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Modelling Approaches

The Case of Schizophrenia

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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 covariates 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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Notes

  1. A probabilistic simulation generally requires a minimum of 1000 simulations of cohorts. This may be quite time consuming for a patient-level simulation model, as each cohort requires a number of patient simulations to generate the cohort. If the probabilistic simulation takes too long, one could consider using a meta model. By using a meta model, one reduces one layer of simulations, i.e. the patient-level simulation, of the micro-simulation models. This substantially reduces simulation time of the probabilistic sensitivity analysis. A recently introduced meta-modelling approach is the Gaussian process. Gaussian process models have the advantage that they can be fitted on fewer probabilistic input and output combinations and that they can be applied for nonlinear models.

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Acknowledgements

Johnson & Johnson provided funding to Pharmerit BV to conduct the analysis and prepare the article. Sue Caleo is an employee of Johnson & Johnson. All remaining authors are employees of Pharmerit BV, which acted as a paid consultant to Johnson & Johnson, who market various antipsychotics.

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Authors and Affiliations

  1. Pharmerit BV, Marten Meesweg 143, 3068 AV, Rotterdam, the Netherlands

    Bart M. S. Heeg, Joep Damen, Erik Buskens, Frank de Charro & Ben A. van Hout

  2. Janssen Pharmaceutica NV, Beerse, Belgium

    Sue Caleo

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  1. Bart M. S. Heeg
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  2. Joep Damen
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  3. Erik Buskens
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  4. Sue Caleo
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Correspondence to Bart M. S. Heeg.

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Heeg, B.M.S., Damen, J., Buskens, E. et al. Modelling Approaches. Pharmacoeconomics 26, 633–648 (2008). https://doi.org/10.2165/00019053-200826080-00002

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