Every week, we select a recently published Open Access article to feature. This week’s article is from Pharmaceutical Statistics and proposes Bayesian model-assisted designs should be one of the go to designs for early phase oncology trials.
The article’s abstract is given below, with the full article available to read here.
Improving early phase oncology clinical trial design: The case for finding the optimal biological dose. Pharmaceutical Statistics. 2023; 1– 9. doi:10.1002/pst.2291, .
Historically early phase oncology drug development programmes have been based on the belief that “more is better”. Furthermore, rule-based study designs such as the “3 + 3” design are still often used to identify the MTD. Phillips and Clark argue that newer Bayesian model-assisted designs such as the BOIN design should become the go to designs for statisticians for MTD finding. This short communication goes one stage further and argues that Bayesian model-assisted designs such as the BOIN12 which balances risk-benefit should be included as one of the go to designs for early phase oncology trials, depending on the study objectives. Identifying the optimal biological dose for future research for many modern targeted drugs, immunotherapies, cell therapies and vaccine therapies can save significant time and resources.More Details