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

A local influence approach applied to binary data from a psychiatric study.

Ivy Jansen1, Geert Molenberghs, Marc Aerts

  • 1Biostatistics, Center for Statistics, Limburgs Universitair Centrum, Universitaire Campus, B-3590 Diepenbeek, Belgium.

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

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

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

Pharmaceutical statistics·2026
Same author

A review of evidence supporting amyloid beta reduction as a surrogate endpoint in Alzheimer's disease.

The journal of prevention of Alzheimer's disease·2026
Same author

Predicting unplanned hospital revisits among community-dwelling older adults: a dynamic cohort study.

European journal of hospital pharmacy : science and practice·2025
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 a new method for analyzing incomplete multivariate and longitudinal binary data. It enhances statistical model robustness by assessing sensitivity to unverifiable assumptions using local influence.

Area of Science:

  • Statistics
  • Biostatistics
  • Data Analysis

Background:

  • Statistical models for incomplete data require unverifiable assumptions.
  • Existing methods primarily address longitudinal data with selection models.
  • Sensitivity analysis tools are crucial for model validation.

Purpose of the Study:

  • To develop a local influence strategy for multivariate and longitudinal binary data with nonmonotone missingness.
  • To extend the Baker et al. (1992) model for covariate inclusion.
  • To assess the sensitivity of statistical models to assumptions in complex data scenarios.

Main Methods:

  • Extension of the Baker et al. (1992) model to incorporate covariates.
  • Application of local influence (Cook, 1986) for sensitivity analysis.

Related Experiment Videos

  • Analysis of multivariate and longitudinal binary data with nonmonotone missingness.

    • Main Results:

    • A local influence strategy is developed for the extended Baker et al. model.
    • The proposed method supports the model-building process for incomplete binary data.
    • Analytical insights into local influence graph behavior are provided.

    Conclusions:

    • The developed local influence strategy is applicable to multivariate and longitudinal binary data with nonmonotone missingness.
    • The extended model and sensitivity analysis enhance the reliability of statistical modeling.
    • Limitations related to conditional model parameters are acknowledged.