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Competing-Risks Duration Models with Correlated Random Effects: An Application to Dementia Patients’ Transition Histories

Author

Listed:
  • Hess , Wolfgang

    () (Department of Economics, Lund University)

  • Schwarzkopf , Larissa

    () (Institute of Health Economics and Health Care Management, Helmholtz Zentrum)

  • Hunger , Matthias

    () (Institute of Health Economics and Health Care Management, Helmholtz Zentrum)

  • Holle , Rolf

    () (Institute of Health Economics and Health Care Management, Helmholtz Zentrum)

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.

Suggested Citation

  • Hess , Wolfgang & Schwarzkopf , Larissa & Hunger , Matthias & Holle , Rolf, 2013. "Competing-Risks Duration Models with Correlated Random Effects: An Application to Dementia Patients’ Transition Histories," Working Papers 2013:28, Lund University, Department of Economics.
  • Handle: RePEc:hhs:lunewp:2013_028
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    References listed on IDEAS

    as
    1. Peter Haan & Arne Uhlendorff, 2006. "Estimation of multinomial logit models with unobserved heterogeneity using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 6(2), pages 229-245, June.
    2. 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.
    3. Thomas A. Mroz & Yaraslau V. Zayats, 2008. "Arbitrarily Normalized Coefficients, Information Sets, and False Reports of "Biases" in Binary Outcome Models," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 406-413, August.
    4. 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.
    5. 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.
    6. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
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    More about this item

    Keywords

    Competing risks; Dementia; Discrete-time duration model; Multinomial logit; Random effects; Transition;

    JEL classification:

    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • I10 - Health, Education, and Welfare - - Health - - - General

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