COPSS-NISS COVID-19 Data Science Webinar Series

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Flying the Plane While Improving It – Learning from COVID Patient Data in Close to Real Time

About this Webinar Series

The COPSS-NISS COVID-19 Data Science webinar series is co-organized by the Committee of the Presidents of Statistical Societies (COPSS) and its five charter member societies (ASA, ENAR, IMS, SSC, and WNAR), as well as NISS.  This bi-weekly seminar features the latest research that is positioned on the cusp of new understanding and analysis of COVID-19 pandemic data, and promotes data-driven research and decision making to combat COVID-19. Find out more about this series and view all the previous sessions on the Webinar Series page.

Abstract

In the current pandemic the need to translate clinical data into actionable research to inform patient care has never been more urgent. Using the Precision Medicine Analytics Platform at Johns Hopkins University, a team of clinicians, biostatisticians and information technologists at the Precision Medicine Center of Excellence for COVID-19 rapidly created a COVID-19 patient registry and deployed an array of biostatistical methods to understand patient trajectories, conduct comparative effectiveness research for therapeutics, learn about pathobiology of COVID-19 and implement a real-time prediction model into the electronic health record (EHR). Join this session to hear about the lessons learned from conducting research in real time while building a complex data registry. 

Participants

Speakers

Scott Zeger, (Johns Hopkins University)

Scott Zeger's methodologic research is to develop statistical models that support scientific learning about human health. Earlier work was on regression models for correlated responses that arise when observations come in clusters, for example in longitudinal research or in sample surveys or when data are observed over time or space. We have extended generalized linear models (logistic, linear, log-linear and survival models) to be applicable in these cases. More recently, my work has been on Bayesian models for "individualized health", that is to use population data to improve decisions about an individual's health state, trajectory or likely benefits and costs of competing interventions. We have applied our novel methods to estimate the etiology of children's pneumonia, trajectory of mental disorders and to predict whether a man's prostate cancer is indolent or aggressive.

Brian Garibaldi, (Johns Hopkins University)

Brian Garibaldi is an associate professor in the Division of Pulmonary and Critical Care Medicine, where he attends in the Medical Intensive Care Unit (MICU) and the Interstitial Lung Disease clinic. He is medical director of the Johns Hopkins Biocontainment Unit (BCU), a federally-funded special pathogens treatment center. He is also the associate program director of the Osler Medical Residency Program, where he leads curriculum development and implementation.

Moderator

Karen Bandeen-Roche, (Johns Hopkins University)

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Organizing Committee

Xihong Lin (Chair) (IMS), Harvard University 
Karen Bandeen-Roche (NISS), Johns Hopkins University 
Chris Barker (ASA), Statistical Planning and Analysis Services, Inc
Gary Chan (WNAR), University of Washington 
Rob Deardon (SSC), University of Calgary 
Natalie Dean (COPSS), University of Florida
Debashree Ray (COPSS), Johns Hopkins University
Jie Peng (WNAR), University of California at Davis 
Nathaniel Stevens (SSC), University of Waterloo 
Elizabeth Stuart (ENAR), Johns Hopkins University 
Ryan Tibshirani (IMS), Carnegie Mellon University  
Lily Wang (ASA), Iowa State University 
Lingzhou Xue (NISS), Pennsylvania State University
Lili Zhao (ENAR), University of Michigan 
Glenn Johnson (Web Communications), NISS