Improving statistical inference when interest focuses on the identification of extreme random effects in clustered data

项目摘要 产生预测随机效果的统计模型被广泛用于评估状态和排名 来自纵向和集群数据的患者、医生、医院和健康计划。预测的随机效应 平均而言，已被证明优于更简单的方法，如标准回归模型。 这些预测的随机效应通常被用来识别极端或离群值，例如老年患者 他们的健康状况迅速下降或医院表现不佳。当人们的兴趣集中在极端情况下时 尽管与平均绩效相比，还没有对最佳方法进行系统的调查。我们建议 开发预测极值或离群值的新方法，并系统地评估其性能 采用理论计算、模拟和实例相结合的方法。 仅仅预测极值或离群值很少是识别极值的充分fi和决策规则 统计上严格的数值也是必要的。我们将开发这样的方法，并提供容易的- 使用软件来实施推荐的方法。

PROJECT ABSTRACT Statistical models that generate predicted random effects are widely used to evaluate the status of and rank patients, physicians, hospitals and health plans from longitudinal and clustered data. Predicted random effects have been proven to outperform simpler approaches such as standard regression models, on average. These predicted random effects are often used to identify extreme or outlying values, such as elderly patients with rapid declines in their health or poorly performing hospitals. When interest focuses on the extremes rather than performance on average, there has been no systematic investigation of best approaches. We propose to develop novel methods for prediction of extreme or outlying values and systematically evaluate their performance using theoretical calculations, simulations and examples. Merely predicting extreme or outlying values is rarely sufficient and decision rules for identifying extreme values in a statistically rigorous manner are also needed. We will develop such approaches and provide easy- to-use software to implement the recommended methods.