Double-counting of populations in evidence synthesis in public health: a call for awareness and future methodological development

BMC Public Health

Research

Clareece R. Nevill¹  Alex J. Sutton¹  Anna Meffen¹  Laura J. Gray¹  Humaira Hussein¹  Sylwia Bujkiewicz¹  Keith R. Abrams² 

[1] Department of Health Sciences, University of Leicester, University Road, LE1 7RH, Leicester, UK;Department of Health Sciences, University of Leicester, University Road, LE1 7RH, Leicester, UK;Department of Statistics, University of Warwick, CV4 7AL, Coventry, UK;

关键词: Evidence synthesis;    meta-analysis;    Network meta-analysis;    Double-counting;    Real-world data;   

DOI  :  10.1186/s12889-022-14213-6

 received in 2022-07-18, accepted in 2022-09-15,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundThere is a growing interest in the inclusion of real-world and observational studies in evidence synthesis such as meta-analysis and network meta-analysis in public health. While this approach offers great epidemiological opportunities, use of such studies often introduce a significant issue of double-counting of participants and databases in a single analysis. Therefore, this study aims to introduce and illustrate the nuances of double-counting of individuals in evidence synthesis including real-world and observational data with a focus on public health.MethodsThe issues associated with double-counting of individuals in evidence synthesis are highlighted with a number of case studies. Further, double-counting of information in varying scenarios is discussed with potential solutions highlighted.ResultsUse of studies of real-world data and/or established cohort studies, for example studies evaluating the effectiveness of therapies using health record data, often introduce a significant issue of double-counting of individuals and databases. This refers to the inclusion of the same individuals multiple times in a single analysis. Double-counting can occur in a number of manners, such as, when multiple studies utilise the same database, when there is overlapping timeframes of analysis or common treatment arms across studies. Some common practices to address this include synthesis of data only from peer-reviewed studies, utilising the study that provides the greatest information (e.g. largest, newest, greater outcomes reported) or analysing outcomes at different time points.ConclusionsWhile common practices currently used can mitigate some of the impact of double-counting of participants in evidence synthesis including real-world and observational studies, there is a clear need for methodological and guideline development to address this increasingly significant issue.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

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