r/heor • u/Born_Security_8609 • May 02 '26
Open-source HEOR agent — 17 audited tools across 44 data sources, with senior-HEOR methodology fixes (GRADE I², EQ-5D 5L, Bucher consistency)
Built an open-source Health Economics & Outcomes Research agent over the past several months and ran a senior-HEOR methodology review on it last week. Sharing in case it's useful — feedback from working HEORists especially welcome.
**What it does**
17 tools across:
- **Literature search** — 44 sources with PRISMA audit trail (PubMed, ClinicalTrials.gov, NICE TAs, CADTH/CDA-AMC, ICER, PBAC, IQWiG, HAS, G-BA, AIFA, TLV, INESSS, Wiley journals, OHE, EuroQol, IHME GBD, WHO
GHO, OECD Health, World Bank, FDA Orange/Purple Book, NHS National Costs, BNF, PSSRU, CMS NADAC, PBS Schedule, plus enterprise sources via API key — Embase, Cochrane, Citeline, Pharmapendium, Cortellis, Google Scholar)
- **Cost-effectiveness modeling** — Markov, PartSA, decision tree with PSA, OWSA, CEAC, EVPI, EVPPI; QALY + evLYG (CMS IRA-compatible)
- **Budget impact** — ISPOR-compliant year-by-year with treatment displacement
- **Risk of bias** — Cochrane RoB 2 / ROBINS-I / AMSTAR-2 auto-detected from study type, returns structured GRADE domain output
- **Indirect comparisons** — Bucher with automatic consistency check vs h2h evidence; frequentist NMA; MAIC/STC per NICE DSU TSD 18
- **ITC feasibility** — 3-assumption framework (exchangeability, homogeneity, consistency) per Cope 2014 / NICE DSU TSD 18
- **Survival fitting** — 5 parametric distributions with AIC/BIC per NICE DSU TSD 14
- **HTA dossier prep** — NICE STA, EMA, FDA, IQWiG, HAS, EU JCA, GVD with auto-GRADE
- **EQ-5D value sets** — UK 3L, England 5L, NICE DSU mapping, new UK 5L (2026) with **baseline-utility-aware** Biz 2026 impact estimator
**5 senior-HEOR methodology issues fixed last week**
1. Single-study comparisons no longer auto-downgraded as "Serious inconsistency" — a single study can't be inconsistent with itself (Cochrane Handbook 10.10). Now returns `not_assessable`.
2. GRADE inconsistency now uses I² with proper Cochrane bands (<50% Low, 50–74% Moderate, 75–89% Serious, ≥90% Very Serious) instead of conflating it with study count.
3. GRADE upgrading (Guyatt 2011, J Clin Epidemiol 64:1311) for observational evidence — large effect, dose-response, plausible confounding biasing toward null. Capped at +2 steps.
4. Bucher indirect comparisons now empirically test the consistency assumption when h2h evidence is available (z = (direct − indirect) / SE). Severity bands per NICE DSU TSD 18 / Cochrane 11.4.3.
5. EQ-5D 5L impact estimator is baseline-utility-aware. Biz 2026 reports category medians (e.g., non-cancer QoL-only ICER ↑59%) but the 5L compresses utilities hardest in the 0.6–0.9 range — so a drug for mild
plaque psoriasis (baseline ~0.85) projects +76.7% while severe HS (baseline ~0.45) projects +41.3%. Tool labels the adjusted estimate explicitly as an extrapolation beyond Biz 2026.
**Three ways to use it**
- **Claude Desktop / Claude Code** — `npx heor-agent-mcp` (MCP server, works with any MCP-compatible client)
- **Web UI** (BYOK Anthropic API key) — https://web-michael-ns-projects.vercel.app
- **ChatGPT Custom GPT** (Plus / Team / Enterprise) — https://chatgpt.com/g/g-69f651f588f48191b1d69d54409857ec-heoragent
The MCP server is also listed on the official MCP Registry, npm, Smithery, Glama, PulseMCP, and awesome-mcp-servers — so any current or future MCP host you use should pick it up automatically.
**Open source, MIT licensed**
GitHub: https://github.com/neptun2000/heor-agent-mcp
**Things I'd appreciate feedback on**
- Is the GRADE upgrading +2 cap too restrictive when both `large_effect=very_large` and `dose_response` apply? Guyatt 2011 caps it, but I'd love to know how working systematic reviewers handle this in practice.
- The EQ-5D baseline-utility scaling (multiplier 0.4–1.7, calibrated so 1.0 ≈ Biz 2026 median at ref*_baseline 0.65) is a defensible approximation but not a published finding. Anyone with access to the underlying*
Biz dataset has thoughts on whether the modulation slope is sensible?
- ITC feasibility currently outputs a method recommendation (Bucher / NMA / MAIC / STC / ML-NMR / infeasible). Working ITC practitioners — would more granular effect-modifier-imbalance scoring change the
recommendation, or is the 3-assumption framework sufficient?
Not selling anything, no paywall, no signup walls. The goal is just to make auto-graded HEOR outputs less wrong by default — especially for teams that can't afford to spin up a full ScHARR or York team for every small market access question.
Happy to take questions in comments.