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UID:362@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20250331T140000
DTEND;TZID=Europe/Paris:20250331T140000
DTSTAMP:20260828T094417Z
URL:https://isdm.umontpellier.fr/events/optimal-treatment-rules-for-the-ne
 t-benefit-of-a-treatment-2/
SUMMARY:Optimal treatment rules for the net benefit of a treatment
DESCRIPTION:Room 02.124\, Building 5\, St Priest campus\n\nMachine Learning
  in Montpellier\, Theory &amp\; Practice - François Petit (Inserm)\n\nWe 
 developed a mathematical setup inspired by Buyse&#x27\;s generalized pairw
 ise comparisons to define the notion of an optimal individualized treatmen
 t rule (ITR) in the presence of a prioritized outcomes in a randomized con
 trolled trial\, terming such an ITR pairwise optimal. We present two appro
 aches to estimate pairwise optimal ITRs. The first is a variant of the k-n
 earest neighbors algorithm. The second is a meta-learner based on a random
 ized bagging scheme\, allowing the use of any classification algorithm for
  constructing an ITR. We study the behavior of these estimation schemes fr
 om a theoretical standpoint and through Monte Carlo simulations and illust
 rate their use on trials data.\n\nMachine Learning in Montpellier\, Theory
  &amp\; Practice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
LOCATION:Room 02.124 Building 5\, St Priest Campus\, Montpellier\, 34000\, 
 France
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