Evidence map›Paper›PMID 35596238›Full record

ArticleStatistics in medicine2022

The polytomous discrimination index for prediction involving multistate processes under intermittent observation.

Shu Jiang, Richard J Cook

Open access · greenAbstract read
In one paragraph

Article in Statistics in medicine, 2022. 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
0.5field-weighted citation impact, top 35% of its field
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, 2 citations in OpenAlex.

  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

2 authors at 2 institutions in 2 countries.

Shu JiangDivision of Public Health Sciences, Washington University School of Medicine, St. Louis, Missouri, USA.ORCID 0000-0003-1464-4838
Richard J CookDepartment of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada.ORCID 0000-0002-1414-4908
University of Waterloo · CAWashington University in St. Louis · US

Funding

Washington University Center for Cellular ImagingP30CA091842 · NCI · WASHINGTON UNIVERSITY · PI TIMOTHY J. EBERLEIN · 2001 to 2026
$128.0M
The St. Jude Lifetime CohortU01CA195547 · NCI · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI HUDSON, MELISSA M, NESS, KIRSTEN KIMBERLIE · 2015 to 2024
$14.9M
NCI NIH HHS P30 CA091842NCI NIH HHS U01 CA195547
6 · The paper itself

Abstract

With the increasing importance of predictive modeling in health research comes the need for methods to rigorously assess predictive accuracy. We consider the problem of evaluating the accuracy of predictive models for nominal outcomes when outcome data are coarsened at random. We first consider the problem in the context of a multinomial response modeled by polytomous logistic regression. Attention is then directed to the motivating setting in which class membership corresponds to the state occupied in a multistate disease process at a time horizon of interest. Here, class (state) membership may be unknown at the time horizon since disease processes are under intermittent observation. We propose a novel extension to the polytomous discrimination index to address this and evaluate the predictive accuracy of an intensity-based model in the context of a study involving patients with arthritis from a registry at the University of Toronto Centre for Prognosis Studies in Rheumatic Diseases.

Indexed as

Logistic ModelsHumansPrognosisclassificationcoarseningdiscriminationintermittent observationmultistate processespredictive modelrisk scores

Identifiers

PMID35596238
PMCPMC9308735
OpenAlexW4281251350

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.