Evidence map›Paper›PMID 35545260›Full record

ReviewInfluenza and other respiratory viruses2022

Methods to account for measured and unmeasured confounders in influenza relative vaccine effectiveness studies: A brief review of the literature.

Matthew M Loiacono, Robertus Van Aalst, Darya Pokutnaya, Salaheddin M Mahmud, Joshua Nealon

Abstract readReview
In one paragraph

Review in Influenza and other respiratory viruses, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

5 authors.

Matthew M LoiaconoGlobal Medical Evidence Generation, Sanofi, Swiftwater, Pennsylvania, USA.ORCID 0000-0001-8100-465X
Robertus Van AalstDepartment of Modeling, Epidemiology, and Data Science, Sanofi, Lyon, France.ORCID 0000-0003-3025-7811
Darya PokutnayaGraduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Salaheddin M MahmudVaccine and Drug Evaluation Centre, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada.
Joshua NealonSchool of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.ORCID 0000-0003-1538-4636

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Observational seasonal influenza relative vaccine effectiveness (rVE) studies employ a variety of statistical methods to account for confounding and biases. To better understand the range of methods employed and implications for policy, we conducted a brief literature review. Across 37 included rVE studies, 10 different types of statistical methods were identified, and only eight studies reported methods to detect residual confounding, highlighting the heterogeneous state of the literature. To improve the comparability and credibility of future rVE research, researchers should clearly explain methods and design choices and implement methods to detect and quantify residual confounding.

Indexed as

Influenza, HumanInfluenza VaccinesBiasHumansVaccine EfficacyInfluenza Vaccinescomparative effectiveness researchconfounding factors, epidemiologicinfluenza vaccinesretrospective studiesreview literature as topic

Identifiers

PMID35545260
PMCPMC9343322

What OpenQuestion holds

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LicenceCC BY
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.