SeminarsThe Mass and the Paneitz operator in CR geometry
155
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Partly Conditional Survival Models for Longitudinal Data. In this talk, we will present the paper by Zheng and Heagerty (2003). It presents the joint analysis of survival and repeated measures data adopts a time-varying covariate regression model for the event time hazard. This 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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Ming-Sian Wu
2007-11-20
13:30:00 - 15:00:00
405 , Mathematics Research Center Building (ori. New Math. Bldg.)
Partly Conditional Survival Models for Longitudinal Data. In this talk, we will present the paper by Zheng and Heagerty (2003). It presents the joint analysis of survival and repeated measures data adopts a time-varying covariate regression model for the event time hazard. This 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.