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

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  • Thomas, Laine
  • Stefanski, Leonard A.
  • Davidian, Marie

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.

Suggested Citation

  • Thomas, Laine & Stefanski, Leonard A. & Davidian, Marie, 2013. "Moment adjusted imputation for multivariate measurement error data with applications to logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 15-24.
    • Handle: RePEc:eee:csdana:v:67:y:2013:i:c:p:15-24
    DOI: 10.1016/j.csda.2013.04.017
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    References listed on IDEAS

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    1. Laurence S. Freedman & Vitaly Fainberg & Victor Kipnis & Douglas Midthune & Raymond J. Carroll, 2004. "A New Method for Dealing with Measurement Error in Explanatory Variables of Regression Models," Biometrics, The International Biometric Society, vol. 60(1), pages 172-181, March.
    2. Raymond J. Carroll & Peter Hall, 2004. "Low order approximations in deconvolution and regression with errors in variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 31-46, February.
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    Cited by:

    1. Cornelis J. Potgieter & Rubin Wei & Victor Kipnis & Laurence S. Freedman & Raymond J. Carroll, 2016. "Moment reconstruction and moment‐adjusted imputation when exposure is generated by a complex, nonlinear random effects modeling process," Biometrics, The International Biometric Society, vol. 72(4), pages 1369-1377, December.

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