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Partly Conditional Survival Models for Longitudinal Data
Author(s) -
Zheng Yingye,
Heagerty Patrick J.
Publication year - 2005
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00323.x
Subject(s) - covariate , proportional hazards model , censoring (clinical trials) , statistics , econometrics , event (particle physics) , survival analysis , accelerated failure time model , hazard , hazard ratio , mathematics , computer science , confidence interval , physics , chemistry , organic chemistry , quantum mechanics
Summary 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 semiparametric 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, Journal of the American Statistical Association 92, 991–998) 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.