ArticleBiometrics2020
Variable selection in joint frailty models of recurrent and terminal events.
Article in Biometrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Bayesian feature selection in joint models with application to a cardiovascular disease cohort study.Statistical methods in medical research · 2026Article
- Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Capturing heterogeneity in repeated measures data by fusion penalty.Statistics in medicine · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
Recurrent event data are commonly encountered in biomedical studies. In many situations, they are subject to an informative terminal event, for example, death. Joint modeling of recurrent and terminal events has attracted substantial recent research interests. On the other hand, there may exist a large number of covariates in such data. How to conduct variable selection for joint frailty proportional hazards models has become a challenge in practical data analysis. We tackle this issue on the basis of the "minimum approximated information criterion" method. The proposed method can be conveniently implemented in SAS Proc NLMIXED for commonly used frailty distributions. Its finite-sample behavior is evaluated through simulation studies. We apply the proposed method to model recurrent opportunistic diseases in the presence of death in an AIDS study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.