title: Multiple-imputation for measurement-error correction.
abstract: BACKGROUND
  There are many methods for measurement-error correction. These methods remain rarely used despite the ubiquity of measurement error.
  
  
  METHODS
  Treating measurement error as a missing-data problem, the authors show how multiple-imputation for measurement-error (MIME) correction can be done using SAS software and evaluate the approach with a simulation experiment.
  
  
  RESULTS
  Based on hypothetical data from a planned cohort study of 600 children with chronic kidney disease, the estimated hazard ratio for end-stage renal disease from the complete data was 2.0 [95% confidence limits (95% CL) 1.4, 2.8] and was reduced to 1.5 (95% CL 1.1, 2.1) using a misclassified exposure of low glomerular filtration rate at study entry (sensitivity of 0.9 and specificity of 0.7). The MIME correction hazard ratio was 2.0 (95% CL 1.2, 3.3), the regression calibration (RC) hazard ratio was 2.0 (95% CL 1.1, 3.7), and restriction to a 25% validation substudy yielded a hazard ratio of 2.0 (95% CL 1.0, 3.7). Based on Monte Carlo simulations across eight scenarios, MIME was approximately unbiased, had approximately correct coverage, and was sometimes more powerful than misclassified or RC analyses. Using root mean squared error as a criterion, the MIME bias correction is sometimes outweighed by added imprecision.
  
  
  CONCLUSIONS
  The choice between MIME and RC depends on performance, ease, and objectives. The usefulness of MIME correction in specific applications will depend upon the sample size or the proportion validated. MIME correction may be valuable in interpreting imperfectly measured epidemiological data.
authors.affiliation: Department of Epidemiology, 615 Norht Wolfe Street, E7640, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. scole@jhsph.edu
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Cole
authors.forename: Haitao
authors.initials: H
authors.lastname: Chu
authors.forename: Sander
authors.initials: S
authors.lastname: Greenland
mesh_terms: D015982:Bias; D002648:Child; D015331:Cohort Studies; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015340:Epidemiologic Research Design; D006801:Humans; D007676:Kidney Failure, Chronic; D009010:Monte Carlo Method; D016017:Odds Ratio; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D012306:Risk; D012680:Sensitivity and Specificity