Subgroup Analysis with Time-to-Event Data Under a Logistic-Cox Mixture Model

摘要

Subgroup detection has received increasing attention recently in different fields such as clinical trials, public management and market segmentation analysis. In these fields, people often face time-to-event data, which are commonly subject to right censoring. This paper proposes a semiparametric Logistic-Cox mixture model for subgroup analysis when the interested outcome is event time with right censoring. The proposed method mainly consists of a likelihood ratio-based testing procedure for testing the existence of subgroups. The expectation-maximization iteration is applied to improve the testing power, and a model-based bootstrap approach is developed to implement the testing procedure. When there exist subgroups, one can also use the proposed model to estimate the subgroup effect and construct predictive scores for the subgroup membership. The large sample properties of the proposed method are studied. The finite sample performance of the proposed method is assessed by simulation studies. A real data example is also provided for illustration.