Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models

ABSTRACT: To increase power and minimize bias in statistical analyses, quantitative outcomes are often adjusted for precision and confounding variables using standard regression approaches. The outcome is modeled as a linear function of the precision variables and confounders; however, for many complex phenotypes, the assumptions of the linear regression models are not always met. As an alternative, we used neural networks for the modeling of complex phenotypes and covariate adjustments. We compared the prediction accuracy of the neural network models to that of classical approaches based on linear regression. Using data from the UK Biobank, COPDGene study, and Childhood Asthma Management Program (CAMP), we examined the features of neural networks in this context and compared them with traditional r

SUBMITTER: Voorhies K 

PROVIDER: S-EPMC9094505 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature