Novel Statistical Approaches to Assessing the Risk of QT Prolongation and Sample Size Calculations in 'thorough QT/QTc studies'

Bayesian Methodology;Flexible Bayesian Models;Thorough QT/QTc Study;Sample Size Calculations;Monte Carlo Simulations;ICH E14 Guidelines

Anand, Suraj P. ; Dr. Sharon C. Murray, Committee Member,Dr. Sujit K. Ghosh, Committee Chair,Dr. Dennis D. Boos, Committee Member,Dr. Jung-Ying Tzeng, Committee Member,Dr. Wenbin Lu, Committee Member,Anand, Suraj P. ; Dr. Sharon C. Murray ; Committee Member ; Dr. Sujit K. Ghosh ; Committee Chair ; Dr. Dennis D. Boos ; Committee Member ; Dr. Jung-Ying Tzeng ; Committee Member ; Dr. Wenbin Lu ; Committee Member

University:North Carolina State University

关键词: Bayesian Methodology;    Flexible Bayesian Models;    Thorough QT/QTc Study;    Sample Size Calculations;    Monte Carlo Simulations;    ICH E14 Guidelines;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/3026/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

ANAND, SURAJ P. Novel Statistical Approaches to Assessing the Risk of QT Prolongationand Sample Size Calculations in ‘thorough QT/QTc studies’. (Under the direction ofProfessor S. K. Ghosh).The ICH E14 guidelines mandate performing a ‘thorough QT/QTc study’ on anynon-antiarrythmic drug, to assess its potential effect on cardiac repolarization, as detectedby QT prolongation, before it can be approved and marketed. The standard way of analyzinga thorough QT (TQT) study to assess a drug for its potential for QT prolongationis to construct a 90% two-sided (or a 95% one-sided) confidence interval (CI), for the differencein baseline-corrected mean QTc (heart-rate corrected version of QT) between drugand placebo at each time point, and to conclude non-inferiority if the upper limit for eachCI is less than 10 ms. The ICH E14 guidelines define a negative thorough QT study asone in which the upper 95% CI for the maximum time-matched mean effect of the drugas compared to placebo is less than 10 ms. A Monte Carlo simulation-based Bayesian approachis proposed to resolve this problem by constructing a posterior credible interval forthe maximum difference parameter.While an interval estimation-based approach may be a way to address the QTprolongation problem, it does not necessarily confirm to the actual intent of the ICH E14guidelines, which is to establish that the mean effect of the drug is less than 5 ms. Alsoproposed is a novel Bayesian approach that attempts to directly calculate the probabilitythat the mean effect is no larger than 5 ms, thereby, providing a direct measure of evidence ofwhether the drug prolongs mean QTc beyond the tolerable threshold of 5 ms. Performanceof the proposed approaches has been assessed using simulated data, and illustrations of themethods have been provided through real data sets obtained from TQT studies conductedat GlaxoSmithKline (GSK).Both these proposed methods as well as the other methods for analyzing QTc dataare based on multivariate normal models, with common covariance structure for both drugand placebo. Such modeling assumptions may be violated and when the sample sizes aresmall the statistical inference can be sensitive to such stringent assumptions.A flexible class of parametric models is proposed to address the above-mentioned limitationsof the currently used models. A Bayesian methodology is used for data analysis, andmodel comparisons are performed using the deviance information criterion (DIC). Superiorperformance of the proposed models over the currently used models is illustrated througha real data set obtained from a GSK-conducted TQT study. Both the proposed methodsfor analyzing QT data can be extended to this flexible class of models.Another major aspect of TQT studies is the sample size determination. Costsinvolved in conducting such studies are substantial and hence sample size calculations playa very important role in ensuring a small but adequate TQT study. A variety of methodshave been proposed to perform sample size calculations under the frequentist paradigm.Such methods have a limited scope and usually apply in the context of linear mixed models,with some assumed covariance structure for the observations. A sample size determinationmethod, using the proposed novel Bayesian method involving estimation of the probabilityof concluding a thorough QT study negative, is provided, which would ensure that the totalerror rate in the context of declaring a TQT study negative is restricted to a desired lowlevel. This method does not rely on any restrictive covariance assumptions.