A novel approach for identifying and addressing case-mix heterogeneity in individual participant data meta-analysis

TL;DR

This paper introduces a new method for meta-analyzing individual patient data that explicitly accounts for case-mix heterogeneity, improving the accuracy of treatment effect estimates across diverse patient populations.

Contribution

It develops a novel approach using outcome regression and inverse probability weighting to distinguish heterogeneity caused by case-mix from other factors in IPD meta-analyses.

Findings

Enables explicit modeling of case-mix heterogeneity

Allows for more accurate treatment effect estimation

Disentangles heterogeneity sources in meta-analysis

Abstract

Case-mix heterogeneity across studies complicates meta-analyses. As a result of this, treatments that are equally effective on patient subgroups may appear to have different effectiveness on patient populations with different case mix. It is therefore important that meta-analyses be explicit for what patient population they describe the treatment effect. To achieve this, we develop a new approach for meta-analysis of randomized clinical trials, which use individual patient data (IPD) from all trials to infer the treatment effect for the patient population in a given trial, based on direct standardization using either outcome regression (OCR) or inverse probability weighting (IPW). Accompanying random-effect meta-analysis models are developed. The new approach enables disentangling heterogeneity due to case mix from that due to beyond case-mix reasons.