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.

中文翻译：

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通过基线调整对负二项式回归进行盲目样本大小重新估计。

在随机临床试验中，按照国际指南的建议，标准是在基线分析中将基线变量作为协变量包括在内。为了使研究设计与分析一致，在计算样本量以适当增加试验效果时，还应考虑这些变量。由于样本量计算中所做的假设始终会存在一定程度的不确定性，因此建议在必要时采用盲目样本量重估（BSSR）来调整样本量。在本文中，我们引入了BSSR方法来计算带有基线协变量的数据结果。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加重次数，复发，并在多发性硬化症和癫痫发作中扫描病变。引入的方法基于Wald和似然比检验统计数据。癫痫的临床试验说明了这些方法。提议的BSSR程序在蒙特卡洛模拟研究中进行了比较，并显示出产生的功率值接近目标值，同时又不会增加I型错误率。