Modelling Methods for Pharmacoeconomics and Health Technology Assessment

Abstract

This paper provides an overview of, and guidance as to when, why and how to choose and use, different simulation modelling methods as applied to healthcare. What simulation is and why it is necessary in addressing healthcare problems are discussed. In addition, key criteria for choosing an appropriate method (project type, population resolution, interactivity, treatment of time and space, resource constraints, autonomy and how knowledge is embedded) are covered. Key concepts for each method, moving from the simplest to most complex methods, are reviewed in some detail.

Economic Evaluations with Agent-Based Modelling: An Introduction

“…Considering these factors, ABM can be considered as an extension of DES. More details about comparison of DES and ABMs can be found elsewhere [43,44].…”

Burden of Diabetes Type 2 Through Modelling and Simulation

“…Process descriptions should be suitably chosen regarding modelling goal(s), modelling phase and other important design factors. As indicated in (Stahl, 2008) the investigators should choose the modelling methods that fit the defined problem. It means that different descriptions can predict similar or complementary results using, for example, different complexity (nonlinear or linear descriptions, high or low order descriptions) or different granularity (observation of the whole population, observation of an individual patient, observation of dynamical processes on molecular level), different aggregation (development of one or several diseases) or perhaps only different model presentations are suitable when performing different analysis functions (state space or transfer function description, continuous or discrete time description, time or frequency domain description, ...).…”

“…In problems that lead to long -term differences in outcome such presentation can be insufficient. One way to avoid (at least partly) this drawback is to use the description in the form of the so called Markov process (Stahl, 2008;Shih, 2007) or to extend the decision tree to include also Markov processes, for example at the outcome node as illustrated in Fig. 4 (Arnold, 2010).…”

“…The advantages of such presentations are that they are relatively simple, frequently used and therefore also well accepted as abstract problem presentation. But there are also two main limitations (Stahl, 2008;Arnold, 2010). State transitions can only occur at the end of a cycle, which can create some biases, and Markov cycle time may force the analyst to make simplifying assumptions regarding transition probabilities.…”

“…The last two facts are very important also for the user of developed model. This is perhaps also one of important reasons why very different mathematical approaches and descriptions are used, where the disciplines like medicine, pharmacy and the life sciences are no exception (Hoppensteadt & Peskin, 2002;Atanasijević-Kunc et al, 2008a;Stahl, 2008;Belič, 2009;Arnold 2010). This is true even in the cases where the same problem, like certain disease development (diabetes for example) is observed.…”