Emil Coman, Ph.D.

Emil ComanResearch Associate II

Dr. Coman is a seasoned statistician with expertise in complex designs and analyses, with particular application in analyzing health disparities and comparing effectiveness of interventions and treatments from a patient centered perspective. His main focus is on precise and reliable systematic measurement of health related outcomes, dealing with data and modeling challenges to ensure sound causal conclusions that can inform policy, and answering specific health disparities and comparative effectiveness questions about delivery of equitable health care. His interests center on statistically assessing evidence and translating it to providers. He has worked on projects like developing patient and provider versions of Care Coordination measures, utilizing peer-supporters to help diabetic patients, the development of a Health Disparities/Equity hospital dashboard, and preventing diabetes in at risk racial/ethnic minorities. He specializes in latent variable modeling (or SEM), evaluation of intervention effects (e.g., with propensity score matching and complier average causal effects models), complex issues of measurement (scales vs. indices, dichotomous indicators), longitudinal and dynamical modeling, models with feedback loops, social network analysis, mixture analysis (unobserved/latent groupings), and simple and modern causal mediation.

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Structural Equation Modeling (SEM)