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 Experiment Videos

Methods of correcting for multiple testing: operating characteristics

B W Brown1, K Russell

  • 1Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston 77030, USA.

Statistics in Medicine
|December 24, 1997
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Treatment-Related Adverse Events and Health-Related Quality of Life Associated With Immune Checkpoint Inhibitor Treatment for Mucosal Head and Neck Squamous Cell Carcinoma: A Scoping Review.

Head & neck·2026
Same author

Aboriginal and Torres Strait Islander youth suicide mortality and previous mental health, suicidality and service use in Queensland, Australia, from 2001 to 2021.

Journal of affective disorders·2024
Same author

Development and validation of a sleep questionnaire, SNoRE 3.0, to evaluate sleep in companion dogs.

Scientific reports·2023
Same author

Technology-enabled multidisciplinary team in-reach for oral corticosteroid stewardship and optimizing care of suspected airways disease exacerbations.

Acute medicine·2023
Same author

The impact of a vaccine mandate and the COVID-19 pandemic on influenza vaccination uptake in Western Australian health care students.

Vaccine·2022
Same author

As the pandemic evolves, so must global monitoring of COVID-19.

Public health·2022
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Evaluating 17 multiple testing correction methods on synthetic data revealed no single best approach. Four methods performed well, but a recommended strategy involves using progressively lenient family-wise error rate controls.

Area of Science:

  • Statistics
  • Biostatistics
  • Computational Biology

Background:

  • Multiple testing is common in data analysis, increasing the risk of false positives.
  • Existing p-value correction methods aim to control family-wise error rates.
  • The performance of these methods can vary depending on data characteristics.

Purpose of the Study:

  • To systematically evaluate 17 p-value correction methods.
  • To identify methods with optimal operating characteristics.
  • To compare false negative error rates against acceptable family-wise error rates.

Main Methods:

  • Utilized synthetic datasets with known statistical properties.
  • Varied factors: number of p-values, proportion of false null hypotheses, p-value distribution, and correlation.

Related Experiment Videos

  • Assessed family-wise and false negative error rates for each method.

    • Main Results:

    • No single method uniformly outperformed others across all scenarios.
    • Four methods demonstrated superior performance without being surpassed.
    • Performance was influenced by the number of tests and the underlying data structure.

    Conclusions:

    • The choice of p-value correction method depends on specific research contexts.
    • A sequential approach using increasingly lenient error controls is suggested.
    • A computer program for these corrections is available for practical application.