BACKGROUND/AIMS Protecting patient safety is an essential component of the conduct of clinical trials. Rigorous safety monitoring schemes are implemented for these studies to guard against excess toxicity risk from study therapies. They often include protocol-specified stopping rules dictating that an excessive number of safety events will trigger a halt of the study. Statistical methods are useful for constructing rules that protect patients from exposure to excessive toxicity while also maintaining the chance of a false safety signal at a low level. Several statistical techniques have been proposed for this purpose, but the current literature lacks a rigorous comparison to determine which method may be best suitable for a given trial design. The aims of this article are (1) to describe a general framework for repeated monitoring of safety events in clinical trials; (2) to survey common statistical techniques for creating safety stopping criteria; and (3) to provide investigators with a software tool for constructing and assessing these stopping rules. METHODS The properties and operating characteristics of stopping rules produced by Pocock and O'Brien-Fleming tests, Bayesian Beta-Binomial models, and sequential probability ratio tests (SPRTs) are studied and compared for common scenarios that may arise in phase II and III trials. We developed the R package "stoppingrule" for constructing and evaluating stopping rules from these methods. Its usage is demonstrated through a redesign of a stopping rule for BMT CTN 0601 (registered at Clinicaltrials.gov as NCT00745420), a phase II, single-arm clinical trial that evaluated outcomes in pediatric sickle cell disease patients treated by bone marrow transplant. RESULTS Methods with aggressive stopping criteria early in the trial, such as the Pocock test and Bayesian Beta-Binomial models with weak priors, have permissive stopping criteria at late stages. This results in a trade-off where rules with aggressive early monitoring generally will have a smaller number of expected toxicities but also lower power than rules with more conservative early stopping, such as the O-Brien-Fleming test and Beta-Binomial models with strong priors. The modified SPRT method is sensitive to the choice of alternative toxicity rate. The maximized SPRT generally has a higher number of expected toxicities and/or worse power than other methods. CONCLUSIONS Because the goal is to minimize the number of patients exposed to and experiencing toxicities from an unsafe therapy, we recommend using the Pocock or Beta-Binomial, weak prior methods for constructing safety stopping rules. At the design stage, the operating characteristics of candidate rules should be evaluated under various possible toxicity rates in order to guide the choice of rule(s) for a given trial; our R package facilitates this evaluation.

中文翻译：

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临床试验安全性监测统计规则。

背景/目的 保护患者安全是进行临床试验的重要组成部分。这些研究实施了严格的安全监测计划，以防止研究疗法产生过度的毒性风险。它们通常包括方案指定的停止规则，规定过多的安全事件将触发研究的停止。统计方法可用于构建规则，保护患者免受过度毒性的影响，同时将错误安全信号的可能性保持在较低水平。为此目的，已经提出了几种统计技术，但目前的文献缺乏严格的比较来确定哪种方法最适合给定的试验设计。本文的目的是（1）描述临床试验中重复监测安全事件的总体框架；(2) 调查创建安全停止标准的常用统计技术；(3) 为研究人员提供用于构建和评估这些停止规则的软件工具。方法 研究 Pocock 和 O'Brien-Fleming 检验、贝叶斯 Beta 二项式模型和序贯概率比检验 (SPRT) 产生的停止规则的属性和操作特征，并针对 II 期和 III 期试验中可能出现的常见情况进行比较。我们开发了 R 包“stoppingrule”，用于根据这些方法构建和评估停止规则。它的用途是通过重新设计 BMT CTN 0601（在 ClinicalTrials.gov 注册为 NCT00745420）的停止规则来证明的，这是一项 II 期单臂临床试验，评估接受骨髓移植治疗的儿童镰状细胞病患者的结果。结果 在试验早期采用激进停止标准的方法，例如 Pocock 检验和先验较弱的贝叶斯 Beta 二项式模型，在后期阶段采用宽松的停止标准。这导致了一种权衡，即与更保守的早期停止规则相比，具有积极早期监控的规则通常具有较少数量的预期毒性，但功效也较低，例如 O-Brien-Fleming 检验和具有强的 Beta-二项式模型。先验。改进的 SPRT 方法对替代毒性率的选择很敏感。与其他方法相比，最大化 SPRT 通常具有更高数量的预期毒性和/或更差的功效。结论 因为我们的目标是尽量减少接触和经历不安全治疗毒性的患者数量，所以我们建议使用 Pocock 或 Beta-二项式弱先验方法来构建安全停止规则。在设计阶段，应在各种可能的毒性率下评估候选规则的操作特性，以指导给定试验的规则选择；我们的 R 包有助于这种评估。