Evidence map›Paper›PMID 40980019›Full record

ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2025

A patient similarity-embedded Bayesian approach to prognostic biomarker inference with application to thoracic cancer immunity.

Duo Yu, Meilin Huang, Michael J Kane, Brian P Hobbs

Abstract read
In one paragraph

Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2025. 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.

Duo YuDivision of Biostatistics, Data Science Institute, Medical College of Wisconsin, Milwaukee, WI, USA.ORCID 0000-0002-8988-1646
Meilin HuangGenentech, South San Francisco, CA, USA.
Michael J KaneDepartment of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Brian P HobbsDepartment of Population Health, The University of Texas at Austin, Austin, TX, USA.

Funding

Project 3: Enhanced Sensitivity of Tumors to Proton Beam Therapy: Mechanisms and Biomarkers.P01CA261669 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI HONG, THEODORE S · 2021 to 2025
$14.0M
NCI NIH HHS P01 CA261669
6 · The paper itself

Abstract

This paper introduces a novel statistical methodology integrating machine learning (ML) and Bayesian modelling to facilitate personalized prognostic predictions with application to oncology. Utilizing power priors, we construct 'patient-similarity embeddings' that identify localized patterns of prognosis. The methodology is applied to study the prognostic value of markers of anticancer immunity within the tumour microenvironment of nonsmall cell lung cancer while adjusting for established clinical characteristics. The method outperforms traditional regression and ML models, while accurately identifying subgroup patterns, thereby enhancing statistical inference and hypothesis testing.

Indexed as

nonsmall cell lung cancer (NSCLC)power priorprecision oncologypredictive probabilitysubgroup identificationtumour microenvironment

Identifiers

PMID40980019
PMCPMC12449836

What OpenQuestion holds

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LicenceTDM
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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.