A note on the calculation of reference change values for two consecutive normally distributed laboratory results

Population reference limits are inadequate for personalized analyses of medical laboratory results. Reference change values have been recommended as a valid alternative in assessing individual changes across sequential measurements. In this paper, we investigate the accuracy (type I error) and power (complement of type II error) of reference change values under three different statistical modeling scenarios and show that oversimplified hypotheses lead to misinterpretation of laboratory results. The power is strongly affected by the statistical modeling assumptions: it is shown that positive shifts in the individual average health condition are difficult to detect, while it is much easier to identify negative shifts.