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UID:20260515T214514EDT-3397DNRxjs@132.216.98.100
DTSTAMP:20260516T014514Z
DESCRIPTION:A Bayesian finite mixture of bivariate regressions model for ca
 usal mediation analyses.\n\nAbstract: Building on the work of Schwartz\, G
 elfand and Miranda (Statistics in Medicine (2010)\; 29(16)\, 1710-23)\, we
  propose a Bayesian finite mixture of bivariate regressions model for caus
 al mediation analyses. Using an identifiability condition within each comp
 onent of the mixture\, we express the natural direct and indirect effects 
 of the exposure on the outcome as functions of the component-specific regr
 ession coefficients. On the basis of simulated data\, we examine the behav
 iour of the model for estimating these effects in situations where the ass
 ociations between exposure\, mediator and outcome are confounded\, or not.
  Additionally\, we demonstrate that this mixture model can be used to acco
 unt for heterogeneity arising through unmeasured binary mediator-outcome c
 onfounders. Finally\, we apply our mediation mixture model to estimate the
  natural direct and indirect effects of exposure to inhaled corticosteroid
 s during pregnancy on birthweight using a cohort of asthmatic women from t
 he province of Québec.\n
DTSTART:20161014T193000Z
DTEND:20161014T203000Z
LOCATION:room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Geneviève Lefebvre\, UQAM
URL:https://www.mcgill.ca/mathstat/channels/event/genevieve-lefebvre-uqam-2
 63393
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