Zapf Antonia, Asendorf Thomas, Anten Christoph, Mütze Tobias, Friede Tim
Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Stat Med. 2020 Jun 30;39(14):1980-1998. doi: 10.1002/sim.8525. Epub 2020 Mar 23.
In randomized clinical trials, it is standard to include baseline variables in the primary analysis as covariates, as it is recommended by international guidelines. For the study design to be consistent with the analysis, these variables should also be taken into account when calculating the sample size to appropriately power the trial. Because assumptions made in the sample size calculation are always subject to some degree of uncertainty, a blinded sample size reestimation (BSSR) is recommended to adjust the sample size when necessary. In this article, we introduce a BSSR approach for count data outcomes with baseline covariates. Count outcomes are common in clinical trials and examples include the number of exacerbations in asthma and chronic obstructive pulmonary disease, relapses, and scan lesions in multiple sclerosis and seizures in epilepsy. The introduced methods are based on Wald and likelihood ratio test statistics. The approaches are illustrated by a clinical trial in epilepsy. The BSSR procedures proposed are compared in a Monte Carlo simulation study and shown to yield power values close to the target while not inflating the type I error rate.
在随机临床试验中，按照国际指南的建议，在主要分析中将基线变量作为协变量纳入是标准做法。为使研究设计与分析保持一致，在计算样本量以为试验提供适当效能时，也应考虑这些变量。由于样本量计算中所做的假设总是存在一定程度的不确定性，因此建议进行盲法样本量重新估计（BSSR），以便在必要时调整样本量。在本文中，我们介绍一种针对具有基线协变量的计数数据结果的BSSR方法。计数结果在临床试验中很常见，例如哮喘和慢性阻塞性肺疾病的急性加重次数、复发次数、多发性硬化症的扫描病变数量以及癫痫发作次数。所介绍的方法基于 Wald 检验统计量和似然比检验统计量。通过一项癫痫临床试验对这些方法进行了说明。在蒙特卡罗模拟研究中对所提出的BSSR程序进行了比较，结果表明这些程序产生的效能值接近目标值，同时不会使I型错误率膨胀。
