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Abstract：
BACKGROUND: Futility (inefficacy) interim monitoring is an important component in the conduct of phase III clinical trials, especially in life-threatening diseases. Desirable futility monitoring guidelines allow timely stopping if the new therapy is harmful or if it is unlikely to demonstrate to be sufficiently effective if the trial were to continue to its final analysis. There are a number of analytical approaches that are used to construct futility monitoring boundaries. The most common approaches are based on conditional power, sequential testing of the alternative hypothesis, or sequential confidence intervals. The resulting futility boundaries vary considerably with respect to the level of evidence required for recommending stopping the study.
OBJECTIVE: We evaluate the performance of commonly used methods using event histories from completed phase III clinical trials of the Radiation Therapy Oncology Group, Cancer and Leukemia Group B, and North Central Cancer Treatment Group.
METHODS: We considered published superiority phase III trials with survival endpoints initiated after 1990. There are 52 studies available for this analysis from different disease sites. Total sample size and maximum number of events (statistical information) for each study were calculated using protocol-specified effect size, type I and type II error rates. In addition to the common futility approaches, we considered a recently proposed linear inefficacy boundary approach with an early harm look followed by several lack-of-efficacy analyses. For each futility approach, interim test statistics were generated for three schedules with different analysis frequency, and early stopping was recommended if the interim result crossed a futility stopping boundary. For trials not demonstrating superiority, the impact of each rule is summarized as savings on sample size, study duration, and information time scales.
RESULTS: For negative studies, our results show that the futility approaches based on testing the alternative hypothesis and repeated confidence interval rules yielded less savings (compared to the other two rules). These boundaries are too conservative, especially during the first half of the study (<50% of information). The conditional power rules are too aggressive during the second half of the study (>50% of information) and may stop a trial even when there is a clinically meaningful treatment effect. The linear inefficacy boundary with three or more interim analyses provided the best results. For positive studies, we demonstrated that none of the futility rules would have stopped the trials.
CONCLUSIONS: The linear inefficacy boundary futility approach is attractive from statistical, clinical, and logistical standpoints in clinical trials evaluating new anti-cancer agents.
OBJECTIVE: We evaluate the performance of commonly used methods using event histories from completed phase III clinical trials of the Radiation Therapy Oncology Group, Cancer and Leukemia Group B, and North Central Cancer Treatment Group.
METHODS: We considered published superiority phase III trials with survival endpoints initiated after 1990. There are 52 studies available for this analysis from different disease sites. Total sample size and maximum number of events (statistical information) for each study were calculated using protocol-specified effect size, type I and type II error rates. In addition to the common futility approaches, we considered a recently proposed linear inefficacy boundary approach with an early harm look followed by several lack-of-efficacy analyses. For each futility approach, interim test statistics were generated for three schedules with different analysis frequency, and early stopping was recommended if the interim result crossed a futility stopping boundary. For trials not demonstrating superiority, the impact of each rule is summarized as savings on sample size, study duration, and information time scales.
RESULTS: For negative studies, our results show that the futility approaches based on testing the alternative hypothesis and repeated confidence interval rules yielded less savings (compared to the other two rules). These boundaries are too conservative, especially during the first half of the study (<50% of information). The conditional power rules are too aggressive during the second half of the study (>50% of information) and may stop a trial even when there is a clinically meaningful treatment effect. The linear inefficacy boundary with three or more interim analyses provided the best results. For positive studies, we demonstrated that none of the futility rules would have stopped the trials.
CONCLUSIONS: The linear inefficacy boundary futility approach is attractive from statistical, clinical, and logistical standpoints in clinical trials evaluating new anti-cancer agents.
摘要：
背景：实用（无效）中期监测是进行III期临床试验的重要组成部分，尤其是在危及生命的疾病中。如果新疗法有害，或者如果试验继续进行最终分析，则理想的无效监测指南允许及时停止。有许多分析方法用于构建徒劳的监测边界。最常见的方法是基于条件幂，替代假设的顺序检验，或顺序置信区间。相对于建议停止研究所需的证据水平，所得的徒劳界限差异很大。
目的：我们使用放射治疗肿瘤学小组已完成的III期临床试验的事件史来评估常用方法的性能，癌症和白血病B组，和北中央癌症治疗小组。
方法：我们考虑了已发表的在1990年后开始的生存终点的优势III期试验。有52项研究可用于来自不同疾病部位的这种分析。使用方案指定的效应大小计算每个研究的总样本量和最大事件数（统计信息），I型和II型错误率。除了常见的徒劳方法之外，我们考虑了最近提出的线性无效性边界方法,该方法具有早期损害外观,随后进行了几项无效性分析.对于每一种徒劳的方法，中期测试统计数据是为三个不同分析频率的时间表生成的，如果临时结果越过徒劳的停止边界，则建议尽早停止。对于没有表现出优越性的试验，每个规则的影响被总结为对样本量的节省，研究持续时间，和信息时间尺度。
结果：对于阴性研究，我们的研究结果表明，基于替代假设和重复置信区间规则的无效方法产生的储蓄较少（与其他两个规则相比）。这些界限太保守了,特别是在研究的前半部分(<50%的信息)。在研究的后半部分（>50％的信息），条件权力规则过于激进，即使有临床意义的治疗效果，也可能会停止试验。具有三个或更多个中期分析的线性无效边界提供了最佳结果。对于积极的研究，我们证明，没有一个无效的规则会停止试验。
结论：线性无效边界方法从统计学上具有吸引力，临床,以及评估新抗癌药的临床试验中的后勤观点。
目的：我们使用放射治疗肿瘤学小组已完成的III期临床试验的事件史来评估常用方法的性能，癌症和白血病B组，和北中央癌症治疗小组。
方法：我们考虑了已发表的在1990年后开始的生存终点的优势III期试验。有52项研究可用于来自不同疾病部位的这种分析。使用方案指定的效应大小计算每个研究的总样本量和最大事件数（统计信息），I型和II型错误率。除了常见的徒劳方法之外，我们考虑了最近提出的线性无效性边界方法,该方法具有早期损害外观,随后进行了几项无效性分析.对于每一种徒劳的方法，中期测试统计数据是为三个不同分析频率的时间表生成的，如果临时结果越过徒劳的停止边界，则建议尽早停止。对于没有表现出优越性的试验，每个规则的影响被总结为对样本量的节省，研究持续时间，和信息时间尺度。
结果：对于阴性研究，我们的研究结果表明，基于替代假设和重复置信区间规则的无效方法产生的储蓄较少（与其他两个规则相比）。这些界限太保守了,特别是在研究的前半部分(<50%的信息)。在研究的后半部分（>50％的信息），条件权力规则过于激进，即使有临床意义的治疗效果，也可能会停止试验。具有三个或更多个中期分析的线性无效边界提供了最佳结果。对于积极的研究，我们证明，没有一个无效的规则会停止试验。
结论：线性无效边界方法从统计学上具有吸引力，临床,以及评估新抗癌药的临床试验中的后勤观点。
