{"success":1,"msg":"","color":"rgb(28, 35, 49)","title":"Handling Sampling and Selection Bias in Association Studies Embedded in Electronic Health Records<\/b>","description":"webinar","title2":"","start":"2020-10-16 15:30","end":"2020-10-16 16:30","responsable":"Simon Mak <\/i><\/a>","speaker":"Bhramar Mukherjee","id":"9","type":"webinar","timezone":"America\/New_York","activity":"JoinZoom Meeting \r\n\r\nhttps:\/\/duke.zoom.us\/j\/92397382385?pwd=NW5aTmZSWUpzOCtneTJDaFZFV0l4dz09\r\n\r\n \r\n\r\nMeetingID: 923 9738 2385 \r\nPasscode: 425966 \r\nOne tap mobile \r\n+13126266799,,92397382385# US (Chicago) \r\n+16468769923,,92397382385# US (New York)\r\n\r\nDial by your location \r\n +1 312 626 6799 US (Chicago) \r\n +1 646 876 9923 US (New York) \r\n +1 301 715 8592 US(Germantown) \r\n +1 669 900 6833 US (San Jose) \r\n +1 253 215 8782 US (Tacoma) \r\n +1 346 248 7799 US (Houston) \r\n +1 408 638 0968 US (San Jose) \r\nMeeting ID: 923 9738 2385 \r\nFind your local number: https:\/\/duke.zoom.us\/u\/ab1gm63ZHV\r\n\r\nJoin bySIP \r\n92397382385@zoomcrc.com\r\n\r\nJoin byH.323 \r\n162.255.37.11 (US West) \r\n162.255.36.11 (US East) \r\n115.114.131.7 (India Mumbai) \r\n115.114.115.7 (India Hyderabad) \r\n213.19.144.110 (EMEA) \r\n103.122.166.55 (Australia) \r\n64.211.144.160 (Brazil) \r\n69.174.57.160 (Canada) \r\n207.226.132.110 (Japan) \r\nMeeting ID: 923 9738 2385 \r\nPasscode: 425966","abstract":"In this talk we will discuss statistical challenges and opportunities with joint analysis of electronic health records and genomic data through \"Genome and Phenome-Wide Association Studies (GWAS and PheWAS)\". We posit a modeling framework that helps us to understand the effect of both selection bias and outcome misclassification in assessing genetic associations across the medical phenome. We will propose various inferential strategies that handle both sources of bias to yield improved inference. We will use data from the UK Biobank and the Michigan Genomics Initiative, a longitudinal biorepository at Michigan Medicine, launched in 2012 to illustrate the analytic framework. The examples illustrate that understanding sampling design and selection bias matters for big data, and are at the heart of doing good science with data. This is joint work with Lauren Beesley at the University of Michigan."}