Bias Correction Methods for Misclassified Covariates in the Cox Model: comparison offive correction methods by simulation and data analysis.
Measurement error/misclassification is commonplace in research when variable(s) can notbe measured accurately. A number of statistical methods have been developed to tackle this problemin a variety of settings and contexts. However, relatively few methods are available to handlemisclassified categorical exposure variable(s) in the Cox proportional hazards regression model. Inthis paper, we aim to review and compare different methods to handle this problem - naïvemethods, regression calibration, pooled estimation, multiple imputation, corrected score estimation,and MC-SIMEX - by simulation. These methods are also applied to a life course study with recalleddata and historical records. In practice, the issue of measurement error/misclassification should beaccounted for in design and analysis, whenever possible. Also, in the analysis, it could be moreideal to implement more than one correction method for estimation and inference, with properunderstanding of underlying assumptions.
Division of Biostatistics, Department of Public Health Sciences, University ofCalifornia, Davis, CA, USA.
Heejung
H
Bang
Ya-Lin
YL
Chiu
Jay S
JS
Kaufman
Mehul D
MD
Patel
Gerardo
G
Heiss
Kathryn M
KM
Rose
ARIC; Childhood SES; Cox proportional hazards regression; Measurement error; Misclassification; Recalled error