A Bayesian phase I/II biomarker-based design for identifying subgroup-specific optimal dose for immunotherapy. (June 2022)

• Record Type: Journal Article
• Authors: Guo, Beibei; Zang, Yong
• Is Part Of: Statistical methods in medical research. Volume 31:Number 6(2022)
• Page Start: 1104
• Page End: 1119
• Publication Date: 2022-06
• Subjects: Immunotherapy -- subgroups -- biomarker -- phase I/II trial -- dose finding -- immune response -- risk–benefit tradeoff -- Bayesian adaptive design; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/09622802221080753
• Languages: English
• ISSNs: 0962-2802

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.