Abstract #301141

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JSM 2003 Abstract #301141
Activity Number: 355
Type: Topic Contributed
Date/Time: Wednesday, August 6, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #301141
Title: Nonparametric and Semiparametric Estimation of Survival
Author(s): Steven D. Mark*+
Companies: National Cancer Institute
Address: 6120 Executive Blvd., Rockville, MD, 20852-4906,
Keywords: survival ; missing data ; semiparametric efficiency ; two-stage ; nested cohort
Abstract:

Frequently in epidemiologic studies, one is interested in assessing the effect of exposure V, on a time to event outcome T, while controlling for covariates J. If the cost of measuring V is disproportionate to the cost of J, it is inefficient to measure V on everyone. Various study designs have been developed where J is observed on all members of a cohort, and V observed only a sub-sample. Estimating equations, generally focusing on the relative risk parameter in the semiparametric Cox model, have been proposed. Rather than relative risk, our focus is on estimating survival as a functional of the cumulative hazards. We characterize the class of all nonparametric and semi-parametric RAL estimators of these cumulative hazards. Using the results of Robins, Rotnitzky, and Zhao (1994), we show that the estimating equations can be regarded as weighted versions of the Nelson-Aalen and the Breslow estimators, respectively. We derive expressions for the efficient estimator in each class; discuss the data structures under which these estimators are identifiable; and examine the implications of the form of the efficient estimator for study design and analysis.

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