论文标题
非正常模型的公差和预测间隔
Tolerance and Prediction Intervals for Non-normal Models
论文作者
论文摘要
预测间隔涵盖了重复采样中的随机过程的未来观察，通常是通过识别一个辅助统计量的关键数量来构建的。类似地，公差间隔涵盖了重复抽样中的人群百分位，并且通常基于关键量。我们在非正常模型中考虑的一种方法利用了链接函数，导致大约正态分布的关键量。在这种正常近似不存在的设置中，我们考虑了基于平均值的置信区间的第二种公差和预测方法。这些方法是直观的，易于实现，具有适当的操作特征，并且与贝叶斯，重新采样和机器学习方法相比，计算上有效。这在多站点临床试验招募的背景下证明了这一点，并具有交错的现场启动，现实的治疗时间以及临床终点的终止成功。
A prediction interval covers a future observation from a random process in repeated sampling, and is typically constructed by identifying a pivotal quantity that is also an ancillary statistic. Analogously, a tolerance interval covers a population percentile in repeated sampling and is often based on a pivotal quantity. One approach we consider in non-normal models leverages a link function resulting in a pivotal quantity that is approximately normally distributed. In settings where this normal approximation does not hold we consider a second approach for tolerance and prediction based on a confidence interval for the mean. These methods are intuitive, simple to implement, have proper operating characteristics, and are computationally efficient compared to Bayesian, re-sampling, and machine learning methods. This is demonstrated in the context of multi-site clinical trial recruitment with staggered site initiation, real-world time on treatment, and end-of-study success for a clinical endpoint.
