Calculation of limits for significant unidirectional changes in two or more serial results of a biomarker based on a computer simulation model

Flemming Lund (Lead / Corresponding author), Per Hyltoft Petersen, Callum G. Fraser, György Sölétormos

    Research output: Contribution to journalArticle

    16 Citations (Scopus)

    Abstract

    Background: Reference change values (RCVs) were introduced more than 30 years ago and provide objective tools for assessment of the significance of differences in two consecutive results from an individual. However, in practice, more results are usually available for monitoring, and using the RCV concept on more than two results will increase the number of false-positive results. Therefore, a simple method is needed to interpret the significance of a difference when all available serial biomarker results are considered. Methods: A computer simulation model using Excel was developed. Based on 10,000 simulated data from healthy individuals, a series of up to 20 results from an 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 measurement result. These successive serial relative differences were computed to give limits for significant unidirectional differences with a constant cumulated maximum probability of both 95% (P<0.05) and 99%(P<0.01). Results: Factors used to multiply the first result from an individual were calculated to create the limits for constant cumulated significant differences. The factors were shown to become a simple function of the number of results and the total coefficient of variation. Conclusions: To interpret unidirectional differences in two or more serial results of a biomarker, the limits for significances are easily calculated using the presented factors. The first result is multiplied by the appropriate factor for increase or decrease, which gives the limits for a significant difference.

    Original languageEnglish
    Pages (from-to)237-244
    Number of pages8
    JournalAnnals of Clinical Biochemistry
    Volume52
    Issue number2
    DOIs
    Publication statusPublished - Mar 2015

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    Biomarkers
    Computer Simulation
    Reference Values
    Computer simulation
    Monitoring

    Keywords

    • Biological variation
    • Log-normal (Gaussian) distribution
    • Monitoring
    • Normal (Gaussian) distribution
    • Reference change value
    • Simulation models
    • Unidirectional differences

    Cite this

    Lund, Flemming ; Petersen, Per Hyltoft ; Fraser, Callum G. ; Sölétormos, György. / Calculation of limits for significant unidirectional changes in two or more serial results of a biomarker based on a computer simulation model. In: Annals of Clinical Biochemistry. 2015 ; Vol. 52, No. 2. pp. 237-244.
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    abstract = "Background: Reference change values (RCVs) were introduced more than 30 years ago and provide objective tools for assessment of the significance of differences in two consecutive results from an individual. However, in practice, more results are usually available for monitoring, and using the RCV concept on more than two results will increase the number of false-positive results. Therefore, a simple method is needed to interpret the significance of a difference when all available serial biomarker results are considered. Methods: A computer simulation model using Excel was developed. Based on 10,000 simulated data from healthy individuals, a series of up to 20 results from an 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 measurement result. These successive serial relative differences were computed to give limits for significant unidirectional differences with a constant cumulated maximum probability of both 95{\%} (P<0.05) and 99{\%}(P<0.01). Results: Factors used to multiply the first result from an individual were calculated to create the limits for constant cumulated significant differences. The factors were shown to become a simple function of the number of results and the total coefficient of variation. Conclusions: To interpret unidirectional differences in two or more serial results of a biomarker, the limits for significances are easily calculated using the presented factors. The first result is multiplied by the appropriate factor for increase or decrease, which gives the limits for a significant difference.",
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    Calculation of limits for significant unidirectional changes in two or more serial results of a biomarker based on a computer simulation model. / Lund, Flemming (Lead / Corresponding author); Petersen, Per Hyltoft; Fraser, Callum G.; Sölétormos, György.

    In: Annals of Clinical Biochemistry, Vol. 52, No. 2, 03.2015, p. 237-244.

    Research output: Contribution to journalArticle

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    AU - Lund, Flemming

    AU - Petersen, Per Hyltoft

    AU - Fraser, Callum G.

    AU - Sölétormos, György

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    N2 - Background: Reference change values (RCVs) were introduced more than 30 years ago and provide objective tools for assessment of the significance of differences in two consecutive results from an individual. However, in practice, more results are usually available for monitoring, and using the RCV concept on more than two results will increase the number of false-positive results. Therefore, a simple method is needed to interpret the significance of a difference when all available serial biomarker results are considered. Methods: A computer simulation model using Excel was developed. Based on 10,000 simulated data from healthy individuals, a series of up to 20 results from an 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 measurement result. These successive serial relative differences were computed to give limits for significant unidirectional differences with a constant cumulated maximum probability of both 95% (P<0.05) and 99%(P<0.01). Results: Factors used to multiply the first result from an individual were calculated to create the limits for constant cumulated significant differences. The factors were shown to become a simple function of the number of results and the total coefficient of variation. Conclusions: To interpret unidirectional differences in two or more serial results of a biomarker, the limits for significances are easily calculated using the presented factors. The first result is multiplied by the appropriate factor for increase or decrease, which gives the limits for a significant difference.

    AB - Background: Reference change values (RCVs) were introduced more than 30 years ago and provide objective tools for assessment of the significance of differences in two consecutive results from an individual. However, in practice, more results are usually available for monitoring, and using the RCV concept on more than two results will increase the number of false-positive results. Therefore, a simple method is needed to interpret the significance of a difference when all available serial biomarker results are considered. Methods: A computer simulation model using Excel was developed. Based on 10,000 simulated data from healthy individuals, a series of up to 20 results from an 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 measurement result. These successive serial relative differences were computed to give limits for significant unidirectional differences with a constant cumulated maximum probability of both 95% (P<0.05) and 99%(P<0.01). Results: Factors used to multiply the first result from an individual were calculated to create the limits for constant cumulated significant differences. The factors were shown to become a simple function of the number of results and the total coefficient of variation. Conclusions: To interpret unidirectional differences in two or more serial results of a biomarker, the limits for significances are easily calculated using the presented factors. The first result is multiplied by the appropriate factor for increase or decrease, which gives the limits for a significant difference.

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