Modeling Treatment Effect Modification in Multidrug-Resistant Tuberculosis in an Individual Patient Data Meta-Analysis

TL;DR

This paper develops a statistical framework using marginal structural models and targeted maximum likelihood estimation to analyze how patient characteristics modify treatment effects in multidrug-resistant tuberculosis across multiple observational studies.

Contribution

It introduces a novel approach combining MSM and TMLE for effect modification analysis in IPD meta-analyses with complex confounding and treatment availability issues.

Findings

Effective estimation of effect modification in MDR-TB treatments.

Robust methodology accounting for confounding and study heterogeneity.

Application to real-world observational data.

Abstract

Effect modification occurs while the effect of the treatment is not homogeneous across the different strata of patient characteristics. When the effect of treatment may vary from individual to individual, precision medicine can be improved by identifying patient covariates to estimate the size and direction of the effect at the individual level. However, this task is statistically challenging and typically requires large amounts of data. Investigators may be interested in using the individual patient data (IPD) from multiple studies to estimate these treatment effect models. Our data arise from a systematic review of observational studies contrasting different treatments for multidrug-resistant tuberculosis (MDR-TB), where multiple antimicrobial agents are taken concurrently to cure the infection. We propose a marginal structural model (MSM) for effect modification by different patient characteristics and co-medications in a meta-analysis of observational IPD. We develop, evaluate, and apply a targeted maximum likelihood estimator (TMLE) for the doubly robust estimation of the parameters of the proposed MSM in this context. In particular, we allow for differential availability of treatments across studies, measured confounding within and across studies, and random effects by study.