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.

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

Dissertation Defense

I’m finally defending! Join the fun on Zoom on May 10 at 10am ET. Contact me for details.

More Details

Outstanding Graduate Student Instructor Award

I am thrilled and honored to have won an Outstanding Graduate Student Instructor Award from Rackham Graduate School at the University of Michigan, which is given annually to twenty GSIs across the University.

Learn More

Previous Next

Recent Publications

Sample Size Considerations for Comparing Dynamic Treatment Regimens in a Sequential Multiple-Assignment Randomized Trial with a Continuous Longitudinal Outcome

Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen is a …
Nicholas J. Seewald, Kelley M. Kidwell, Inbal Nahum-Shani, Tianshuang Wu, James R. McKay, Daniel Almirall
DOI Supplement arXiv

Practical Considerations for Data Collection and Management in Mobile Health Micro-Randomized Trials

There is a growing interest in leveraging the prevalence of mobile technology to improve health by delivering momentary, contextualized …
Nicholas J. Seewald, Shawna N. Smith, Andy Jinseok Lee, Predrag Klasnja, Susan A. Murphy
DOI arXiv
See all publications

Recent & Upcoming Talks

Dissertation Defense: Design and Analytic Considerations for Sequential, Multiple-Assignment Randomized Trials with Longitudinal Outcomes

Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen …
May 10, 2021 10:00 AM — 11:30 AM
Slides Notation Cheat Sheet longsmart

Budgeting SMART: Sample Size and Repeated Measures with a Cost Constraint in a Longitudinal Sequential, Multiple-Assignment Randomized Trial

Clinical practice often involves delivering a sequence of treatments which adapts to a patient’s changing needs. A dynamic treatment …
Mar 17, 2021 3:15 PM — 3:19 PM Virtual
Nicholas J. Seewald, Daniel Almirall
Slides

Sample Size and Timepoint Tradeoffs for Comparing Dynamic Treatment Regimens in a Longitudinal SMART

Clinical practice often involves delivering a sequence of treatments which adapts to a patient’s changing needs. A dynamic treatment …
Aug 4, 2020 10:00 AM — 2:00 PM Virtual
Nicholas J. Seewald, Daniel Almirall
Video Slides Video Script

Sample Size and Timepoint Tradeoffs for Comparing Dynamic Treatment Regimens in a Longitudinal SMART

Mar 23, 2020 11:30 AM — Mar 1, 2020 11:45 AM Virtual
Nicholas J. Seewald, Daniel Almirall
Slides

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

  • 323 West Hall, 1085 South University, Ann Arbor, MI 48109
  • DM Me