The regression-calibration method for fitting generalized linear models with additive measurement error

James W. Hardin, Henrik Schmeidiche and Raymond J. Carroll
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James W. Hardin: Arnold School of Public Health, University of South Carolina
Henrik Schmeidiche: Department of Statistics, Texas A&M University
Raymond J. Carroll: Department of Statistics, Texas A&M University

Stata Journal, 2003, vol. 3, issue 4, 361-372

Abstract: This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on software developed as part of a small business innovation research (SBIR) grant from the National Institutes of Health (NIH). Copyright 2003 by StataCorp LP.

Keywords: regression calibration; measurement error; instrumental variables; replicate measures; generalized linear models (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (9)

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