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Analysis of associations with change in a multivariate outcome variable when baseline is subject to measurement error
Author(s) -
Chambless Lloyd E.,
Davis Vicki
Publication year - 2003
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1352
Subject(s) - multivariate statistics , statistics , covariate , observational error , multivariate analysis , baseline (sea) , standard error , variable (mathematics) , range (aeronautics) , bayesian multivariate linear regression , mathematics , regression analysis , confidence interval , mean squared error , mathematical analysis , oceanography , materials science , composite material , geology
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. Copyright © 2003 John Wiley & Sons, Ltd.