Scott F Grey

Dr. Scott F Grey is an Assistant Professor in the Department of Surgery at the Uniformed Services University of the Health Sciences (USUHS) School of Medicine. He currently serves as Associate Director of Biostatistics for the Surgical Critical Care Initiative (SC2i), where he manages the Bioinformatics Core Services (BiCS) group. His work focuses on developing machine learning algorithms and computerized decision support tools for critically injured patients, with particular expertise in statistical methods for causal inference.

Dr. Grey earned his BS in Physical Education from Kent State University, followed by MS and PhD degrees in Epidemiology and Biostatistics from Case Western Reserve University. His doctoral dissertation examined how different definitions of compliance impacted estimates of treatment efficacy in complier average causal effects (CACE) analysis.

Dr. Grey's research centers on the development of computerized decision support tools to improve clinical decision-making, with a focus on statistical methods for causal inference. His work spans military medicine, trauma care, and critical care, applying advanced biostatistical techniques to complex clinical problems. He has particular expertise in machine learning applications for risk stratification and prediction modeling in vascular surgery, trauma, and critical care settings.

Analysis of Dr. Grey's recent publications reveals a strong focus on military medicine and combat casualty care, with particular emphasis on trauma outcomes, wound healing, and infection prediction. His work frequently employs advanced statistical methods, machine learning, and genomic analysis to address complex clinical problems in critical care settings. A significant portion of his research involves developing clinical decision support tools to improve diagnostic accuracy and treatment decisions in emergency and trauma situations.

Dr. Grey has served as lead statistician for multiple NIH clinical trial networks and large prospective observational studies. His expertise 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.

As Associate Director of Biostatistics for SC2i, Dr. Grey leads a team of biostatisticians, bioinformaticians, and programmers developing machine learning algorithms that utilize complex clinical and biomarker data. His Bioinformatics Core Services group provides analytic support to various research projects at SC2i, including the development of productive algorithms for computer decision support tools, clinical trials to evaluate CDSTs, and various observational studies of critical care.