Nick Seewald

Nick Seewald

Postdoctoral Fellow
Department of Health Policy and Management

Johns Hopkins Bloomberg School of Public Health

Hi, I’m Nick.

I develop statistical methodology for the design and analysis of studies involving complex longitudinal data to answer questions about health policy and precision health. Specifically, I focus on methods that aid in the construction of decision rules which specify for whom to provide what treatment and when. I take a broad approach to this, seeking to make an impact in both statistics and domain sciences from a project’s inception to the dissemination of results.

Currently, I am a postdoctoral fellow in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health, working with Elizabeth Stuart, Ph.D. and Beth McGinty, Ph.D. on causal inference for health policy evaluation.

My Ph.D. was supervised by Daniel Almirall, Ph.D..

Learn More

  • Dynamic Treatment Regimens
  • Sequential, Multiple-Assignment Randomized Trials
  • Randomized Trial Design
  • Complex Longitudinal Data
  • PhD in Statistics, 2021

    University of Michigan

  • MA in Statistics, 2018

    University of Michigan

  • MS in Biostatistics, 2015

    University of Michigan

  • BS in Mathematics, 2013

    University of Notre Dame

Recent Publications

Scaling Interventions to Manage Chronic Disease: Innovative Methods at the Intersection of Health Policy Research and Implementation Science
Policy implementation is a key component of scaling effective chronic disease prevention and management interventions. Policy can …


MRT-SS Calculator
An interactive sample size calculator for micro-randomized trials
MRT-SS Calculator
An online sample size calculator for binary- and continuous-outcome SMARTs


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