  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%), and -12.8% (IQR: -13.2;-11.0%),
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% 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 calibration
is recommended for sensitivity analysis for measurement error.
