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SUMMARY:Causal inference in epidemiology using target trial principles: Ap
 plications in pregnancy and prostate cancer
DTSTART:20210917T151500
DTEND:20210917T170000
DTSTAMP:20260510T164732Z
UID:f777eefb145d9db2ae264c18dce75f24a86e4edf56ecc07f4f87687e
CATEGORIES:Conferences - Seminars
DESCRIPTION:Louisa H. Smith\, Harvard\nMuch of the evidence about what imp
 roves and impairs human health is derived from observational data. However
 \, naïve analyses can result in biased and misleading conclusions. One to
 ol that can help us better use observational data for causal inference is 
 the target trial\, which invokes the principles of randomized controlled t
 rials to design and analyze observational studies.\nIn this talk I will pr
 esent the design and analysis of two observational studies using these pri
 nciples. First\, I will describe some pitfalls in estimating the risk of p
 reterm birth after COVID-19 and introduce a strategy to produce meaningful
  comparisons between pregnancies that were and weren’t affected by COVID
 -19. Using this method in a registry of pregnant women during the pandemic
 \, we find a large increase in risk of preterm birth after severe COVID-19
  late in pregnancy\, but not after milder disease or earlier in pregnancy.
 \nNext\, I will discuss an open question about prostate cancer treatment t
 hat is unlikely to ever be addressed in a trial\, and I will describe how 
 to answer it in observational data using inverse probability weighting or 
 the parametric g-formula. A comparison of the results from each analysis p
 oints to the importance of fully specifying a sustained treatment strategy
 \, as well as to inadequacies in our data for answering questions about th
 is particular strategy.\n 
LOCATION:MED 0 1418 https://plan.epfl.ch/?room==MED%200%201418 https://epf
 l.zoom.us/j/61830901642
STATUS:CONFIRMED
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