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UID:133@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20250224T160000
DTEND;TZID=Europe/Paris:20250224T160000
DTSTAMP:20260602T130232Z
URL:https://isdm.umontpellier.fr/events/doubly-robust-and-efficient-calibr
 ation-of-prediction-sets-for-censored-time-to-event-outcomes-2/
SUMMARY:Doubly Robust and Efficient Calibration of Prediction Sets for Cens
 ored Time-to-Event Outcomes
DESCRIPTION:Campus St Priest\nRebecca Farina\n\nOur objective is to constru
 ct well-calibrated prediction sets for a time-to-event outcome subject to 
 right-censoring with guaranteed coverage. Our approach is inspired by mode
 rn conformal inference literature\, in that\, unlike classical frameworks\
 , we obviate the need for a well-specified parametric or semi-parametric s
 urvival model to accomplish our goal. In contrast to existing conformal pr
 ediction methods for survival data\, which restrict censoring to be of Typ
 e I\, whereby potential censoring times are assumed to be fully observed o
 n all units in both training and validation samples\, we consider the more
  common right-censoring setting in which either only the censoring time or
  only the event time of primary interest is directly observed\, whichever 
 comes first. Under a standard conditional independence assumption between 
 the potential survival and censoring times given covariates\, we propose a
 nd analyze two methods to construct valid and efficient lower predictive b
 ounds for the survival time of a future observation. The proposed methods 
 build upon modern semiparametric efficiency theory for censored data\, in 
 that the first approach incorporates inverse-probability-of-censoring weig
 hting (IPCW)\, while the second approach is based on augmented-inverse-pro
 bability-of-censoring weighting (AIPCW). For both methods\, we formally es
 tablish asymptotic coverage guarantees\, and demonstrate both via theory a
 nd empirical experiments that AIPCW substantially improves efficiency over
  IPCW in the sense that its coverage error bound is of second-order mixed 
 bias type\, that is doubly robust\, and therefore guaranteed to be asympto
 tically negligible relative to the coverage error of IPCW.\n\nLabéliséHa
 llesIA
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