Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation
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Sensitivity analysis for measurement error can be applied in the absence of validation data by means of regression calibration and simulation-extrapolation. These have not been compared for this purpose. A simulation study was conducted comparing the performance of regression calibration and simulation-extrapolation in a multivariable model. The performance of the two methods was evaluated in terms of bias, mean squared error (MSE) and confidence interval coverage, for ranging reliability of the error-prone measurement (0.2-0.9), sample size (125-1,000), number of replicates (2-10), and R-squared (0.03-0.75). It was assumed that no validation data were available about the error-free measures, while measurement error variance was correctly estimated. In various scenarios, regression calibration was unbiased while simulation-extrapolation was biased: median bias was 1.4 (interquartile range (IQR): 0.8;2 respectively. A small gain in efficiency was observed for simulation-extrapolation (median MSE: 0.005, IQR: 0.004;0.006) versus regression calibration (median MSE: 0.006, IQR: 0.004;0.007). Confidence interval coverage was at the nominal level of 95 and smaller than 95 85;94 is recommended for sensitivity analysis for measurement error.
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