A Weighted Prognostic Covariate Adjustment Method for Efficient and Powerful Treatment Effect Inferences in Randomized Controlled Trials
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随机试验中高效处理效应推断的加权预后协变量调整
- 研究问题
- 随机试验结果存在异方差时，如何利用历史对照数据生成的预后信息，提高处理效应估计效率和检验功效？
- 方法与思路
- 作者以数字孪生生成器预测对照潜在结果分布，提出Weighted PROCOVA，将预测均值和方差信息用于回归调整。
- 材料陈述的贡献
- 摘要称，方法具有无偏性，并在模拟和阿尔茨海默病案例中降低估计方差、维持一类错误率及提高检验功效。
- 阅读关注点
- 应核对无偏性证明条件、权重构造和历史数据迁移假设，以及预测方差信息与功效增益之间的实验关系。
- 阅读边界
- 这是arXiv预印本，同行评审或期刊接收未获核验；摘要所报功效增益有特定条件，未说明全部理论条件和外推范围。
原文摘要
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.