Location
This workshop was held at SAS Hall on the campus of NC State University.
Description
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 | |
| BREAK | ||
| 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 | ||
| Adjourn |
Questions: email [email protected]