Adjustment for unmeasured confounding through informative priors for the confounder-outcome relation.

ABSTRACT:

Background

Observational studies of medical interventions or risk factors are potentially biased by unmeasured confounding. In this paper we propose a Bayesian approach by defining an informative prior for the confounder-outcome relation, to reduce bias due to unmeasured confounding. This approach was motivated by the phenomenon that the presence of unmeasured confounding may be reflected in observed confounder-outcome relations being unexpected in terms of direction or magnitude.

Methods

The approach was tested using simulation studies and was illustrated in an empirical example of the relation between LDL cholesterol levels and systolic blood pressure. In simulated data, a comparison of the estimated exposure-outcome relation was made between two frequentist multivariable

SUBMITTER: Groenwold RHH 

PROVIDER: S-EPMC6303957 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature