Anand Suraj P, Murray Sharon C, Koch Gary G
Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA.
J Biopharm Stat. 2010 May;20(3):587-603. doi: 10.1080/10543400903582000.
The cost for conducting a "thorough QT/QTc study" is substantial and an unsuccessful outcome of the study can be detrimental to the safety profile of the drug, so sample size calculations play a very important role in ensuring adequate power for a thorough QT study. Current literature offers some help in designing such studies, but these methods have limitations and mostly apply only in the context of linear mixed models with compound symmetry covariance structure. It is not evident that such models can satisfactorily be employed to represent all kinds of QTc data, and the existing literature inadequately addresses whether there is a change in sample size and power for more general covariance structures for the linear mixed models. We assess the use of some of the existing methods to design a thorough QT study through data arising from a GlaxoSmithKline (GSK)-conducted thorough QT study, and explore newer models for sample size calculation. We also provide a new method to calculate the sample size required to detect assay sensitivity with adequate power.
进行一项“全面QT/QTc研究”的成本很高,而且研究结果不成功可能会对药物的安全性产生不利影响,因此样本量计算在确保全面QT研究有足够效能方面起着非常重要的作用。当前的文献在设计此类研究方面提供了一些帮助,但这些方法存在局限性,且大多仅适用于具有复合对称协方差结构的线性混合模型。目前尚不清楚此类模型是否能够令人满意地用于表示各种QTc数据,而且现有文献也没有充分探讨对于线性混合模型更一般的协方差结构,样本量和效能是否会发生变化。我们通过葛兰素史克(GSK)进行的一项全面QT研究产生的数据,评估一些现有方法在设计全面QT研究中的应用,并探索用于样本量计算的新模型。我们还提供了一种新方法,用于计算在有足够效能的情况下检测分析灵敏度所需的样本量。