Incorporating latent survival trajectories and covariate heterogeneity in time-to-event data analysis: a joint mixture model approach
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 devel...
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2025-05-01
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| Online Access: | https://doi.org/10.1186/s12874-025-02580-8 |
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| author | Fu-Wen Liang Wenyaw Chan Michael D. Swartz Bouthaina S. Dabaja |
| author_facet | Fu-Wen Liang Wenyaw Chan Michael D. Swartz Bouthaina S. Dabaja |
| author_sort | Fu-Wen Liang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-686c3688a63f40a29c647c9b8ecce12f |
| institution | OA Journals |
| issn | 1471-2288 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
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| series | BMC Medical Research Methodology |
| spelling | doaj-art-686c3688a63f40a29c647c9b8ecce12f2025-08-20T02:25:11ZengBMCBMC Medical Research Methodology1471-22882025-05-0125111110.1186/s12874-025-02580-8Incorporating latent survival trajectories and covariate heterogeneity in time-to-event data analysis: a joint mixture model approachFu-Wen Liang0Wenyaw Chan1Michael D. Swartz2Bouthaina S. Dabaja3Department of Public Health, College of Health Sciences, Kaohsiung Medical UniversityDepartment of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at HoustonDepartment of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at HoustonDepartment of Radiation Oncology, University of Texas MD Anderson Cancer CenterAbstract 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.https://doi.org/10.1186/s12874-025-02580-8Joint modelingMixture modelsTime-to-event dataLatent class analysisSurvival trajectoriesUnobserved heterogeneity |
| spellingShingle | Fu-Wen Liang Wenyaw Chan Michael D. Swartz Bouthaina S. Dabaja Incorporating latent survival trajectories and covariate heterogeneity in time-to-event data analysis: a joint mixture model approach BMC Medical Research Methodology Joint modeling Mixture models Time-to-event data Latent class analysis Survival trajectories Unobserved heterogeneity |
| title | Incorporating latent survival trajectories and covariate heterogeneity in time-to-event data analysis: a joint mixture model approach |
| title_full | Incorporating latent survival trajectories and covariate heterogeneity in time-to-event data analysis: a joint mixture model approach |
| title_fullStr | Incorporating latent survival trajectories and covariate heterogeneity in time-to-event data analysis: a joint mixture model approach |
| title_full_unstemmed | Incorporating latent survival trajectories and covariate heterogeneity in time-to-event data analysis: a joint mixture model approach |
| title_short | Incorporating latent survival trajectories and covariate heterogeneity in time-to-event data analysis: a joint mixture model approach |
| title_sort | incorporating latent survival trajectories and covariate heterogeneity in time to event data analysis a joint mixture model approach |
| topic | Joint modeling Mixture models Time-to-event data Latent class analysis Survival trajectories Unobserved heterogeneity |
| url | https://doi.org/10.1186/s12874-025-02580-8 |
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