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Analysis of functional status transitions by using a semi‐Markov process model in the presence of left‐censored spells

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  • Liming Cai
  • Nathaniel Schenker
  • James Lubitz

Abstract

Summary. To analyse functional status transitions in the older population better, we fit a semi‐Markov process model to data from the 1992–2002 Medicare Current Beneficiary Survey. We used an analogue of the stochastic EM algorithm to address the problem of left censoring of spells in longitudinal data. The iterative algorithm converged robustly under various initial values for the unobserved elapsed durations of spells in progress at base‐line. Results on life expectancy and recovery from functional limitations based on the semi‐Markov process model differ from those based on the traditional multistate life‐table method. The proposed treatment of left‐censored spells has the potential to expand the modelling capability that is available to researchers in fields where left censoring is a concern.

Suggested Citation

  • Liming Cai & Nathaniel Schenker & James Lubitz, 2006. "Analysis of functional status transitions by using a semi‐Markov process model in the presence of left‐censored spells," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(4), pages 477-491, August.
    • Handle: RePEc:bla:jorssc:v:55:y:2006:i:4:p:477-491
    DOI: 10.1111/j.1467-9876.2006.00548.x
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    Cited by:

    1. Douglas A. Wolf & Thomas M. Gill, 2008. "Fitting Event-History Models to Uneventful Data," Center for Policy Research Working Papers 101, Center for Policy Research, Maxwell School, Syracuse University.
    2. Ardo Van Den Hout & Carol Jagger & Fiona E. Matthews, 2009. "Estimating life expectancy in health and ill health by using a hidden Markov model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(4), pages 449-465, September.
    3. Marco Raffaella Piccarreta & Marco Bonetti & Stefano Lombardi, 2018. "Comparing models for sequence data: prediction and dissimilarities," Working Papers 113, "Carlo F. Dondena" Centre for Research on Social Dynamics (DONDENA), Università Commerciale Luigi Bocconi.
    4. Scott Lynch & J. Brown, 2010. "Obtaining multistate life table distributions for highly refined subpopulations from cross-sectional data: A bayesian extension of sullivan’s method," Demography, Springer;Population Association of America (PAA), vol. 47(4), pages 1053-1077, November.
    5. Wouterse, B. & Meijboom, B.R. & Polder, J.J., 2011. "The relationship between baseline health and longitudinal costs of hospital use," Other publications TiSEM bdedc33c-9737-4bfc-beee-0, Tilburg University, School of Economics and Management.
    6. Douglas Wolf & Thomas Gill, 2009. "Modeling transition rates using panel current-status data: How serious is the bias?," Demography, Springer;Population Association of America (PAA), vol. 46(2), pages 371-386, May.
    7. Liming Cai & Mark D. Hayward & Yasuhiko Saito & James Lubitz & Aaron Hagedorn & Eileen Crimmins, 2010. "Estimation of multi-state life table functions and their variability from complex survey data using the SPACE Program," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 22(6), pages 129-158.
    8. Frans Willekens & Hein Putter, 2014. "Software for multistate analysis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 31(14), pages 381-420.
    9. Bram Wouterse & Bert R. Meijboom & Johan J. Polder, 2011. "The relationship between baseline health and longitudinal costs of hospital use," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 985-1008, August.

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