Design of Cost-Effective QC Procedures for Clinical Chemistry Assays

Abstract

depends on the fluctuations in the system, expressed as covariance P(K) and the measuring error R (K). A Kalman filter enables the on-line estimation of calibration parameters, intercept, sensitivity and drift (of both intercept and sensitivity). The filter requires a model of the system, including system noise and measurement noise. When a good model is available, the filter can predict future values or estimate best values of the changing parameters. These figures may be used to determine when a recalibration is required. Making a number of assumptions, the usual Kalman filter algorithms can be used resulting in [4,5,6]