Modelling approaches: the case of schizophrenia.
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.
Heeg
Bart M S
BM
Pharmerit BV, Rotterdam, the Netherlands.
Damen
Joep
J
Buskens
Erik
E
Caleo
Sue
S
de Charro
Frank
F
van Hout
Ben A
BA
D003362:Cost-Benefit Analysis / Q000191:economics*; D003657:Decision Making; D003661:Decision Support Techniques; D006801:Humans; D008390:Markov Chains; D018803:Models, Economic; D012559:Schizophrenia / Q000191:economics* / Q000628:therapy