Correcting for multivariate measurement error by regression calibration in meta-analyses of epidemiological studies
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Correcting for multivariate measurement error by regression calibration in meta-analyses of epidemiological studies. / Tybjærg-Hansen, Anne.
I: Statistics in Medicine, Bind 28, Nr. 7, 2009, s. 1067-1092.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning
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TY - JOUR
T1 - Correcting for multivariate measurement error by regression calibration in meta-analyses of epidemiological studies
AU - Tybjærg-Hansen, Anne
N1 - The Fibrinogen Studies Collaboration.The Copenhagen City Heart Study
PY - 2009
Y1 - 2009
N2 - Within-person variability in measured values of multiple risk factors can bias their associations with disease. The multivariate regression calibration (RC) approach can correct for such measurement error and has been applied to studies in which true values or independent repeat measurements of the risk factors are observed on a subsample. We extend the multivariate RC techniques to a meta-analysis framework where multiple studies provide independent repeat measurements and information on disease outcome. We consider the cases where some or all studies have repeat measurements, and compare study-specific, averaged and empirical Bayes estimates of RC parameters. Additionally, we allow for binary covariates (e.g. smoking status) and for uncertainty and time trends in the measurement error corrections. Our methods are illustrated using a subset of individual participant data from prospective long-term studies in the Fibrinogen Studies Collaboration to assess the relationship between usual levels of plasma fibrinogen and the risk of coronary heart disease, allowing for measurement error in plasma fibrinogen and several confounders Udgivelsesdato: 2009/3/30
AB - Within-person variability in measured values of multiple risk factors can bias their associations with disease. The multivariate regression calibration (RC) approach can correct for such measurement error and has been applied to studies in which true values or independent repeat measurements of the risk factors are observed on a subsample. We extend the multivariate RC techniques to a meta-analysis framework where multiple studies provide independent repeat measurements and information on disease outcome. We consider the cases where some or all studies have repeat measurements, and compare study-specific, averaged and empirical Bayes estimates of RC parameters. Additionally, we allow for binary covariates (e.g. smoking status) and for uncertainty and time trends in the measurement error corrections. Our methods are illustrated using a subset of individual participant data from prospective long-term studies in the Fibrinogen Studies Collaboration to assess the relationship between usual levels of plasma fibrinogen and the risk of coronary heart disease, allowing for measurement error in plasma fibrinogen and several confounders Udgivelsesdato: 2009/3/30
M3 - Journal article
VL - 28
SP - 1067
EP - 1092
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 7
ER -
ID: 19819946