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UID:20250511T072850EDT-0231ZPRt0X@132.216.98.100
DTSTAMP:20250511T112850Z
DESCRIPTION:Lawrence McCandless\, PhD\n\nAssociate Professor\, Faculty of H
 ealth Sciences\, Simon Fraser University\n\nUnmeasured confounding\, large
  datasets\, and the role of Bayesian statistics\n\nAbstract:\n\nUnmeasured
  confounding creates terrible problems in observational studies using larg
 e administrative databases.  The massive sample size crushes p-values and 
 standard errors to zero that are calculated from standard biostatistical t
 echniques.  While this may delight researchers who discover that everythin
 g is significant\, it obscures the role of bias\, including unmeasured con
 founding.  The Bayesian approach to statistics provides an appealing way f
 orward because uncertainty about bias can be brought into the analysis usi
 ng prior distributions.  In this talk I will illustrate Bayesian sensitivi
 ty analysis for unmeasured confounding in observational studies using admi
 nistrative data from British Columbia.  I will show how to use Stan\, whic
 h is new software developed by Andrew Gelman and others.  Stan allows the 
 careful study of posterior distribution in a vast collection of Bayesian m
 odels\, including nonidentifiable models for bias in epidemiology\, which 
 are poorly suited to conventional Gibbs sampling. \n\nBio: \n\nLawrence Mc
 Candless is associate professor in the Faculty of Health Sciences at Simon
  Fraser University.  His research interests include epidemiology and Bayes
 ian causal inference. \n
DTSTART:20151006T193000Z
DTEND:20151006T203000Z
LOCATION:Room 24\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:Biostatistics Seminar
URL:/epi-biostat-occh/channels/event/biostatistics-sem
 inar-255483
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