Evidence map›Paper›PMID 28824834›Full record

ArticleCurrent epidemiology reports2016

Methodologic Issues When Estimating Risks in Pharmacoepidemiology.

Jessie K Edwards, Laura L Hester, Mugdha Gokhale, Catherine R Lesko

Abstract read
In one paragraph

Article in Current epidemiology reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Which heart failure patients benefit most from non-invasive telemedicine? An overview of current evidence and future directions.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2024
    Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. A Framework for Descriptive Epidemiology.American journal of epidemiology · 2022
    Article
  15. Article
  16. Medication adherence with denosumab in patients with bone metastases from solid tumors treated in routine clinical settings: a retrospective study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2022
    Observational
  17. Article
  18. Article
  19. Article
  20. Article
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.

Jessie K EdwardsDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Laura L HesterDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Mugdha GokhaleDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Catherine R LeskoDepartment of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA.

Funding

UNC CLINICAL AND TRANSLATIONAL SCIENCE AWARD: BPCAUL1RR025747 · NCRR · UNIV OF NORTH CAROLINA CHAPEL HILL · PI RUNGE, MARSCHALL S. · 2008 to 2011
$44.6M
North Carolina Translational & Clinical Sciences Institute (NC TraCS)UL1TR001111 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BUSE, JOHN BERNARD, CAREY, TIMOTHY S · 2013 to 2017
$43.9M
Johns Hopkins HIV Clinical CohortU01DA036935 · NIDA · JOHNS HOPKINS UNIVERSITY · PI MOORE, RICHARD DOUGLAS · 2014 to 2024
$14.9M
Cancer Training GrantR25CA116339 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BASCH, ETHAN M., REEVE, BRYCE B · 2008 to 2017
$4.0M
Lung and HIV Analytical Data Coordinating Center (LHAD-CC)U01HL121812 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI LAU, BRYAN · 2013 to 2017
$3.4M
Structural models for treatment and exposure effects in clinical HIV cohortsR01AI100654 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI COLE, STEPHEN R · 2013 to 2016
$1.6M
NCATS NIH HHS UL1 TR001111NCI NIH HHS R25 CA116339NCRR NIH HHS UL1 RR025747NHLBI NIH HHS U01 HL121812NIAID NIH HHS R01 AI100654NIDA NIH HHS U01 DA036935
6 · The paper itself

Abstract

Risk is an important parameter to describe the occurrence of health outcomes over time. However, many outcomes of interest in healthcare settings, such as disease incidence, treatment initiation, and cause-specific mortality, may be precluded from occurring by other events, often referred to as competing events. Here, we review straightforward approaches to estimate risk in the presence of competing events. We illustrate the application of these methods using timely examples in pharmacoepidemiologic research and compare results to those obtained using analytic simplifications commonly used to handle competing events. These examples demonstrate how the analytic methods used to account for competing events affect the interpretation of results from pharmacoepidemiologic studies.

Indexed as

Bias (Epidemiology)Epidemiologic MethodsPharmacoepidemiologySurvival Analysis

Identifiers

PMID28824834
PMCPMC5557056

What OpenQuestion holds

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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.