A missing composite covariate in survival analysis: a case study of the Chinese Longitudinal Health and Longevity Survey
AbstractWe 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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Bibliographic InfoPaper provided by Max Planck Institute for Demographic Research, Rostock, Germany in its series MPIDR Working Papers with number WP-2008-022.
Length: 9 pages
Date of creation: Sep 2008
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Web page: http://www.demogr.mpg.de/
Find related papers by JEL classification:
- J1 - Labor and Demographic Economics - - Demographic Economics
- Z0 - Other Special Topics - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-11-04 (All new papers)
- NEP-CNA-2008-11-04 (China)
- NEP-HEA-2008-11-04 (Health Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
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