Random-Effects Models for Serial Observations with Binary Response

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ID: 305878
1984
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Abstract
This paper presents a general mixed model for the analysis of serial dichotomous responses provided by a panel of study participants. Each subject's serial responses are assumed to arise from a logistic model, but with regression coefficients that vary between subjects. The logistic regression parameters are assumed to be normally distributed in the population. Inference is based upon maximum likelihood estimation of fixed effects and variance components, and empirical Bayes estimation of random effects. Exact solutions are analytically and computationally infeasible, but an approximation based on the mode of the posterior distribution of the random parameters is proposed, and is implemented by means of the EM algorithm. This approximate method is compared with a simpler two-step method proposed by Korn and Whittemore (1979, Biometrics 35, 795-804), using data from a panel study of asthmatics originally described in that paper. One advantage of the estimation strategy described here is the ability to use all of the data, including that from subjects with insufficient data to permit fitting of a separate logistic regression model, as required by the Korn and Whittemore method. However, the new method is computationally intensive.
Reference Key
openalex_W2024085858 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Robert G. Stiratelli, Nan M. Laird, James H. Ware
Journal biometrics
Year 1984
DOI
10.2307/2531147
URL
https://doi.org/10.2307/2531147
Keywords Keywords not found

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