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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., (© The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.)

Background: Cancer antigen 125 (CA125) is used to monitor tumor burden among patients with advanced serous epithelial ovarian cancer. The purpose is to compare the monitoring performance of seven previously proposed criteria., Materials & Methods: The CA125 assessment criteria were applied to simulated datasets. We investigated the ability to provide information on CA125 increments as well as their robustness against false positive signals., Results: For baseline concentrations above cut-off, the best performing criterion was based on a confirmed increment ≥2.5-times the nadir concentration. For baseline concentrations below cut-off, the best performing criterion was based on a confirmed increment from ≤ cut-off to >two-times cut-off., Discussion: Computer simulation models may be useful for a preclinical validation of criteria to be investigated in clinical trials.

Background Reference change values provide objective tools to assess the significance of a change in two consecutive results for a biomarker from an individual. The reference change value calculation is based on the assumption that within-subject biological variation has random fluctuation around a homeostatic set point that follows a normal (Gaussian) distribution. This set point (or baseline in steady-state) should be estimated from a set of previous samples, but, in practice, decisions based on reference change value are often based on only two consecutive results. The original reference change value was based on standard deviations according to the assumption of normality, but was soon changed to coefficients of variation (CV) in the formula (reference change value = ± Z ċ 2½ ċ CV). Z is being dependent on the desired probability of significance, which also defines the percentages of false-positive results. The aim of this study was to investigate false-positive results using five different published methods for calculation of reference change value. Methods The five reference change value methods were examined using normally and ln-normally distributed simulated data. Results One method performed best in approaching the theoretical false-positive percentages on normally distributed data and another method performed best on ln-normally distributed data. The commonly used reference change value method based on two results (without use of estimated set point) performed worst both on normally distributed and ln-normally distributed data. Conclusions The optimal choice of method to calculate reference change value limits requires knowledge of the distribution of data (normal or ln-normal) and, if possible, knowledge of the homeostatic set point. [ABSTRACT FROM AUTHOR]

Background: In the management of breast cancer, several algorithms for interpretation of measured concentrations during monitoring have been introduced, without objective evaluation of their performance regarding the distance of the starting concentration from the cut-off concentration. Methods: A computer simulation model has developed using parameters for the tumour biomarker CA 15-3 regarding biological variation and different rates of increase during progressive disease. Seven different algorithms, which include the cut-off point in the calculations, are applied to the simulated data corresponding to 1000 surrogate patients. Steady-state variation (CV-within-subject=14.9%) is based on Gaussian random numbers and an exponential increase in tumour growth (λ=0.009, 0.021, and 0.090 kU/day). Results: The main outcome of the simulations was that low starting concentrations (baseline concentrations) delay the detection of progressive disease (true-positive), whereas, baseline concentrations just below the cut-off value results in false-positive results of progression during steady-state situations. The algorithms investigated show varying susceptibility for baseline concentrations approaching the cut-off. Thus, three of the algorithms show >90% false-positives and three algorithms <5% false-positives when baseline concentrations were just below the cut-off point. Conclusions: Based on the simulations, it is possible to select the optimal algorithms for early detection of progressive disease and a low percentage of false-positives. [ABSTRACT FROM AUTHOR]

Abstract: Background: Several criteria have been proposed to interpret increments in serological cancer biomarker concentrations starting from low baseline concentrations crossing the cut-off. None of the criteria have been compared for their ability to signal tumour growth when ≤2% false positive results are accepted. Methods: The cancer biomarker Tissue Polypeptide Antigen was used as an example. Seven criteria to interpret increments in concentrations were investigated by computer simulations. Firstly, for each criterion, we identified a baseline concentration stratified for three levels of biological variation providing ≤2% false positive signals of tumour growth during one year of monitoring. Secondly, combining the steady state concentrations with rates of marker increase during tumour growth allowed calculation of the lengths of tumour detection times for each criterion. Results: The number of false positive marker signals depended on the baseline concentration, the magnitude of biological variation, and the magnitude of the required increment defined in the criterion. The lengths of the tumour detection times also depended on the rates of marker increase. Conclusions: The results suggest that different types of criteria should be used within different intervals of below cut-off level concentrations if the rate of false positive signals of marker increments should be kept ≤2%. [Copyright &y& Elsevier]