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arXiv · 1908.10613

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

Tat-Thang Vo·Raphael Porcher·Anna Chaimani·Stijn Vansteelandt
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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.

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Tat-Thang Vo, Raphael Porcher, Anna Chaimani, Stijn Vansteelandt. 2019-08-28. A novel approach for identifying and addressing case-mix heterogeneity in individual participant data meta-analysis. https://doi.org/10.1002/jrsm.1382

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