This course introduces the principles and practices required to conduct rigorous and reproducible research across the translational spectrum. Rigor and reproducibility are quite appropriately receiving greater emphasis across all levels of research, and are receiving greater attention from scientific journals and funders of research alike. At the National Institutes for Health (NIH), rigor and reproducibility are being promoted in their guidance to grant applicants as well as grant reviewers (https://grants.nih.gov/reproducibility/index.htm). The NIH is in fact implementing policies “requiring formal instruction in scientific rigor and transparency to enhance reproducibility for all individuals supported by institutional training grants, institutional career development awards, or individual fellowships.” (https://grants.nih.gov/grants/guide/notice-files/NOT-OD-16-034.html). Thus, it is essential that researchers understand best practices in research to ensure rigor and reproducibility of their research. In this course, students learn these best practices, including sound study planning and design, consideration of all relevant biomedical variables, sound data management practices, statistical considerations and techniques, and transparency in reporting research results.
- Understand the importance of rigor and reproducibility in research across the translational spectrum
- Identify key characteristics, strengths, and weaknesses of various study designs necessary to ensure scientific rigor
- Recognize key biomedical variables necessary for a given research question
- Implement best practices in data collection and management
- Understand the importance of selecting appropriate data analysis techniques to ensure reproducible results
- Report and present results from a research study in a fully transparent manner
PREREQUISITES: GMS 6861 (Applied Biostatistics I), or equivalent
FORMAT: Online, Synchronous (through Canvas)
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