Impact of Subgroup Prevalences on Partitioning of Gaussian-distributed Reference Values

Abstract Background: The aims of this report were to examine how unequal subgroup prevalences in the source population may affect reference interval partitioning decisions and to develop generally applicable guidelines for partitioning gaussian-distributed data. Methods: We recently proposed a new model for partitioning reference intervals when the underlying data distribution is gaussian. This model is based on controlling the proportions of the subgroup distributions that fall outside each of the common reference limits, using the distances between the reference limits of the subgroup distributions as functions to these proportions. We examine the significance of the unequal prevalence effect for the partitioning problem and quantify it for distance partitioning criteria by deriving analytical expressions to express these criteria as a function of the ratio of prevalences. An application example, illustrating various aspects of the importance of the prevalence effect, is also presented. Results: Dramatic shrinkage of the critical distances between reference limits of the subgroups needed for partitioning was observed as the ratio of prevalences, the larger one divided by the smaller one, was increased from unity. Because of this shrinkage, the same critical distances are not valid for all ratios of prevalences, but specific critical distances should be used for each particular value of this ratio. Although proportion criteria used in determining the need for reference interval partitioning are not dependent on the prevalence effect, this effect should be accounted for when these criteria are being applied by adjusting the sample sizes of the subgroups to make them correspond to the ratio of prevalences. Conclusions: The prevalences of subgroups in the reference population should be known and observed in the calculations for every reference interval study, irrespective of whether distance or proportion criteria are being used to determine the need for reference interval partitioning. We present detailed methods to account for the prevalences when applying each of these types of criteria. Analytical expressions for the distance criteria, to be used when high precision is needed, and approximate distances, to be used in practical work, are derived. General guidelines for partitioning gaussian distributed data are presented. Following these guidelines and using the new model, we suggest that partitioning can be performed more reliably than with any of the earlier models because the new model not only offers an improved correspondence between the critical distances and the critical proportions, but also accounts for the prevalence effect.

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Partitioning biochemical reference data intosubgroups: comparison of existing methods

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm.2004.123 ◽

2004 ◽

Vol 42 (7) ◽

Cited By ~ 14

Author(s):

Ari Lahti

Keyword(s):

Reference Data ◽

Reference Interval ◽

Reference Intervals ◽

New Method ◽

Poor Correlation ◽

Specific Reference ◽

Common Reference ◽

Reference Limits ◽

The Common ◽

The Ideal

AbstractFour existing methods for partitioning biochemical reference data into subgroups are compared. Two of these, the method of Sinton et al. and that of Ichihara and Kawai, are based on a quotient of a difference between the subgroups and the reference interval for the combined distribution. The criterion of Sinton et al. appears rather stringent and could lead to recommendations to apply a common reference interval in many cases where establishment of group-specific reference intervals would be more useful. The method of Ichihara and Kawai is similar to that of Sinton et al., but their criterion, based on a quantity derived from between-group and within-group variances, seems to lead to inconsistent results when applied to some model cases. These two methods have the common weakness of using gross differences between subgroup distributions as an indicator of differences between their reference limits, while distributions with different means can actually have equal reference limits and those with equal means can have different reference limits. The idea of Harris and Boyd to require that the proportions of the subgroup distributions outside the common reference limits be kept reasonably close to the ideal value of 2.5% as a prerequisite for using common reference limits seems to have been a major improvement. The other two methods considered, that of Harris and Boyd and the “new method” follow this idea. The partitioning criteria of Harris and Boyd have previously been shown to provide a poor correlation to those proportions, however, and the weaknesses of their method are summarized in a list of five drawbacks. Different versions of the new method offer improvements to these drawbacks.

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Albumin and calcium reference interval using healthy individuals and a data-mining approach

Annals of Clinical Biochemistry International Journal of Laboratory Medicine ◽

10.1177/0004563220944204 ◽

2020 ◽

Vol 57 (5) ◽

pp. 373-381 ◽

Cited By ~ 2

Author(s):

N Jassam ◽

A Luvai ◽

D Narayanan ◽

D Turnock ◽

G Lee ◽

...

Keyword(s):

Metrological Traceability ◽

Reference Interval ◽

Reference Population ◽

Reference Intervals ◽

Healthy Individuals ◽

Reference Limit ◽

Common Reference ◽

Age Related ◽

Upper Reference Limit ◽

Analytical Platforms

Background Harmonization of reference intervals for analytes that have a sound calibration and metrological traceability is a widely recommended practice. The UK Pathology Harmony has recently harmonized reference intervals for calcium and albumin. In this study, we have determined the reference intervals for calcium and albumin on the UK’s most commonly used analytical platforms. Method A prospective reference population of healthy individuals was recruited according to the IFCC CRIDL criteria. A second indirect population was collected from 14 primary care setting and measured in laboratories using various analytical platforms and methods (Roche, Abbott, Beckman and Siemens analytical platforms). Results In total, 299 subjects were recruited; the central 95th centile values for calcium for three out of four analytical platforms were in a close agreement with UK Pathology Harmony reference intervals of 2.2–2.6 mmol/L. Reference intervals of BCG methods from both cohorts and irrespective of analytical platforms were higher for both lower and upper reference limits than those for BCP. In comparison, the indirect study showed an age-related variation. The younger population reference intervals varied by up to 5.7% at the lower reference limit and up to 12% at the upper reference limit compared with Pathology Harmony reference intervals, and the older population showed a variation of up to 14% at both limits. Conclusion While calcium reference intervals can be a subject for harmonization, albumin reference intervals studied showed large variation which is unsupportive of embracing a common reference interval for albumin.

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Objective Criteria for Partitioning Gaussian-distributed Reference Values into Subgroups

Clinical Chemistry ◽

10.1093/clinchem/48.2.338 ◽

2002 ◽

Vol 48 (2) ◽

pp. 338-352 ◽

Cited By ~ 31

Author(s):

Ari Lahti ◽

Per Hyltoft Petersen ◽

James C Boyd ◽

Callum G Fraser ◽

Nils Jørgensen

Keyword(s):

Reference Values ◽

Geographic Area ◽

Reference Intervals ◽

Critical Values ◽

Final Decision ◽

Gaussian Distributions ◽

New Model ◽

Reference Limit ◽

Common Reference ◽

Reference Limits

Abstract Background: The aim of this study was to develop new and useful criteria for partitioning reference values into subgroups applicable to gaussian distributions and to distributions that can be transformed to gaussian distributions. Methods: The proposed criteria relate to percentages of the subgroups outside each of the reference limits of the combined distribution. Critical values suggested as partitioning criteria for these percentages were derived from analytical bias quality specifications for using common reference intervals throughout a geographic area. As alternative partitioning criteria to the actual percentages, these were transformed mathematically to critical distances between the reference limits of the subgroup distributions, to be applied to each pair of reference limits, the upper and the lower, at a time. The new criteria were tested using data on various plasma proteins collected from ∼500 reference individuals, and the outcomes were compared with those given by the currently widely applied and recommended partitioning model of Harris and Boyd, the “Harris-Boyd model”. Results: We suggest 4.1% as the critical minimum percentage outside that would justify partitioning into subgroups, and 3.2% as the critical maximum percentage outside that would justify combining them. Percentages between these two values should be classified as marginal, implying that nonstatistical considerations are required to make the final decision on partitioning. The correlation between the critical percentages and the critical distances was mathematically precise in the new model, whereas this correlation is rather approximate in the Harris-Boyd model because focus on the difference between means in this model makes high precision hard to achieve. The application examples suggested that the new model is more radical than the Harris-Boyd model. Conclusions: New percentage and distance criteria, to be used for partitioning gaussian-distributed data, have been developed. The distance criteria, applied separately to both reference limit pairs of the subgroup distributions, seemed more reliable and correlated more accurately with the critical percentages than the distance criteria of the Harris-Boyd model. As opposed to the Harris-Boyd model, the new model is easily adjustable to new critical values of the percentages, should they need to be changed in the future.

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Are the currently used reference intervals for creatine kinase (CK) reflecting the general population? The Tromsø Study

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm.2011.776 ◽

2012 ◽

Vol 50 (5) ◽

Author(s):

Hallvard Lilleng ◽

Stein Harald Johnsen ◽

Tom Wilsgaard ◽

Svein Ivar Bekkelund

Keyword(s):

Creatine Kinase ◽

General Population ◽

Reference Interval ◽

Reference Intervals ◽

Control Analysis ◽

Age Group ◽

Background Population ◽

Norwegian Population ◽

Reference Limits ◽

Laboratory Reference

AbstractLaboratory reference intervals are not necessarily reflecting the range in the background population. This study compared creatine kinase (CK) reference intervals calculated from a large sample from a Norwegian population with those elaborated by the Nordic Reference Interval Project (NORIP). It also assessed the pattern of CK-normalization after standardized control analyses.New upper reference limits (URL) CK values were calculated after exclusion of individuals with risk of hyperCKemia and including individuals with incidentally detected hyperCKemia after they had completed a standardized control analysis. After exclusion of 5924 individuals with possible causes of hyperCKemia, CK samples were analyzed in 6904 individuals participating in the 6th survey of The Tromsø Study. URL was defined as the 97.5 percentile.New URL in women was 207 U/L. In men <50 years it was 395 U/L and in men ≥50 years 340 U/L. In individuals with elevated CK, normalization grade after control analysis was inversely correlated to the CK level (p<0.04).URL CK values in women and in men <50 years of age were in accordance with URL CK values given by the NORIP. In men ≥50 years, a higher URL was found and the findings suggest an upward adjustment of URL in this age group.

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Critical comments to a recent EFLM recommendation for the review of reference intervals

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2016-1112 ◽

2017 ◽

Vol 55 (3) ◽

Cited By ~ 5

Author(s):

Rainer Haeckel ◽

Werner Wosniok ◽

Farhad Arzideh ◽

Jakob Zierk ◽

Eberhard Gurr ◽

...

Keyword(s):

Gold Standard ◽

Reference Intervals ◽

Direct Approach ◽

Confidence Limits ◽

Common Reference ◽

Reference Limits ◽

Age Range

AbstractIn a recent EFLM recommendation on reference intervals by Henny et al., the direct approach for determining reference intervals was proposed as the only presently accepted “gold” standard. Some essential drawbacks of the direct approach were not sufficiently emphasized, such as unacceptably wide confidence limits due to the limited number of observations claimed and the practical usability for only a limited age range. Indirect procedures avoid these disadvantages of the direct approach. Furthermore, indirect approaches are well suited for reference limits with large variations during lifetime and for common reference limits.

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A multi-stage Gaussian transformation algorithm for clinical laboratory data.

Clinical Chemistry ◽

10.1093/clinchem/28.8.1735 ◽

1982 ◽

Vol 28 (8) ◽

pp. 1735-1741 ◽

Cited By ~ 26

Author(s):

J C Boyd ◽

D A Lacher

Keyword(s):

Mean Squared Error ◽

Clinical Laboratory ◽

Statistical Tests ◽

Random Noise ◽

Laboratory Data ◽

Reference Interval ◽

Reference Intervals ◽

Distributed Data ◽

Multi Stage ◽

Positive Coefficients

Abstract We have developed a multi-stage computer algorithm to transform non-normally distributed data to a normal distribution. This transformation is of value for calculation of laboratory reference intervals and for normalization of clinical laboratory variates before applying statistical procedures in which underlying data normality is assumed. The algorithm is able to normalize most laboratory data distributions with either negative or positive coefficients of skewness or kurtosis. Stepwise, a logarithmic transform removes asymmetry (skewness), then a Z-score transform and power function transform remove residual peakedness or flatness (kurtosis). Powerful statistical tests of data normality in the procedure help the user evaluate both the necessity for and the success of the data transformation. Erroneous assessments of data normality caused by rounded laboratory test values have been minimized by introducing computer-generated random noise into the data values. Reference interval endpoints that were estimated parametrically (mean +/- 2 SD) by using successfully transformed data were found to have a smaller root-mean-squared error than those estimated by the non-parametric percentile technique.

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Descriptive analytical data and consequences for calculation of common reference intervals in the Nordic Reference Interval Project 2000

Scandinavian Journal of Clinical and Laboratory Investigation ◽

10.1080/00365510410006306 ◽

2004 ◽

Vol 64 (4) ◽

pp. 343-370 ◽

Cited By ~ 37

Author(s):

P. Rustad ◽

P. Felding ◽

A. Lahti ◽

P. Hyltoft Petersen

Keyword(s):

Analytical Data ◽

Reference Interval ◽

Reference Intervals ◽

Common Reference ◽

Project 2000

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DETERMINING REFERENCE RANGES FOR TREC AND KREC ASSAYS IN IMMUNE DEFICIENCY SCREENING OF NEWBORNS IN RUSSIAN FEDERATION

Medical Immunology (Russia) ◽

10.15789/1563-0625-2019-3-527-538 ◽

2019 ◽

Vol 21 (3) ◽

pp. 527-538

Author(s):

M. A. Gordukova ◽

I. A. Korsunsky ◽

Yu. V. Chursinova ◽

M. M. Byakhova ◽

I. P. Oscorbin ◽

...

Keyword(s):

Reference Interval ◽

Reference Population ◽

Dried Blood Spots ◽

Reference Intervals ◽

Asymmetric Distribution ◽

Reference Ranges ◽

Individual Values ◽

Experimental Values ◽

Deficiency Screening ◽

Guthrie Cards

In this work, we used a reference population of newborns and sampled dried blood spots on Guthrie cards of 2,739 individual samples to determine the reference intervals for TRECs and KRECs values, in order to diagnose primary immunodeficiency by means of neonatal screening. The median absolute values for TRECs and KRECs were 195 (CI95%: 185-206) and 185 (CI95%: 176-197) copies per μl, respectively; the normalized value for TRECs was 2780 (CI95%: 2690-2840), and for KRECs, 2790 (CI95%: 2700-2900) copies per 2 × 105 copies of the albumin gene or 105 cells. The reference interval was calculated for 99 and 99.9 percentiles of total TRECs and KRECs individual values. Due to asymmetric distribution of data, the outliers were filtered off, using the Tukey’s criterion applied after logarithmic transformation of the data. When analyzing absolute values for TREC/KREC (per μL of blood), no “drop-down” TRECs values were identified; for KRECs, 18 experimental values were excluded from further analysis (from 9.8 to 13.5). The outlying values were not identified among the normalized values of TRECs/KRECs. The obtained reference values for TRECs and KRECs (at the 0.1 percentile level) were, respectively, 458 and 32 per 105 cells, or 23 and 17 per μl of blood samples from neonates.

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Indirect methods for reference interval determination – review and recommendations

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2018-0073 ◽

2018 ◽

Vol 0 (0) ◽

Cited By ~ 6

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.

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Recommendation for the review of biological reference intervals in medical laboratories

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2016-0793 ◽

2016 ◽

Vol 54 (12) ◽

Cited By ~ 22

Author(s):

Joseph Henny ◽

Anne Vassault ◽

Guilaine Boursier ◽

Ines Vukasovic ◽

Pika Mesko Brguljan ◽

...

Keyword(s):

Expert Panel ◽

Clinical Chemistry ◽

Simple Procedure ◽

Reference Population ◽

Reference Intervals ◽

Medical Laboratories ◽

Reference Limits ◽

Original Procedure ◽

Selection Of

AbstractThis document is based on the original recommendation of the Expert Panel on the Theory of Reference Values of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC), updated guidelines were recently published under the auspices of the IFCC and the Clinical and Laboratory Standards Institute (CLSI). This document summarizes proposals for recommendations on: (i) The terminology, which is often confusing, noticeably concerning the terms of reference limits and decision limits. (ii) The method for the determination of reference limits according to the original procedure and the conditions, which should be used. (iii) A simple procedure allowing the medical laboratories to fulfill the requirements of the regulation and standards. The updated document proposes to verify that published reference limits are applicable to the laboratory involved. Finally, the strengths and limits of the revised recommendations (especially the selection of the reference population, the maintenance of the analytical quality, the choice of the statistical method used…) will be briefly discussed.

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