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Reliably optimizing a new treatment in humans is a critical first
step in clinical evaluation since choosing a suboptimal dose or
schedule may lead to failure in later trials. At the same time, if
promising preclinical results do not translate into a real
treatment advance, it is important to determine this quickly and
terminate the clinical evaluation process to avoid wasting
resources. Bayesian Designs for Phase I-II Clinical Trials
describes how phase I-II designs can serve as a bridge or
protective barrier between preclinical studies and large
confirmatory clinical trials. It illustrates many of the severe
drawbacks with conventional methods used for early-phase clinical
trials and presents numerous Bayesian designs for human clinical
trials of new experimental treatment regimes. Written by research
leaders from the University of Texas MD Anderson Cancer Center,
this book shows how Bayesian designs for early-phase clinical
trials can explore, refine, and optimize new experimental
treatments. It emphasizes the importance of basing decisions on
both efficacy and toxicity.
Reliably optimizing a new treatment in humans is a critical first
step in clinical evaluation since choosing a suboptimal dose or
schedule may lead to failure in later trials. At the same time, if
promising preclinical results do not translate into a real
treatment advance, it is important to determine this quickly and
terminate the clinical evaluation process to avoid wasting
resources. Bayesian Designs for Phase I-II Clinical Trials
describes how phase I-II designs can serve as a bridge or
protective barrier between preclinical studies and large
confirmatory clinical trials. It illustrates many of the severe
drawbacks with conventional methods used for early-phase clinical
trials and presents numerous Bayesian designs for human clinical
trials of new experimental treatment regimes. Written by research
leaders from the University of Texas MD Anderson Cancer Center,
this book shows how Bayesian designs for early-phase clinical
trials can explore, refine, and optimize new experimental
treatments. It emphasizes the importance of basing decisions on
both efficacy and toxicity.
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