ABSTRACT

The novel mechanism of action of immunotherapy agents, in treatment of various types of cancer, poses unique challenges during the designing of clinical trials. It is important to account for possibility of a delayed treatment effect and adjust sample size accordingly. This paper provides an analytical approach for computing sample size in the presence of a delayed effect using a piece-wise proportional hazards model. Failing to account for an anticipated treatment delay may result in considerable loss in power. The overall hazard ratio (HR), which now represents the average HR across the entire treatment period, can remain a meaningful measure of average benefit to patients in the trial. We show that, special consideration needs to be given for the designing of interim analyses related to futility, so as not to increase the probability of incorrectly stopping an effective agent. It is shown that the weighted log-rank test, using the Fleming-Harrington class of weights, can be used as supportive analysis to better reflect the impact of a delayed effect and possible long-term benefit in a subset of the overall population.

摘要

免疫治疗剂在治疗各种类型癌症中的新作用机制在临床试验的设计过程中提出了独特的挑战。重要的是要考虑延迟治疗效果的可能性并相应地调整样本量。本文提供了一种分析方法，用于使用分段比例风险模型在存在延迟效应的情况下计算样本量。未能考虑预期的治疗延迟可能会导致相当大的功率损失。现在代表整个治疗期间的平均 HR 的总体风险比 (HR) 仍然是衡量试验中患者平均受益的有意义的衡量标准。我们表明，在设计与无效性相关的中期分析时需要特别考虑，以免增加错误停止有效代理的可能性。结果表明，使用 Fleming-Harrington 类权重的加权对数秩检验可用作支持性分析，以更好地反映延迟效应的影响和可能对总人口子集的长期益处。