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Related Concept Videos

Causality in Epidemiology01:21

Causality in Epidemiology

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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...
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

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The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
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What is an Experiment?01:12

What is an Experiment?

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An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
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Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
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Observational Studies01:11

Observational Studies

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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
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Related Experiment Video

Updated: Mar 16, 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

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Mendelian Randomization as an Approach to Assess Causality Using Observational Data.

Peggy Sekula1, Fabiola Del Greco M2, Cristian Pattaro2

  • 1Division of Genetic Epidemiology, Institute for Medical Biometry and Statistics and ps@imbi.uni-freiburg.de.

Journal of the American Society of Nephrology : JASN
|August 4, 2016
PubMed
Summary

Mendelian randomization uses genetic variants to assess if an exposure causally affects an outcome. This method overcomes limitations of observational studies, offering a cost-efficient way to explore causal relationships.

Keywords:
causalitymendelian randomizationstatistical method

Related Experiment Videos

Last Updated: Mar 16, 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

15.4K

Area of Science:

  • Epidemiology
  • Genetics
  • Biostatistics

Background:

  • Observational studies often show biased associations due to confounding or reverse causality.
  • Randomized controlled trials are not always feasible for establishing causality.
  • Mendelian randomization (MR) offers a robust alternative to assess causal inference.

Purpose of the Study:

  • To provide an overview of the Mendelian randomization method.
  • To discuss assumptions, implications, and applications of MR.
  • To highlight specific considerations for MR studies in nephrology.

Main Methods:

  • Utilizes genetic variants as instrumental variables for a modifiable exposure.
  • Leverages the random allocation of alleles, akin to randomized controlled trials.
  • Employs publicly available genetic association data to identify suitable instrumental variables.

Main Results:

  • An association between genetic instrumental variables and an outcome supports a causal link between the exposure and outcome.
  • MR is a time- and cost-efficient approach for causal inference.
  • Illustrative examples and special considerations in nephrology are discussed.

Conclusions:

  • Mendelian randomization is a powerful tool for causal inference when RCTs are not feasible.
  • The method addresses confounding and reverse causality inherent in observational studies.
  • MR presents significant opportunities for research in kidney function and disease.