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

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