Regulatory-Grade R to XPT Pipeline with Attribute Control for CDISC-Compliant Clinical Trial Data Exchange

Abstract

The exchange of clinical trial data in regulatory submissions increasingly relies on the Transport format (XPT) as specified by the Clinical Data Interchange Standards Consortium (CDISC). While the R programming environment offers powerful tools for data transformation and analysis, the generation of XPT files that meet regulatory-grade requirements for metadata fidelity, attribute preservation, and compliance auditing remains a significant challenge. This paper presents a comprehensive pipeline architecture that converts R data frames into CDISC-compliant XPT files while enforcing strict attribute control over variable labels, formats, lengths, and associated metadata. We examine the structural trade-offs between the flexibility of R’s data structures and the rigid specifications of CDISC standards, particularly with respect to character encoding, missing value representation, and dataset-level metadata. The proposed pipeline integrates modular validation checkpoints, automated attribute mapping, and versioned governance workflows that align with both Food and Drug Administration (FDA) submission guidelines and international regulatory frameworks. Infrastructure considerations such as containerized deployment, continuous integration for validation, and scalability to large multi-study datasets are discussed from an operational perspective. Robustness is achieved through configurable rule engines that detect and remediate attribute drift, while fairness in data representation is addressed by ensuring consistent handling of sparse or incomplete trial data across heterogeneous sources. Policy implications include audit trail requirements, reproducibility mandates, and the evolving role of machine-readable metadata in regulatory review processes. Cross-domain comparisons with financial and geospatial data exchange standards provide insight into broader socio-technical lessons. The paper concludes with forward-looking perspectives on the integration of artificial intelligence for automated attribute inference and the potential for real-time compliance feedback. This work provides a foundational framework for researchers and practitioners seeking to operationalize regulatory-grade data exchange pipelines within open-source statistical environments.