Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Bias01:22

Bias

7.2K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
7.2K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

6.1K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
6.1K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.2K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.2K
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

5.5K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
5.5K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

545
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
545
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Episodic memory trajectories of older adults with and without HIV: A longitudinal population-based study in rural South Africa.

PLOS global public health·2026
Same author

How should covariates be handled in randomized trials? Empirical evidence from 50 trials and recommendations for practice.

Journal of clinical epidemiology·2026
Same author

Doubly Robust Estimators of the Restricted Mean Time in Favor Estimands in Individual- and Cluster-Randomized Trials.

Statistics in medicine·2026
Same author

Test-Negative Designs With Multiple Testing Sources.

Statistics in medicine·2026
Same author

EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.

Annals of statistics·2026
Same author

Unpacking sources of transmission in HIV prevention trials with deep-sequence pathogen data.

Nature communications·2026
Same journal

Can the All of Us sample be reweighted to mirror a nationally representative sample? A comparison of mortality predictors.

Epidemiology (Cambridge, Mass.)·2026
Same journal

Gut health, systemic inflammation, and linear growth among Indonesian infants: findings from the Action Against Stunting Hub observation cohort: Erratum.

Epidemiology (Cambridge, Mass.)·2026
Same journal

Evaluating Estimators in Partially Identified Models.

Epidemiology (Cambridge, Mass.)·2026
Same journal

Stratification and accumulation? Explaining changing mortality inequities between business owners and non-owners in the U.S. (1984-2022).

Epidemiology (Cambridge, Mass.)·2026
Same journal

Be wary of age-stratum aging in early-onset cancer trends.

Epidemiology (Cambridge, Mass.)·2026
Same journal

The Authors Respond.

Epidemiology (Cambridge, Mass.)·2026
See all related articles

Related Experiment Video

Updated: Jan 7, 2026

Measuring Attentional Biases for Threat in Children and Adults
08:25

Measuring Attentional Biases for Threat in Children and Adults

Published on: October 19, 2014

15.8K

Test-negative Designs with Various Reasons for Testing: Statistical Bias and Solution.

Mengxin Yu1, Tom Hongyi Liu2, Kendrick Qijun Li3

  • 1From the The Statistics and Data Science Department of the Wharton School, University of Pennsylvania, Philadelphia, PA.

Epidemiology (Cambridge, Mass.)
|December 31, 2025
PubMed
Summary
This summary is machine-generated.

Modified test-negative designs for vaccine effectiveness can be biased. This study introduces a stratified estimator to account for various testing reasons, improving precision and reducing bias in post-market vaccine evaluation.

Keywords:
COVID-19PrecisionStratificationVaccine effectiveness

More Related Videos

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
06:58

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

Published on: January 24, 2020

7.7K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.3K

Related Experiment Videos

Last Updated: Jan 7, 2026

Measuring Attentional Biases for Threat in Children and Adults
08:25

Measuring Attentional Biases for Threat in Children and Adults

Published on: October 19, 2014

15.8K
Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
06:58

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

Published on: January 24, 2020

7.7K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.3K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Vaccinology

Background:

  • Test-negative designs are crucial for post-market vaccine effectiveness evaluation when randomized trials are infeasible.
  • Recent adaptations include individuals with diverse testing reasons, potentially introducing bias.
  • Formal statistical examination is needed for these modified designs.

Purpose of the Study:

  • To statistically examine potential bias in modified test-negative designs.
  • To develop methods for unbiased estimation of vaccine effectiveness.
  • To improve precision by incorporating multiple reasons for testing.

Main Methods:

  • Statistical derivations and causal graphs were used to analyze bias.
  • Reasons for testing were categorized into symptoms, mandatory screening, and contact tracing.
  • Stratification was employed for consistent estimation and bias elimination.
  • A novel stratified estimator was proposed and evaluated.

Main Results:

  • The standard odds ratio estimator can be biased if diverse testing reasons are not considered.
  • Stratification effectively eliminates bias and allows for consistent estimation of vaccine effectiveness.
  • The proposed stratified estimator can improve precision by incorporating multiple testing reasons.

Conclusions:

  • Modified test-negative designs require careful statistical consideration of testing reasons.
  • The proposed stratification method provides a robust approach for vaccine effectiveness estimation.
  • This work enhances the reliability of post-market vaccine safety and effectiveness surveillance.