Multiple Instrument Methods Comparison by Precision weighted Deming Regression

Abstract

In methods comparison (MC) studies, specimens are tested using two or more instruments with the objective of establishing the statistical relationship between the different instruments readings. Unlike regular regression, this is an errors in variables problem. Relationships may be fitted parametrically (Deming regression) or non-parametrically (Passing Bablok or PB regression.) In clinical chemistry settings, the measurement variability is rarely constant, but generally increases with increasing analyte values. Precision weighted Deming regression models this variability and incorporates it into the fitting. The simplest setting of comparing two instruments is discussed in (1) and implemented in an R package (2). PB makes minimal distributional assumptions. Its classical two-instrument implementation has recently been extended to multiple instruments (3). This work extends the two-instrument Deming model of (1) to multiple instruments, developing algorithms for fitting, for formal inference, for residual analysis, and for outlier detection and diagnosis.