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March 2021 Random-effects meta-analysis of Phase I dose-finding studies using stochastic process priors
Moreno Ursino, Christian Röver, Sarah Zohar, Tim Friede
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Ann. Appl. Stat. 15(1): 174-193 (March 2021). DOI: 10.1214/20-AOAS1390

Abstract

Phase I dose-finding studies aim at identifying the maximum tolerated dose (MTD). Often, several dose-finding studies are conducted with some variation in the administration mode or dose panel. For instance, sorafenib (BAY 43-900) was used as monotherapy in 36 phase I trials, according to a recent clinicaltrials.gov search. 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.

Acknowledgements

We thank the anonymous reviewer, the Editor and, especially, the anonymous Associate Editor for their careful reading of our manuscript and their many insightful comments and suggestions.

The first author is also affiliated with F-CRIN PARTNERS platform, AP-HP, Université de Paris.

Citation

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Moreno Ursino. Christian Röver. Sarah Zohar. Tim Friede. "Random-effects meta-analysis of Phase I dose-finding studies using stochastic process priors." Ann. Appl. Stat. 15 (1) 174 - 193, March 2021. https://doi.org/10.1214/20-AOAS1390

Information

Received: 1 August 2019; Revised: 1 July 2020; Published: March 2021
First available in Project Euclid: 18 March 2021

Digital Object Identifier: 10.1214/20-AOAS1390

Rights: Copyright © 2021 Institute of Mathematical Statistics

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