The analysis of QT interval data is now an essential part of the assessment of drug safety. As the QT interval is inversely associated with heart rate, an appropriate correction must be applied in order to evaluate QT data in clinical trials. The aim is to characterize changes in QT interval at a standard heart rate, taking into account the correlation between these two variables to adjust for heart rate changes during the course of the trial. It has been shown that the relationship between the RR interval (=1/heart rate) and the QT interval is highly variable between individuals but stable over time within each individual.Many mathematical models have been developed to describe the QT-RR relationship. However, there has been less emphasis on the derivation of suitable statistical models that account for the multilevel structure of the ECG data.An important example is the interpretation of the so-called population-specific heart rate corrections, which are based on data pooled from different subjects. Often, simple regression techniques are used to quantify the population correction, disregarding the subject level and leading to biased parameter estimates. Instead, population-based corrections that account for individual intercepts should be used, in order to distinguish within-subject-effects from between-subject effects. Therefore, population-specific corrections cannot be derived solely from the cross-sectional data. The impact of the different statistical models is illustrated by data from the baseline periods of six clinical QT studies.