A group sequential, response-adaptive design for randomized clinical trials
A group sequential, response-adaptive design for randomized clinical trials
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DOI：
10.1016/s0197-2456(03)00092-8
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发表时间：
2003-10-01
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影响因子：
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通讯作者：
Chappell, R
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文献类型：
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作者：
Karrison, TG;Huo, DZ;Chappell, R
关于临床试验的响应自适应设计已经有相当多的方法学研究，但很少在实践中使用。 Rosenberger 和 Lachin 的一篇文章总结了造成这种情况的许多原因，但通常引用的两个主要原因是后勤困难和由于选择效应、患者特征或风险因素随时间的“漂移”以及其他来源而可能产生的偏差。詹尼森和特恩布尔考虑对连续结果变量进行群体顺序、响应自适应设计，部分解决这些问题，同时允许提前停止。组序贯方法（其中随机化概率在序贯组内保持恒定）的主要优点是分层分析将消除由于漂移引起的偏差。在本文中，我们考虑二元结果和一种根据累积证据的强度来改变分配比率的算法。具体来说，患者被纳入大小为 n(Ak)、n(Bk)、k = 1、2、...K 的组，其中 n(Ak)、n(Bk) 是连续组 k 中治疗组 A 和 B 的样本量。患者最初按 1:1 的比例分配。第 k 次中期分析后，如果两个治疗组比较结果的 z 值绝对值小于 1.0，则比率保持 1:1；如果 z 值超过 1.0，则按比率 R 分配下一个连续组，有利于当前效果较好的治疗；如果 z 统计量超过 1.5，则分配比率为 R-2；如果 z 值超过 2.0，则分配比率为 R-3。如果超出奥布莱恩-弗莱明监测边界，试验将终止。当分配比率超过 1 时，向上调整组样本大小以保持信息的相等增量。 z 统计量源自按顺序组分层的加权对数优势比。在各种场景和分配规则下进行了模拟研究和理论计算。结果表明，即使患者群体存在显着漂移，该方法也能维持名义 I 类错误率。当存在真正的治疗差异时，分配到较差治疗组的患者数量的适度减少可以以相对于非适应性设计总样本量较小的增加为代价来实现。讨论了局限性，例如观察结果延迟的影响，以及进一步研究的领域。我们的结论是，响应式适应性设计可能对某些目的有用，特别是在存在较大治疗效果的情况下，尽管允许提前停止会最大限度地减少益处。如果进行这样的设计，则应对随机化和分析进行分层，以避免由于时间趋势而产生偏差。 (C) 2003 Elsevier Inc. 保留所有权利。
There has been considerable methodological research on response-adaptive designs for clinical trials but they have seldom been used in practice. The many reasons for this are summarized in an article by Rosenberger and Lachin, but the two main reasons generally cited are logistical difficulties and the potential for bias due to selection effects, "drift" in patient characteristics or risk factors over time, and other sources. Jennison and Turnbull consider a group sequential, response-adaptive design for continuous outcome variables that partially addresses these concerns while at the same time allowing for early stopping. The key advantage of a group sequential approach in which randomization probabilities are kept constant within sequential groups is that a stratified analysis will eliminate bias due to drift. In this article we consider binary outcomes and an algorithm for altering the allocation ratio that depends on the strength of the accumulated evidence. Specifically, patients are enrolled in groups of size n(Ak), n(Bk), k = 1, 2, ... K, where n(Ak), n(Bk) are the sample sizes in treatment arms A and B in sequential group k. Patients are initially allocated in a 1: 1 ratio. After the kth interim analysis, if the z-value comparing outcomes in the two treatment groups is less than 1.0 in absolute value, the ratio remains 1:1; if the z-value exceeds 1.0, the next sequential group is allocated in the ratio R, favoring the currently better-performing treatment; if the z-statistic exceeds 1.5, the allocation ratio is R-2, and if the z-value exceeds 2.0, the allocation ratio is R-3. If the O'Brien-Fleming monitoring boundary is exceeded the trial is terminated. Group sample-sizes are adjusted upward to maintain equal increments of information when allocation ratios exceed one. The z-statistic is derived from a weighted log-odds ratio stratified by sequential group. Simulation studies and theoretical calculations were performed under a variety of scenarios and allocation rules. Results indicate that the method maintains the nominal type I error rate even when there is substantial drift in the patient population. When a true treatment difference exists, a modest reduction in the number of patients assigned to the inferior treatment arm can be achieved at the expense of smaller increases in the total sample size relative to a nonadaptive design. Limitations, such as the impact of delays in observing outcomes, are discussed, as well as areas for further research. We conclude that responsive adaptive designs may be useful for some purposes, particularly in the presence of large treatment effects, although allowing early stopping minimizes the benefits. If such a design is undertaken, the randomization and analysis should be stratified in order to avoid bias due to time trends. (C) 2003 Elsevier Inc. All rights reserved.