Correcting for nonlinear measurement errors in the dependent variable in the general linear model |
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Authors: | John P. Buonaccorsi Tor D. Tosteson |
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Affiliation: | 1. Department of Mathematics and Statistics , University of Massachusetts Amherst , MA, 01003;2. Department of Community and Family Medicine Dartmouth Medical School , Biostatistics and Epidemiology Group , Hanover, NH, 03755-3861 |
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Abstract: | Linear models are considered in which measurement error is present in the dependent variable. Observed values are related to true values via nonlinear regression models with the parameters in the measurement error models being estimated with the use of independent, external data, collected using standards. Pseudo-maximum likelihood estimators and their asymptotic properties are developed under normality assumptions and the common approach of simply analyzing imputed values obtained from the nestimated calibration curves is assessed. A small simulation evaluates the procedures. An example is presented in which urinary neopterin (measured via radioimmunoassay) is nbeing compared between two groups of individuals. |
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Keywords: | calibration errors-in-variables EM algorithm nonlinear regression pseudo-maximum likelihood radioimmunoassay |
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