Background Reference change values provide objective tools to assess the significance of a change in two consecutive results of a biomarker from an individual. However, in practice, more results are usually available and using the reference change value concept on more than two results will increase the number of false positive results. Methods A computer simulation model was developed using Excel. Based on 10,000 simulated measurements among healthy individuals, a series of up to 20 results of a biomarker from each individual was generated using different values for the within-subject biological variation plus the analytical variation. Each new result in this series was compared to the initial result. These successive serial differences were computed to give limits for significant bidirectional changes with constant cumulated maximum probabilities of 95% (p<0.05) and 99% (p<0.01). Results From an individual factors used to multiply the first result were calculated to create limits for constant cumulated significant changes. The factors were shown to become a function of the number of results included and the total coefficient of variation. Conclusions The first result should be multiplied by the appropriate factors for increase and decrease to give the limits for a significant bidirectional change in several consecutive measurements.
- False positives
- Initial concentration
- Log-normal (Gaussian) distribution
- Normal (Gaussian) distribution
- Reference change values