Dealing with covariate measurement error in a clustered cross-sectional survey

Abstract

Abstract Many surveys are often complex cross-sectional studies that involve clustered data. Such surveys can have the additional complexity of the measurement error problem. Ignoring the measurement error problem and the clustering aspect may lead to incorrect inferences and conclusions. The purpose of this study was to demonstrate the application of regression calibration to correct for covariate measurement error in a clustered cross-sectional survey in a generalized estimating equations (GEE) framework. Methods that ignore both covariate measurement error and within-cluster correlation structure are compared to the proposed regression calibration-GEE method. The study found that clustering does not affect the association estimates adjusted for measurement error using regression calibration. However, the standard errors of the coefficient estimates are overestimated or underestimated in methods that ignore the within-cluster dependency despite adjusting for measurement error. Specifically, for clusters of size 10 and under unstructured and exchangeable correlation structure, the standard error was about 10.3% higher and 13.6% lower, respectively, in the method that ignores the within-cluster dependency than in the proposed method. From the findings of this study, we conclude that it is important to adjust for covariate measurement error in clustered data, while accounting for the within-cluster correlation.