A Bayesian Selection Model for Correcting Outcome Reporting Bias With Application to a Meta-analysis on Heart Failure Interventions

TL;DR

This paper introduces a Bayesian selection model to correct outcome reporting bias in multivariate meta-analyses, demonstrating its impact on effect estimates and conclusions, especially in clinical interventions for heart failure.

Contribution

It develops a novel Bayesian model to adjust for outcome reporting bias in MMA and proposes a measure to quantify ORB's impact on results.

Findings

Adjusting for ORB changed the significance of the intervention effect on hospital readmission.

The model was validated on 748 meta-analyses from the Cochrane Database.

Application to heart failure interventions showed substantial bias correction.

Abstract

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.