Moment Adjusted Imputation for Multivariate Measurement Error Data with Applications to Logistic Regression.


Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Thomas, L; Stefanski, LA; Davidian, M

Published Date

  • November 1, 2013

Published In

Volume / Issue

  • 67 /

Start / End Page

  • 15 - 24

PubMed ID

  • 24072947

Pubmed Central ID

  • 24072947

International Standard Serial Number (ISSN)

  • 0167-9473

Digital Object Identifier (DOI)

  • 10.1016/j.csda.2013.04.017


  • eng

Conference Location

  • Netherlands