Abstract #301068

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JSM 2003 Abstract #301068
Activity Number: 448
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract - #301068
Title: Incorporating Time-Dependent Covariates in the Cox Regression Model Using Time-Decaying Covariate Effects
Author(s): Yali Liu*+ and Bruce A. Craig
Companies: Purdue University and Purdue University
Address: 136-15 Nimitz Dr., Apt. 15, West Lafayette, IN, 47906,
Keywords: survival ; longitudinal ; Cox regression
Abstract:

In many longitudinal studies, there are time-dependent covariates which are observed intermittently. To incorporate these covariates into the Cox regression model, the subject-specific value of each covariate must be known at each failure time. Because this rarely occurs, it must be estimated from the observations. Early approaches have involved using the last observed value or estimating a function of covariate over time. Such methods, however, can lead to biased and misleading inference on the Cox model parameters. We propose a time-decaying covariate effect model to estimate the unobserved covariate value for each subject at each failure time. This approach utilizes the flanking observed covariate values and the time before and after these observations. We illustrate the approach through a simulation study and by application to WESDR data with glycosylated hemoglobin as the time-dependent covariate.

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