ENBIS-17 in Naples
9 – 14 September 2017; Naples (Italy)
Abstract submission: 21 November 2016 – 10 May 2017
BivRegBLS: A New R Package in Method Comparison Studies with Tolerance Intervals and (Correlated)-Errors-in-Variables Regressions
11 September 2017, 12:00 – 12:20
- Submitted by
- Bernard Francq
- Bernard Francq (GSK), Marion Berger (Sanofi)
- The need of laboratories to quickly assess the quality of samples leads to the development of new measurement methods. These methods should lead to results comparable with those obtained by a standard method.
Two main methodologies are presented in the literature. The first one is the Bland–Altman approach with its agreement intervals (AIs) in a (M=(X+Y)/2,D=Y-1) space, where two methods (X and Y) are interchangeable if their differences are not clinically significant. The second approach is based on errors-in-variables regression in a classical (X,Y) plot, whereby two methods are considered equivalent when providing similar measures notwithstanding the random measurement errors.
A consistent correlated-errors-in-variables (CEIV) regression is introduced as the errors are shown to be correlated in the Bland–Altman plot. The coverage probabilities collapse drastically and the biases soar when this correlation is ignored. Robust novel tolerance intervals (based on unreplicated or replicated designs) are shown to be better than AIs, and novel predictive intervals in the (X,Y) plot and in the (M,D) plot are introduced with excellent coverage probabilities.
Guidelines for practitioners will be discussed and illustrated with the new and promising R package BivRegBLS. It will be explained how to model and plot the data in the (X,Y) space with the BLS regression (Bivariate Least Square) or in the (M,D) space with the CBLS regression (Correlated-BLS) by using BivRegBLS. The main functions will be explored with an emphasis on the output and how to plot the results.
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