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Sverre Sandberg - One of the best experts on this subject based on the ideXlab platform.

  • A Bayesian Approach to Biological Variation Analysis
    Clinical chemistry, 2019
    Co-Authors: Thomas Røraas, Sverre Sandberg, Aasne K. Aarsand, Bård Støve
    Abstract:

    BACKGROUND: Biological Variation (BV) data have many applications for diagnosing and monitoring disease. The standard statistical approaches for estimating BV are sensitive to “noisy data” and assume homogeneity of within-participant CV. Prior knowledge about BV is mostly ignored. The aims of this study were to develop Bayesian models to calculate BV that (a) are robust to “noisy data,” (b) allow heterogeneity in the within-participant CVs, and (c) take advantage of prior knowledge. METHOD: We explored Bayesian models with different degrees of robustness using adaptive Student t distributions instead of the normal distributions and when the possibility of heterogeneity of the within-participant CV was allowed. Results were compared to more standard approaches using chloride and triglyceride data from the European Biological Variation Study. RESULTS: Using the most robust Bayesian approach on a raw data set gave results comparable to a standard approach with outlier assessments and removal. The posterior distribution of the fitted model gives access to credible intervals for all parameters that can be used to assess reliability. Reliable and relevant priors proved valuable for prediction. CONCLUSIONS: The recommended Bayesian approach gives a clear picture of the degree of heterogeneity, and the ability to crudely estimate personal within-participant CVs can be used to explore relevant subgroups. Because BV experiments are expensive and time-consuming, prior knowledge and estimates should be considered of high value and applied accordingly. By including reliable prior knowledge, precise estimates are possible even with small data sets.

  • Harmonization initiatives in the generation, reporting and application of Biological Variation data
    Clinical chemistry and laboratory medicine, 2018
    Co-Authors: Aasne K. Aarsand, Pilar Fernandez-calle, Jorge Díaz-garzón, Thomas Røraas, Anna Carobene, William A. Bartlett, Federica Braga, Abdurrahman Coskun, Niels Jonker, Sverre Sandberg
    Abstract:

    Abstract Biological Variation (BV) data have many applications in laboratory medicine. However, concern has been raised that some BV estimates in use today may be irrelevant or of unacceptable quality. A number of initiatives have been launched by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) and other parties to deliver a more harmonized practice in the generation, reporting and application of BV data. Resulting from a necessary focus upon the veracity of historical BV studies, critical appraisal and meta-analysis of published BV studies is possible through application of the Biological Variation Data Critical Appraisal Checklist (BIVAC), published in 2017. The BIVAC compliant large-scale European Biological Variation Study delivers updated high-quality BV data for a wide range of measurands. Other significant developments include the publication of a Medical Subject Heading term for BV and recommendations for common terminology for reporting of BV data. In the near future, global BV estimates derived from meta-analysis of BIVAC appraised publications will be accessible in a Biological Variation Database at the EFLM website. The availability of these high-quality data, which have many applications that impact on the quality and interpretation of clinical laboratory results, will afford improved patient care.

  • Biological Variation: Evaluation of methods for constructing confidence intervals for estimates of within-person Biological Variation for different distributions of the within-person effect.
    Clinica Chimica Acta, 2017
    Co-Authors: Thomas Røraas, Bård Støve, Per Hyltoft Petersen, Sverre Sandberg
    Abstract:

    Abstract Background Precise estimates of the within-person Biological Variation, CV I , can be essential both for monitoring patients and for setting analytical performance specifications. The confidence interval, CI, may be used to evaluate the reliability of an estimate, as it is a good measure of the uncertainty of the estimated CV I . The aim of the present study is to evaluate and establish methods for constructing a CI with the correct coverage probability and non-cover probability when estimating CV I . Method Data based on 3 models for distributions for the within-person effect were simulated to assess the performance of 3 methods for constructing confidence intervals; the formula based method for the nested ANOVA, the percentile bootstrap and the bootstrap-t methods. Results The performance of the evaluated methods for constructing a CI varied, both dependent on the size of the CV I and the type of distributions. The bootstrap-t CI have good and stable performance for the models evaluated, while the formula based are more distribution dependent. The percentile bootstrap performs poorly. Conclusion CI is an essential part of estimation of the within-person Biological Variation. Good coverage probability and non-cover probabilities for CI are achievable by using the bootstrap-t combined with CV-ANOVA. Supplemental R-code is provided online.

  • Biological Variation reliable data is essential
    Clinical Chemistry and Laboratory Medicine, 2015
    Co-Authors: Aasne K. Aarsand, Thomas Røraas, Sverre Sandberg
    Abstract:

    Biological Variation (BV) data is a cornerstone in the interpretation of laboratory test results, being the basis for many of the decisions we make every day both in the laboratory and in clinical practise. Among the many applications is its use in diagnosis and monitoring of disease. Most typically this occurs when comparing a person’s level of the analyte of interest against a reference interval, based on the between-subject Variation, CV G , or when comparing a change against the reference change value, based on the within-subject Variation, CV I . Furthermore, BV data is probably the most commonly used approach for setting analytical quality specifications for bias, imprecision and total error for many laboratory constituents. General assumptions for the uses of BV data are that estimates are reliable, i.e., adequately collected and calculated, and that the estimates are representative for the specific population and setting for which they will be applied.

  • A checklist for critical appraisal of studies of Biological Variation.
    Clinical chemistry and laboratory medicine, 2015
    Co-Authors: William A. Bartlett, Pilar Fernandez-calle, Thomas Røraas, Anna Carobene, Federica Braga, Abdurrahman Coskun, Richard Prusa, Neils Jonker, Sverre Sandberg
    Abstract:

    Data on Biological Variation are used for many purposes in laboratory medicine but concern exists over the validity of the data reported in some studies. A critical appraisal checklist has been produced by a working group established by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) to enable standardised assessment of existing and future publications of Biological Variation data. The checklist identifies key elements to be reported in studies to enable safe accurate and effective transport of Biological Variation data sets across healthcare systems. The checklist is mapped to the domains of a minimum data set required to enable this process.

Per Hyltoft Petersen - One of the best experts on this subject based on the ideXlab platform.

  • Biological Variation: Evaluation of methods for constructing confidence intervals for estimates of within-person Biological Variation for different distributions of the within-person effect.
    Clinica Chimica Acta, 2017
    Co-Authors: Thomas Røraas, Bård Støve, Per Hyltoft Petersen, Sverre Sandberg
    Abstract:

    Abstract Background Precise estimates of the within-person Biological Variation, CV I , can be essential both for monitoring patients and for setting analytical performance specifications. The confidence interval, CI, may be used to evaluate the reliability of an estimate, as it is a good measure of the uncertainty of the estimated CV I . The aim of the present study is to evaluate and establish methods for constructing a CI with the correct coverage probability and non-cover probability when estimating CV I . Method Data based on 3 models for distributions for the within-person effect were simulated to assess the performance of 3 methods for constructing confidence intervals; the formula based method for the nested ANOVA, the percentile bootstrap and the bootstrap-t methods. Results The performance of the evaluated methods for constructing a CI varied, both dependent on the size of the CV I and the type of distributions. The bootstrap-t CI have good and stable performance for the models evaluated, while the formula based are more distribution dependent. The percentile bootstrap performs poorly. Conclusion CI is an essential part of estimation of the within-person Biological Variation. Good coverage probability and non-cover probabilities for CI are achievable by using the bootstrap-t combined with CV-ANOVA. Supplemental R-code is provided online.

  • confidence intervals and power calculations for within person Biological Variation effect of analytical imprecision number of replicates number of samples and number of individuals
    Clinical Chemistry, 2012
    Co-Authors: Thomas Røraas, Per Hyltoft Petersen, Sverre Sandberg
    Abstract:

    BACKGROUND: Reliable estimates of within-person Biological Variation and reference change value are of great importance when interpreting test results, monitoring patients, and setting quality specifications. Little information has been published regarding what experimental design is optimal to achieve the best estimates of within-person Biological Variation. METHOD: Expected CIs were calculated for different balanced designs for a 2-level nested variance analysis model with varying analytical imprecision. We also simulated data sets based on the model to calculate the power of different study designs for detection of within-person Biological Variation. RESULTS: The reliability of an estimate for Biological Variation and a study's power is very much influenced by the study design and by the ratio between analytical imprecision and within-person Biological Variation. For a fixed number of measurements, it is preferable to have a high number of samples from each individual. Shortcomings in analytical imprecision can be controlled by increasing the number of replicates. CONCLUSIONS: The design of an experiment to estimate Biological Variation should take into account the analytical imprecision of the method and focus on obtaining the highest possible reliability. Estimates of Biological Variation should always be reported with CIs.

  • Within-subject Biological Variation of glucose and HbA(1c) in healthy persons and in type 1 diabetes patients.
    Clinical chemistry and laboratory medicine, 2011
    Co-Authors: Siri Carlsen, Per Hyltoft Petersen, Svein Skeie, Øyvind Skadberg, Sverre Sandberg
    Abstract:

    Background Several articles describing within-subject Biological Variation of fasting glucose and HbA(1c) in healthy populations have been published, but information about Biological Variation of glucose and HbA(1c) in patients with type 1 diabetes is scarce. It is reasonable to assume that type 1 diabetics differ from their healthy counterparts in this matter. The aim of our study was to estimate the Biological Variation of glucose and HbA(1c) in healthy subjects and in patients with type 1 diabetes. Methods Fifteen healthy individuals and 15 type 1 diabetes patients were included. Biological Variations were calculated based on blood samples collected weekly for 10 consecutive weeks from the healthy and the eligible of the type 1 diabetes patients. Results The within-subject Variations of glucose were approximately 5% in healthy individuals and 30% in diabetes patients, and for HbA(1c) they were 1.2% in healthy individuals and 1.7% in diabetes patients. Conclusions In conclusion, we found a high within-subject Biological Variation of glucose in diabetes patients as expected compared to healthy individuals (30% vs. 5%). The short-term (2 months) within-subject Biological Variation of HbA(1c) did not differ significantly between well regulated type 1 diabetes patients and healthy individuals (1.7% vs. 1.2%).

  • Biological Variation of total prostate specific antigen a survey of published estimates and consequences for clinical practice
    Clinical Chemistry, 2005
    Co-Authors: György Sölétormos, Per Hyltoft Petersen, Axel Semjonow, Paul Sibley, Rolf Lamerz, Walter Albrecht, Peter Bialk, Massimo Gion, Frank Junker, Hans-peter Schmid
    Abstract:

    Background: The objectives of this study were to determine whether a single result for total prostate-specific antigen (tPSA) can be used confidently to guide the need for prostate biopsy and by how much serial tPSA measurements must differ to be significant. tPSA measurements include both analytical and Biological components of Variation. The European Group on Tumor Markers conducted a literature survey to determine both the magnitude and impact of Biological Variation on single, the mean of replicate, and serial tPSA measurements. Methods: The survey yielded 27 studies addressing the topic, and estimates for the Biological Variation of tPSA could be derived from 12 of these studies. Results: The mean Biological Variation was 20% in the concentration range 0.1–20 μg/L for men over 50 years. The Biological Variation means that the one-sided 95% confidence interval (CI) of the dispersion for a single tPSA result is ∼33%. Three replicate samples with one analysis on each narrow the one-sided 95% CI for the mean concentration to ∼20% and facilitate decisions on prostate biopsy. During monitoring of serial measurements, the change needed for significance is ∼50% ( P <0.05). Conclusions: The Biological Variation of tPSA has implications for screening, diagnosis, and monitoring. Single measurements may not be sufficiently precise for screening and diagnosis. Replicate samples and calculation of the mean concentration may improve precision by reducing the dispersion. Monitoring of tPSA requires an estimate of either the change needed for significance or, alternatively, of the significance of the change.

  • Biological Variation of Total Prostate-Specific Antigen: A Survey of Published Estimates and Consequences for Clinical Practice
    Clinical chemistry, 2005
    Co-Authors: György Sölétormos, Per Hyltoft Petersen, Axel Semjonow, Paul Sibley, Rolf Lamerz, Walter Albrecht, Peter Bialk, Massimo Gion, Frank Junker, Hans-peter Schmid
    Abstract:

    Background: The objectives of this study were to determine whether a single result for total prostate-specific antigen (tPSA) can be used confidently to guide the need for prostate biopsy and by how much serial tPSA measurements must differ to be significant. tPSA measurements include both analytical and Biological components of Variation. The European Group on Tumor Markers conducted a literature survey to determine both the magnitude and impact of Biological Variation on single, the mean of replicate, and serial tPSA measurements. Methods: The survey yielded 27 studies addressing the topic, and estimates for the Biological Variation of tPSA could be derived from 12 of these studies. Results: The mean Biological Variation was 20% in the concentration range 0.1–20 μg/L for men over 50 years. The Biological Variation means that the one-sided 95% confidence interval (CI) of the dispersion for a single tPSA result is ∼33%. Three replicate samples with one analysis on each narrow the one-sided 95% CI for the mean concentration to ∼20% and facilitate decisions on prostate biopsy. During monitoring of serial measurements, the change needed for significance is ∼50% ( P

Hans-peter Schmid - One of the best experts on this subject based on the ideXlab platform.

  • Biological Variation of total prostate specific antigen a survey of published estimates and consequences for clinical practice
    Clinical Chemistry, 2005
    Co-Authors: György Sölétormos, Per Hyltoft Petersen, Axel Semjonow, Paul Sibley, Rolf Lamerz, Walter Albrecht, Peter Bialk, Massimo Gion, Frank Junker, Hans-peter Schmid
    Abstract:

    Background: The objectives of this study were to determine whether a single result for total prostate-specific antigen (tPSA) can be used confidently to guide the need for prostate biopsy and by how much serial tPSA measurements must differ to be significant. tPSA measurements include both analytical and Biological components of Variation. The European Group on Tumor Markers conducted a literature survey to determine both the magnitude and impact of Biological Variation on single, the mean of replicate, and serial tPSA measurements. Methods: The survey yielded 27 studies addressing the topic, and estimates for the Biological Variation of tPSA could be derived from 12 of these studies. Results: The mean Biological Variation was 20% in the concentration range 0.1–20 μg/L for men over 50 years. The Biological Variation means that the one-sided 95% confidence interval (CI) of the dispersion for a single tPSA result is ∼33%. Three replicate samples with one analysis on each narrow the one-sided 95% CI for the mean concentration to ∼20% and facilitate decisions on prostate biopsy. During monitoring of serial measurements, the change needed for significance is ∼50% ( P <0.05). Conclusions: The Biological Variation of tPSA has implications for screening, diagnosis, and monitoring. Single measurements may not be sufficiently precise for screening and diagnosis. Replicate samples and calculation of the mean concentration may improve precision by reducing the dispersion. Monitoring of tPSA requires an estimate of either the change needed for significance or, alternatively, of the significance of the change.

  • Biological Variation of Total Prostate-Specific Antigen: A Survey of Published Estimates and Consequences for Clinical Practice
    Clinical chemistry, 2005
    Co-Authors: György Sölétormos, Per Hyltoft Petersen, Axel Semjonow, Paul Sibley, Rolf Lamerz, Walter Albrecht, Peter Bialk, Massimo Gion, Frank Junker, Hans-peter Schmid
    Abstract:

    Background: The objectives of this study were to determine whether a single result for total prostate-specific antigen (tPSA) can be used confidently to guide the need for prostate biopsy and by how much serial tPSA measurements must differ to be significant. tPSA measurements include both analytical and Biological components of Variation. The European Group on Tumor Markers conducted a literature survey to determine both the magnitude and impact of Biological Variation on single, the mean of replicate, and serial tPSA measurements. Methods: The survey yielded 27 studies addressing the topic, and estimates for the Biological Variation of tPSA could be derived from 12 of these studies. Results: The mean Biological Variation was 20% in the concentration range 0.1–20 μg/L for men over 50 years. The Biological Variation means that the one-sided 95% confidence interval (CI) of the dispersion for a single tPSA result is ∼33%. Three replicate samples with one analysis on each narrow the one-sided 95% CI for the mean concentration to ∼20% and facilitate decisions on prostate biopsy. During monitoring of serial measurements, the change needed for significance is ∼50% ( P

György Sölétormos - One of the best experts on this subject based on the ideXlab platform.

  • Biological Variation of total prostate specific antigen a survey of published estimates and consequences for clinical practice
    Clinical Chemistry, 2005
    Co-Authors: György Sölétormos, Per Hyltoft Petersen, Axel Semjonow, Paul Sibley, Rolf Lamerz, Walter Albrecht, Peter Bialk, Massimo Gion, Frank Junker, Hans-peter Schmid
    Abstract:

    Background: The objectives of this study were to determine whether a single result for total prostate-specific antigen (tPSA) can be used confidently to guide the need for prostate biopsy and by how much serial tPSA measurements must differ to be significant. tPSA measurements include both analytical and Biological components of Variation. The European Group on Tumor Markers conducted a literature survey to determine both the magnitude and impact of Biological Variation on single, the mean of replicate, and serial tPSA measurements. Methods: The survey yielded 27 studies addressing the topic, and estimates for the Biological Variation of tPSA could be derived from 12 of these studies. Results: The mean Biological Variation was 20% in the concentration range 0.1–20 μg/L for men over 50 years. The Biological Variation means that the one-sided 95% confidence interval (CI) of the dispersion for a single tPSA result is ∼33%. Three replicate samples with one analysis on each narrow the one-sided 95% CI for the mean concentration to ∼20% and facilitate decisions on prostate biopsy. During monitoring of serial measurements, the change needed for significance is ∼50% ( P <0.05). Conclusions: The Biological Variation of tPSA has implications for screening, diagnosis, and monitoring. Single measurements may not be sufficiently precise for screening and diagnosis. Replicate samples and calculation of the mean concentration may improve precision by reducing the dispersion. Monitoring of tPSA requires an estimate of either the change needed for significance or, alternatively, of the significance of the change.

  • Biological Variation of Total Prostate-Specific Antigen: A Survey of Published Estimates and Consequences for Clinical Practice
    Clinical chemistry, 2005
    Co-Authors: György Sölétormos, Per Hyltoft Petersen, Axel Semjonow, Paul Sibley, Rolf Lamerz, Walter Albrecht, Peter Bialk, Massimo Gion, Frank Junker, Hans-peter Schmid
    Abstract:

    Background: The objectives of this study were to determine whether a single result for total prostate-specific antigen (tPSA) can be used confidently to guide the need for prostate biopsy and by how much serial tPSA measurements must differ to be significant. tPSA measurements include both analytical and Biological components of Variation. The European Group on Tumor Markers conducted a literature survey to determine both the magnitude and impact of Biological Variation on single, the mean of replicate, and serial tPSA measurements. Methods: The survey yielded 27 studies addressing the topic, and estimates for the Biological Variation of tPSA could be derived from 12 of these studies. Results: The mean Biological Variation was 20% in the concentration range 0.1–20 μg/L for men over 50 years. The Biological Variation means that the one-sided 95% confidence interval (CI) of the dispersion for a single tPSA result is ∼33%. Three replicate samples with one analysis on each narrow the one-sided 95% CI for the mean concentration to ∼20% and facilitate decisions on prostate biopsy. During monitoring of serial measurements, the change needed for significance is ∼50% ( P

  • Biological Variation and analytical imprecision of CA 125 in patients with ovarian cancer.
    Scandinavian journal of clinical and laboratory investigation, 2000
    Co-Authors: Malgorzata K. Tuxen, György Sölétormos, Gordon J. S. Rustin, A. E. Nelstrop, P. Dombernowsky
    Abstract:

    Despite the availability of serial data on CA 125 in ovarian cancer, the problem of interpreting a change over time is still unsolved. Changes in marker concentrations are due not only to patients improving or deteriorating but also to analytical imprecision and normal intra-individual Biological Variation. The aim of this study was to assess the analytical imprecision (CVA) and the intraand inter-individual Biological Variation (CVI and CVG

Wj Hayward Vermaak - One of the best experts on this subject based on the ideXlab platform.

  • Biological Variation in sweat sodium chloride conductivity.
    Annals of Clinical Biochemistry, 2002
    Co-Authors: Da-elene Van Der Merwe, Johan B. Ubbink, Rhena Delport, Piet J. Becker, Gurdeep S. Dhatt, Wj Hayward Vermaak
    Abstract:

    Background Sweat conductivity, which is equivalent to sweat NaCI concentration, is used as a screening test to identify possible cystic fibrosis (CF) patients. No data exist on the Biological Variation of this variable and the influence it may have on the interpretation of sweat testing. The aim of this study was to determine the components of Biological Variation for sweat sodium chloride conductivity and to apply Biological Variation parameters in the interpretation of sweat conductivity. Methods Sweat conductivity was determined once a week for 5 consecutive weeks on 15 healthy volunteers, 20 healthy infants and 20 known CF patients. Results The analytical coefficient of Variation (CV A ) was 1.15% for the high-level control material, with a value of 123 mmol/L, and 1.32% for the normal-level control material with a value of 40 mmoL/L. The within-subject (CV 1 ) and between-subject (CV G ) Biological Variations were 12.0% and 30.0%, respectively, for healthy controls; 18% and 20% for healthy infants; and 7.3% and 6.5% for CF patients, respectively. Using the CV A , CV G and CV I , the 95% reference ranges were determined for the above-mentioned three groups. The calculated 95% ranges for the healthy babies and CF patients were 18-60 mmoL/L and 96-144 mmoL/L. Conclusions Our data support a decision level of > 60 mmoL/L for confirmatory CF testing. A lower decision level will result in an unacceptable high rate of unnecessary confirmation testing.

  • Biological Variation in sweat sodium chloride conductivity.
    Annals of clinical biochemistry, 2002
    Co-Authors: Da-elene Van Der Merwe, Johan B. Ubbink, Rhena Delport, Gurdeep S. Dhatt, Piet Becker, Wj Hayward Vermaak
    Abstract:

    Sweat conductivity, which is equivalent to sweat NaCl concentration, is used as a screening test to identify possible cystic fibrosis (CF) patients. No data exist on the Biological Variation of this variable and the influence it may have on the interpretation of sweat testing. The aim of this study was to determine the components of Biological Variation for sweat sodium chloride conductivity and to apply Biological Variation parameters in the interpretation of sweat conductivity. Sweat conductivity was determined once a week for 5 consecutive weeks on 15 healthy volunteers, 20 healthy infants and 20 known CF patients. The analytical coefficient of Variation (CV(A)) was 1.15% for the high-level control material, with a value of 123 mmol/L, and 1.32% for the normal-level control material with a value of 40 mmoL/L. The within-subject (CV) and between-subject (CV(G)) Biological Variations were 12.0% and 30.0%, respectively, for healthy controls; 18% and 20% for healthy infants; and 7.3% and 6.5% for CF patients, respectively. Using the CV(A), CV(G) and CV(I), the 95% reference ranges were determined for the above-mentioned three groups. The calculated 95% ranges for the healthy babies and CF patients were 18-60 mmoL/L and 96-144 mmoL/L. Our data support a decision level of > 60 mmoL/L for confirmatory CF testing. A lower decision level will result in an unacceptable high rate of unnecessary confirmation testing.