Abstract

Repeated samplings and measurements in the monitoring ofpatients to look for changes are common clinical problems. The “reference change value”, calculated as z_P×[2×(CV_I ² + CV_A ²)]^1/2, where z_P is the z-statistic and CV_I and CV_A 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 ofmissing significant changes in serial results from individuals (probability off alse-negatives) is investigated in this work by means of power functions. Therefore, when an analyte is being monitored in a patient, power functions estimate the probability of detecting a defined real change by measuring the difference. Thus, when a measured difference is the same as the calculated reference change value, then it will be detected in only 50% of situations.