ArticleStatistics in medicine2026
Applying the Partial Order Continual Reassessment Method to High-Dimensional Treatment Combinations.
Article in Statistics in medicine, 2026. 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
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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
1 citing paper in PubMed.
- Applying the Partial Order Continual Reassessment Method to High-Dimensional Treatment Combinations.Statistics in medicine · 2026Article
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Authors and funding
4 authors.
Funding
Abstract
Recent years have seen increasing popularities in therapies that combines several drugs and/or schedules, which imposes great difficulties on the design of appropriate Phase I clinical trials. The Partial Ordering Continual Reassessment Method (POCRM), among others, is a popular design that can be easily adapted to those complex settings. However, the design is introduced and evaluated in the setting of the combination of two drugs, and the combination of more than two drugs has rarely been investigated. In particular, the specification of toxicity orderings has always been an essential part of the POCRM, which becomes more difficult in high-dimensional settings due to the combinatorially increasing nature of the number of possible orderings of combination/schedules. This article proposes a systematic approach to specify orderings based on asymptotic properties in the setting of the combination of more than two drugs. Large simulation studies show that this novel ordering specification method leads to better design performance both asymptotically and with finite sample sizes.
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Registered trials
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