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

1.1K
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:
1.1K
Study Design in Statistics01:15

Study Design in Statistics

10.1K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
10.1K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

628
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:
628
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.4K
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:  
1.4K
Causality in Epidemiology01:21

Causality in Epidemiology

1.8K
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...
1.8K
Biostatistics: Overview01:20

Biostatistics: Overview

937
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
937

You might also read

Related Articles

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

Sort by
Same author

Digital health and consumer health informatics: past and future.

Medical research archives·2026
Same author

Evaluation Framework for Bruise Detection: Systematic ALS/White-Light Training and Skin-Tone Balancing with Deep Learning.

Sensors (Basel, Switzerland)·2026
Same author

Optimal insurance coverage and pricing of outpatient drugs in Iran: a cost- and chronicity-based adaptation of the vertical equity model.

International journal for equity in health·2026
Same author

The association of prenatal adiposity characteristics with early childhood overweight and obesity: findings from a large and diverse mother-child cohort.

International journal of obesity (2005)·2026
Same author

An Interoperable Vaccine Record: A Roadmap to Realization.

Vaccines·2026
Same author

Model Quality in AI-based Bruise Detection: Rethinking IoU and Confidence Thresholds.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Related Experiment Video

Updated: Feb 24, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K

Which decisions affect cohort distribution in COVID-19 data analytics?

Atefehsadat Haghighathoseini1, Janusz Wojtusiak1, Lemba Priscille Ngana1

  • 1George Mason University, Fairfax, VA, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|February 23, 2026
PubMed
Summary
This summary is machine-generated.

Data analytics for COVID-19 research requires diverse patient cohorts. Strategic decision-making in data preprocessing is crucial for ensuring cohort representativeness and achieving equitable health outcomes.

Keywords:
Cohort distributionData ProcessingDecision-makingNational COVID Cohort Collaborative (N3C)

More Related Videos

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

15.4K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

Related Experiment Videos

Last Updated: Feb 24, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K
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

15.4K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

Area of Science:

  • Public Health
  • Data Science
  • Epidemiology

Background:

  • Data analytics is vital for understanding COVID-19 impacts, but contradictory results highlight issues with data.
  • Cohort representativeness is essential for accurate insights into diverse patient populations.
  • Existing research often lacks transparency regarding how cohort selection influences demographic distribution.

Purpose of the Study:

  • To investigate how decision-making processes during data preprocessing affect cohort diversity.
  • To analyze the impact of these decisions on demographic representation (sex, race, ethnicity).
  • To underscore the need for informed strategies in data analysis for equitable health outcomes.

Main Methods:

  • Analysis of decision-making points in data preprocessing and cohort construction.
  • Quantification of demographic distribution changes based on specific data handling choices.
  • Examination of the influence of seemingly unrelated factors on patient distribution.

Main Results:

  • Data preprocessing decisions significantly increase variability in demographic representation.
  • Variations observed include: female representation (0.77%-2.68%), Black race (1.17%-5.15%), and Hispanic/Latino ethnicity (5.84%-8.21%).
  • Timing and provider selection also impact patient distribution and outcomes, independent of demographics.

Conclusions:

  • Arbitrary data decisions can lead to skewed results and impact health equity.
  • Evidence-based, strategic decision-making is necessary for consistent and reliable data analytics.
  • Informed strategies enhance resource utilization and promote more equitable public health policies.