Build a Health Economic Model – Step by Step

Building a health economic model requires you to move from a clearly defined decision problem and an understanding of the clinical evidence to an appropriate modeling approach, model structure, analyses, validation, and communication of results.

The following steps provide a practical framework for developing a health economic model and show how TreeAge Pro supports the modeling process from beginning to end.

  1. Define the Decision Problem
  2. Understand Your Clinical Evidence
  3. Choose the Modeling Approach
  4. Build Patient Pathways
  5. Add Model Inputs
  6. Analyze the Model
  7. Validate and Explore Model Behavior
  8. Assess Uncertainty
  9. Communicate the Model and Results

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Define the Decision Problem

Before building the model, clearly define the decision problem it needs to address. This establishes the population, strategies, perspective, time horizon, and outcomes that will guide the modeling process.

Consider the following key elements:

  • Population
    • What patient population will the model represent?
    • Examples:
      • Patients diagnosed with a specific type of cancer
      • Women with high risk pregnancies
  • Strategies
    • What treatment, diagnostic, screening, or other strategies will be compared?
    • Examples:
      • Radiation therapy vs. surgery vs. combination therapy
      • Breast cancer screening strategies
      • Different colonoscopy screening intervals
  • Perspective
    • Whose perspective will guide the analysis?
    • Examples:
      • Healthcare payer
      • Societal
      • Patient
  • Time horizon
    • What time horizon is appropriate for capturing the relevant outcomes?
    • Examples:
      • Short-term time horizon
      • Lifetime horizon
  • Outcomes
    • What outcomes will be used to compare strategies?
    • Examples:
      • Cost per quality-adjusted life year (QALY)
      • Cost per death avoided
    • What additional clinical or model outcomes should be reported?
      • Infections prevented
      • Healthy births
      • Cases detected

Understand Your Clinical Evidence

Before choosing how to represent disease progression, understand what your available evidence tells you and how risk and outcomes change over time.

Clinical evidence may include Kaplan-Meier curves or tables, patient-level data, fitted distributions, published probabilities, hazard ratios, treatment effects, and other sources.

Consider:

  • What outcomes or events does the evidence represent?
  • How does risk change over time?
  • How far does the observed evidence extend?
  • Will outcomes need to be extrapolated beyond the observed period?
  • Does the evidence describe overall outcomes or specific events that should drive disease progression in the model?

TreeAge Pro provides tools to explore and interpret clinical evidence, understand how risk changes over time, and use that evidence to inform your modeling approach.

Choose the Modeling Approach

Choose the modeling approach that best represents the decision problem, available evidence, and disease process.

  • Decision Tree
    • Useful for problems with a defined sequence of events and a relatively limited number of pathways and outcomes.
    • Examples:
      • Emergency department visits
      • Orthopedic procedures
  • Markov Model
    • Represent disease progression over time using health states, transitions, and recurring events.
    • Examples:
      • Cancer treatment with adverse events
      • Asthma management
      • Chronic pain management
  • Partitioned Survival Model
    • Represent disease progression using survival outcomes to determine membership in health states over time.
    • Example:
      • Oncology treatments
  • Patient Simulation
    • Simulate individual patients when patient characteristics, history, and prior events influence future events and outcomes.
    • Examples:
      • Colorectal cancer screening based on prior screening history
      • Subgroup analyses based on patient characteristics
  • Discrete Event Simulation
    • Represent disease progression through the timing of individual and competing events.
    • Examples:
      • Complex care pathways where event timing is critical

Build Patient Pathways

Define the model structure and patient pathways before focusing on the data needed to populate them.

Build pathways that represent the important health states, events, decisions, and disease progression in your model. Starting with the structure helps clarify what the model needs to represent—and what inputs will actually be required.

  • Decision Nodes and Strategies
    • The model typically begins with a decision node containing one branch for each strategy under evaluation.
    • Each branch represents a strategy being evaluated and provides the starting point for its corresponding patient pathways.
  • Patient Pathways
    • Patient pathways represent the important health states and events patients may experience.
    • Branches represent alternative events or outcomes as patients move through the model.
  • Markov Models
    • Markov models consist of health states, events, and cycles that represent disease progression over time.
    • Key characteristics:
      • The overall time horizon is divided into cycles.
      • Health states represent a patient’s current condition.
      • During each cycle, patients may experience events that move them to other health states.
      • The process repeats until the end of the time horizon.
  • Partitioned Survival Models
    • Commonly, Progression-Free Survival (PFS) and Overall Survival (OS) curves are modeled.
    • Survival functions determine how membership in health states changes over time.
  • Patient Simulations
    • Patient simulation models individual patients as they move through pathways and experience events over time.
    • Patient-level modeling supports:
      • Incorporation of patient characteristics
      • Tracking of patient history
      • Greater modeling flexibility
  • Discrete Event Simulation
    • Discrete event simulation uses sampled event times to determine when individual and competing events occur rather than progressing through fixed cycles.

Add Model Inputs

Once the model structure is established, add the clinical, economic, and other inputs needed to drive the model.

Inputs may include probabilities, survival functions, treatment effects, costs, utilities, and other parameters. Add inputs where they apply within the model structure so their role in calculations and outcomes remains clear.

  • Disease Progression
    • These inputs determine how patients move through the model and how disease progression is represented over time.
    • Examples:
      • Markov Models: transition probabilities
      • Partitioned Survival Models: survival or hazard functions
      • Discrete Event Simulations: event-time distributions
      • Patient Simulations: individual characteristics, history, and events can influence risks and outcomes
  • Costs
    • Include costs associated with:
      • Treatments
      • Diagnostics
      • Hospitalizations
      • Follow-up care
  • Utilities
    • Utilities represent health-related quality of life associated with health states, events, and time spent in those states.
  • Other Clinical and Model Inputs
    • Examples include:
      • Test sensitivity and specificity
      • Hazard ratios
      • Relative risks
      • Epidemiological parameters

Analyze the Model

Once the model structure and inputs are established, analyze the model to understand disease progression, compare strategies, and evaluate clinical and economic outcomes.

TreeAge Pro provides integrated analysis and reporting tools that follow the model structure and inputs you define.

Depending on the modeling approach and decision problem, analyses may include:

  • Cost-Effectiveness Analysis – Compare strategies using costs, effectiveness, ICERs, net benefits, and other cost-effectiveness outcomes.
  • Markov Cohort Analysis – Examine disease progression, state membership, costs, utilities, and other outcomes over time.
  • Partitioned Survival Analysis – Analyze disease progression and outcomes using survival curves and related clinical evidence.
  • Patient Simulation – Explore patient-level variation, characteristics, histories, and events through simulation.
  • Discrete Event Simulation – Explore individual patient pathways and competing events using time-to-event distributions.
  • Model Reports and Visualizations – Explore model outcomes and behavior using detailed reports, graphs, and visualizations.

Incremental Cost-Effectiveness Ratio (ICER)

Cost-effectiveness analysis can use the Incremental Cost-Effectiveness Ratio (ICER) to compare the difference in costs between strategies with the difference in effectiveness:

ICER = Incremental Cost (IC) ÷ Incremental Effectiveness (IE)

ICERs can be considered in relation to an appropriate willingness-to-pay (WTP) threshold when interpreting cost-effectiveness results.

Validate and Explore Model Behavior

Examine model structure, disease progression, calculations, and outputs to understand how the model is behaving and validate that it reflects your assumptions and evidence.

  • Visualize and Review Disease Progression
    • Compare modeled disease progression with observed clinical evidence to understand how closely model behavior reflects the available evidence.
  • Review Model Calculations
    • Inspect costs, utilities, probabilities, and other calculations throughout the model to understand how inputs and pathways produce results.
  • Examine Patient Traces
    • Review patient traces to understand how patients move through the model.
    • Review model behavior at the cohort level or examine individual patient histories in simulation models.
  • Test Complex Calculations
    • Review and test formulas, assumptions, and custom calculations to verify that they behave as intended.
  • Make the Model Easier to Review
    • Visible model structure, assumptions, calculations, and results help colleagues, clients, and reviewers understand and scrutinize the model—even when they did not build it themselves.

Assess Uncertainty

Explore how uncertainty in model inputs and assumptions affects results and conclusions.

Many model parameters are estimated and therefore uncertain. Sensitivity analyses help you understand how changes in inputs and assumptions affect model results.

Deterministic Sensitivity Analysis

  • Deterministic sensitivity analysis evaluates the effect of changing one parameter at a time across a plausible range of values.
  • Results across multiple parameters can be summarized using tornado diagrams to show which parameters have the greatest impact on results.

Probabilistic Sensitivity Analysis

  • Probabilistic sensitivity analysis evaluates uncertainty across multiple parameters simultaneously.
  • Parameter values are sampled from probability distributions to explore the combined impact of parameter uncertainty on model results.

Communicate the Model and Results

Communicate model structure, assumptions, analyses, and results clearly to colleagues, clients, reviewers, and decision-makers.

TreeAge Pro provides multiple ways to communicate and share your modeling work:

  • Copy and export graphs for presentations, reports, publications, and other documents.
  • Share models through TreeAge Pro Web.
    • Let colleagues, clients, and reviewers examine the model, change inputs, and run analyses without installing TreeAge Pro.
    • Give others a direct way to understand and explore the model and its results.
    • Control access to determine who can view and analyze the model.
  • Export reports and model outputs to Excel for further review and communication.

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