ArticleStatistics in medicine2022
Extending the Continual Reassessment Method to accommodate step-up dosing in Phase I 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 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed, 1 citations in OpenAlex.
- Extending the Continual Reassessment Method to accommodate step-up dosing in Phase I trials.Statistics in medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Continual Reassessment Method (CRM) was developed for Phase I trials to identify a maximum-tolerated dose of an agent using a design in which each participant is treated with a single administration of the agent. We propose an extension of the CRM in which participants receive multiple administrations of an agent using a so-called step-up dosing procedure in which participants receive one or more administrations of lower doses of the agent before they receive their penultimate dose. We use methods developed for the CRM to model the probability of DLT for each administration, which leads to the use of conditional probability models to model the joint probability of DLT across multiple administrations. We compare our approach to two existing methods that use time-to-event modeling methods for modeling the probability of DLT. We demonstrate through simulations that our approach has operating characteristics similar to existing methods, but due to its foundations in the CRM, ours is simpler to implement than existing approaches and is therefore more likely to be adopted in practice.
Indexed as
Identifiers
What OpenQuestion holds
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.