Evidence map›Paper›PMID 29424000›Full record

ArticleStatistics in medicine2018

A threshold-free summary index of prediction accuracy for censored time to event data.

Yan Yuan, Qian M Zhou, Bingying Li, Hengrui Cai, Eric J Chow, Gregory T Armstrong

Abstract read
In one paragraph

Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
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  4. Basal cell carcinoma risk prediction in survivors of childhood cancer.Journal of the National Cancer Institute · 2025
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  15. Assessing the Adequacy of a Prediction Model.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine
    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

6 authors.

Yan YuanSchool of Public Health, University of Alberta, Edmonton, AB T6G1C9, Canada.ORCID 0000-0002-2073-9924
Qian M ZhouDepartment of Mathematics and Statistics, Mississippi State University, Starkville, Mississippi 39762, USA.
Bingying LiDepartment of Statistics and Actuarial Science, Simon Fraser University, Burnaby, B.C. V5A1S6, Canada.
Hengrui CaiSchool of Public Health, University of Alberta, Edmonton, AB T6G1C9, Canada.
Eric J ChowFred Hutchinson Cancer Research Center, Seattle Children's Hospital, University of Washington, Seattle, Washington, USA.
Gregory T ArmstrongDepartment of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, 262 Danny Thomas Place, MS 735,, Memphis, TN 38105, USA.

Funding

Mach-LETSGO: Machine-LEarning of Treatment, Survey, and Genetics towards Obtaining Correct Classification of Chronic Conditions in Adult Survivors in the Childhood Cancer Survivor Study - CCSS SupplU24CA055727 · NCI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI Gregory Armstrong · 1999 to 2026
$96.7M
CIHRNCI NIH HHS U24 CA055727
6 · The paper itself

Abstract

Prediction performance of a risk scoring system needs to be carefully assessed before its adoption in clinical practice. Clinical preventive care often uses risk scores to screen asymptomatic population. The primary clinical interest is to predict the risk of having an event by a prespecified future time t

Indexed as

Statistics, NonparametricBiometryCancer SurvivorsComputer SimulationHeart FailureHumansPredictive Value of TestsRisk AssessmentRisk FactorsTime Factorscensored event timepositive predictive valueprecision-recall curverisk predictionscreeningtime-dependent prediction accuracy

Identifiers

PMID29424000
PMCPMC5895543

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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.