Clinical trials are critical in advancing medical treatments but often suffer from immense time and financial burden. Advances in statistical methodologies and artificial intelligence (AI) present opportunities to address these inefficiencies. Here we introduce Prognostic Covariate-Adjusted Mixed Models for Repeated Measures (PROCOVA-MMRM) as an advantageous combination of prognostic covariate adjustment (PROCOVA) and Mixed Models for Repeated Measures (MMRM). PROCOVA-MMRM utilizes time-matched prognostic scores generated from AI models to enhance the precision of treatment effect estimators for longitudinal continuous outcomes, enabling reductions in sample size and enrollment times. We first provide a description of the background and implementation of PROCOVA-MMRM, followed by two case study reanalyses where we compare the performance of PROCOVA-MMRM versus the unadjusted MMRM. These reanalyses demonstrate significant improvements in statistical power and precision in clinical indications with unmet medical need, specifically Alzheimer's Disease (AD) and Amyotrophic Lateral Sclerosis (ALS). We also explore the potential for sample size reduction with the prospective implementation of PROCOVA-MMRM, finding that the same or better results could have been achieved with fewer participants in these historical trials if the enhanced precision provided by PROCOVA-MMRM had been prospectively leveraged. We also confirm the robustness of the statistical properties of PROCOVA-MMRM in a variety of realistic simulation scenarios. Altogether, PROCOVA-MMRM represents a rigorous method of incorporating advances in the prediction of time-matched prognostic scores generated by AI into longitudinal analysis, potentially reducing both the cost and time required to bring new treatments to patients while adhering to regulatory standards.

翻译：临床试验在推进医学治疗中至关重要，但常面临巨大的时间和资金负担。统计学方法与人工智能的进步为解决这些低效问题创造了机遇。本文提出预后协变量调整重复测量混合模型(PROCOVA-MMRM)，作为预后协变量调整(PROCOVA)与重复测量混合模型(MMRM)的优势结合。PROCOVA-MMRM利用AI模型生成的时间匹配预后评分，增强纵向连续结局治疗效应估计的精度，从而减少样本量和入组时间。我们首先阐述PROCOVA-MMRM的背景与实现方法，随后通过两项案例研究重新分析，比较PROCOVA-MMRM与未调整MMRM的性能。这些再分析表明，在阿尔茨海默病和肌萎缩侧索硬化症等未满足临床需求的适应症中，统计功效和精度获得显著提升。我们还探索了前瞻性应用PROCOVA-MMRM的样本量缩减潜力，发现若提前利用该模型提供的增强精度，这些历史试验中更少的受试者即可获得相同或更优的结果。通过多种逼真模拟场景验证，PROCOVA-MMRM统计属性的稳健性得到进一步确认。总体而言，PROCOVA-MMRM代表了一种将AI生成的时间匹配预后评分预测进展严格整合至纵向分析的方法，有望在遵循监管标准的前提下，减少新疗法惠及患者所需的时间和成本。