James Hung H M, Wang Sue-Jane
Division of Biometrics I, OB/OTS/CDER, U.S. Food and Drug Administration, Silver Spring , MD 20993-0002, USA.
J Biopharm Stat. 2012;22(4):679-86. doi: 10.1080/10543406.2012.676533.
Statistical testing in clinical trials can be complex when the statistical distribution of the test statistic involves a nuisance parameter. Some type of nuisance parameters such as standard deviation of a continuous response variable can be handled without too much difficulty. Other type of nuisance parameters, specifically associated with the main parameter under testing, can be difficult to handle. Without knowledge of the possible value of such a nuisance parameter, the maximum type I error associated with testing the main parameter may occur at an extreme value of the nuisance parameter. A well known example is the intersection-union test for comparing a combination drug with its two component drugs where the nuisance parameter is the mean difference between the two components. Knowledge of the possible range of value of this mean difference may help enhance the clinical trial design. For instance, if the interim internal data suggest that this mean difference falls into a possible range of value, then the sample size may be reallocated after the interim look to possibly improve the efficiency of statistical testing. This research sheds some light into possible power advantage from such a sample size reallocation at the interim look.
在临床试验中，当检验统计量的统计分布涉及一个干扰参数时，统计检验可能会很复杂。某些类型的干扰参数，如连续反应变量的标准差，可以相对轻松地处理。而其他类型的干扰参数，特别是与正在检验的主要参数相关的那些，可能难以处理。如果不知道这样一个干扰参数的可能值，与检验主要参数相关的最大I型错误可能会出现在干扰参数的一个极端值处。一个著名的例子是用于比较复方药物与其两种成分药物的交-并检验，其中干扰参数是两种成分之间的平均差异。了解这个平均差异的可能取值范围可能有助于优化临床试验设计。例如，如果中期内部数据表明这个平均差异落入一个可能的取值范围，那么在中期观察之后可以重新分配样本量，以可能提高统计检验的效率。这项研究揭示了在中期观察时进行这种样本量重新分配可能带来的检验效能优势。
