Updated: Apr 11, 2019
Together with Erasmus MC we developed a discrete event simulation model that helps to determine the cost-effectiveness of medical intervention. Discrete event models can model patients at an individual level in contrary to the more commonly used Markov models. This allows taking into account more sophisticated patient pathways.
Results of this study were presented in the article: “Simulation study on the impact of direct meta-analysis methods on health economic outcomes” by P. Vemer (Pepijn), M.J. Al (Maiwenn), M. Oppe (Mark) and M.P.M.H. Rutten-van Mölken (Maureen)
Decision-analytic cost-effectiveness (CE) models combine many different parameters like transition probabilities, event probabilities, utilities and costs, which are often obtained after meta-analysis. The method of meta-analysis may affect the CE estimate. Aim: Our aim was to perform a simulation study that compares the performance of different methods of meta-analysis, especially with respect to model-based health economic (HE) outcomes. Methods: A reference patient population of 50,000 was simulated from which sets of samples were drawn. Each sample drawn represented a clinical trial comparing two fictitious interventions. In several scenarios, the heterogeneity between these trials was varied, by drawing one or more of the trials from predefined subpopulations. Parameter estimates from these trials were combined using frequentist fixed (FFE) and random effects (FRE), and Bayesian fixed (BFE) and random effects (BRE) meta-analysis.