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

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 …

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 …

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 …

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 …

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 …

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

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