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Activity Number:
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229
- Random Effect/Mixed Models
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Type:
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Contributed
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Date/Time:
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Monday, July 31, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract #323171
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Title:
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Marginally Interpretable Generalized Linear Mixed Models
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Author(s):
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Jeffrey J Gory* and Peter F Craigmile and Steven MacEachern
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Companies:
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The Ohio State University and The Ohio State University and The Ohio State University
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Keywords:
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marginal model ;
conditional model ;
population-averaged ;
subject-specific predictions ;
logistic-normal integral
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Abstract:
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Two popular approaches for relating correlated measurements of a non-Gaussian response variable to a set of predictors are to fit a marginal model using generalized estimating equations and to fit a generalized linear mixed model by introducing latent random variables. The first approach is effective for parameter estimation, but leaves one without a formal model for the data with which to assess quality of fit or make predictions for future observations. The second approach overcomes these deficiencies, but leads to parameter estimates that must be interpreted conditional on the latent variables. Further complicating matters, obtaining marginal summaries from a generalized linear mixed model often requires evaluation of an analytically intractable integral or use of attenuation factors that are not exact. We define a class of marginally interpretable generalized linear mixed models that lead to parameter estimates with a marginal interpretation while maintaining the desirable statistical properties of a conditionally-specified model. We discuss the form of these models under various common link functions and also address computational issues associated with these models.
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Authors who are presenting talks have a * after their name.
