Evidence map›Paper›PMID 28913837›Full record

ArticleStatistics in medicine2017

Practical recommendations for reporting Fine-Gray model analyses for competing risk data.

Peter C Austin, Jason P Fine

2 registry-linked trialsAbstract read
In one paragraph

Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 680 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
680citing papers in PubMed, 1 pooled it
–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.

NCT04359810 phase2completednot on this mapstarted 2020, after this paper: background citation

A Phase 2, Multi-Center, Randomized Clinical Trial to Evaluate the Efficacy and Safety of Human Anti-SARS-CoV-2 Convalescent Plasma in Severely Ill Adults With COVID-19

TypeinterventionalSponsorMax O'DonnellRan2020 to 2020Enrolled223ConditionsSARS-CoV-2 InfectionArmsConvalescent Plasma (anti-SARS-CoV-2 plasma), Non-convalescent Plasma (control plasma)
NCT05293418 completednot on this mapstarted 2020, after this paper: background citation

Retrospective Single-center Cohort Study to Evaluate the Incidence of Colonization by Multidrug-resistant Organisms in Mechanically Ventilated Patients With Severe COVID-19 Admitted to a Large Intensive Care Unit in Milan, Italy

TypeobservationalSponsorFondazione IRCCS Ca' Granda, Ospedale Maggiore PoliclinicoRan2020 to 2022Enrolled435ConditionsCOVID-19, Bacterial Infections, Colonization, AsymptomaticArmsdata collection
3 · Its place in the literature

Who cites it

680 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  4. Effect of Initiation and Continuous Adherence to ARBs Versus ACEIs on Risk of Adjudicated Mild Cognitive Impairment or Dementia.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025
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  5. Perceived Control, Cognitive Training, and Incident Alzheimer's Disease and Related Dementias in the ACTIVE Study.The journals of gerontology. Series B, Psychological sciences and social sciences · 2025
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620 more citing papers are in PubMed but not listed here.

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.

Peter C AustinInstitute for Clinical Evaluative Sciences, Toronto, Ontario, Canada.ORCID http://orcid.org/0000-0003-3337-233X
Jason P FineDepartment of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In survival analysis, a competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. Outcomes in medical research are frequently subject to competing risks. In survival analysis, there are 2 key questions that can be addressed using competing risk regression models: first, which covariates affect the rate at which events occur, and second, which covariates affect the probability of an event occurring over time. The cause-specific hazard model estimates the effect of covariates on the rate at which events occur in subjects who are currently event-free. Subdistribution hazard ratios obtained from the Fine-Gray model describe the relative effect of covariates on the subdistribution hazard function. Hence, the covariates in this model can also be interpreted as having an effect on the cumulative incidence function or on the probability of events occurring over time. We conducted a review of the use and interpretation of the Fine-Gray subdistribution hazard model in articles published in the medical literature in 2015. We found that many authors provided an unclear or incorrect interpretation of the regression coefficients associated with this model. An incorrect and inconsistent interpretation of regression coefficients may lead to confusion when comparing results across different studies. Furthermore, an incorrect interpretation of estimated regression coefficients can result in an incorrect understanding about the magnitude of the association between exposure and the incidence of the outcome. The objective of this article is to clarify how these regression coefficients should be reported and to propose suggestions for interpreting these coefficients.

Indexed as

Data Interpretation, StatisticalModels, StatisticalHumansProbabilityProportional Hazards ModelsRegression AnalysisRisk AssessmentSurvival Analysiscompeting riskscumulative incidence functionsubdistribution hazard modelsurvival analysis

Identifiers

PMID28913837
PMCPMC5698744

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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.