Pharmacoeconomic-evaluation

This skill provides comprehensive guidance and tools for conducting pharmacoeconomic evaluations including cost-effectiveness analysis (CEA), cost-utility an...

Security Scan

The skill's code, documentation, and runtime instructions are coherent with a pharmacoeconomic evaluation toolset and do not request unrelated credentials or perform suspicious I/O; minor documentation/manifest inconsistencies are present but not security-critical.

Purpose & Capability

The name, description, SKILL.md and the included Python scripts (CEA, BIA, Monte Carlo) align: the skill provides calculation and simulation tools for pharmacoeconomic analyses. There are no unexpected required binaries, environment variables, or credentials.

Instruction Scope

SKILL.md instructs use of the scripts in scripts/, and the scripts are implementation-consistent and do not reference network endpoints or system credentials. However, SKILL.md and README reference additional files (references/model_methods.md, scripts/example.py) that are not present in the provided manifest — a documentation/packaging inconsistency that could lead to missing examples or references at runtime.

Install Mechanism

No install spec is declared (instruction-only), and the repository contains only plain Python scripts with a requirements.txt listing common numeric libraries (numpy, pandas, scipy, tqdm). No external downloads, obscure URLs, or archive extraction are used.

Credentials

The skill does not request any environment variables, credentials, or config paths. The operations in the code are local numeric computations and do not access system secrets or external services.

Persistence & Privilege

The skill does not request 'always: true' and uses default agent invocation settings. There is no indication it modifies other skills or system-wide agent configuration.

Assessment

This skill appears coherent and code-only: it provides local Python scripts for pharmacoeconomic analyses and does not ask for credentials or perform network I/O. Before installing or running, consider: (1) run it in an isolated environment (virtualenv/container) and install requirements.txt; (2) be aware Monte Carlo/PSA routines can be CPU/memory intensive for large simulation counts; (3) the package references additional docs/examples that are missing from the manifest — you may want to request the missing reference files or examples from the publisher; (4) review the scripts if you will run them on sensitive patient data to ensure they don't include unintended logging or export of PII; and (5) if you need automated/autonomous invocation by an agent, limit input data and test with synthetic datasets first.

Pharmacoeconomic Evaluation Skill

Overview

This skill provides comprehensive guidance for conducting pharmacoeconomic evaluations, including cost-effectiveness analysis, cost-utility analysis, cost-benefit analysis, budget impact analysis, sensitivity analysis, and model construction. Following the Chinese Pharmacoeconomic Evaluation Guidelines (2023 Edition), it provides complete workflows, calculation tools, and reference materials for economic evaluation of healthcare interventions.

Evaluation Types

Choose the appropriate evaluation type based on research objectives and data characteristics:

• Cost-Effectiveness Analysis (CEA): Use when intervention effect can be measured by a single clinical indicator (e.g., life years, survival rate)
• Cost-Utility Analysis (CUA): Use when both quality and quantity of life need to be considered; outcome measure is QALYs
• Cost-Benefit Analysis (CBA): Use when intervention effects can be expressed in monetary terms
• Cost-Minimization Analysis (CMA): Use when two interventions have proven equivalent efficacy; only compare costs
• Budget Impact Analysis (BIA): Use to assess financial impact of new drugs or technologies on healthcare insurance funds

Core Workflow

Step 1: Define Research Framework

1. Define research question

   • Identify target disease and population
   • Determine intervention and comparator
   • Set perspective (recommended: societal)

2. Select evaluation type

   • Choose CEA, CUA, CBA, or CMA based on outcome measure
   • Consider discounting for long-term studies (both costs and outcomes)
   • Recommended discount rate: 3.5%

3. Determine time horizon

   • Chronic diseases: lifetime or sufficiently long
   • Acute diseases: short-term follow-up (1-3 years)
   • Budget impact analysis: typically 3-5 years

Step 2: Identify and Measure Costs

Identify costs following Chinese Pharmacoeconomic Evaluation Guidelines:

Direct Medical Costs

• Medication costs
• Outpatient costs
• Inpatient costs
• Diagnostic and test costs
• Surgical treatment costs
• Adverse event treatment costs

Direct Non-Medical Costs

• Transportation
• Accommodation
• Nutritional support
• Unprofessional caregiving

Indirect Costs

• Productivity loss (premature death or sick leave)
• Caregiver burden

Intangible Costs

• Pain, anxiety, quality of life reduction not included in monetary costs; considered in utility analysis

Cost Data Sources:

• Hospital Information Systems
• Insurance Databases
• Epidemiological Studies
• Literature Review
• Questionnaire Surveys

Step 3: Measure Effects/Utilities

Effect Measure Selection

• Survival indicators: Life Years (LY), Survival Rate
• Disease-specific indicators: Event-free survival, Symptom improvement
• Others: Complication rate, Hospitalization frequency

Utility Measurement (Recommended Indirect Methods)

• EQ-5D (EuroQol Five-Dimensional Questionnaire)
• SF-6D (Based on SF-36)
• QWB (Quality of Well-Being Index)

Utility Value Source Priority:

1. Primary data from target population (best)
2. Published Chinese population utility values
3. Data from other countries (requires adjustment)

Step 4: Build Decision Analytic Models

Select appropriate model type based on research characteristics:

Decision Tree Model

• Scenarios: Short-term, single decision, clear event sequence
• Advantages: Intuitive, easy to understand, suitable for analyzing decision processes
• Steps:
  1. Define decision nodes, chance nodes, terminal nodes
  2. Assign probabilities to each chance node (sum to 1)
  3. Assign costs and effects to each terminal node
  4. Roll back to calculate expected values
  5. Compare decision options

Markov Model

• Usage scenario: Chronic diseases, long-term follow-up, recurrent events
• Advantages: Can handle cyclical state transitions, clear structure
• Steps:
  1. Define health states (e.g., healthy, mild, moderate, severe, death)
  2. Build transition matrix (describe state-to-state transition probabilities)
  3. Estimate transition probabilities (from incidence, survival curves, or literature)
  4. Assign cycle costs and utilities to each state
  5. Set cycle length (typically 1 year) and model time horizon
  6. Run Markov simulation

Discrete Event Simulation (DES)

• Scenarios: Large individual variation, irregular event timing, resource constraints
• Advantages: Most flexible, can simulate individual paths, precise time-dependent modeling
• 步骤:
  1. Define entities (patients) and their attributes
  2. Define possible event types
  3. Establish event scheduling mechanism
  4. Run simulation
  5. Aggregate results

Partitioned Survival Model (PSM)

• Scenarios: Oncology research, based on survival curves
• Advantages: Directly based on survival data, reasonable extrapolation
• Steps:
  1. Obtain PFS and OS survival curves
  2. Fit parametric distributions (exponential, Weibull, etc.)
  3. Extrapolate to model time horizon
  4. Calculate population distribution across partitions
  5. Accumulate costs and utilities

See references/model_methods.md for detailed modeling methods.

Step 5: Calculate Key Metrics

Use calculation tools in scripts/:

Incremental Cost-Effectiveness Ratio (ICER)

Use calculate_icere() from scripts/cost_effectiveness_analysis.py:

from cost_effectiveness_analysis import calculate_icere

result = calculate_icere(
    cost_intervention,  # Intervention group cost
    effect_intervention,  # Intervention group effect (e.g., QALYs)
    cost_control,  # Control group cost
    effect_control,  # Control group effect
    threshold=30000  # Threshold (30KUSD for US & UK, and close to 2x GDP per QALY of China)
)

ICER Formula: [ ICER = \frac{C_A - C_B}{E_A - E_B} = \frac{\Delta C}{\Delta E} ]

Quality-Adjusted Life Years (QALYs)

Use calculate_qaly() from scripts/cost_effectiveness_analysis.py:

from cost_effectiveness_analysis import calculate_qaly

qalys = calculate_qaly(
    life_years=10,  # Life years
    utility_scores=np.array([...]),  # Utility scores for each period
    discount_rate=0.03  # Discount rate
)

QALY Formula: [ QALY = \sum_{t=1}^{T} U_t \times \frac{1}{(1+r)^{t-1}} ]

Net Benefit

[ NB = \lambda \times E - C ]

Where:

• NB = Net Benefit
• λ = Willingness-to-pay threshold
• E = Effect
• C = Cost

Budget Impact Analysis

Use BudgetImpactModel from scripts/budget_impact_analysis.py:

from budget_impact_analysis import BudgetImpactModel

model = BudgetImpactModel(
    target_population=100000,
    treatment_cost_new=15000,
    treatment_cost_old=10000,
    horizon_years=5,
    uptake_rate=0.2,
    discount_rate=0.03
)

# Calculate multi-scenario budget impact
scenarios = {
    "Base Case": [0.2, 0.3, 0.4, 0.5, 0.6],
    "Optimistic": [0.3, 0.5, 0.7, 0.8, 0.9],
    "Conservative": [0.1, 0.15, 0.2, 0.25, 0.3]
}

results = model.compare_scenarios(
    scenarios,
    population_growth_rate=0.02,
    treatment_cost_inflation=0.01
)

Step 6: Conduct Sensitivity Analysis

One-Way Sensitivity Analysis

Use deterministic_sensitivity_analysis() from scripts/cost_effectiveness_analysis.py:

from cost_effectiveness_analysis import deterministic_sensitivity_analysis

# Define parameter ranges
param_ranges = {
    'drug_cost': (10000, 20000),
    'hospital_cost': (5000, 15000),
    'effectiveness': (0.8, 1.2)
}

# Run sensitivity analysis
results_df = deterministic_sensitivity_analysis(
    base_params=base_parameters,
    param_ranges=param_ranges,
    outcome_func=outcome_function
)

Tornado Plot Data: Use tornado_plot_data() function

Probabilistic Sensitivity Analysis (PSA)

Use MonteCarloSimulator from scripts/monte_carlo_simulation.py:

from monte_carlo_simulation import MonteCarloSimulator

# Create simulator
simulator = MonteCarloSimulator(n_simulations=10000, seed=42)

# Define parameter distributions
parameters = {
    'cost': {
        'distribution': 'gamma',
        'params': (2, 15000),  # shape, scale
        'min_value': 0
    },
    'effect': {
        'distribution': 'beta',
        'params': (5, 3),  # alpha, beta
        'min_value': 0,
        'max_value': 10
    }
}

# Run PSA
results_df = simulator.probabilistic_sensitivity_analysis(
    parameters=parameters,
    outcome_func=outcome_function,
    threshold=120000
)

Generate CEAC: Use generate_ceac() function

Value of Information (VOI): Use value_of_information_analysis() function

Step 7: Interpret and Report Results

Willingness-to-Pay Threshold (Reference)

• 1x GDP/QALY: ~¥120,000
• 2x GDP/QALY: ~¥240,000
• 3x GDP/QALY: ~¥360,000

Interpretation

• ICER ≤ Threshold: Cost-effective
• ICER > Threshold: Not cost-effective
• Strict Dominance: Lower cost and better effect
• Strict Disadvantage: Higher cost and worse effect

Reporting Requirements

Follow CHEERS 2022 and Chinese Pharmacoeconomic Evaluation Guidelines:

1. Clearly describe research design and methods
2. Report baseline analysis results
3. Provide sensitivity analysis results (one-way and probabilistic)
4. Report confidence intervals
5. Discuss limitations and generalizability
6. Clearly state funding sources and potential conflicts of interest

See references/guidelines.md for detailed guidelines.

Scripts Guide

cost_effectiveness_analysis.py

Core functions: Cost-effectiveness analysis, ICER calculation, QALY calculation, deterministic sensitivity analysis

Main functions:

• calculate_icere(): Calculate ICER
• calculate_qaly(): Calculate QALYs
• calculate_ceac(): Calculate Cost-Effectiveness Acceptability Curve
• deterministic_sensitivity_analysis(): One-way sensitivity analysis
• tornado_plot_data(): Prepare tornado plot data
• markov_model_transition(): Markov model simulation
• discount_costs(): Cost discounting

budget_impact_analysis.py

Core functions: Budget impact analysis model

Main classes and methods:

• BudgetImpactModel: Budget impact analysis model
  • calculate_budget_impact_scenario(): Calculate single scenario budget impact
  • compare_scenarios(): Compare multiple scenarios
  • sensitivity_analysis(): Sensitivity analysis
  • generate_summary(): Generate analysis summary
• calculate_incremental_budget_impact(): Calculate incremental budget impact
• budget_impact_report(): Generate budget impact report

monte_carlo_simulation.py

Core functions: Monte Carlo simulation, probabilistic sensitivity analysis, value of information analysis

Main classes and methods:

• MonteCarloSimulator: Monte Carlo simulator
  • generate_samples(): Generate samples from specified distribution
  • probabilistic_sensitivity_analysis(): Run PSA
  • generate_ceac(): Generate CEAC
  • value_of_information_analysis(): VOI analysis
  • scatter_plot_data(): Prepare cost-effectiveness scatter plot data

References Guide

guidelines.md

Summary of key content from ISPOR Good Practices, including:

• Evaluation framework and perspective
• Cost identification and measurement
• Effect/utility measurement
• Model construction methods
• Discounting principles
• Sensitivity analysis requirements
• Result presentation and reporting standards
• Common calculation formulas

Use case: Query specific requirements, standards, and methods for Chinese pharmacoeconomic evaluation

model_methods.md

Detailed decision analytic model construction methods, including:

• Markov model (basic concepts, transition matrix, probability estimation)
• Decision tree model (structure, probability assignment, rollback calculation)
• Discrete event simulation (core elements, advantages/disadvantages)
• Partitioned survival model (survival curve fitting)
• Model comparison and selection
• Modeling best practices

Use case: Learn specific modeling methods, build decision analytic models

Common Task Scenarios

Scenario 1: Conduct Cost-Effectiveness Analysis for New Drug

1. Determine research perspective (societal)
2. Identify direct medical and non-medical costs
3. Collect clinical trial data for effect measures (survival, QALYs)
4. Build Markov model to simulate disease progression
5. Calculate ICER and compare with threshold
6. Conduct one-way and probabilistic sensitivity analysis
7. Write report following CHEERS standards

Scenario 2: Budget Impact Analysis

1. Determine target population size
2. Obtain costs for new drug and comparator
3. Set uptake rate scenarios (base, optimistic, conservative)
4. Use BudgetImpactModel to calculate budget impact for each scenario
5. Conduct sensitivity analysis
6. Generate budget impact report

Scenario 3: Model Building and Validation

1. Select model type based on disease characteristics
2. Learn modeling methods from references/model_methods.md
3. Estimate model parameters from literature or clinical trials
4. Validate model (internal and external validation)
5. Run baseline analysis
6. Conduct sensitivity analysis to verify model stability

Scenario 4: Probabilistic Sensitivity Analysis

1. Specify probability distributions for each key parameter
2. Run 10,000+ simulations using MonteCarloSimulator
3. Generate cost-effectiveness scatter plot
4. Generate Cost-Effectiveness Acceptability Curve (CEAC)
5. Conduct Value of Information (VOI) analysis
6. Report cost-effectiveness probability and confidence intervals

Parameter Management Best Practices

Parameter Organization

Organize parameters by category:

• Research Framework Parameters: Perspective, time horizon, discount rate, threshold
• Model Structure Parameters: Health states, initial distribution
• Transition Probability Parameters: State-to-state transition probabilities
• Cost Parameters: Annual costs by state
• Utility Parameters: Utility values by state
• Sensitivity Analysis Parameters: Parameter ranges and probability distributions
• Simulation Parameters: Number of simulations, random seed, etc.

Parameter Source Documentation

Each parameter value must have a clear data source:

• Literature Citation: Author, journal, year, pages
• Database: Database name, version, access date
• Guidelines/Standards: Guideline name, version, issuing organization
• Expert Opinion: Expert source and judgment basis
• Research Assumption: Rationale for assumption

Example Code Format

# ========== Parameter Category Title ==========
PARAMETER_NAME = {
    'parameter_key': value,  # Source: Detailed source description
    'another_key': value,    # Source: Reference [Author, Journal, Year]
}

See scripts/example.py for complete parameter organization format.

Important Notes

1. Follow Chinese Guidelines: Ensure research methods meet requirements of Chinese Pharmacoeconomic Evaluation Guidelines (2023)

2. Transparency: Clearly describe all assumptions, data sources, and calculation methods

3. Parameter Source Documentation: All parameter values must cite sources for traceability and verification

4. Discounting: Both costs and outcomes need discounting; recommended rate is 3.5%

5. Sensitivity Analysis: Conduct sufficient sensitivity analysis to evaluate uncertainty

6. Model Validation: Validate model internally; conduct external validation if possible

7. Reporting Standards: Follow CHEERS 2022 reporting standards

8. Threshold: Clearly state the threshold used and its basis (Reference: 1-3x GDP/QALY)

9. Time Horizon: Select sufficiently long time horizon to capture all relevant costs and outcomes

10. Cost Measurement: Avoid using payment prices (reimbursed prices); use actual costs or standardized charges

11. Utility Measurement: Prioritize Chinese population utility values; note applicability of measurement tools

12. Parameter Organization: Reference format in scripts/example.py, organize parameters neatly and document sources in detail