Introduction: To account for heart rate-induced changes in the QT interval, correction formulas are generally applied to normalize the QT interval for heart rate. None of these formulas is entirely accurate because correction or normalization of any parameter in biology may introduce an additional source of variation in estimating the parameter. In this article, a one-step approach for the statistical analysis of the QT interval was proposed based on modeling the functional relationship between the QT interval and heart rate.
Methods: The QT-HR relationship was incorporated into the statistical analysis to provide a model-based correction. This was accomplished by including HR as a covariate in the QT interval analysis. The approach was demonstrated using data generated from Lilly Research Laboratories. We compared the false positive rate and statistical power of QT, QTcF, and the proposed one-step method.
Results: We found the one-step method demonstrated the greatest sensitivity in detecting a QT interval change without an increase in the false positive rate. It was shown that the one-step QT analysis could detect a 5%-6% increment of the QT interval. This is approximately equivalent to an increase of 11-13 ms in QT interval in beagle dogs.
Discussion: Several advantages and unique features of the one-step method are discussed. These include evaluating treatment effect on QT without applying a heart rate correction formula and estimating QT difference flexibly at any selected heart rate. In addition to the linear QT-HR relationship, other functional relationships can be easily implemented to this approach.