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\footnotesize
\noindent Statistica Sinica {\bf 17}(2007), 531-547

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\centerline{\large\bf BAYESIAN DOSE FINDING IN PHASE
I CLINICAL TRIALS}
\vspace{2pt}
\centerline{\large\bf BASED ON A NEW STATISTICAL FRAMEWORK}
\vspace{.25cm}
\author{Y. Ji, Y. Li and G. Yin}
\vspace{.4cm}
\centerline{\it The University of Texas M. D.
Anderson Cancer Center}
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\begin{quotation}
\noindent {\it Abstract:}
 Phase I clinical trials aim to find the
maximum tolerated dose of an
  experimental drug. We consider dose escalation, de-escalation, or
  staying at the current dose as three different stochastic moves over
  the lattice of a sequence of prespecified
  dose levels. Each move is chosen by minimizing an expected penalty
  that determines the dose level for treating the next cohort of
  patients. We develop a stopping rule under which the termination
  of the trial ensures that the posterior probability that the current dose is
  the maximum tolerated dose is larger than a prespecified value. Under a new class of priors,
  posterior estimates for the dose toxicity probabilities are obtained using the Markov chain
  Monte Carlo method. We demonstrate the new designs using a real phase I clinical trial.\par
\vspace{9pt}
\noindent {\it Key words and phrases:}
Markov chain
Monte Carlo, penalty, stopping rule, stochastic moves, toxicity. \par
\end{quotation}

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