  Passing-Bablok regression is a standard tool for method and assay comparison
studies thanks to its place in industry guidelines such as CLSI. Unfortunately,
its computational cost is high as a naive approach requires O(n2) time. This
makes it impossible to compute the Passing-Bablok regression estimator on large
datasets. Additionally, even on smaller datasets it can be difficult to perform
bootstrap-based inference. We introduce the first quasilinear time algorithm
for the equivariant Passing-Bablok estimator. In contrast to the naive
algorithm, our algorithm runs in O(n log(n)) expected time using O(n) space,
allowing for its application to much larger data sets. Additionally, we
introduce a fast estimator for the variance of the Passing-Bablok slope and
discuss statistical inference based on bootstrap and this variance estimate.
Finally, we propose a diagnostic plot to identify influential points in
Passing-Bablok regression. The superior performance of the proposed methods is
illustrated on real data examples of clinical method comparison studies.
