2021/10/17 by Ray Bai, Bai, Ray, Xiaokang Liu +12
Decision Sciences · Mathematics · Medicine · #Advanced Causal Inference Techniques #Applications (stat.AP) #Artificial intelligence #Bayesian probability #Bivariate analysis #Computer science #Confidence interval #FOS: Computer and information sciences #Internal medicine #Machine learning #Medicine #Meta-analysis #Meta-analysis and systematic reviews #Orb (optics) #Psychological intervention #Statistical Methods in Clinical Trials #stat.AP
paper · pdf · doi:10.48550/arxiv.2110.08849
26 pages, 5 tables, 8 figures
arxiv created 2021/10/17 · openalex publication_date 2021/10/17 · arxiv updated 2021/10/19 · openalex created_date 2021/10/25 · openalex updated_date 2026/08/05
Multivariate meta-analysis (MMA) is a powerful tool for jointly estimating multiple outcomes' treatment effects. However, the validity of results from MMA is potentially compromised by outcome reporting bias (ORB), or the tendency for studies to selectively report outcomes. Until recently, ORB has been understudied. Since ORB can lead to biased conclusions, it is crucial to correct the estimates of effect sizes and quantify their uncertainty in the presence of ORB. With this goal, we develop a Bayesian selection model to adjust for ORB in MMA. We further propose a measure for quantifying the impact of ORB on the results from MMA. We evaluate our approaches through a meta-evaluation of 748 bivariate meta-analyses from the Cochrane Database of Systematic Reviews. Our model is motivated by and applied to a meta-analysis of interventions on hospital readmission and quality of life for heart failure patients. In our analysis, the relative risk (RR) of hospital readmission for the intervention group changes from a significant decrease (RR: 0.931, 95% confidence interval [CI]: 0.862-0.993) to a statistically nonsignificant effect (RR: 0.955, 95% CI: 0.876-1.051) after adjusting for ORB. This study demonstrates that failing to account for ORB can lead to different conclusions in a meta-analysis.