Addressing Outcome Reporting Bias in Meta-analysis: A Selection Model Perspective

TL;DR

This paper investigates outcome reporting bias in meta-analyses, proposing selection model-based adjustment techniques, and evaluates their effectiveness through real data application and simulations to improve treatment effect estimates.

Contribution

It extends existing methods by applying selection models to address outcome reporting bias in meta-analyses, especially considering heterogeneity.

Findings

Selection models can effectively adjust for outcome reporting bias.

Application to real clinical data demonstrates improved treatment effect estimation.

Simulation results show robustness of the proposed adjustment methods.

Abstract

Outcome Reporting Bias (ORB) poses significant threats to the validity of meta-analytic findings. It occurs when researchers selectively report outcomes based on the significance or direction of results, potentially leading to distorted treatment effect estimates. Despite its critical implications, ORB remains an under-recognized issue, with few comprehensive adjustment methods available. The goal of this research is to investigate ORB-adjustment techniques through a selection model lens, thereby extending some of the existing methodological approaches available in the literature. To gain a better insight into the effects of ORB in meta-analysis of clinical trials, specifically in the presence of heterogeneity, and to assess the effectiveness of ORB-adjustment techniques, we apply the methodology to real clinical data affected by ORB and conduct a simulation study focusing on treatment effect estimation with a secondary interest in heterogeneity quantification.