CLINICAL TRIAL DESIGN & INTERIM ANALYSIS--PERSPECTIVE

这项提议总体目标是开发新的序列 设计和分析临床试验的统计方法 补充当前分组序贯方法。临床试验需求 对伦理、经济和科学的中期分析和监测 理由。关键的统计问题是重复的中期分析 可能会严重地使常规数据的使用失效 试验结束时比较处理的固定样本检验 通过夸大第I类错误。因此，临床试验应该利用 允许提前停止的(一组)顺序程序。然后是 问题是，我们在统计信息方面损失了什么(在 与固定样本测试的比较)如果我们提前停止试验？ 而不是使用随机截断来计算条件 POWER，我们建议使用一种更直接的度量来计算机会 序贯检验与固定样本检验不一致。多数 重要的是，我们提出了一种形式的(组)序列边界，它允许 提前停止并保留固定样本设计的有效性 如下意义：(1)固定样本的全幂函数 测试和顺序设计几乎相同；(2) 序贯检验得出结论的不一致概率 与固定样本相反的检验可以忽略不计；(3)最大值 样本大小不大于相应的固定大小 样本量测试；预期样本量基本上是 小于固定样本测试的大小。因此，这样的一个 序贯计划为决策者提供了更有力的客观证据 何时需要提前停止，而不是当前组按顺序 以及随机条件概率比检验和置信度 正态分布试验的序贯停止后的间隔 回应和二分结果；(2)扩大不和谐音的使用 临床试验监测的可能性；(3)开发方法 具有重复测量数据或事件发生时间结果的临床试验； (4)将结果扩展到多个重复测量结果，和/或 事件间隔时间结果；(5)扩展快速计算算法 不同试验设计的运行特点，并使 适用于当前组序列边界的算法以及 比较它们；(6)将所提出的方法应用于美国国立卫生研究院赞助的四个 多中心试验和癌症试验。

The broad objective of this proposal is to develop new sequential statistical methods for the design and analysis of clinical trials that complement current group sequential methods. Clinical trials demand interim analyses and monitoring for ethical, economical and scientific reasons. The key statistical issue is that repeated interim analyses of accumulating data may seriously invalidate the use of a conventional fixed sample test in comparing the treatments at the end of the trial by inflating the type I error. Therefore clinical trials should utilize a (group) sequential procedures that allow early stopping. Then the issue is, What do we lose in terms of statistical information (in comparison with the fixed sample test) if we stopped the trial early? In stead of using stochastic curtailment that calculates a conditional power, we propose to use a more direct measure that computes the chance that the sequential test and the fixed sample test do not agree. Most importantly, we propose a formal (group) sequential boundary that allows early stopping and retains the validity of a fixed sample design in the following sense: (1) the entire power functions of the fixed sample test and the sequential design are virtually the same; (2) the discordant probability that the sequential test leads to a conclusion opposite to that of the fixed sample test is negligible; (3) the maximum sample size is no greater than the size of the corresponding fixed sample size test; and the expected sample sizes are substantially smaller than the size of the fixed sample test. Therefore, such a sequential plan provides stronger objective evidence to decision makers when early stopping is necessary than do the current group sequential and stochastic conditional probability ratio test and confidence intervals after sequential stopping for trials with normally distributed responses and dichotomous outcomes; (2) to extend the use of discordant probability to clinical trials monitoring; (3) to develop methods for clinical trials with repeated measures data or time-to-event outcomes; (4) to extend the results to multiple repeated measures outcomes, and/or time-to-event outcomes; (5) to extend the fast algorithm for calculating operating characteristics to different trial designs and make the algorithm applicable to current group sequential boundaries as well and compare them; (6) to apply the proposed methods to four NIH sponsored multicenter trials and a cancer trial.