Open AccessModeling treatment effect modification in multidrug-resistant tuberculosis in an individual patientdata meta-analysis
Author(s) -
Yan Liu,
Mireille E. Schnitzer,
Guanbo Wang,
Edward H. Kennedy,
Piret Viiklepp,
Mario H. Vargas,
Giovanni Sotgiu,
Dick Menzies,
Andrea Benedetti
Publication year - 2021
Publication title -
statistical methods in medical research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.952
H-Index - 85
eISSN - 1477-0334
pISSN - 0962-2802
DOI - 10.1177/09622802211046383
Subject(s) - observational study , meta analysis , covariate , confounding , random effects model , context (archaeology) , marginal structural model , estimator , econometrics , computer science , medicine , treatment effect , statistics , mathematics , biology , paleontology , traditional medicine
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 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, where multiple antimicrobial agents are taken concurrently to cure the infection. We propose a marginal structural model for effect modification by different patient characteristics and co-medications in a meta-analysis of observational individual patient data. We develop, evaluate, and apply a targeted maximum likelihood estimator for the doubly robust estimation of the parameters of the proposed marginal structural model 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.