Improving Predictions When Interest Focuses on Extreme Random Effects
针对传统随机效应模型在预测极端值（如表现差的医院或健康快速下降的患者）时表现不佳的问题，提出了新的预测方法，并在骨关节炎数据中验证了其优越性。
Statistical models that generate predicted random effects are widely used to evaluate the performance of and rank patients, physicians, hospitals and health plans from longitudinal and clustered data. Predicted random effects have been proven to outperform treating clusters as fixed effects (essentially a categorical predictor variable) and using standard regression models, on average. These predicted random effects are often used to identify extreme or outlying values, such as poorly performing hospitals or patients with rapid declines in their health. When interest focuses on the extremes rather than performance on average, there has been no systematic investigation of best approaches. We develop novel methods for prediction of extreme values, evaluate their performance, and illustrate their application using data from the Osteoarthritis Initiative to predict walking speed in older adults. The new methods substantially outperform standard random and fixed-effects approaches for extreme values.