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UID:20260728T191712EDT-9394koUVV9@132.216.98.100
DTSTAMP:20260728T231712Z
DESCRIPTION:Sherri Rose\, PhD\n\nAssistant Professor\, Department of Health
  Care Policy\, Harvard Medical School\n\nA Robust Machine-Learning Approac
 h for Variable Importance in Health Spending\n\nALL ARE WELCOME\n\nAbstrac
 t:\n\nThe impact of medical conditions on health care spending has almost 
 exclusively been examined in parametric regression for health plan payment
  risk adjustment. This paper presents nonparametric machine-learning-based
  effect estimators for variable importance to understand the role of indiv
 idual medical condition categories in health spending among commercially i
 nsured enrollees. We evaluate how much more\, on average\, enrollees with 
 each medical condition cost after controlling for demographic information 
 and other medical conditions. This is accomplished within the targeted lea
 rning framework using targeted maximum likelihood estimation and super lea
 rning to estimate the effects of these medical conditions. Our results dem
 onstrate that multiple sclerosis\, congestive heart failure\, severe cance
 rs\, major depression and bipolar disorders\, and chronic hepatitis are th
 e most costly medical conditions on average per individual.  In contrast\,
  standard parametric regression formulas for plan payment risk adjustment 
 differed nontrivially both in the size of effect estimates and relative ra
 nks. The health spending literature may be considerably underestimating th
 e spending contributions of a number of medical conditions\, which is a po
 tentially critical oversight. If current risk-adjustment methods are not c
 apturing the true incremental effect of medical conditions\, undesirable i
 ncentives related to adverse selection in health insurance markets may rem
 ain.\n\nBio:\n\nwww.drsherrirose.com\n\n \n\n \n
DTSTART:20160405T193000Z
DTEND:20160405T203000Z
LOCATION:Room 24\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:Biostatistics Seminar: 'A Robust Machine-Learning Approach for Vari
 able Importance in Health Spending'
URL:https://www.mcgill.ca/epi-biostat-occh/channels/event/biostatistics-sem
 inar-robust-machine-learning-approach-variable-importance-health-spending-
 259541
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