TY - JOUR
T1 - Shuffling adaptive clinical trials
AU - Gokhale, Sanjay G.
AU - Gokhale, Sankalp
N1 - Publisher Copyright:
© 2013 Wolters Kluwer Health, Inc.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - adaptive trials
KW - clinical relevance
KW - real-time data
KW - shuffling participants
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U2 - 10.1097/MJT.0b013e31827e978a
DO - 10.1097/MJT.0b013e31827e978a
M3 - Article
C2 - 23751329
AN - SCOPUS:84878633815
SN - 1075-2765
VL - 23
SP - e663-e669
JO - American Journal of Therapeutics
JF - American Journal of Therapeutics
IS - 3
ER -