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Related Concept Videos

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
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Strategies for Assessing and Addressing Confounding01:25

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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.
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Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

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Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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Group Design02:01

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Regression Toward the Mean01:52

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

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Body:Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to...
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Mendelian randomisation for mediation analysis: current methods and challenges for implementation.

Alice R Carter1,2, Eleanor Sanderson3,4, Gemma Hammerton3,4,5

  • 1MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK. alice.carter@bristol.ac.uk.

European Journal of Epidemiology
|May 7, 2021
PubMed
Summary

Mendelian randomization (MR) offers improved causal inference for mediation analysis, overcoming limitations of traditional methods. MR approaches like multivariable MR and two-step MR are robust to confounding and measurement error.

Keywords:
Mediation analysisMendelian randomisationMultivariable Mendelian randomisationTwo-step Mendelian randomisation

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Area of Science:

  • Epidemiology
  • Statistical Genetics
  • Causal Inference

Background:

  • Traditional mediation analysis faces challenges like confounding and measurement error.
  • Mendelian randomization (MR) utilizes genetic variants to enhance causal inference.
  • Existing MR methods require adaptation for mediation analysis.

Purpose of the Study:

  • To present and demonstrate two Mendelian randomization (MR) approaches for mediation analysis: multivariable MR (MVMR) and two-step MR.
  • To outline the assumptions, advantages, and potential issues of MR-based mediation analysis.
  • To provide practical guidance and code for implementing these methods.

Main Methods:

  • Description of multivariable MR (MVMR) for mediation analysis.
  • Description of two-step MR for mediation analysis.
  • Simulation studies and real-data examples to illustrate the methods.

Main Results:

  • MR mediation methods are robust to confounding and non-differential measurement error.
  • Simulations confirm the validity of MR approaches under specified assumptions.
  • Both MVMR and two-step MR are applicable to individual-level and summary-data MR.

Conclusions:

  • Mendelian randomization (MR) provides a powerful framework to improve causal inference in mediation analysis.
  • MR mediation methods offer advantages over traditional approaches, particularly in handling confounding and measurement error.
  • Careful consideration of MR assumptions is crucial for reliable mediation analysis.