ROMI: A Randomized Two-Stage Basket Trial Design To Optimize Doses for Multiple Indications

Authors

Shuqi Wang
Peter F Thall
Kentaro Takeda
Ying Yuan

Publication Date

10-3-2024

Journal

Biometrics

DOI

10.1093/biomtc/ujae105

PMID

39360905

PMCID

PMC11447723

PubMedCentral® Posted Date

10-3-2024

PubMedCentral® Full Text Version

Post-print

Abstract

Optimizing doses for multiple indications is challenging. The pooled approach of finding a single optimal biological dose (OBD) for all indications ignores that dose-response or dose-toxicity curves may differ between indications, resulting in varying OBDs. Conversely, indication-specific dose optimization often requires a large sample size. To address this challenge, we propose a Randomized two-stage basket trial design that Optimizes doses in Multiple Indications (ROMI). In stage 1, for each indication, response and toxicity are evaluated for a high dose, which may be a previously obtained maximum tolerated dose, with a rule that stops accrual to indications where the high dose is unsafe or ineffective. Indications not terminated proceed to stage 2, where patients are randomized between the high dose and a specified lower dose. A latent-cluster Bayesian hierarchical model is employed to borrow information between indications, while considering the potential heterogeneity of OBD across indications. Indication-specific utilities are used to quantify response-toxicity trade-offs. At the end of stage 2, for each indication with at least one acceptable dose, the dose with highest posterior mean utility is selected as optimal. Two versions of ROMI are presented, one using only stage 2 data for dose optimization and the other optimizing doses using data from both stages. Simulations show that both versions have desirable operating characteristics compared to designs that either ignore indications or optimize dose independently for each indication.

Keywords

Humans, Bayes Theorem, Dose-Response Relationship, Drug, Randomized Controlled Trials as Topic, Maximum Tolerated Dose, Research Design, Computer Simulation, Sample Size, Models, Statistical, Biometry, Bayesian hierarchical model, dose optimization, multiple indications, Project Optimus, randomization, utility

Published Open-Access

yes

Recommended Citation

Wang, Shuqi; Thall, Peter F; Takeda, Kentaro; et al., "ROMI: A Randomized Two-Stage Basket Trial Design To Optimize Doses for Multiple Indications" (2024). Faculty, Staff and Student Publications. 6833.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6833