title: A nonparametric approach to QT interval correction for heart rate.
abstract: 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.
authors.affiliation: Medical Statistics Unit, Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom. Duolao.Wang@lshtm.ac.uk
authors.forename: Duolao
authors.initials: D
authors.lastname: Wang
authors.forename: Yin Bun
authors.initials: YB
authors.lastname: Cheung
authors.forename: Radivoj
authors.initials: R
authors.lastname: Arezina
authors.forename: Jorg
authors.initials: J
authors.lastname: Taubel
authors.forename: Alan John
authors.initials: AJ
authors.lastname: Camm
mesh_terms: D000328:Adult; D001145:Arrhythmias, Cardiac / Q000139:chemically induced* / Q000175:diagnosis / Q000503:physiopathology; D002940:Circadian Rhythm; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004562:Electrocardiography / Q000706:statistics & numerical data; D006339:Heart Rate / Q000187:drug effects*; D006801:Humans; D016014:Linear Models; D008875:Middle Aged; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D018709:Statistics, Nonparametric; D013997:Time Factors; D055815:Young Adult