Evidence map›Paper›PMID 40817779›Full record

ArticleStatistics in medicine2025

Score Test for Functional Markov Process With Image Predictor.

Yang Wang, Graham A Colditz, Shu Jiang

Abstract read
In one paragraph

Article in Statistics in medicine, 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. Review
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

3 authors.

Yang WangDivision of Public Health Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.ORCID https://orcid.org/0000-0002-6948-0909
Graham A ColditzDivision of Public Health Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.ORCID https://orcid.org/0000-0002-7307-0291
Shu JiangDivision of Public Health Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.

Funding

Dynamic prediction incorporating time-varying covariates for the onset of breast cancerR37CA256810 · NCI · WASHINGTON UNIVERSITY · PI JIANG, SHU · 2021 to 2025
$2.4M
Breast Cancer Research Foundation BCRF24-028NCI NIH HHS R37 CA256810
6 · The paper itself

Abstract

A functional multistate model is presented, which accommodates Markov processes governing disease transition in a finite set of states. Importantly, we consider a setting where the set of predictors contains a high-dimensional image with the goal of quantifying the association between the image and the transition of disease states. In the motivating application of breast cancer, women start from normal breast tissue, go through benign lesions, and then to the onset of DCIS/invasive cancer. As in the real data application, we consider the setting in which the individuals are observed intermittently and the transition times are interval censored. A score test is developed to test the nullity of the coefficient function for the image predictor at different transitions between states. The asymptotic distribution of the score statistic is provided. An application involving progression to the development of breast cancer with mammogram image data provides illustration. Our results demonstrate an important association between the mammogram image and the probability of transition in breast cancer.

Indexed as

Breast NeoplasmsMammographyMarkov ChainsModels, StatisticalComputer SimulationDisease ProgressionFemaleHumansbreast cancerfunctional dataimage datamultistate modelscore test

Identifiers

PMID40817779
PMCPMC13216885

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.