Use of posterior predictive assessments to evaluate model fit in multilevel logistic regression

Authors
Green, M.J., Medley, G.F. and Browne, W.J.
Year
2009
Journal
Veterinary Research, 40:4, 10
DOI
10.1051/vetres/2009013
Abstract

Assessing the fit of a model is an important final step in any statistical analysis, but this is not straightforward when complex discrete response models are used. Cross validation and posterior predictions have been suggested as methods to aid model criticism. In this paper a comparison is made between four methods of model predictive assessment in the context of a three level logistic regression model for clinical mastitis in dairy cattle; cross validation, a prediction using the full posterior predictive distribution and two mixed predictive methods that incorporate higher level random effects simulated from the underlying model distribution. Cross validation is considered a gold standard method but is computationally intensive and thus a comparison is made between posterior predictive assessments and cross validation. The analyses revealed that mixed prediction methods produced results close to cross validation whilst the full posterior predictive assessment gave predictions that were over-optimistic (closer to the observed disease rates) compared with cross validation. A mixed prediction method that simulated random effects from both higher levels was best at identifying the outlying level two (farm-year) units of interest. It is concluded that this mixed prediction method, simulating random effects from both higher levels, is straightforward and may be of value in model criticism of multilevel logistic regression, a technique commonly used for animal health data with a hierarchical structure.

Number of levels
3
Model data structure
Response types
Multivariate response model?
No
Longitudinal data?
No
Further model keywords
Substantive discipline
Substantive keywords
Impact

Paper compares methods for assessing model fit in multilevel logistic regressions with mastitis application

Paper submitted by
William Browne, Bristol Veterinary School, University of Bristol, william.browne@bristol.ac.uk
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