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Approximate conditional inference in mixed-effects models with binary data

Author

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  • Lee, Woojoo
  • Shi, Jian Qing
  • Lee, Youngjo

Abstract

The conditional likelihood approach is a sensible choice for a 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.

Suggested Citation

  • Lee, Woojoo & Shi, Jian Qing & Lee, Youngjo, 2010. "Approximate conditional inference in mixed-effects models with binary data," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 173-184, January.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:1:p:173-184
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    References listed on IDEAS

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    1. Jian Qing Shi & John Copas, 2002. "Publication bias and meta‐analysis for 2×2 tables: an average Markov chain Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 221-236, May.
    2. Shih, Joanna H. & Lu, Shou-En, 2009. "Semiparametric estimation of a nested random effects model for the analysis of multi-level clustered failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3864-3871, September.
    3. J. G. Liao, 1999. "A Hierarchical Bayesian Model for Combining Multiple 2 × 2 Tables Using Conditional Likelihoods," Biometrics, The International Biometric Society, vol. 55(1), pages 268-272, March.
    4. N. Sartori, 2003. "Modified profile likelihoods in models with stratum nuisance parameters," Biometrika, Biometrika Trust, vol. 90(3), pages 533-549, September.
    5. Ruggero Bellio & Nicola Sartori, 2003. "Extending conditional likelihood in models for stratified binary data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(2), pages 121-132, December.
    6. Noh, Maengseok & Lee, Youngjo, 2007. "REML estimation for binary data in GLMMs," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 896-915, May.
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