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: current perspectives.

Michael G Kenward1, James Carpenter

  • 1Medical Statistics Unit, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK. mike.kenward@lshtm.ac.uk

Statistical Methods in Medical Research
|July 11, 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

Multi-arm multi-stage platform trials for neurological disease: accelerating progress.

Lancet (London, England)·2026
Same author

Repurposed drug prioritization pipeline for a multi-arm platform trial in clinical Alzheimer's disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Sample Size Calculation for the ROCI Design.

Statistics in medicine·2026
Same author

Bayesian analysis of the causal reference-based model for missing data in clinical trials.

Journal of biopharmaceutical statistics·2026
Same author

Patient and public involvement and engagement in clinical trials at scale: Analysis of the first 3250 responses on the POrtal for Patient and Public Engagement in Dementia (POPPED).

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Distinct Chiral Nanostructures of Graphene Quantum Dots Govern Divergent Passive and Active Enantioselective Transport across Biological Membranes.

bioRxiv : the preprint server for biology·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

Multiple imputation is a powerful statistical method for handling missing data in medical research, particularly in clinical trials. This approach offers robust solutions for incomplete datasets, improving the reliability of research findings.

Area of Science:

  • Biostatistics
  • Medical Research Methodology
  • Data Science

Background:

  • Missing data is a pervasive challenge in medical research, impacting the validity of clinical trials and observational studies.
  • Traditional methods for handling missing data can introduce bias and reduce statistical power.

Purpose of the Study:

  • To provide a comprehensive overview of multiple imputation (MI) techniques.
  • To discuss current perspectives on the application and rationale of MI in medical research.
  • To highlight the relevance of MI for longitudinal studies and analyses with missing covariates.

Main Methods:

  • Review of the general problem of missing data.
  • Detailed explanation of the multiple imputation process and its underlying principles.

Related Experiment Videos

  • Exploration of two primary approaches for generating imputations: fully multivariate models and iterated conditional univariate models.
  • Discussion on the utility of uncongenial imputation models for sensitivity analyses and clinical trial settings.

    • Main Results:

    • Multiple imputation offers a robust framework for addressing missing data in complex medical research designs.
    • Uncongenial imputation models are valuable for sensitivity analyses and specific clinical trial applications.
    • The paper identifies open questions and future research directions in the field of multiple imputation.

    Conclusions:

    • Multiple imputation is an essential tool for modern medical research, enabling more accurate and reliable analyses of incomplete data.
    • Further research is needed to refine imputation methods and address emerging challenges in statistical analysis.
    • The effective use of multiple imputation enhances the integrity of evidence generated from clinical trials and observational studies.