OBJECTIVES: In the last years, the application of discrete event simulation (DES) increased considerably. According to a literature review, DES gives similar results but is much more time consuming. The objective is to compare Markov models and DES for chronic obstructive lung disease (COPD) using the simulation software ARENA. METHODS: PubMed and EMBASE were searched for articles comparing both approaches. Criteria were extracted to compare a Markov model for COPD with a DES model evolved in this study. The base COPD model is coextensive with a Markov model implemented in Excel, the DES model additionally incorporates an age distribution, which was derived from a study of the Robert Koch Institute (RKI). Otherwise, both models were based on the same data and probabilities. The models’ quality was validated by the criteria list of Philips et al. (2006) securing quality standards for decision analytic models. RESULTS: Comparison of both modeling approaches demonstrated significant advantages of the flexibility of DES. This was not outweighed by more complex and time consuming data evaluation, modeling, and simulation. DES enables more scopes for development and increasing modeling flexibility by integrating extensions to standard Markov models. However, possible advantages and problems of DES were only assessed with regard to the integration of an age distribution. This distribution reflects the prevalence of chronic bronchitis and therefore differs from the real age-related prevalence of COPD. However, due to lacking data it was necessary to implement this distribution. CONCLUSIONS: DES allows modeling complex diseases with different disease stages and various influences. Compared to Markov models, DES is more flexible in its application and reflection of reality. It provides significant advantages in data integration and is able to gather, process, and analyze a multiple of information. The disadvantages of DES concerning complex data evaluation, modeling, and simulation could not be followed.