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DTSTART:20241103T020000
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DTSTART:20250309T020000
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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 S
 ebastien Haneuse\, Ph.D. Title- Double sampling and semiparametric method
 s for informatively missing not at random data Abstract Missing data aris
 e in almost all applied settings and are ubiquitous in electronic health 
 records (EHR). When data are missing not at random (MNAR) with respect to
  measured covariates\, sensitivity analyses are often considered. These s
 olutions\, however\, are unsatisfying in that they are not guaranteed to 
 yield actionable conclusions. Motivated by an EHR-based study of long-ter
 m outcomes following bariatric surgery\, we consider the use of double sa
 mpling as a means to mitigate MNAR outcome data when the statistical goal
 s are estimation and inference regarding causal contrasts based on mean c
 ounterfactuals. We describe identification assumptions and derive efficie
 nt and robust estimators of the average causal treatment effect under a n
 onparametric model as well as under a model assuming the missing outcomes
  were\, in fact\, initially missing at random (MAR). We compare these in 
 simulations to an approach that adaptively estimates based on evidence of
  violation of the MAR assumption. Finally\, we show how the methods can b
 e extended to: (i) estimation/inference regarding causal quantile treatme
 nt effects\; and (ii) hypothesis testing regarding MNAR.\n\nAdmission:\nF
 ree\n\nDetails URL:\nhttps://medicine.yale.edu/event/ysph-biostatistics-s
 eminar-8/\n
DTEND;TZID=America/New_York:20260303T130000
DTSTAMP:20261030T151123Z
DTSTART;TZID=America/New_York:20260303T120000
GEO:41.302961;-72.931638
LOCATION:106 A&B\, 47 College Street\, New Haven\, CT\, United States
SEQUENCE:0
STATUS:Confirmed
SUMMARY:YSPH Biostatistics Seminar- Double sampling and semiparametric met
 hods for informatively missing not at random data
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