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DTSTART:20241103T020000
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DESCRIPTION:NOTE: BIS 526 students are required to attend in person. Other
 s are invited to attend in person\, but may also attend via zoom. Speaker
 - Abdus S. Wahed\, PhD Title: “Imputation-based Q-learning for optimizing
  dynamic treatment regimes with right-censored survival and competing ris
 k outcomes” Abstract Q-learning has been one of the most commonly used me
 thods for optimizing dynamic treatment regimes (DTRs) in multistage decis
 ion-making. Right-censored survival outcome poses a significant challenge
  to Q-Learning due to its reliance on parametric models for counterfactua
 l estimation which are subject to misspecification and sensitive to missi
 ng covariates. In this paper\, we propose an imputation-based Q-learning 
 (IQ-learning) where flexible nonparametric or semiparametric models are e
 mployed to estimate optimal treatment rules for each stage and then weigh
 ted hot-deck multiple imputation (MI) and direct-draw MI are used to pred
 ict optimal potential survival times. Missing data are handled using inve
 rse probability weighting and MI\, and the nonrandom treatment assignment
  among the observed is accounted for using a propensity-score approach. W
 e investigate the performance of IQ-learning via extensive simulations an
 d show that it is more robust to model misspecification than existing Q-l
 earning methods\, imputes only plausible potential survival times contrar
 y to parametric models\, and provides more flexibility in terms of baseli
 ne hazard shape. Using IQ-learning\, we developed an optimal DTR for leuk
 emia treatment based on a randomized trial with observational follow-up t
 hat motivated this study.\n\nSpeaker:\nAbdus S. Wahed\, Ph.D.\n\nAdmissio
 n:\nFree\n\nDetails URL:\nhttps://medicine.yale.edu/event/ysph-biostatist
 ics-seminar-tba3-5/\n
DTEND;TZID=America/New_York:20240305T130000
DTSTAMP:20261009T062740Z
DTSTART;TZID=America/New_York:20240305T120000
SEQUENCE:0
STATUS:Confirmed
SUMMARY:YSPH Biostatistics Seminar: “Imputation-based Q-learning for optim
 izing dynamic treatment regimes with right-censored survival and competin
 g risk outcomes”
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