BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//ZContent.net//ZapCalLib 1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
SUMMARY:Statistics Seminar
DTSTART;VALUE=DATE:20251104
DTEND;VALUE=DATE:20251104
UID:2026-07-19-03-05-16@demo.icalendar.org
DTSTAMP:20260719T030516
DESCRIPTION:Abstract: Pseudo-observation methods have become useful tools f
 or regression with censored survival data, especially when the quantity o
 f interest is not a standard hazard-based parameter. Most current applicat
 ions focus on estimating outcomes at a fixed and limited number of time p
 oints, such as survival probability, cumulative incidence, or restricted 
 mean survival. In many applications, however, the main goal is to describe
  how the entire outcome curve evolves over time for patients with differe
 nt covariate profiles.\n\nWe propose a general framework that extends pseu
 do-observation regression from a small set of time points to the full tim
 e course of a survival outcome. The idea is to construct pseudo-observatio
 ns over time and combine them with a process-regression approach, so that
  covariate effects can be studied continuously rather than only at select
 ed times. This yields an estimated covariate-specific survival curve and i
 ncludes existing pseudo-observation regression methods as a special case.
   The proposed framework is flexible and can be used for several survival
 -type outcomes, including overall survival, cumulative incidence in compe
 ting risks, and related functionals. It also provides a bridge between fam
 iliar pseudo-observation methods and broader regression approaches for tim
 e-varying outcomes. We discuss the main methodological ideas, practical c
 omputation, and extensions to settings with covariate-dependent censoring
 . The approach is motivated by the need for interpretable regression metho
 ds that target clinically meaningful quantities directly, while making fu
 ller use of follow-up information over time. This work aims to broaden th
 e scope of pseudo-observation methods from pointwise analysis to process-l
 evel estimation and prediction.\n\n \n\nJoint work with Omer Moyal
RRULE:FREQ=WEEKLY;BYDAY=TUE
END:VEVENT
END:VCALENDAR