Bayesian decision procedures for dose escalation-a reanalysis

Zhou et.al (2006) developed Bayesian dose-escalation procedures for
early phase I clinical trials in oncology.They are based on with discrete
measures of undesirable events and continuous measures of therapeutic
benefit. The objective is to find the optimal dose associated with some
low probability of an adverse event.

To understand their methodology I tried to reproduce their results
using a hierarchical linear model (Lindley and Smith (1972)) with different
orderings of the data. Computations were done in R. I found my results
were consistent with one another but different to the published results.
I then also programmed the model using ``WinBugs'' and again found the
results to be consistent with mine. I concluded that the published results
were in error.

My main interests are in Bayesian approaches for the design and analysis
of dose escalation trials, which involves prior information concerning
parameters of the relationships between dose and the risk of an adverse
event, with the chance to update after every dosing period using Bayes
theorem. In this talk I will discuss some of these issues and also shall
report my current work.