Stratification by quality-induced selection bias in a meta-analysis of clinical trials

Jennifer Stone, Usha Gurunathan, Kathryn Glass, Zachary Munn, Peter Tugwell, Suhail A.R. Doi*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    45 Citations (Scopus)


    Objectives: The inconsistency demonstrated across strata when using different scales has been attributed to quality scores, and stratification continues to be done using risk of bias domain judgments. This study examines if restricting primary meta-analyses to studies at low risk of bias or presenting meta-analyses stratified according to risk of bias is indeed the right approach to explore potential methodological bias. Study Design and Setting: Reanalysis of the impact of quality subgroupings in an existing meta-analysis based on 25 different scales. Results: We demonstrate that quality stratification itself is the problem because it induces a spurious association between effect size and precision within stratum. Studies with larger effects or lesser precision tend to be of lower quality—a form of collider-stratification bias (stratum being the common effect of the reasons for these two outcomes) that leads to inconsistent results across scales. We also show that the extent of this association determines the variability in effect size and statistical significance across strata when conditioning on quality. Conclusions: We conclude that stratification by quality leads to a form of selection bias (collider-stratification bias) and should be avoided. We demonstrate consistent results with an alternative method that includes all studies.

    Original languageEnglish
    Pages (from-to)51-59
    Number of pages9
    JournalJournal of Clinical Epidemiology
    Publication statusPublished - Mar 2019


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