01931nas a2200241   4500008004100000022001400041245012100055210006900176260001600245300001200261490000700273520115700280653000901437653002801446653002601474653001501500653002701515653002401542653002601566653002401592110003701616856003601653       2009                        eng d  a0277-671500aCorrecting for multivariate measurement error by regression calibration in meta-analyses of epidemiological studies.0 aCorrecting for multivariate measurement error by regression cali  c2009 Mar 30  a1067-920 v283 a<p>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.</p>10aBias10aCardiovascular Diseases10aEpidemiologic Studies10aFibrinogen10aMeta-Analysis as Topic10aModels, Statistical10aMultivariate Analysis10aRegression Analysis1 aFibrinogen Studies Collaboration  uhttps://chs-nhlbi.org/node/1078