A crucial task for a randomized controlled trial (RCT) is to specify a statistical method that can yield an efficient estimator and powerful test for the treatment effect. A novel and effective strategy to obtain efficient and powerful treatment effect inferences is to incorporate predictions from generative artificial intelligence (AI) algorithms into covariate adjustment for the regression analysis of a RCT. Training a generative AI algorithm on historical control data enables one to construct a digital twin generator (DTG) for RCT participants, which utilizes a participant's baseline covariates to generate a probability distribution for their potential control outcome. Summaries of the probability distribution from the DTG are highly predictive of the trial outcome, and adjusting for these features via regression can thus improve the quality of treatment effect inferences, while satisfying regulatory guidelines on statistical analyses, for a RCT. However, a critical assumption in this strategy is homoskedasticity, or constant variance of the outcome conditional on the covariates. In the case of heteroskedasticity, existing covariate adjustment methods yield inefficient estimators and underpowered tests. We propose to address heteroskedasticity via a weighted prognostic covariate adjustment methodology (Weighted PROCOVA) that adjusts for both the mean and variance of the regression model using information obtained from the DTG. We prove that our method yields unbiased treatment effect estimators, and demonstrate via comprehensive simulation studies and case studies from Alzheimer's disease that it can reduce the variance of the treatment effect estimator, maintain the Type I error rate, and increase the power of the test for the treatment effect from 80% to 85%~90% when the variances from the DTG can explain 5%~10% of the variation in the RCT participants' outcomes.

翻译：随机对照试验（RCT）的一项关键任务是指定一种统计方法，以生成治疗效应的有效估计量并进行强有力的检验。一种新颖且有效的策略是通过将生成式人工智能（AI）算法的预测纳入回归分析的协变量调整，从而在RCT中获得高效且有力的治疗效果推断。基于历史对照数据训练生成式AI算法，可以构建RCT参与者的数字孪生生成器（DTG），该生成器利用参与者的基线协变量生成其潜在对照结局的概率分布。DTG输出的概率分布汇总统计量对试验结局具有高度预测性，通过回归调整这些特征可提升治疗效果推断的质量，同时满足监管机构对统计分析的指导要求。然而，该策略的一个关键假设是同方差性，即结局变量在给定协变量条件下方差恒定。在异方差情况下，现有协变量调整方法会产生低效的估计量和检验效能不足的假设检验。我们提出通过加权预后协变量调整方法（Weighted PROCOVA）解决异方差问题，该方法利用DTG获取的信息同步调整回归模型的均值和方差。我们证明该方法可生成无偏的治疗效应估计量，并通过阿尔茨海默病的综合性模拟研究与案例研究验证：当DTG提供的方差能解释RCT参与者结局变异的5%~10%时，该方法可将治疗效应估计量的方差降低，维持第一类错误率，并将治疗效应检验的统计效能从80%提升至85%~90%。