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Logistic Regression with Exposure Biomarkers and Flexible Measurement Error
Author(s) -
Sugar Elizabeth A.,
Wang ChingYun,
Prentice Ross L.
Publication year - 2007
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2006.00632.x
Subject(s) - statistics , logistic regression , calibration , cohort , regression , econometrics , variance (accounting) , regression analysis , body mass index , biomarker , observational error , computer science , medicine , mathematics , biology , biochemistry , accounting , business
Summary Regression calibration, refined regression calibration, and conditional scores estimation procedures are extended to a measurement model that is motivated by nutritional and physical activity epidemiology. Biomarker data, available on a small subset of a study cohort for reasons of cost, are assumed to adhere to a classical measurement error model, while corresponding self‐report nutrient consumption or activity‐related energy expenditure data are available for the entire cohort. The self‐report assessment measurement model includes a person‐specific random effect, the mean and variance of which may depend on individual characteristics such as body mass index or ethnicity. Logistic regression is used to relate the disease odds ratio to the actual, but unmeasured, dietary or physical activity exposure. Simulation studies are presented to evaluate and contrast the three estimation procedures, and to provide insight into preferred biomarker subsample size under selected cohort study configurations.