Huang Dalong Patrick, Xiao Shan, Dang Qianyu, Tsong Yi
US Food and Drug Administration, Office of Biostatics, Office of Translational Sciences, Center for Drug Evaluation and Research, Silver Spring, MD, USA.
Biogen Inc., Cambridge, MA, USA.
Pharm Stat. 2018 Sep;17(5):607-614. doi: 10.1002/pst.1874. Epub 2018 Jun 28.
The revised ICH E14 Question and Answer (R3) document issued in December 2015 enables pharmaceutical companies to use concentration-QTc (C-QTc) modeling as the primary analysis for assessing QTc prolongation risk of new drugs. A new approach by including the time effect into the current C-QTc model is introduced. Through a simulation study, we evaluated performances of different C-QTc modeling with different dependent variables, covariates, and covariance structures. This simulation study shows that C-QTc models with ΔQTc being dependent variable without time effect inflate false negative rate and that fitting C-QTc models with different dependent variables, covariates, and covariance structures impacts the control of false negative and false positive rates. Appropriate C-QTc modeling strategies with good control of false negative rate and false positive rate are recommended.
2015年12月发布的修订版ICH E14问答（R3）文件使制药公司能够将浓度-校正QT间期（C-QTc）建模作为评估新药QTc延长风险的主要分析方法。引入了一种将时间效应纳入当前C-QTc模型的新方法。通过模拟研究，我们评估了不同依赖变量、协变量和协方差结构的不同C-QTc建模的性能。该模拟研究表明，以ΔQTc为依赖变量且无时间效应的C-QTc模型会夸大假阴性率，并且拟合具有不同依赖变量、协变量和协方差结构的C-QTc模型会影响假阴性率和假阳性率的控制。建议采用能良好控制假阴性率和假阳性率的适当C-QTc建模策略。
