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 imputation inference for multivariate multilevel continuous data with ignorable non-response.

Recai M Yucel1

  • 1Department of Epidemiology and Biostatistics, University at Albany, School of Public Health, One University Place, Room 139, Rensselaer, NY 12144, USA. ryucel@albany.edu

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|April 15, 2008
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

Time-Varying Path-Specific Direct and Indirect Effects: A Novel Approach to Examine Dynamic Behavioral Processes with Application to Smoking Cessation.

Multivariate behavioral research·2026
Same author

A computationally efficient sequential regression imputation algorithm for multilevel data.

Journal of applied statistics·2024
Same author

Organizational Health Literacy and Health Among New York State Medicaid Members.

Health literacy research and practice·2023
Same author

Organ Donation Willingness Among Asian Americans: Results from a National Study.

Journal of racial and ethnic health disparities·2022
Same author

Toxic elements in follicular fluid adversely influence the likelihood of pregnancy and live birth in women undergoing IVF.

Human reproduction open·2021
Same author

Variability of essential and non-essential trace elements in the follicular fluid of women undergoing in vitro fertilization (IVF).

Ecotoxicology and environmental safety·2021
Same journal

Correction to: 'Stokes settling and particle-laden plumes: implications for deep-sea mining and volcanic eruption plumes' (2020), by Mingotti et al.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

A stable hothouse triggered by a tipping mechanism.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Beyond distance: quantifying point cloud dynamics with persistent homology and dynamic optimal transport.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Global stability of the Atlantic overturning circulation: edge state, long transients and boundary crisis under CO2 forcing.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Morse index classification and landscape of Kuramoto system for Hebbian-based binary pattern recognition.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same journal

Interpretable and equation-free response theory for complex systems.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
See all related articles

This study introduces Bayesian multiple imputation for handling missing data in multilevel models. It addresses missing values at any observational level, improving statistical analysis for complex datasets.

Area of Science:

  • Statistics
  • Computational Statistics
  • Biostatistics

Background:

  • Missing data methods have advanced significantly in general statistical analyses.
  • However, methods for missing data in multilevel applications lag behind.
  • Sophisticated consumers of statistics increasingly demand robust missing-data handling.

Purpose of the Study:

  • To adapt and apply multiple imputation techniques for missing values within multilevel models.
  • To specifically address missing data occurring at any level of observational units.
  • To utilize Bayesian inference for imputing missing data.

Main Methods:

  • Employing Bayesian arguments to draw multiple imputations from the posterior predictive distribution.
  • Utilizing multivariate extensions of mixed-effects models to simulate this distribution.

Related Experiment Videos

  • Applying the developed methods to a real-world dataset concerning unmet mental health care needs in children.

    • Main Results:

    • Demonstrates the feasibility of Bayesian multiple imputation in multilevel contexts.
    • Provides a framework for handling arbitrarily missing data across different levels.
    • The application successfully identifies correlates of unmet mental health care needs.

    Conclusions:

    • Bayesian multiple imputation offers a powerful approach for missing data in multilevel modeling.
    • This method enhances the accuracy and reliability of statistical inferences in complex hierarchical data.
    • The study highlights the importance of advanced missing-data techniques in applied research, particularly in health services research.