Predictive models have been broadly used in medical decision making for cost-effectiveness analysis, comparative effectiveness research, etc. Recent advancements in computing technology have facilitated the development of increasingly intricate predictive models aimed at describing complex health processes and systems. Depending on the specific characteristics there is a large variety of complex predictive models including but not limited to state transition, discrete event simulation, dynamic transmission, compartmental, microsimulation, and agent-based models. Center faculty have extensive expertise in complex predictive modeling. Dr Chrysanthopoulou specializes in statistical techniques for calibration, validation, and predictive accuracy assessment of microsimulation models. She has developed the open-source MIcrosimulation Lung Cancer (MILC) model of the natural history of lung cancer, and is involved in collaborative projects with Brown and Boston University on building complex models for cancer, dementia, opioid use disorder, and sexually transmitted diseases.