Megan Schuler, PhD is a Marshall J. Seidman Fellow in Health Care Policy at Harvard Medical School. She is an applied statistician with a strong background in substance use and mental health disorders. Her methodological research involves the estimation of causal effects in observational health data through application of propensity score methods. Additionally, her research examines the effectiveness of treatment for substance use and mental health disorders, barriers to treatment, and the comorbidity of substance use and mental health disorders.
She is working with Dr. Laura Hatfield to develop methods to optimize medical treatment decision-making by jointly considering multiple patient outcomes as well as patient preferences. In other work, they are investigating statistical methods to model longitudinal trajectories of health service utilization, including end-of-life care for oncology patients and psychotherapy and antidepressant use among youth with depression. Additionally, she authored an overview of targeted maximum likelihood estimation for causal inference with Dr. Sherri Rose. She is a member of the Health Policy Data Science Lab.
Dr. Schuler received her PhD in Mental Health in 2013 from the Johns Hopkins Bloomberg School of Public Health, under the mentorship of Dr. Elizabeth Stuart. Her dissertation work sought to integrate latent variable and causal inference methods in order to estimate the causal effects of latent classes on a distal outcome. Dr. Schuler’s doctoral research was funded through Hopkins’ Sommer Scholar program. Dr. Schuler completed a NIDA-funded postdoctoral research fellowship at Penn State, where she was part of The Methodology Center and The Prevention Center. Dr. Schuler received her MS in Biostatistics from the Medical University of South Carolina and her BS in Mathematics from Tulane University.