A longitudinal measurement error model with a semicontinuous covariate |
| |
Authors: | Li Liang Shao Jun Palta Mari |
| |
Affiliation: | Department of Biostatistics and Epidemiology/Wb4, Cleveland Clinic Foundation, Cleveland, Ohio 44195, USA. lli@bio.ri.ccf.org |
| |
Abstract: | Covariate measurement error in regression is typically assumed to act in an additive or multiplicative manner on the true covariate value. However, such an assumption does not hold for the measurement error of sleep-disordered breathing (SDB) in the Wisconsin Sleep Cohort Study (WSCS). The true covariate is the severity of SDB, and the observed surrogate is the number of breathing pauses per unit time of sleep, which has a nonnegative semicontinuous distribution with a point mass at zero. We propose a latent variable measurement error model for the error structure in this situation and implement it in a linear mixed model. The estimation procedure is similar to regression calibration but involves a distributional assumption for the latent variable. Modeling and model-fitting strategies are explored and illustrated through an example from the WSCS. |
| |
Keywords: | Latent variable Longitudinal model Measurement error |
本文献已被 PubMed 等数据库收录! |
|