JSM 2004 - Toronto

Abstract #300542

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Activity Number: 378
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: General Methodology
Abstract - #300542
Title: Semiparametric Models for Missing Covariates in Generalized Linear Mixed Models
Author(s): Qingxia Chen*+ and Joseph G. Ibrahim
Companies: University of North Carolina, Chapel Hill and University of North Carolina, Chapel Hill
Address: Dept. of Biostatistics, School of Public Health, Chapel Hill, NC, 27599,
Keywords: generalized additive model ; EM algorithm ; Gibbs sampling ; Monte Carlo EM
Abstract:

We consider a class of semiparametric models for the covariate distribution and missing data mechanism for missing covariate and/or response data for generalized linear models and generalized linear mixed models. Ignorable and Nonignorable missing covariate and/or response data is considered. A generalized additive model (GAM) is considered for the covariate distribution and/or the missing data mechanism. Penalized regression splines are used to express the GAM's as a generalized linear mixed effects model, in which the variance of the corresponding random effects provides an intuitive index for choosing between the semiparametric and parametric model. Maximum likelihood estimates are obtained via the EM algorithm. Simulations are given to demonstrate the methodology and two real datasets from a melanoma cancer and breast cancer clinical trials are analyzed using the proposed methods.

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