A Bayesian phase I/II biomarker-based design for identifying subgroup-specific optimal dose for immunotherapy

Authors

Beibei Guo, Louisiana State Univ, Dept Expt Stat, Baton Rouge, LA 70803 USAFollow
Yong Zang, Indiana Univ, Dept Biostat & Hlth Data Sci, Indianapolis, IN 46204 USA ; Indiana Univ, Ctr Computat Biol & Bioinformat, Indianapolis, IN 46204 USA

Author ORCID Identifier

zang, yong https://orcid.org/0000-0001-5036-5815

Document Type

Article

Publication Date

2-2022

Abstract

Immunotherapy is an innovative treatment that enlists the patient's immune system to battle tumors. The optimal dose for treating patients with an immunotherapeutic agent may differ according to their biomarker status. In this article, we propose a biomarker-based phase I/II dose-finding design for identifying subgroup-specific optimal dose for immunotherapy (BSOI) that jointly models the immune response, toxicity, and efficacy outcomes. We propose parsimonious yet flexible models to borrow information across different types of outcomes and subgroups. We quantify the desirability of the dose using a utility function and adopt a two-stage dose-finding algorithm to find the optimal dose for each subgroup. Simulation studies show that the BSOI design has desirable operating characteristics in selecting the subgroup-specific optimal doses and allocating patients to those optimal doses, and outperforms conventional designs.

Publication Source (Journal or Book title)

STATISTICAL METHODS IN MEDICAL RESEARCH

Recommended Citation

Guo, B., & Zang, Y. (2022). A Bayesian phase I/II biomarker-based design for identifying subgroup-specific optimal dose for immunotherapy. STATISTICAL METHODS IN MEDICAL RESEARCH https://doi.org/10.1177/09622802221080753