Prolongation of the QT interval on a surface electrocardiogram is a biomarker for a potentially life threatening arrhythmia. It is used by drug developers and regulatory agencies as a measure of drug safety. Heart rate or RR interval (the inverse of heart rate) correction of the QT interval is necessary because of the QT interval shortening that accompanies physiologic decreases in the RR interval. When a drug alters the RR interval, it is important to distinguish a QT change that is due to a drug effect versus an artifact of a heart rate change. Two common heart rate correction methods used in clinical practice are Bazett's and Fridericia's corrections. However, it has been recognized that the relationship between heart rate and QT interval is individual specific. Thus the application of a population level correction such as Bazett's or Fridericia's may not be optimal to correct heart rate in a specific individual. Malik proposed to characterize an inidividual's QT/RR relationship obtained from off-drug or placebo data, and then apply subject-specific correction factors derived from the off-drug data to the on-drug dataset. However, as Malik's method assumes an un-changed QT/RR relationship after the drug treatment, it could lead to a biased QTc prolongation estimate. This problem was addressed by Li in a joint QT correction for both off and on drug data. In this paper, for the first time, four prescribed methods are summarized together. Their biases and efficiencies in QTc and QTc prolongation estimations are compared theoretically. Their performances are illustrated through a haloperidol QT analysis. In addition, clinical trial design issues are discussed, specifically crossover versus parallel group studies.