ArticleStatistics in medicine2022
A nonparametric Bayesian method for dose finding in drug combinations cancer trials.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- N-of-1 medicine.Singapore medical journal · 2024Article
- Two-dimensional calibration-free odds design for phase I drug-combination trials.Frontiers in oncology · 2023Article
- A software tool for both the maximum tolerated dose and the optimal biological dose finding trials in early phase designs.Contemporary clinical trials communications · 2022Article
- Modeling synergism in early phase cancer trials with drug combination with continuous dose levels: is there an added value?Brazilian journal of biometrics · 2022Article
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Authors and funding
3 authors.
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Abstract
We propose an adaptive design for early-phase drug-combination cancer trials with the goal of estimating the maximum tolerated dose (MTD). A nonparametric Bayesian model, using beta priors truncated to the set of partially ordered dose combinations, is used to describe the probability of dose limiting toxicity (DLT). Dose allocation between successive cohorts of patients is estimated using a modified continual reassessment scheme. The updated probabilities of DLT are calculated with a Gibbs sampler that employs a weighting mechanism to calibrate the influence of data vs the prior. At the end of the trial, we recommend one or more dose combinations as the MTD based on our proposed algorithm. We apply our method to a Phase I clinical trial of CB-839 and Gemcitabine that motivated this nonparametric design. The design operating characteristics indicate that our method is comparable with existing methods.
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