Decision-analytic models for breast cancer: Do currently published models meet the requirements of personalized medicine?

To give an insight into the structure and methodology of simulation approaches used for evaluating interventions in breast-cancer. Research increasingly focuses on personalized strategies in medicine. Therefore, conventional clinical studies, genomic and proteomic (-omics) and biomarker studies need to be translated into patient-tailored prediction rules and treatment decisions. Recommendations for future comprehensive breast-cancer simulation are derived based on published simulations and requirements arising from personalized medicine, A systematic literature review is performed and information on study design, simulation framework etc. are extracted. Strengths and limitations are assesed. The review shows how currently available models encompass the underlying biologic disease progression, treatment effects including complications and economic outcomes. We expect that these models are limited with respect to information from -omics and biomarker studies. Discrete-event-simulation and agent-based simulation approaches are discussed. This work provides an essential basis for further simulation approaches to evaluate personalized breast-cancer treatment.