Abstract #302233

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JSM 2003 Abstract #302233
Activity Number: 180
Type: Contributed
Date/Time: Monday, August 4, 2003 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #302233
Title: Weighted Estimators for Proportional Hazards Model with Missing Covariates
Author(s): Lihong Qi*+ and C. Y. Wang and Ross L. Prentice
Companies: University of Washington and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center
Address: 3801 Brooklyn Ave. NE #M402-B, Seattle, WA, 98105-6769,
Keywords: missing data ; nonparametric methods ; pseudolikelihood ; weighted estimating equation ; case-cohort ; nested case-control
Abstract:

Regression parameter estimation in the Cox proportional hazards model is considered when certain covariates are observed for all study subjects and other covariate data of interest are collected only for a subset. We study a class of weighted estimators which make use of all available data. These weighted estimators are applicable to cohort sampling procedures, including case-cohort and nested case-control designs. For a simple weighted estimator, and a fully augmented weighted estimator modified from that investigated by Wang and Chen (2001), we employ nonparametric methods to estimate the inclusion probabilities and the conditional expectation of a pseudolikelihood score function. The asymptotic distribution theory of these estimators is derived and their asymptotic equivalence is shown when the inclusion probabilities and the conditional expectation of the pseudolikelihood score only involve discrete variables. We examine some properties of these estimators via simulation and by application to a dataset.

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