  Phase I dose-finding studies aim at identifying the maximal tolerated dose
(MTD). It is not uncommon that several dose-finding studies are conducted,
although often with some variation in the administration mode or dose panel.
For instance, sorafenib (BAY 43-900) was used as monotherapy in at least 29
phase I trials according to a recent search in clinicaltrials.gov. Since the
toxicity may not be directly related to the specific indication, synthesizing
the information from several studies might be worthwhile. However, this is
rarely done in practice and only a fixed-effect meta-analysis framework was
proposed to date. We developed a Bayesian random-effects meta-analysis
methodology to pool several phase I trials and suggest the MTD. A curve free
hierarchical model on the logistic scale with random effects, accounting for
between-trial heterogeneity, is used to model the probability of toxicity
across the investigated doses. An Ornstein-Uhlenbeck Gaussian process is
adopted for the random effects structure. Prior distributions for the curve
free model are based on a latent Gamma process. An extensive simulation study
showed good performance of the proposed method also under model deviations.
Sharing information between phase I studies can improve the precision of MTD
selection, at least when the number of trials is reasonably large.
