Evaluating Uncertainties in Health Economic Models: A Review and Guide

Abstract

In health economics, decision-makers rely on models to assess the cost-effectiveness of healthcare interventions and guide resource allocation. Health Technology Assessment (HTA) agencies employ cost-effectiveness models to determine the approval and market access of new therapies within their respective jurisdictions. Health economists use quantitative techniques to synthesize clinical, epidemiological, and economic data to model the costs and effectiveness of a new drug compared to the current standard of care over the lifetime of the patients. These models frequently integrate a wide range of assumptions and data inputs from various sources, which renders them vulnerable to a significant level of uncertainty. Economic models commonly confront multiple forms of uncertainty, such as stochastic uncertainty (first-order), which differs from parameter uncertainty (second-order), as well as the presence of heterogeneity within patient populations. Additionally, structural uncertainty related to the model itself adds another layer of complexity. Uncertainty assessment is essential in model-based health economic evaluations that inform regulatory and reimbursement decisions. Understanding these sources of uncertainty, taking steps to minimize their impact, and analyzing, quantifying, and reporting these inherent uncertainties are crucial for ensuring that health economic models provide robust and reliable insights for effective decision-making. This article examines different types of uncertainty in health economic models and methods to analyze and quantify them, offering practical guidelines with examples from recent literature.