Evidence map›Paper›PMID 41013291›Full record

SynthesisBMC infectious diseases2025

A meta-analysis of vaccine efficacy from phase III clinical trials of approved vaccines against SARS-CoV-2 and variants.

Dipesh Dhayfule, Yu-Heng Wu, Akram Ashyani, Ming-Chi Li, Chin-Shiang Tsai, Po-Lin Chen, Torbjörn E M Nordling

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Dipesh Dhayfule *Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan.
Yu-Heng Wu *Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan.
Akram AshyaniDepartment of Mechanical Engineering, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan.
Ming-Chi LiDepartment of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 138 Sheng - Li Rd, Tainan, 701, Taiwan.
Chin-Shiang TsaiDepartment of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 138 Sheng - Li Rd, Tainan, 701, Taiwan.
Po-Lin ChenDepartment of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 138 Sheng - Li Rd, Tainan, 701, Taiwan.
Torbjörn E M NordlingDepartment of Mechanical Engineering, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan. torbjorn.nordling@nordlinglab.org.

Funding

Ministry of Science and Technology, Taiwan 105-2218-E-006-016-MY2National Science and Technology Council 111-2221-E-006-186
6 · The paper itself

Abstract

backgroundAs we emerge from the COVID-19 pandemic and transition to a post-pandemic era, it is crucial to reflect on our experiences and prepare for future pandemics. Here we evaluate the impact of different methods for calculating the vaccine efficacy of COVID-19 vaccines, which has not been done previously.

methodsWe conducted a meta-analysis of 38 approved COVID-19 vaccines using data from phase III clinical trials between May 4, 2020, and June 10, 2022. We analyze vaccine efficacy against multiple SARS-CoV-2 variants including the original strain, Alpha, Beta, Delta, and Kappa using multiple endpoints. Clinical endpoints are categorized into a tree structure including asymptomatic infection, symptomatic infection, mild to critical illness, and death. We employ re-estimated vaccine efficacies, including relative risk and Poisson regression with robust error variance, for equitable cross-vaccine comparisons.

resultsWe re-estimated 63 vaccine efficacies, revealing a 3% to 6% difference in five efficacies compared to the original study. Four efficacies exhibited lower bounds below the critical 50% threshold for the endpoint asymptomatic, symptomatic, moderate, and severe, contrary to the initial reports. However, efficacy consistently surpasses the 50% threshold against symptomatic COVID-19. Overall efficacies range from 34.2% to 100%, 50.3% to 100% against symptomatic, and 66.8% to 100% against severe, and 65% to 95% against variants.

conclusionsOur systematic classification of vaccine endpoints enables more statistically rigorous meta-analyses across studies. Beyond the quantitative results, our study emphasizes the need to standardize the estimation method for robust assessments of vaccine efficacy. We highlight the incompleteness of the knowledge about different vaccine efficacy in the middle of the pandemic, in particular the need to identify variants during the trials and report on multiple endpoints. We encourage all authors to publicly share their data, fostering additional impartial investigations. This data collection enables comparisons with real-world effectiveness data, enabling future studies of the predictive power of efficacy.

Indexed as

COVID-19COVID-19 VaccinesSARS-CoV-2Vaccine EfficacyClinical Trials, Phase III as TopicHumansCOVID-19 VaccinesCOVID-19Meta-analysisPhase III clinical trialsSARS-CoV-2Vaccine efficacy

Identifiers

PMID41013291
PMCPMC12465431

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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.