One approach to assessing reference material (RM) commutability and agreement with clinical samples (CS) is to use ordinary least squares or Deming regression with prediction intervals. This approach assumes constant variance that may not be fulfilled by the measurement procedures. Flexible regression frameworks which relax this assumption, such as quantile regression or generalized additive models for location, scale, and shape (GAMLSS), have recently been implemented, which can model the changing variance with measurand concentration.
We simulated four imprecision profiles, ranging from simple constant variance to complex mixtures of constant and proportional variance, and examined the effects on commutability assessment outcomes with above four regression frameworks and varying the number of CS, data transformations and RM location relative to CS concentration. Regression framework performance was determined by the proportion of false rejections of commutability from prediction intervals or centiles across relative RM concentrations and was compared with the expected nominal probability coverage.
In simple variance profiles (constant or proportional variance), Deming regression, without or with logarithmic transformation respectively, is the most efficient approach. In mixed variance profiles, GAMLSS with smoothing techniques are more appropriate, with consideration given to increasing the number of CS and the relative location of RM. In the case where analytical coefficients of variation profiles are U-shaped, even the more flexible regression frameworks may not be entirely suitable.
In commutability assessments, variance profiles of measurement procedures and location of RM in respect to clinical sample concentration significantly influence the false rejection rate of commutability.
Research funding: None declared.
Author contributions: CM, TL, WR and WH conceived the requirement for simulation. CM performed simulation and produced numerical and graphical summary results. RT and CL had oversight and approval of statistical analysis. CM drafted the original manuscript, with all authors contributing to the content and editorial review of manuscript. All authors have read and approved the final manuscript.
Competing interests: Authors state no conflict of interest.
Ethical approval: Simulated data is exempt from review by the local Institutional Ethics Board.
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The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2022-0205).
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