Multiple-imputation for measurement-error correction
نویسندگان
چکیده
منابع مشابه
Multiple-imputation for measurement-error correction.
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 hy...
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Cole et al. in this issue 2 propose that MI may also be useful in dealing with a second problem rife in epidemiology: exposure measurement error, which typically causes underestimation of exposure–disease associations (regression dilution bias). 3 They coin the acronym MIME (multiple imputation for measurement error) and show that this method can indeed remove regression dilution bias. How wide...
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OBJECTIVES Structured association tests (SAT), like any statistical model, assumes that all variables are measured without error. Measurement error can bias parameter estimates and confound residual variance in linear models. It has been shown that admixture estimates can be contaminated with measurement error causing SAT models to suffer from the same afflictions. Multiple imputation (MI) is p...
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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 sta...
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ژورنال
عنوان ژورنال: International Journal of Epidemiology
سال: 2006
ISSN: 1464-3685,0300-5771
DOI: 10.1093/ije/dyl097