Evidence map›Paper›PMID 38117882›Full record

ReviewScience advances2023

Design and analysis heterogeneity in observational studies of COVID-19 booster effectiveness: A review and case study.

Sabir Meah, Xu Shi, Lars G Fritsche, Maxwell Salvatore, Abram Wagner, Emily T Martin, Bhramar Mukherjee

Abstract readReview
In one paragraph

Review in Science advances, 2023. 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. Integrating Genomic Data into Test-negative Designs for Estimating Lineage-specific COVID-19 Vaccine Effectiveness.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
    Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Sabir MeahDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.ORCID 0009-0006-4101-3221
Xu ShiDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.ORCID 0000-0001-8566-9552
Lars G FritscheDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.ORCID 0000-0002-2110-1690
Maxwell SalvatoreCenter for Precision Health Data Science, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0002-3659-1514
Abram WagnerDepartment of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.ORCID 0000-0003-4691-7802
Emily T MartinDepartment of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.ORCID 0000-0002-5771-8895
Bhramar MukherjeeDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.ORCID 0000-0003-0118-4561

Funding

Accounting for Hidden Bias in Vaccine Studies: A Negative Control FrameworkR01GM139926 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SHI, XU, TCHETGEN TCHETGEN, ERIC JOEL · 2021 to 2024
$1.5M
NIGMS NIH HHS R01 GM139926
6 · The paper itself

Abstract

We investigated the design and analysis of observational booster vaccine effectiveness (VE) studies by performing a scoping review of booster VE literature with a focus on study design and analytic choices. We then applied 20 different approaches, including those found in the literature, to a single dataset from Michigan Medicine. We identified 80 studies in our review, including over 150 million observations in total. We found that while protection against infection is variable and dependent on several factors including the study population and time period, both monovalent boosters and particularly the bivalent booster offer strong protection against severe COVID-19. In addition, VE analyses with a severe disease outcome (hospitalization, intensive care unit admission, or death) appear to be more robust to design and analytic choices than an infection endpoint. In terms of design choices, we found that test-negative designs and their variants may offer advantages in statistical efficiency compared to cohort designs.

Indexed as

COVID-19HospitalizationHumansIntensive Care UnitsMichiganObservational Studies as Topic

Identifiers

PMID38117882
PMCPMC10732535

What OpenQuestion holds

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Registered trials

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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.