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Laboratory Management |
Departments of
1
Mathematical Sciences and
2
Pathology and Laboratory Medicine, University of Cincinnati, Cincinnati, OH 45221.
a Address correspondence to this author at: Department of Mathematical Sciences, University of Cincinnati, PO Box 210025, Cincinnati, OH 45221-0025. Fax 513-556-3417; e-mail paul.horn{at}uc.edu.
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 
2 distributions of 1,
4, 7, and 10 df. 2 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.
The following articles in journals at HighWire Press have cited this article:
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  E. Schwedhelm, V. Xanthakis, R. Maas, L. M. Sullivan, F. Schulze, U. Riederer, R. A. Benndorf, R. H. Boger, and R. S. Vasan Asymmetric Dimethylarginine Reference Intervals Determined with Liquid Chromatography-Tandem Mass Spectrometry: Results from the Framingham Offspring Cohort Clin. Chem., August 1, 2009; 55(8): 1539 - 1545. [Abstract] [Full Text] [PDF]
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 F. Ceriotti, R. Hinzmann, and M. Panteghini Reference intervals: the way forward Ann Clin Biochem, January 1, 2009; 46(1): 8 - 17. [Abstract] [Full Text] [PDF]
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 J. Jund, M. Rabilloud, M. Wallon, and R. Ecochard Methods to Estimate the Optimal Threshold for Normally or Log-Normally Distributed Biological Tests Med Decis Making, July 1, 2005; 25(4): 406 - 415. [Abstract] [PDF]
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 H. M. Blanck, B. A. Bowman, G. R. Cooper, G. L. Myers, and D. T. Miller Laboratory Issues: Use of Nutritional Biomarkers J. Nutr., March 1, 2003; 133(3): 888S - 894. [Abstract] [Full Text] [PDF]
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P. S. Horn and A. J. Pesce Effect of Ethnicity on Reference Intervals Clin. Chem., October 1, 2002; 48(10): 1802 - 1804. [Full Text] [PDF]
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P. S. Horn, L. Feng, Y. Li, and A. J. Pesce Effect of Outliers and Nonhealthy Individuals on Reference Interval Estimation Clin. Chem., December 1, 2001; 47(12): 2137 - 2145. [Abstract] [Full Text] [PDF]
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P. S. Horn, A. J. Pesce, and B. E. Copeland Reference Interval Computation Using Robust vs Parametric and Nonparametric Analyses Clin. Chem., December 1, 1999; 45(12): 2284 - 2285. [Full Text] [PDF]
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 E. M Wright and P. Royston Calculating reference intervals for laboratory measurements Statistical Methods in Medical Research, April 1, 1999; 8(2): 93 - 112. [Abstract] [PDF]
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