ArticleBMC medical research methodology2023
Correction of vaccine effectiveness derived from test-negative case-control studies.
Article in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed, 13 citations in OpenAlex.
- Power and sample size considerations for test-negative design with bias correction: a case study on the world first malaria vaccine.BMC medical research methodology · 2025Article
- Estimating the number of probable new SARS-CoV-2 infections among tested subjects from the number of confirmed cases.BMC medical research methodology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDetermining the vaccine effectiveness (VE) is an important part of studying every new vaccine. Test-negative case-control (TNCC) studies have recently been used to determine the VE. However, the estimated VE derived from a TNCC design depends on the test sensitivity and specificity. Herein, a method for correction of the value of VE derived from a TNCC study is presented.
methodsAn analytical method is presented to compute the corrected VE based on the sensitivity and specificity of the diagnostic test utilized. To show the application of the method proposed, a hypothetical TNCC study is presented. In this in silico study, 100 000 individuals referring to a healthcare system for COVID-19-like illness were tested with diagnostic tests with sensitivities of 0.6, 0.8, and 1.0, and specificities ranging from 0.85 to 1.00. A vaccination coverage of 60%, an attack rate of 0.05 for COVID-19 in unvaccinated group, and a true VE of 0.70, were assumed. In this simulation, a COVID-19-like illness with an attack rate of 0.30 could also affect all the studied population regardless of their vaccination status.
resultsThe observed VE ranged from 0.11 (computed for a test sensitivity of 0.60 and specificity of 0.85) to 0.71 (computed for a test sensitivity and specificity of 1.0). The mean computed corrected VE derived from the proposed method was 0.71 (the standard deviation of 0.02).
conclusionsThe observed VE derived from TNCC studies can be corrected easily. An acceptable estimate for VE can be computed regardless of the diagnostic test sensitivity and specificity used in the study.
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Registered trials
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.