OBJECTIVES: Many models are available to evaluate the economic impact of antipsychotics in schizophrenia. However, the majority is developed using cohort-level simulation, not accounting for patient heterogeneity. Our objective was to define the specifications of a new comprehensive discrete event simulation (DES) model that could be used to evaluate the health and economic outcomes associated with alternative treatment strategies based on antipsychotics for patients with schizophrenia.

METHODS: A systematic review of pharmacoeconomic models for schizophrenia was conducted to identify the key model structures and limitations of existing models. Clinical and methodological experts were consulted to define the structure of the new model, and to determine the outcomes to be considered.

RESULTS: After the review (previously published) of 79 economic models, including 2 original DES models, and expert consultations, the following key structural aspects of the model were defined: health states (stable, relapse), treatment effect (relapse prevention), side effect (short-term: extrapyramidal symptoms, sedation, sexual dysfunction, weight gain; long-term: diabetes, cardiovascular disease), treatment-related events (compliance, discontinuation, switch) and death. In the model, patients transitioned between relapse and stable states, with antipsychotics reducing the risk of relapse. Antipsyshotics were also associated with side effects lowering patients’ quality of life, and leading to possible treatment discontinuation or reduced compliance. The new model combines the unique features of previous DES models, including compliance, flexible treatment sequence and discontinuation due to patient’s choice. In addition, the new model incorporates long-term side effects and associated death. The model was programmed in R 3.4.3.

CONCLUSIONS: This new economic model provides a tool for the evaluation of treatment strategies for schizophrenia with high flexibility in a transparent way. Next steps include an extensive model validation. The model codes will be made available open-source so that other researchers can check, use and update it.