenario.

We also carried out a sensitivity analysis where the effects of these deprivation by cohort interactions were allowed to vary across the three different groups offered vaccination (ie, we used 15 terms instead of five). For cervical cancer, owing to small numbers in cohort 7, we fitted a reduced model where the effects of these interactions were constrained to be the same for cohorts 6 and 7.

All analyses were performed in Stata, version 17. 38

Patient and public involvement

Patient and public involvement contributors were not formally involved in this research. We did, however, engage with Cancer Research UK (CRUK), Jo’s Cervical Cancer Trust, and the HPV Coalition on the importance of these analyses and the dissemination of the results. This included taking part in a video produced by ITN Business for World Cancer Day 2023, writing a piece for the 20th anniversary of the creation of CRUK, and engaging with international media about our research findings on the effect of the English HPV vaccination programme. We have also discussed the research and a draft of this paper with individual patients, journalists, and patient and public involvement representatives linked to broader research programmes.

Table 1 lists the characteristics of the birth cohorts included in the study. We defined the different cohorts so that each cohort is homogeneous in terms of the age women would have been offered HPV vaccination (if at all) and the age at which they would have first been invited for cervical screening.

Overall, there were 231.1 million women years of observation between 1 January 2006 and 30 June 2020 on women aged 20-64 years in England. During this time, 29 968 women received a diagnosis of invasive cervical cancer and 335 228 a diagnosis of CIN3 ( table 2 ). Observations between 1 July 2019 and 30 June 2020 have not been reported previously. With these additional 12 months of follow-up, there are, in the routine vaccination group (cohort 7), about twice the number of diagnoses compared with the same group in our previous study (we now have 13 v 7 previously for cervical cancer, 109 v 49 for CIN3; see supplementary table S2).

Summary statistics of study population

Our previously published findings on the effect of the national HPV vaccination were largely confirmed with the new data ( table 3 , also see supplementary table S3). The analysis showed that the previously observed low rates of disease and the estimated high effectiveness of the immunisation programme continued during the additional 12 months of follow-up (diagnoses in July 2019 to June 2020) among women born since 1 September 1990. In particular, the estimated effects of vaccination for that later period in cohort 7 (those born since 1 September 1995) imply a reduction in incidence of 83.9% (95% confidence interval (CI) 63.8% to 92.8%) for cervical cancer and 94.3% (92.6% to 95.7%) for CIN3 ( table 3 ). The relative risk reduction estimates for the earlier period are not identical to those reported previously because we also had new data for the unvaccinated cohorts that affected the baseline rates.

Estimated relative risk reductions (percentages) in incidence of invasive cervical cancer and CIN3 in the three cohorts offered HPV vaccination compared with the most recent unvaccinated cohort

Supplementary table S4 shows the full estimates from modelling the effects of vaccination in different levels of socioeconomic deprivation, with summary results reported in table 4 , table 5 , and table 6 . The highest incidence rates for invasive cervical cancer were observed among women living in the most deprived areas (first fifth) but, while in the reference unvaccinated group there was a strong downward gradient moving from women in the most deprived areas to those in the least deprived, little difference was found between the second and fifth fifths of deprivation in the groups offered vaccination. In both the reference and the vaccination cohorts the highest rates of CIN3 occurred in those from the most deprived areas, but no clear trend was observed among the other four fifths of deprivation (see supplementary tables S5 and S6).

Estimated number of invasive cervical cancers and CIN3s predicted and prevented by mid-2020 in the three cohorts of women offered HPV vaccination

Estimated cohort specific numbers of invasive cervical cancers predicted and prevented by mid-2020 among women in the least and most deprived areas

Estimated cohort specific numbers of CIN3 predicted and prevented by mid-2020 among women in the least and most deprived areas

Overall, our model estimated that 687 (95% CI 556 to 819) cervical cancers and 23 192 (22 163 to 24 220) CIN3s had been prevented by the vaccination programme up to mid-2020 among young women in England ( table 4 ). The greatest numbers for cervical cancer were prevented in women in the most deprived areas (192 and 199 for first and second fifths, respectively) and the fewest in women in the least deprived fifth (61 cancers prevented). The number of women with CIN3 prevented was high across all deprivation groups but greatest among women living in the more deprived areas: 5121 and 5773 for first and second fifths, respectively, compared with 4173 and 3309 in the fourth and fifth fifths, respectively. When we looked at the corresponding cohort specific figures ( table 5 and table 6 ), we noticed differences between the cohorts, particularly for CIN3. In all three cohorts offered vaccination the numbers and rates of prevented cervical cancers were much higher in women from the most deprived areas than least deprived areas ( table 5 ). The proportion of women with prevented cervical cancer in each cohort was, however, similar between the first and fifth fifths of deprivation. For CIN3 ( table 6 ), the results were more complicated. In women offered vaccination at age 16-18 years (cohort 5), the proportion of cervical cancers prevented was substantially less in those from the most deprived areas (29.6%) compared with those from the least deprived areas (40.6%). An inequality still existed in cohorts 6 and 7, but it was greatly reduced (67.7% v 72.8% in cohort 6 and 95.3% v 96.1% in cohort 7).

In England, the social-class gradient for cervical cancer is one of the steepest of any cancers: women in the most deprived fifth have had double the risk of those in the least deprived fifth. 39 40 Some of this results from differences in exposure to HPV and risk of an infection becoming persistent, 41 but differential uptake of cervical screening has also been an important factor. Previous research has highlighted the need for new engagement strategies to improve attendance for cervical screening among young women living in more socially deprived areas. 42 Encouragingly, the coverage of HPV vaccination has been (at least for the routine campaign and before the covid-19 pandemic) uniformly high. 43 It is, however, important to investigate whether immunisation—including the indirect effects achieved by high uptake—is helping to reduce health inequalities.

Using population based cancer registrations updated to mid-2020, which provided information on about twice the expected number of cancers in women offered HPV vaccination aged 12-13 years than in our previous analysis, we were able to show that the high vaccination effectiveness seen previously was confirmed with more recent data. The largest differences between the old and the new data were found for cohort 6 (the catch-up group offered the vaccine at age 14-16 years): for cervical cancer the estimated effectiveness increased, whereas for CIN3 it decreased. The reasons behind these differences are unclear. The results for cohorts 6 and 7 in the new data are more in keeping with what we would have expected given that the proportion of disease caused by HPV types 16 and 18 is greater for invasive cancer than for CIN3.

We also investigated the effect of the HPV immunisation programme by socioeconomic deprivation. Overall, we found that the programme was associated with a substantial reduction in the expected number of women with cervical cancers and CIN3 in all fifths of deprivation. For cervical cancer before vaccination, the downward gradient with decreasing deprivation was strong. In all cohorts offered vaccination, the highest rate was still seen among women living in the most deprived areas, but little difference was observed between women living in the second to fifth deprived areas. For CIN3, similar patterns were observed for the reference unvaccinated group and the three cohorts offered vaccination, but rates were greatly reduced in all fifths of deprivation in the latter. When we compared women in the most deprived areas with those in the least deprived areas in terms of percentage of disease averted, we observed differences across the cohorts for CIN3, with women in the least deprived areas in the older catch-up cohort (vaccine offered at age 16-18 years) having a greater proportion of averted CIN3s after HPV immunisation than women in the most deprived area (40.6% v 29.6%). The same, although to a much less extent, was observed for the younger catch-up cohort (72.8% v 67.7%). For invasive cervical cancer, we found no evidence of a less beneficial impact (in terms of percentage of cases averted) of the vaccination in women living in the most deprived areas; in fact, especially for the older catch-up cohort, the percentage was slightly higher in women in the most deprived areas compared with those in the least deprived areas.

The observed incidences of cervical cancer and CIN3 depend on three key factors: the intensity of exposure to HPV infections (including age at first exposure), the uptake of cervical screening, and HPV vaccination coverage. It is therefore difficult to disentangle the effects of these three drivers on the index of multiple deprivation specific rates with the data at hand. The health inequality in CIN3 in cohort 5 might result from the lower vaccination coverage among women in the most deprived areas since at age 16-18 years when they became eligible for vaccination more of those from the most deprived fifth may not have been in school or, for other reasons, may have missed the offer of HPV immunisation. These observations are consistent with previous understanding that higher uptake of catch-up vaccination was associated, although not as strongly as in some countries, with lower deprivation. 25 It is, however, reassuring that cohorts 6 and 7 showed little inequality in relative reductions in cancer (as in vaccination coverage).

However, since the UK has recently announced a change to a one dose schedule for routine HPV vaccination, ensuring this change achieves high coverage (including in the birth cohorts currently with lower coverage owing to covid-19 related interruption to schooling, and to immunisation services) is important to maintain the effects we have seen on cervical disease and on inequalities. Further investigations could be carried out in the future to check for any effect on cancer incidence caused by covid-19, gender neutral vaccination (since 2019), a change in the type of vaccine used, or reduced dose schedules.

Strengths and limitations of this study

Our analysis has several strengths. Our study provides direct evidence for the effect of a public health intervention (such as HPV vaccination) on cancer rates by deprivation. We used high quality data from population based cancer registries and were able to investigate the extent of socioeconomic inequalities in cohorts offered vaccination and whether the effectiveness of the HPV immunisation continued in an additional year of follow-up. The code for the analysis was written and tested using simulated data and an independent analyst later ran the code on the real dataset, guaranteeing reliable and robust results and preserving patient confidentiality.

The main limitations of our study are that it was observational and individual level data on vaccination status were not available. However, previous published research 14 provided detailed information on potential confounding factors and the best way to adjust for these in the analysis. Additionally, the discontinuities in vaccine uptake with date of birth makes this study powerful and less prone to biases from unobserved confounders than an analysis based on individual level data on HPV vaccination status.

Women born after 1 September 1999 were offered the Gardasil vaccine from 1 September 2012. As these women were at most aged 20 years and 10 months at the end of the study follow-up (30 June 2020), it is not yet possible with the data available to compare the effectiveness of the programme among those offered Cervarix and those offered Gardasil. This additional comparative analysis will become feasible with a longer follow-up on the recipients of Gardasil.

Policy implications

We found that the high effectiveness of the national HPV immunisation continued in the additional year of follow-up (July 2019 to June 2020). This is encouraging as it validates the previously published results and further supports consideration of more limited cervical screening for cohorts with high vaccination coverage aged 12-13 years. Moreover, although women living in the most deprived areas are still at higher risk of cervical cancer than those in less deprived areas, the HPV vaccination programme is associated with substantially lowered rates of disease across all fifths of socioeconomic deprivation. For cervical cancer, this has led to the levelling-up of the rates across the second to fifth fifths of deprivation so that the strong downward gradient observed in the reference unvaccinated cohort is no longer present in the cohorts offered vaccination. For CIN3, in the older catch-up cohorts women living in the least deprived areas seem to have benefited more from vaccination than those living in the most deprived areas, but the rates were still greatly reduced in all socioeconomic groups. Cervical screening strategies for women offered vaccination should carefully consider the differential effect both on rates of disease and on inequalities that are evident among women offered catch-up vaccination.

Conclusions

The HPV vaccination programme in England has not only been associated with a substantial reduction in incidence of cervical neoplasia in targeted cohorts, but also in all socioeconomic groups. This shows that well planned and executed public health interventions can both improve health and reduce health inequalities.

What is already known on this topic

In England, immunisation against human papillomavirus (HPV) has been associated with greatly reduced incidence rates of cervical cancer and grade 3 cervical intraepithelial neoplasia (CIN3) up to June 2019, especially among women offered routine vaccination at age 12-13 years

The social-class gradient for cervical cancer incidence has been one of the steepest of any cancers

Concern has been raised that HPV vaccination could least benefit those at highest risk of cervical cancer

What this study adds

The high effectiveness of vaccination against HPV seen previously continued during an additional year of follow-up, from July 2019 to June 2020

The English HPV vaccination programme was associated with substantially lower rates of cervical cancer and CIN3 in all fifths of socioeconomic deprivation, although the highest rates remained among women in the most deprived areas

For cervical cancer, the strong downward gradient from high to low deprivation observed in the reference unvaccinated cohort was no longer present among those offered vaccination

Ethics statements

Ethical approval.

Not required as the study used aggregated data from the National Disease Registration Service as well as publicly available information from the Office for National Statistics website.

Data availability statement

The cancer registry data analysed for this paper are securely held by the National Disease Registration Service (NDRS). Requests to access the data can be made through NHS England’s DARS service ( https://digital.nhs.uk/services/data-access-request-service-dars ). The Simulacrum ( https://simulacrum.healthdatainsight.org.uk/ ) is a synthetic dataset developed by Health Data Insight and derived from anonymous cancer data provided by NHS England’s NDRS. Mid-year population estimates are freely downloadable from the Office for National Statistics website ( https://www.ons.gov.uk/ ).

Acknowledgments

We thank Alejandra Castañon (LCP Health Analytics), Marta Checchi (UK Health Security Agency), and Lucy Elliss-Brookes (NHS England) for helpful comments on the study protocol, and Kwok Wong (NHS England) for contributing to the quality assurance of the data extraction code.

Contributors: PS had the original idea. He is the guarantor. MF and PS conceptualised the study and prepared the study protocol, which was subsequently reviewed by the other co-authors. MF wrote and tested the Stata code (checked by PS) for the data analysis and drafted the manuscript. BN extracted the dataset and ran the Stata code on it. All authors critically reviewed and approved the final submitted version. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This work was supported by Cancer Research UK (grant No C8162/A27047). The funder had no role in the study design or in the collection, analysis, interpretation of data, writing of the report or decision to submit the article for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare support from Cancer Research UK for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Transparency: The lead author (the manuscript’s guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: The results of this research will be disseminated through the media, blogs and scientific meetings and will inform the design and implementation of interventions to reduce health inequalities. We will also work with others to produce information for the public to support human papillomavirus immunisation and cervical screening programmes and, if the opportunity arises, to contribute summary data for an international meta-analysis of similar studies.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ .

  • ↵ IARC. Human papillomaviruses. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, Volume 90. 2007.
  • ↵ World Health Organization (WHO). Global Market Study: HPV 2022 https://cdn.who.int/media/docs/default-source/immunization/mi4a/who-mi4a-global-market-study-hpv.pdf?sfvrsn=649561b3_1&download=true .
  • Cuschieri K ,
  • Hibbitts S ,
  • ↵ Public Health England (PHE). Human Papillomavirus (HPV) vaccine coverage in England, 2008/09 to 2013/14. A review of the full six years of the three-dose schedule: Public Health England (PHE); 2015. https://www.gov.uk/government/publications/human-papillomavirus-hpv-immunisation-programme-review-2008-to-2014 ; accessed 6 January 2021.
  • ↵ UK Health Security Agency. HPV vaccination: guidance for healthcare practitioners (version 6) 2022 [updated April 2022]. https://www.gov.uk/government/publications/hpv-universal-vaccination-guidance-for-health-professionals ; accessed 24 August 2022.
  • Lévesque LE ,
  • Kaufman JS ,
  • Brisson M ,
  • HPV Vaccination Impact Study Group
  • Thomas SL ,
  • Tabrizi SN ,
  • Brotherton JM ,
  • Kaldor JM ,
  • Markowitz LE ,
  • Steinau M ,
  • Hernandez-Aguado JJ ,
  • Sánchez Torres DA ,
  • Martínez Lamela E ,
  • Lehtinen M ,
  • Lagheden C ,
  • Luostarinen T ,
  • Falcaro M ,
  • Castañon A ,
  • Wallace L ,
  • Pollock KG ,
  • Elfström KM ,
  • Skorstengaard M ,
  • Thamsborg LH ,
  • Dillner J ,
  • Dehlendorff C ,
  • Belmonte F ,
  • ↵ NHS. The NHS long term plan 2019. https://www.longtermplan.nhs.uk/ ; accessed 24 August 2022.
  • Johnson HC ,
  • Lafferty EI ,
  • Roberts SA ,
  • Stretch R ,
  • Sheridan A ,
  • Pappas-Gogos G ,
  • Douglas E ,
  • McLennan D ,
  • Henson KE ,
  • Elliss-Brookes L ,
  • Coupland VH ,
  • ↵ Office for National Statistics. https://www.ons.gov.uk/ ; accessed 24 October 2022.
  • Carstensen B
  • Sasieni P ,
  • ↵ Huber P. The behavior of maximum likelihood estimates under nonstandard conditions. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability: University of California Press, 1967:221-33.
  • Health Data Insight
  • Lancucki L ,
  • Patnick J ,
  • Castanon A ,
  • Thomson CS ,
  • UK Association of Cancer Registries
  • ↵ Cancer Research UK. Cervical Cancer Incidence Statistics 2015. https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/cervical-cancer/incidence ; accessed 14 March 2023.
  • Currin LG ,
  • Linklater KM ,
  • Rahman MA ,
  • Paranjothy S
  • ↵ UK Health Security Agency (UKHSA). HPV vaccine uptake 2023. https://www.gov.uk/government/collections/vaccine-uptake#hpv-vaccine-uptake ; accessed 12 March 2023.

statistical research journal

Log in using your username and password

  • Search More Search for this keyword Advanced search
  • Latest content
  • Publish with us
  • About the journal
  • Meet the editors
  • Specialist reviews
  • BMJ Journals More You are viewing from: Google Indexer

You are here

  • Volume 3, Issue 1
  • Regular use of fish oil supplements and course of cardiovascular diseases: prospective cohort study
  • Article Text
  • Article info
  • Citation Tools
  • Rapid Responses
  • Article metrics

Download PDF

  • Ge Chen 1 ,
  • Zhengmin (Min) Qian 2 ,
  • Junguo Zhang 1 ,
  • Shiyu Zhang 1 ,
  • http://orcid.org/0000-0002-7003-6565 Zilong Zhang 1 ,
  • Michael G Vaughn 3 ,
  • Hannah E Aaron 2 ,
  • Chuangshi Wang 4 ,
  • Gregory YH Lip 5 , 6 and
  • http://orcid.org/0000-0002-3643-9408 Hualiang Lin 1
  • 1 Department of Epidemiology , Sun Yat-Sen University , Guangzhou , China
  • 2 Department of Epidemiology and Biostatistics, College for Public Health and Social Justice , Saint Louis University , Saint Louis , Missouri , USA
  • 3 School of Social Work, College for Public Health and Social Justice , Saint Louis University , Saint Louis , Missouri , USA
  • 4 Medical Research and Biometrics Centre , Fuwai Hospital, National Centre for Cardiovascular Diseases, Peking Union Medical College , Beijing , China
  • 5 Liverpool Centre for Cardiovascular Science , University of Liverpool and Liverpool Heart and Chest Hospital , Liverpool , UK
  • 6 Department of Clinical Medicine , Aalborg University , Aalborg , Denmark
  • Correspondence to Dr Hualiang Lin, Department of Epidemiology, Sun Yat-Sen University, Guangzhou, Guangdong 510080, China; linhualiang{at}mail.sysu.edu.cn

Objective To examine the effects of fish oil supplements on the clinical course of cardiovascular disease, from a healthy state to atrial fibrillation, major adverse cardiovascular events, and subsequently death.

Design Prospective cohort study.

Setting UK Biobank study, 1 January 2006 to 31 December 2010, with follow-up to 31 March 2021 (median follow-up 11.9 years).

Participants 415 737 participants, aged 40-69 years, enrolled in the UK Biobank study.

Main outcome measures Incident cases of atrial fibrillation, major adverse cardiovascular events, and death, identified by linkage to hospital inpatient records and death registries. Role of fish oil supplements in different progressive stages of cardiovascular diseases, from healthy status (primary stage), to atrial fibrillation (secondary stage), major adverse cardiovascular events (tertiary stage), and death (end stage).

Results Among 415 737 participants free of cardiovascular diseases, 18 367 patients with incident atrial fibrillation, 22 636 with major adverse cardiovascular events, and 22 140 deaths during follow-up were identified. Regular use of fish oil supplements had different roles in the transitions from healthy status to atrial fibrillation, to major adverse cardiovascular events, and then to death. For people without cardiovascular disease, hazard ratios were 1.13 (95% confidence interval 1.10 to 1.17) for the transition from healthy status to atrial fibrillation and 1.05 (1.00 to 1.11) from healthy status to stroke. For participants with a diagnosis of a known cardiovascular disease, regular use of fish oil supplements was beneficial for transitions from atrial fibrillation to major adverse cardiovascular events (hazard ratio 0.92, 0.87 to 0.98), atrial fibrillation to myocardial infarction (0.85, 0.76 to 0.96), and heart failure to death (0.91, 0.84 to 0.99).

Conclusions Regular use of fish oil supplements might be a risk factor for atrial fibrillation and stroke among the general population but could be beneficial for progression of cardiovascular disease from atrial fibrillation to major adverse cardiovascular events, and from atrial fibrillation to death. Further studies are needed to determine the precise mechanisms for the development and prognosis of cardiovascular disease events with regular use of fish oil supplements.

  • Health policy
  • Nutritional sciences
  • Public health

Data availability statement

Data are available upon reasonable request. UK Biobank is an open access resource. Bona fide researchers can apply to use the UK Biobank dataset by registering and applying at http://ukbiobank.ac.uk/register-apply/ .

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/bmjmed-2022-000451

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

WHAT IS ALREADY KNOWN ON THIS TOPIC

Findings of the effects of omega 3 fatty acids or fish oil on the risk of cardiovascular disease are controversial

Most previous studies focused on one health outcome and did not characterise specific cardiovascular disease outcomes (eg, atrial fibrillation, myocardial infarction, stroke, heart failure, and major adverse cardiovascular events)

Whether fish oil could differentially affect the dynamic course of cardiovascular diseases, from atrial fibrillation to major adverse cardiovascular events, to other specific cardiovascular disease outcomes, or even to death, is unclear

WHAT THIS STUDY ADDS

In people with no known cardiovascular disease, regular use of fish oil supplements was associated with an increased relative risk of atrial fibrillation and stroke

In people with known cardiovascular disease, the beneficial effects of fish oil supplements were seen on transitions from atrial fibrillation to major adverse cardiovascular events, atrial fibrillation to myocardial infarction, and heart failure to death

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE, OR POLICY

Regular use of fish oil supplements might have different roles in the progression of cardiovascular disease

Further studies are needed to determine the precise mechanisms for the development and prognosis of cardiovascular disease events with regular use of fish oil supplements

Introduction

Cardiovascular disease is the leading cause of death worldwide, accounting for about one sixth of overall mortality in the UK. 1 2 Fish oil, a rich source of omega 3 fatty acids, containing eicosapentaenoic acid and docosahexaenoic acid, has been recommended as a dietary measure to prevent cardiovascular disease. 3 The UK National Institute for Health and Care Excellence recommends that people with or at high risk of cardiovascular disease consume at least one portion of oily fish a week, and the use of fish oil supplements has become popular in the UK and other western countries in recent years. 4 5

Although some epidemiological and clinical studies have assessed the effect of omega 3 fatty acids or fish oil on cardiovascular disease and its risk factors, the findings are controversial. The Agency for Healthcare Research and Quality systematically reviewed 37 observational studies and 61 randomised controlled trials, and found evidence indicating the beneficial effects of higher consumption of fish oil supplements on ischaemic stroke, whereas no beneficial effect was found for atrial fibrillation, major adverse cardiovascular events, myocardial infarction, total stroke, or all cause death. 6 In contrast, the Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial (REDUCE-IT) reported a decreased risk of major adverse cardiovascular events with icosapent ethyl in patients with raised levels of triglycerides, regardless of the use of statins. 7 Most of these findings, however, tended to assess the role of fish oil at a certain stage of cardiovascular disease. For example, some studies restricted the study population to people with a specific cardiovascular disease or at a high risk of cardiovascular disease, 8 9 whereas others evaluated databases of generally healthy populations. 10 All of these factors might preclude direct comparison of the effects of omega 3 fatty acids on atrial fibrillation events or on further deterioration of cardiovascular disease. Few studies have fully characterised specific cardiovascular disease outcomes or accounted for differential effects based on the complex disease characteristics of participants. Hence, in this study, we hypothesised that fish oil supplements might have harmful, beneficial, or no effect on different cardiovascular disease events in patients with varying health conditions.

Most previous studies on the association between fish oil and cardiovascular diseases generally focused on one health outcome. Also, no study highlighted the dynamic progressive course of cardiovascular diseases, from healthy status (primary stage), to atrial fibrillation (secondary stage), major adverse cardiovascular events (tertiary stage), and death (end stage). Clarifying this complex pathway in relation to the detailed progression of cardiovascular diseases would provide substantial insights into the prevention or treatment of future disease at critical stages. Whether fish oil could differentially affect the dynamic course of cardiovascular disease (ie, from atrial fibrillation to major adverse cardiovascular events, to other specific cardiovascular disease outcomes, or even to death) is unclear.

To deal with this evidence gap, we conducted a longitudinal cohort study to estimate the associations between fish oil supplements and specific clinical cardiovascular disease outcomes, including atrial fibrillation, major adverse cardiovascular events, and all cause death in people with no known cardiovascular disease or at high risk of cardiovascular disease for the purpose of primary prevention. We also assessed the modifying effects of fish oil supplements on the disease process, from atrial fibrillation to other outcomes, in people with known cardiovascular disease for the purpose of secondary prevention.

The UK Biobank is a community based cohort study with more than half a million UK inhabitants aged 40-69 years at recruitment. 11–13 Participants were invited to participate in this study if they were registered with the NHS and lived within 35 km of one of 22 Biobank assessment centres. Between 1 March 2006 and 31 July 2010, a baseline survey was conducted, based on a touch screen questionnaire and face-to-face interviews, to collect detailed personal, socioeconomic, and lifestyle characteristics, and information on diseases. 11–13

We excluded patients who had a diagnosis of atrial fibrillation (n=8326), heart failure (n=2748), myocardial infarction (n=11 949), stroke (n=7943), or cancer (n=48 624) at baseline; who withdrew from the study during follow-up (n=1299); or who had incomplete or outlier data for the main information (n=11 748). Because we focused only on a specific sequence of progression of cardiovascular disease (ie, from healthy status to atrial fibrillation, to major adverse cardiovascular events, and then to death), we excluded 1983 participants with other transition patterns. The remaining 415 737 participants were included in this analysis ( figure 1 ).

  • Download figure
  • Open in new tab
  • Download powerpoint

Flowchart of selection of participants in study. The count of diagnosed diseases does not equate to the total number of individuals, because each person could have multiple diagnoses

Determining use of fish oil supplements

Information on regular use of fish oil supplements was collected from a self-reported touchscreen questionnaire during the baseline survey. 14 15 Each participant was asked whether they regularly used any fish oil supplement. Trained staff conducted a verbal interview with participants, asking if they were currently receiving treatments or taking any medicines, including omega 3 or fish oil supplements. Based on this information, we classified participants as regular users of fish oil supplements and non-users.

Follow-up and outcomes

Participants were followed up from the time of recruitment to death, loss to follow-up, or the end date of follow-up (31 March 2021), whichever came first. Incident cases of interest, including atrial fibrillation, heart failure, stroke, and myocardial infarction, were identified by linkage to death registries, primary care records, and hospital inpatient records. 11 Information on deaths was obtained from death registries of the NHS Information Centre, for participants in England and Wales, and from the NHS Central Register Scotland, for participants in Scotland. 11 Outcomes were defined by a three character ICD-10 (international classification of diseases, 10th revision) code. In this study, atrial fibrillation was defined by ICD-10 code I48, and major adverse cardiovascular events was determined by a combination of heart failure (I50, I11.0, I13.0, and I13.2), stroke (I60-I64), and myocardial infarction (I21, I22, I23, I24.1, and I25.2) codes.

We collected baseline data on age (<65 years and ≥65 years), sex (men and women), ethnic group (white and non-white), Townsend deprivation index (with a higher score indicating higher levels of deprivation), smoking status (never, previous, and current smokers), and alcohol consumption (never, previous, and current drinkers). Data for sex were taken from information in UK Biobank rather than from patient reported gender. Baseline dietary data were obtained from a dietary questionnaire completed by the patient or by an interviewer. The questionnaire was established for each nation (ie, England, Scotland, and Wales) to assess an individual's usual food intake (oily fish, non-oily fish, vegetables, fruit, and red meat). Diabetes mellitus was defined by ICD-10 codes E10-E14, self-reported physician's diagnosis, self-reported use of antidiabetic drugs, or haemoglobin A1c level ≥6.5% at baseline. Hypertension was defined by ICD-10 code I10 or I15, self-reported physician's diagnosis, self-reported use of antihypertensive drugs, or measured systolic and diastolic blood pressure ≥130/85 mm Hg at baseline. Information on other comorbidities (obesity (ICD-10 code E66), chronic obstructive pulmonary disease (J44), and chronic renal failure (N18)) was extracted from the first occurrence (UKB category ID 1712). Information on the use of drugs, including antihypertensive drugs, antidiabetic drug, and statins, was extracted from treatment and drug use records. Biochemistry markers were measured immediately at the central laboratory from serum samples collected at baseline. Binge drinking was defined as consumption of ≥6 standard drinks/day for women or ≥8 standard drinks/day for men. Detailed information on alcohol consumption and binge drinking in the UK Biobank was reported previously. 16

Statistical analysis

Characteristics of participants are summarised as number (percentages) for categorical variables and mean (standard deviation (SD)) for continuous variables. Comparisons between regular users of fish oil supplements and non-users were made with the χ 2 test or Student's t test.

We used a multi-state regression model to assess the role of regular use of fish oil supplements in the temporal disease progression from healthy status to atrial fibrillation, to major adverse cardiovascular events, and subsequently to death. The multi-state model is an extension of competing risks survival analysis. 17–19 The model allows simultaneous estimation of the role of risk factors in transitions from a healthy state to atrial fibrillation (transition A), healthy state to major adverse cardiovascular events (transition B), healthy state to death (transition C), atrial fibrillation to major adverse cardiovascular events (transition D), atrial fibrillation to death (transition E), and major adverse cardiovascular events to death (transition F) (transition pattern I, figure 2 ). The focus on these six transitions rather than on all possible health state transitions was preplanned and evidence based. If participants entered different states on the same date, we used the date of the theoretically previous state as the entry date of the latter state minus 0.5 days.

Numbers of participants in transition pattern I, from baseline to atrial fibrillation, major adverse cardiovascular events, and death

We further examined the effects of regular use of fish oil supplements on other pathways. For example, we divided major adverse cardiovascular events into three individual diseases (heart failure, stroke, and myocardial infarction), resulting in three independent pathways (transition patterns II, III, and IV, online supplemental figures S1–S3 ). All models were adjusted for age, sex, ethnic group, Townsend deprivation index, consumption of oily fish, consumption of non-oily fish, smoking status, alcohol consumption, obesity, hypertension, diabetes mellitus, chronic obstructive pulmonary disease, chronic renal failure, and use of statins, antidiabetic drugs, and antihypertensive drugs.

Supplemental material

We conducted several sensitivity analyses for the multi-state analyses of transition pattern A: additionally adjusting for setting (urban and rural), body mass index (underweight, normal, overweight, and obese), and physical activity (low, moderate, and high) in the model; adjusting for binge drinking rather than alcohol consumption; additionally adjusting for other variables of dietary intake (consumption of vegetables, fruit, and red meat); calculating participants' entry date into the previous state with different time intervals (0.5 years, one year, and two years); excluding participants who entered different states on the same date; excluding events occurring in the first two years of follow-up; restricting the follow-up date to 31 March 2020 to evaluate the influence of the covid-19 pandemic; and the use of the inverse probability weighted method to deal with biases between the regular users and non-users of fish oil supplements. Also, we conducted grouped analyses for sex, age group, ethnic group, smoking status, consumption of oily fish, consumption of non-oily fish, hypertension, and drug use, to examine effect modification. The interactions were tested with the likelihood ratio test. All analyses were carried out with R software (version 4.0.3), and the multi-model analysis was performed with the mstate package. A two tailed P value <0.05 was considered significant.

Patient and public involvement

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research. Participants were involved in developing the ethics and governance framework for UK Biobank and have been engaged in the progress of UK Biobank through follow-up questionnaires and additional assessment visits. UK Biobank keeps participants informed of all research output through the study website ( https://www.ukbiobank.ac.uk/explore-your-participation ), participant events, and newsletters.

A total of 415 737 participants (mean age 55.9 (SD 8.1) years; 55% women), aged 40-69 years, were analysed, and 31.4% (n=1 30 365) of participants reported regular use of fish oil supplements at baseline ( figure 1 ). Table 1 shows the characteristics of regular users (n=130 365) and non-users (n=285 372) of fish oil supplements. In the group of regular users of fish oil supplements, we found higher proportions of elderly people (22.6% v 13.9%), white people (95.1% v 94.2%), and women (57.6% v 53.9%), and higher consumption of alcohol (93.1% v 92.0%), oily fish (22.1% v 15.4%), and non-oily fish (18.0% v 15.4%) than non-users. The Townsend deprivation index (mean −1.5 (SD 3.0) v −1.3 (3.0)) and the proportion of current smokers (8.1% v 11.4%) were lower in regular users of fish oil supplements. Online supplemental table S1 provides more details on patient characteristics and online supplemental table S2 compares the basic characteristics of included and excluded people.

  • View inline

Baseline characteristics of study participants grouped by use of fish oil supplements

Over a median follow-up time of of 11.9 years, 18 367 participants had atrial fibrillation (transition A) and 17 826 participants had major adverse cardiovascular events (transition B); 14 902 participants died without having atrial fibrillation or major adverse cardiovascular events (transition C). Among patients with incident atrial fibrillation, 4810 developed major adverse cardiovascular events (transition D) and 1653 died (transition E). Among patients with incident major adverse cardiovascular events, 5585 died during follow-up (transition F, figure 2 ). In separate analyses for individual diseases (transition patterns II, III, and IV, online supplemental figures S1–S3 ), in patients with atrial fibrillation, 3085 developed heart failure, 1180 had a stroke, and 1415 had a myocardial infarction. During follow-up, 2436, 2088, and 2098 deaths occurred in patients with heart failure, stroke, and myocardial infarction, respectively.

Multi-state regression results

Table 2 shows the different roles of regular use of fish oil supplements in transitions from healthy status to atrial fibrillation, to major adverse cardiovascular events, and then to death. For individuals in the primary stage (healthy status), we found that the use of fish oil supplements had a harmful effect on the transition from health to atrial fibrillation, with an adjusted hazard ratio of 1.13 (95% CI 1.10 to 1.17, transition A). The hazard ratio for transition B (from health to major adverse cardiovascular events) was 1.00 (95% CI 0.97 to 1.04) and for transition C (from health to death) was 0.98 (0.95 to 1.02).

Hazard ratios (95% confidence intervals) for each transition, for different transition patterns for progressive cardiovascular disease by regular use of fish oil supplements

For individuals in the secondary stage (atrial fibrillation) at the beginning of the study, regular use of fish oil supplements decreased the risk of major adverse cardiovascular events (transition D, hazard ratio 0.92, 95% CI 0.87 to 0.98), and had a borderline protective effect on the transition from atrial fibrillation to death (transition E, 0.91, 0.82 to 1.01). For transition F, from major adverse cardiovascular events to death, after adjusting for covariates, the hazard ratio was 0.99 (0.94 to 1.06, transition pattern I, table 2 ).

We divided major adverse cardiovascular events into three individual diseases (ie, heart failure, stroke, and myocardial infarction) and found that regular use of fish oil supplements was marginally associated with an increased risk of stroke in people with a healthy cardiovascular state (hazard ratio 1.05, 95% CI 1.00 to 1.11), whereas a protective effect was found in transitions from healthy cardiovascular states to heart failure (0.92, 0.86 to 0.98). For patients with atrial fibrillation, we found that the beneficial effects of regular use of fish oil supplements were for transitions from atrial fibrillation to myocardial infarction (0.85, 0.76 to 0.96), and from atrial fibrillation to death (0.88, 0.81 to 0.95) for transition pattern IV. For patients with heart failure, we found a protective effect of regular use of fish oil supplements on the risk of mortality (0.91, 0.84 to 0.99) (transition patterns II, III, and IV, table 2 ).

Stratified and sensitivity analyses

We found that age, sex, smoking, consumption of non-oily fish, prevalent hypertension, and use of statins and antihypertensive drugs modified the associations between regular use of fish oil supplements and the transition from healthy states to atrial fibrillation ( online supplemental figure S4 ). We found that the association between regular use of fish oil supplements and risk of transition from healthy states to major adverse cardiovascular events was greater in women (hazard ratio 1.06, 95% CI 1.00 to 1.11, P value for interaction=0.005) and non-smoking participants (1.06, 1.06 to 1.11, P value for interaction=0.001) ( online supplemental figure S4 ). The protective effect of regular use of fish oil supplements on the transition from healthy states to death was greater in men (hazard ratio 0.93, 95% CI 0.89 to 0.98, P value for interaction=0.003) and older participants (0.91, 0.86 to o 0.96, P value for interaction=0.002) ( online supplemental figures S5 and S6 ). The results were not substantially changed in the sensitivity analyses ( online supplemental table S3 ).

Principal findings

Our study characterised the regular use of fish oil supplements on the progressive course of cardiovascular disease, from a healthy state (primary stage), to atrial fibrillation (secondary stage), major adverse cardiovascular events (tertiary stage), and death (end stage). In this prospective analysis of more than 400 000 UK adults, we found that regular use of fish oil supplements could have a differential role in the progression of cardiovascular disease. For people with a healthy cardiovascular profile, regular use of fish oil supplements, a choice of primary prevention, was associated with an increased risk of atrial fibrillation. For participants with a diagnosis of atrial fibrillation, however, regular use of fish oil supplements, as secondary prevention, had a protective effect or no effect on transitions from atrial fibrillation to major adverse cardiovascular events, atrial fibrillation to death, and major adverse cardiovascular events to death. When we divided major adverse cardiovascular events into three individual diseases (ie, heart failure, stroke, and myocardial infarction), we found associations that could suggest a mildly harmful effect between regular use of fish oil supplements and transitions from a healthy cardiovascular state to stroke, whereas potential beneficial associations were found between regular use of fish oil supplements and transitions from atrial fibrillation to myocardial infarction, atrial fibrillation to death, and heart failure to death.

Comparison with other studies

Primary prevention.

The cardiovascular benefits of regular use of fish oil supplements have been examined in numerous studies but the results are controversial. Extending previous reports, our study estimated the associations between regular use of fish oil supplements and specific clinical cardiovascular disease outcomes in people with no known cardiovascular disease. Our findings are in agreement with the results of several previous randomised controlled trials and meta-analyses. The Long-Term Outcomes Study to Assess Statin Residual Risk with Epanova in High Cardiovascular Risk Patients with Hypertriglyceridaemia (STRENGTH) reported that consumption of 4 g/day of marine omega 3 fatty acids was associated with a 69% higher risk of new onset atrial fibrillation in people at high risk of cardiovascular disease. 20 A meta-analysis of seven randomised controlled trials showed that users of marine omega 3 fatty acids supplements had a higher risk of atrial fibrillation events, with a hazard ratio of 1.25 (95% CI 1.07 to 1.46, P=0.013). 21 The Vitamin D and Omega-3 Trial (VITAL Rhythm study), a large trial of omega 3 fatty acids for the primary prevention of cardiovascular disease in adults aged ≥50 years, however, found no effects on incident atrial fibrillation, major adverse cardiovascular events, or cardiovascular disease mortality among those treated with 840 mg/day of marine omega 3 fatty acids compared with placebo. 10 22

One possible explanation for the inconsistent results in these studies is that adverse effects might be related to dose and composition. Higher doses of omega 3 fatty acids used in previous studies might have had an important role in causing an adverse effect on atrial fibrillation. 21 One study found that high concentrations of fish oil altered cell membrane properties and inhibited Na-K-ATPase pump activity, whereas a low concentration of fish oil minimised peroxidation potential and optimised activity. 23 In another study, individuals with atrial fibrillation or flutter had higher percentages of total polyunsaturated fatty acids, and n-3 and n-6 polyunsaturated fatty acids, on red blood cell membranes than healthy controls. 24

In terms of composition of omega 3 fatty acids, a recent meta-analysis showed that eicosapentaenoic acid alone can be more effective at reducing the risk of cardiovascular disease than the combined effect of eicosapentaenoic acid and docosahexaenoic acid. 25 Similar outcomes were reported in the INSPIRE study, which showed that higher levels of docosahexaenoic acid reduced the cardiovascular benefits of eicosapentaenoic acid when given as a combination. 26 Another possible explanation is that age, sex, ethnic group, smoking status, dietary patterns, and use of statins and antidiabetic drugs by participants might modify the effects of regular use of fish oil supplements on cardiovascular disease events. Despite these differences in risk estimates, our findings do not support the use of fish oil or omega 3 fatty acid supplements for the primary prevention of incident atrial fibrillation or other specific clinical cardiovascular disease events in generally healthy individuals. Caution might be warranted when fish oil supplements are used for primary prevention because of the uncertain cardiovascular benefits.

Secondary prevention

Our large scale cohort study assessed the role of regular use of fish oil supplements on the disease process, from atrial fibrillation to more serious cardiovascular disease stages, to death, in people with known cardiovascular disease. Contrary to the observations for primary prevention, we found associations that could suggest beneficial effects between regular use of fish oil supplements and most cardiovascular disease transitions. No associations were found between regular use of fish oil supplements and transitions from atrial fibrillation to death, or from major adverse cardiovascular events to death.

Consistent with our hypothesis, the Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto Miocardico (GISSI) Prevenzione study reported an association between administration of low dose prescriptions of n-3 polyunsaturated fatty acids and reduced cardiovascular events in patients with recent myocardial infarction. 27 A meta-analysis of 16 randomised controlled trials also reported a tendency towards a greater beneficial effect for secondary prevention in patients with cardiovascular disease. 28 Why patients with previous atrial fibrillation benefit is unclear. These findings indicate that triglyceride independent effects of omega 3 fatty acids might in part be responsible for the benefits in cardiovascular disease seen in previous trials. 29–31 No proven biological mechanism for this explanation exists, however, and the dose and formulation of omega 3 fatty acids used in clinical practice are not known.

For the disease process, from cardiovascular disease to death, our findings are consistent with the results of secondary prevention trials of omega 3 fatty acids, which have mostly shown a weak or neutral preventive effect in all cause mortality with oil fish supplements. The GISSI heart failure trial (GISSI-HF), conducted in 6975 patients with chronic heart failure, reported that supplemental omega 3 fatty acids reduced the risk of all cause mortality by 9% (hazard ratio 0.91, 95% CI 0.833 to 0.998, P=0.041). 32 Zelniker et al showed that omega 3 fatty acids were inversely associated with a lower incidence of sudden cardiac death in patients with non-ST segment elevation acute coronary syndrome. 33 A meta-analysis found that use of omega 3 supplements of ≤1 capsule/day was not associated with all cause mortality, but among participants with a risk of cardiovascular disease, taking a higher dose was associated with a reduction in cardiac death and sudden death. 28 Individuals who might benefit the most from fish oil or omega 3 fatty acid supplements are possibly more vulnerable individuals, such as those with previous cardiovascular diseases and those who can no longer live in the community. How fish oil supplements stop further deterioration of cardiovascular disease is unclear, but the theory that supplemental omega 3 fatty acids might protect the coronary artery is biologically plausible, suggesting that omega 3 fatty acids have anti-inflammatory and anti-hypertriglyceridaemia effects, contributing to a reduction in thrombosis and improvement in endothelial function. 34–41 Nevertheless, the effects of omega 3 fatty acids vary according to an individual's previous use of statins, which might partly explain the different effects of fish oil supplements in people with and without cardiovascular disease.

Many studies of omega 3 fatty acids, including large scale clinical trials and meta-analyses, have not produced entirely consistent results. 21 25 42 Our study mainly explored the varied potential effects of regular use of fish oil supplements on progression of cardiovascular disease, offering an initial overview of this ongoing discussion. Our findings suggest caution in the use of fish oil supplements for primary prevention because of the uncertain cardiovascular benefits and adverse effects. Further studies are needed to determine whether potential confounders modify the effects of oil fish supplements and the precise mechanisms related to the development and prognosis of cardiovascular disease events.

Strengths and limitations of this study

The strengths of our study were the large sample size, long follow-up period, which allowed us to analyse clinically diagnosed incident diseases, and complete data on health outcomes. Another strength was our analytical strategy. The multi-state model gives less biased estimates than the conventional Cox model, and distinguished the effect of regular use of fish oil supplements on each transition in the course of cardiovascular disease.

Our study had some limitations. Firstly, as an observational study, no causal relations can be drawn from our findings. Secondly, although we adjusted for multiple covariates, residual confounding could still exist. Thirdly, information on dose and formulation of the fish oil supplements was not available in this study, so we could not evaluate potential dose dependent effects or differentiate between the effects of different fish oil formulations. Fourthly, the use of hospital inpatient data for determining atrial fibrillation events could have excluded some events triggered by acute episodes, such as surgery, trauma, and similar conditions, resulting in underestimation of the true risk because undiagnosed atrial fibrillation is a common occurrence. 43 Fifthly, most of the participants in this study were from the white ethnic group and whether the findings can be generalised to other ethnic groups is not known. Finally, our study did not consider behavioural changes in populations with different cardiovascular profiles because of limited information, and variations in outcomes for different cardiovascular states merits further exploration.

Conclusions

This large scale prospective study of a UK cohort suggested that regular use of fish oil supplements might have differential roles in the course of cardiovascular diseases. Regular use of fish oil supplements might be a risk factor for atrial fibrillation and stroke among the general population but could be beneficial for disease progression, from atrial fibrillation to major adverse cardiovascular events, and from atrial fibrillation to death. Further studies are needed to determine whether potential confounders modify the effects of oil fish supplements and the precise mechanisms for the development and prognosis of cardiovascular disease events.

Ethics statements

Patient consent for publication.

Consent obtained directly from patients.

Ethics approval

The UK Biobank study obtained ethical approval from the North West Multicentre Research ethics committee, Information Advisory Group, and the Community Health Index Advisory Group (REC reference for UK Biobank 11/NW/0382). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

This study was conducted with UK Biobank Resource (application No: 69550). We appreciate all participants and professionals contributing to UK Biobank.

  • Mensah GA ,
  • Johnson CO , et al
  • Gao MM , et al
  • Saravanan P ,
  • Davidson NC ,
  • Schmidt EB , et al
  • National Institute for Health and Care Excellence
  • Lichtenstein AH ,
  • Vadiveloo M , et al
  • Djuricic I ,
  • Miller M , et al
  • Kesse-Guyot E ,
  • Czernichow S , et al
  • Klemsdal TO ,
  • Sandvik L , et al
  • Manson JE ,
  • Lee I-M , et al
  • Gallacher J ,
  • Allen N , et al
  • Littlejohns TJ ,
  • Sudlow C , et al
  • Allen NE , et al
  • Zhong W-F ,
  • Liu S , et al
  • Wu Z , et al
  • Gallagher C ,
  • Elliott AD , et al
  • Qian SE , et al
  • Nicholls SJ ,
  • Lincoff AM ,
  • Garcia M , et al
  • Djousse L ,
  • Al-Ramady OT , et al
  • Bassuk SS ,
  • Cook NR , et al
  • Cazzola R ,
  • Della Porta M ,
  • Castiglioni S , et al
  • Viviani Anselmi C ,
  • Ferreri C ,
  • Novelli V , et al
  • Khan MS , et al
  • Knowlton K , et al
  • Marchioli R ,
  • Bomba E , et al
  • Olmastroni E ,
  • Gazzotti M , et al
  • Al Rifai M , et al
  • Tavazzi L ,
  • Maggioni AP ,
  • Marchioli R , et al
  • Zelniker TA ,
  • Morrow DA ,
  • Scirica BM , et al
  • Limonte CP ,
  • Zelnick LR ,
  • Ruzinski J , et al
  • Nelson JR ,
  • Miller PE ,
  • Van Elswyk M ,
  • Alexander DD
  • Mozaffarian D ,
  • Bornfeldt KE
  • Harris WS ,
  • Ginsberg HN ,
  • Arunakul N , et al
  • Markozannes G ,
  • Tsapas A , et al
  • Svennberg E ,
  • Engdahl J ,
  • Al-Khalili F , et al

Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

  • Data supplement 1
  • Data supplement 2

GYL and HL are joint senior authors.

Contributors HL supervised the whole project and designed the work. GC and HL directly accessed and verified the underlying data reported in the manuscript. GC contributed to data interpretation and writing of the report. ZQ, SZ, JZ, ZZ, MGV, HEA, CW, and GYHL contributed to the discussion and data interpretation, and revised the manuscript. All authors had full access to all of the data in the study and had final responsibility for the decision to submit for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. HL is the guarantor. Transparency: The lead author (guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Funding This work was supported by the Bill and Melinda Gates Foundation (grant No INV-016826). Under the grant conditions of the foundation, a creative commons attribution 4.0 generic license has already been assigned to the author accepted manuscript version that might arise from this submission. The funder had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Competing interests All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from Bill and Melinda Gates Foundation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Read the full text or download the PDF:

IMAGES

  1. Research & Reviews: Journal of Statistics vol 5 issue 3 by STM Journals

    statistical research journal

  2. How To Write a Statistical Research Paper: Tips, Topics, Outline

    statistical research journal

  3. Journal of Statistical Theory and Practice

    statistical research journal

  4. Statistics Journal|Journal of Statistics|Research Journal of Statistics

    statistical research journal

  5. Basic Statistical Tools in Research and Data Analysis || An Introduction to Research Methodology

    statistical research journal

  6. (PDF) The most-cited statistical papers

    statistical research journal

VIDEO

  1. Research Methodology and Statistical Analysis Paper Mcom 2023 #questionpaper #exam #ignou #ignoumec

  2. Statistical Review

  3. Statistical Review

  4. Finding Scopus Indexed Journals

  5. Statistical Review

  6. COPSS-NISS Leadership in Translational Statistical Research, April 30, 2024

COMMENTS

  1. JSR

    Journal of Statistical Research (JSR) is the official journal of the Institute of Statistical Research and Training since 1970. Since its inception, it has been an excellent means of transfer and communication of statistical knowledge across the globe. It publishes original research articles relating to the methodology and practice of statistics.

  2. Home

    Overview. Statistical Papers is a forum for presentation and critical assessment of statistical methods encouraging the discussion of methodological foundations and potential applications. The Journal stresses statistical methods that have broad applications, giving special attention to those relevant to the economic and social sciences.

  3. Research in Statistics

    Journal metrics Editorial board. Taylor & Francis are currently supporting a 100% APC discount for all authors. Research in Statistics is a broad open access journal publishing original research in all areas of statistics and probability. The journal focuses on broadening existing research fields, and in facilitating international collaboration ...

  4. Statistical Methods in Medical Research: Sage Journals

    Statistical Methods in Medical Research is a highly ranked, peer reviewed scholarly journal and is the leading vehicle for articles in all the main areas of medical statistics and therefore an essential reference for all medical statisticians. It is particularly useful for medical researchers dealing with data and provides a key resource for medical and statistical libraries, as well as ...

  5. Journal of the American Statistical Association

    Journal overview. Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association ( JASA ) has long been considered the premier journal of statistical science. Articles focus on statistical applications, theory, and methods throughout all disciplines that make use of data ...

  6. International Statistical Review

    International Statistical Review is the flagship journal of the International Statistical Institute (ISI) and its family of Associations. We publish research articles of broad and general interest in statistics and probability. Key topics include reviews of significant theoretical or methodological developments, statistical computing and graphics, statistics education, and application areas.

  7. Statistics

    Statistics is the application of mathematical concepts to understanding and analysing large collections of data. A central tenet of statistics is to describe the variations in a data set or ...

  8. Statistics

    Statistics is a leading international research journal that publishes high-quality research articles which develop new theory, methods and applications in any active field of statistics and statistical learning. Papers submitted for consideration should provide novel contributions to statistical theory, with rigorous mathematical proofs; or relevant statistical applications, with well ...

  9. Statistical Science

    The aim of Statistical Science is to present the full range of contemporary statistical thought at a technical level accessible to the broad community of practitioners, teachers, researchers, and students of statistics and probability. The journal publishes discussions of methodological and theoretical topics of current interest and importance, surveys of substantive research areas with ...

  10. Journals

    Journal of Survey Statistics and Methodology Sponsored by the ASA and American Association for Public Opinion Research, this journal's objective is to include cutting-edge scholarly articles on statistical and methodological issues for sample surveys, censuses, administrative record systems, and other related data.

  11. Journal Rankings on Statistics and Probability

    International Scientific Journal & Country Ranking. SCImago Institutions Rankings SCImago Media Rankings SCImago Iber SCImago Research Centers Ranking SCImago Graphica Ediciones Profesionales de la Información

  12. Stat

    In recognition of this, we are excited to announce a special issue of the journal Stat focusing on models for consulting and collaboration in higher education. We invite consultants, collaborators, and program leaders to submit manuscripts that address building and running data science and/or statistical research infrastructure and support ...

  13. Statistical Modelling: Sage Journals

    The journal aims to be the major resource for statistical modelling, covering both methodology and practice. Its goal is to be multidisciplinary in nature, promoting the cross-fertilization of ideas between substantive research areas, as well as providing a common forum for the comparison, unification and nurturing of modelling issues across different subjects.

  14. Home

    Journal of Statistical Theory and Practice is a broad-based journal that publishes original research and reviews in statistical sciences. Submission of original research papers, significant review articles, and book reviews are encouraged. Publishes editorials on 'Life and Works' of eminent scholars. Regularly publishes special issues on ...

  15. Introduction to Research Statistical Analysis: An Overview of the

    Introduction. Statistical analysis is necessary for any research project seeking to make quantitative conclusions. The following is a primer for research-based statistical analysis. It is intended to be a high-level overview of appropriate statistical testing, while not diving too deep into any specific methodology.

  16. Statistics

    Read the latest Research articles in Statistics from Scientific Reports. ... View all journals; Search; My ... Improved data quality and statistical power of trial-level event-related potentials ...

  17. Statistical methods used in the public health literature and ...

    Statistical literacy and knowledge is needed to read and understand the public health literature. The purpose of this study was to quantify basic and advanced statistical methods used in public health research. We randomly sampled 216 published articles from seven top tier general public health journals. Studies were reviewed by two readers and a standardized data collection form completed for ...

  18. Understanding Statistical Testing

    Abstract. Statistical hypothesis testing is common in research, but a conventional understanding sometimes leads to mistaken application and misinterpretation. The logic of hypothesis testing presented in this article provides for a clearer understanding, application, and interpretation. Key conclusions are that (a) the magnitude of an estimate ...

  19. Statistics: Vol 58, No 2 (Current issue)

    Modelling additive extremile regression by iteratively penalized least asymmetric weighted squares and gradient descent boosting. Ziwen Geng. Article | Published online: 30 Apr 2024. View all latest articles. Explore the current issue of Statistics, Volume 58, Issue 2, 2024.

  20. IASE

    SERJ - Statistics Education Research Journal. SERJ is a peer-reviewed electronic journal of the International Association for Statistical Education (IASE) and the International Statistical Institute (ISI). SERJ is published three times a year (one special edition in June, and two regular issues in February and November) and it is free.

  21. Basic statistical tools in research and data analysis

    Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if ...

  22. Principles of Statistical Analyses: Learning from Randomized

    Extract. Aiming to provide a concise and thorough guide for readers with (some) mathematical experience, Principles of Statistical Analyses achieves this exceptionally well. Part I builds the foundation for statistical inference by introducing the axioms of probability theory, presenting discrete, continuous, and multivariate distributions, and covering concentration inequalities and limit ...

  23. TREND Reporting Guidelines for Nonrandomized/Quasi-Experimental Study

    The Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) guidelines were first published in 2004, in response to the perceived value and effect of the Consolidated Standards of Reporting Trials (CONSORT) guidelines that had been introduced a decade earlier. 1 The initial development of these guidelines was spearheaded by the US Centers for Disease Control and Prevention HIV ...

  24. Journal of Applied Statistics

    The Journal publishes original research papers, review articles, and short application notes. In general, ... The Journal of Applied Statistics Best Paper Prize is awarded annually, as decided by the Editor-in-Chief with the support of the Associate Editors. The winning article receives a £500 prize, and their paper will be made free to view ...

  25. CFA‐Based Splicing Forgery Localization Method via Statistical Analysis

    Chinese Journal of Electronics (2021-2022) Cognitive Computation and Systems; ... Benefiting from the use of statistical methods as tools in fusion framework for practical applications, the improvement of single statistical method still makes sense. ... This work was supported by the Technical Research Program of Ministry of Public Security ...

  26. A Statistical Analysis on the Functional Changes of Dialect and

    The diachronic description method and statistical analysis are combined to detect time-varying growth and decline trends in Nanchang citizens' verbal function from 2021 to 2022 and 2006 to 2007. ... which is sociological research method that compares and analyzes the similarities and differences of social phenomena in different periods to ...

  27. Unleashing economic potential: decoding the FDI-economic ...

    The findings of this research hold significant potential for policymakers, providing them with valuable insights into harnessing the economic advantages of foreign investment while leveraging the unique strengths and abilities of each host country. 2.1.1 Research philosophy and design. The research philosophy adopted in this study is positivism.

  28. Effect of the HPV vaccination programme on incidence of ...

    Objectives To replicate previous analyses on the effectiveness of the English human papillomavirus (HPV) vaccination programme on incidence of cervical cancer and grade 3 cervical intraepithelial neoplasia (CIN3) using 12 additional months of follow-up, and to investigate effectiveness across levels of socioeconomic deprivation. Design Observational study. Setting England, UK. Participants ...

  29. The American Statistician

    Forum for gold, open-access research on Statistics and Data Science Education, includes curricular reform, team learning, pedagogy efficacy and data literacy. ... Journal of the American Statistical Association is a journal of statistical science that publishes research in statistical applications, theory and methods. Partial Access.

  30. Regular use of fish oil supplements and course of cardiovascular

    Objective To examine the effects of fish oil supplements on the clinical course of cardiovascular disease, from a healthy state to atrial fibrillation, major adverse cardiovascular events, and subsequently death. Design Prospective cohort study. Setting UK Biobank study, 1 January 2006 to 31 December 2010, with follow-up to 31 March 2021 (median follow-up 11.9 years). Participants 415 737 ...