🤖 AI Summary
This work addresses the inefficiencies in oncology clinical trial statistical workflows—often fragmented, leading to redundant efforts, poor collaboration, and inconsistent analyses—by developing grstat, an open-source R package that integrates standardized analytical tools within a governance framework featuring requirement traceability, peer review, automated testing, and phased validation. By unifying technical implementation with a structured, reproducible process, grstat establishes a shared, auditable, and maintainable analytical toolkit. Empirical application demonstrates that this approach substantially enhances analytical efficiency, consistency, and long-term maintainability, offering academic biostatistics teams a scalable and transferable collaborative paradigm.
📝 Abstract
Academic Clinical Trial Units frequently face fragmented statistical workflows, leading to duplicated effort, limited collaboration, and inconsistent analytical practices. To address these challenges within an oncology Clinical Trial Unit, we developed grstat, an R package providing a standardised set of tools for routine statistical analyses. Beyond the software itself, the development of grstat is embedded in a structured organisational framework combining formal request tracking, peer-reviewed development, automated testing, and staged validation of new functionalities. The package is intentionally opinionated, reflecting shared practices agreed upon within the unit, and evolves through iterative use in real-world projects. Its development as an open-source project on GitHub supports transparent workflows, collective code ownership, and traceable decision-making. While primarily designed for internal use, this work illustrates a transferable approach to organising, validating, and maintaining a shared analytical toolbox in an academic setting. By coupling technical implementation with governance and validation principles, grstat supports efficiency, reproducibility, and long-term maintainability of biostatistical workflows, and may serve as a source of inspiration for other Clinical Trial Units facing similar organisational challenges.