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Subgroup Identification in Survival Outcome Data Based on Concordance Probability Measurement

Author

Listed:
  • 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)

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.

Suggested Citation

  • Shengli An & Peter Zhang & Hong-Bin Fang, 2023. "Subgroup Identification in Survival Outcome Data Based on Concordance Probability Measurement," Mathematics, MDPI, vol. 11(13), pages 1-10, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2855-:d:1179377
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    References listed on IDEAS

    as
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