Misspecification of confounder-exposure and confounder-outcome associations leads to bias in effect estimates.

ABSTRACT:

Background

Confounding is a common issue in epidemiological research. Commonly used confounder-adjustment methods include multivariable regression analysis and propensity score methods. Although it is common practice to assess the linearity assumption for the exposure-outcome effect, most researchers do not assess linearity of the relationship between the confounder and the exposure and between the confounder and the outcome before adjusting for the confounder in the analysis. Failing to take the true non-linear functional form of the confounder-exposure and confounder-outcome associations into account may result in an under- or overestimation of the true exposure effect. Therefore, this paper aims to demonstrate the importance of assessing the linearity assumption for confounder-e

SUBMITTER: Schuster NA 

PROVIDER: S-EPMC9835340 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature