In this work, we proposed a novel inferential procedure assisted by machine learning based adjustment for randomized control trials. The method was developed under the Rosenbaum's framework of exact tests in randomized experiments with covariate adjustments. Through extensive simulation experiments, we showed the proposed method can robustly control the type I error and can boost the inference efficiency for a randomized controlled trial (RCT). This advantage was further demonstrated in a real world example. The simplicity and robustness of the proposed method makes it a competitive candidate as a routine inference procedure for RCTs, especially when the number of baseline covariates is large, and when nonlinear association or interaction among covariates is expected. Its application may remarkably reduce the required sample size and cost of RCTs, such as phase III clinical trials.

翻译：本文提出了一种基于机器学习辅助调整的新型推断方法，用于随机对照试验。该方法在Rosenbaum协变量调整随机实验精确检验框架下开发。通过大量模拟实验表明，所提方法能够稳健控制第一类错误，并提升随机对照试验的推断效率。该优势进一步通过真实案例得到验证。所提方法的简洁性与稳健性使其成为随机对照试验常规推断程序的强有力候选方案，尤其适用于基线协变量数量较多、且协变量间存在非线性关联或交互作用的情形。其应用可显著降低随机对照试验（如III期临床试验）所需样本量与试验成本。