Sample size calculations for crossover thorough QT studies: satisfaction of regulatory threshold and assay sensitivity.

The cost for conducting a "thorough QT/QTc study" is substantial and an unsuccessful outcome of the study can be detrimental to the safety profile of the drug, so sample size calculations play a very important role in ensuring adequate power for a thorough QT study. Current literature offers some help in designing such studies, but these methods have limitations and mostly apply only in the context of linear mixed models with compound symmetry covariance structure. It is not evident that such models can satisfactorily be employed to represent all kinds of QTc data, and the existing literature inadequately addresses whether there is a change in sample size and power for more general covariance structures for the linear mixed models.

A flexible class of models for data arising from a 'thorough QT/QTc study'.

The standard methods for analyzing data arising from a 'thorough QT/QTc study' are based on multivariate normal models with common variance structure for both drug and placebo. Such modeling assumptions may be violated and when the sample sizes are small, the statistical inference can be sensitive to such stringent assumptions. This article proposes a flexible class of parametric models to address the above-mentioned limitations of the currently used models.

A Bayesian Approach for Investigating the Risk of QT Prolongation.

The standard approach to investigating a drug for its potential for QT prolongation is to construct a 90% two-sided (or a 95% one-sided) confidence interval (CI), for the difference in baseline corrected mean QTc (heart-rate corrected version of QT) between drug and placebo at each time-point, and to conclude non-inferiority if the upper limit for each CI is less than a pre-specified constant. An alternative approach is to base the non-inferiority inference on the largest difference in population mean QTc (baseline corrected) between drug and placebo. In this paper, we propose a Bayesian approach to resolving this problem using a Monte Carlo simulation method.

Physiographic and entomologic risk factors of malaria in Assam, India.

Fever surveys were conducted in several districts of the Indian state of Assam to ascertain the prevalence of malaria in relation to vector abundance, entomologic inoculation rates (EIRs), and geographic location of human settlements. Anopheles minimus were incriminated, but their relative abundance and biting rates varied among districts, and no significant correlation was observed between these two indicators (r = 0.43, P = 0.