ABSTRACT In immunotherapy, both the dose and the schedule of drug administration can significantly influence therapeutic effects by modulating immune system activation. Incorporating immune response measures into clinical trial designs offers an opportunity to enhance decision‐making by leveraging their close association with therapeutic efficacy and toxicity. Motivated by settings where biomarker data indicate improved efficacy in biomarker‐positive patients, we propose a dose–schedule optimization strategy tailored to each biomarker‐defined subgroup, based on elicited utility functions that capture risk‐benefit tradeoffs. We introduce a joint modeling framework that simultaneously evaluates immune response, toxicity, and efficacy, enabling information sharing across outcome types and patient subgroups. Our approach utilizes parsimonious yet flexible models designed specifically to address challenges due to small sample sizes commonly encountered in early‐phase trials. Simulation studies demonstrate that the proposed design achieves desirable operating characteristics and effectively informs dose–schedule optimization.
Qiu et al. (Thu,) studied this question.