Competing-risks duration models with correlated random effects: an application to dementia patients’ transition histories
(2014) In Statistics in Medicine 33(22). p.3919-3931- Abstract
- Abstract in Undetermined
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... (More) - Abstract in Undetermined
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. Copyright © 2014 John Wiley & Sons, Ltd. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4449092
- author
- Hess, Wolfgang LU ; Schwarzkopf, Larissa ; Hunger, Matthias and Holle, Rolf
- organization
- publishing date
- 2014
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Statistics in Medicine
- volume
- 33
- issue
- 22
- pages
- 3919 - 3931
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- pmid:24827139
- wos:000341824600011
- scopus:84906943583
- pmid:24827139
- ISSN
- 1097-0258
- DOI
- 10.1002/sim.6206
- language
- English
- LU publication?
- yes
- id
- ad01b5f8-ccd6-4faf-973c-0ef5edd251be (old id 4449092)
- date added to LUP
- 2016-04-01 10:37:04
- date last changed
- 2022-01-26 00:56:24
@article{ad01b5f8-ccd6-4faf-973c-0ef5edd251be,
abstract = {{Abstract in Undetermined<br>
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. Copyright © 2014 John Wiley & Sons, Ltd.}},
author = {{Hess, Wolfgang and Schwarzkopf, Larissa and Hunger, Matthias and Holle, Rolf}},
issn = {{1097-0258}},
language = {{eng}},
number = {{22}},
pages = {{3919--3931}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Statistics in Medicine}},
title = {{Competing-risks duration models with correlated random effects: an application to dementia patients’ transition histories}},
url = {{http://dx.doi.org/10.1002/sim.6206}},
doi = {{10.1002/sim.6206}},
volume = {{33}},
year = {{2014}},
}