A Nonparametric Approach to QT Interval Correction for Heart Rate

• Published in: Journal of biopharmaceutical statistics Vol. 20; no. 3; pp. 508 - 522
• Main Authors: Wang, Duolao, Cheung, Yin Bun, Arezina, Radivoj, Taubel, Jorg, Camm, Alan John
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis Group 01.05.2010; Taylor & Francis Ltd
• Subjects: Adult; Arrhythmias, Cardiac - chemically induced; Arrhythmias, Cardiac - diagnosis; Arrhythmias, Cardiac - physiopathology; Circadian Rhythm; Clinical trial; Clinical trials; Comparative analysis; Computer Simulation; Data Interpretation, Statistical; Electrocardiography - statistics & numerical data; Generalized additive model; Heart rate; Heart Rate - drug effects; Humans; Intervals; Linear Models; Mathematical models; Middle Aged; Models, Statistical; Nonparametric regression model; QT interval correction; QTc; Randomized Controlled Trials as Topic - statistics & numerical data; Simulation; Statistics; Statistics, Nonparametric; Time Factors; Young Adult
• ISSN: 1054-3406; 1520-5711
• DOI: 10.1080/10543400903581952

We propose to use generalized additive models to fit the relationship between QT interval and RR (RR = 60/heart rate), and develop two new methods for correcting the QT for heart rate: the linear additive model and log-transformed linear additive model. The proposed methods are compared with six commonly used parametric models that were used in four clinical trial data sets and a simulated data set. The results show that the linear additive models provide the best fit for the vast majority of individual QT-RR profiles. Moreover, the QT correction formula derived from the linear additive model outperforms other correction methods.