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Sensitivity analysis for measurement error can be applied in the absence ofvalidation data by means of regression calibration andsimulation-extrapolation. These have not been compared for this purpose. Asimulation study was conducted comparing the performance of regressioncalibration and simulation-extrapolation in a multivariable model. Theperformance of the two methods was evaluated in terms of bias, mean squarederror (MSE) and confidence interval coverage, for ranging reliability of theerror-prone measurement (0.2-0.9), sample size (125-1,000), number ofreplicates (2-10), and R-squared (0.03-0.75). It was assumed that no validationdata were available about the error-free measures, while measurement errorvariance was correctly estimated. In various scenarios, regression calibrationwas unbiased while simulation-extrapolation was biased: median bias was 1.4%(interquartile range (IQR): 0.8;2%), and -12.8% (IQR: -13.2;-11.0%),respectively. A small gain in efficiency was observed forsimulation-extrapolation (median MSE: 0.005, IQR: 0.004;0.006) versusregression calibration (median MSE: 0.006, IQR: 0.004;0.007). Confidenceinterval coverage was at the nominal level of 95% for regression calibration,and smaller than 95% for simulation-extrapolation (median coverage: 92%, IQR:85;94%). In the absence of validation data, the use of regression calibrationis recommended for sensitivity analysis for measurement error.

Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation