Dose Selection Balancing Efficacy and Toxicity Using Bayesian Model Averaging
A. Lawrence Gould

TL;DR
This paper introduces a Bayesian model averaging approach to dose selection in drug development, balancing efficacy and toxicity without relying on specific functional forms, and demonstrates its effectiveness through simulations.
Contribution
It develops a model-agnostic Bayesian model averaging framework for dose selection that accounts for efficacy-toxicity relationships in a flexible, practical manner.
Findings
The BMA-Mod approach reliably identifies acceptable dose ranges.
It performs well across different copula models and categorical responses.
Simulation results show improved dose prediction accuracy.
Abstract
Successful pharmaceutical drug development requires finding correct doses that provide an optimum balance between efficacy and toxicity. Competing responses to dose such as efficacy and toxicity often will increase with dose, and it is important to identify a range of doses to provide an acceptable efficacy response (minimum effective dose) while not causing unacceptable intolerance or toxicity (maximum tolerated dose). How this should be done is not self-evident. Relating efficacy to dose conditionally on possible toxicity may be problematic because whether toxicity occurs will not be known when a dose for a patient needs to be chosen. Copula models provide an appealing approach for incorporating an efficacy-toxicity association when the functional forms of the efficacy and toxicity dose-response models are known but may be less appealing in practice when the functional forms of the…
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Taxonomy
TopicsStatistical Methods in Clinical Trials · Computational Drug Discovery Methods · Optimal Experimental Design Methods
