Pharmacoeconomic Evaluation

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