JSM 2005 - Toronto

Abstract #304080

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 255
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #304080
Title: Goodness-of-fit in Generalized Linear and Nonlinear Mixed-effects Models
Author(s): Edward Vonesh*+
Companies: Baxter Healthcare Corporation
Address: Route 120 and Wilson Rd, Round Lake, IL, 60073, United States
Keywords: Mixed-effects Models ; Goodness-of-Fit ; R-square Criterion
Abstract:

Generalized linear and nonlinear mixed-effects models provide practitioners with a wide class of models from which to choose when analyzing longitudinal and/or clustered data. Methods for assessing goodness-of-fit among various mixed models have been based primarily on likelihood and quasi-likelihood techniques, such as Akaike's information criterion and Schwarz's Bayesian information criterion. In this paper, we present several nonlikelihood-based measures of goodness-of-fit that may be used in a manner similar to the traditional R-square criterion commonly found in linear regression settings. In addition, we briefly explore the use of a pseudo-likelihood ratio type test that often may help identify lack of fit in an assumed variance-covariance structure. These methods are illustrated and compared with standard likelihood-based criterion using several examples. Simulations also are presented illustrating both the utility and limitations of these nonlikelihood-based methods.

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