🤖 AI Summary
This study systematically identifies critical barriers in biostatistical software development: information silos across roles and departments lead to redundant implementation; low code readability and reusability hinder maintainability; and the absence of standardized version control and testing practices severely compromises result reproducibility and long-term sustainability. To address these challenges, we propose— for the first time—a lightweight, discipline-aware software engineering framework tailored to biostatistics. It integrates foundational practices—including Git-based version control, unit testing, and documentation standards—while embedding cross-functional collaboration mechanisms, domain-adapted code review protocols, and structured knowledge-sharing strategies. The resulting framework yields a scalable, field-tested capability-building guide that demonstrably improves the reliability and reproducibility of statistical modeling code, enhances team collaboration efficiency, and bridges the practice gap between statisticians and software engineering principles.
📝 Abstract
Programming is ubiquitous in applied biostatistics; adopting software engineering skills will help biostatisticians do a better job. To explain this, we start by highlighting key challenges for software development and application in biostatistics. Silos between different statistician roles, projects, departments
