Effects of Omitting Non-confounding Predictors From General Relative-Risk Models for Binary Outcomes.

ABSTRACT:

Background

The effects, in terms of bias and precision, of omitting non-confounding predictive covariates from generalized linear models have been well studied, and it is known that such omission results in attenuation bias but increased precision with logistic regression. However, many epidemiologic risk analyses utilize alternative models that are not based on a linear predictor, and the effect of omitting non-confounding predictive covariates from such models has not been characterized.

Methods

We employed simulation to study the effects on risk estimation of omitting non-confounding predictive covariates from an excess relative risk (ERR) model and a general additive-multiplicative relative-risk mixture model for binary outcome data in a case-control setting. We also com

SUBMITTER: Cologne J 

PROVIDER: S-EPMC6375815 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature