Accounting for unobserved covariates with varying degrees of estimability in high-dimensional biological data.

ABSTRACT: An important phenomenon in high-throughput biological data is the presence of unobserved covariates that can have a significant impact on the measured response. When these covariates are also correlated with the covariate of interest, ignoring or improperly estimating them can lead to inaccurate estimates of and spurious inference on the corresponding coefficients of interest in a multivariate linear model. We first prove that existing methods to account for these unobserved covariates often inflate Type I error for the null hypothesis that a given coefficient of interest is zero. We then provide alternative estimators for the coefficients of interest that correct the inflation, and prove that our estimators are asymptotically equivalent to the ordinary least squares estimators obtained wh

SUBMITTER: McKennan C 

PROVIDER: S-EPMC6845853 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature