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

Study Designs in Epidemiology01:20

Study Designs in Epidemiology

1.1K
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...
1.1K
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

861
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
861
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
Harmonic Mean01:09

Harmonic Mean

3.8K
The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
Take the example of the speed of a car, which is the measure of the rate of distance traveled. If the vehicle traverses the same distance back-and-forth, its average speed equals the total distance traveled divided by the total time taken. However, if the car moves with varying speeds, then the arithmetic mean is more skewed...
3.8K
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
Introduction to Epidemiology01:26

Introduction to Epidemiology

2.0K
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,...
2.0K

You might also read

Related Articles

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

Sort by
Same author

Sex- and age- specific lung cancer incidence and mortality among Medicaid beneficiaries with and without HIV, 2001-2015.

BMC cancer·2026
Same author

Association between housing instability and HIV care outcomes among people with HIV in the United States.

Journal of acquired immune deficiency syndromes (1999)·2026
Same author

Association between social vulnerability index and rates of carbapenem-resistant enterobacterales infections in the Denver metropolitan region, 2016-2019.

Infection control and hospital epidemiology·2026
Same author

Residential mobility and health among people with hiv: a scoping review.

AIDS (London, England)·2026
Same author

Association of Comorbid and Incident Depression and Other Mental Health Conditions With Long-COVID: Results From the Johns Hopkins COVID Long Study.

Journal of medical virology·2026
Same author

Incidence of frailty-related fracture among Medicaid beneficiaries living with HIV and cancer: A cohort study.

PloS one·2026

Related Experiment Video

Updated: Feb 14, 2026

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

19.7K

Collaborative, pooled and harmonized study designs for epidemiologic research: challenges and opportunities.

Catherine R Lesko1, Lisa P Jacobson1, Keri N Althoff1

  • 1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

International Journal of Epidemiology
|February 14, 2018
PubMed
Summary

Collaborative study designs (CSDs) leverage multiple independent contributing studies (ICSs) for increased power and diverse insights. Despite challenges, CSDs enable robust population health research and generalizable findings.

More Related Videos

Harmonic Nanoparticles for Regenerative Research
09:23

Harmonic Nanoparticles for Regenerative Research

Published on: May 1, 2014

12.2K
Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

5.0K

Related Experiment Videos

Last Updated: Feb 14, 2026

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

19.7K
Harmonic Nanoparticles for Regenerative Research
09:23

Harmonic Nanoparticles for Regenerative Research

Published on: May 1, 2014

12.2K
Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

5.0K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health Research

Background:

  • Collaborative study designs (CSDs) integrating individual-level data from independent contributing studies (ICSs) are increasingly prevalent.
  • CSDs offer advantages such as enhanced statistical power, investigation of effect heterogeneity, cost-efficiency, and fostering collaborative research.
  • CSDs present unique challenges including political, logistical, and methodological hurdles, alongside data harmonization complexities.

Purpose of the Study:

  • To highlight the advantages and challenges associated with collaborative study designs (CSDs).
  • To explore the potential of CSDs in leveraging heterogeneous data for advanced analyses.
  • To discuss the generalizability of findings from CSDs and methods to address analytic challenges.

Main Methods:

  • Combining individual-level data from multiple independent contributing studies (ICSs).
  • Leveraging heterogeneous data across ICSs to investigate measurement error and residual confounding.
  • Developing methods to address challenges in data harmonization and analysis of diverse study designs.

Main Results:

  • CSDs significantly increase statistical power and the ability to study effect heterogeneity.
  • Opportunities exist to use heterogeneous data for measurement error and confounding investigations.
  • CSDs facilitate consistent description of population health and generalization of results.

Conclusions:

  • Collaborative study designs (CSDs) are powerful tools for advancing research beyond the scope of single studies.
  • Addressing challenges in data harmonization and analysis is crucial for maximizing the benefits of CSDs.
  • CSDs enable robust, generalizable insights into population health across diverse settings and populations.