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A missing composite covariate in survival analysis: a case study of the Chinese Longitudinal Health and Longevity Survey

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  • Francesco Lagona

    (Max Planck Institute for Demographic Research, Rostock, Germany)

  • Zhen Zhang

    (Max Planck Institute for Demographic Research, Rostock, Germany)

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    Abstract

    We estimate a Cox proportional hazards model where one of the covariates measures the level of a subject´s cognitive functioning by grading the total score obtained by the subject on the items of a questionnaire. A case study is presented where the sample includes partial respondents, who did not answer some or all of the questionnaire items. The total score takes hence the form of an interval-censored variable and, as a result, the level of cognitive functioning is missing on some subjects. We handle partial respondents by taking a likelihood-based approach where survival time is jointly modelled with the censored total score and the size of the censoring interval. Parameter estimates are obtained by an E-M-type algorithm that essentially reduces to the iterative maximization of three complete log-likelihood functions derived from two augmented datasets with case weights, alternated with weights updating. This methodology is exploited to assess the Mini Mental State Examination index as a prognostic factor of survival in a sample of Chinese older adults.

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    File URL: http://www.demogr.mpg.de/papers/working/wp-2008-022.pdf
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    Bibliographic Info

    Paper provided by Max Planck Institute for Demographic Research, Rostock, Germany in its series MPIDR Working Papers with number WP-2008-022.

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    Length: 9 pages
    Date of creation: Sep 2008
    Date of revision:
    Handle: RePEc:dem:wpaper:wp-2008-022

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    Web page: http://www.demogr.mpg.de/

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    Keywords: China; health;

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    1. Joseph G. Ibrahim & Ming-Hui Chen & Stuart R. Lipsitz & Amy H. Herring, 2005. "Missing-Data Methods for Generalized Linear Models: A Comparative Review," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 100, pages 332-346, March.
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