A moment-adjusted imputation method for measurement error models

Biometrics. 2011 Dec;67(4):1461-70. doi: 10.1111/j.1541-0420.2011.01569.x. Epub 2011 Mar 8.


Studies of clinical characteristics frequently measure covariates with a single observation. This may be a mismeasured version of the "true" phenomenon due to sources of variability like biological fluctuations and device error. Descriptive analyses and outcome models that are based on mismeasured data generally will not reflect the corresponding analyses based on the "true" covariate. Many statistical methods are available to adjust for measurement error. Imputation methods like regression calibration and moment reconstruction are easily implemented but are not always adequate. Sophisticated methods have been proposed for specific applications like density estimation, logistic regression, and survival analysis. However, it is frequently infeasible for an analyst to adjust each analysis separately, especially in preliminary studies where resources are limited. We propose an imputation approach called moment-adjusted imputation that is flexible and relatively automatic. Like other imputation methods, it can be used to adjust a variety of analyses quickly, and it performs well under a broad range of circumstances. We illustrate the method via simulation and apply it to a study of systolic blood pressure and health outcomes in patients hospitalized with acute heart failure.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms*
  • Blood Pressure Determination / methods*
  • Computer Simulation
  • Data Interpretation, Statistical*
  • Heart Failure / diagnosis*
  • Heart Failure / epidemiology*
  • Humans
  • Models, Statistical*
  • Reproducibility of Results
  • Sensitivity and Specificity