Nonconventional clinical trial designs: approaches to provide more precise estimates of treatment effects with a smaller sample size, but at a cost.
Statistical sciences have recently made advancements that allow improved precision or reduced sample size in clinical research studies. Herein, we review 4 of the more promising: (1) improvements in approaches for dose selection trials, (2) approaches for sample size adjustment, (3) selection of study end point and associated statistical methods, and (4) frequentist versus Bayesian statistical methods. Whereas each of these holds the opportunity for more efficient trials, each are associated with the need for more stringent assumptions or increased complexity in the interpretation of results. The opportunities for these promising approaches, and their associated "costs," are reviewed.
Howard
George
G
Department of Biostatistics, University of Alabama at Birmingham School of Public Health, Birmingham, AL 35294-0022, USA. ghoward@uab.edu
D001499:Bayes Theorem; D023381:Endpoint Determination; D006801:Humans; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000592:standards*; D012107:Research Design; D018401:Sample Size; D012680:Sensitivity and Specificity; D016896:Treatment Outcome