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SUMMARY:BSU Seminar: &quot\;A Bayesian framework for incorporating exposur
 e uncertainty into health analyses with application to air pollution and s
 tillbirth&quot\; - Joshua Warren\, Yale School of Public Health
DTSTART:20240618T130000Z
DTEND:20240618T140000Z
UID:TALK217912@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:Studies of the relationships between environmental exposures a
 nd adverse health outcomes often rely on a two-stage statistical modeling 
 approach\, where exposure is modeled/predicted in the first stage and used
  as input to a separately fit health outcome analysis in the second stage.
  Uncertainty in these predictions is frequently ignored\, or accounted for
  in an overly simplistic manner when estimating the associations of intere
 st. Working in the Bayesian setting\, we propose a flexible kernel density
  estimation (KDE) approach for fully utilizing posterior output from the f
 irst stage modeling/prediction to make accurate inference on the associati
 on between exposure and health in the second stage\, derive the full condi
 tional distributions needed for efficient model fitting\, detail its conne
 ctions with existing approaches\, and compare its performance through simu
 lation. Our KDE approach is shown to generally have improved performance a
 cross several settings and model comparison metrics. Using competing appro
 aches\, we investigate the association between lagged daily ambient fine p
 articulate matter levels and stillbirth counts in New Jersey (2011–2015)
 \, observing an increase in risk with elevated exposure 3 days prior to de
 livery. The newly developed methods are available in the R package KDExp.
LOCATION:MRC Biostatistics Unit\, East Forvie Building\, Forvie Site Robin
 son Way Cambridge CB2 0SR.
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