Evidence map›Paper›PMID 27550392›Full record

ReviewJournal of the American Geriatrics Society2016

Use and Interpretation of Propensity Scores in Aging Research: A Guide for Clinical Researchers.

Dae Hyun Kim, Carl F Pieper, Ali Ahmed, Cathleen S Colón-Emeric

Abstract readReview
In one paragraph

Review in Journal of the American Geriatrics Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Trial
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  5. Article
  6. Physical Function Assessment of Older Veterans With Serious Mental Illness.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2023
    Article
  7. Article
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  19. Observational
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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.

Dae Hyun KimDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
Carl F PieperDepartment of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina.
Ali AhmedDivision of Geriatrics, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama.
Cathleen S Colón-EmericSchool of Medicine, Duke University, Durham, North Carolina. colon001@mc.duke.edu.

Funding

Resource Core 3 - Metabolomics CoreP30AG028716 · NIA · DUKE UNIVERSITY · PI Sarah B. Peskoe · 2006 to 2026
$24.6M
Harvard Clinical and Translational Science CenterKL2TR001100 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2013 to 2017
$8.5M
EXTRAMURAL RESEARCH FACILITIES IMPROVEMENT: PHYSIOLOGYC06RR020136 · NCRR · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI MARCHASE, RICHARD BANFIELD · 2004 to 2004
$3.6M
Development and Validation of a Frailty Index Using Claims Data for Pharmacoepidemiologic Studies in Older AdultsK08AG051187 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI KIM, DAE HYUN · 2015 to 2017
$636k
Mentoring Intervention Development in Fall and Fracture PreventionK24AG049077 · NIA · DUKE UNIVERSITY · PI COLON-EMERIC, CATHLEEN S · 2015 to 2019
$361k
NCATS NIH HHS KL2 TR001100NCRR NIH HHS C06 RR020136NIA NIH HHS K08 AG051187NIA NIH HHS K24 AG049077NIA NIH HHS P30 AG028716
6 · The paper itself

Abstract

Observational studies are an important source of evidence for evaluating treatment benefits and harms in older adults, but lack of comparability in the outcome risk factors between the treatment groups leads to confounding. Propensity score (PS) analysis is widely used in aging research to reduce confounding. Understanding the assumptions and pitfalls of common PS analysis methods is fundamental to applying and interpreting PS analysis. This review was developed based on a symposium of the American Geriatrics Society Annual Meeting on the use and interpretation of PS analysis in May 2014. PS analysis involves two steps: estimation of PS and estimation of the treatment effect using PS. Typically estimated from a logistic model, PS reflects the probability of receiving a treatment given observed characteristics of an individual. PS can be viewed as a summary score that contains information on multiple confounders and is used in matching, weighting, or stratification to achieve confounder balance between the treatment groups to estimate the treatment effect. Of these methods, matching and weighting generally reduce confounding more effectively than stratification. Although PS is often included as a covariate in the outcome regression model, this is no longer a best practice because of its sensitivity to modeling assumption. None of these methods reduce confounding by unmeasured variables. The rationale, best practices, and caveats in conducting PS analysis are explained in this review using a case study that examined the effective of angiotensin-converting enzyme inhibitors on mortality and hospitalization in older adults with heart failure.

Indexed as

Biomedical ResearchGeriatricsPropensity ScoreAgedEvidence-Based MedicineGeriatric AssessmentHumansObservational Studies as TopicOutcome Assessment, Health Careconfoundingobservational researchpropensity score

Identifiers

PMID27550392
PMCPMC5072994

What OpenQuestion holds

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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.