Abstract #300475

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JSM 2003 Abstract #300475
Activity Number: 133
Type: Contributed
Date/Time: Monday, August 4, 2003 : 12:00 PM to 1:50 PM
Sponsor: Biometrics Section
Abstract - #300475
Title: Inference for the Power Variance Function Frailty Model
Author(s): Hong Wang*+ and John P. Klein
Companies: Medical College of Wisconsin and Medical College of Wisconsin
Address: 9130 W Bluemound Rd., Milwaukee, WI, 53226-4590,
Keywords: frailty models ; power variance function distribution ; EM algorithm ; censored data
Abstract:

In survival analysis, frailty models are commonly used to model dependent event times. Models that assume individuals within the same group sharing an unobservable random effect are called shared frailty models. We consider a shared frailty model where the frailty distribution belongs to a very general distribution family called Power Variance Function (PVF) distributions. Many popular frailty models such as the gamma, inverse Gaussian, and positive stable frailty model are special cases of the PVF frailty model. The EM algorithm is employed to estimate the risk coefficients and frailty parameters for the PVF frailty model. A simulation study is used to evaluate performance of these estimators. Estimate of the variance-covariance matrix will be presented. We will also discuss properties of different hypothesis tests to identify frailty distribution.

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