Performance of four regression frameworks with varying precision profiles in simulated reference material commutability assessment

Abstract Objectives 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. Methods 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. Results 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. Conclusions 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.