BEGIN:VCALENDAR
VERSION:2.0
PRODID:-// - ECPv6.15.15//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://isdm.umontpellier.fr
X-WR-CALDESC:Évènements pour 
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20240331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20241027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20250224T160000
DTEND;TZID=Europe/Paris:20250224T160000
DTSTAMP:20260414T170917
CREATED:20250603T090114Z
LAST-MODIFIED:20250603T090114Z
UID:7060-1740412800-1740412800@isdm.umontpellier.fr
SUMMARY:Doubly Robust and Efficient Calibration of Prediction Sets for Censored Time-to-Event Outcomes
DESCRIPTION:Campus St Priest\nRebecca Farina \nOur objective is to construct well-calibrated prediction sets for a time-to-event outcome subject to right-censoring with guaranteed coverage. Our approach is inspired by modern conformal inference literature\, in that\, unlike classical frameworks\, we obviate the need for a well-specified parametric or semi-parametric survival model to accomplish our goal. In contrast to existing conformal prediction methods for survival data\, which restrict censoring to be of Type I\, whereby potential censoring times are assumed to be fully observed on 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 and analyze two methods to construct valid and efficient lower predictive bounds 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 weighting (IPCW)\, while the second approach is based on augmented-inverse-probability-of-censoring weighting (AIPCW). For both methods\, we formally establish asymptotic coverage guarantees\, and demonstrate both via theory and 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 asymptotically negligible relative to the coverage error of IPCW. \n            Visio
URL:https://isdm.umontpellier.fr/event/doubly-robust-and-efficient-calibration-of-prediction-sets-for-censored-time-to-event-outcomes/
CATEGORIES:Séminaire
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/2025/02/ml-mpt-1.jpg
END:VEVENT
END:VCALENDAR