QC and Validation
This page outlines how I apply QC and validation practices to support accurate, traceable, and review-ready statistical and programming deliverables.
Why QC and validation matter
In regulated clinical workflows, quality is not only about getting the final result—it is about ensuring that results are:
- Correct
- Consistent
- Reproducible
- Traceable
- Reviewable
QC and validation practices help reduce rework, improve confidence in deliverables, and support efficient internal/external review.
What I focus on
QC-first execution
I prefer to build QC checkpoints into the process rather than relying only on end-stage review. This helps identify issues earlier and reduces delivery risk.
Validation discipline
I focus on validating key outputs, assumptions, and data/output consistency in a way that supports both technical confidence and reviewer clarity.
Traceability support
I maintain clear links across: - specifications / shells / SAP intent - datasets and derivations - output programs and final results
Review readiness
I organize deliverables so reviewers can quickly understand: - what was produced, - what was checked, - what issues were identified, - and how they were resolved.
Practical QC / validation activities
Examples of activities I commonly support include:
- Dataset and output consistency checks
- Population and label verification across outputs
- Shell-to-output alignment review
- Cross-checks between analysis definitions and reported results
- Review of formatting, footnotes, and interpretation-sensitive elements
- Validation summaries and issue tracking documentation
How I structure QC and validation work
1) Plan checks early
- Identify high-risk outputs/variables
- Clarify key review expectations
- Align checks to deliverable timelines
2) Perform targeted and repeatable checks
- Use standardized checks where possible
- Prioritize decision-critical outputs and values
- Document review scope and findings clearly
3) Resolve and document issues
- Track issues to closure
- Record rationale for decisions and corrections
- Maintain consistency across related deliverables after fixes
4) Prepare for review
- Organize files logically
- Make traceability easier for reviewers
- Summarize what was checked and any notable decisions
Common pitfalls I help prevent
- QC concentrated only at the end of production
- Inconsistent populations/results across tables and figures
- Weak documentation of review findings and resolutions
- Fixes applied in one output but not propagated consistently
- Deliverables that are hard for reviewers to navigate
Working style
My QC/validation approach is practical and risk-based:
- Focus first on what could materially affect interpretation or delivery
- Build consistency checks into the workflow
- Communicate issues early and clearly
- Keep documentation concise but audit-friendly
- Balance thoroughness with timeline discipline