title: A new outlier identification test for method comparison studies based on robust regression.
abstract: The identification of outliers in method comparison studies (MCS) is an important part of data analysis, as outliers can indicate serious errors in the measurement process. Common outlier tests proposed in the literature usually require a homogeneous sample distribution and homoscedastic random error variances. However, datasets in MCS usually do not meet these assumptions. In this work, a new outlier test based on robust linear regression is proposed to overcome these special problems. The LORELIA (local reliability) residual test is based on a local, robust residual variance estimator, given as a weighted sum of the observed residuals. The new test is compared to a standard test proposed in the literature by a Monte Carlo simulation. Its performance is illustrated in examples.
authors.affiliation: Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany. geraldine_r@web.de
authors.forename: Geraldine
authors.initials: G
authors.lastname: Rauch
authors.forename: Andrea
authors.initials: A
authors.lastname: Geistanger
authors.forename: Jürgen
authors.initials: J
authors.lastname: Timm
mesh_terms: D000704:Analysis of Variance; D002986:Clinical Trials as Topic; D016001:Confidence Intervals; D005069:Evaluation Studies as Topic; D006801:Humans; D016014:Linear Models; D009010:Monte Carlo Method; D012107:Research Design / Q000706:statistics & numerical data*