Evidence map›Paper›PMID 24520806›Full record

ArticleJournal of internal medicine2014

Propensity scores for confounder adjustment when assessing the effects of medical interventions using nonexperimental study designs.

T Stürmer, R Wyss, R J Glynn, M A Brookhart

Abstract read
In one paragraph

Article in Journal of internal medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 173 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
173citing 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.

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

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

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113 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

4 authors.

T StürmerDepartment of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
R Wyss
R J Glynn
M A Brookhart

Funding

Virology Research Program (Program 4)P30CA016086 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Deborah F. Tate · 1985 to 2026
$201.5M
Propensity Scores and Preventive Drug Use in the ElderlyR01AG023178 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI STURMER, TIL · 2005 to 2014
$2.7M
NCI NIH HHS P30 CA016086NIA NIH HHS R01 AG023178
6 · The paper itself

Abstract

Treatment effects, especially when comparing two or more therapeutic alternatives as in comparative effectiveness research, are likely to be heterogeneous across age, gender, co-morbidities and co-medications. Propensity scores (PSs), an alternative to multivariable outcome models to control for measured confounding, have specific advantages in the presence of heterogeneous treatment effects. Implementing PSs using matching or weighting allows us to estimate different overall treatment effects in differently defined populations. Heterogeneous treatment effects can also be due to unmeasured confounding concentrated in those treated contrary to prediction. Sensitivity analyses based on PSs can help to assess such unmeasured confounding. PSs should be considered a primary or secondary analytic strategy in nonexperimental medical research, including pharmacoepidemiology and nonexperimental comparative effectiveness research.

Indexed as

Comparative Effectiveness ResearchConfounding Factors, EpidemiologicPropensity ScoreAge FactorsComorbidityDrug Therapy, CombinationEpidemiologic Research DesignHumansOutcome Assessment, Health CarePharmacoepidemiologySex Factorscomparative effectiveness researchconfoundingepidemiologic methodsheterogeneitypharmacoepidemiologypropensity scores

Identifiers

PMID24520806
PMCPMC4037382

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.