ArticleBiometrical journal. Biometrische Zeitschrift2019
Cancer phase I trial design using drug combinations when a fraction of dose limiting toxicities is attributable to one or more agents.
Article in Biometrical journal. Biometrische Zeitschrift, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Chaperone directed heterobifunctional molecules circumvent KRASCancer letters · 2025Article
- A Bayesian adaptive design for dual-agent phase I-II oncology trials integrating efficacy data across stages.Biometrical journal. Biometrische Zeitschrift · 2023Article
- Modeling synergism in early phase cancer trials with drug combination with continuous dose levels: is there an added value?Brazilian journal of biometrics · 2022Article
- Flexible use of copula-type model for dose-finding in drug combination clinical trials.Biometrics · 2022Article
- Combining cytotoxic agents with continuous dose levels in seamless phase I-II clinical trials.Journal of the Royal Statistical Society. Series C, Applied statistics · 2022Article
- A nonparametric Bayesian method for dose finding in drug combinations cancer trials.Statistics in medicine · 2022Article
- Dose Correlation ofEvidence-based complementary and alternative medicine : eCAM · 2021Article
- A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations with Ordinal Toxicity Grades.Stats · 2020Article
- A Bayesian seamless phase I-II trial design with two stages for cancer clinical trials with drug combinations.Biometrical journal. Biometrische Zeitschrift · 2020Article
- A proposal for a new PhD level curriculum on quantitative methods for drug development.Pharmaceutical statistics · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Drug combination trials are increasingly common nowadays in clinical research. However, very few methods have been developed to consider toxicity attributions in the dose escalation process. We are motivated by a trial in which the clinician is able to identify certain toxicities that can be attributed to one of the agents. We present a Bayesian adaptive design in which toxicity attributions are modeled via copula regression and the maximum tolerated dose (MTD) curve is estimated as a function of model parameters. The dose escalation algorithm uses cohorts of two patients, following the continual reassessment method (CRM) scheme, where at each stage of the trial, we search for the dose of one agent given the current dose of the other agent. The performance of the design is studied by evaluating its operating characteristics when the underlying model is either correctly specified or misspecified. We show that this method can be extended to accommodate discrete dose combinations.
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