ArticleBiometrics2010
A hierarchical Bayesian design for phase I trials of novel combinations of cancer therapeutic agents.
Article in Biometrics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predicting Successful Phase Advancement and Regulatory Approval in Multiple Myeloma From Phase I Overall Response Rates.JCO clinical cancer informatics · 2017Pooled it
- Seamless monotherapy-combination phase I dose-escalation model-based design.Clinical trials (London, England) · 2025Article
- Comb-BOIN12: A Utility-Based Bayesian Optimal Interval Design for Dose Optimization in Cancer Drug-Combination Trials.Statistics in biopharmaceutical research · 2025Article
- Toxicity Adaptive Lists Design: A Practical Design for Phase I Drug Combination Trials in Oncology.JCO precision oncology · 2024Article
- Applications of the partial-order continual reassessment method in the early development of treatment combinations.Clinical trials (London, England) · 2024Article
- Local continual reassessment methods for dose finding and optimization in drug-combination trials.Statistical methods in medical research · 2023Article
- A clinical phase I dose-finding design with adaptive shrinking boundaries for drug combination trials.BMC medical research methodology · 2023Article
- Evaluation of phase I clinical trial designs for combinational agents along with guidance based on simulation studies.Journal of applied statistics · 2023Review
- Modeling synergism in early phase cancer trials with drug combination with continuous dose levels: is there an added value?Brazilian journal of biometrics · 2022Article
- A NOVEL FRAMEWORK TO ESTIMATE MULTIDIMENSIONAL MINIMUM EFFECTIVE DOSES USING ASYMMETRIC POSTERIOR GAIN ANDThe annals of applied statistics · 2022Article
- Adaptive Phase 1 Design in Radiation Therapy Trials.International journal of radiation oncology, biology, physics · 2022Article
- Accuracy and Safety of Novel Designs for Phase I Drug-Combination Oncology Trials.Statistics in biopharmaceutical research · 2022Article
- SEMIPARAMETRIC DOSE FINDING METHODS FOR PARTIALLY ORDERED DRUG COMBINATIONS.Statistica Sinica · 2022Article
- BOIN: a novel Bayesian design platform to accelerate early phase brain tumor clinical trials.Neuro-oncology practice · 2021Review
- Adapting isotonic dose-finding to a dynamic set of drug combinations with application to a phase I leukemia trial.Clinical trials (London, England) · 2021Article
- A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations with Ordinal Toxicity Grades.Stats · 2020Article
- CRM2DIM: A SAS macro for implementing the dual-agent Bayesian continual reassessment method.Computer methods and programs in biomedicine · 2019Article
- Cancer phase I trial design using drug combinations when a fraction of dose limiting toxicities is attributable to one or more agents.Biometrical journal. Biometrische Zeitschrift · 2019Article
- AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual-agent dose finding trials.Journal of the Royal Statistical Society. Series C, Applied statistics · 2019Article
- How to design a dose-finding study on combined agents: Choice of design and development of R functions.PloS one · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We propose a hierarchical model for the probability of dose-limiting toxicity (DLT) for combinations of doses of two therapeutic agents. We apply this model to an adaptive Bayesian trial algorithm whose goal is to identify combinations with DLT rates close to a prespecified target rate. We describe methods for generating prior distributions for the parameters in our model from a basic set of information elicited from clinical investigators. We survey the performance of our algorithm in a series of simulations of a hypothetical trial that examines combinations of four doses of two agents. We also compare the performance of our approach to two existing methods and assess the sensitivity of our approach to the chosen prior distribution.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.