Traces
From Data to Bedside · the pathway walked on real cases
A trace takes a clinical recommendation that already exists and walks it back down the pathway to the measurement it rests on, checking at each rung whether it holds. Each trace is the same pathway walked on a real case, tagged to the rung it tests hardest and linked back to the pathway rung by rung, so a method above and its worked example here point at each other. They are grouped by the condition they concern, by ICD-10 code, with a methods group for the data-standards and trial work that no single condition owns.
Hypertension · I10All hypertension traces →
Measurement rung
The 120 mmHg systolic target
The 2017 ACC/AHA intensive target leans on the SPRINT trial, which measured blood pressure with automated, rested, averaged readings that run lower than the single manual cuff most clinics use. The trace walks the recommendation down to that measurement choice.
Acute ischemic stroke · I63All stroke traces →
Model & estimate rungs
Tenecteplase versus alteplase
The 2026 AHA/ASA guideline made the two thrombolytics co-equal first-line. The strongest head-to-head evidence is non-inferiority, not superiority, so the trace asks what a “not worse by more than a set margin” result, plus a logistical advantage, can and cannot support.
Type 2 diabetes · E11
Difference-in-differences
The $35 insulin cap · Part D
A two-way fixed-effects difference-in-differences on the IRA insulin cap: event study, parallel-trends defense, placebo years. End-to-end on real Part D data, measurement through defend-it.
Cardiometabolic risk · E88.81
Survey-weighted analysis
Cardiometabolic risk in NHANES
Survey-weighted prevalence and risk, multiple imputation, calibration versus AUC, and a Pooled Cohort Equations head-to-head. End-to-end on NHANES, framing through defend-it.
Methods
Data-standards and trial work that no single condition owns, each built end-to-end on real data.
Real-world data
Medicaid spending outliers
Peer-group robust z-scores, BH-FDR multiplicity, an isolation-forest second opinion, and a county cost atlas. Measurement through defend-it.
Trial data standards · SAS & R
CDISC clinical-trial programming
Double-programming an FDA-grade analysis package in SAS and R on the CDISC pilot data: SDTM to ADaM, a medication-level dataset, dictionary coding, tables, figures, and listings, and PROC COMPARE QC. Measurement through defend-it.
Survey methods · SAS
Survey data in SAS
Design-aware analysis with the SURVEY procedures: weights, strata, and clusters, plus skip-pattern handling and restructuring repeated measures between wide and long form.
More conditions and analyses are in progress, each traced on the same pathway: statin primary-prevention thresholds and the pooled-risk equations behind them, the glycemic targets in type 2 diabetes, and the screening-interval recommendations for breast and prostate cancer. They publish here as each one is fully worked and sourced.
Methodology frameworks
A separate strand of work develops methodological frameworks at the boundary of evidence synthesis and clinical AI evaluation. These are public drafts intended to be redlined.
Risk-of-bias appraisal for AI training corpora (v0.1). Adapting Cochrane RoB 2 / ROBINS-I logic to the text an LLM was actually trained on. Six bias domains with inline signaling questions and a stylized worked example end-to-end.
Additional frameworks (GRADE for AI-synthesized claims, PRISMA-style reporting checklist for clinical AI as evidence synthesizer) are in progress and will publish as v0.1 drafts when ready.
Methods essays
Shorter explainers on a single method, each tied to the pathway rung it belongs to.
When the hazard ratio misleads. Non-proportional hazards and the case for restricted mean survival time, connected to the survival tools in the Estimate rung. Read →
Across the traces
Read one at a time, a trace appraises a single recommendation. Read together, the traces start to show patterns: the rungs where guideline confidence and statistical support most often diverge, the measurement choices that quietly decide an estimate, the difference between a number that is precise and a number that is portable. As the set grows, this section will carry that synthesis: how a reader who understands both the clinical stakes and the statistics should weight the evidence behind a given recommendation.
That synthesis is methodological commentary, not clinical advice. It is about what the numbers can and cannot support, written for people who design studies, defend methods, and read the literature critically. It is not guidance for treating a patient, and it is not a substitute for the guidelines themselves or for clinical judgment.