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

Author

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  • Francesco Lagona

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

  • Zhen Zhang

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

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.

Suggested Citation

  • Francesco Lagona & Zhen Zhang, 2008. "A missing composite covariate in survival analysis: a case study of the Chinese Longitudinal Health and Longevity Survey," MPIDR Working Papers WP-2008-022, Max Planck Institute for Demographic Research, Rostock, Germany.
    • Handle: RePEc:dem:wpaper:wp-2008-022
    DOI: 10.4054/MPIDR-WP-2008-022
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    References listed on IDEAS

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    2. Zhenmei Zhang, 2006. "Gender Differentials in Cognitive Impairment and Decline of the Oldest Old in China," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 61(2), pages 107-115.
    3. 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, vol. 100, pages 332-346, March.
    4. Amy H. Herring & Joseph G. Ibrahim & Stuart R. Lipsitz, 2004. "Non‐ignorable missing covariate data in survival analysis: a case‐study of an International Breast Cancer Study Group trial," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(2), pages 293-310, April.
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    More about this item

    Keywords

    China; health;

    JEL classification:

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

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