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Related Concept Videos

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Test-Negative Designs With Multiple Testing Sources.

Mengxin Yu1, Nicholas P Jewell2

  • 1Department of Statistics and Data Science, School of Art and Science, School of Public Health, Washington University in St. Louis, St. Louis, Missouri, USA.

Statistics in Medicine
|April 30, 2026
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Summary
This summary is machine-generated.

Test-negative designs (TND) are crucial for evaluating infectious disease interventions like vaccines. This study proposes a method to address bias from multiple testing reasons in TND, ensuring accurate vaccine efficacy assessment.

Keywords:
Ebolacase‐cohort studytest‐negative design

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Area of Science:

  • Epidemiology
  • Vaccinology
  • Biostatistics

Background:

  • Test-negative designs (TND) are widely used to evaluate infectious disease interventions, including vaccines for influenza and COVID-19.
  • Traditional TND relies on symptomatic individuals, but modern applications include asymptomatic cases, potentially introducing bias.
  • The 'multiple reasons for testing' problem arises when individuals are tested for various reasons beyond symptoms, complicating efficacy assessments.

Purpose of the Study:

  • To address bias in test-negative designs caused by aggregating symptomatic and asymptomatic test results.
  • To propose and examine a method for estimating vaccine efficacy in the context of multiple testing sources, using an Ebola vaccine trial as a case study.
  • To assess whether vaccine efficacy is consistent across different sources of test results (symptomatic vs. contact tracing).

Main Methods:

  • Utilized a modified test-negative design framework inspired by an Ebola viral disease (EVD) vaccine trial.
  • Incorporated data from both symptomatic individuals presenting for care and asymptomatic close contacts of confirmed cases.
  • Developed an approach to estimate common vaccine efficacy and assess its consistency across different testing pathways.

Main Results:

  • The study examines a novel approach to estimate vaccine efficacy from combined symptomatic and asymptomatic testing data.
  • It provides a method to evaluate if the intervention's effectiveness differs between individuals tested due to symptoms versus those tested via contact tracing.
  • The proposed methodology is crucial for accurate efficacy assessment in complex trial designs.

Conclusions:

  • The proposed method offers a way to mitigate bias in test-negative designs arising from multiple reasons for testing.
  • This approach is vital for accurately evaluating vaccine efficacy, particularly in scenarios involving both symptomatic and asymptomatic individuals.
  • The methodology remains relevant for future infectious disease vaccine trials, especially if the EVD trial is recommenced.