|Title||Moment Adjusted Imputation for Multivariate Measurement Error Data with Applications to Logistic Regression.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Thomas, Laine, Leonard A. Stefanski, and Marie Davidian|
|Journal||Comput Stat Data Anal|
|Date Published||2013 Nov 01|
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.
|Alternate Journal||Comput Stat Data Anal|
|Original Publication||Moment adjusted imputation for multivariate measurement error data with applications to logistic regression.|
|PubMed Central ID||PMC3780432|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA085848 / CA / NCI NIH HHS / United States
R37 AI031789 / AI / NIAID NIH HHS / United States
T32 HL079896 / HL / NHLBI NIH HHS / United States
Moment Adjusted Imputation for Multivariate Measurement Error Data with Applications to Logistic Regression.