A taxonomy of model structures for economic evaluation of health technologies

Abstract

Models for the economic evaluation of health technologies provide valuable information to decision makers. The choice of model structure is rarely discussed in published studies and can affect the results produced. Many papers describe good modelling practice, but few describe how to choose from the many types of available models. This paper develops a new taxonomy of model structures. The horizontal axis of the taxonomy describes assumptions about the role of expected values, randomness, the heterogeneity of entities, and the degree of non-Markovian structure. Commonly used aggregate models, including decision trees and Markov models require large population numbers, homogeneous sub-groups and linear interactions. Individual models are more flexible, but may require replications with different random numbers to estimate expected values. The vertical axis of the taxonomy describes potential interactions between the individual actors, as well as how the interactions occur through time. Models using interactions, such as system dynamics, some Markov models, and discrete event simulation are fairly uncommon in the health economics but are necessary for modelling infectious diseases and systems with constrained resources. The paper provides guidance for choosing a model, based on key requirements, including output requirements, the population size, and system complexity.

Discrete event simulation for performance modelling in health care: a review of the literature

“…In addition, although not focused on DES, Brennan et al (2006), suggests a taxonomy of model structures for economic evaluation of health technologies and, in so doing, identifies the role of DES in health economics. Fone et al (2003) is a report of a systematic review of the literature on health-care simulation between 1980 and 1999 and follows a route familiar to readers of systematic reviews in health care.…”

Economic modelling of diagnostic and treatment pathways in National Institute for Health and Care Excellence clinical guidelines: the Modelling Algorithm Pathways in Guidelines (MAPGuide) project

“…[40][41][42] These may be useful for situations where decision-makers want a model that can be adapted over time to evaluate emerging technologies or to incorporate new evidence. 43 One well-known example is the pioneering coronary heart disease (CHD) policy model. 44,45 This was designed to estimate CHD incidence, prevalence, mortality and related resource costs across a population.…”

Simulation sample sizes for Monte Carlo partial EVPI calculations

“…It can be applied to patient simulation models (Brennan et al, 2006), where there is the additional complication of how many patient simulations per parameter set to use . It is also applicable to models with any characterisation of uncertainty and is not limited to continuous or parametric distributions.…”