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SUMMARY:A flexible Monte Carlo quantitative bias analysis for unmeasured c
 onfounding - Rachael Hughes (University of Bristol)
DTSTART:20260422T083000Z
DTEND:20260422T091500Z
UID:TALK246619@talks.cam.ac.uk
DESCRIPTION:Quantitative bias analysis (QBA) for unmeasured confounding is
  essential for observational studies. A probabilistic QBA incorporates ext
 ernal information about the unmeasured confounders via prior distributions
  for certain parameters (known as bias parameters) that specify the relati
 onship between the unmeasured confounders\,) for unmeasured confounding is
  essential for observational studies. A probabilistic QBA incorporates ext
 ernal information about the unmeasured confounders via prior distributions
  for certain parameters (known as bias parameters) that specify the relati
 onship between the unmeasured confounders\, U\, and the study data. It can
  be implemented as a Bayesian or Monte Carlo QBA. Software implementations
  of probabilistic QBAs to unmeasured confounding are scarce and mainly lim
 ited to unadjusted analyses of binary variables. We propose a Monte Carlo 
 QBA applicable to a generalised linear model or survival proportional haza
 rds model that allows for: (i) binary\, continuous\, or categorical exposu
 re and measured confounders C\, (ii) correlation between C and U\, and (ii
 i) one or multiple binary or continuous U. To minimize the number of bias 
 parameters\, our proposed bias model does not model U directly but instead
  models the part of U not explained by C. Using a data example and simulat
 ion studies\, we show that our Monte Carlo QBA\, with informative priors\,
  virtually eliminates the bias due to unmeasured confounding in a wide ran
 ge of scenarios and performs as well as a Bayesian QBA. Our Monte Carlo QB
 A will be available as Stata command qbaconfound. &nbsp\;\nAuthors: Rachel
  Hughes (Bristol)\, Emily Kawabata (Bristol)\, Chin Yang Shapland (Bristol
 ) Tom Palmer (Bristol) David Carslake (Bristol) Kate Tilling (Bristol)\n&n
 bsp\;
LOCATION:External
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