Improving Rigor and Transparency in Observational Research
Observational studies often require substantial data exploration before formal analyses begin. Decisions regarding data quality, missing data, variable coding, eligibility criteria, and analytical methods can influence study results. The SAPI project addresses this gap by creating a structured framework that:
- Integrates Initial Data Analysis (IDA) into the statistical analysis planning process.
- Encourages transparent documentation of data issues and analytical decisions.
- Tracks deviations from original analysis plans.
- Promotes reproducibility, transparency, and research integrity in observational studies.
The guideline is being developed using a rigorous consensus process (Delphi). The resulting checklist reflects CSTAT's commitment to reproducible research, statistical rigor, and transparent data analysis. By advancing best practices for documenting analytical decisions, the project supports higher-quality research across clinical, public health, environmental, and biomedical sciences. The SAPI checklist is available here: Link
