Equivariant Passing-Bablok regression in quasilinear time

英文摘要：

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.