ASA Princeton-Trenton Chapter 2019 Half-Day Traveling Course

Dear Fellow Statisticians,

The ASA Princeton-Trenton chapter is hosting a half-day 2019 ASA traveling short course:

Analysis of Big Healthcare Databases

Instructor: Rebecca Hubbard, University of Pennsylvania

When: Friday, September 20, 2019 from 9:15 AM to 2:30 PM

Where:

West Lecture Hall, Rutgers Robert Wood Johnson Medical School

675 Hoes Lane West,

Piscataway, NJ 08854

Note that refreshments and a lunch buffet are included in each registration, and parking is complimentary and available in Lots A, B & C. Event Parking signage will be at the entrance to Lots A, B & C. Please note that all event parking at Rutgers now requires an online registration:

https://rudots.nupark.com/events/Events/Register/0315a48c-d7af-40d7-8873-1e893e17c774

Until this process is completed your vehicles are not registered and may receive a citation.

Agenda:

Time

Topic

8:45 – 9:10 am

Registration, Coffee, Breakfast, and Check In

9:10 – 9:15 am

Greetings and Introduction

9:15 – 10:30 am

Data structure and extracting data elements from the EHR

10:30 – 10:45 am

Break

10:45 – 12:00 PM

Missing data and outcome misclassification

12:00 – 1:00 pm

LUNCH Break

1:00 – 2:30 pm

Correcting for bias due to EHR data errors

This is a great opportunity for learning and networking. We look forward to seeing you at this exciting event.

ASA Princeton-Trenton Chapter

Abstract: The widespread adoption of electronic health records (EHR) as a means of documenting medical care has created a vast resource for the study of health conditions, interventions, and outcomes in routine clinical practice. Using healthcare databases, including EHR and administrative claims data, for research facilitates the efficient creation of large research databases, execution of pragmatic clinical trials, and study of rare diseases. Despite these advantages, there are many challenges for research conducted using these data. To make valid inference, statisticians must be aware of data generation, capture, and availability issues and utilize appropriate study designs and statistical analysis methods to account for these issues. In this course, we will discuss topics related to the design and analysis of research studies using big healthcare databases. We will cover issues related to the structure and quality of the data, including data types and methods for extracting variables of interest; sources of missing data; error in covariates and outcomes extracted from EHR and claims data; and data capture considerations such as informative visit processes and medical records coding procedures. In the second half of the course, we will discuss statistical approaches to address some of the challenges and unique features of healthcare databases, including missing data and error in automated algorithm-derived covariates and outcomes. We will also discuss some cutting-edge methods developed to address the unique challenges of this context such as privacy-preserving computation for use in distributed research networks. The overarching objective of this course is to provide participants with an introduction to the structure and content of healthcare databases and equip them with a set of appropriate tools to investigate and analyze this rich data resource.

About the instructor: Rebecca Hubbard is an Associate Professor of Biostatistics in the Department of Biostatistics, Epidemiology and Informatics at the University of Pennsylvania. Her methodological research emphasizes development of statistical tools to support valid inference for EHR-based analyses, accounting for complex data availability and data quality issues, and has been applied across a variety of domains including studies of cancer epidemiology, aging and dementia, and pharmacoepidemiology. She has experience conducting analyses using data from a number of large healthcare databases including Medicare, PCORnet, Kaiser Permanente, Flatiron Health, and Optum. Results of this work have been published in over 100 peer-reviewed papers in the statistical and medical literature. She has taught short courses at ENAR, the FDA, and the Summer Institutes in Statistical Genetics and Statistics for Clinical Research at the University of Washington over the past 10 years.