PMED Transition Workshop: May 20-21, 2019


This workshop was held at SAS Hall on the campus of NC State University.


The transition workshop for the PMED program was an opportunity for the active working groups in the program to exchange results and share their perspectives on common issues. This workshop focused on recent research progress that has been made in connection with the many research areas spanned by the PMED program. Sessions of talks dedicated to each working group were presented by active members. The workshop’s goal was to facilitate planning for continuing collaborations on further research questions to extend beyond the period of the PMED program.

Schedule and Supporting Media

Printed Schedule
Speaker Titles/Abstracts
Participant List

Monday, May 20, 2019
Room 1102, SAS Hall, N.C. State University, Raleigh, NC

Description Speaker Slides
Theme 1 (Tumor Heterogeneity)
Intro and Overview on Tumor Heterogeneity Working Group Kevin Flores, N.C. State University
John Nardini, SAMSI & N.C. State University
Virtual Tumor Populations from a Randomized Reaction-Diffusion Modely Nick Henscheid, University of Arizona
Nonlinear Mixed Effects Models Applied to Tumor Heterogeneity Rebecca Everett, N.C. State University
Creating Virtual Populations for Modeling Tumor Heterogeneity John Nardini, SAMSI & N.C. State University
Applications of Machine Learning to Heterogeneous Population Data John Lagergren, N.C. State University
Non-parametric Techniques for Estimating Tumor Heterogeneity Erica Rutter, N.C. State University
Theme 2 (Sequential Decision Making (Observational Data))
Session title: Real World Challenges in Observational Data DTR Analyses
Intro and Overview on Theme 2 Erica Moodie, McGill University
Dynamic Treatment Regimes via Reward Ignorant Modeling Michael Wallace, University of Waterloo
Using Inverse Conditional Probability Weights to Adjust for Unmeasured Cluster-Specific Confounding in Clustered Data Zulin He, Iowa State University
Estimation and Optimization of Composite Outcomes Daniel Luckett, University of North Carolina, Chapel Hill
Theme 3 (Observational Microbiome)
Introduction to the Observational Microbiome Working Group Session Li Ma, Duke University
Network Methods for Integrating Compositional Microbiome Data with Machine Learning Andrew Hinton, University of North Carolina, Chapel Hill
Bayesian Graphical Compositional Regression for Microbiome Data Jialiang Mao, Duke University
MIMIX: a Bayesian Mixed-Effects Model for Microbiome Data from Designed Experiments Brian Reich, N.C. State University

Tuesday, May 21, 2019
Room 1102, SAS Hall, N.C. State University, Raleigh, NC

Description Speaker Slides Video
Plenary Talk 1: A Bayesian Model for Joint Longitudinal and Survival Outcomes in the Presence of Subpopulation Heterogeneity Elizabeth Slate, Florida State University
Plenary Talk 2: Some Recent Advances in Precision Medicine and Machine Learning Michael Kosorok, University of North Carolina, Chapel Hill
Plenary Talk 3: Machine Learning Methods to Learn Improved Electrophysiological Biomarkers in Clinical Trials David Carlson, Duke University

Questions: email