Subgroup Identification in Survival Outcome Data Based on Concordance Probability Measurement

Shengli An, Peter Zhang and Hong-Bin Fang ()
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Shengli An: Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, China
Peter Zhang: Otsuka Pharmaceutical Development & Commercialization Inc., Rockville, MD 20878, USA
Hong-Bin Fang: Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington, DC 20057, USA

Mathematics, 2023, vol. 11, issue 13, 1-10

Abstract: Identifying a subgroup of patients who may have an enhanced treatment effect in a randomized clinical trial has received increasing attention recently. For time-to-event outcomes, it is a challenge to define the effectiveness of a treatment and to choose a cutoff time point for identifying subgroup membership, especially in trials in which the two treatment arms do not differ in overall survival. In this paper, we propose a mixture cure model to identify a subgroup for a new treatment that was compared to a classical treatment (or placebo) in a randomized clinical trial with respect to survival time. Using the concordance probability measurement ( K -index), we propose a statistic to test the existence of subgroups with effective treatments in the treatment arm. Subsequently, the subgroup is defined by a limited number of covariates based on the estimated area under the curve (AUC). The performance of this method in different scenarios is assessed through simulation studies. A real data example is also provided for illustration.

Keywords: concordance probability; K -index; mixture models; randomized clinical trials; subgroups; survival analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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