# Random-effects meta-analysis of phase I dose-finding studies using stochastic process priors

## TL;DR

This paper introduces a Bayesian random-effects meta-analysis method using stochastic process priors to combine multiple phase I dose-finding studies, improving MTD estimation by accounting for heterogeneity across trials.

## Contribution

It develops a novel hierarchical Bayesian model with Gaussian process priors for meta-analyzing phase I trials, addressing between-study variability.

## Key findings

- Good performance in simulations even with model deviations
- Sharing information improves MTD precision with multiple trials
- Method effectively accounts for heterogeneity across studies

## Abstract

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.