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Best practices in statistical computing
Ricardo Sanchez1, Beth Ann Griffin2, Joseph Pane3
1UnitedHealthcare, Minnetonka, Minnesota, USA.
Statistics in Medicine
|September 6, 2021
Summary
Implementing code quality assurance (QA) is crucial for researchers. Following best practices in coding, documentation, version control, data management, and testing ensures accurate and reproducible research findings.
Area of Science:
- Data Science
- Computational Statistics
- Research Methodology
Background:
- Increasing data complexity and advanced computational methods heighten the risk of errors in research code.
- Coding and data preparation mistakes can lead to substantial consequences, impacting study findings and reproducibility.
Purpose of the Study:
- To outline key steps for implementing a robust code quality assurance (QA) process for researchers.
- To enhance the reliability and reproducibility of data, code, analyses, and results in scientific studies.
Main Methods:
- Adherence to best practices in code writing and style.
- Clear written documentation of code, workflow, and analytical decisions.
- Implementation of rigorous version control, data management, and regular testing/review procedures.
Main Results:
- Following the proposed QA steps significantly improves the accuracy and reproducibility of research results.
- A systematic approach to code QA minimizes errors and enhances the integrity of scientific findings.
Conclusions:
- Code quality assurance is essential for reliable scientific research in an increasingly complex data landscape.
- Shared responsibility for code QA extends to researchers, institutions, journals, and funding agencies to uphold research integrity.
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