Becoming a Clinical Statistical Programmer in 2026: A Realistic Guide
What the job is, who gets hired, and how to train for it: a realistic 2026 guide to becoming a clinical statistical programmer, from SAS base to AI-assisted practice.
Topic
#cdisc
11 posts
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The 2026 CDISC AI Challenge: Winners, Open Code, and a Synthetic ADaM Showdown
The 2026 CDISC AI Innovation Challenge winners present today in Denver. None published code — except one adjacent R package. We read synadam's source and compare it to our own synthetic ADaM pipeline.
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Clinical R in Practice: The Open-Source Stack in Pharma
A 15-part series on R in pharma: pharmaverse ADaM, ARD tables, risk-based validation, targets pipelines, GxP Shiny, LLM agents — collected into a free bilingual book.
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From SDTM to Submission: The Clinical Data Flow, End to End
One map of every dataset, standard, and hand-off between the clinic and the regulator — SDTM, ADaM, define.xml, TLFs, and the submission package — and where R now sits at each stage.
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From Natural Language to CDISC Datasets
NL-to-CDISC generation, LLM-built analysis apps, auto slide decks — how close is the fully automatic submission, and which bottleneck is regulatory rather than technical?
SDTM Domain Basics: The Model Every Clinical Programmer Must Know
SDTM domains explained: domain classes, the topic-timing-qualifier variable pattern, USUBJID, controlled terminology, SUPPQUAL, and a day-one reading order.
▶ video ✎ interactive lesson
ADSL Derivation Walkthrough: TRT01SDT, Flags, and the Merge Traps
How ADSL is built: deriving treatment dates and population flags from DM/EX/DS/SV, the one-row-per-subject rule, and the QC checks that catch real discrepancies.
▶ video ✎ interactive lesson
The Clinical SP Bootcamp: A Learning Path from Raw Data to TLF
A systematic path into clinical statistical programming: CDISC fundamentals, modern SCE workflow, and where AI agents fit — every part with runnable takeaways.
▶ video ✎ interactive lesson
Clinical SAS Interview Questions: What Actually Gets Asked
How clinical SAS interviews actually work: four rounds from SAS mechanics to GxP habits, real-format questions, and the signals strong answers contain.
One Number That Explains Where Rule-Based Validation Stops
69.7% of oncology CORE rules port cleanly to a graph constraint language. The 30% that don't are exactly where the dangerous errors live.
The Contradictions Your Validator Can't See
Rule-based SDTM validators structurally miss cross-domain contradictions. SHACL-SPARQL graph constraints plus a deterministic agent layer catch all 20 archetypes.