Testing effect of a drug using multiple nested models for the dose–response
Testing effect of a drug using multiple nested models for the dose–response
复制标题
使用多个嵌套模型进行剂量反应测试药物的效果
作者：
C. Baayen;C. Baayen;Philip Hougaard;C. Pipper
在药物开发期间，剂量的选择通常基于II期剂量探索试验，其中选定的剂量与安慰剂一起纳入。分析此类试验的两种常用统计学剂量探索方法是单独比较每种剂量与安慰剂（使用多重比较程序）或基于模型的策略（其中将剂量-效应模型拟合到所有数据）。第一种方法在患者集中于几种剂量时效果最好，但不能对未测试的剂量得出结论。基于模型的方法允许在剂量之间进行插值，但有效性取决于假设的剂量-反应模型的正确性。Bretz等人（2005，Biometrics 61，738-748）提出了一种组合方法，该方法使用多重比较程序从一组候选模型中选择一个或多个合适的模型。该方法最初需要对候选模型的任何非线性参数进行先验估计，因此仍然可能存在一定程度的模型错误指定，并且只能评估一般模型的一个或几个特殊情况。我们提出了一种替代的多重检验程序，它评估一组候选的合理的剂量反应模型对彼此选择一个最终的模型。该方法不需要任何先验参数估计，并控制选择太复杂的模型的I类错误率。
During development of a drug, typically the choice of dose is based on a Phase II dose‐finding trial, where selected doses are included with placebo. Two common statistical dose‐finding methods to analyze such trials are separate comparisons of each dose to placebo (using a multiple comparison procedure) or a model‐based strategy (where a dose–response model is fitted to all data). The first approach works best when patients are concentrated on few doses, but cannot conclude on doses not tested. Model‐based methods allow for interpolation between doses, but the validity depends on the correctness of the assumed dose–response model. Bretz et al. (2005, Biometrics 61, 738–748) suggested a combined approach, which selects one or more suitable models from a set of candidate models using a multiple comparison procedure. The method initially requires a priori estimates of any non‐linear parameters of the candidate models, such that there is still a degree of model misspecification possible and one can only evaluate one or a few special cases of a general model. We propose an alternative multiple testing procedure, which evaluates a candidate set of plausible dose–response models against each other to select one final model. The method does not require any a priori parameter estimates and controls the Type I error rate of selecting a too complex model.