Moment Adjusted Imputation for Multivariate Measurement Error Data with Applications to Logistic Regression.
In clinical studies, covariates are often measured with error due to biological fluctuations, device error and other sources. Summary statistics and regression models that are based on mismeasured data will differ from the corresponding analysis based on the "true" covariate. Statistical analysis can be adjusted for measurement error, however various methods exhibit a tradeo between convenience and performance. Moment Adjusted Imputation (MAI) is method for measurement error in a scalar latent variable that is easy to implement and performs well in a variety of settings. In practice, multiple covariates may be similarly influenced by biological fluctuastions, inducing correlated multivariate measurement error. The extension of MAI to the setting of multivariate latent variables involves unique challenges. Alternative strategies are described, including a computationally feasible option that is shown to perform well.
Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27705, U.S.A.
Laine
L
Thomas
Leonard A
LA
Stefanski
Marie
M
Davidian
Logistic Regression; Moment adjusted imputation; Multivariate measurement error; Regression calibration