Validation, QC & Defensive Programming

How a derived clinical dataset earns a regulator’s trust. This series teaches the validation and quality-control discipline that sits between “the code ran” and “the dataset is submittable”: independent double programming with diffdf, defensive assertions, reproducibility, and R package qualification — each worked on public pharmaverse data, so every check runs.

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A derived clinical dataset is not trusted because the code ran without error. It is trusted because a second, independent programmer reproduced it and the two outputs matched exactly, because it checks its own assumptions while it runs, and because the environment that produced it is qualified and reproducible. That discipline — validation and quality control (QC) — is what turns a working script into a submittable deliverable. This series teaches it in R, the practical way, on public pharmaverse data so every check runs. The runtime is the judge.

The core idea is independent verification: the production programmer and the QC programmer work from the same specification but write separate code, and the two datasets must agree to the value. Zero differences means validated. The arc:

1. QC by double programming with diffdf — the industry-standard pattern for validating a derived dataset. Independently re-derive a key variable, compare with diffdf(), read the difference report, catch a seeded discrepancy, fix it, and iterate to zero.
2. Defensive programming and assertions — make a script fail loudly and early when its input data violates expectations. Assert the structure, types, ranges, uniqueness, and allowed values of an ADaM dataset before you compute, with base stopifnot() plus the checkmate and assertthat packages.
3. Reproducibility with renv — make the analysis reproduce exactly on another machine and years later. Pin every package version in a project-local library and a committed renv.lock, and understand why a reproducible, qualified environment is part of a submission’s audit trail.
4. An auditable SOP for AI-assisted programming — use AI assistants to draft clinical R code without compromising submission integrity: treat AI output as unqualified until it passes the same QC gates, and keep a documented prompt → draft → review → approved audit trail, grounded in GAMP-style validation and the FDA’s draft risk-based credibility principles.
5. R package qualification with riskmetric — answer the regulator’s question “how do you know the R packages you depend on are fit for use?” Score an installed package’s risk with the R Validation Hub’s riskmetric — maintenance, documentation, testing, and activity metrics — read the results honestly (including which metrics need network and return NA in a sealed validated environment), and fit it into a package-qualification SOP.

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