The effects of mixture distribution misspecification when fitting mixed-effects logistic models

SUMMARY Mixed-effects logistic models are often used to analyze binary response data which have been gathered in clusters, or groups. Responses are assumed to follow a logistic model with in clusters, with an intercept which varies across clusters according to a specified probability distribution G....

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Bibliographic Details
Published in:Biometrika 1992-12, Vol.79 (4), p.755-762
Main Authors: NEUHAUS, J. M., HAUCK, W. W., KALBFLEISCH, J. D.
Format: Article
Language:English
Subjects:
Approximation
Asymptotic bias
Binary data
Clustered data
Consistent estimators
Estimation bias
Exact sciences and technology
Linear inference, regression
Logistics
Mathematics
Maximum likelihood estimation
Modeling
Parametric models
Probability and statistics
Regression coefficients
Robust inference
Sciences and techniques of general use
Statistical variance
Statistics
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Summary:SUMMARY Mixed-effects logistic models are often used to analyze binary response data which have been gathered in clusters, or groups. Responses are assumed to follow a logistic model with in clusters, with an intercept which varies across clusters according to a specified probability distribution G. In this paper we examine the performance of mixed-effects logistic regression analysis when a main component of the model, the mixture distribution, is misspecified. We show that, when the mixture distribution is misspecified, estimates of model parameters, including the effects of covariates, typically are asymptotically biased, i.e. inconsistent. However, we present some approximations which suggest that the magnitude of the bias in the estimated covariate effects is typically small. These findings are corroborated by a set of simulations which also suggest that valid variance estimates of estimated covariate effects can be obtained when the mixture distribution is misspecified.
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/79.4.755