Bayesian Modeling of QT Interval: Focus on Baseline Measurements
Ihab G. Girgis, Jeremy Hing, Partha Nandy, Filip De Ridder, and Surya Mohanty
Johnson & Johnson Pharmaceutical Research & Development
Detecting drug-induced effects on cardiac repolarization, measured by the length of the QT interval on an ECG, is a closely monitored safety element in drug development. More recently, it is thoroughly scrutinized in regulatory submissions. Baseline QT intervals can be influenced by a number of factors, such as, heart rate (HR), administration of placebo, gender, and natural circadian rhythm. In addition, there might be other unknown factors making it highly variable across population and the analysis of such data is complex. Accurate modeling of baseline QT becomes an important first step in evaluating effects of drugs. Changes to this baseline model after the administration of the investigational drug will reflect the effect on the QT/QTc interval.The objective of this work is to model the baseline QT data in healthy subjects using a hierarchical Bayesian approach and to explore the influence of gender, RR interval (RR= 60/HR), and circadian rhythm on the QT interval. Also, the effect of placebo was tested.
