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

Introduction to Epidemiology01:26

Introduction to Epidemiology

2.3K
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,...
2.3K
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
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

919
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...
919
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

1.3K
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...
1.3K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.5K
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:  
1.5K
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

You might also read

Related Articles

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

Sort by
Same author

The role of glycaemic and lipid risk factors in mediating the effect of BMI on coronary heart disease: a two-step, two-sample Mendelian randomisation study.

Diabetologia·2017
Same author

Prospective associations of psychosocial adversity in childhood with risk factors for cardiovascular disease in adulthood: the MRC National Survey of Health and Development.

International journal for equity in health·2017
Same author

Are parents' motivations to exercise and intention to engage in regular family-based activity associated with both adult and child physical activity?

BMJ open sport & exercise medicine·2017
Same author

Different strategies for diagnosing gestational diabetes to improve maternal and infant health.

The Cochrane database of systematic reviews·2017
Same author

Association of pre-pregnancy body mass index with offspring metabolic profile: Analyses of 3 European prospective birth cohorts.

PLoS medicine·2017
Same author

Cardiometabolic phenotypes and mitochondrial DNA copy number in two cohorts of UK women.

Mitochondrion·2017
Same journal

Age at menarche and adverse pregnancy and perinatal outcomes: triangulating evidence from multivariable and Mendelian randomization analyses.

International journal of epidemiology·2026
Same journal

Life-course trajectories of cardiovascular disease risk factors in rural India: Andhra Pradesh Children and Parents Study (APCAPS) 2003-2023.

International journal of epidemiology·2026
Same journal

Cohort Profile Update: The Young Lives study.

International journal of epidemiology·2026
Same journal

From the departing Editors in Chief.

International journal of epidemiology·2026
Same journal

Data Resource Profile: Cheeloo Lifespan Electronic-health reseArch Data-library (Cheeloo LEAD).

International journal of epidemiology·2026
Same journal

Cohort Profile Update: The Swiss Childhood Cancer Survivor Cohort.

International journal of epidemiology·2026
See all related articles

Related Experiment Video

Updated: Mar 8, 2026

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis
10:33

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

Published on: June 17, 2019

11.4K

Triangulation in aetiological epidemiology.

Debbie A Lawlor1,2, Kate Tilling1,2, George Davey Smith1,2

  • 1MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.

International Journal of Epidemiology
|January 22, 2017
PubMed
Summary
This summary is machine-generated.

Triangulation integrates multiple research approaches to strengthen causal inference in epidemiological studies. By comparing findings and understanding biases, researchers can achieve more reliable answers to complex health questions.

Keywords:
Aetiological epidemiologyMendelian randomizationRCTscausalityinstrumental variablesnatural experimentsnegative control studiestriangulationwithin-sibships studies

More Related Videos

Enactive Phenomenological Approach to the Trier Social Stress Test: A Mixed Methods Point of View
05:26

Enactive Phenomenological Approach to the Trier Social Stress Test: A Mixed Methods Point of View

Published on: January 7, 2019

7.3K
Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
11:10

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Published on: December 27, 2010

12.8K

Related Experiment Videos

Last Updated: Mar 8, 2026

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis
10:33

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

Published on: June 17, 2019

11.4K
Enactive Phenomenological Approach to the Trier Social Stress Test: A Mixed Methods Point of View
05:26

Enactive Phenomenological Approach to the Trier Social Stress Test: A Mixed Methods Point of View

Published on: January 7, 2019

7.3K
Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
11:10

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Published on: December 27, 2010

12.8K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Causal inference in aetiological epidemiology relies on integrating diverse evidence.
  • Different research methods possess unique, unrelated sources of potential bias.
  • Consistent findings across methods with opposing biases increase confidence in results.

Purpose of the Study:

  • To illustrate the application of triangulation for enhancing causal inference in aetiological epidemiology.
  • To propose criteria for triangulation in epidemiological research.
  • To identify and integrate key sources of bias from various approaches within a framework.

Main Methods:

  • Developing a minimum set of criteria for triangulation in aetiological epidemiology.
  • Summarizing key bias sources for several epidemiological approaches.
  • Describing a framework for integrating results from different approaches.

Main Results:

  • Triangulation strengthens confidence in causal findings when results converge, especially if biases would predict opposite directions.
  • Understanding bias directions helps identify necessary further research when inconsistencies arise.
  • Considering exposure duration and timing is crucial when comparing results across studies.

Conclusions:

  • Triangulation is a valuable strategy for improving causal inference in aetiological epidemiology.
  • Explicitly stating expected bias directions and seeking opposing biases enhances triangulation effectiveness.
  • This framework aids in robustly addressing causal questions in public health research.