ADaM
TipWhat this section covers
This section presents ADaM-focused work including analysis dataset design, derivation traceability, validation/QC workflows, and submission-supporting documentation (including ADRG).
Overview
ADaM enables reproducible statistical analysis and direct support for TLF production. My ADaM approach emphasizes:
- Analysis readiness aligned to SAP/planned analyses
- Traceable derivations from SDTM to analysis variables
- Clear population and endpoint definitions
- QC and validation discipline
- Submission-ready documentation and reviewer support
The focus is on building datasets that are scientifically useful, operationally reliable, and easy to audit.
What you’ll find in this section
ADaM Overview
- ADaM strategy and analysis-use framing
- Relationship to SAP/TLF planning
- Traceability and reproducibility mindset
Domain Examples
- ADSL
- ADAE
- ADVS
- ADTTE
- Example derivation and analysis-use considerations
Submission and Documentation Support
- Submission package organization
- Define metadata support
- ADRG preparation and reviewer-oriented explanations
Build Quality, Programs, Validation, and QC
- Program organization and derivation flow
- Validation and consistency checks
- QC approaches for datasets and analysis variables
- Review and issue-resolution practices
Standards
- ADaM standards alignment and implementation consistency
- Practical standards interpretation across studies/endpoints
Representative ADaM contributions
- Translate SAP analysis intent into reproducible analysis datasets
- Build derivations with explicit traceability to source/SDTM inputs
- Support QC and validation reviews with documented rationale
- Prepare ADaM/ADRG deliverables for reviewer-friendly navigation
Working style
My ADaM workflow is analysis-driven and traceability-first:
- Start from analysis questions and TLF needs
- Define derivation logic clearly and consistently
- Validate key variables and populations early
- Package outputs and documentation for efficient review