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Related Concept Videos

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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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:  
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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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Frustration and Conflict: Avoidance-Avoidance, Double-Approach Avoidance01:14

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Avoidance-avoidance conflict refers to a psychological situation where a person must choose between two or more unpleasant alternatives. These conflicts are particularly stressful because neither option is desirable. This dilemma is often expressed in sayings like "caught between a rock and a hard place" or "between the devil and the deep blue sea." For instance, individuals who fear dental procedures may find themselves torn between enduring a painful toothache or facing the...
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Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
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Diode: Forward bias01:20

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In semiconductor devices, diodes play a crucial role in directing current flow, and its operation is primarily categorized into forward bias and reverse bias. A diode is said to be forward-biased when its p-type region is connected to the positive terminal of a battery and its n-type region is linked to the negative terminal. This configuration reduces the potential barrier within the diode, allowing current to flow easily from the p to the n-type region.
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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
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Avoiding selection bias in metabolomics studies: a tutorial.

S C Boone1, S le Cessie2,3, K Willems van Dijk4,5,6

  • 1Department of Clinical Epidemiology, Department C7-P, Leiden University Medical Center (LUMC), PO Box 9600, 2300 RC, Leiden, The Netherlands. s.c.boone@lumc.nl.

Metabolomics : Official Journal of the Metabolomic Society
|March 5, 2019
PubMed
Summary
This summary is machine-generated.

Selection bias can skew metabolomics findings in epidemiologic studies. Inverse probability weighting offers a solution for unbiased estimation in secondary analyses of selected populations.

Keywords:
Collider biasEpidemiologyInverse probability weightingMetabolomicsSelection bias

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Area of Science:

  • Epidemiologic Research
  • Metabolomics
  • Biostatistics

Background:

  • Metabolomics assays are costly, leading to small study populations and secondary analyses.
  • Selection criteria in original studies can introduce bias in these secondary analyses.
  • Unaccounted selection can lead to misleading results in metabolomics research.

Purpose of the Study:

  • To theoretically introduce selection bias in metabolomics.
  • To demonstrate selection bias using a nested case-control study.
  • To evaluate methods for mitigating selection bias in metabolomics.

Main Methods:

  • Theoretical introduction to selection bias.
  • Demonstration of bias in metabolomics data from a nested case-control study.
  • Evaluation of standard analytical methods versus inverse probability weighting.

Main Results:

  • Standard regression adjustments can induce bias in selected metabolomics data.
  • Inverse probability weighting (survey weighting) can provide unbiased estimates.
  • Selection bias is a critical concern in secondary metabolomics analyses.

Conclusions:

  • Careful consideration of selection bias is crucial in metabolomics.
  • Inverse probability weighting is a viable method to address selection bias.
  • Biostatistical methods must be adapted for metabolomics in selected populations.