Evidence map›Paper›PMID 31660633›Full record

ReviewStatistics in medicine2020

A review of the use of time-varying covariates in the Fine-Gray subdistribution hazard competing risk regression model.

Peter C Austin, Aurélien Latouche, Jason P Fine

Abstract readReview
In one paragraph

Review in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.

0numbers the graph read from it
0cells of the map it votes in
96citing 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

96 citing papers in PubMed.

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  6. The kidney failure risk equation in people with CKD and multimorbidity: the effect of competing mortality risks.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2026
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36 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Peter C AustinICES, Toronto, Ontario, Canada.ORCID 0000-0003-3337-233X
Aurélien LatoucheConservatoire National des Arts et Métiers, Paris, France.
Jason P FineDepartment of Biostatistics, University of North Carolina, Chapel Hill, North Carolina.

Funding

Canadian Institutes of Health Research (CIHR) MOP 86508
6 · The paper itself

Abstract

In survival analysis, time-varying covariates are covariates whose value can change during follow-up. Outcomes in medical research are frequently subject to competing risks (events precluding the occurrence of the primary outcome). We review the types of time-varying covariates and highlight the effect of their inclusion in the subdistribution hazard model. External time-dependent covariates are external to the subject, can effect the failure process, but are not otherwise involved in the failure mechanism. Internal time-varying covariates are measured on the subject, can effect the failure process directly, and may also be impacted by the failure mechanism. In the absence of competing risks, a consequence of including internal time-dependent covariates in the Cox model is that one cannot estimate the survival function or the effect of covariates on the survival function. In the presence of competing risks, the inclusion of internal time-varying covariates in a subdistribution hazard model results in the loss of the ability to estimate the cumulative incidence function (CIF) or the effect of covariates on the CIF. Furthermore, the definition of the risk set for the subdistribution hazard function can make defining internal time-varying covariates difficult or impossible. We conducted a review of the use of time-varying covariates in subdistribution hazard models in articles published in the medical literature in 2015 and in the first 5 months of 2019. Seven percent of articles published included a time-varying covariate. Several inappropriately described a time-varying covariate as having an association with the risk of the outcome.

Indexed as

Survival AnalysisHumansRegression AnalysisRisk AssessmentRisk FactorsTimecompeting riskssubdistribution hazard modelsurvival analysistime-varying covariate

Identifiers

PMID31660633
PMCPMC6916372

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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.