Dataset for: Confidence interval estimation for the changepoint of treatment stratification in the presence of a qualitative covariate-treatment interaction

The goal in stratified medicine is to administer the ‘best’ treatment to a patient. Not all patients might benefit from the same treatment, the choice of best treatment can depend on certain patient characteristics. In this article, it is assumed that a time-to-event outcome is considered as a patient-relevant outcome and a qualitative interaction between a continuous covariate and treatment exists, i.e. that patients with different values of one specific covariate should be treated differently. We suggest and investigate different methods for confidence interval estimation for the covariate value, where the treatment recommendation should be changed based on data collected in a randomized clinical trial. An adaptation of Fieller’s theorem, the delta method and different bootstrap approaches (normal, percentile-based, wild bootstrap) are investigated and compared in a simulation study. Extensions to multivariable problems are presented and evaluated.We observed appropriate confidence interval coverage following Fieller’s theorem irrespective of sample size, but at the cost of very wide or even infinite confidence intervals. The delta method and the
wild bootstrap approach provided the smallest intervals, but inadequate coverage for small to moderate event numbers, also depending on the location of the true changepoint. For the percentile-based bootstrap, wide intervals were observed and it was
slightly conservative regarding coverage, whereas the normal bootstrap did not provide acceptable results for many scenarios. The described methods were also applied to data from a randomized clinical trial comparing two treatments for patients with
symptomatic, severe carotid artery stenosis, considering patient’s age as predictive marker.

在分层医疗的目标是为患者提供最适宜的治疗。并非所有患者都能从同一种治疗中获益，最佳治疗方案的选择可能取决于患者的特定特征。在本文中，假定时间至事件结果被视为与患者相关的结果，并且存在一个连续协变量与治疗之间的定性交互作用，即具有不同特定协变量值的患者应接受不同的治疗。我们提出并研究了不同方法来估计协变量值的置信区间，治疗建议应根据随机临床试验收集的数据进行调整。研究了菲勒定理的改编、delta方法和不同的bootstrap方法（正态分布、百分位数基础、wild bootstrap）的模拟研究，并进行了比较。提出了多变量问题的扩展，并进行了评估。我们观察到，无论样本大小如何，根据菲勒定理均观察到适当的置信区间覆盖率，但代价是置信区间非常宽泛，甚至无限。delta方法和wild bootstrap方法提供了最小的区间，但对于小到中等的事件数量，覆盖率不足，也取决于真实变化点的位置。基于百分位数的bootstrap观察到区间较宽，并且对覆盖率的保守性稍显过度，而正态bootstrap在许多场景下未提供可接受的结果。所述方法还被应用于比较两种治疗对有症状、严重颈动脉狭窄患者的随机临床试验数据，其中将患者的年龄视为预测标志。