Chapter 4: Conducting an Economic Evaluation – Methods and Data

This chapter provides practical guidance on the key steps involved in conducting an economic evaluation, from defining the problem to analyzing and appraising data. By following a structured approach, researchers and decision-makers can ensure that evaluations are robust, transparent, and relevant to healthcare policy and practice.

4.1 Steps in Conducting an Economic Evaluation

Conducting an economic evaluation generally follows a sequence of structured steps:

1. Define the Decision Problem

• Specify the intervention(s) being compared.
• Identify the setting (e.g., hospital, community).
• Determine the perspective (societal, payer, or institutional).
• Define the time horizon and target population.
  This step is similar to developing a research question in an academic study.

2. Select Appropriate Health Outcomes

• Choice of outcome measure distinguishes one type of economic evaluation from another (e.g., cost-effectiveness analysis vs cost-utility analysis).

3. Determine the Perspective

The perspective defines which costs and benefits are included:

• Societal Perspective: Includes all costs, regardless of who pays (e.g., lost productivity, patient travel).
• Payer Perspective: Includes costs borne by the health system or insurance provider.
• Institutional/ Provider Perspective: Narrower, focused on costs relevant to a specific hospital or clinic.

4. Design the Model

Models simplify real-world healthcare scenarios:

• Decision Trees: Represent short-term or one-time events, calculating costs and outcomes at terminal nodes.
• Markov Models: Better suited for chronic diseases, representing transitions between health states over time.

5. Populate the Model with Data

When populating a model, data inputs typically include costs, outcomes, and probabilities of different events (e.g., disease progression, treatment success, or adverse effects). Data inputs include:

• Clinical Data: Treatment effectiveness and outcomes, often from guidelines or trial evidence.
• Economic Data (Costs): From hospital databases, national administrative datasets, fee schedules, formularies, wage data, costing studies, or published literature.
• Probability Data: These probabilities are essential for simulating realistic health outcomes in decision-analytic models.

6. Test Robustness and Uncertainty (Sensitivity Analysis)

• Models must be tested against uncertainty by varying key inputs and assumptions.
• Sensitivity analyses ensure reliability and highlight the impact of uncertainty on results.

4.2 Measuring Quality-Adjusted Life Years (QALYs)

Before introducing QALYs, it is important to understand that health outcomes can be measured not only by life years gained but also by the quality of those years. Economic evaluations often require a common unit that combines both quantity and quality of life — leading to the concept of the Quality-Adjusted Life Year (QALY). QALYs are a standard outcome measure in cost-utility analysis, combining quality and length of life into a single index.

• Health Utility Measurement Tools:
  • EQ-5D (European Quality of Life 5-Dimension)
  • SF-36 (36-Item Short Form Survey)
  • HUI3 (Health Utilities Index Mark III)

  These tools are often embedded into clinical trials for economic evaluations.

• Disability-Adjusted Life Year (DALY):

  • Widely used in global health, especially in low- and middle-income countries.

  • Combines years of life lost due to premature mortality and years lived with disability.

4.3 Appraising Evidence and Considering the Audience

1. Quality Assurance

High-quality economic evaluations adhere to best practices:

• The CHEERS Checklist (Consolidated Health Economic Evaluation Reporting Standards) provides 17 items to guide evaluation and reporting.
• Essential reporting elements include objective, population, time horizon, perspective, comparators, outcomes, discount rate, costs, assumptions, analytic methods, and uncertainty analysis.

2. Audience Considerations

Economic evaluations must be tailored to their audience:

• Health Technology Assessment (HTA) agencies for national policy.
• Local decision-makers (e.g., hospital administrators) for budgeting.
• Pharmacy and Therapeutics Committees for formulary inclusion decisions.
• Academic audiences for methodological contributions.