Dataset for: Generalized Multiple Contrast Tests in Dose-Response Studies

In the process of developing drugs, proof-of-concept studies can be helpful in determining whether there is any evidence of a dose-response relationship. A global test for this purpose that has gained popularity is a component of the MCP-Mod procedure, which involves the specification of a candidate set of several plausible dose-response models. For each model, a test is performed for significance of an optimally chosen contrast among the sample means. An overall p-value is obtained from the distribution of the maximum of the contrast statistics. This is equivalent to basing the test on the minimum of the p-values arising from these contrast statistics and, hence, can be viewed as a method for combining dependent p-values. We generalize this idea to the use of different statistics for combining the dependent p-values, such as Fisher's combination method or the inverse normal combination method. Simulation studies show that the generalized multiple contrast tests (GMCTs) based on the Fisher and inverse normal methods are generally more powerful than the MCP-Mod procedure based on the minimum of the p-values except for cases where the true dose-response model is, in a sense, near the extremes of the candidate set of dose-response models. The proposed GMCTs can also be used for model selection and dosage selection by employing a closed testing procedure.

在药物研发过程中，概念验证（proof-of-concept）研究有助于判断是否存在剂量-反应（dose-response）关系的相关证据。为此目的而广受欢迎的全局检验是MCP-Mod流程的组成部分，该流程需要明确指定一组包含若干合理剂量-反应模型的候选集。针对每个模型，均会对样本均值间经最优选择的对比的显著性开展检验，并基于对比统计量的最大值分布得到全局p值。该全局检验等价于基于上述对比统计量所产生的p值的最小值构建检验，因此可被视为一种合并相依p值的方法。本文将该思路推广至使用不同统计量合并相依p值的场景，例如费希尔（Fisher）合并法或逆正态合并法。模拟研究表明，基于费希尔法与逆正态法的广义多重对比检验（generalized multiple contrast tests, GMCTs）通常比基于p值最小值的MCP-Mod流程具有更高的检验效能，仅当真实剂量-反应模型在某种意义上接近剂量-反应模型候选集的极端值时除外。所提出的GMCTs还可通过闭合检验（closed testing）流程实现模型选择与剂量选择。