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

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:
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
Causality in Epidemiology01:21

Causality in Epidemiology

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...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

You might also read

Related Articles

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

Sort by
Same author

Effectiveness of a Nutrition Counseling Intervention on Food Consumption, According to the Degree of Processing: A Community-Based Non-Randomized Trial of Quilombola Communities in South Brazil.

International journal of public health·2024
Same author

Investigation of anti-Leptospira spp. antibodies and leptospiruria in cats attended to a veterinary teaching hospital in southern Brazil.

Comparative immunology, microbiology and infectious diseases·2024
Same author

Impact of dentists and equipment in the performing dental imaging examinations: a longitudinal analysis.

Brazilian oral research·2022
Same author

[Congenital anomalies from the perspective of social determinants of health].

Cadernos de saude publica·2022
Same author

[Analysis of records for women that have undergone legally authorized abortions in Porto Alegre, Rio Grande do Sul State, Brazil].

Cadernos de saude publica·2021
Same author

Dental biofilm of symptomatic COVID-19 patients harbours SARS-CoV-2.

Journal of clinical periodontology·2021

Related Experiment Video

Updated: Jun 25, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

[Multiple imputations for missing data: a simulation with epidemiological data].

Luciana Neves Nunes1, Mariza Machado Klück, Jandyra Maria Guimarães Fachel

  • 1Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil. lununes@mat.ufrgs.br

Cadernos De Saude Publica
|February 17, 2009
PubMed
Summary

Multiple imputation effectively handles missing data in surgical patient studies, yielding results comparable to complete datasets. This statistical method improves upon analyses that exclude cases with missing information, reducing potential bias.

Related Experiment Videos

Last Updated: Jun 25, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Biostatistics
  • Medical Informatics

Context:

  • Missing data in statistical analyses can lead to biased estimates.
  • Traditional methods often exclude subjects with incomplete data, limiting study power.
  • Imputation, the process of filling in missing data, offers a potential solution.

Purpose:

  • To present and evaluate a multiple imputation method for handling missing data.
  • To compare the performance of multiple imputation against complete case analysis and analysis of incomplete datasets.

Summary:

  • Logistic regression models for death were developed using a dataset of 470 surgical patients.
  • Two incomplete datasets were created with 5% and 20% missing data in a single variable.
  • Multiple imputation produced results similar to the complete dataset, outperforming analyses that excluded cases with missing data.

Impact:

  • Multiple imputation provides a more robust and less biased approach to statistical analysis when dealing with missing data.
  • This method enhances the reliability of findings from datasets with missing values, particularly in clinical research.
  • It offers a superior alternative to excluding incomplete cases, thereby preserving statistical power and data integrity.