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Aligning Test and Reference Methods by Simultaneous Correction of Proportional and Constant Bias in Comparative
Jesse Albert Canchola1, Benjamin Blue La Brot1, Daniel Jarem1
1Global Medical Affairs, Infection and Immunity, Roche Molecular Systems, Inc., Pleasanton, CA, United States.
Background:
Biases between test and reference methods, specifically constant and proportional biases, can affect diagnostic accuracy. Conventional methods address these biases separately, limiting precision.
Methods:
We introduce a unified regression-based correction method for both constant and proportional bias, demonstrated using simulated polymerase chain reaction viral load data. The method applies linear and log-transformed regression adjustments with validation using both regression and Bland-Altman plots.
Results:
The correction, when plotting the test against the reference, yielded a fitted regression line with slope and intercept confidence intervals containing the ideal values of 1 and 0, respectively, confirming elimination of bias. Residual analysis provided evidence of model linearity assumptions.
Conclusions:
This unified correction enhances reliability in comparative studies and provides a reproducible foundation for recalibration across diagnostic platforms.
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