Multiple-imputation for measurement-error correction

Stephen R. Cole, Haitao Chu, Sander Greenland

Research output: Contribution to journalArticlepeer-review

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. Conclusion: 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.

Original languageEnglish (US)
Pages (from-to)1074-1081
Number of pages8
JournalInternational journal of epidemiology
Volume35
Issue number4
DOIs
StatePublished - Aug 2006
Externally publishedYes

Bibliographical note

Funding Information:
The authors thank the editor and reviewers for suggestions, which improved this manuscript. Drs Cole and Chu were supported in part by the National Institutes of Health through the data coordinating centres for the paediatric Chronic Kidney Disease cohort study (UO1-DK-066116), Multicenter AIDS Cohort Study (UO1-AI-35043), and the Women’s Interagency HIV Study (UO1-AI-42590).

Keywords

  • Bias
  • Measurement error
  • Misclassification
  • Missing data
  • Multiple-imputation

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