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SUMMARY:Defining\, identifying\, and estimating\, causal effects of genera
 lized time-varying treatment strategies on repeated\, informatively measur
 ed outcomes truncated by death
DTSTART:20261002T151500
DTEND:20261002T164500
DTSTAMP:20260922T203415Z
UID:f3304e83ca0d354a52111b5a2c1da809d5ef02b197810c9d6c5517a5
CATEGORIES:Conferences - Seminars
DESCRIPTION:Jessica Young\, Harvard\nResearchers often express interest in
  estimating causal effects of (possibly time-varying) treatment strategies
  on the mean of an outcome that is undefined after an individual dies.  F
 or example\, the Medications and Weight Gain in PCORnet (MedWeight) study 
 was an observational study leveraging electronic health records (EHR) that
  aimed to estimate the effect of initiating (and subsequently adhering to)
  different medications for the same indication on future weight change at 
 different times up to 24 months post treatment initiation in an adult\, cl
 inical population where some individuals die in every month. This renders 
 the standard notion of an average total treatment effect undefined at any 
 time.  Truncation by death is a ubiquitous problem\, even in randomized t
 rials\, that cannot be solved with any particular choice of estimator (sta
 tistic).  Rather\, truncation by death creates the more fundamental chall
 enge of defining an alternative effect notion to the total effect that ali
 gns with the true study motivation\, which often involves some clinical de
 cision. At the same time\, an outcome like weight change is not only infor
 matively measured\, but also sparsely measured in an EHR at any specifical
 ly chosen follow-up time such that estimators which can time-smooth in ava
 ilable repeated outcomes beyond that chosen time are desirable for improve
 d precision.  In this talk\, we consider a studyable estimand for truncat
 ion by death settings that\, under a testable isolation condition\, is a c
 ausal effect which connects to a conditional version of the recently posed
  separable adherence effects.  We pose time-smoothed estimators of these 
 effects for EHR data that can smooth over all available outcome measures f
 or increased precision. \n 
LOCATION:CM 1 517 https://plan.epfl.ch/?room==CM%201%20517
STATUS:CONFIRMED
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