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.