Covariate adjustment and methods of incorporating historical data in randomized clinical trials (RCTs) each provide opportunities to increase trial power. We unite these approaches for the analysis of RCTs with binary outcomes based on the Cochran-Mantel-Haenszel (CMH) test for marginal risk ratio (RR). In PROCOVA-CMH, subjects are stratified on a single prognostic covariate reflective of their predicted outcome on the control treatment (e.g. placebo). This prognostic score is generated based on baseline covariates through a model trained on historical data. We propose two closed-form prospective estimators for the asymptotic sampling variance of the log RR that rely only on values obtainable from observed historical outcomes and the prognostic model. Importantly, these estimators can be used to inform sample size during trial planning. PROCOVA-CMH demonstrates type I error control and appropriate asymptotic coverage for valid inference. Like other covariate adjustment methods, PROCOVA-CMH can reduce the variance of the treatment effect estimate when compared to an unadjusted (unstratified) CMH analysis. In addition to statistical methods, simulations and a case study in Alzheimer's Disease are given to demonstrate performance. Results show that PROCOVA-CMH can provide a gain in power, which can be used to conduct smaller trials.
翻译:在随机临床试验(CRCTs)中,对历史数据的调整和将历史数据纳入随机化临床试验(RCTs)的方法都提供了提高试验力的机会。我们根据Cochran-Mantel-Haenszel(CMH)的边际风险比率测试(RR),将这些分析RCT的方法与二进制结果结合起来。在PROCOVA-CMH(PROCOVA-CMH)中,根据单一预测性共变反应其在控制治疗方面的预测结果(例如安慰剂)进行分级。这种预测性评分是根据基准共变数产生的,通过经过历史数据培训的模型,我们建议用两个封闭式的预期估计器来分析RR对日志的无预防性抽样差异。在从所观察到的历史结果和预测模型模型中只能获得的数值。重要的是,这些估计器可用于在试验规划期间告知样本的大小。 PROCOVAH(POBA-CH)可以显示对正确性判断力的测算方法,PROCO-CM(在进行非调整性分析时可以提供较小的测算结果分析。