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Modeling the Role of Baseline Risk and Additional Study-Level Covariates in Meta-Analysis of Treatment Effects
Phuc T Tran1, Annamaria Guolo1
1Department of Statistical Sciences, University of Padova, Padova, Italy.
Statistics in Medicine
|October 23, 2025
Summary
This study introduces a new meta-analysis method to better understand treatment effects by incorporating baseline risk and other factors. The approach uses measurement error correction for more reliable results in clinical trials.
Area of Science:
- Biostatistics
- Epidemiology
- Clinical Trials
Background:
- Investigating treatment effect heterogeneity in meta-analyses is crucial.
- Baseline risk is a key factor but difficult to measure accurately.
- Existing methods use proxies with inherent measurement errors.
Purpose of the Study:
- To extend classical meta-analysis by incorporating baseline risk and additional covariates.
- To address measurement errors in study-level aggregated data.
- To improve the reliability of meta-analysis inference.
Main Methods:
- Developed a likelihood-based inference framework with measurement error correction.
- Computed within-study covariances using Taylor expansions.
- Proposed a pseudo-likelihood solution for computational ease and when subgroup information is unavailable.
Main Results:
- Simulation studies evaluated method performance under various conditions (sample size, heterogeneity, risk distribution).
- The proposed methods were applied to a meta-analysis on COVID-19 and schizophrenia.
- The techniques aim to provide more accurate and reliable meta-analysis results.
Conclusions:
- The extended meta-analysis framework enhances the investigation of treatment effect heterogeneity.
- Measurement error correction is vital for robust inference when using aggregated study-level data.
- The methods offer practical solutions for complex meta-analytic challenges.
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