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Related Concept Videos

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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:
Causality in Epidemiology01:21

Causality in Epidemiology

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...
Introduction to Epidemiology01:26

Introduction to Epidemiology

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,...
Bias01:22

Bias

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...
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

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 case-control studies.
Observational Studies01:11

Observational Studies

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.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One example of...

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Oral Bacterial Infection and Shedding in Drosophila melanogaster
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Potential biases in observational infectious disease studies: examples using directed acyclic graphs.

Erlangga Yusuf1, Oksana Martinuka2, Frits R Rosendaal3

  • 1Department of Medical Microbiology and Infectious Diseases, Erasmus MC, Rotterdam, The Netherlands.

Clinical Microbiology and Infection : the Official Publication of the European Society of Clinical Microbiology and Infectious Diseases
|May 30, 2026
PubMed
Summary

This review explains common biases in infectious disease research and introduces directed acyclic graphs (DAGs) to identify and mitigate them. It serves as a guide for improving study design and critical appraisal.

Keywords:
BiasObservational studiesRandomized control trialsSelection biasTime-related bias

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Area of Science:

  • Epidemiology
  • Medical Research Methodology

Background:

  • Observational studies in infectious diseases are susceptible to systematic errors (bias) due to non-randomization and reliance on existing data.
  • Bias compromises the internal validity and reliability of research findings in this field.

Purpose of the Study:

  • To present a comprehensive overview of prevalent biases in observational infectious disease research.
  • To introduce directed acyclic graphs (DAGs) as a tool for bias identification.
  • To outline practical strategies for mitigating identified biases.

Main Methods:

  • A narrative review approach was employed, drawing from authors' editorial, review, and clinical experiences.
  • Literature search conducted via PubMed and recent reviews from related medical fields.
  • Biases categorized into selection, time-related, and information bias, with examples from infectious disease studies.

Main Results:

  • Common biases in observational infectious disease research were identified and categorized.
  • Directed acyclic graphs (DAGs) are presented as a conceptual framework for understanding and visualizing bias.
  • Specific examples and strategies for detecting and reducing bias are provided.

Conclusions:

  • This paper serves as an introductory guide for clinicians, researchers, and reviewers.
  • Aims to enhance critical appraisal skills and inform better study design in infectious disease research.
  • Acknowledges that the review does not encompass all potential biases.