title: Shuffling Adaptive Clinical Trials.
abstract: Clinical trials are interventional studies on human beings, designed to test the hypothesis for diagnostic techniques, treatments, and disease preventions. Any novel medical technology should be evaluated for its efficacy and safety by clinical trials. The costs associated with developing drugs have increased dramatically over the past decade, and fewer drugs are obtaining regulatory approval. Because of this, the pharmaceutical industry is continually exploring new ways of improving drug developments, and one area of focus is adaptive clinical trial designs. Adaptive designs, which allow for some types of prospectively planned mid-study changes, can improve the efficiency of a trial and maximize the chance of success without undermining validity and integrity of the trial. However it is felt that in adaptive trials; perhaps by using accrued data the actual patient population after the adaptations could deviate from the originally target patient population and so to overcome this drawback; special methods like Bayesian Statistics, predicted probability are used to deduce data-analysis. Here, in this study, mathematical model of a new adaptive design (shuffling adaptive trial) is suggested which uses real-time data, and because there is no gap between expected and observed data, statistical modifications are not needed. Results are obviously clinically relevant.
authors.affiliation: 1Department of Pediatrics and Neonatology, Rajhans Hospital, Saphale, India; and 2Department of Neurology, Duke University, Durham, NC.
authors.forename: Sanjay G
authors.initials: SG
authors.lastname: Gokhale
authors.forename: Sankalp
authors.initials: S
authors.lastname: Gokhale
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic; D003627:Data Interpretation, Statistical; D006801:Humans; D008962:Models, Theoretical; D011336:Probability; D012107:Research Design