A Bayesian Approach for Investigating the Risk of QT Prolongation.

The standard approach to investigating a drug for its potential for QT prolongation is to construct a 90% two-sided (or a 95% one-sided) confidence interval (CI), for the difference in baseline corrected mean QTc (heart-rate corrected version of QT) between drug and placebo at each time-point, and to conclude non-inferiority if the upper limit for each CI is less than a pre-specified constant. An alternative approach is to base the non-inferiority inference on the largest difference in population mean QTc (baseline corrected) between drug and placebo. In this paper, we propose a Bayesian approach to resolving this problem using a Monte Carlo simulation method. We use simulated data to assess the performance of the proposed approach, discuss its advantages over the standard approach, and illustrate the method by applying it to a real data set obtained from a thorough QT study conducted at GlaxoSmithKline (GSK).