Bias due to Berkson error: issues when using predicted values in place of observed covariates.

ABSTRACT: Studies often want to test for the association between an unmeasured covariate and an outcome. In the absence of a measurement, the study may substitute values generated from a prediction model. Justification for such methods can be found by noting that, with standard assumptions, this is equivalent to fitting a regression model for an outcome variable when at least one covariate is measured with Berkson error. Under this setting, it is known that consistent or nearly consistent inference can be obtained under many linear and nonlinear outcome models. In this article, we focus on the linear regression outcome model and show that this consistency property does not hold when there is unmeasured confounding in the outcome model, in which case the marginal inference based on a covariate measur

SUBMITTER: Haber G 

PROVIDER: S-EPMC8511945 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature