Topic
12 posts
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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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A complete ADaM derivation built from composable bricks: the derive_* mental model, ADSL and BDS patterns, and why company templates sit on top of admiral instead of replacing it.
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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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AI pair programmers for ADaM, LLM-assisted tables, QC drafting — the production case ledger from 2024-2026, and the exact wall each case hit when the rubber met validation.
ADTTE step by step: event and censoring definitions from the SAP, the censoring date cascade, CNSR semantics, partial dates at the event, and QC listings per subject.
▶ video ✎ interactive lesson
ADAE from the OCCDS side: one row per event, the AE-to-ADSL merge, treatment-emergent flags driven by TRT01SDT, serious flags, and the QC defects reviewers catch.
▶ video ✎ interactive lesson
The BDS skeleton behind ADaM analysis datasets: PARAM/PARAMCD/AVAL, baseline flags, change from baseline, and how ADVS and ADLB are built visit by visit.
▶ video ✎ interactive lesson
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.
CDISC CORE is a free, open-source rule engine for SDTM and ADaM validation. How its YAML rules work, how to run it, and where rule-based checking stops.
Independent QC re-programming costs 30–50% of clinical programming effort. An AI framework matched 97.1–100% of variables while keeping independence intact.
Schema-only synthetic ADaM generation plateaus at 0.45 overall quality; enriching schemas from protocol/SAP/CRF knowledge graphs plus templates reaches 0.70.
#synthetic-data#adam#knowledge-graphs#llm#statistical-programming
Base Llama 3.1 8B scores 0.36 on admiral code generation. LoRA fine-tuning plus knowledge-graph validation gets it to 0.82 — without sending data to an API.