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 Concept Videos

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

927
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
927
Incomplete Dominance01:43

Incomplete Dominance

29.8K
Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
29.8K
Introduction to Epidemiology01:26

Introduction to Epidemiology

1.7K
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
1.7K
Causality in Epidemiology01:21

Causality in Epidemiology

1.5K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.5K
Multiple Allele Traits01:49

Multiple Allele Traits

38.0K
The Concept of Multiple Allelism
38.0K
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

933
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
933

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Heterogeneity in Aging: Exploring Grip Strength Variability in Population-Based Age-Sex Cohorts.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same author

Commentary on "Resurrecting complete-case analysis: a defense": the loss of information remains unresolved.

American journal of epidemiology·2026
Same author

Subunit-Specific Immunodominance in Clinically Distinct Populations With AChR+ Myasthenia Gravis: A Multiparametric Cross-Sectional Analysis.

Neurology·2025
Same author

The daily auditory environments of people with tinnitus.

Scientific reports·2025
Same author

Bayesian Multilevel Latent Class Profile Analysis: Inference and Estimation for Exploring the Diverse Pathways to Academic Proficiency.

Multivariate behavioral research·2025
Same author

Physical health and function trajectories in adults with cancer: psychosocial predictors of class membership.

Journal of cancer survivorship : research and practice·2024

Related Experiment Video

Updated: Jan 24, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.0K

Multiple Imputation for Incomplete Data in Environmental Epidemiology Research.

Prince Addo Allotey1, Ofer Harel2

  • 1Department of Statistics, College of Liberal Arts and Sciences, University of Connecticut, 215 Glenbrook Rd Unit, Storrs, CT, 4120, USA.

Current Environmental Health Reports
|May 16, 2019
PubMed
Summary

Multiple imputation (MI) methods outperform complete case analysis (CCA) for handling missing data in environmental epidemiology. MI improves parameter estimates by accounting for uncertainty from incomplete datasets.

Keywords:
Complete case analysisComplete dataMissing dataMultiple imputationSpontaneous abortionTraditional statistical methods

More Related Videos

Fundus Photography as a Convenient Tool to Study Microvascular Responses to Cardiovascular Disease Risk Factors in Epidemiological Studies
10:11

Fundus Photography as a Convenient Tool to Study Microvascular Responses to Cardiovascular Disease Risk Factors in Epidemiological Studies

Published on: October 22, 2014

19.6K
Data Collection on Marine Litter Ingestion in Sea Turtles and Thresholds for Good Environmental Status
13:18

Data Collection on Marine Litter Ingestion in Sea Turtles and Thresholds for Good Environmental Status

Published on: May 18, 2019

12.6K

Related Experiment Videos

Last Updated: Jan 24, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.0K
Fundus Photography as a Convenient Tool to Study Microvascular Responses to Cardiovascular Disease Risk Factors in Epidemiological Studies
10:11

Fundus Photography as a Convenient Tool to Study Microvascular Responses to Cardiovascular Disease Risk Factors in Epidemiological Studies

Published on: October 22, 2014

19.6K
Data Collection on Marine Litter Ingestion in Sea Turtles and Thresholds for Good Environmental Status
13:18

Data Collection on Marine Litter Ingestion in Sea Turtles and Thresholds for Good Environmental Status

Published on: May 18, 2019

12.6K

Area of Science:

  • Environmental Epidemiology
  • Biostatistics
  • Statistical Analysis

Background:

  • Incomplete data present a significant challenge in statistical analyses within environmental epidemiology.
  • Many researchers overlook the complexities introduced by missing data, potentially compromising study validity.
  • Multiple imputation (MI) offers a robust approach to address missing data, contrasting with simpler methods like complete case analysis (CCA).

Purpose of the Study:

  • To evaluate the performance of two multiple imputation (MI) methods: fully conditional specification and multivariate normal.
  • To compare the efficacy of MI techniques against the complete case analysis (CCA) method for handling missing data.
  • To discuss practical considerations and potential issues encountered when implementing these missing data handling strategies.

Main Methods:

  • Utilized two distinct multiple imputation (MI) techniques: fully conditional specification and multivariate normal.
  • Employed complete case analysis (CCA) as a benchmark for comparison.
  • Applied these methods to analyze data from 944 women in the Collaborative Perinatal Project, examining associations between maternal smoking and birth outcomes (birth weight, spontaneous abortion).

Main Results:

  • Multiple imputation (MI) demonstrated superior performance in handling incomplete data compared to complete case analysis (CCA).
  • MI methods yielded significant improvements in parameter estimates.
  • Both MI approaches produced comparable point estimates, though minor variations in standard errors were observed.

Conclusions:

  • Multiple imputation (MI) is a more appropriate method for addressing missing data in environmental epidemiological research than complete case analysis (CCA).
  • MI techniques enhance the reliability and accuracy of statistical parameter estimates.
  • Researchers should consider MI methods to mitigate the impact of missing data and improve analytical outcomes.