We explore adaptive (data-driven) approaches in the analysis of clinical QT data in order to find scientifically-sound solutions for correcting the QT interval for heart rate and for analysis of extreme QT measurements. We demonstrate that predefined QT correction formulas (eg, Bazett and Fridericia formulas) are unreliable when the investigational drug induces heart rate changes. Further, simple data-driven approaches (eg, QT correction formulas derived from baseline data in clinical trials) lead to a substantial inflation of the false positive rate. We discuss a QT interval analysis framework based on repeated-measures models that account for correlation among serial ECG measurements collected on the same subject and drug-induced heart rate changes. We also assess the performance of reference ranges for QT interval currently used in clinical trials.
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