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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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Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

349
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...
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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.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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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.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Statistical Methods for Analyzing Epidemiological Data01:25

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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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Related Experiment Video

Updated: Nov 8, 2025

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
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Mitigating Biases in CORD-19 for Analyzing COVID-19 Literature.

Anshul Kanakia1, Kuansan Wang1, Yuxiao Dong1

  • 1Microsoft Research, Redmond, WA, United States.

Frontiers in Research Metrics and Analytics
|April 19, 2021
PubMed
Summary

The COVID-19 Research Dataset (CORD-19) has topical biases. Expanding the dataset reveals a broader, interdisciplinary research landscape, improving question-answering systems for pandemic research.

Keywords:
CORD-19Microsoft Academic Servicescitation analysisclosure graphdata biasesscientometrics

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Area of Science:

  • Bibliometrics
  • Data Science
  • Scientific Communication

Background:

  • The COVID-19 Research Dataset (CORD-19) was created to aid AI-driven research discovery.
  • Machine learning systems can exhibit biases due to training data limitations.
  • Ethical considerations necessitate bias detection in scientific datasets.

Purpose of the Study:

  • To develop a framework for examining biases in the CORD-19 dataset.
  • To compare CORD-19 properties with citation behaviors of the broader scientific community.
  • To assess the impact of dataset expansion on understanding COVID-19 research.

Main Methods:

  • Created three expanded datasets: CORD-19E (enclosure), CORD-19C (full closure), and CORD-19I (inflection closure).
  • Analyzed dataset properties by comparing them to citation network characteristics.
  • Evaluated topical coverage, interdisciplinarity, and collaboration patterns.

Main Results:

  • CORD-19 shows a bias towards recent, topically focused articles.
  • Expanded datasets reveal COVID-19 research spans a longer time frame and is highly interdisciplinary.
  • Collaboration patterns (team size, geography) are well-represented in the original CORD-19 dataset.

Conclusions:

  • Expanding the CORD-19 dataset provides a more comprehensive view of pandemic research.
  • A question-answering system utilizing expanded knowledge may offer improved performance.
  • While topical biases exist, CORD-19 effectively captures collaboration dynamics.