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

What do we do with missing data? Some options for analysis of incomplete data.

Trivellore E Raghunathan1

  • 1Department of Biostatistics and Institute for Social Research, University of Michigan, Ann Arbor, Michigan 48109, USA. teraghu@umich.edu

Annual Review of Public Health
|March 16, 2004
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

A SEMIPARAMETRIC MULTIPLE IMPUTATION APPROACH TO FULLY SYNTHETIC DATA FOR COMPLEX SURVEYS.

Journal of survey statistics and methodology·2024
Same author

Small Area Estimation of Cancer Risk Factors and Screening Behaviors in US Counties by Combining Two Large National Health Surveys.

Preventing chronic disease·2019
Same author

Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study.

Biometrics·2019
Same author

Explaining The Slowdown In Medical Spending Growth Among The Elderly, 1999-2012.

Health affairs (Project Hope)·2019
Same author

Effect of Group-Administered Behavioral Treatment on Urinary Incontinence in Older Women: A Randomized Clinical Trial.

JAMA internal medicine·2018
Same author

Longitudinal study of adolescent tobacco use and tobacco control policies in India.

BMC public health·2018

Missing data in public health studies can bias results. This review covers weighting, multiple imputation, and likelihood-based methods to address incomplete data and improve analysis accuracy.

Area of Science:

  • Public Health
  • Biostatistics
  • Epidemiology

Background:

  • Missing data is a common challenge in public health research.
  • Excluding subjects with incomplete data can lead to biased estimates.
  • Systematic differences between included and excluded subjects exacerbate bias.

Purpose of the Study:

  • To review methods for analyzing incomplete data in public health investigations.
  • To illustrate potential biases from complete-case analysis.
  • To introduce techniques for handling missing data effectively.

Main Methods:

  • Review of three primary approaches for incomplete data analysis.
  • Weighting methods to compensate for excluded subjects.
  • Multiple imputation to replace missing values with plausible estimates.

Related Experiment Videos

  • Likelihood-based methods using observed incomplete data.

    • Main Results:

    • Simulation study demonstrates bias severity in logistic regression with missing data.
    • Illustrates concepts and methodology using a logistic regression example.
    • Discusses available software packages for incomplete data analysis.

    Conclusions:

    • Standard complete-case analysis can yield biased results.
    • Weighting, multiple imputation, and likelihood methods offer alternatives.
    • These methods aim to provide more accurate estimates from incomplete datasets.