A regression model for longitudinal change in the presence of measurement error.
OBJECTIVE
The analysis of change in measured variables has become quite popular in studies where data are collected repeatedly over time. The authors describe some of the potential pitfalls in the analysis of change when the variable for change is measured with error. They show that regression analysis is often biased, possibly leading to erroneous results.


METHODS
A simple method to correct for measurement error bias in regression models that model change is presented.


CONCLUSIONS
The two examples illustrate how measurement error can adversely affect an analysis. The bias-corrected approach yields valid results.
Yanez
N David
ND
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
Kronmal
Richard A
RA
Shemanski
Lynn R
LR
Psaty
Bruce M
BM
D000368:Aged; D015982:Bias; D003327:Coronary Disease / Q000209:etiology*; D006801:Humans; D008074:Lipoproteins / Q000097:blood; D015233:Models, Statistical; D012044:Regression Analysis; D012307:Risk Factors
D008074:Lipoproteins