Evidence map›Paper›PMID 42455565›Full record

ArticleStatistics in medicine2026

Efficiency Enhancement in Testing Treatment Efficacy Across Multiple Populations Using Treatment Crossover Data.

Ryo Emoto, Kiyoaki Ishii, Toshinari Takamura, Shigeyuki Matsui

Abstract read
PubMed Full text at PMC Publisher
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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ryo EmotoDepartment of Biostatistics and Data Science, Graduate School of Medicine, Kyoto University, Kyoto, Japan.ORCID https://orcid.org/0000-0003-1722-6762
Kiyoaki IshiiDepartment of Endocrinology and Metabolism, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan.
Toshinari TakamuraDepartment of Endocrinology and Metabolism, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan.
Shigeyuki MatsuiDepartment of Biostatistics and Data Science, Graduate School of Medicine, Kyoto University, Kyoto, Japan.

Funding

Japan Science and Technology Agency JPMJCR21D3Japan Society for the Promotion of Science 16H06299Japan Society for the Promotion of Science 21H04874
6 · The paper itself

Abstract

Recent advances in biotechnology and personalized medicine have driven the development of efficient clinical trial methodologies for assessing treatment efficacy across multiple populations defined by treatment effect modifiers. Within-patient comparison of different treatments is a promising approach for improving study efficiency across multiple populations by eliminating between-patient variability in treatment evaluation. This study provides a framework for evaluating treatment efficacy in multiple populations for

Indexed as

Clinical Trials as TopicComputer SimulationCross-Over StudiesDiabetes Mellitus, Type 2HumansModels, StatisticalPrecision MedicineSample SizeTreatment Effect HeterogeneityTreatment Outcomecrossover trialspersonalized medicinerequired sample sizestatistical powertreatment effect heterogeneity

Identifiers

PMID42455565
PMCPMC13371832

What OpenQuestion holds

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Registered trials

None linked

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.