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An Assessment of the Treatment Effect in Treatment of Physician Choice Trials
Statistics in Biopharmaceutical Research ( IF 1.8 ) Pub Date : 2020-09-08 , DOI: 10.1080/19466315.2020.1801496
Philani Mpofu 1 , Stella W. Karuri 2
Affiliation  

Abstract

In treatment of physician choice (TPC) trials, patients randomized to the control arm are treated with one of several drugs which are predetermined prior to the initiation of the trial. Statistical analysis in TPC trials typically hinge on a comparison of the experimental arm to the control arm, with the data from the control arm pooled across treatments. The robustness of the treatment effect for time-to-event endpoints estimated using the Cox proportional hazard model depends on the proportionality assumption. This assumption is violated if the magnitudes of the effects of the control treatments relative to the experimental treatment vary. The resulting effect is an averaged effect and is sensitive to censoring. We express the resulting hazard ratio from the Cox model where data from the control arm are pooled, as a weighted average of the head-to-head effects comparing controls treatments to the experimental treatments. These weights are function of the physicians preferences of the control treatments. Through simulation studies, we propose a method of identifying trial scenarios where the heterogeneity of the control treatments might lead to misleading results. We also propose an alternative estimator for the averaged effect which is unaffected by censoring.



中文翻译:

医师选择试验治疗效果评估

摘要

在医师选择 (TPC) 试验的治疗中,随机分配到对照组的患者使用在试验开始之前预先确定的几种药物中的一种进行治疗。TPC 试验中的统计分析通常取决于实验组与控制组的比较,其中来自控制组的数据在治疗中汇集。使用 Cox 比例风险模型估计的事件发生时间终点的治疗效果的稳健性取决于比例假设。如果对照处理相对于实验处理的影响大小发生变化,则违反该假设。由此产生的效果是一种平均效果,对审查很敏感。我们表达了来自 Cox 模型的结果风险比,其中汇集了来自控制臂的数据,作为比较对照处理与实验处理的头对头效应的加权平均值。这些权重是医生对对照治疗的偏好的函数。通过模拟研究,我们提出了一种识别试验情景的方法,其中对照处理的异质性可能导致误导性结果。我们还提出了一种不受审查影响的平均效应的替代估计量。

更新日期:2020-09-08
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