论文标题
在最小化和其他协变量自适应随机方法下的平均治疗效果的推断
Inference on Average Treatment Effect under Minimization and Other Covariate-Adaptive Randomization Methods
论文作者
论文摘要
经常在临床试验中使用协变量自适应随机方案，例如最小化和分层排序的块，以平衡跨预后因素的治疗分配。协变量随机化后推理的现有理论发展大部分仅限于可以指定响应和协变量之间正确模型的情况，或者随机化方法具有良好的属性。基于在随机化中使用的协变量水平的分层，并对未用于随机化的协变量进行进一步调整，在本文中，我们提出了几个估计量，以自由推断平均治疗效果定义为两种治疗中的响应平均值之间的差异。我们在所有流行的协变量自适应随机化方案下建立了拟议估计量的渐近态性，包括最小化的理论特性尚不清楚，我们表明，渐近分布在协方差自适应随机方法方面是不变的。为渐近推断构建了一致的方差估计器。还研究了估计量的渐近相对效率和有限样本特性。我们建议在协变量自适应随机化后使用我们提出的估计器之一进行有效和模型的自由推理。
Covariate-adaptive randomization schemes such as the minimization and stratified permuted blocks are often applied in clinical trials to balance treatment assignments across prognostic factors. The existing theoretical developments on inference after covariate-adaptive randomization are mostly limited to situations where a correct model between the response and covariates can be specified or the randomization method has well-understood properties. Based on stratification with covariate levels utilized in randomization and a further adjusting for covariates not used in randomization, in this article we propose several estimators for model free inference on average treatment effect defined as the difference between response means under two treatments. We establish asymptotic normality of the proposed estimators under all popular covariate-adaptive randomization schemes including the minimization whose theoretical property is unclear, and we show that the asymptotic distributions are invariant with respect to covariate-adaptive randomization methods. Consistent variance estimators are constructed for asymptotic inference. Asymptotic relative efficiencies and finite sample properties of estimators are also studied. We recommend using one of our proposed estimators for valid and model free inference after covariate-adaptive randomization.
