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Evaluation of biomarkers for treatment selection using individual participant data from multiple clinical trials
Author(s) -
Kang Chaeryon,
Janes Holly,
Tajik Parvin,
Groen Henk,
Mol Ben,
Koopmans Corine,
Broekhuijsen Kim,
Zwertbroek Eva,
van Pampus Maria,
Franssen Maureen
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7608
Subject(s) - clinical trial , medicine , selection (genetic algorithm) , disease , meta analysis , outcome (game theory) , biomarker , randomized controlled trial , pregnancy , intensive care medicine , computer science , machine learning , mathematics , biochemistry , chemistry , mathematical economics , biology , genetics
Biomarkers that predict treatment effects may be used to guide treatment decisions, thus improving patient outcomes. A meta‐analysis of individual participant data (IPD) is potentially more powerful than a single‐study data analysis in evaluating markers for treatment selection. Our study was motivated by the IPD that were collected from 2 randomized controlled trials of hypertension and preeclampsia among pregnant women to evaluate the effect of labor induction over expectant management of the pregnancy in preventing progression to severe maternal disease. The existing literature on statistical methods for biomarker evaluation in IPD meta‐analysis have evaluated a marker's performance in terms of its ability to predict risk of disease outcome, which do not directly apply to the treatment selection problem. In this study, we propose a statistical framework for evaluating a marker for treatment selection given IPD from a small number of individual clinical trials. We derive marker‐based treatment rules by minimizing the average expected outcome across studies. The application of the proposed methods to the IPD from 2 studies in women with hypertension in pregnancy is presented.