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Approximate conditional inference in mixed-effects models with binary data

Lookup NU author(s): Dr Jian Shi

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Abstract

The conditional likelihood approach is a sensible choice fora hierarchical logistic regression model or other generalized regression models with binary data. However, its heavy computational burden limits its use, especially for the related mixed-effects model. A modified profile likelihood is used as an accurate approximation to conditional likelihood, and then the use of two methods for inferences for the hierarchical generalized regression models with mixed effects is proposed. One is based on a hierarchical likelihood and Laplace approximation method, and the other is based on a Markov chain Monte Carlo EM algorithm. The methods are applied to a meta-analysis model for trend estimation and the model for multi-arm trials. A simulation study is conducted to illustrate the performance of the proposed methods. (C) 2009 Elsevier B.V. All rights reserved.

Publication metadata

Author(s): Lee W, Shi JQ, Lee Y

Publication type: Article

Publication status: Published

Journal: Computational Statistics & Data Analysis

Year: 2010

Volume: 54

Issue: 1

Pages: 173-184

Print publication date: 01/01/2010

ISSN (print): 0167-9473

ISSN (electronic): 1872-7352

Publisher: Elsevier BV

URL: http://dx.doi.org/10.1016/j.csda.2009.07.027

DOI: 10.1016/j.csda.2009.07.027

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