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Abstract:
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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, for the difference in mean QTc (heart-rate corrected version of QT) between drug and placebo at each time point, and to conclude noninferiority if the upper limits for all these CIs are less than a prespecified constant. One alternative approach is to base the noninferiority inference on largest difference in population mean QTc between drug and placebo. In this paper, we propose a Bayesian solution to 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 appropriateness of this approach and apply the method to a real dataset on QTc to make statistical inference.
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