Abstract #301792

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JSM 2003 Abstract #301792
Activity Number: 59
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract - #301792
Title: Estimating Equations in Censored Survival Data: Some Applications
Author(s): David C. Gruben*+
Companies:
Address: 937 Bullock Ave., Yeadon, PA, 19050-3714,
Keywords: censoring ; survival analysis ; estimating equations ; quasi-likelihood ; generalized linear model ; GEE
Abstract:

In their book Statistical Analysis with Missing Data, Little and Rubin give an example of independently and identically distributed exponential data with mean theta.The data are censored at some known time-point c, so that only values less than or equal to c are observed and the rest are missing. (An example of this mechanism might be a clinical trial whose follow-up ends at time c for all patients.) In this case, the cause of the missing data is not ignorable in the estimation of theta. Little and Rubin derive the maximum likelihood estimator for theta that incorporates the missing data mechanism. In this presentation, an estimator based on an estimating-equations approach is proposed as a solution to this problem. The resulting estimator of theta will be contrasted with the maximum-likelihood estimator. Estimating equations allow for a variety of methods (e.g., generalized linear model, generalized estimating equations) to handle different applications (e.g., longitudinal or correlated data) , and these methods and their applications, as well as extensions to other distributions, will be discussed briefly.

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