Optimal Designs for Comparing Regression Curves: Dependence Within and Between Groups

We consider the problem of designing experiments for the comparison of two regression curves describing the relation between a predictor and a response in two groups, where the data between and within the group may be dependent. In order to derive efficient designs we use results from stochastic analysis to identify the best linear unbiased estimator (BLUE) in a corresponding continuous model. It is demonstrated that in general simultaneous estimation using the data from both groups yields more precise results than estimation of the parameters separately in the two groups. Using the BLUE from simultaneous estimation, we then construct an efficient linear estimator for finite sample size by minimizing the mean squared error between the optimal solution in the continuous model and its discrete approximation with respect to the weights (of the linear estimator). Finally, the optimal design points are determined by minimizing the maximal width of a simultaneous confidence band for the difference of the two regression functions. The advantages of the new approach are illustrated by means of a simulation study, where it is shown that the use of the optimal designs yields substantially narrower confidence bands than the application of uniform designs.

Testing for similarity of binary efficacy-toxicity responses

Clinical trials often aim to compare two groups of patients for efficacy and/or toxicity depending on covariates such as dose. Examples include the comparison of populations from different geographic regions or age classes or, alternatively, of different treatment groups. Similarity of these groups can be claimed if the difference in average outcome is below a certain margin over the entire covariate range. In this article, we consider the problem of testing for similarity in the case that efficacy and toxicity are measured as binary outcome variables. We develop a new test for the assessment of similarity of two groups for a single binary endpoint. Our approach is based on estimating the maximal deviation between the curves describing the responses of the two groups, followed by a parametric bootstrap test. Further, using a two-dimensional Gumbel-type model we develop methodology to establish similarity for (correlated) binary efficacy-toxicity outcomes. We investigate the operating characteristics of the proposed methodology by means of a simulation study and present a case study as an illustration.

Safety considerations selecting antiseizure medications for the treatment of individuals with Dravet syndrome

Introduction: Management of individuals with Dravet Syndrome has evolved significantly over the past 10 years. Progress has been made in understanding the pathophysiology, the long-term outcome and possible consequences of inappropriate therapies, new drugs have been approved by the regulatory authorities and patients and families expressed their needs beyond seizures' control.Areas covered: The authors aimed at providing an overview of the main antiseizure medications used in Dravet syndrome with a particular focus on safety considerations. As the highly active phase of seizures takes place before the age of 5 years, the characteristics of antiseizure medications in infancy and childhood have also been considered due to their impact on antiseizure medication safety.Expert opinion: Recent treatments, evaluated via randomized clinical trials, are promising in terms of efficacy and safety in individuals with DS. However, the balance between expected benefits and risks taken must be accurately assessed on an individual basis. There is a lack of data to understand the needs of patients and families, a major point particularly in this population, where the evaluation of efficacy and safety beyond seizures is difficult due to cognitive delay and behavioral disorders and where this evaluation is coming almost exclusively from caregivers.