Target Trial Emulation for Evaluating Mental Health Policy

This methods webinar is part of a series put on by the NIMH-funded Johns Hopkins ALACRITY Center for Health and Longevity in Mental Illness. The focus of the Johns Hopkins ALACRITY Center is to translate evidence-based interventions to reduce premature mortality among consumers with serious mental illness (SMI) into community mental health settings in Maryland and Nationwide. This workshop series aims to highlight advanced quantitative methods and mixed methods to help answer important questions in mental health services research. About this Presentation: Estimating the impact of mental health policy is challenging, often due to small sample sizes and high heterogeneity across different policy implementations. Target trial emulation, an approach to designing rigorous nonexperimental studies by “emulating” key features of a clinical trial, can help. Most commonly used outside of policy contexts, this approach is also valuable for policy evaluation as policies typically are not randomly assigned. In this webinar, I introduce the policy trial emulation framework and discuss how it can be used to assess the effect of a health policy on clinical or population health outcomes. Speaker Bio: Nicholas J Seewald, PhD is an Assistant Professor of Biostatistics, in the Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania Perelman School of Medicine. He develops and applies statistical methodology to answer key questions in public health and medicine through thoughtful study design and analysis combined with deep collaboration with applied scientists. His work is motivated by problems across a wide array of applications, including physical activity, oncology, and substance use and related policy, and spans the entire investigative process from formulating a research question through study design and data analysis. Dr. Seewald’s goal is to develop statistical methods that empower scientists to make impactful contributions in their fields. His methodological work involves building tools to address important statistical issues in a way that is accessible and understandable to applied researchers. His work is primarily related to causal inference – the use of data to make causal conclusions through precise assumptions, strong study design, and estimation techniques – with complex repeated-measures data. In July 2023, Dr. Seewald completed his postdoctoral fellowship in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health. Prior to joining us at Hopkins, he studied at the University of Michigan where he earned his PhD in statistics, a master’s in statistics, and a master’s in biostatistics. He earned his bachelor’s degree in mathematics from the University of Notre Dame.