Latent class analysis with discrete failure time model

Qinmengge Li, Kevin He, Lam C. Tsoi, Jian Kang

The Annals of Applied Statistics · 2026 · Volume 20 · Issue 1

Abstract

In survival analysis, accurate identification of latent classes is essential to effectively account for potential hidden population heterogeneity. In response to this challenge, we introduce the latent class discrete survival (LaCDS) model. LaCDS employs a finite-mixture model structure within the context of the discrete failure time model and implements the expectation-maximization algorithm for efficient optimization. Through extensive simulation studies, we evaluate the performance of LaCDS in comparison to other methods. Our results demonstrate LaCDS’s superior ability to identify population heterogeneities, both in terms of baseline hazards and coefficients. Additionally, it is robust under both discrete and continuous simulation mechanisms. We apply LaCDS and other methods to identify subgroups among kidney transplant patients within the Organ Procurement and Transplantation Network (OPTN) study. Our findings underscore the superior accuracy of LaCDS in subgrouping homogeneous patients compared to existing methods.

Keywords

Finite mixture model; latent class; Survival analysis;

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