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SUMMARY:Virtual BSU Seminar: 'Stochastic treatment interventions in causal
  survival analysis' - Dr Lan Wen\, Harvard University 
DTSTART:20201013T130000Z
DTEND:20201013T140000Z
UID:TALK150814@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:Several methods are available for estimating the causal effect
  of time-varying treatment strategies on survival outcomes in observationa
 l studies. These include singly robust methods such as inverse probability
  weighting (IPW) that requires a sequence of correctly specified models of
  the observed treatment distribution (the propensity score)\, and iterativ
 e conditional expectation (ICE) that require a sequence of correctly speci
 fied models of the nested conditional outcome means. Alternatively\, doubl
 y robust estimators that combine IPW and ICE require that only one of the 
 sequences of models be correctly specified\, and thus offer more than one 
 opportunity for valid estimation. In recent years\, these methods have bee
 n generalized to accommodate effects of stochastic strategies such that tr
 eatment assignment at each time is non-deterministic within levels of the 
 measured past.  Many authors have considered stochastic strategies that de
 pend on the propensity score which would suggest that doubly robust estima
 tors are not possible to construct. However\, this is not the case. In thi
 s talk\, I will give an intuition into why some strategies that depend on 
 the propensity score can still be estimated by doubly robust estimators\, 
 and describe a class of stochastic treatment interventions that will alway
 s have doubly robust estimators in point treatment processes and multiply 
 robust estimators in longitudinal observational studies. I also propose a 
 new stochastic treatment intervention dependent on the propensity score mo
 tivated by an application to Pre-Exposure Prophylaxis (PrEP) initiation st
 udies that allows doubly and multiply robust estimators.
LOCATION:Virtual Seminar 
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