Evidence map›Paper›PMID 41758769›Full record

ArticleStatistics in medicine2026

Applying the Partial Order Continual Reassessment Method to High-Dimensional Treatment Combinations.

Weishi Chen, Li Liu, Nolan A Wages, Pavel Mozgunov

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Weishi ChenMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.ORCID https://orcid.org/0000-0003-0718-5901
Li LiuDepartment of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, USA.ORCID https://orcid.org/0009-0004-5536-3850
Nolan A WagesDepartment of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, USA.
Pavel MozgunovMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

Funding

Gates Cambridge TrustMassey Comprehensive Cancer CenterMedical Research Council MC UU 00002/19National Institute for Health Research NIHR300576
6 · The paper itself

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.

Indexed as

Clinical Trials, Phase I as TopicComputer SimulationDrug Therapy, CombinationHumansModels, StatisticalSample Sizecombination trialsdose‐findingordering specificationpartial ordering continual reassessment methods

Identifiers

PMID41758769
PMCPMC12948264

What OpenQuestion holds

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Registered trials

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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.