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Patient subgroup identification for clinical drug development
Author(s) -
Huang Xin,
Sun Yan,
Trow Paul,
Chatterjee Saptarshi,
Chakravartty Arunava,
Tian Lu,
Devanarayan Viswanath
Publication year - 2017
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.7236
Subject(s) - context (archaeology) , drug development , clinical trial , identification (biology) , computer science , multivariate statistics , biomarker , medicine , outcome (game theory) , data mining , intensive care medicine , data science , machine learning , drug , mathematics , pharmacology , paleontology , biochemistry , chemistry , botany , mathematical economics , biology
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.