Search

We discuss group-sequential three-arm noninferiority clinical trial designs that include active and placebo controls for evaluating both assay sensitivity and noninferiority. We extend two existing approaches, the fixed margin and fraction approaches, into a group-sequential setting with two decision-making frameworks. We investigate the operating characteristics including power, Type I error rate, maximum, and expected sample sizes, as design factors vary. In addition, we discuss sample size recalculation and its impact on the power and Type I error rate via a simulation study. [ABSTRACT FROM PUBLISHER]

Clinical patient management is dynamic. It is not based on a single decision but on a sequence of decisions, with adjustments of therapy overtime, where adjustment are personalized to individual patients. However, strategies allowing for such adjustments are infrequently studied. In the treatment of serious bacterial infections, there are two therapeutic decision points: empirical therapy when the patient is first seen by the clinician, and definitive therapy when drug susceptibility results are available to help guide therapeutic choices. COMparing Personalized Antibiotic StrategieS (COMPASS) is a trial design that compares strategies consistent with clinical practice rather than specific treatments. A strategy is a decision-rule guiding treatments of bacterial infections both at the empirical and at the definitive stages. Sequential multiple assignment randomized (SMART) COMPASS allows evaluation of strategies when there are multiple definitive therapy choices. SMART COMPASS is a pragmatic design, mirroring antibiotic treatment decision-making as they unfold in clinical practice and addressing the most relevant question for treating patients: identification of the patient-management strategy that optimizes ultimate patient outcomes. Complex statistical challenges are encountered during the design of SMART COMPASS including how to: appropriately estimate effects and associated standard errors within the context of sequential randomization, identify the best of several strategies while controlling trial-wise error, and calculate sample size and power when trying to identify the optimal strategy. Design considerations for SMART COMPASS are discussed. [ABSTRACT FROM AUTHOR]

The effects of interventions are multidimensional. Use of more than one primary endpoint offers an attractive design feature in clinical trials as they capture more complete characterization of the effects of an intervention and provide more informative intervention comparisons. For these reasons, multiple primary endpoints have become a common design feature in many disease areas such as oncology, infectious disease, and cardiovascular disease. More specifically in medical product development, multiple endpoints are used as co-primary to evaluate the effect of the new interventions. Although methodologies to address continuous co-primary endpoints are well-developed, methodologies for binary endpoints are limited. In this article, we describe power and sample size determination for clinical trials with multiple correlated binary endpoints, when relative risks are evaluated as co-primary. We consider a scenario where the objective is to evaluate evidence for superiority of a test intervention compared with a control intervention, for all of the relative risks. We discuss the normal approximation methods for power and sample size calculations and evaluate how the required sample size, power, and Type I error vary as a function of the correlations among the endpoints. Also we discuss a simple, but conservative procedure for appropriate sample size calculation. We then extend the methods allowing for interim monitoring using group-sequential methods. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

We discuss the decision-making frameworks for clinical trials with multiple co-primary endpoints in a group-sequential setting. The decision-making frameworks can account for flexibilities, such as a varying number of analyses, equally or unequally spaced increments of information, and fixed or adaptive Type I error allocation among endpoints. The frameworks can provide efficiency, that is, potentially fewer trial participants, than the fixed sample size designs. We investigate the operating characteristics of the decision-making frameworks and provide guidance on constructing efficient group-sequential strategies in clinical trials with multiple co-primary endpoints. [ABSTRACT FROM AUTHOR]