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 randomized trials which yield complex longitudinal data for 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

Interests
  • Dynamic Treatment Regimens
  • Sequential, Multiple-Assignment Randomized Trials
  • Randomized Trial Design
  • Complex Longitudinal Data
Education
  • 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

Software

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

Contact

  • Hampton House 501, 624 N Broadway, Baltimore, MD 21205
  • DM Me