JSM 2005 - Toronto

Abstract #302960

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Activity Number: 400
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract - #302960
Title: Seminonparametric Mixed Models for Longitudinal Data
Author(s): Guei-Feng Tsai*+ and Annie Qu
Companies: Oregon State University and Oregon State University
Address: 1061 SW Washington Ave Apt204, Corvallis, OR, 97333, United States
Keywords: longitudinal data ; quadratic inference function ; microarray
Abstract:

We develop nonparametric, marginal, and random effects models for the analysis of longitudinal data. In particular, we consider a time-varying coefficient model because it provides a flexible statistical model and takes both time and covariates effects into account for longitudinal data. In order to incorporate correlation of longitudinal measurements, the quadratic inference function method is applied. We perform a goodness-of-fit test to determine whether the coefficients are time varying in marginal models method. Furthermore, we take subject-specific effects into consideration and develop random effects models in our setting. In addition, we construct additional estimating equations for estimating variance components. We also propose a goodness-of-fit test for testing whether the variance components of random effects are significant. A real data example on cell cycle microarray data and simulations are illustrated using our methods.

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