Health Economic Modeling & Cost-Effectiveness Analysis
Build rigorous cost-effectiveness models for health policy decisions
What You Can Do
You can structure comprehensive health economic models from scratch, validate key assumptions against published literature, and synthesize evidence to support policy decisions. The skill helps you identify methodological gaps, document assumptions transparently, and calculate cost-effectiveness ratios with documented uncertainty ranges. You'll produce policy-ready analyses that clearly communicate the economic case for interventions to stakeholders.
Features
Create decision trees, Markov models, or agent-based frameworks tailored to your intervention and decision context
Systematically validate costs, outcomes, and transition probabilities against published studies and guidelines
Conduct one-way sensitivity analyses, probabilistic sensitivity analysis, and scenario modeling to test model robustness
Compute ICERs, NMBs, and cost-benefit ratios with transparent documentation of all assumptions and inputs
Maintain detailed assumption registers documenting source, rationale, and uncertainty for every parameter in your model
Synthesize disparate data sources (RCTs, observational studies, administrative claims) into consistent model inputs
Translate economic results into policy briefs with clear messaging about budget impact, equity implications, and implementation feasibility
Example Output
Example 1: Cost-Effectiveness Model Structure
Decision Tree for Screening Program
├── Screening (Cost: $500, Sensitivity: 92%)
│ ├── Disease Found (p=0.08)
│ │ ├── Early Treatment (Cost: $15,000, Effect: 3.2 QALYs)
│ │ └── Advanced Treatment (Cost: $45,000, Effect: 1.8 QALYs)
│ └── No Disease (p=0.92)
│ └── No Treatment (Cost: $0, Effect: 0 QALYs)
└── No Screening (Cost: $0)
├── Disease Progresses (p=0.08)
│ └── Late Treatment (Cost: $60,000, Effect: 0.9 QALYs)
└── No Disease (p=0.92)
└── No Treatment (Cost: $0, Effect: 0 QALYs)
ICER = $28,400/QALY (baseline scenario)
Example 2: Assumption Register
| Parameter | Base Case | Source | Range | Rationale |
|---|---|---|---|---|
| Screening Sensitivity | 92% | Smith et al. 2021 RCT | 85%-97% | Varies by population demographics |
| Program Cost per Screen | $500 | Regional invoice data | $400-$650 | Includes staff + supplies + overhead |
| Treatment Response Rate | 76% | Meta-analysis (12 studies) | 68%-84% | Confidence interval from meta |
| Discount Rate | 3% | WHO guideline | 0%-5% | Standard practice in health econ |
Example 3: Sensitivity Analysis Report
"Tornado diagram shows model is most sensitive to treatment response rate (±$12k ICER range) and discount rate (±$8k range). At 5% discount rate and 68% response, ICER rises to $38,200/QALY. Screening sensitivity has minimal impact. Recommend targeted research to reduce treatment response uncertainty."
What's Included
- Health Economic Model Templates: Ready-to-adapt frameworks for decision trees, Markov models, and budget impact models across common interventions
- Assumption Validation Checklist: Systematic checklist to review each model input against guidelines (NICE, WHO, ACE) and published literature
- Sensitivity Analysis Framework: Structured approach to one-way, two-way, and probabilistic sensitivity analyses with interpretation guidance
- Policy Brief Template: Template for translating economic findings into decision-maker-ready summaries with equity and feasibility considerations
- Literature Synthesis Workflow: Process for extracting, validating, and integrating data from multiple sources into consistent model parameters
Who It's For
- Health Economists
- Health Policy Analysts
- Public Health Officials & Planners
- Pharmaceutical & Medical Device Researchers
- Healthcare System Decision-Makers
Best For
- Building cost-effectiveness analyses for new health interventions
- Validating methodological assumptions in published models
- Preparing health economic evidence for policy decisions
- Synthesizing multi-source data into model parameters
- Communicating cost-effectiveness findings to non-technical stakeholders
You might also like
This skill helps you construct econometric forecasting models from time series data without writing code. You define variables, specify relationships, and Claude generates ARIMA, VAR, and regression models with diagnostic tests. Get confidence intervals, residual analysis, and publication-ready forecasts in minutes.
Estimate demand elasticity coefficients from your data, perform comprehensive sensitivity analyses across price points, and develop evidence-based pricing strategies grounded in microeconomic principles. You'll understand how price changes affect demand, model revenue and profit scenarios, and identify optimal pricing that maximizes both profitability and market positioning.
You can conduct rigorous condition assessments of historic building fabric, document material compositions and deterioration mechanisms, and translate field observations into technically sound restoration specifications that withstand peer review and agency approval. This protocol creates an evidence-based foundation for restoration decisions, particularly for complex masonry, timber, or plaster assemblies, and helps you justify material and technique choices for grant applications and heritage preservation projects.
This skill helps you design defensible policy evaluations from the ground up, diagnose threats to causal inference in your methodology, and translate complex econometric results into persuasive arguments for non-technical stakeholders. You get structured frameworks for evaluation design, systematic checklists for identifying methodological vulnerabilities, and communication strategies that build policy maker confidence in your findings.
You can systematize your analysis of environmental policies through rigorous economic frameworks, modeling cost-benefit scenarios and projecting long-term impacts with structured workflows. This skill automates the labor-intensive process of weaving policy objectives, economic theory, and quantitative modeling into coherent impact assessments. You'll produce defensible, evidence-based evaluations that translate environmental goals into measurable economic outcomes.
Receive actionable guidance on econometric model selection, specification testing, and uncertainty quantification for your economic forecasts. This skill helps you choose the right model (ARIMA, VAR, GARCH) based on your data characteristics, diagnose specification problems, validate assumptions, and communicate forecast uncertainty to stakeholders. You'll get structured workflows that turn raw economic data into defensible, statistically sound forecasts.
Develop econometric models with proper specification testing, validate all statistical assumptions, and generate point forecasts with confidence intervals and error diagnostics. You'll follow a systematic workflow that catches common pitfalls—autocorrelation, heteroscedasticity, multicollinearity, structural breaks—before your model reaches production. Every forecast comes with documented assumptions, sensitivity analysis, and a reproducible methodology.
Systematically diagnose market structures (perfect competition, monopolistic competition, oligopoly, monopoly), evaluate firm conduct and pricing strategies, and assess competitive positioning. You'll receive detailed frameworks to identify barriers to entry, estimate market power, and develop strategic positioning recommendations backed by microeconomic principles.