Faculty, Staff and Student Publications
Language
English
Publication Date
5-14-2025
Journal
BMC Medical Research Methodology
DOI
10.1186/s12874-025-02580-8
PMID
40369452
PMCID
PMC12079916
PubMedCentral® Posted Date
5-14-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Background: Finite mixture models have been recently applied in time-to-event data to identify subgroups with distinct hazard functions, yet they often assume differing covariate effects on failure times across latent classes but homogeneous covariate distributions. This study aimed to develop a method for analyzing time-to-event data while accounting for unobserved heterogeneity within a mixture modeling framework.
Methods: A joint model was developed to incorporate latent survival trajectories and observed information for the joint analysis of time-to-event outcomes, correlated discrete and continuous covariates, and a latent class variable. It assumed covariate effects on survival times and covariate distributions vary across latent classes. Unobservable trajectories were identified by estimating the probability of belonging to a particular class based on observed information. This method was applied to a Hodgkin lymphoma study, identifying four distinct classes in terms of long-term survival and distributions of prognostic factors.
Results: Results from simulation studies and the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. Four unobserved subgroups were identified, each characterized by distinct survival parameters and varying distributions of prognostic factors. A notable decreasing trend in the incidence of second malignancy over time was noted, along with different effects of second malignancy and relapse on survival across subgroups, providing deeper insights into disease progression over time.
Conclusions: The proposed joint model effectively identifies latent subgroups, revealing unobserved heterogeneity in survival outcomes and prognostic factors. Its flexibility enables more precise estimation of survival trajectories, with broad applicability in survival analysis.
Keywords
Humans, Hodgkin Disease, Survival Analysis, Computer Simulation, Models, Statistical, Prognosis, Joint modeling, Mixture models, Time-to-event data, Latent class analysis, Survival trajectories, Unobserved heterogeneity
Published Open-Access
yes
Recommended Citation
Liang, Fu-Wen; Chan, Wenyaw; Swartz, Michael D; et al., "Incorporating Latent Survival Trajectories and Covariate Heterogeneity in Time-to-Event Data Analysis: A Joint Mixture Model Approach" (2025). Faculty, Staff and Student Publications. 1079.
https://digitalcommons.library.tmc.edu/uthsph_docs/1079