BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Drupal iCal API//EN
X-WR-CALNAME:Events items teaser
X-WR-TIMEZONE:America/Toronto
BEGIN:VTIMEZONE
TZID:America/Toronto
X-LIC-LOCATION:America/Toronto
BEGIN:DAYLIGHT
TZNAME:EDT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
DTSTART:20190310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:EST
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
DTSTART:20191103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:68281384609a4
DTSTART;TZID=America/Toronto:20200120T100000
SEQUENCE:0
TRANSP:TRANSPARENT
DTEND;TZID=America/Toronto:20200120T100000
URL:/statistics-and-actuarial-science/events/department
 -seminar-jared-huling-ohio-state-university
SUMMARY:Department seminar by Jared Huling\, Ohio State University
CLASS:PUBLIC
DESCRIPTION:Summary \n\nSUFFICIENT DIMENSION REDUCTION FOR POPULATIONS WITH
  STRUCTURED\nHETEROGENEITY\n\nRisk modeling has become a crucial component
  in the effective delivery\nof health care. A key challenge in building ef
 fective risk models is\naccounting for patient heterogeneity among the div
 erse populations\npresent in health systems. Incorporating heterogeneity b
 ased on the\npresence of various comorbidities into risk models is crucial
  for the\ndevelopment of tailored care strategies\, as it can provide\npat
 ient-centered information and can result in more accurate risk\nprediction
 . Yet\, in the presence of high dimensional covariates\,\naccounting for t
 his type of heterogeneity can exacerbate estimation\ndifficulties even wit
 h large sample sizes. Towards this aim\, we\npropose a flexible and interp
 retable risk modeling approach based on\nsemiparametric sufficient dimensi
 on reduction. The approach accounts\nfor patient heterogeneity\, borrows s
 trength in estimation across\nrelated subpopulations to improve both estim
 ation efficiency and\ninterpretability\, and can serve as a useful explora
 tory tool or as a\npowerful predictive model. In simulated examples\, we s
 how that our\napproach can improve estimation performance in the presence 
 of\nheterogeneity and is quite robust to deviations from its key\nunderlyi
 ng assumption. We demonstrate the utility of our approach in\nthe predicti
 on of hospital admission risk for a large health system\nwhen tested on fu
 rther follow-up data.\n 
DTSTAMP:20250517T044140Z
END:VEVENT
END:VCALENDAR