JSM 2005 - Toronto

Abstract #303999

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 225
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #303999
Title: Smooth Inference for Survival Functions with Arbitrarily Censored Data
Author(s): Kirsten Doehler*+ and Marie Davidian
Companies: North Carolina State University and North Carolina State University
Address: Campus Box 8203, Raleigh, NC, 27695,
Keywords: censored data ; seminonparametric density ; survival analysis
Abstract:

Standard methods for inference on survival functions with right- or interval-censored data are traditionally nonparametric, and hence impose no assumptions on the true survival distribution. We propose a new procedure for estimation of survival functions that allows a unified approach to handling different kinds of censoring based on the premise that, if one is willing to make mild smoothness assumptions on the underlying true survival distribution, efficiency gains and computational advantages over nonparametric methods may be possible. The approach assumes the survival distribution has a "smooth" density, which is approximated by the so-called seminonparametric (SNP) density. The SNP has a flexible "parametric" representation that admits a convenient expression for the likelihood and allows it to capture arbitrary shapes through choice of a tuning parameter, which may be carried out based on standard criteria such as AIC and BIC. We describe the approach and its implementation and validate its performance in empirical studies. For right-censored data, we demonstrate that the method can greatly outperform the Kaplan-Meier estimator under a variety of scenarios.

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Revised March 2005