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<p>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, replacing the traditional linear model with nonparametric models that capture the complex relationships between covariates and outcomes. Through extensive simulation experiments, we showed the proposed method can robustly control the type I error and can boost the statistical efficiency for a randomized controlled trial (RCT). This advantage was further demonstrated in a real-world example. The simplicity, flexibility, and robustness of the proposed method makes it a competitive candidate as a routine inference procedure for RCTs, especially 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.</p>

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Machine learning assisted adjustment boosts efficiency of exact inference in randomized controlled trials

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月8日

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  • Han Yu,
  • Alan Hutson,
  • Xiaoyi Ma

摘要

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, replacing the traditional linear model with nonparametric models that capture the complex relationships between covariates and outcomes. Through extensive simulation experiments, we showed the proposed method can robustly control the type I error and can boost the statistical efficiency for a randomized controlled trial (RCT). This advantage was further demonstrated in a real-world example. The simplicity, flexibility, and robustness of the proposed method makes it a competitive candidate as a routine inference procedure for RCTs, especially 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.