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Best practices in statistical computing.

Ricardo Sanchez1, Beth Ann Griffin2, Joseph Pane3

  • 1UnitedHealthcare, Minnetonka, Minnesota, USA.

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|September 6, 2021
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
data managementmethodologyversion control

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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.