Evidence map›Paper›PMID 32314782›Full record

ArticleAmerican journal of epidemiology2020

Invited Commentary: Opportunities That Come With Studying the Co-Occurrence of Multiple Outcomes.

Sebastien Haneuse, Deborah Schrag, Daniel Nevo

Open access · greenAbstract readComment
In one paragraph

Article in American journal of epidemiology, 2020. 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
0.3field-weighted citation impact, top 24% 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, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Sebastien Haneuse
Deborah Schrag
Daniel Nevo

Funding

Clustered semi-competing risks analysis in quality of end-of-life care studiesR01CA181360 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANEUSE, SEBASTIEN · 2014 to 2017
$1.8M
NCI NIH HHS R01 CA181360
6 · The paper itself

Abstract

In almost all clinical settings, patients are at risk for multiple potential events and, in consultation with health-care providers, must weigh the potential benefits and harms across these events when making decisions. As researchers seek to build an evidence base to inform these decisions, they must contend with a choice as to how they will handle the different events. One approach, arguably the standard approach in the literature, is to consider the events individually by conducting analyses and publishing results for each one at a time. Doing so, however, fails to acknowledge or exploit the inherent multivariate nature of the data, represents a lost opportunity, and results in an evidence base that is not aligned with how clinical decision-making is actually performed. The article by Prentice et al. (Am J Epidemiol. 2020;189(9):972-981) in this issue of the Journal moves beyond this standard by illustrating recently developed methods that directly take advantage of information on the co-occurrence of multiple events. Moreover, their article highlights the role of modern methods in deriving additional information and insight from studies of multiple clinical outcomes by making full use of multivariate data, with the goal being to complement, not replace, existing methods.

Indexed as

Decision MakingResearchFemaleHumansIntention to Treat AnalysisWomen's Healthdecision-makingmultivariate data analysissemicompeting riskstime-to-event data

Identifiers

PMID32314782
PMCPMC7443758
OpenAlexW3016539684

What OpenQuestion holds

Textmetadata
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.