Recent developments in the field of sensor devices provide new possibilities to measure a variety of health related aspects in a precise and fine-grained manner. Subsequently, more empirical data will be generated than ever before. While this greatly improves the opportunities for creating accurate predictive models, other types of models besides the more traditional machine learning approaches can provide insights into temporal relationships in the data. Models that express temporal relationships between states in a mathematical manner are examples of such models. However, the evaluation methods traditionally used in the field of predictive modeling are not appropriate for those models, making it difficult to distinguish them in terms of validity. Appropriate assessment methodology is therefore necessary to drive the research of mathematical modeling forward. In this paper we investigate the applicability of such a formalized method. The method takes into account important model aspects, namely descriptive and predictive capability, parameter sensitivity and model complexity. As a case study the method is applied to a mathematical model in the domain of mental health, showing that the method generates useful insights into the behavior of the model.