Building effective health economic models: key components for success

Health economic modeling provides a simplified but credible representation of complex realities within a mathematical framework, helping decision-makers assess the value of new treatments and optimize resource allocation.

By integrating clinical, epidemiological and economic data, health economic models inform critical decisions related to cost-effectiveness (CE), reimbursement, and resource management.

A truly impactful health economic model answers critical research questions while ensuring its outputs are accurate, relevant, and representative. At its core, the effectiveness of such a model often depends on the quality and comprehensiveness of its data inputs. Let’s explore the foundational components that researchers must consider when building robust health economic models capable of driving meaningful transformation and progress.

Health economic models support informed decision-making by systematically evaluating clinical outcomes, costs, and value. They help answer complex questions such as:

• How effective is a new treatment compared to existing options?
• How do clinical benefits balance against treatment costs?
• What are the financial implications of adopting a new healthcare policy?
• What uncertainties exist, and how might they influence decision-making?

For instance, a new cancer therapy might improve survival but also significantly increase costs. An effective model would clearly quantify benefits, examine trade-offs between cost and clinical outcomes, and highlight uncertainty and variability in results. However, all models have limitations; thus, transparency about assumptions, rigorous validation, and awareness of uncertainty are essential to ensure accurate and reliable outcomes.

Building a health economic model is follows a process similar to constructing a sturdy building – it requires careful planning, high quality materials, and a design that fits its purpose. Below are the essential elements.

Model Structure: The model must represent the disease progression accurately, considering the duration, interdependence of disease stages, and timing of events. Structures range from simple decision trees to complex models like Markov models, partitioned survival analyses, and patient-level simulations. The model structure should be tailored to the disease’s clinical characteristics.

Setting: Economic models, especially those designed to support payer reimbursement discussions, must ultimately be relevant to a specific geography. A typical development process is to create a global model that whose structure and comparators are applicable across geographies with input values specific to a core country (e.g., United Kingdom-specific costs, patient demographics, etc.). A global model can later be adapted to non-core countries using country-specific parameter values to create a local model.

Epidemiological Inputs: Disease-related risks and event probabilities are informed by evidence from clinical trials, observational studies, and quantitative systems pharmacology (QSP)/physiologically based pharmacokinetic (PBPK) models, or expert opinions.

Time Horizon: The time horizon should reflect the natural disease history—lifetime for chronic conditions and shorter for acute diseases.

Analytical Approach: Various approaches such as cost-utility analysis (CUA), cost-effectiveness analysis (CEA), cost-minimization analysis (CMA), and cost-benefit analysis (CBA) can be applied, depending on clinical outcomes and data availability.

Costs and Perspectives: The scope of costs considered may vary depending on the perspective taken, but those considered are generally associated with the use of the compared health technologies and the course of the disease. Direct medical costs are usually calculated but non-medical, indirect costs such as costs from lost productivity or informal care may be included as well (if appropriate for the disease or required in the context of specific indications or geographies).

Utilities: Used in CUA to assess quality-of-life changes, utilities are valued between 0 and 1, where 1 represents perfect health, and 0 represents death.

Validation

Validation is critical to ensure model accuracy and credibility. It includes:

• Internal Validation: Ensuring technical correctness and accurate implementation of calculations and algorithms.
• External Validation (Expert Review): Incorporating external experts in the validation process to review model assumptions, structure, and inputs.
• External Validation (Outcome Comparison): Validating model outcomes against independent data sources or datasets not used during model development.

Sensitivity Analysis

Sensitivity analysis assesses the robustness and reliability of the model by examining how uncertainty in model inputs and assumptions affects outcomes. Deterministic and probabilistic sensitivity analyses, as well as scenario analyses, are typically conducted to explore different assumptions and validate the stability of model results.

Model Maintainability and Updating

Models should be designed with a clear and organized structure that facilitates regular updating and maintenance. This ensures that models remain relevant and accurate as new data, evidence, or healthcare technologies emerge.

Effective modeling and development of cost-effectiveness, budget-impact analysis, and all reimbursement submissions require a team of experts familiar with the reimbursement challenges in different markets. Partner with Certara Evidence & Access to create models that deliver real-world value.