Statistical Assessment of QT/QTc Prolongation Based on Maximum of Correlated Normal Random Variables

A powerful test for the maximum treatment effect in thorough QT/QTc studies

Parallel-group thorough QT/QTc studies focus on the change of QT/QTc values at several time-matched points from a pretreatment day (baseline) to a posttreatment day for different groups of treatment. The International Council for Harmonisation E14 stresses that QTc prolongation beyond a threshold represents high cardiac risk and calls for a test on the largest time-matched treatment effect (QTc prolongation). QT/QTc analysis usually assumes a jointly multivariate normal (MVN) distribution of pretreatment and posttreatment QT/QTc values, with a blocked compound symmetry covariance matrix. Existing methods use an analysis of covariance (ANCOVA) model including day-averaged baseline as a covariate to deal with the MVN model. However, the ANCOVA model tends to underestimate the variation of the estimator for treatment effects, resulting in the inflation of empirical type I error rate when testing whether the largest QTc prolongation is beyond a threshold. In this article, we propose two new methods to estimate the time-matched treatment effects under the MVN model, including maximum likelihood estimation and ordinary-least-square-based two-stage estimation. These two methods take advantage of the covariance structure and are asymptotically efficient. Based on these estimators, powerful tests for QT/QTc prolongation are constructed. Simulation shows that the proposed estimators have smaller mean square error, and the tests can control the type I error rate with high power. The proposed methods are applied on testing the carryover effect of diltiazem to inhibit dofetilide in a randomized phase 1 trial.

Lack of significant effect of bilastine administered at therapeutic and supratherapeutic doses and concomitantly with ketoconazole on ventricular repolarization: results of a thorough QT study (TQTS) with QT-concentration analysis

The effect of bilastine on cardiac repolarization was studied in 30 healthy participants during a multiple-dose, triple-dummy, crossover, thorough QT study that included 5 arms: placebo, active control (400 mg moxifloxacin), bilastine at therapeutic and supratherapeutic doses (20 mg and 100 mg once daily, respectively), and bilastine 20 mg administered with ketoconazole 400 mg. Time-matched, triplicate electrocardiograms (ECGs) were recorded with 13 time points extracted predose and 16 extracted over 72 hours post day 4 dosing. Four QT/RR corrections were implemented: QTcB; QTcF; a linear individual correction (QTcNi), the primary correction; and a nonlinear one (QTcNnl). Moxifloxacin was associated with a significant increase in QTcNi at all time points between 1 and 12 hours, inclusively. Bilastine administration at 20 mg and 100 mg had no clinically significant impact on QTc (maximum increase in QTcNi, 5.02 ms; upper confidence limit [UCL] of the 1-sided, 95% confidence interval, 7.87 ms). Concomitant administration of ketoconazole and bilastine 20 mg induced a clinically relevant increase in QTc (maximum increase in QTcNi, 9.3 ms; UCL, 12.16 ms). This result was most likely related to the cardiac effect of ketoconazole because for all time points, bilastine plasma concentrations were lower than those observed following the supratherapeutic dose.

Multiple comparisons of repeatedly measured response: issues of validation testing in thorough QT/QTc clinical trials

In order to validate the results of a thorough QT/QTc clinical trial, ICH E14 recommended that a concurrent positive control treatment be included in the trial. Zhang (2008) recommended that the study results are validated if the positive control establishes assay sensitivity, i.e., has an effect on the mean QT/QTc interval of 5 ms or more. Zhang (2008) and Tsong et al. (2008) discussed the intersection-union test approach and an alternative global average test approach for testing assay sensitivity during the validation process. In this article, we further discuss the multiple comparison issues of the repeatedly measured QT difference between positive control treatment and placebo in the validation test. We describe and discuss several approaches for type I error rate adjustment that are applicable to the situation.