Subgroup Analysis of Interval-censored Failure Time Data With Application to Alzheimer's Disease

Subgroup analysis provides a useful tool for dealing with the heterogeneity in various situations, such as disease treatments, and has attracted a lot of attention in many areas, such as precision medicine. In this paper, we discuss the subgroup analysis for interval-censored failure time data under a heterogeneous Cox proportional hazards model, and a sieve penalized maximum likelihood estimation procedure is proposed. The proposed method can classify study subjects into different subgroups and determine the number of subgroups, the important predictors, and their estimated effects simultaneously. Also, the theoretical justification of the approach is provided, and an extensive simulation study is conducted to evaluate its empirical performance, which indicates that it works well in practical situations. In addition, the proposed methodology is applied to a set of real data on Alzheimer's Disease that motivated this study.