Improved prediction and flagging of extreme random effects for non-Gaussian outcomes using weighted methods.

ABSTRACT: Investigators often focus on predicting extreme random effects from mixed effects models fitted to longitudinal or clustered data, and on identifying or "flagging" outliers such as poorly performing hospitals or rapidly deteriorating patients. Our recent work with Gaussian outcomes showed that weighted prediction methods can substantially reduce mean square error of prediction for extremes and substantially increase correct flagging rates compared to previous methods, while controlling the incorrect flagging rates. This paper extends the weighted prediction methods to non-Gaussian outcomes such as binary and count data. Closed-form expressions for predicted random effects and probabilities of correct and incorrect flagging are not available for the usual non-Gaussian outcomes, and the comp

SUBMITTER: Neuhaus J 

PROVIDER: S-EPMC12309285 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature