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Hess, W.* ; Schwarzkopf, L. ; Hunger, M. ; Holle, R.

Competing-risks duration models with correlated random effects: An application to dementia patients' transition histories.

Stat. Med. 33, 3919-3931 (2014)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
Multi-state transition models are widely applied tools to analyze individual event histories in the medical or social sciences. In this paper, we propose the use of (discrete-time) competing-risks duration models to analyze multi-transition data. Unlike conventional Markov transition models, these models allow the estimated transition probabilities to depend on the time spent in the current state. Moreover, the models can be readily extended to allow for correlated transition probabilities. A further virtue of these models is that they can be estimated using conventional regression tools for discrete-response data, such as the multinomial logit model. The latter is implemented in many statistical software packages and can be readily applied by empirical researchers. Moreover, model estimation is feasible, even when dealing with very large data sets, and simultaneously allowing for a flexible form of duration dependence and correlation between transition probabilities. We derive the likelihood function for a model with three competing target states and discuss a feasible and readily applicable estimation method. We also present the results from a simulation study, which indicate adequate performance of the proposed approach. In an empirical application, we analyze dementia patients' transition probabilities from the domestic setting, taking into account several, partly duration-dependent covariates.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Competing Risks ; Dementia ; Discrete-time Duration Model ; Multinomial Logit ; Random Effects ; Transition; Maximum Simulated Likelihood; Insurance Claims Data; Aged Leila 75+; Unobserved Heterogeneity; Incident Dementia; Economic-impact; Institutionalization; Regression; German; Death
ISSN (print) / ISBN 0277-6715
e-ISSN 1097-0258
Journal Statistics in Medicine
Quellenangaben Volume: 33, Issue: 22, Pages: 3919-3931 Article Number: , Supplement: ,
Publisher Wiley
Publishing Place Hoboken
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Health Economics and Health Care Management (IGM)