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Investigating healthy life expectancy using a multi-state model in the presence of missing data and misclassification

Author

Listed:
  • Ardo van den Hout

    (University College London (UCL))

  • Ekaterina Ogurtsova

    (Max-Planck-Institut für Demografische Forschung)

  • Jutta Gampe

    (Max-Planck-Institut für Demografische Forschung)

  • Fiona Matthews

    (University of Cambridge)

Abstract

Background: A continuous-time three-state model can be used to describe change in cognitive function in the older population. State 1 corresponds to normal cognitive function, state 2 to cognitive impairment, and state 3 to dead. For statistical inference, longitudinal data are available from the UK Medical Research Council Cognitive Function and Ageing Study. Objective: The aim is statistical analysis of longitudinal multi-state data taking into account missing data and potential misclassification of state. In addition, methods for long-term prediction of the transition process are of interest, specifically when applied to the study of healthy life expectancy. Methods: Cognitive function in the older population is assumed to be stable or declining. For this reason, observed improvement of cognitive function is assumed to be caused by misclassification of either state 1 or 2. Regression models for the transition intensities are formulated to incorporate covariate information. Maximum likelihood is used for statistical inference. Results: It is shown that missing values for the state at a pre-scheduled time can easily be taken into account. Long-term prediction is explained and illustrated by the estimation of statespecific life expectancies. In addition, it is shown how microsimulation can be used to further explore predictions based on a fitted multi-state model. Conclusions: Statistical analysis of longitudinal multi-state data can take into account missing data and potential misclassification of state. With respect to long-term prediction, microsimulation is a useful tool for summarising and displaying characteristics of cognitive decline and survival. Comments: ---

Suggested Citation

  • Ardo van den Hout & Ekaterina Ogurtsova & Jutta Gampe & Fiona Matthews, 2014. "Investigating healthy life expectancy using a multi-state model in the presence of missing data and misclassification," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(42), pages 1219-1244.
    • Handle: RePEc:dem:demres:v:30:y:2014:i:42
    DOI: 10.4054/DemRes.2014.30.42
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    References listed on IDEAS

    as
    1. Ben D. MacArthur & Richard O. C. Oreffo, 2005. "Bridging the gap," Nature, Nature, vol. 433(7021), pages 19-19, January.
    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. Glen A. Satten & Ira M. Longini, 1996. "Markov Chains with Measurement Error: Estimating the ‘True’ Course of a Marker of the Progression of Human Immunodeficiency Virus Disease," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(3), pages 275-295, September.
    4. Jackson, Christopher, 2011. "Multi-State Models for Panel Data: The msm Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i08).
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    Cited by:

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    4. Christian Dudel, 2017. "Expanding the Markov chain tool box: distributions of occupation times and waiting times," MPIDR Working Papers WP-2017-017, Max Planck Institute for Demographic Research, Rostock, Germany.

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    More about this item

    Keywords

    micro-simulation; panel data; misclassification; cognitive functioning;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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