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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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Related Experiment Video

Updated: Feb 24, 2026

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Reproducing Epidemiologic Research and Ensuring Transparency.

Steven S Coughlin

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    |August 24, 2017
    PubMed
    Summary

    Ensuring epidemiologic study reproducibility involves sharing data and software. A new quasi-reproducible method allows sharing statistical methods and code for sensitive data, enhancing transparency and collaboration.

    Area of Science:

    • Epidemiology
    • Biostatistics
    • Data Science

    Background:

    • Reproducibility in epidemiologic studies is crucial for verifying findings and ensuring data integrity.
    • Recent advancements include global data-sharing platforms and updated policies from major health organizations.
    • Challenges remain, particularly with sensitive data that cannot be openly shared due to legal or ethical constraints.

    Purpose of the Study:

    • To outline a pragmatic approach for achieving reproducible research with sensitive data.
    • To introduce a quasi-reproducible method that facilitates sharing of statistical methods and code.
    • To enhance transparency and collaboration in epidemiologic research.

    Main Methods:

    • Discusses the importance of data and software availability for verifying epidemiologic study findings.
    Keywords:
    clinical trialsconfidentialityde-identificationprivacyreproducible research

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  • Highlights recent developments in data sharing, including global platforms and institutional policy updates.
  • Introduces a quasi-reproducible approach for studies with sensitive data, focusing on disseminating methods and code.

    • Main Results:

    • The proposed quasi-reproducible approach enables the dissemination of statistical methods and code for sensitive data.
    • This method allows independent researchers to scrutinize statistical approaches without accessing raw data.
    • Both full reproducibility and quasi-reproducibility enhance critical evaluation and idea exchange.

    Conclusions:

    • Reproducibility and quasi-reproducibility are vital for increasing transparency in epidemiologic research.
    • The quasi-reproducible approach offers a practical solution for studies involving sensitive data.
    • Facilitating the sharing of methods and code accelerates scientific progress and fosters collaboration.