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BEGIN:VEVENT
DTSTAMP:20260508T114351Z
DTSTART;VALUE=DATE-TIME:20221124T120000
DTEND;VALUE=DATE-TIME:20221124T130000
SUMMARY:Health Sciences Seminar Series - Prof Bhramar Mukherjee
TZID:Europe/London
UID:20221124-8a17841a8452edfb018460db07c873e1@warwick.ac.uk
CREATED:20221117T143820Z
DESCRIPTION:Using Electronic Health Records for Scientific Research: Prom
 ises and Perils Professor Bhramar Mukherjee Bhramar Mukherjee is the Joh
 n D. Kalbfleisch Collegiate Professor and chair of the Department of Bio
 statistics at the University of Michigan School of Public Health. She is
  also a professor in the Department of Epidemiology and a professor of G
 lobal Public Health at the School of Public Health. Mukherjee's research
  focuses on the development and application of statistical methods in ep
 idemiology\, environmental health\, cancer research and disease risk ass
 essment. She has authored more than 340 publications in statistics\, bio
 statistics\, epidemiology and medical journals and has led several impac
 tful extramural grants as a principal investigator from both the NSF and
  the NIH. Dedicated to diversifying the statistical and data science wor
 kforce\, Mukherjee has been leading a flagship undergraduate summer prog
 ram in Big Data since 2015. This program has trained nearly 300 undergra
 duates\, more than 60% of whom go on to pursue graduate school in a quan
 titative field. She is a fellow of the American Statistical Association 
 and the American Association for the Advancement of Science. She is reci
 pient of many awards\, including the Janet Norwood Award and the Sarah G
 oddard Power award in 2021. In 2022\, she was elected to the National Ac
 ademy of Medicine\, one of the highest honors for researchers in health 
 and medicine. Using Electronic Health Records for Scientific Research: P
 romises and Perils Electronic Health Records (EHR) linked with other aux
 iliary data sources hold tremendous potential for conducting real time a
 ctionable research. However\, one has to answer two fundamental question
 s before conducting inference: "Who is in my study?" and "What is the ta
 rget population of Inference?". Without accounting for selection bias\, 
 one can quickly produce rapid but inaccurate conclusions. In this talk\,
  I will discuss a statistical framework for jointly considering selectio
 n bias and phenotype misclassification in analyzing EHR data. Examples w
 ill include genome and phenome-wide association studies of Cancer and CO
 VID-19 outcomes using data from the Michigan Genomics initiative and the
  UK Biobank. This is joint work with Lars Fritsche\, Lauren Beesley and 
 Maxwell Salvatore at the University of Michigan School of Public Health.
  _______________________________________________________________________
 _________ Microsoft Teams meeting Join on your computer\, mobile app or 
 room device Click here to join the meeting Meeting ID: 369 491 860 696 P
 asscode: Phh5tJ
LOCATION:A042 ÌÇÐÄTV Medical School and Teams
CATEGORIES:EventsAndOpendays,HealthGRP,GRPEvents,HS_APC,HS_SSSH,HS_STATS,
 HS_MHWB,HealthSciences
LAST-MODIFIED:20221117T143820Z
ORGANIZER;CN=Roulla Philippou:
END:VEVENT
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