Pub Date : 2025-12-13DOI: 10.1016/j.jclinepi.2025.112101
Chao Zhang , Ruohua Yan , Xiaohang Liu, Xiaolu Nie, Yaguang Peng, Xiaoxia Peng
<div><h3>Background and Objective</h3><div>To systematically evaluate the performance of <em>k</em>-fold cross-validation and bootstrap-based optimism correction methods for internal validation of statistical and machine learning models.</div></div><div><h3>Methods</h3><div>A total of 239,415 inpatients were extracted from an open access database named Medical Information Mart for Intensive Care IV, of which 39,145 were randomly sampled as a predefined reference dataset. Among the remaining simulation dataset with 200,000 inpatients, training sets with sample sizes ranging from 595 to 5946 were randomly selected, and multiple prediction models were developed in each training set using various modeling strategies, including logistic regression, least absolute shrinkage and selection operator regression, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Light Gradient Boosting Machine, and Random Forest. The dependent variable of the model was acute kidney injury (AKI), a binary outcome with an incidence of 18.5%, and the independent variables included 22 common predictors of AKI. For each model, 2-fold, 5-fold, and 10-fold cross-validation were used for internal validation to calculate area under the receiver-operating characteristic curve (AUC), which is a common metric for quantifying the overall ability of a model to discriminate between positive or negative classifications. In addition, the Harrell, .632, and .632+ AUC estimators were calculated for internal validation based on bootstrapping. The above simulation process was repeated 1000 times to obtain 1000 estimates of AUC for each internal validation method of each model. The model performance was simultaneously evaluated in the reference dataset to obtain an empirical AUC (analogous to the “gold standard”). Then, by comparing the 1000 AUC estimates with the empirical AUC, the accuracy of internal validation methods for different models was assessed.</div></div><div><h3>Results</h3><div>For parametric models, the .632+ estimator provided the most accurate estimates of AUC, followed by 10-fold cross-validation with only slight bias. In contrast, for nonparametric models, all bootstrap-based optimism correction methods significantly overestimated AUC, and the overestimation was not reduced by increasing the sample size. Most strikingly, 10-fold cross-validation demonstrated stable and good performance across all scenarios considered, regardless of the modeling strategy or sample size.</div></div><div><h3>Conclusion</h3><div>The performance of bootstrap-based optimism correction methods can be affected by model complexity, although the .632+ estimator performs best in parameter models based on small-sample training. In comparison, 10-fold cross-validation is more robust and easier to implement. Therefore, it is recommended to prioritize 10-fold cross-validation as the internal validation method for prediction models.</div></div><div><h3>Plain Language Summary</h3><div>With the exponen
{"title":"Empirical simulation of internal validation methods for prediction models: comparing k-fold cross-validation with bootstrap-based optimism correction","authors":"Chao Zhang , Ruohua Yan , Xiaohang Liu, Xiaolu Nie, Yaguang Peng, Xiaoxia Peng","doi":"10.1016/j.jclinepi.2025.112101","DOIUrl":"10.1016/j.jclinepi.2025.112101","url":null,"abstract":"<div><h3>Background and Objective</h3><div>To systematically evaluate the performance of <em>k</em>-fold cross-validation and bootstrap-based optimism correction methods for internal validation of statistical and machine learning models.</div></div><div><h3>Methods</h3><div>A total of 239,415 inpatients were extracted from an open access database named Medical Information Mart for Intensive Care IV, of which 39,145 were randomly sampled as a predefined reference dataset. Among the remaining simulation dataset with 200,000 inpatients, training sets with sample sizes ranging from 595 to 5946 were randomly selected, and multiple prediction models were developed in each training set using various modeling strategies, including logistic regression, least absolute shrinkage and selection operator regression, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Light Gradient Boosting Machine, and Random Forest. The dependent variable of the model was acute kidney injury (AKI), a binary outcome with an incidence of 18.5%, and the independent variables included 22 common predictors of AKI. For each model, 2-fold, 5-fold, and 10-fold cross-validation were used for internal validation to calculate area under the receiver-operating characteristic curve (AUC), which is a common metric for quantifying the overall ability of a model to discriminate between positive or negative classifications. In addition, the Harrell, .632, and .632+ AUC estimators were calculated for internal validation based on bootstrapping. The above simulation process was repeated 1000 times to obtain 1000 estimates of AUC for each internal validation method of each model. The model performance was simultaneously evaluated in the reference dataset to obtain an empirical AUC (analogous to the “gold standard”). Then, by comparing the 1000 AUC estimates with the empirical AUC, the accuracy of internal validation methods for different models was assessed.</div></div><div><h3>Results</h3><div>For parametric models, the .632+ estimator provided the most accurate estimates of AUC, followed by 10-fold cross-validation with only slight bias. In contrast, for nonparametric models, all bootstrap-based optimism correction methods significantly overestimated AUC, and the overestimation was not reduced by increasing the sample size. Most strikingly, 10-fold cross-validation demonstrated stable and good performance across all scenarios considered, regardless of the modeling strategy or sample size.</div></div><div><h3>Conclusion</h3><div>The performance of bootstrap-based optimism correction methods can be affected by model complexity, although the .632+ estimator performs best in parameter models based on small-sample training. In comparison, 10-fold cross-validation is more robust and easier to implement. Therefore, it is recommended to prioritize 10-fold cross-validation as the internal validation method for prediction models.</div></div><div><h3>Plain Language Summary</h3><div>With the exponen","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"190 ","pages":"Article 112101"},"PeriodicalIF":5.2,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145758308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-13DOI: 10.1016/j.jclinepi.2025.112108
Shyam Sundar Sah, Abhishek Kumbhalwar
{"title":"Comment on \"Most methodological characteristics do not exaggerate effect estimates in nutrition RCTs: findings from a metaepidemiological study\".","authors":"Shyam Sundar Sah, Abhishek Kumbhalwar","doi":"10.1016/j.jclinepi.2025.112108","DOIUrl":"10.1016/j.jclinepi.2025.112108","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112108"},"PeriodicalIF":5.2,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145764453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-13DOI: 10.1016/j.jclinepi.2025.112106
Ulrike Paschen, Stefan Sauerland
{"title":"Frameworks for assessing diagnostic interventions are useful for HTA work, but context-dependent","authors":"Ulrike Paschen, Stefan Sauerland","doi":"10.1016/j.jclinepi.2025.112106","DOIUrl":"10.1016/j.jclinepi.2025.112106","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"189 ","pages":"Article 112106"},"PeriodicalIF":5.2,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145764521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-13DOI: 10.1016/j.jclinepi.2025.112107
Werner Vach
{"title":"Comment on “In humble defense of unexplainable black box prediction models in healthcare”","authors":"Werner Vach","doi":"10.1016/j.jclinepi.2025.112107","DOIUrl":"10.1016/j.jclinepi.2025.112107","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"191 ","pages":"Article 112107"},"PeriodicalIF":5.2,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145764464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1016/j.jclinepi.2025.112103
Elizabeth A. Terhune , Mahederemariam Bayleyegn Dagne , Miriam Barsoum , Meera Viswanathan , Rania Ali , Vivian Welch , Ana B. Pizarro , Nila Sathe , Tiffany Duque , Damian Francis , Anita Rizvi , Dru Riddle , Robert W. Turner II , Tamara A. Baker , Patricia C. Heyn
Objectives
The current literature lacks an established and adoptable definition of “racial health equity.” This study aimed to catalog and evaluate, via thematic analyses, definitions and terminology related to racial health equity across the specific studies from the Robert Wood Johnson Foundation and Cochrane-US (United States) (RWJF-Cochrane) “Centering Racial Health Equity in Systematic Reviews” project and to propose a working definition based on study findings.
Study Design and Setting
We employed an integrative review framework to analyze current definitions of racial health equity terms identified within published studies from the RWJF-Cochrane project. Definitions of racial health equity were identified via dual reviewer screening of all identified studies and interview transcripts, which included recent systematic reviews (published since 2020), theoretical and conceptual health literature, and listening exercises with interest holders involved in systematic reviews addressing health equity. Identified definitions were analyzed via thematic coding using the Braun and Clarke framework.
Results
We reviewed 157 systematic reviews, 29 interviews, and 16 articles related to racial health equity for the presence of racial health equity definitions. This review resulted in 32 definitions of racial health equity from theoretical and conceptual health literature (n = 16) and interest holder transcripts (n = 16). No systematic reviews contained definitions of racial health equity. Retrieved definitions emphasize equality in health or health care, including outcomes, processes, or care; themes of discrimination in health-care settings; and acknowledgments of the intersections of social determinants of health with health equity. Definitions varied on the role of improving health-care access in achieving racial health equity. A working definition of racial health equity is proposed using common themes identified across definitions.
Conclusion
Our findings highlight that a clear and consistent definition of racial health equity will assist researchers, practitioners, and policymakers with developing metrics and interventions aimed at reducing racial health inequities. Thus, we propose a working definition for racial health equity, which emphasizes 1) fairness and justice in health, 2) equality in health outcomes and access across racialized groups, 3) a recognition that social consequences of one's race and/or ethnicity may influence health or the quality of health care received. We also note areas of variability in understandings that require further discussion.
{"title":"Defining racial health equity: an integrative analysis of terminology and conceptualizations","authors":"Elizabeth A. Terhune , Mahederemariam Bayleyegn Dagne , Miriam Barsoum , Meera Viswanathan , Rania Ali , Vivian Welch , Ana B. Pizarro , Nila Sathe , Tiffany Duque , Damian Francis , Anita Rizvi , Dru Riddle , Robert W. Turner II , Tamara A. Baker , Patricia C. Heyn","doi":"10.1016/j.jclinepi.2025.112103","DOIUrl":"10.1016/j.jclinepi.2025.112103","url":null,"abstract":"<div><h3>Objectives</h3><div>The current literature lacks an established and adoptable definition of “racial health equity.” This study aimed to catalog and evaluate, via thematic analyses, definitions and terminology related to racial health equity across the specific studies from the Robert Wood Johnson Foundation and Cochrane-US (United States) (RWJF-Cochrane) “Centering Racial Health Equity in Systematic Reviews” project and to propose a working definition based on study findings.</div></div><div><h3>Study Design and Setting</h3><div>We employed an integrative review framework to analyze current definitions of racial health equity terms identified within published studies from the RWJF-Cochrane project. Definitions of racial health equity were identified via dual reviewer screening of all identified studies and interview transcripts, which included recent systematic reviews (published since 2020), theoretical and conceptual health literature, and listening exercises with interest holders involved in systematic reviews addressing health equity. Identified definitions were analyzed via thematic coding using the Braun and Clarke framework.</div></div><div><h3>Results</h3><div>We reviewed 157 systematic reviews, 29 interviews, and 16 articles related to racial health equity for the presence of racial health equity definitions. This review resulted in 32 definitions of racial health equity from theoretical and conceptual health literature (<em>n</em> = 16) and interest holder transcripts (<em>n</em> = 16). No systematic reviews contained definitions of racial health equity. Retrieved definitions emphasize equality in health or health care, including outcomes, processes, or care; themes of discrimination in health-care settings; and acknowledgments of the intersections of social determinants of health with health equity. Definitions varied on the role of improving health-care access in achieving racial health equity. A working definition of racial health equity is proposed using common themes identified across definitions.</div></div><div><h3>Conclusion</h3><div>Our findings highlight that a clear and consistent definition of racial health equity will assist researchers, practitioners, and policymakers with developing metrics and interventions aimed at reducing racial health inequities. Thus, we propose a working definition for racial health equity, which emphasizes 1) fairness and justice in health, 2) equality in health outcomes and access across racialized groups, 3) a recognition that social consequences of one's race and/or ethnicity may influence health or the quality of health care received. We also note areas of variability in understandings that require further discussion.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"190 ","pages":"Article 112103"},"PeriodicalIF":5.2,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145758359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1016/j.jclinepi.2025.112105
Eibhlín Looney , Moira Duffy , Dimity Dutch , Victoria Brown , John Browne , Declan Devane , Janas M. Harrington , Catherine Hayes , Brittany J. Johnson , Patricia M. Kearney , Jamie J. Kirkham , Patricia Leahy-Warren , Andrew W. Murphy , Sarah Redsell , Anna Lene Seidler , Helen Skouteris , Darren Dahly , Karen Matvienko-Sikar
Background and Objective
Heterogeneity in what and how outcomes are measured in childhood obesity prevention trials limits evidence synthesis and evaluation of intervention effectiveness. Core Outcome Sets (COS) and Core Outcome Measurement Sets (COMS) can standardize measurement and reporting across trials, but only if they are used by trialists. This study examined trialists’ awareness and attitudes toward two childhood obesity–related COS and factors influencing their use; characteristics of Outcome Measurement Instruments (OMIs) used in childhood obesity prevention trials; and how trialists choose these OMIs.
Methods
An online, international, cross-sectional survey was conducted including trialists engaged in designing and/or conducting childhood obesity prevention trials in children aged 0–5 years. Trialists were recruited via peer-reviewed publications, the Transforming Obesity Prevention for CHILDren Collaboration and professional contacts. The survey examined trialist characteristics, awareness, and use of existing COS, OMI characteristics, and factors influencing trialist selection of OMIs. Quantitative data were analyzed descriptively; qualitative data were analyzed using content analysis.
Results
The majority of the 46 trialists who completed the survey were senior-career researchers (61%; n = 28), with 1 to 38 years' experience in childhood obesity prevention trials. Seventy percent (n = 32) were familiar with COS in general; 84% (n = 26) of these were familiar with one or both childhood obesity–related COS. These trialists' COS use was limited by perceived participant burden, cost, and lack of knowledge; availability of guidelines, and resources facilitated COS use. Trialists favored measuring outcomes using existing (83%; n = 38) and adapted (80%; n = 37) questionnaires, and anthropometric measures (80%; n = 37). Quantitative and qualitative data indicated that measurement properties (eg, reliability, validity), cost, perceived burden, ease of use, and feasibility were the most important factors influencing trialists’ OMI choice.
Conclusion
Trialists’ awareness and use of childhood obesity–related COS is positive, and may be enhanced through provision of guidance and resources to support COS and COMS use. Development of COMS should consider trialist-reported factors related to feasibility and measurement properties. Such considerations can enhance COS and COMS use in trials, reducing outcome heterogeneity, and improving evaluation of intervention effectiveness to prevent childhood obesity.
{"title":"Factors influencing use and choice of Core Outcome Sets and Outcome Measurement Instruments in trials of interventions to prevent childhood obesity: a mixed-methods survey","authors":"Eibhlín Looney , Moira Duffy , Dimity Dutch , Victoria Brown , John Browne , Declan Devane , Janas M. Harrington , Catherine Hayes , Brittany J. Johnson , Patricia M. Kearney , Jamie J. Kirkham , Patricia Leahy-Warren , Andrew W. Murphy , Sarah Redsell , Anna Lene Seidler , Helen Skouteris , Darren Dahly , Karen Matvienko-Sikar","doi":"10.1016/j.jclinepi.2025.112105","DOIUrl":"10.1016/j.jclinepi.2025.112105","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Heterogeneity in what and how outcomes are measured in childhood obesity prevention trials limits evidence synthesis and evaluation of intervention effectiveness. Core Outcome Sets (COS) and Core Outcome Measurement Sets (COMS) can standardize measurement and reporting across trials, but only if they are used by trialists. This study examined trialists’ awareness and attitudes toward two childhood obesity–related COS and factors influencing their use; characteristics of Outcome Measurement Instruments (OMIs) used in childhood obesity prevention trials; and how trialists choose these OMIs.</div></div><div><h3>Methods</h3><div>An online, international, cross-sectional survey was conducted including trialists engaged in designing and/or conducting childhood obesity prevention trials in children aged 0–5 years. Trialists were recruited via peer-reviewed publications, the Transforming Obesity Prevention for CHILDren Collaboration and professional contacts. The survey examined trialist characteristics, awareness, and use of existing COS, OMI characteristics, and factors influencing trialist selection of OMIs. Quantitative data were analyzed descriptively; qualitative data were analyzed using content analysis.</div></div><div><h3>Results</h3><div>The majority of the 46 trialists who completed the survey were senior-career researchers (61%; <em>n</em> = 28), with 1 to 38 years' experience in childhood obesity prevention trials. Seventy percent (<em>n</em> = 32) were familiar with COS in general; 84% (<em>n</em> = 26) of these were familiar with one or both childhood obesity–related COS. These trialists' COS use was limited by perceived participant burden, cost, and lack of knowledge; availability of guidelines, and resources facilitated COS use. Trialists favored measuring outcomes using existing (83%; <em>n</em> = 38) and adapted (80%; <em>n</em> = 37) questionnaires, and anthropometric measures (80%; <em>n</em> = 37). Quantitative and qualitative data indicated that measurement properties (eg, reliability, validity), cost, perceived burden, ease of use, and feasibility were the most important factors influencing trialists’ OMI choice.</div></div><div><h3>Conclusion</h3><div>Trialists’ awareness and use of childhood obesity–related COS is positive, and may be enhanced through provision of guidance and resources to support COS and COMS use. Development of COMS should consider trialist-reported factors related to feasibility and measurement properties. Such considerations can enhance COS and COMS use in trials, reducing outcome heterogeneity, and improving evaluation of intervention effectiveness to prevent childhood obesity.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"191 ","pages":"Article 112105"},"PeriodicalIF":5.2,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145758311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 10.1016/j.jclinepi.2025.112104
Gowri Gopalakrishna
Research integrity is foundational to clinical epidemiology, particularly in an increasingly transparent scientific landscape. As the field of research integrity navigates the evolving demands of open science, data transparency, and collaborative research, it must also grapple with the influence systemic challenges, such as research fairness and diversity, equity, and inclusion (DEI), have on research quality. This Key Concepts article provides a concise overview of research integrity for clinical epidemiologists. It summarizes key principles in research integrity and the emerging overlap with open science, research fairness, and DEI in upholding the integrity of epidemiological research. Practical guidance is provided at every stage of the research lifecycle—from preparing a research proposal to study protocol development and data collection to publication and dissemination of research findings. It addresses how these overlapping concepts demonstrate that research integrity is not merely about methodological rigor, but is a scientific imperative that requires a broader definition of research integrity to produce high-quality research that is responsible and inclusive.
{"title":"Research integrity in clinical epidemiology: core concepts and contemporary challenges","authors":"Gowri Gopalakrishna","doi":"10.1016/j.jclinepi.2025.112104","DOIUrl":"10.1016/j.jclinepi.2025.112104","url":null,"abstract":"<div><div>Research integrity is foundational to clinical epidemiology, particularly in an increasingly transparent scientific landscape. As the field of research integrity navigates the evolving demands of open science, data transparency, and collaborative research, it must also grapple with the influence systemic challenges, such as research fairness and diversity, equity, and inclusion (DEI), have on research quality. This <em>Key Concepts</em> article provides a concise overview of research integrity for clinical epidemiologists. It summarizes key principles in research integrity and the emerging overlap with open science, research fairness, and DEI in upholding the integrity of epidemiological research. Practical guidance is provided at every stage of the research lifecycle—from preparing a research proposal to study protocol development and data collection to publication and dissemination of research findings. It addresses how these overlapping concepts demonstrate that research integrity is not merely about methodological rigor, but is a scientific imperative that requires a broader definition of research integrity to produce high-quality research that is responsible and inclusive.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"191 ","pages":"Article 112104"},"PeriodicalIF":5.2,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145752446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-08DOI: 10.1016/j.jclinepi.2025.112098
Shannon M. Ruzycki , Kirstie C. Lithgow , Claire Song , Sarah Taylor , Abinaya Subramanian , Miriam Li , Stephanie Happ , Mark Shea , Debby Oladimeji , Wayne Clark , Dean A. Fergusson , Sarina R. Isenberg , Patricia Li , Sangeeta Mehta , Stuart G. Nicholls , Courtney L. Pollock , Louise Pilote , Amity E. Quinn , Syamala Buragadda , David Collister
<div><h3>Objectives</h3><div>To describe the demographic and social identities of participants in contemporary Canadian randomized clinical trials (RCTs).</div></div><div><h3>Study Design and Setting</h3><div>A meta-epidemiologic study included published reports of phase 2 and 3 RCTs that exclusively recruited adults living in Canada and were registered on ClinicalTrials.gov between January 1, 2010, and December 31, 2019. Study design and participant demographics were abstracted from eligible articles in duplicate using frameworks for understanding participant diversity such as PROGRESS-PLUS.</div></div><div><h3>Results</h3><div>We identified 118 RCTs with 17,387 participants. Most reported participant sex (<em>n</em> = 105, 89.0%), few reported gender (<em>n</em> = 12, 10.2%), and none reported both. Among articles reporting sex, there were 11,066 female (63.6%), 5402 male (32.8%), and one intersex (<0.1%) participants. There were 477 women (54.1%) and 404 men (45.9%) participants. No studies reported gender diverse participants. When excluding studies that only recruited one sex and/or gender, 51.8% of participants were male (<em>n</em> = 4774/9219) and 47.5% were men (<em>n</em> = 446/850). Race and/or ethnicity was reported for 4124 participants (23.7%) in 31 of 118 (26.3%) of RCTs; of these, 72.0% were White (<em>n</em> = 2969), 2.7% were Black (<em>n</em> = 113), and 0.2% were Indigenous (<em>n</em> = 7). Eligibility criteria related to specific PROGRESS-PLUS factors were rare except for cognition (<em>n</em> = 42, 35.6%), substance use (<em>n</em> = 25, 21.7%), pregnancy (<em>n</em> = 29, 24.5%), breastfeeding (<em>n</em> = 16, 13.6%), and older age (<em>n</em> = 26, 22.0%).</div></div><div><h3>Conclusion</h3><div>The data are encouraging regarding representation of female and women participants in Canadian trials. Due to underreporting of other identities, we cannot identify additional groups who may be underrepresented. Work to improve reporting of race and/or ethnicity, among other identities, is needed.</div></div><div><h3>Plain Language Summary</h3><div>Clinical trials tell us what drugs and procedures are helpful for patients. In certain specialties, like cancer and heart disease, clinical trials are made up mostly of men, White people, and younger people. This means that the results of these trials may be different for other groups of people, especially older people, women, and racialized people, who are more likely to have these diseases. We looked at the demographic identities of all participants in 118 Canadian clinical trials that were done between 2010 and 2019. Of the 17,387 participants, there were 11,066 female, 5402 male, 477 women, 404 men, and one intersex participant. We could find the race and/or ethnicity for only 4124 participants in 31 of the trials. Most participants (72.0%) were White, and only 2.7% were Black and 0.2% were Indigenous. These results tell us that reporting of identities in Canadian clinical trial
{"title":"Participant diversity and inclusive trial design: a meta-epidemiologic study of Canadian randomized clinical trials","authors":"Shannon M. Ruzycki , Kirstie C. Lithgow , Claire Song , Sarah Taylor , Abinaya Subramanian , Miriam Li , Stephanie Happ , Mark Shea , Debby Oladimeji , Wayne Clark , Dean A. Fergusson , Sarina R. Isenberg , Patricia Li , Sangeeta Mehta , Stuart G. Nicholls , Courtney L. Pollock , Louise Pilote , Amity E. Quinn , Syamala Buragadda , David Collister","doi":"10.1016/j.jclinepi.2025.112098","DOIUrl":"10.1016/j.jclinepi.2025.112098","url":null,"abstract":"<div><h3>Objectives</h3><div>To describe the demographic and social identities of participants in contemporary Canadian randomized clinical trials (RCTs).</div></div><div><h3>Study Design and Setting</h3><div>A meta-epidemiologic study included published reports of phase 2 and 3 RCTs that exclusively recruited adults living in Canada and were registered on ClinicalTrials.gov between January 1, 2010, and December 31, 2019. Study design and participant demographics were abstracted from eligible articles in duplicate using frameworks for understanding participant diversity such as PROGRESS-PLUS.</div></div><div><h3>Results</h3><div>We identified 118 RCTs with 17,387 participants. Most reported participant sex (<em>n</em> = 105, 89.0%), few reported gender (<em>n</em> = 12, 10.2%), and none reported both. Among articles reporting sex, there were 11,066 female (63.6%), 5402 male (32.8%), and one intersex (<0.1%) participants. There were 477 women (54.1%) and 404 men (45.9%) participants. No studies reported gender diverse participants. When excluding studies that only recruited one sex and/or gender, 51.8% of participants were male (<em>n</em> = 4774/9219) and 47.5% were men (<em>n</em> = 446/850). Race and/or ethnicity was reported for 4124 participants (23.7%) in 31 of 118 (26.3%) of RCTs; of these, 72.0% were White (<em>n</em> = 2969), 2.7% were Black (<em>n</em> = 113), and 0.2% were Indigenous (<em>n</em> = 7). Eligibility criteria related to specific PROGRESS-PLUS factors were rare except for cognition (<em>n</em> = 42, 35.6%), substance use (<em>n</em> = 25, 21.7%), pregnancy (<em>n</em> = 29, 24.5%), breastfeeding (<em>n</em> = 16, 13.6%), and older age (<em>n</em> = 26, 22.0%).</div></div><div><h3>Conclusion</h3><div>The data are encouraging regarding representation of female and women participants in Canadian trials. Due to underreporting of other identities, we cannot identify additional groups who may be underrepresented. Work to improve reporting of race and/or ethnicity, among other identities, is needed.</div></div><div><h3>Plain Language Summary</h3><div>Clinical trials tell us what drugs and procedures are helpful for patients. In certain specialties, like cancer and heart disease, clinical trials are made up mostly of men, White people, and younger people. This means that the results of these trials may be different for other groups of people, especially older people, women, and racialized people, who are more likely to have these diseases. We looked at the demographic identities of all participants in 118 Canadian clinical trials that were done between 2010 and 2019. Of the 17,387 participants, there were 11,066 female, 5402 male, 477 women, 404 men, and one intersex participant. We could find the race and/or ethnicity for only 4124 participants in 31 of the trials. Most participants (72.0%) were White, and only 2.7% were Black and 0.2% were Indigenous. These results tell us that reporting of identities in Canadian clinical trial","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"191 ","pages":"Article 112098"},"PeriodicalIF":5.2,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145727093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-08DOI: 10.1016/j.jclinepi.2025.112100
Gina Bantle, Julia Stadelmaier, Maria Petropoulou, Joerg J Meerpohl, Lukas Schwingshackl
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