Evidence map›Paper›PMID 42673713›Full record

ArticleVaccine2026

Evaluating the influence of vaccine archetypes and epidemiological conditions on clinical trial metrics for tuberculosis vaccine candidates.

Jason R Andrews, Sabine Hermans, Richard G White, Alberto L García-Basteiro, Julio Croda, Frank Cobelens

Abstract read
In one paragraph

Article in Vaccine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

6 authors.

Jason R AndrewsDepartment of Medicine, Stanford University School of Medicine, Stanford, California, USA; Amsterdam UMC, location University of Amsterdam, Department of Global Health, Amsterdam Institute for Global Health and Development, Amsterdam, the Netherlands. Electronic address: jandr@stanford.edu.
Sabine HermansAmsterdam UMC, location University of Amsterdam, Department of Global Health, Amsterdam Institute for Global Health and Development, Amsterdam, the Netherlands; Center for Tropical Medicine and Travel Medicine, Department of Infectious Diseases, Amsterdam UMC, Location University of Amsterdam, Amsterdam, the Netherlands.
Richard G WhiteDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Alberto L García-BasteiroCentro de Investigação em Saúde de Manhiça, Manhiça, Mozambique; Centro de Investigación Biomédica en Red de Enfermedades Infecciosas, ISGlobal, Hospital Clínic Barcelona, Universitat de Barcelona, Barcelona, Spain.
Julio CrodaFederal University of Mato Grosso do Sul, Campo Grande, Brazil; Oswaldo Cruz Foundation, Campo Grande, Brazil; Yale University School of Public Health, New Haven, CT, USA.
Frank CobelensAmsterdam UMC, location University of Amsterdam, Department of Global Health, Amsterdam Institute for Global Health and Development, Amsterdam, the Netherlands. Electronic address: f.cobelens@aighd.org.

Funding

Defining drivers of TB transmission in the era of universal ART, and implications for finding the walking wellR01AI147321 · NIAID · LONDON SCH/HYGIENE & TROPICAL MEDICINE · PI GRANT, ALISON · 2019 to 2024
$2.3M
Mentoring patient-oriented translational research in tuberculosisK24AI182647 · NIAID · STANFORD UNIVERSITY · PI Jason Randolph Andrews · 2024 to 2026
$512k
Bill & Melinda Gates Foundation INV-035506NIAID NIH HHS K24 AI182647NIAID NIH HHS R01 AI147321Wellcome Trust
6 · The paper itself

Abstract

backgroundConducting tuberculosis vaccine trials in high-transmission settings offers potential gains in efficiency, yet the impact of repeated exposure in the presence of incomplete protection on measured vaccine efficacy is uncertain. We evaluated the effect of vaccine characteristics and epidemiological context on estimates of vaccine efficacy and requisite sample size for tuberculosis vaccine trials.

methodsWe simulated clinical trials of hypothetical tuberculosis vaccines using a stochastic compartmental model of tuberculosis natural history. We considered vaccines that confer "leaky" (50% per-exposure reduction) versus "all-or-none" protection against infection and/or disease that were evaluated in QuantiFERON Gold Plus (QFT) positive, negative or mixed populations with 2% (medium burden; incidence 400 per 100,000), 5% (high burden; 800 per 100,000) or 50% (very high burden; 4500 per 100,000) annual risk of infection (ARI), including protection against infection alone. We compared estimates of vaccine efficacy and statistical power to detect differences according to sample size for each vaccine archetype and epidemiologic setting.

findingsSimulated trials of vaccines protecting against disease, alone or with protection against infection, gave comparable estimates of efficacy across epidemiological settings, whether all-or-none or leaky. The largest reduction was for a leaky vaccine protecting against both infection and disease in QFT negative participants, whose efficacy fell from 75% at medium ARI to 67% at very high ARI (relative reduction 11%). A vaccine acting only by preventing infection fell further, from 50% to 36% (28%). Trials at very high ARI could nonetheless achieve 90% power with 80-96% fewer participants than at medium ARI.

interpretationWhile vaccines conferring leaky protection against infection and disease tested in settings with very high ARI could modestly underestimate efficacy, trials could be adequately powered with a fraction of the sample size in such settings. Further, trials could enroll a mixture of QFT negative and positive individuals, increasing generalizability.

Indexed as

Clinical Trials as TopicTuberculosisTuberculosis VaccinesVaccine EfficacyComputer SimulationHumansMycobacterium tuberculosisSample SizeTuberculosis VaccinesClinical trialsPowerSample sizeTuberculosisVaccines

Identifiers

PMID42673713
PMCPMC13623288

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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.