JSM 2004 - Toronto

Abstract #300753

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Activity Number: 413
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #300753
Title: Empirical Likelihood-based Semiparametric Inference of the Treatment Effect in the Two-sample Problem
Author(s): Hua Liang*+
Companies: St. Jude Children's Research Hospital
Address: 332 N. Lauderdale St., Memphis, TN, 38105,
Keywords: estimating equation ; confidence interval ; empirical likelihood ratio/function ; Kaplan-Meier estimation ;
Abstract:

To compare two samples of censored data, we propose a unified semiparametric inference for the parameter of interest when one sample is parametric and the other is nonparametric. The parameter of interest may represent, for example, a comparison of means, survival probabilities, survival competition probability. The confidence interval drawn from the semiparametric inference, which is based on the empirical likelihood principle, improves the counterpart constructed from the common estimating equation. The empirical likelihood ratio, which can be used to construct test and confidence intervals of the parameter of interest, is shown to be asymptotically chi-squared. Simulation experiments illustrate that our method based on the empirical likelihood substantially outperforms the method of the estimating equation. A real dataset is analyzed with the proposed methods.

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