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Fitting Event-History Models to Uneventful Data

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Abstract

Data with which to study disability dynamics usually take the form of successive current-status measures of disability rather than a record of events or spell durations. One recent paper presented a semi-Markov model of disability dynamics in which spell durations were inferred from sequences of current-status measures taken at 12-month intervals. In that analysis, it was assumed that no unobserved disablement transitions occurred between annual interviews. We use data from a longitudinal survey in which participants' disability was measured at monthy intervals, and simulate the survival curves for remaining disabled that would be obtained with 1- and 12-month follow-up intervals. The median length of an episode of disability based on the 12-month interval data is over 22 months, while the "true" median, based on the 1-month interval data, is only one month.

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  • 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.
    • Handle: RePEc:max:cprwps:101
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    File URL: https://surface.syr.edu/cpr/65/
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    1. Scott M. Lynch & J. Scott Brown & Katherine G. Harmsen, 2003. "The Effect of Altering ADL Thresholds on Active Life Expectancy Estimates for Older Persons," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 58(3), pages 171-178.
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    4. 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.
    5. Mira Hidajat & Mark Hayward & Yasuhiko Saito, 2007. "Indonesia’s social capacity for population health: the educational gap in active life expectancy," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(2), pages 219-234, April.
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    More about this item

    Keywords

    Disability; semi-Markov process; duration analysis;
    All these keywords.

    JEL classification:

    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I19 - Health, Education, and Welfare - - Health - - - Other

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