Bootstrap in errors-in-variables regressions applied to methods comparison studies

In method comparison studies, the measurements taken by two methods are compared to assess whether they are equivalent. If there is no analytical bias between the methods, they should provide the same results on average notwithstanding the measurement errors. This equivalence can be assessed with regression techniques by taking into account the measurement errors. Among them, the paper focuses on Deming Regression (DR) and Bivariate Least-Squares regression (BLS). The confidence intervals (CI's) of the regression parameters are useful to assess the presence or absence of bias. These CI's computed by errors-in-variables regressions are approximate (except the one for slope estimated by DR), which leads to coverage probabilities lower than the nominal value. Six bootstrap approaches and the jackknife are assessed in the paper as means to improve the coverage probabilities of the CI's