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Related Experiment Video

Updated: Jul 2, 2026

Comprehensive & Cost Effective Laboratory Monitoring of HIV/AIDS: an African Role Model
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Published on: October 31, 2010

Use of routine clinical laboratory data to define reference intervals.

Brian Shine1

  • 1Department of Clinical Biochemistry, John Radcliffe Hospital, Oxford, UK. brian.shine@orh.nhs.uk

Annals of Clinical Biochemistry
|August 30, 2008
PubMed
Summary

This study explores how to use routine lab data to define normal ranges for plasma alkaline phosphatase (ALP), a blood enzyme. ALP levels change with age and sex, so standard methods aren't always reliable. The researchers tested three ways to normalize the data and found that all worked well, with one method (LMS) being slightly better. They also showed that dividing the data into age groups improved accuracy. This approach could help labs set more accurate normal ranges using everyday clinical data.

Keywords:
reference intervalclinical data analysisnormalization methodsalkaline phosphatase

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Area of Science:

  • Clinical laboratory science
  • Biostatistical methods
  • Reference interval analysis

Background:

Reference intervals help identify deviations from normal health. Establishing these intervals typically requires data from healthy individuals, but this is rarely feasible. Prior research has shown that clinical data from general populations can be used if outliers are removed. However, no prior work had resolved how to model age and sex-related changes effectively. This gap motivated the current study. Existing methods struggle with non-normal distributions and variable age patterns. This paper addresses that uncertainty by exploring transformation techniques. The need to model ALP levels across age and sex remains unmet. No prior work had tested Cole's LMS alongside Box-Cox and logarithmic methods.

Purpose Of The Study:

This study aimed to demonstrate how clinical data can be used to define reference intervals for plasma alkaline phosphatase. ALP levels change with age and sex, making standard methods inadequate. The authors sought to compare normalization techniques for large datasets. They focused on removing outliers and modeling age-related patterns. The goal was to find a reliable transformation method. They also wanted to assess how data segmentation affects accuracy. The study tested three normalization approaches. The findings could improve reference interval establishment in routine labs.

Main Methods:

The study used ALP data from 75,328 individuals aged 0–80 years. Data were grouped by sex and age into bins. Three normalization methods were applied: logarithmic, Box-Cox, and Cole's LMS. Outliers were removed after initial transformation. The process was repeated on the cleaned data. Normality was assessed using normal score plots. The Kolmogorov-Smirnov test confirmed distribution fit. Fractional polynomials modeled transformation parameters.

Main Results:

All three methods produced normally distributed data. The LMS method showed the best fit with normality. Outlier rates were consistent across methods. ALP levels peaked in adolescence and stabilized in early adulthood. Fractional polynomials captured age-related trends well. Reference intervals were derived for each sex and age group. Data segmentation improved model accuracy. Overlapping age ranges ensured smooth transitions.

Conclusions:

The study shows that routine clinical data can define reference intervals. Outlier removal and normalization are essential. LMS transformation provided the best normality fit. ALP levels change predictably with age and sex. Data segmentation improved model accuracy. The methods tested are suitable for large datasets. This approach can be applied to other analytes. The findings support using clinical data for reference interval derivation.

All three methods produced normally distributed data, but LMS showed the best fit.

To compare how each method handles non-normal distributions and age-related changes.

They reapplied transformations and tested normality with score plots and the Kolmogorov-Smirnov test.

They modeled transformation parameters to capture age-related trends in ALP levels.

Data were segmented into overlapping age ranges to capture the adolescent peak and adult stability.

They propose that such data can define reference intervals if outliers are removed and transformations are applied.