Evidence map›Paper›PMID 37328600›Full record

ArticleDrug safety2023

Serially Combining Epidemiological Designs Does Not Improve Overall Signal Detection in Vaccine Safety Surveillance.

Faaizah Arshad, Martijn J Schuemie, Fan Bu, Evan P Minty, Thamir M Alshammari, Lana Y H Lai, Talita Duarte-Salles, Stephen Fortin, Fredrik Nyberg, Patrick B Ryan and 3 more

Abstract read
In one paragraph

Article in Drug safety, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Faaizah ArshadDepartment of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA.
Martijn J SchuemieDepartment of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA.
Fan BuDepartment of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA.
Evan P MintyO'Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, AB, Canada.
Thamir M AlshammariMedication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia.
Lana Y H LaiDivision of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK.
Talita Duarte-SallesFundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
Stephen FortinObservational Health Data Analytics, Janssen R&D, Titusville, NJ, USA.
Fredrik NybergSchool of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden.
Patrick B RyanObservational Health Data Sciences and Informatics, New York, NY, USA.
George HripcsakObservational Health Data Sciences and Informatics, New York, NY, USA.
Daniel Prieto-AlhambraCentre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK.
Marc A SuchardDepartment of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA. msuchard@ucla.edu.ORCID http://orcid.org/0000-0001-9818-479X

Funding

Statistical Innovation to Integrate Sequences and Phenotypes for Scalable Phylodynamic InferenceR01AI153044 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Marc A. Suchard · 2021 to 2026
$2.4M
NIAID NIH HHS R01 AI153044
6 · The paper itself

Abstract

introductionVaccine safety surveillance commonly includes a serial testing approach with a sensitive method for 'signal generation' and specific method for 'signal validation.' The extent to which serial testing in real-world studies improves or hinders overall performance in terms of sensitivity and specificity remains unknown.

methodsWe assessed the overall performance of serial testing using three administrative claims and one electronic health record database. We compared type I and II errors before and after empirical calibration for historical comparator, self-controlled case series (SCCS), and the serial combination of those designs against six vaccine exposure groups with 93 negative control and 279 imputed positive control outcomes.

resultsThe historical comparator design mostly had fewer type II errors than SCCS. SCCS had fewer type I errors than the historical comparator. Before empirical calibration, the serial combination increased specificity and decreased sensitivity. Type II errors mostly exceeded 50%. After empirical calibration, type I errors returned to nominal; sensitivity was lowest when the methods were combined.

conclusionWhile serial combination produced fewer false-positive signals compared with the most specific method, it generated more false-negative signals compared with the most sensitive method. Using a historical comparator design followed by an SCCS analysis yielded decreased sensitivity in evaluating safety signals relative to a one-stage SCCS approach. While the current use of serial testing in vaccine surveillance may provide a practical paradigm for signal identification and triage, single epidemiological designs should be explored as valuable approaches to detecting signals.

Indexed as

VaccinesDatabases, FactualElectronic Health RecordsHumansResearch DesignSensitivity and SpecificityVaccines

Identifiers

PMID37328600
PMCPMC10345011

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.