Abstract

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.

摘要

生成预测随机效应的统计模型被广泛用于根据纵向和集群数据评估患者、医生、医院和健康计划的表现并对其进行排名。平均而言，预测的随机效应已被证明优于将聚类视为固定效应（本质上是分类预测变量）并使用标准回归模型。这些预测的随机效应通常用于识别极端值或异常值，例如绩效不佳的医院或健康状况迅速下降的患者。当兴趣集中在极端情况而不是平均表现时，就没有对最佳方法进行系统的调查。我们开发了预测极值的新方法，评估了它们的性能，并使用来自骨关节炎倡议的数据来说明它们在预测老年人步行速度方面的应用。对于极值，新方法大大优于标准随机和固定效应方法。