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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

9.3K
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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Introduction to Epidemiology01:26

Introduction to Epidemiology

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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,...
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Study Designs in Epidemiology01:20

Study Designs in Epidemiology

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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...
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Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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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:
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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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Law of Independent Assortment02:03

Law of Independent Assortment

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While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
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Related Experiment Video

Updated: Mar 19, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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[Mendelian randomisation - a genetic approach to an epidemiological method].

Mats Julius Stensrud1

  • 1Avdeling for biostatistikk Oslo Centre for Biostatistics and Epidemiology (OCBE) Universitetet i Oslo.

Tidsskrift for Den Norske Laegeforening : Tidsskrift for Praktisk Medicin, Ny Raekke
|June 22, 2016
PubMed
Summary

Mendelian randomisation uses genetic variants to investigate disease causes from observational data. This method overcomes confounding and reverse causation, offering a powerful tool for causal inference in genetic research.

Related Experiment Videos

Last Updated: Mar 19, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • Advancements in genetic information accessibility and analytical methods.
  • Mendelian randomisation leverages random gene variant distribution during meiosis for causal inference.
  • Utilizes genetic variants influencing risk factors but not the disease directly.

Purpose of the Study:

  • To explain the principles of Mendelian randomisation.
  • To present the applications of this genetic methodology.
  • To review existing literature on Mendelian randomisation.

Main Methods:

  • Systematic review of methodology articles on Mendelian randomisation.
  • Searches conducted in PubMed and McMaster Plus databases.
  • Inclusion of supplementary clinical studies identified through PubMed.

Main Results:

  • Mendelian randomisation studies are robust against confounding and reverse causation, unlike traditional observational studies.
  • The methodology has yielded significant insights into disease risk factors.
  • Acknowledges the inherent limitations of the Mendelian randomisation approach.

Conclusions:

  • Mendelian randomisation is a promising tool for establishing causality in disease research.
  • Properly conducted studies can identify both modifiable and non-modifiable disease causes.
  • Expected widespread adoption of Mendelian randomisation due to rapid genetic research progress.