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Title: Evaluation of biomarkers for treatment selection using individual participant data from multiple clinical trials Authors:  Chae Ryon Kang - University of Pittsburgh (United States) [presenting]
Holly Janes - Fred Hutchinson Cancer Research Center (United States)
Parvin Tajik - University of Amsterdam (Netherlands)
Henk Groen - University of Groningen (Netherlands)
Ben Mol - University of Adelaide (Australia)
Abstract: 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. The motivation comes from the IPD that were collected from two randomized controlled trials of hypertension and pre-eclampsia 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 markers performance in terms of its ability to predict risk of disease outcome, which do not directly apply to the treatment selection problem. 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 two studies in women with hypertension in pregnancy is presented.