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UID:20260620T193015EDT-903290oVK7@132.216.98.100
DTSTAMP:20260620T233015Z
DESCRIPTION:\n	Abstract:\n\n\nAdvances in wearables and digital technology n
 ow make it possible to deliver behavioral\, mobile health\, interventions 
 to individuals in their every-day life. The micro-randomized trial (MRT) i
 s increasingly used to provide data to inform the construction of these in
 terventions. This work is motivated by multiple MRTs that have been conduc
 ted or are currently in the field in which the primary outcome is a longit
 udinal binary outcome. The first\, often called the primary\, analysis in 
 these trials is a marginal analysis that seeks to answer whether the data 
 indicates that a particular intervention component has an effect on the lo
 ngitudinal binary outcome. Under rather restrictive assumptions one can\, 
 based on existing literature\, derive a semi-parametric\, locally efficien
 t estimator of the causal effect. In this talk\, starting from this estima
 tor\, we develop multiple estimators that can be used as the basis of a pr
 imary analysis under more plausible assumptions. Simulation studies are co
 nducted to compare the estimators. We illustrate the developed methods usi
 ng data from the MRT\, BariFit. In BariFit\, the goal is to support weight
  maintenance for individuals who received bariatric surgery.\n\n\n	Speaker
 \n\n\nTianchen Qian is a Postdoctoral Fellow in the Department of Statisti
 cs at Harvard University. His research interest includes Causal inference\
 , Mobile health and reinforcement learning\, Clinical trial design\, Semip
 arametric methods\n
DTSTART:20190412T193000Z
DTEND:20190412T203000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Tianchen Qian (Harvard University)
URL:/mathstat/channels/event/tianchen-qian-harvard-uni
 versity-296087
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