Modelling Approaches

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.

Systematic narrative review of decision frameworks to select the appropriate modelling approaches for health economic evaluations

“…All decision frameworks were published in the past 10 years with two specific to infectious diseases [6, 8] and the remainder being generic/non-disease specific [7, 9–13]. Each framework covered different model types, although all of them involved a decision between a traditional modelling approach (i.e.…”

“…Decision frameworks were visually represented by flow charts [6, 9, 12, 13], radar graphs [7], or tables [8, 10, 11] and Table 3 further details the selection criteria that were considered within each framework. The definition of common structural features and practical consideration that were identified from this review of frameworks are presented in Table 4.…”

“…This is an important issue since each approach can introduce constraints to a model’s development and its conceptualization in terms of what elements can be captured and the ease to which they can be incorporated into the model [6]. This may lead to a different focus on the decision problem and, thereby, generate conflicting results and diverging policy recommendations [7]. …”

Economic evaluation of the use of a pharmacogenetic diagnostic test in schizophrenia

“…As noted above, one way to collect the data would be by means of prospective studies. The availability of patient level data would also allow considering different analytic approaches, such as discrete event simulation, as discussed by Berg et al [41] Nevertheless, one does not have to consider only a randomized controlled trial where one study arm involves dosing based on CYP2D6 results. It would also be informative to conduct a retrospective analysis to see how well CYP2D6 is able to predict later clinical outcomes such as treatment failure or intolerance in all patients treated using the "traditional dosing" approach.…”

Systematic review of pharmacoeconomic models for schizophrenia

“…Cost analysis was also considered as a type of economic analysis, since while early economic evaluations primarily compared costs, they also presented health outcomes. Type of model included DT, CLMM, PLMM, DES model [7,13], and others. Perspective was classified into societal, payer, and patient perspective [14].…”