Trial reportContemporary clinical trials2015
Dose escalation with over-dose and under-dose controls in Phase I/II clinical trials.
Trial report in Contemporary clinical trials, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Defining Utility as a Measure of Preference Under Uncertainty in Phase I-II Oncology Dose Finding Trials.Statistics in medicine · 2026Article
- Structure-Tissue Exposure/Selectivity Relationship (STR) on Carbamates of Cannabidiol.International journal of molecular sciences · 2024Article
- Development of consensus-driven SPIRIT and CONSORT extensions for early phase dose-finding trials: the DEFINE study.BMC medicine · 2023Review
- 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 Bayesian seamless phase I-II trial design with two stages for cancer clinical trials with drug combinations.Biometrical journal. Biometrische Zeitschrift · 2020Article
- Rapid enrollment design for finding the optimal dose in immunotherapy trials with ordered groups.Journal of biopharmaceutical statistics · 2019Article
- Two-stage design for phase I-II cancer clinical trials using continuous dose combinations of cytotoxic agents.Journal of the Royal Statistical Society. Series C, Applied statistics · 2019Article
- Interactive calculator for operating characteristics of phase I cancer clinical trials using standard 3+3 designs.Contemporary clinical trials communications · 2018Article
- Analyses of drug combinations using missing data shortens trial periods in phase I/II oncology trials.Contemporary clinical trials communications · 2017Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
To save valuable time and resources in new drug development, Phase I/II clinical trials with toxicity control and drug efficacy as dual primary endpoints have become increasingly popular. Escalation with over-dose control (the EWOC) is a Bayesian adaptive Phase I clinical trial design that can accurately estimate the maximum tolerated dose (MTD) level and control the probability of overdosing patients during the dose allocation phase. In this paper, we extend EWOC to Phase I/II clinical trials by controlling for under-dosing with a Gumbel Copula model to provide patients with at least minimum drug efficacy. We propose a utility function to measure the composite effect of toxicity and efficacy and select the optimal dose. To deal with the common issue that the efficacy endpoint often cannot be quickly ascertained, we employ Bayesian data augmentation to handle delayed efficacy and allow for flexible patient accrual without a waiting period. Extensive simulations demonstrate that the proposed new design not only provides better therapeutic effect by reducing the probability of treating patients at under-dose levels while protecting patients from being overdosed, but also improves trial efficiency and increases the accuracy of dose recommendation for subsequent clinical trials. We apply the proposed design to a Phase I/II solid tumor trial.
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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.