A Bayesian Approach to Assessing the Risk of QT Prolongation
by
Suraj Anand
Department of Statistics, North Carolina State Univ.
Coauthors: Dr Sujit Ghosh

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. The proposed method has several advantages over some of the other existing methods and is easy to implement in practice. We use simulated data to assess the performance of the proposed approach, and illustrate the method by applying it to a real data set obtained from a definitive QT study.

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Date received: July 3, 2007