Rank power of metrics used to assess QTc interval prolongation by clinical trial simulation

Monte Carlo simulation was used to assess the type I error rate and rank order of power for six different metrics using linear mixed-effect models, including two variables recommended by the European Agency for the Evaluation of Medicinal Products (EMEA) in the analysis of QTc interval data. The metrics analyzed were maximal change in QTc interval from baseline, maximal QTc interval, area under the QTc interval-time curve (AUC), average QTc interval, maximal QTc interval with baseline QTc interval as covariate, and AUC with baseline QTc interval as covariate. Two dosing regimens were studied: multiple-dose oral and multiple-dose continuous intravenous infusion. Both regimens were designed to produce similar maximal plasma concentrations, albeit with the infusion regimen maintaining maximal plasma concentrations for a longer period of time. The ability of the metrics to detect a drug effect was examined, assuming drug effect followed either an Emax or linear model. All statistics had a type I error rate near the nominal value. Regardless of pharmacokinetic or pharmacodynamic model, AUC with baseline QTc interval as a covariate had greater power than any other metric examined. The simulations also suggest that mean QTc interval data not be used.