Abstract
Objective
In the analysis of observational data, the argument is sometimes made that if adjustment
for measured confounders induces little change in the treatment–outcome association,
then there is less concern about the extent to which the association is driven by
unmeasured confounding. We quantify this reasoning using Bayesian sensitivity analysis
(BSA) for unmeasured confounding. Using hierarchical models, the confounding effect
of a binary unmeasured variable is modeled as arising from the same distribution as
that of measured confounders. Our objective is to investigate the performance of the
method compared to sensitivity analysis, which assumes that there is no relationship
between measured and unmeasured confounders.
Study Design and Setting
We apply the method in an observational study of the effectiveness of beta-blocker
therapy in heart failure patients.
Results
BSA for unmeasured confounding using hierarchical prior distributions yields an odds
ratio (OR) of 0.72, 95% credible interval (CrI): 0.56, 0.93 for the association between
beta-blockers and mortality, whereas using independent priors yields OR=0.72, 95% CrI: 0.45, 1.15.
Conclusion
If the confounding effect of a binary unmeasured variable is similar to that of measured
confounders, then conventional sensitivity analysis may give results that overstate
the uncertainty about bias.
Keywords
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Article info
Publication history
Published online: October 16, 2007
Accepted:
May 28,
2007
Identification
Copyright
© 2008 Elsevier Inc. Published by Elsevier Inc. All rights reserved.
