Competing-Risks Duration Models with Correlated Random Effects: An Application to Dementia Patients’ Transition Histories
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.
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|Date of creation:||09 Sep 2013|
|Date of revision:|
|Publication status:||Forthcoming as Hess, Wolfgang, Larissa Schwarzkopf, Matthias Hunger and Rolf Holle, 'Competing-Risks Duration Models with Correlated Random Effects: An Application to Dementia Patients’ Transition Histories' in Statistics in Medicine .|
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- James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer, vol. 16(3), pages 439-454, August.
- Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
- Concetta Rondinelli & Cheti Nicoletti, 2009.
"The (mis)specification of discrete duration models with unobserved heterogeneity: a Monte Carlo study,"
Temi di discussione (Economic working papers)
705, Bank of Italy, Economic Research and International Relations Area.
- Nicoletti, Cheti & Rondinelli, Concetta, 2010. "The (mis)specification of discrete duration models with unobserved heterogeneity: A Monte Carlo study," Journal of Econometrics, Elsevier, vol. 159(1), pages 1-13, November.
- Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2002. "Reliable estimation of generalized linear mixed models using adaptive quadrature," Stata Journal, StataCorp LP, vol. 2(1), pages 1-21, February.
- Peter Haan & Arne Uhlendorff, 2006.
"Estimation of multinomial logit models with unobserved heterogeneity using maximum simulated likelihood,"
StataCorp LP, vol. 6(2), pages 229-245, June.
- Peter Haan & Arne Uhlendorff, 2006. "Estimation of Multinomial Logit Models with Unobserved Heterogeneity Using Maximum Simulated Likelihood," Discussion Papers of DIW Berlin 573, DIW Berlin, German Institute for Economic Research.
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