Patient subgroup identification for clinical drug development. (1st February 2017)

• Record Type: Journal Article
• Authors: Huang, Xin; Sun, Yan; Trow, Paul; Chatterjee, Saptarshi; Chakravartty, Arunava; Tian, Lu; Devanarayan, Viswanath
• Is Part Of: Statistics in medicine. Volume 36:Number 9(2017)
• Page Start: 1414
• Page End: 1428
• Publication Date: 2017-02-01
• Subjects: subgroup identification -- precision medicine -- clinical trial -- variable selection -- cutoff estimation -- predictive modeling -- cross‐validation -- predictive significance; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.7236
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Causal mechanism of relationship between the clinical outcome (efficacy or safety endpoints) and putative biomarkers, clinical baseline, and related predictors is usually unknown and must be deduced empirically from experimental data. Such relationships enable the development of tailored therapeutics and implementation of a precision medicine strategy in clinical trials to help stratify patients in terms of disease progression, clinical response, treatment differentiation, and so on. These relationships often require complex modeling to develop the prognostic and predictive signatures. For the purpose of easier interpretation and implementation in clinical practice, defining a multivariate biomarker signature in terms of thresholds (cutoffs/cut points) on individual biomarkers is preferable. In this paper, we propose some methods for developing such signatures in the context of continuous, binary and time‐to‐event endpoints. Results from simulations and case study illustration are also provided. Copyright © 2017 John Wiley & Sons, Ltd.