ROMI: a randomized two-stage basket trial design to optimize doses for multiple indications.

Area of Science:

• Clinical Trial Design
• Pharmacometrics
• Biostatistics

Background:

• Optimizing drug doses for multiple indications presents challenges due to varying dose-response and dose-toxicity curves.
• Existing methods like pooled or indication-specific optimization have limitations, including ignoring heterogeneity or requiring large sample sizes.

Purpose of the Study:

• To propose a novel Randomized two-stage basket trial design that Optimizes doses in Multiple Indications (ROMI).
• To address the challenge of finding optimal biological doses (OBD) across diverse patient populations and diseases.

Main Methods:

• A two-stage basket trial design evaluating high doses in stage 1 and randomizing between high and lower doses in stage 2.
• Utilizing a latent-cluster Bayesian hierarchical model to share information across indications while acknowledging heterogeneity.
• Employing indication-specific utilities to balance response and toxicity trade-offs.

Main Results:

• Simulations demonstrate that both versions of the ROMI design exhibit favorable operating characteristics.
• The proposed ROMI design outperforms methods that ignore indications or optimize doses independently.

Conclusions:

• The ROMI design offers an effective approach for optimizing drug doses in multi-indication settings.
• This adaptive trial design balances efficiency and accuracy by leveraging data across indications.