Thomas Røraas

Background: Good estimates of within-person biological variation, CV_I, are essential for diagnosing and monitoring patients and for setting analytical performance specifications. The aim of the present study was to use computer simulations to evaluate the impact of various measurement distributions on different methods for estimating CV_I and reference change value (RCV). Method: Data were simulated on the basis of 3 models for distributions of the within-person effect. We evaluated 3 different methods for estimating CV_I: standard ANOVA, ln-ANOVA, and CV-ANOVA, and 3 different methods for calculating RCV: classic, ln-RCV, and a nonparametric method. We estimated CV_I and RCV with the different methods and compared the results with the true values. Results: The performance of the methods varied, depending on both the size of the CV_I and the type of distributions. The CV-ANOVA model performed well for the estimation of CV_I with all simulated data. The ln-RCV method performed best if data were ln-normal distributed or CV_I was less than approximately 12%. The nonparametric RCV method performed well for all simulated data but was less precise. Conclusions: The CV-ANOVA model is recommended for both calculation of CV_I and the step-by-step approach of checking for outliers and homogeneity in replicates and samples. The standard method for calculation of RCV should not be used when using CVs.

In order to improve recruitment of patients to the Norwegian diabetes register for adults, a questionnaire was designed to collect data directly from patients. The main aim of this study was to assess the agreement of questionnaire data with data reported to the Register from health care personnel during routine consultations. Patient data were obtained by sending a questionnaire with 27 of the 41 Register variables to 3714 members of the Norwegian Diabetes Association. Questionnaire data were compared with data already in the Register. Paired t-tests, percentages of total agreement, percentages of “positive” answers and kappa coefficients (k) were used for comparing data. Of the 1645 replies (44.3 %), the Register already had data on 324 patients for comparison. Response rate for most variables was better from patients (ranging from 76–100 %) compared with health care professionals (33–100 %). For 17 of 25 assessable variables including diabetes duration, height, weight, HbA1c, drug treatment and several diabetes complications, agreement was substantial or better with kappa >0.60. Data on family history of premature heart disease (k–0.59), foot examination (k = 0.26), foot ulcer (k = 0.32) and arterial surgery (k = 0.24) seemed to be difficult to answer by patients, whereas data on physical activity and self-monitoring of glucose seemed to be better when reported by patients. Patient response rate was acceptable, and data had good concordance with data from health care professionals for most variables. However, registers using patient questionnaires should compare questionnaire data with data from professionals at regular intervals.

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.