The continual reassessment method (CRM) is an adaptive design for Phase I trials whose operating characteristics, including appropriate sample size, probability of correctly identifying the maximum tolerated dose, and the expected proportion of participants assigned to each dose, can only be determined via simulation. The actual time to determine a final “best” design can take several hours or days, depending on the number of scenarios that are examined. The computational cost increases as the kernel of the one‐parameter CRM design is expanded to other settings, including additional parameters, monitoring of both toxicity and efficacy, and studies of combinations of two agents. For a given vector of true DLT probabilities, we have developed an approach that replaces a simulation study of thousands of hypothetical trials with a single simulation. Our approach, which is founded on the consistency of the CRM, very accurately reflects the results produced by the simulation study, but does so in a fraction of time required by the simulation study. Relative to traditional simulations, we extensively examine how our method is able to assess the operating characteristics of a CRM design for a hypothetical trial whose characteristics are based upon a previously published Phase I trial. We also provide a metric of nonconsistency and demonstrate that although nonconsistency can impact the operating characteristics of our method, the degree of over‐ or under‐estimation is unpredictable. As a solution, we provide an algorithm for maintaining the consistency of a chosen CRM design so that our method is applicable for any trial.

中文翻译：

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一种评估持续重新评估方法性能的无模拟方法。

持续重新评估方法 (CRM) 是 I 期试验的适应性设计，其操作特征（包括适当的样本量、正确识别最大耐受剂量的概率以及分配到每个剂量的预期参与者比例）只能通过模拟来确定。确定最终“最佳”设计的实际时间可能需要几个小时或几天，具体取决于所检查的场景数量。随着单参数 CRM 设计的核心扩展到其他设置，包括附加参数、毒性和功效监测以及两种药物组合的研究，计算成本会增加。对于给定的真实 DLT 概率向量，我们开发了一种方法，用单次模拟代替数千次假设试验的模拟研究。我们的方法建立在 CRM 一致性的基础上，非常准确地反映了模拟研究产生的结果，但只用了模拟研究所需时间的一小部分。相对于传统模拟，我们广泛研究了我们的方法如何能够评估假设试验的 CRM 设计的操作特征，该假设试验的特征基于先前发布的 I 期试验。我们还提供了不一致性的度量，并证明虽然不一致性会影响我们方法的操作特性，但高估或低估的程度是不可预测的。作为解决方案，我们提供了一种算法来维持所选 CRM 设计的一致性，以便我们的方法适用于任何试验。