Some Controversial Multiple Testing Problems in Regulatory Applications
Some Controversial Multiple Testing Problems in Regulatory Applications
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DOI：
10.1080/10543400802541693
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发表时间：
2009-01-01
影响因子：
1.1
通讯作者：
Wang, Sue-Jane
中科院分区：
文献类型：
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作者：
Hung, H. M. James;Wang, Sue-Jane
在监管应用中的多个测试问题往往比处理一组表示测试中的多个零假设的数学符号的问题更具挑战性。在联合交叉设置中，重要的是定义一系列与临床问题相关的零假设。在不同的临床应用中，需要适当考虑主要终点和次要终点之间的区别。在没有适当考虑的情况下，广泛使用的序贯门控策略通常会施加太多的逻辑限制，特别是在处理多剂量和多个终点的检验问题、复合终点及其组分终点的检验问题以及存在多个终点时的优效性和非劣效性检验问题时。将封闭检验中涉及的零假设划分为临床相关的排序或集合可以是解决临床试验者在设计阶段定义临床假设或临床问题时需要更多关注的不合逻辑问题的可行替代方案。在交集-并集设置中，几乎没有空间来减轻每个端点必须满足相同的预期α水平的要求的严格性，除非零假设下的参数空间可以被实质性地限制。这类限制往往需要提出难以克服的理由，而且通常无法得到内部数据的支持。因此，一种可能的补救方法，以减轻可能的保守性，作为这一要求的结果是一个组序贯设计策略，开始与保守的样本量规划，然后利用α消耗函数可能提前得出结论。
Multiple testing problems in regulatory applications are often more challenging than the problems of handling a set of mathematical symbols representing multiple null hypotheses under testing. In the union-intersection setting, it is important to define a family of null hypotheses relevant to the clinical questions at issue. The distinction between primary endpoint and secondary endpoint needs to be considered properly in different clinical applications. Without proper consideration, the widely used sequential gate keeping strategies often impose too many logical restrictions to make sense, particularly to deal with the problem of testing multiple doses and multiple endpoints, the problem of testing a composite endpoint and its component endpoints, and the problem of testing superiority and noninferiority in the presence of multiple endpoints. Partitioning the null hypotheses involved in closed testing into clinical relevant orderings or sets can be a viable alternative to resolving the illogical problems requiring more attention from clinical trialists in defining the clinical hypotheses or clinical question(s) at the design stage. In the intersection-union setting there is little room for alleviating the stringency of the requirement that each endpoint must meet the same intended alpha level, unless the parameter space under the null hypothesis can be substantially restricted. Such restriction often requires insurmountable justification and usually cannot be supported by the internal data. Thus, a possible remedial approach to alleviate the possible conservatism as a result of this requirement is a group-sequential design strategy that starts with a conservative sample size planning and then utilizes an alpha spending function to possibly reach the conclusion early.