On casting random-effects models in a survival framework

Ramani S. Pilla, Yongdai Kim, Hakbae Lee

Research output: Contribution to journalArticle

Abstract

Logistic random-effects models are often employed in the analysis of correlated binary data. However, fitting these models is challenging, since the marginal distribution of the response variables is analytically intractable. Often, the random effects are treated as missing data for constructing traditional data augmentation algorithms. We create a novel alternative data augmentation scheme that simplifies the likelihood-based inference for logistic random-effects models. We cast the random-effects model in a 'survival framework', where each binary response is the censoring indicator for a survival time that is treated as additional missing data. Under this augmentation framework, the conditional expectations are free of unknown regression parameters. Such a construction has a particular advantage that, in the case of discrete covariates, the score equations for regression parameters have analytical solutions. Consequently, one does not need to resort to a search algorithm in estimating the regression parameters. We further create a parameter expansion scheme for logistic random-effects models under this survival data augmentation framework. The proposed data augmentation is illustrated when the random-effects distribution follows a multivariate Gaussian and multivariate t-distribution. The performance of the method is assessed through simulation studies and a real data analysis.

Original languageEnglish
Pages (from-to)629-642
Number of pages14
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume70
Issue number3
DOIs
Publication statusPublished - 2008 Jul 1

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Data Augmentation
Random Effects Model
Casting
Logistic Model
Regression
Random Effects
Missing Data
Correlated Binary Data
Multivariate T-distribution
Binary Response
Survival Data
Survival Time
Model Fitting
Conditional Expectation
Augmentation
Censoring
Marginal Distribution
Search Algorithm
Covariates
Likelihood

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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On casting random-effects models in a survival framework. / Pilla, Ramani S.; Kim, Yongdai; Lee, Hakbae.

In: Journal of the Royal Statistical Society. Series B: Statistical Methodology, Vol. 70, No. 3, 01.07.2008, p. 629-642.

Research output: Contribution to journalArticle

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