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Partly Conditional Survival Models for Longitudinal Data

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Author Info

  • Yingye Zheng

    (Fred Hutchinson Cancer Research Center)

  • Patrick Heagerty

    (University of Washington)

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    Abstract

    It is common in longitudinal studies to collect information on the time until a key clinical event, such as death, and to measure markers of patient health at multiple follow-up times. One approach to the joint analysis of survival and repeated measures data adopts a time-varying covariate regression model for the event time hazard. Using this standard approach the instantaneous risk of death at time t is specified as a possibly semi-parametric function of covariate information that has accrued through time t. In this manuscript we decouple the time scale for modeling the hazard from the time scale for accrual of available longitudinal covariate information. Specifically, we propose a class of models that condition on the covariate information through time s and then specifies the conditional hazard for times t where t > s. Our approach parallels the "partly conditional" models proposed by Pepe and Couper (1997} for pure repeated measures applications. Estimation is based on the use of estimating equations applied to clusters of data formed through the creation of derived survival times that measure the time from measurement of covariates to the end of follow-up. Patient follow-up may be terminated either by the occurrence of the event or by censoring. The proposed methods allow a flexible characterization of the association between a longitudinal covariate process and a survival time, and facilitate the direct prediction of survival probabilities in the time-varying covariate setting.

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    File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1053&context=uwbiostat
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    Bibliographic Info

    Paper provided by Berkeley Electronic Press in its series UW Biostatistics Working Paper Series with number 1053.

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    Date of creation: 11 Jul 2004
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    Handle: RePEc:bep:uwabio:1053

    Note: oai:bepress.com:uwbiostat-1053
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    Related research

    Keywords: joint model; estimating equation; multivariate survival;

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    1. Skates S. J & Pauler D. K & Jacobs I. J, 2001. "Screening Based on the Risk of Cancer Calculation From Bayesian Hierarchical Changepoint and Mixture Models of Longitudinal Markers," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 429-439, June.
    2. repec:wop:humbsf:2000-50 is not listed on IDEAS
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