Wiens Brian L, Heyse Joseph F
Amgen Inc., Thousand Oaks, California 93120-1799, USA.
J Biopharm Stat. 2003 Feb;13(1):103-15. doi: 10.1081/BIP-120017729.
We consider the role of interaction tests in the context of active-controlled clinical trials that aim to demonstrate the noninferiority of an experimental treatment compared to a standard (control) treatment. When the subjects can be grouped into strata (e.g., study sites, gender, race, etc.), there may be a desire to determine whether the experimental treatment is noninferior to the standard in each of the strata. We present five possible analysis strategies to test for heterogeneity of relative treatment effects among strata. These strategies are either identical to or straightforward modifications of strategies that can be used to test for interaction when the objective of the study is to show differences rather than noninferiority. The various analysis strategies implicitly depend on different definitions of interaction. Power of the various tests will be low, a phenomenon that often occurs when testing for interaction. We present simulation results to quantify the power and type I error rates under different scenarios and an example to demonstrate the proposed tests. None of the analysis strategies is best under every parameter configuration. The tests may be best used in a descriptive or exploratory manner. Extensions to two-sided equivalence testing are also discussed.
我们考虑交互作用检验在主动对照临床试验中的作用，这类试验旨在证明试验性治疗相对于标准（对照）治疗的非劣效性。当受试者可被分组为不同层次（如研究地点、性别、种族等）时，可能会希望确定试验性治疗在各层次中是否均不劣于标准治疗。我们提出了五种可能的分析策略，用于检验各层次间相对治疗效果的异质性。这些策略要么与用于检验交互作用的策略相同，要么是其直接的修改，前提是研究目的是显示差异而非非劣效性。各种分析策略隐含地依赖于不同的交互作用定义。各种检验的效能会较低，这是检验交互作用时经常出现的现象。我们给出模拟结果以量化不同情形下的效能和I型错误率，并给出一个示例以展示所提出的检验方法。没有一种分析策略在所有参数配置下都是最佳的。这些检验方法最好以描述性或探索性方式使用。还讨论了双侧等效性检验的扩展。
