A Bayesian Phase I/II Trial Design for Immunotherapy

Suyu Liu, Beibei Guo and Ying Yuan

Journal of the American Statistical Association, 2018, vol. 113, issue 523, 1016-1027

Abstract: Immunotherapy is an innovative treatment approach that stimulates a patient’s immune system to fight cancer. It demonstrates characteristics distinct from conventional chemotherapy and stands to revolutionize cancer treatment. We propose a Bayesian phase I/II dose-finding design that incorporates the unique features of immunotherapy by simultaneously considering three outcomes: immune response, toxicity, and efficacy. The objective is to identify the biologically optimal dose, defined as the dose with the highest desirability in the risk–benefit tradeoff. An Emax model is utilized to describe the marginal distribution of the immune response. Conditional on the immune response, we jointly model toxicity and efficacy using a latent variable approach. Using the accumulating data, we adaptively randomize patients to experimental doses based on the continuously updated model estimates. A simulation study shows that our proposed design has good operating characteristics in terms of selecting the target dose and allocating patients to the target dose. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Date: 2018
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DOI: 10.1080/01621459.2017.1383260

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