Advanced Search
MyIDEAS: Login to save this paper or follow this series

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

Contents:

Author Info

  • 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.

Download Info

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.

Bibliographic Info

Paper provided by Lund University, Department of Economics in its series Working Papers with number 2013:28.

as in new window
Length: 15 pages
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 .
Handle: RePEc:hhs:lunewp:2013_028

Contact details of provider:
Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund,Sweden
Phone: +46 +46 222 0000
Fax: +46 +46 2224613
Web page: http://www.nek.lu.se/en
More information through EDIRC

Related research

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

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. 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.
  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. 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.
  4. 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.
  5. 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.
Full references (including those not matched with items on IDEAS)

Citations

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:hhs:lunewp:2013_028. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (David Edgerton).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.