Evidence map›Paper›PMID 30076511›Full record

ReviewLifetime data analysis2019

Parametric estimation of association in bivariate failure-time data subject to competing risks: sensitivity to underlying assumptions.

Jeongyong Kim, Karen Bandeen-Roche

Abstract readReview
In one paragraph

Review in Lifetime data analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jeongyong KimDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA. jkim339@jhu.edu.ORCID 0000-0001-5023-8218
Karen Bandeen-RocheDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.

Funding

'T V ASSESSMENT - MEMORY DISORDERED PATIENTS'P50AG005146 · NIA · JOHNS HOPKINS UNIVERSITY · PI HUGANIR, RICHARD L · 1985 to 2019
$37.6M
Research Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI Corinne Pettigrew · 2020 to 2026
$29.3M
NIA NIH HHS P30 AG066507NIA NIH HHS P50 AG005146NIA NIH HHS P50AG005146
6 · The paper itself

Abstract

There has arisen a considerable body of research addressing the estimation of association between paired failure times in the presence of competing risks. In a 2002 paper, Bandeen-Roche and Liang proposed the conditional cause-specific hazard ratio (CCSHR) as a measure of this association and a parametric method by which to estimate it. The method features an interpretable decomposition of the CCSHR into factors describing the association between a pair's times to first failure among multiple failure causes and the association in pair members' propensities to fail due to a common cause. There were indications of sensitivity to model assumptions, however, in the 2002 work. Here we report a detailed study of the method's sensitivity to its parametric assumptions. We conclude that the method's performance is most sensitive to mis-specification of temporality in the association between pair members' first-failure times and of correlation between propensity to fail early or late and the propensity to fail of a specific cause. Implications for methods development are highlighted.

Indexed as

Computer SimulationData AccuracyProportional Hazards ModelsAlgorithmsData AnalysisHumansLikelihood FunctionsModels, StatisticalMultivariate AnalysisSensitivity and SpecificityConditional cause-specific hazard ratioConditional hazard ratioDependenceFrailtyMultivariateSurvival

Identifiers

PMID30076511
PMCPMC6360142

What OpenQuestion holds

Textmetadata
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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.