Moment adjusted imputation for multivariate measurement error data with applications to logistic regression

Laine Thomas, Leonard A. Stefanski and Marie Davidian

Computational Statistics & Data Analysis, 2013, vol. 67, issue C, 15-24

Abstract: 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 mis-measured 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 tradeoff between convenience and performance. Moment Adjusted Imputation (MAI) is a 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 fluctuations, 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.

Keywords: Moment adjusted imputation; Multivariate measurement error; Logistic regression; Regression calibration (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:67:y:2013:i:c:p:15-24

DOI: 10.1016/j.csda.2013.04.017

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