Department of Mathematics, Paris Lodron University, Salzburg, Austria.
Department of Neurology, Christian Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria.
J Biopharm Stat. 2020;30(1):143-159. doi: 10.1080/10543406.2019.1632871. Epub 2019 Jul 21.
When testing for superiority in a parallel-group setting with a continuous outcome, adjusting for covariates is usually recommended. For this purpose, the analysis of covariance is frequently used, and recently several exact and approximate sample size calculation procedures have been proposed. However, in case of multiple covariates, the planning might pose some practical challenges and pitfalls. Therefore, we propose a method, which allows for blinded re-estimation of the sample size during the course of the trial. Simulations confirm that the proposed method provides reliable results in many practically relevant situations, and applicability is illustrated by a real-life data example.
在平行组设计中,对于连续结局的研究,通常建议对协变量进行调整。为此,通常采用协方差分析,并且最近提出了几种精确和近似的样本量计算程序。然而,在存在多个协变量的情况下,计划可能会带来一些实际的挑战和陷阱。因此,我们提出了一种方法,允许在试验过程中对样本量进行盲法重新估计。模拟结果证实,该方法在许多实际相关情况下提供了可靠的结果,并且通过一个实际数据示例说明了其适用性。