DESnets: A Graphical Representation for Discrete Event Simulation and Cost-Effectiveness Analysis

Abstract

Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost. Discrete event simulation (DES) is playing an increasing role in CEA thanks to several advantages, such as the possibility of modeling time and heterogeneous populations. It is usually implemented with general-purpose programming languages or commercial software packages. To our knowledge, no artificial intelligence technique has been applied to DES for CEA. Our objective is to develop a graphical representation, an algorithm, and a software tool that allows non-programmers to easily build models and perform CEA. We present DESnets (discrete event simulation networks) as a new type of probabilistic graphical model inspired by probabilistic influence diagrams, an algorithm for evaluating and an implementation as an OpenMarkov plug-in. DESnets are compared qualitatively and empirically with six alternative tools using as a running example a model about osteoporosis by the British National Institute for Health and Care Excellence (NICE). In our experiments, the implementation of DESnets allowed the building of a typical DES model declaratively. Its evaluation process ranked among the most efficient. DESnets compare favorably with alternative tools in terms of ease of use, expressive power, transparency, and computational efficiency.

Keywords

• cost-effectiveness analysis
• discrete event simulation
• probabilistic graphical models
• influence diagrams
• decision support