Abstract #301873

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JSM 2003 Abstract #301873
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 - #301873
Title: Discriminating Between Competing Models for Survival Data Based on Covariate Information
Author(s): Zdenek Valenta*+ and Chung-Chou H. Chang and Lisa A. Weissfeld
Companies: EuroMISE Center and University of Pittsburgh and University of Pittsburgh
Address: Skalni 496 / 7, Liberec, , 460 01, Czech Republic
Keywords: survival function ; competing models ; discriminating ; survival status
Abstract:

Analysis of right-censored survival data often permits postulating several competing models, each satisfying its binding assumptions. One can arrive, for instance, at different semiparametric survival models by either assuming proportionality of the hazard functions over time, or by relaxing this assumption via inclusion of penalized splines, while both models may appear to be equally plausible for the data at hand. Competing models, such as Cox proportional hazards (PH) model and Gray's time-varying coefficients (TVC) model, do however, often render apparently different conditional survival function estimates for an individual with covariate values X. Using the survival status Y(t) for each available subject as a trivial unbiased estimator of conditional probability of survival given X=x, we demonstrate that we can discriminate between competing models on the basis of the expected conditional variance of Y(t) given X. Examples based on the United Network for Organ Sharing (UNOS) posttransplant survival data are shown for illustration.

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