Transforming Daily Diary Data to CDISC Compliant Datasets: A Methodological Approach

Daily diary data offers valuable insights into patient experiences but presents unique challenges when transforming into Clinical Data Interchange Standards Consortium (CDISC) compliant formats. This transformation involves mapping temporally dense observations as discrete visits in the Study Data Tabulation Model (SDTM) and converting them into analytical cycles in the Analysis Data Model (ADaM) datasets. Traditional approaches often result in validation issues, extended programming timelines, and regulatory queries due to structural misalignments with CDISC frameworks originally designed for visit-based paradigms. The implementation of strategic intermediate datasets bridges these structural gaps while maintaining data integrity and regulatory acceptability. This innovative technique demonstrates substantial improvements across validation metrics, programming efficiency, and regulatory timelines. Validation testing confirms CDISC compliance despite the unconventional nature of diary data structures, with marked reductions in critical findings and information requests. The resulting datasets support robust endpoint analysis with enhanced statistical power while maintaining clear traceability from raw data to final results, ultimately improving submission quality and accelerating regulatory approval processes for clinical trials incorporating patient-reported outcomes through daily diary collection methods.