Assessment of QT interval prolongation is an integral part of clinical studies in drug development because a prolonged QT interval can cause sudden cardiac death. Traditionally a linear or non-linear regression method is applied to estimate subject- or group-specific heart rate corrected QT intervals (QTc) on which comparisons are based among treatment groups. These regression models rely on a fundamental assumption that the predictor variable (RR interval) is measured without error. However, the fact is that both QT and RR intervals measured in electrocardiogram (ECG) are subject to not only measurement error, but also fluctuation that is caused by physiological and biological factors. Hence the assumption in the regression models is most likely violated. In this paper we propose a Bayesian hierarchical measurement error model to evaluate QTc interval and prolongation. The proposed approach is illustrated using a real data set. Simulation studies show that our proposed Bayesian measurement error approach outperforms the current most commonly used frequentist methods.