A Weighted Prognostic Covariate Adjustment Method for Efficient and Powerful Treatment Effect Inferences in Randomized Controlled Trials

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.Comment: 49 pages, 6 figures, 12 table

T2 Protect AD: Achieving a rapid recruitment timeline in a multisite clinical trial for individuals with mild to moderate Alzheimer's disease

Abstract Introduction The reporting of approaches facilitating the most efficient and timely recruitment of Alzheimer's disease (AD) patients into pharmacologic trials is fundamental to much‐needed therapeutic progress. Methods T2 Protect AD (T2), a phase 2 randomized placebo‐controlled trial of troriluzole in mild to moderate AD, used multiple recruitment strategies. Results T2 exceeded its recruitment target, enrolling 350 participants between July 2018 and December 2019 (randomization rate: 0.87 randomizations/site/month, or 3‐fold greater than recent trials of mild to moderate AD). The vast majority (98%) of participants were enrolled during a 10‐month window of intense promotion in news media, TV and radio advertisements, and social media. The distribution of primary recruitment sources included: existing patient lists at participating sites (72.3%), news media (12.3%), physician referral (6.0%), word of mouth (3.1%), and paid advertising (2.9%). Discussion The rapid recruitment of participants with mild to moderate AD was achieved through a range of approaches with varying success