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Jonathan Chipman, Ph.D.

Languages spoken: English, Portuguese

Academic Information

Departments: Population Health Sciences - Assistant Professor, Family & Preventive Medicine - Adjunct Assistant Professor

Divisions: Biostatistics, DFPM Administration

Academic Office Information

Jonathan.Chipman@hci.utah.edu

(801) 213-8474

Huntsman Cancer Hospital

1950 Circle of Hope, Room:
Salt Lake City, UT 84112

Research Interests

  • Adaptive Monitoring Schemes
  • Randomization Inference
  • Covariate-Adjusted Randomization
  • Causal Inference

Dr. Chipman is an Assistant Professor of Biostatistics within the Department of Population Health Sciences. He is a member of the Cancer Biostatistics Shared Resource at the Huntsman Cancer Institute (HCI) and serves on the HCI Data Safety and Monitoring Committee as well as multiple single-study DSMCs.

Dr. Chipman received his PhD in Biostatistics from Vanderbilt University and joined the University of Utah in September 2019. His personal research focuses on methods to increase the effective sample size of clinical trials using covariate-adjusted randomization and to adaptively monitor trials and observational studies. Dr. Chipman's research efforts focus on not only establishing statistical significance but also scientific relevance. In his collaborative research, Dr. Chipman has worked extensively on observational and experimental studies evaluating the quality of life and comparative effectiveness of interventions.

Prior to receiving his PhD, he earned a BS and MS in statistics. Between his MS and PhD studies, he worked four years as a collaborative statistician at the Dana-Farber Cancer Institute. And, while earning his PhD, he worked three years as a collaborative statistician for the Tennessee Valley Veteran’s Health Affairs.

Research Statement

I am trained in the design and analysis of clinical trials and observational studies.

My students and I develop methods that increase a study’s efficiency and effectiveness. Our current emphasis is in developing covariate-adjusted randomization and sample-size adaptive monitoring methods.

These methods reduce on average the noise in estimating an effect, improve the ability to study key subgroups, and allow for following studies until drawing clinically meaningful conclusions regarding the strength of the true effect.

Education History

Doctoral Training Vanderbilt University
Biostatistics
PhD
Graduate Training University of Minnesota
Biostatistics
MS
Undergraduate Brigham Young University
Major: Statistical Science; Minors: Mathematics and Business Management
BS