Importance  Randomized clinical trials (RCTs) currently apply the same statistical threshold of alpha = 2.5% for controlling for false-positive results or type 1 error, regardless of the burden of disease or patient preferences. Is there an objective and systematic framework for designing RCTs that incorporates these considerations on a case-by-case basis?

Objective  To apply Bayesian decision analysis (BDA) to cancer therapeutics to choose an alpha and sample size that minimize the potential harm to current and future patients under both null and alternative hypotheses.

Data Sources  We used the National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) database and data from the 10 clinical trials of the Alliance for Clinical Trials in Oncology.

Study Selection  The NCI SEER database was used because it is the most comprehensive cancer database in the United States. The Alliance trial data was used owing to the quality and breadth of data, and because of the expertise in these trials of one of us (D.J.S.).

Data Extraction and Synthesis  The NCI SEER and Alliance data have already been thoroughly vetted. Computations were replicated independently by 2 coauthors and reviewed by all coauthors.

Main Outcomes and Measures  Our prior hypothesis was that an alpha of 2.5% would not minimize the overall expected harm to current and future patients for the most deadly cancers, and that a less conservative alpha may be necessary. Our primary study outcomes involve measuring the potential harm to patients under both null and alternative hypotheses using NCI and Alliance data, and then computing BDA-optimal type 1 error rates and sample sizes for oncology RCTs.

Results  We computed BDA-optimal parameters for the 23 most common cancer sites using NCI data, and for the 10 Alliance clinical trials. For RCTs involving therapies for cancers with short survival times, no existing treatments, and low prevalence, the BDA-optimal type 1 error rates were much higher than the traditional 2.5%. For cancers with longer survival times, existing treatments, and high prevalence, the corresponding BDA-optimal error rates were much lower, in some cases even lower than 2.5%.

Conclusions and Relevance  Bayesian decision analysis is a systematic, objective, transparent, and repeatable process for deciding the outcomes of RCTs that explicitly incorporates burden of disease and patient preferences.

重要性  随机临床试验 (RCT) 目前应用相同的统计阈值 alpha = 2.5% 来控制假阳性结果或 1 型错误，无论疾病负担或患者偏好如何。是否有一个客观和系统的框架来设计随机对照试验，并根据具体情况纳入这些考虑因素？

目的  将贝叶斯决策分析 (BDA) 应用于癌症治疗，以选择在无效假设和替代假设下对当前和未来患者的潜在伤害最小化的 alpha 和样本量。

数据源  我们使用了美国国家癌症研究所 (NCI) 的监测、流行病学和最终结果 (SEER) 数据库以及来自肿瘤临床试验联盟的 10 项临床试验的数据。

研究选择  使用 NCI SEER 数据库是因为它是美国最全面的癌症数据库。由于数据的质量和广度，以及我们中的一个人 (DJS) 在这些试验中的专业知识，使用了联盟试验数据。

数据提取和综合  NCI SEER 和 Alliance 数据已经过彻底审查。计算由 2 位合著者独立复制，并由所有合著者审阅。

主要结果和措施  我们之前的假设是 2.5% 的 alpha 不会将最致命癌症对当前和未来患者的总体预期伤害降到最低，并且可能需要不那么保守的 alpha。我们的主要研究结果包括使用 NCI 和 Alliance 数据测量在无效假设和替代假设下对患者的潜在伤害，然后计算 BDA 最佳 1 型错误率和肿瘤 RCT 的样本量。

结果  我们使用 NCI 数据计算了 23 个最常见癌症部位和 10 个联盟临床试验的 BDA 最佳参数。对于涉及生存时间短、没有现有治疗方法和低患病率的癌症治疗的 RCT，BDA 最佳 1 型错误率远高于传统的 2.5%。对于存活时间较长、现有治疗方法和高患病率的癌症，相应的 BDA 最佳错误率要低得多，在某些情况下甚至低于 2.5%。

结论和相关性  贝叶斯决策分析是一个系统、客观、透明和可重复的过程，用于确定明确纳入疾病负担和患者偏好的 RCT 的结果。