Evaluation of biomarkers for treatment selection using individual participant data from multiple clinical trials

Chaeryon Kang* (Corresponding Author), Holly Janes, Parvin Tajik, Henk Groen, Ben Mol, Corine Koopmans, Kim Broekhuijsen, Eva Zwertbroek, Maria van Pampus, Maureen Franssen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1439-1453
Number of pages15
JournalStatistics in Medicine
Issue number9
Early online date14 Feb 2018
Publication statusPublished - 30 Apr 2018


  • HYPITAT trials
  • individual participant data
  • randomized clinical trial
  • treatment selection biomarker


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