Evidence map›Paper›PMID 29238965›Full record

ArticleBiometrics2018

Sieve analysis using the number of infecting pathogens.

Dean Follmann, Chiung-Yu Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrics, 2018. 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

2 authors.

Dean FollmannBiostatistics Research Branch, National Institute of Allergy and Infectious Diseases, 5601 Fishers Lane, Bethesda, Maryland 20892, U.S.A.ORCID 0000-0003-4073-0393
Chiung-Yu HuangDivision of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, 550 N. Broadway, Baltimore, Maryland 21205, U.S.A.ORCID 0000-0003-2313-3562

Funding

Statistical Methods for Survival and Recurrent Event Data in Clinical ResearchR01CA193888 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HUANG, CHIUNG-YU · 2015 to 2019
$1.7M
NCI NIH HHS R01 CA193888
6 · The paper itself

Abstract

Assessment of vaccine efficacy as a function of the similarity of the infecting pathogen to the vaccine is an important scientific goal. Characterization of pathogen strains for which vaccine efficacy is low can increase understanding of the vaccine's mechanism of action and offer targets for vaccine improvement. Traditional sieve analysis estimates differential vaccine efficacy using a single identifiable pathogen for each subject. The similarity between this single entity and the vaccine immunogen is quantified, for example, by exact match or number of mismatched amino acids. With new technology, we can now obtain the actual count of genetically distinct pathogens that infect an individual. Let F be the number of distinct features of a species of pathogen. We assume a log-linear model for the expected number of infecting pathogens with feature "f,"

Indexed as

Clinical Trials as TopicHumansImmunity, HeterologousMalariaModels, StatisticalRegression AnalysisTreatment OutcomeVaccinesVaccinesCompeting risksCox regressionEmpirical processInfectious diseaseMarked processMultiple OutputationWithin Cluster Resampling

Identifiers

PMID29238965
PMCPMC6004265

What OpenQuestion holds

Textmetadata
LicenceTDM
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.