Title:  Bayesian Adaptive Methods for Clinical Trials

Instructor:   Brad Carlin, University of Minnesota

Overview:  Thanks in large part to the rapid development of Markov
chain Monte Carlo (MCMC) methods and software for their
implementation,
Bayesian methods have become ubiquitous in modern biostatistical
analysis. In submissions to regulatory agencies where data on new
drugs or medical devices are often scanty but researchers have access
to large historical databases, Bayesian methods have emerged as
particularly helpful in combining the disparate sources of information
while maintaining traditional frequentist protections regarding Type I
error and power.  Biostatisticians in earlier phases (especially Phase
I
oncology trials) have long appreciated Bayes' ability to get good
answers
quickly.  Finally, an increasing desire for adaptability in clinical
trials (to
react to trial knowledge as it accumulates) has also led to heightened
interest in Bayesian methods.  This lecture series introduces Bayesian
methods, computing, and software, and then goes on to elucidate their
use in Phase I and II clinical trials.  We include desc-riptions of
how
the methods can be implemented in WinBUGS, R, and BRugs, a version
of the BUGS package callable from within R.

Lecture 1:  Introduction to Hierarchical Bayes Methods and Computing
  Bayesian inference:  point and interval estimation, model choice
  Bayesian computing:  MCMC methods; Gibbs sampler;
  Metropolis-Hastings algorithm
  Hierarchical modeling and metaanalysis
  Principles of Bayesian clinical trial design:  predictive
  probability, indifference zone, Bayesian
    and frequentist operating characteristics (power, Type I error)

Lecture 2:  Bayesian design and analysis for Phase I studies
  Rule-based designs for determining the MTD (e.g., 3+3)
  Model-based designs for determining the MTD (CRM, EWOC, TITE
  monitoring, toxicity intervals)
  Dose ranging and optimal biologic dosing
  Efficacy and toxicity
  Examples and software

Lecture 3:  Bayesian design and analysis for Phase II studies
  Standard designs:  Phase IIA (single-arm) vs. Phase IIB (multi-arm)
  Predictive probability-based methods
  Sequential stopping:  for futility, efficacy
  Multi-arm designs with adaptive randomization
  Adaptive confirmatory trials:  adaptive sample size, futility
  analysis, arm dropping
  Bayesian hierarchical methods in safety studies
  Adaptive incorporation of historical data
  Summary and Floor Discussion