Jennifer Stone1, Usha Gurunathan2, Kathryn Glass3, Zachary Munn4, Peter Tugwell5, Suhail A R Doi6. 1. Department of Health Services Research and Policy, Research School of Population Health, Australian National University, Canberra, ACT, Australia. 2. Department of Anaesthesia, The Prince Charles Hospital, Brisbane, Queensland, Australia; School of Population Health, University of Queensland, Brisbane, Queensland, Australia. 3. National Centre for Epidemiology and Population Health, Research School of Population Health, Australian National University, Canberra, ACT, Australia. 4. The Joanna Briggs Institute, The University of Adelaide, Adelaide, South Australia, Australia. 5. Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada. 6. Department of Population Medicine, College of Medicine, Qatar University, Doha, Qatar. Electronic address: sardoi@gmx.net.
Abstract
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.
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.
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