Dr. Grey obtained his BS in physical education from Kent State University and his MS and PhD in epidemiology and biostatistics from Case Western Reserve University. His doctoral dissertation considered how different definitions of compliance impacted estimates of treatment efficacy in complier average causal effects (CACE) analysis. Dr. Grey served as statistician on a large cluster randomized trial of a school-based substance abuse prevention program at University of Akron and the smaller cluster randomized trial to reduce communicable infections in office settings that Kent State University. He then worked on a longitudinal statewide registry of vascular surgeries, carotid and peripheral vascular interventions in the Division of Cardiology at University of Michigan, developing prediction models of procedure outcomes using machine learning to identify important risk factors, risk stratify patients and risk-adjust hospital comparisons.

Dr. Grey currently manages the Bioinformatics Core Services (BiCS) group in the Surgical Critical Care Initiative, and has an appointment as assistant professor in the Department of Surgery. He leads a team of biostatisticians, bioinformaticians and programmers developing machine learning algorithms that utilize complex clinical and biomarker data to create computerized decision support tools (CDSTs) that can be deployed in the care of critically injured patients. The team also aids the development and conduct of clinical trials to provide evidence of safety and efficacy of CDSTs as well as various observational studies of critical care. Dr. Grey’s statistical expertise also includes developing and running individual and cluster randomized trials, and statistical methods for causal inference such as propensity scores, statistical mediation, complier average causal effects and targeted maximum likelihood estimation.