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

arXiv: https://arxiv.org/abs/1908.10613 · HTML: https://mlsanity.com/paper/1908.10613/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [Distinguishing case-mix from context heterogeneity in prognostic regression model synthesis settings](https://mlsanity.com/paper/2608.12885/) (2026)

## Abstract (arXiv)

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.