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

Incomplete hierarchical data.

Caroline Beunckens1, Geert Molenberghs, Herbert Thijs

  • 1Center for Statistics, Hasselt University, Diepenbeek, Belgium. caroline.beunckens@uhasselt.be

Statistical Methods in Medical Research
|July 28, 2007
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

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
Same author

Integrating oral health screening into general practice: validation study of the Oral Health Screener.

Scientific reports·2026
Same author

Time-Scale Target Parameters and Two-Step Estimation in Longitudinal Trials for Progressive Diseases.

Statistics in medicine·2026
Same author

Corrigendum to "Development of a short version of the Delirium Observation Screening Scale (s-DOSS): A psychometric validation study" [Int. J. Nurs. Stud. volume 177, May 2026, 105362].

International journal of nursing studies·2026
Same author

Handling Missing Data in Participants with Baseline but No Post-Baseline Data.

Pharmaceutical statistics·2026
Same author

Development of a short version of the Delirium Observation Screening Scale (s-DOSS): A psychometric validation study.

International journal of nursing studies·2026
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
Same journal

A robust neural network with random effects for subject-specific prediction of clustered count data.

Statistical methods in medical research·2026
Same journal

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.

Statistical methods in medical research·2026
Same journal

Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty.

Statistical methods in medical research·2026
See all related articles

Analyzing incomplete hierarchical data requires careful methods. This study reviews techniques like multiple imputation and generalized estimating equations, emphasizing sensitivity analysis for reliable results.

Area of Science:

  • Statistics
  • Data Analysis

Background:

  • Hierarchical data analysis often faces missing data challenges.
  • Missing data mechanisms can be outside researcher control, necessitating robust analytical approaches.

Purpose of the Study:

  • To outline a framework for analyzing incomplete hierarchical data.
  • To discuss various analytical methods and their assumptions, including ignorability.
  • To explore sensitivity analysis for robust conclusions.

Main Methods:

  • Review of standard methods: direct likelihood, multiple imputation, generalized estimating equations.
  • Exploration of flexible models addressing outcome and missingness processes simultaneously.
  • Illustration of sensitivity to modeling assumptions.

Related Experiment Videos

Main Results:

  • Standard methods often rely on the ignorability assumption.
  • Flexible models can accommodate non-ignorable missingness.
  • Sensitivity analyses are crucial for validating results.

Conclusions:

  • Careful selection and application of analytical methods are vital for incomplete hierarchical data.
  • Sensitivity analysis provides a pathway to assess the robustness of findings.
  • Consideration of feasibility within regulatory environments is important.