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DTSTAMP:20250813T015533Z
DESCRIPTION:Seminar Epidemiology\, Biostatistics\, & Occupational Health\n
 \nTitle: Survival Analysis in Multi-Site Studies Using Summary-Level Risk 
 Set Tables.\n\nDr. Shu is an assistant professor of Biostatistics in the D
 epartment of Biostatistics\, Epidemiology and Informatics in the Perelman 
 School of Medicine at the University of Pennsylvania. She is also the asso
 ciate director for Biostatistics at the Center for Pediatric Clinical Effe
 ctiveness (CPCE) and a Biostatistics faculty member at the Children’s Hosp
 ital of Philadelphia. Dr. Shu is interested in developing and applying sui
 table statistical methods to assess comparative safety and effectiveness o
 f medical products. Her research areas include causal inference\, measurem
 ent error\, pharmacoepidemiology\, and privacy-protecting methods. She com
 pleted her MSc in Statistics (with specialization in Biostatistics) at the
  University of Western Ontario\, and her PhD in Statistics at the Universi
 ty of Waterloo.\n\n\nMedical research often analyzes data from multiple so
 urces to increase statistical power and generalizability. A growing number
  of studies are now conducted within multi-site distributed data networks.
  For example\, the Sentinel System\, funded by the U.S. Food and Drug Admi
 nistration\, monitors the safety of approved medical products using data f
 rom multiple data partners. Within these networks like the Sentinel System
 \, each data partner maintains physical control of their data and may not 
 always be able or willing to share individual-level data for analysis. In 
 this talk\, I will first provide a brief overview of existing methods that
  enable valid survival analysis without pooling individual-level data acro
 ss sites. Then\, I will introduce a one-step method that allows data partn
 ers to share only summary-level risk set tables to estimate overall and si
 te-specific hazard ratios. Finally\, I will discuss how to apply risk set 
 tables to other important measures such as Kaplan–Meier curves as well as 
 some future topics. I will justify the method theoretically\, illustrate i
 ts use\, and demonstrate its statistical performance using both real-world
  and simulated data.\n\n \n\n \n\nVia Zoom: https://mcgill.zoom.us/j/85978
 187693?pwd=WWtJZUpnb0JXK3o5SStnOFcxK3FFUT09\n
DTSTART:20210929T193000Z
DTEND:20210929T203000Z
SUMMARY:Di Shu (University of Pennsylvania Perelman School of Medicine)
URL:/mathstat/channels/event/di-shu-university-pennsyl
 vania-perelman-school-medicine-333654
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