StatDoc brings structure to flexibility — ensuring that even non-standard or exploratory datasets are built with the same attention to quality, transparency, and compliance as every other part of the submission lifecycle.
Every study has unique data challenges that don’t always fit within standard CDISC models. Exploratory endpoints, adaptive trial designs, real-world data integrations, and multi-study summaries often require flexible data structures to answer complex scientific questions. StatDoc designs and delivers derived and custom datasets that maintain the same rigor, traceability, and compliance expected in regulated deliverables — while allowing analytical freedom and scalability.
These datasets are purpose-built to meet evolving analytical needs and to support advanced modeling, integrated analyses, and future data re-use across programs.
Create custom datasets for exploratory endpoints, modeling studies, device data, and non-standard assessments. Develop derived datasets for cross-study or integrated analyses, ensuring alignment across different study designs. Extend standard CDISC structures thoughtfully to capture unique study requirements without compromising compliance.
Combine datasets from multiple studies or sources into consistent, comparable formats. Standardize variable naming, coding, and data structures for integrated safety and efficacy analyses. Build scalable data models supporting integrated summaries, meta-analyses, or long-term program evaluations.
Provide detailed mapping, derivation, and transformation documentation for every dataset. Maintain variable-level lineage from source data through derived outputs. Ensure transparent traceability for audit, replication, or re-analysis at any stage.
Perform comprehensive validation to confirm data accuracy, completeness, and alignment with analytical objectives. Apply independent QC to confirm numeric accuracy, derivation consistency, and record integrity. Verify that datasets integrate correctly with existing SDTM, ADaM, and reporting layers.
Design dataset frameworks adaptable for future endpoints, additional studies, or evolving regulatory expectations. Build reusable, parameter-driven programs to reduce turnaround for subsequent analyses. Support compatibility with visualization, statistical modeling, and machine-learning environments.