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.

chapter 1|28 pages

Why Conduct Phase I–II Trials?

chapter 2|14 pages

The Phase I–II Paradigm

chapter 3|15 pages

Establishing Priors

chapter 5|16 pages

Designs with Late-Onset Outcomes

chapter 6|24 pages

Utility-Based Designs

chapter 7|20 pages

Personalized Dose Finding

chapter 8|30 pages

Combination Trials

chapter 9|24 pages

Optimizing Molecularly Targeted Agents

chapter 10|20 pages

Optimizing Doses in Two Cycles

chapter 11|22 pages

Optimizing Dose and Schedule

chapter 12|8 pages

Dealing with Dropouts

chapter 13|13 pages

Optimizing Intra-Arterial tPA

chapter 14|16 pages

Optimizing Sedative Dose in Preterm Infants