Sample size and power estimation when covariates are measured with error
Measurement error in exposure variables can lead to bias in effect estimates, and methods that aim to correct this bias often come at the price of greater standard errors (and so, lower statistical power). This means that standard sample size calculations are inadequate and that, in general, simulation studies are required. Our routine autopower aims to take the legwork out of this simulation process, restricting attention to univariate logistic regression where exposures are subject to classical measurement error. It can be used to estimate the power of a particular model setup or to search for a suitable sample size for a desired power. The measurement error correction methods that are employed are regression calibration (rcal) and a conditional score method--a Stata routine that we also introduce.
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