ENBIS-18 in Nancy
2 – 25 September 2018; Ecoles des Mines, Nancy (France)
Abstract submission: 20 December 2017 – 4 June 2018
Be Confident, Predictable or Tolerable in Method Comparison Studies. Correlated-Error-in-Variables Regressions in XY and MD Plots.
4 September 2018, 14:10 – 14:30
- Submitted by
- Bernard Francq
- Bernard Francq (GSK, CMC StatS), Marion Berger (Sanofi, Département de Biostatistiques et Programmation)
- The need of laboratories to quickly assess the quality of samples leads to the development of new analytical measurement methods. These methods should, ideally, lead to ‘similar’ or ‘equivalent’ results notwithstanding the measurement errors. This can be assessed in a classical XY plot or MD plot (M=(X+Y)/2, D=Y-X) (Bland-Altman plot).
The well-known Agreement Interval (AI) in the MD plot will be compared to Tolerance Intervals (TIs). It will be explained that TIs are better, easier to explain and to interpret.
The measurement uncertainties in an MD plot are correlated. If this correlation is ignored, the biases soar considerably and the coverage probabilities collapse drastically. Therefore, the Bland-Altman approach leads to substantial misunderstandings and misleading conclusions.
In a recent paper, Francq and Govaerts reconcile the XY plot and MD plot by providing Confidence Intervals (CIs) and Prediction Intervals (PIs) with respectively the BLS and the new and promising CBLS regression ((Correlated)-Bivariate Least Square).
In this talk, we generalize these statistical intervals (CIs and PIs) by introducing the concept of a ‘generalized’ interval (GI). This provides flexibility to the practitioners as the equivalence can be assessed on averages (CI), on individual measures (PI) or on averages of a given number of measures (GI). Simulations, animated graphs and real data will be used to illustrate these techniques.
B.G. Francq, B.B. Govaerts. How to regress and predict in a Bland-Altman plot? Review and contribution based on tolerance intervals and correlated-errors-in-variables models. Statistics in Medicine, 35:2328-2358, 2016.
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