scholarly journals A robust approach to reference interval estimation and evaluation

1998 ◽  
Vol 44 (3) ◽  
pp. 622-631 ◽  
Author(s):  
Paul S Horn ◽  
Amadeo J Pesce ◽  
Bradley E Copeland
Keyword(s):  
Prediction Interval ◽  
Clinical Chemistry ◽  
Real Data ◽  
Reference Interval ◽  
Reference Intervals ◽  
Small Sample ◽  
The Other ◽  
Robust Estimator ◽  
Sample Sizes ◽  
Robust Approach

Abstract We propose a new methodology for the estimation of reference intervals for data sets with small numbers of observations or for those with substantial numbers of outliers. We propose a prediction interval that uses robust estimates of location and scale. The SAS software can be readily modified to do these calculations. We compared four reference interval procedures (nonparametric, transformed, robust with a nonparametric lower limit, and transformed robust) for sample sizes of 20, 40, 60, 80, 100, and 120 from χ2 distributions of 1, 4, 7, and 10 df. χ2 distributions were chosen because they simulate the skewness of distributions often found in clinical chemistry populations. We used the root mean square error as the measure of performance and used computer simulation to calculate this measure. The robust estimator showed the best performance for small sample sizes. As the sample size increased, the performance values converged. The robust method for calculating upper reference interval values yields reasonable results. In two examples using real data for haptoglobin and glucose, the robust estimator provides slightly smaller upper reference limits than the other procedures. Lastly, the robust estimator was compared with the other procedures in a population where 5% of the values were multiplied by a factor of 5. The reference intervals were calculated with and without outlier detection. In this case, the robust approach consistently yielded upper reference interval values that were closer to those of the true underlying distributions. We propose that robust statistical analysis can be of great use for determinations of reference intervals from limited or possibly unreliable data.

scholarly journals Reference Intervals for Hematology and Clinical Chemistry for the African Elephant (Loxodonta africana)

2021 ◽  
Vol 8 ◽  
Author(s):  
Christine Steyrer ◽  
Michele Miller ◽  
Jennie Hewlett ◽  
Peter Buss ◽  
Emma H. Hooijberg
Keyword(s):  
National Parks ◽  
Clinical Chemistry ◽  
Clinical Pathology ◽  
Loxodonta Africana ◽  
Reference Interval ◽  
Reference Intervals ◽  
Small Sample ◽  
African Elephant ◽  
Serum Samples ◽  
American Society

The African elephant (Loxodonta africana) is listed as vulnerable, with wild populations threatened by habitat loss and poaching. Clinical pathology is used to detect and monitor disease and injury, however existing reference interval (RI) studies for this species have been performed with outdated analytical methods, small sample sizes or using only managed animals. The aim of this study was to generate hematology and clinical chemistry RIs, using samples from the free-ranging elephant population in the Kruger National Park, South Africa. Hematology RIs were derived from EDTA whole blood samples automatically analyzed (n = 23); manual PCV measured from 48 samples; and differential cell count results (n = 51) were included. Clinical chemistry RIs were generated from the results of automated analyzers on stored serum samples (n = 50). Reference intervals were generated according to American Society for Veterinary Clinical Pathology guidelines with a strict exclusion of outliers. Hematology RIs were: PCV 34–49%, RBC 2.80–3.96 × 1012/L, HGB 116–163 g/L, MCV 112–134 fL, MCH 35.5–45.2 pg, MCHC 314–364 g/L, PLT 182–386 × 109/L, WBC 7.5–15.2 × 109/L, segmented heterophils 1.5–4.0 × 109/L, band heterophils 0.0–0.2 × 109/L, total monocytes 3.6–7.6 × 109/L (means for “regular” were 35.2%, bilobed 8.6%, round 3.9% of total leukocytes), lymphocytes 1.1–5.5 × 109/L, eosinophils 0.0–0.9 × 109/L, basophils 0.0–0.1 × 109/L. Clinical chemistry RIs were: albumin 41–55 g/L, ALP 30–122 U/L, AST 9–34 U/L, calcium 2.56–3.02 mmol/L, CK 85–322 U/L, GGT 7–16 U/L, globulin 30–59 g/L, magnesium 1.15–1.70 mmol/L, phosphorus 1.28–2.31 mmol/L, total protein 77–109 g/L, urea 1.2–4.6 mmol/L. Reference intervals were narrower than those reported in other studies. These RI will be helpful in the future management of injured or diseased elephants in national parks and zoological settings.

scholarly journals Personalized Reference Intervals in Laboratory Medicine: A New Model Based on Within-Subject Biological Variation

2020 ◽  
Author(s):  
Abdurrahman Coşkun ◽  
Sverre Sandberg ◽  
Ibrahim Unsal ◽  
Coskun Cavusoglu ◽  
Mustafa Serteser ◽  
...  
Keyword(s):  
Steady State ◽  
Personalized Medicine ◽  
Clinical Chemistry ◽  
Reference Interval ◽  
Reference Intervals ◽  
Biological Variation ◽  
Test Results ◽  
Steady State Condition ◽  
Laboratory Test Results ◽  
Measurement Results

Abstract Background The concept of personalized medicine has received widespread attention in the last decade. However, personalized medicine depends on correct diagnosis and monitoring of patients, for which personalized reference intervals for laboratory tests may be beneficial. In this study, we propose a simple model to generate personalized reference intervals based on historical, previously analyzed results, and data on analytical and within-subject biological variation. Methods A model using estimates of analytical and within-subject biological variation and previous test results was developed. We modeled the effect of adding an increasing number of measurement results on the estimation of the personal reference interval. We then used laboratory test results from 784 adult patients (>18 years) considered to be in a steady-state condition to calculate personalized reference intervals for 27 commonly requested clinical chemistry and hematology measurands. Results Increasing the number of measurements had little impact on the total variation around the true homeostatic set point and using ≥3 previous measurement results delivered robust personalized reference intervals. The personalized reference intervals of the study participants were different from one another and, as expected, located within the common reference interval. However, in general they made up only a small proportion of the population-based reference interval. Conclusions Our study shows that, if using results from patients in steady state, only a few previous test results and reliable estimates of within-subject biological variation are required to calculate personalized reference intervals. This may be highly valuable for diagnosing patients as well as for follow-up and treatment.

Independence Estimating Equations for Controlled Clinical Trials with Small Sample Sizes

2006 ◽  
Vol 45 (04) ◽  
pp. 430-434 ◽  
Author(s):  
G. Dahmen ◽  
A. Ziegler
Keyword(s):  
Clinical Trials ◽  
Estimating Equations ◽  
Small Sample ◽  
Repeated Measurements ◽  
Robust Estimator ◽  
Variance Estimator ◽  
Sample Sizes ◽  
Controlled Clinical Trials ◽  
Parallel Group ◽  
Small Sample Sizes

Summary Objectives: The application of independence estimating equations (IEE) for controlled clinical trials (CCTs) has recently been discussed, and recommendations for its use have been derived for testing hypotheses. The robust estimator of variance has been shown to be liberal for small sample sizes. Therefore a series of modifications has been proposed. In this paper we systematically compare confidence intervals (CIs) proposed in the literature for situations that are common in CCTs. Methods: Using Monte-Carlo simulation studies, we compared the coverage probabilities of CIs and non-convergence probabilities for the parameters of the mean structure for small samples using modifications of the variance estimator proposed by Mancl and de Rouen [7], Morel et al. [8] and Pan [3]. Results: None of the proposed modifications behave well in each investigated situation. For parallel group designs with repeated measurements and binary response the method proposed by Pan maintains the nominal level. We observed non-convergence of the IEE algorithm in up to 10% of the replicates depending on response probabilities in the treatment groups. For comparing slopes with continuous responses, the approach of Morel et al. can be recommended. Conclusions: Results of non-convergence probabilities show that IEE should not be used in parallel group designs with binary endpoints and response probabilities close to 0 or 1. Modifications of the robust variance estimator should be used for sample sizes up to 100 clusters for CI estimation.

Reference intervals for 21 clinical chemistry analytes in arterial and venous umbilical cord blood

1993 ◽  
Vol 39 (6) ◽  
pp. 1041-1044 ◽  
Author(s):  
S L Perkins ◽  
J F Livesey ◽  
J Belcher
Keyword(s):  
Alkaline Phosphatase ◽  
Umbilical Cord ◽  
Clinical Chemistry ◽  
Venous Blood ◽  
Reference Interval ◽  
Reference Intervals ◽  
Nonparametric Analysis ◽  
Term Infants ◽  
Male And Female ◽  
Gamma Glutamyltransferase

Abstract Reference intervals were determined for 21 clinical chemistry analytes in umbilical cord arterial and venous blood from healthy term infants. Nonparametric analysis (rank number) was used to determine the central 95% reference interval. No significant differences were observed between male and female infants. Reference intervals for glucose, urea, creatinine, urate, phosphate, calcium, albumin, total protein, cholesterol, triglycerides, alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, creatine kinase, lactate dehydrogenase, gamma-glutamyltransferase, and magnesium all were significantly different from adult values.

Estimation of individual reference intervals in small sample sizes

2007 ◽  
Vol 210 (3-4) ◽  
pp. 471-478 ◽  
Author(s):  
Åse Marie Hansen ◽  
Anne Helene Garde ◽  
Nanna Hurwitz Eller
Keyword(s):  
Reference Intervals ◽  
Small Sample ◽  
Sample Sizes ◽  
Small Sample Sizes

scholarly journals The zlog value as a basis for the standardization of laboratory results

2017 ◽  
Vol 41 (1) ◽  
Author(s):  
Georg Hoffmann ◽  
Frank Klawonn ◽  
Ralf Lichtinghagen ◽  
Matthias Orth
Keyword(s):  
Clinical Chemistry ◽  
Standard Procedure ◽  
Inverse Function ◽  
Real Data ◽  
Reference Interval ◽  
Reference Population ◽  
German Society ◽  
Health Law ◽  
Original Result ◽  
Laboratory Results

AbstractBackground:With regard to the German E-Health Law of 2016, the German Society for Clinical Chemistry and Laboratory Medicine (DGKL) has been invited to develop a standard procedure for the storage and transmission of laboratory results. We suggest the commonly used z-transformation.Methods:This method evaluates by how many standard deviations (SDs) a given result deviates from the mean of the respective reference population. We confirm with real data that laboratory results of healthy individuals can be adjusted to a normal distribution by logarithmic transformation.Results:Thus, knowing the lower and upper reference limits LL and UL, one can transform any result x into a zlog value using the following equation:$\eqalign{ {\rm{zlog}} = & {\rm{(log(x)}}-{\rm{(log(LL)}} + {\rm{log(UL))/2)\cdot3}}{\rm{.92/(log(UL)}} \cr -{\bf{ }}{\rm{log(LL))}} \cr} $The result can easily be interpreted, as its reference interval (RI) is –1.96 to +1.96 by default, and very low or high results yield zlog values around –5 and +5, respectively. For intuitive data presentation, the zlog values may be transformed into a continuous color scale, e.g. from blue via white to orange.Using the inverse function, any zlog value can then be translated into the theoretical result of an analytical method with another RI:(1)$${\rm{x}} = {\rm{L}}{{\rm{L}}^{0.5 - {\rm{zlog}}/3.92}} \cdot {\rm{U}}{{\rm{L}}^{0.5 + {\rm{zlog}}/3.92}}$$Conclusions:Our standardization proposal can easily be put into practice and may effectively contribute to data quality and patient safety in the frame of the German E-health law. We suggest for the future that laboratories should provide the zlog value in addition to the original result, and that the data transmission protocols (e.g. HL7, LDT) should contain a special field for this additional value.

scholarly journals Indirect methods for reference interval determination – review and recommendations

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Graham R.D. Jones ◽  
Rainer Haeckel ◽  
Tze Ping Loh ◽  
Ken Sikaris ◽  
Thomas Streichert ◽  
...  
Keyword(s):  
Clinical Chemistry ◽  
Direct Methods ◽  
Analytical Techniques ◽  
Reference Interval ◽  
Reference Population ◽  
Reference Intervals ◽  
Statistical Techniques ◽  
Indirect Methods ◽  
Indirect Approach ◽  
Patient Health

Abstract Reference intervals are a vital part of the information supplied by clinical laboratories to support interpretation of numerical pathology results such as are produced in clinical chemistry and hematology laboratories. The traditional method for establishing reference intervals, known as the direct approach, is based on collecting samples from members of a preselected reference population, making the measurements and then determining the intervals. An alternative approach is to perform analysis of results generated as part of routine pathology testing and using appropriate statistical techniques to determine reference intervals. This is known as the indirect approach. This paper from a working group of the International Federation of Clinical Chemistry (IFCC) Committee on Reference Intervals and Decision Limits (C-RIDL) aims to summarize current thinking on indirect approaches to reference intervals. The indirect approach has some major potential advantages compared with direct methods. The processes are faster, cheaper and do not involve patient inconvenience, discomfort or the risks associated with generating new patient health information. Indirect methods also use the same preanalytical and analytical techniques used for patient management and can provide very large numbers for assessment. Limitations to the indirect methods include possible effects of diseased subpopulations on the derived interval. The IFCC C-RIDL aims to encourage the use of indirect methods to establish and verify reference intervals, to promote publication of such intervals with clear explanation of the process used and also to support the development of improved statistical techniques for these studies.

scholarly journals Variability in the measurement of C-reactive protein in healthy subjects: implications for reference intervals and epidemiological applications

1997 ◽  
Vol 43 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Elizabeth M Macy ◽  
Timothy E Hayes ◽  
Russell P Tracy
Keyword(s):  
Clinical Chemistry ◽  
Reference Interval ◽  
Reference Intervals ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Analytical Variability ◽  
Subject Variability ◽  
Donor Population ◽  
Index Of Individuality ◽  
Small Index

Abstract We developed a reproducible ELISA for C-reactive protein (CRP), calibrated with WHO Reference Material, for which intra- and interassay CVs were 3.0% and 6.0%, respectively. Analytical recovery was 97.9%. The distribution of CRP in a healthy blood donor population (n = 143) was nongaussian, with 2.5th, 50th, and 97.5th percentile values of 0.08, 0.64, and 3.11 mg/L, respectively. There was no sex-related difference, and the association with age was weak. In a study of variability [by the method of Fraser and Harris (Crit Rev Clin Lab Sci 1989;27:409–37)], the analytical variability was 5.2%; the within-subject variability, CVI, was 42.2%; and the between-subject variability, CVG, was 92.5%. The critical difference for sequential values significant at P ≤0.05 (i.e., the smallest percentage change unlikely to be due to analytical variability or CVI) was calculated as 118%, and the index of individuality, CVI/CVG, was 0.46. This suggests that CRP, like many clinical chemistry analytes, has limited usefulness in detecting early disease-associated changes when used in conjunction with a healthy reference interval. From a molecular epidemiological standpoint, the usefulness of CRP in longitudinal studies is suggested by the small index of individuality and by observations that (a) short-term fluctuations were infrequent, (b) all data stayed within the reference interval, and (c) relative rankings of the subjects over 6 months only moderately deteriorated.

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