Prediction of random effects and effects of misspecification of their distribution
Statistical models that include random effects are commonly used to analyze longitudinal and clustered data. These models are often used to derive predicted values of the random effects, for example in predicting which physicians or hospitals are performing exceptionally well or exceptionally poorly. I start this talk with a brief introduction and several examples of the use of prediction of random effects in practice. In typical applications, the data analyst specifies a parametric distribution for the random effects (often Gaussian) although there is little information available to guide this choice. Are predictions sensitive to this specification? Through theory, simulations, and an example illustrating the prediction of who is likely to go on to develop high blood pressure, I show that misspecification can have a moderate impact on predictions of random effects and describe simple ways to diagnose such sensitivity.
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