Discrete event simulation (DES) is a form of computer-based modeling that provides an intuitive and flexible approach to representing complex systems. It has been used in a wide range of health care applications. Most early applications involved analyses of systems with constrained resources, where the general aim was to improve the organization of delivered services. More recently, DES has increasingly been applied to evaluate specific technologies in the context of health technology assessment. The aim of this article was to provide consensus-based guidelines on the application of DES in a health care setting, covering the range of issues to which DES can be applied. The article works through the different stages of the modeling process: structural development, parameter estimation, model implementation, model analysis, and representation and reporting. For each stage, a brief description is provided, followed by consideration of issues that are of particular relevance to the application of DES in a health care setting. Each section contains a number of best practice recommendations that were iterated among the authors, as well as among the wider modeling task force.

In our opinion, discrete event simulation should be the preferred technique for health economic evaluations today.

BackgroundIn schizophrenia, medication adherence is critical to achieve better patient outcomes and to avoid relapses, which are responsible for a significant proportion of total healthcare costs for this chronic illness. The aim of this study was to assess the cost-effectiveness of olanzapine long-acting injection (OLAI) compared with risperidone long-acting injection (RLAI) in patients with schizophrenia in Spain.MethodsA discrete event simulation (DES) model was developed from a Spanish healthcare system perspective to estimate clinical and economic outcomes for patients with schizophrenia over a five-year period. Patients who had earlier responded to oral medication and have a history of relapse due to adherence problems were considered. Identical model populations were treated with either OLAI or RLAI. In the absence of a head-to-head clinical trial, discontinuation and relapse rates were obtained from open-label studies. The model accounted for age, gender, risks of relapse and discontinuation, relapse management, hospitalization, treatment switching and adverse events. Direct medical costs for the year 2011 and outcomes including relapse avoided, life years (LYs), and quality-adjusted life years (QALYs) were discounted at a rate of 3%.ResultsWhen comparing RLAI and OLAI, the model predicts that OLAI would decrease 5-year costs by €2,940 (Standard Deviation between replications 300.83), and result in a QALY and LY gains of 0.07 (SD 0.019) and 0.04 (SD 0.025), respectively. Patients on OLAI had fewer relapses compared to RLAI (1.392 [SD 0.035] vs. 1.815 [SD 0.035]) and fewer discontinuations (1.222 [SD 0.031] vs. 1.710 [SD 0.039]). Sensitivity analysis indicated that the study was robust and conclusions were largely unaffected by changes in a wide range of parameters.ConclusionsThe present evaluation results in OLAI being dominant over RLAI, meaning that OLAI represents a more effective and less costly alternative compared to RLAI in the treatment of patients with schizophrenia in the Spanish setting.

Discrete event simulation (DES) is a form of computer-based modeling that provides an intuitive and flexible approach to representing complex systems. It has been used in a wide range of health care applications. Most early applications involved analyses of systems with constrained resources, where the general aim was to improve the organization of delivered services. More recently, DES has increasingly been applied to evaluate specific technologies in the context of health technology assessment. The aim of this article is to provide consensus-based guidelines on the application of DES in a health care setting, covering the range of issues to which DES can be applied. The article works through the different stages of the modeling process: structural development, parameter estimation, model implementation, model analysis, and representation and reporting. For each stage, a brief description is provided, followed by consideration of issues that are of particular relevance to the application of DES in a health care setting. Each section contains a number of best practice recommendations that were iterated among the authors, as well as the wider modeling task force.