Generalized linear mixed model for binary outcomes when covariates are subject to measurement errors and detection limits.

ABSTRACT: Longitudinal measurement of biomarkers is important in determining risk factors for binary endpoints such as infection or disease. However, biomarkers are subject to measurement error, and some are also subject to left-censoring due to a lower limit of detection. Statistical methods to address these issues are few. We herein propose a generalized linear mixed model and estimate the model parameters using the Monte Carlo Newton-Raphson (MCNR) method. Inferences regarding the parameters are made by applying Louis's method and the delta method. Simulation studies were conducted to compare the proposed MCNR method with existing methods including the maximum likelihood (ML) method and the ad hoc approach of replacing the left-censored values with half of the detection limit (HDL). The results s

SUBMITTER: Xie X 

PROVIDER: S-EPMC5720942 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature