Bayesian Hierarchical Modeling to Handle Systematically Missing Outcome Data in Meta-Analysis with Individual Patient-Level Data
Elaona Lemoto and Hwanhee Hong, PhD
Abstract
In the interest of conducting pairwise meta-analysis with individual patient-level data, researchers combine randomized control trials that not only compare the same treatments, but also overlap in reported outcomes. However, it is often the case that some trials may not have overlapping outcomes, which causes one outcome to be prioritized and studies not observing such outcome to be removed from the analysis. To address this, we propose a Bayesian hierarchical model that simultaneously considers all reported outcomes where at least one study includes all outcomes of interest. Through simulations, we explore the implications of our approach in scenarios with varying data availability and highlight its inherent constraints. Subsequently, we apply our proposed model to a MA of treatments for major depressive disorder, where discrepancies among reported outcomes are evident.
Date
March 25, 2025
Time
12:00 AM
Location
New Orleans, Louisiana, USA