(), Larissa Schwarzkopf
(), Matthias Hunger
() and Rolf Holle
Wolfgang Hess: Department of Economics, Lund University, Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund, Sweden
Larissa Schwarzkopf: Institute of Health Economics and Health Care Management, Helmholtz Zentrum, Postal: Helmholtz Zentrum, München, Germany
Matthias Hunger: Institute of Health Economics and Health Care Management, Helmholtz Zentrum, Postal: Helmholtz Zentrum, München, Germany
Rolf Holle: Institute of Health Economics and Health Care Management, Helmholtz Zentrum, Postal: Helmholtz Zentrum, München, Germany
Abstract: 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 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.
15 pages, September 9, 2013
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