Friede T, Kieser M
Department of Medical Statistics, University of Göttingen, Göttingen, Germany.
Methods Inf Med. 2011;50(3):237-43. doi: 10.3414/ME09-01-0063. Epub 2010 Mar 8.
Analysis of covariance (ANCOVA) is widely applied in practice and its use is recommended by regulatory guidelines. However, the required sample size for ANCOVA depends on parameters that are usually uncertain in the planning phase of a study. Sample size recalculation within the internal pilot study design allows to cope with this problem. From a regulatory viewpoint it is preferable that the treatment group allocation remains masked and that the type I error is controlled at the specified significance level. The characteristics of blinded sample size reassessment for ANCOVA in non-inferiority studies have not been investigated yet. We propose an appropriate method and evaluate its performance.
In a simulation study, the characteristics of the proposed method with respect to type I error rate, power and sample size are investigated. It is illustrated by a clinical trial example how strict control of the significance level can be achieved.
A slight excess of the type I error rate beyond the nominal significance level was observed. The extent of exceedance increases with increasing non-inferiority margin and increasing correlation between outcome and covariate. The procedure assures the desired power over a wide range of scenarios even if nuisance parameters affecting the sample size are initially mis-specified.
The proposed blinded sample size recalculation procedure protects from insufficient sample sizes due to incorrect assumptions about nuisance parameters in the planning phase. The original procedure may lead to an elevated type I error rate, but methods are available to control the nominal significance level.
协方差分析(ANCOVA)在实践中被广泛应用,监管指南也推荐使用。然而,ANCOVA所需的样本量取决于在研究规划阶段通常不确定的参数。内部预试验设计中的样本量重新计算可以解决这个问题。从监管角度来看,最好保持治疗组分配的隐蔽性,并将I型错误控制在指定的显著性水平。非劣效性研究中ANCOVA的盲法样本量重新评估的特点尚未得到研究。我们提出一种合适的方法并评估其性能。
在一项模拟研究中,研究了所提出方法在I型错误率、检验效能和样本量方面的特点。通过一个临床试验实例说明了如何实现对显著性水平的严格控制。
观察到I型错误率略高于名义显著性水平。超出程度随着非劣效界值的增加以及结局与协变量之间相关性的增加而增加。即使影响样本量的干扰参数最初指定错误,该程序在广泛的场景中也能确保所需的检验效能。
所提出的盲法样本量重新计算程序可防止因在规划阶段对干扰参数的错误假设而导致样本量不足。原始程序可能会导致I型错误率升高,但有方法可以控制名义显著性水平。