Quantitative scientists such as data scientists, biostatisticians, epidemiologists, and bioinformaticians, among others, are expected to exhibit rigor and reproducibility in their contributions to science. This includes ensuring data are fit for purpose, free from bias, measured with known uncertainty, that analyses are traceable to the data, and reporting is sufficient to reproduce the results. Here we provide information on best practices for rigor and reproducibility, including:
- Team and Project Management
- Data Collection and Management
- Clinical Application and Impact
- Clinical Trial Management
- Reporting Guidelines
- Ethical Guidelines for Statistical Practice
Duke BERD offers consultations to develop analytic best practice implementation plans that utilize existing guidance. For more information or to submit other resources that should be included on this page, please contact berdcore@duke.edu.
*Access to publications listed below may require authentication through your academic institution to be accessed free of charge.
Team and Project Management
Data Collection and Management
Clinical Application and Impact
- All models are wrong and yours are useless: Making clinical prediction models impactful for patients npj Precision Oncology (2024)
Clinical Trial Management
- The evolving role of data & safety monitoring boards for real-world clinical trials. Journal of clinical and translational science, 7(1), e179. JCTS (2023)
- Recommendations for data monitoring committees from the Clinical Trials Transformation Initiative. Clinical trials (London, England), 14(4), 342–348. Clinical Trials (2017)
- Guidance on interim analysis methods in clinical trials JCTS (2023)
- World Health Organization's Operational Guidelines for the Establishment and Functioning of Data and Safety Monitoring Boards
- Food and Drug Administration’s Guidance for Clinical Trial Sponsors: Establishment and Operation of clinical Trial Data Monitoring Committees
- European Medicines Agency Committee for Medicinal Products for Human Use (CHMP) Guidelines on Data Monitoring Committees
- Society of Clinical Trials - Data Monitoring Committee (DMC) Training
- ICH Good Clinical Practice (GCP) E6(R3)
Reporting Guidelines
The EQUATOR Network (Enhancing the QUAlity and Transparency Of health Research) is a global initiative aimed at improving the reliability and value of health research literature by promoting transparent and accurate reporting. It provides access to a comprehensive collection of reporting guidelines for various study types, including randomized trials (CONSORT), observational studies (STROBE), systematic reviews (PRISMA), and more.
Ethical Guidelines for Statistical Practice
The American Statistical Association’s Ethical Guidelines for Statistical Practice are intended to help statistical practitioners with ethical reasoning. The guidelines are intended for individuals, but these principles are also relevant to organizations that engage in statistical practice.
Examples and Implementation Plans
COMING SOON….