Random Walk and Parallel Crossing Bayesian Optimal Interval Design for Dose Finding with Combined Drugs

Ruitao Lin () and Guosheng Yin ()
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Ruitao Lin: The University of Hong Kong, Department of Statistics and Actuarial Science
Guosheng Yin: The University of Hong Kong, Department of Statistics and Actuarial Science

A chapter in Frontiers of Biostatistical Methods and Applications in Clinical Oncology, 2017, pp 21-35 from Springer

Abstract: Abstract Interval designs have recently attracted enormous attention due to their simplicity, desirable properties, and superior performance. We study random-walk and parallel-crossing Bayesian optimal interval designsBayesian optimal interval design for dose finding in drug-combination trials. The entire dose-finding procedures of these two designs are nonparametric (or model-free), which are thus robust and also do not require the typical “nonparametric” prephase used in model-based designs for drug-combination trials. Simulation Simulation studies demonstrate the finite-sample performance of the proposed methods under various scenarios. Both designs are illustrated with a phase I two-agent dose-finding trial in prostate cancer.

Keywords: Bayesian method; Dose finding; Drug combination; Interval design; Random walk (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-10-0126-0_3

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DOI: 10.1007/978-981-10-0126-0_3

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