Lizotte Daniel J, Tahmasebi Arezoo
Departments of Computer Science and Epidemiology & Biostatistics, The University of Western Ontario, London, Ontario, Canada.
Stat Methods Med Res. 2017 Aug;26(4):1611-1629. doi: 10.1177/0962280217708662. Epub 2017 Jul 11.
We develop and evaluate tolerance interval methods for dynamic treatment regimes (DTRs) that can provide more detailed prognostic information to patients who will follow an estimated optimal regime. Although the problem of constructing confidence intervals for DTRs has been extensively studied, prediction and tolerance intervals have received little attention. We begin by reviewing in detail different interval estimation and prediction methods and then adapting them to the DTR setting. We illustrate some of the challenges associated with tolerance interval estimation stemming from the fact that we do not typically have data that were generated from the estimated optimal regime. We give an extensive empirical evaluation of the methods and discussed several practical aspects of method choice, and we present an example application using data from a clinical trial. Finally, we discuss future directions within this important emerging area of DTR research.
我们开发并评估了用于动态治疗方案（DTR）的容忍区间方法，这些方法可以为遵循估计的最优方案的患者提供更详细的预后信息。尽管针对DTR构建置信区间的问题已得到广泛研究，但预测区间和容忍区间却很少受到关注。我们首先详细回顾不同的区间估计和预测方法，然后将它们应用于DTR设置。我们说明了与容忍区间估计相关的一些挑战，这些挑战源于我们通常没有从估计的最优方案中生成的数据这一事实。我们对这些方法进行了广泛的实证评估，并讨论了方法选择的几个实际方面，还给出了一个使用来自临床试验数据的示例应用。最后，我们讨论了DTR研究这一重要新兴领域的未来发展方向。
