Decision Sciences Department, Drexel University, Philadelphia, PA 19104, USA.
Stat Med. 2010 Nov 30;29(27):2794-801. doi: 10.1002/sim.4060.
Consider the problem of testing H(0):p ≤ p(0) vs H(1):p > p(0), where p could, for example, represent the response rate to a new drug. The group sequential TT is an efficient alternative to a single-stage test as it can provide a substantial reduction in the expected number of test subjects. Whitehead provides formulas for determining stopping boundaries for this test. Existing research shows that test designs based on these formulas (WTTs) may not meet Type I error and/or power specifications, or may be over-powered at the expense of requiring more test subjects than are necessary. We present a search algorithm, with program available from the author, which provides an alternative approach to triangular test design. The primary advantage of the algorithm is that it generates test designs that consistently meet error specifications. In tests on nearly 1000 example combinations of n (group size), p(0), p(1), α, and β the algorithm-determined triangular test (ATT) design met specified Type I error and power constraints in every case considered, whereas WTT designs met constraints in only 10 cases. Actual Type I error and power values for the ATTs tend to be close to specified values, leading to test designs with favorable average sample number performance. For cases where the WTT designs did meet Type I error and power constraints, the corresponding ATT designs also had the advantage of providing, on average, a modest reduction in average sample numbers calculated at p(0), p(1), and (p(0) + p(1))/2.
考虑检验假设 H(0):p ≤ p(0)与 H(1):p > p(0)的问题,其中 p 可以代表新药的反应率。群组序贯 TT 是一种比单阶段测试更有效的替代方法,因为它可以大大减少预期的测试对象数量。Whitehead 提供了用于确定此类测试停止边界的公式。现有研究表明,基于这些公式的测试设计(WTT)可能不符合 I 型错误和/或功效规格,或者可能因需要更多的测试对象而过度效力,而这些对象是不必要的。我们提出了一种搜索算法,并提供了程序,该算法提供了一种替代的三角测试设计方法。该算法的主要优势在于它生成的测试设计始终符合误差规范。在对近 1000 个示例组合的 n(组大小)、p(0)、p(1)、α和β进行的测试中,算法确定的三角测试(ATT)设计在考虑的每种情况下都符合指定的 I 型错误和功效约束,而 WTT 设计仅在 10 种情况下符合约束。ATT 的实际 I 型错误和功效值往往接近指定值,从而导致具有有利平均样本数量性能的测试设计。对于 WTT 设计确实符合 I 型错误和功效约束的情况,相应的 ATT 设计还具有平均样本数量略有减少的优势,这是在 p(0)、p(1)和(p(0) + p(1))/2 处计算得出的。