Analysis of associations with change in a multivariate outcome variable when baseline is subject to measurement error.
A simple general algorithm is described for correcting for bias caused by measurement error in independent variables in multivariate linear regression. This algorithm, using standard software, is then applied to several approaches to the analysis of change from baseline as a function of baseline value of the outcome measure plus other covariates, any of which might have measurement error. The algorithm may also be used when the independent variables differ by component of the multivariate independent variable. Simulations indicate that under various conditions bias is much reduced, as is mean squared error, and coverage of 95 per cent confidence intervals is good.
Chambless
Lloyd E
LE
Collaborative Studies Coordinating Center, Department of Biostatistics, CB# 8030, The University of North Carolina at Chapel Hill, Bank of America Building, Suite 400, 137 East Franklin Street, 27514-4145, USA. wchambless@unc.edu
Davis
Vicki
V
D000465:Algorithms; D015982:Bias; D003198:Computer Simulation; D003327:Coronary Disease / Q000473:pathology; D006801:Humans; D016014:Linear Models; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D017539:Tunica Intima / Q000000981:diagnostic imaging / Q000473:pathology; D014463:Ultrasonography