Abstract #300252

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JSM 2003 Abstract #300252
Activity Number: 372
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #300252
Title: Empirical and Kernel Estimation of Covariate Distribution Conditional on Survival Time
Author(s): Xiaochun Li*+ and Ronghui Xu
Companies: Dana Farber Cancer Institute and Harvard School of Public Health
Address: 70 Centre St. Apt. 1A, Brookline, MA, 02446-2816,
Keywords: survival ; proportional hazard model ; kernel smoothing ; asymptotics
Abstract:

In biomedical research there is often interest in describing covariate distribution given survival. This is not immediately available due to censoring. In this paper we develop an empirical estimate of the conditional covariate distribution under the proportional hazards regression model. We show that it converges weakly to a Gaussian process and provide its variance estimate. We then apply kernel smoothing to obtain an estimate of the corresponding density function. The asymptotic properties of the density estimator will be discussed. We have developed an R package to implement the methods in this paper. Examples will be shown through this package on PBC data.

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