In subject-based RI, what determines whether a difference between two consecutive measurements is statistically significant (P ≤ 0.05)?

• A. Coefficient of Variation (CV)
• B. Standard Error of Measurement (SEM)
• C. Reference Change Value (RCV)
• D. Confidence Interval (CI)

Answer: (C)

When assessing repeated measurements in the same subject, you need a threshold that reflects how much a value can vary within that individual (due to both the test method and natural biology) before you call a change real. The Reference Change Value provides that threshold. It combines analytical variation (how precise the assay is) with within-subject biological variation and translates it into the amount of change that would be expected 95% of the time if no true change occurred. If the observed difference between two consecutive measurements exceeds this RCV, it’s considered statistically significant at P ≤ 0.05.

Other options serve different purposes. CV describes variability within a set of measurements but not the meaningful change between two time points in the same person. SEM relates to the precision of a single measurement or the spread around a mean, not to whether two individual results differ beyond expected intra-subject variation. Confidence intervals quantify the precision of an estimate but aren’t the direct threshold used for declaring a change between two consecutive subject measurements significant.