Repeated samplings and measurements in the monitoring of patients to look for changes are common clinical problems. The "reference change value", calculated as zp x [2 x (CVI2 + CVA2)](1/2), where zp is the z-statistic and CVI and CVA are within-subject and analytical coefficients of variation, respectively, has been used to detect whether a measured difference between measurements is statistically significant. However, a reference change value only detects the probability of false-positives (type I error), and for this reason, a model to calculate the risk of missing significant changes in serial results from individuals (probability of false-negatives) is investigated in this work by means of power functions.
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