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

Multiple outputation: inference for complex clustered data by averaging analyses from independent data.

Dean Follmann1, Michael Proschan, Eric Leifer

  • 1National Institute of Allergy and Infectious Diseases, 6700B Rockledge Drive MSC 7609, Bethesda, Maryland 20892, USA. dfollmann@niaid.nih.gov

Biometrics
|August 21, 2003
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

Proteomics risk scores and mortality in heart failure: Generalizability across populations.

PloS one·2026
Same author

Donor-Specific Transplant Outcomes from BMTCTN 1702: A Multi-Center Prospective Biological-Assignment Trial.

Blood advances·2026
Same author

Searching for immune correlates in Lassa vaccine development - workshop report.

NPJ vaccines·2026
Same author

Correlates of severe and delta COVID-19 in a phase 3 trial of the AZD1222 vaccine.

NPJ vaccines·2026
Same author

Continuous Infusion of the CXCR4 Antagonist Plerixafor for WHIM Syndrome.

Journal of clinical immunology·2026
Same author

Enhanced accuracy of NIRS-vascular occlusion testing through incorporation of conduit artery diameter.

JPhys photonics·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

This study introduces multiple outputation, a simple method for analyzing clustered data when only independent data methods are available. This approach offers a broadly applicable tool for statistical analysis in various settings.

Area of Science:

  • Statistics
  • Biostatistics
  • Data Analysis

Background:

  • Statistical methods often assume independent data, posing challenges for analyzing clustered datasets.
  • Existing methods for clustered data can be complex or impractical in certain scenarios.

Purpose of the Study:

  • To present and validate the multiple outputation method for analyzing clustered data.
  • To demonstrate the broad applicability of multiple outputation across diverse statistical applications.

Main Methods:

  • Multiple outputation involves randomly selecting one data point from each cluster and applying standard statistical methods.
  • The procedure is repeated multiple times, averaging estimates to derive the final result.
  • Variance estimation is calculated using the average of individual variance estimates and the sample variance of the estimates.

Related Experiment Videos

Main Results:

  • The article proves the asymptotic normality of estimates derived from multiple outputation under weak conditions.
  • Demonstrates successful application of multiple outputation to various data types, including angular data, p-values, and genetics data.
  • Highlights the method's effectiveness even with random cluster sizes.

Conclusions:

  • Multiple outputation is a simple, broadly applicable, and effective method for analyzing clustered data.
  • The technique is particularly useful when standard clustered data methods are impractical.
  • It serves as a quick and straightforward tool for statistical analysis of clustered observations.