The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams.

ABSTRACT:

Background

Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method.

Methods

Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based margi

SUBMITTER: Yu Y 

PROVIDER: S-EPMC5745640 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature