Patient subgroup identification for clinical drug development.
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.
AbbVie, Inc., North Chicago, IL, U.S.A.
Xin
X
Huang
AbbVie, Inc., North Chicago, IL, U.S.A.
Yan
Y
Sun
AbbVie, Inc., North Chicago, IL, U.S.A.
Paul
P
Trow
AbbVie, Inc., North Chicago, IL, U.S.A.
Saptarshi
S
Chatterjee
AbbVie, Inc., North Chicago, IL, U.S.A.|Novartis Oncology, Hyderabad, India.
Arunava
A
Chakravartty
Stanford University School of Medicine, Palo Alto, CA, U.S.A.
Lu
L
Tian
AbbVie, Inc., North Chicago, IL, U.S.A.
Viswanath
V
Devanarayan
D015415:Biomarkers; D002986:Clinical Trials as Topic / Q000379:methods*; D004358:Drug Therapy; D023381:Endpoint Determination / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D013223:Statistics as Topic; D016896:Treatment Outcome
D015415:Biomarkers
clinical trial; cross-validation; cutoff estimation; precision medicine; predictive modeling; predictive significance; subgroup identification; variable selection