Bart M.S. Heeg (Pharmerit BV, Rotterdam, the Netherlands) Joep Damen (Pharmerit BV, Rotterdam, the Netherlands) Erik Buskens (Pharmerit BV, Rotterdam, the Netherlands) Sue Caleo (Janssen Pharmaceutica NV, Beerse, Belgium) Frank de Charro (Pharmerit BV, Rotterdam, the Netherlands) Ben A. van Hout (Pharmerit BV, Rotterdam, the Netherlands)
Abstract
Schizophrenia is a chronic disease characterized by periods of relative stability interrupted by acute episodes (or relapses). The course of the disease may vary considerably between patients. Patient histories show considerable inter- and even intra-individual variability. We provide a critical assessment of the advantages and disadvantages of three modelling techniques that have been used in schizophrenia: decision trees, (cohort and micro-simulation) Markov models and discrete event simulation models. These modelling techniques are compared in terms of building time, data requirements, medico-scientific experience, simulation time, clinical representation, and their ability to deal with patient heterogeneity, the timing of events, prior events, patient interaction, interaction between co-variates and variability (first-order uncertainty). We note that, depending on the research question, the optimal modelling approach should be selected based on the expected differences between the comparators, the number of co-variates, the number of patient subgroups, the interactions between co-variates, and simulation time. Finally, it is argued that in case micro-simulation is required for the cost-effectiveness analysis of schizophrenia treatments, a discrete event simulation model is best suited to accurately capture all of the relevant interdependencies in this chronic, highly heterogeneous disease with limited long-term follow-up data.
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Article provided by Wolters Kluwer Health | Adis in its journal PharmacoEconomics.
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