ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2017
Phase I Designs that Allow for Uncertainty in the Attribution of Adverse Events.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Design of a Phase I Drug Combination Study with Adaptive Allocation Based on Dose-Limiting Toxicity Attribution.Cancers · 2025Article
- A Bayesian dose-finding design for outcomes evaluated with uncertainty.Clinical trials (London, England) · 2021Article
- 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
- Revisiting isotonic phase I design in the era of model-assisted dose-finding.Clinical trials (London, England) · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
In determining dose limiting toxicities in Phase I studies, it is necessary to attribute adverse events (AE) to being drug related or not. Such determination is subjective and may introduce bias. In this paper, we develop methods for removing or at least diminishing the impact of this bias on the estimation of the maximum tolerated dose (MTD). The approach we suggest takes into account the subjectivity in the attribution of AE by using model-based dose escalation designs. The results show that gains can be achieved in terms of accuracy by recovering information lost to biases. These biases are a result of ignoring the errors in toxicity attribution.
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Registered trials
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