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Resampling methods in Microsoft Excel (R) for estimating reference intervals
Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology and Molecular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Clinical Chemistry.
2015 (English)In: Biochemia Medica, ISSN 1330-0962, E-ISSN 1846-7482, Vol. 25, no 3, 311-319 p.Article in journal (Refereed) Published
Abstract [en]

Computer-intensive resampling/bootstrap methods are feasible when calculating reference intervals from non-Gaussian or small reference samples. Microsoft Excel(R) in version 2010 or later includes natural functions, which lend themselves well to this purpose including recommended interpolation procedures for estimating 2.5 and 97.5 percentiles. The purpose of this paper is to introduce the reader to resampling estimation techniques in general and in using Microsoft Excel(R) 2010 for the purpose of estimating reference intervals in particular. Parametric methods are preferable to resampling methods when the distributions of observations in the reference samples is Gaussian or can transformed to that distribution even when the number of reference samples is less than 120. Resampling methods are appropriate when the distribution of data from the reference samples is non-Gaussian and in case the number of reference individuals and corresponding samples are in the order of 40. At least 500-1000 random samples with replacement should be taken from the results of measurement of the reference samples.

Place, publisher, year, edition, pages
CROATIAN SOC MEDICAL BIOCHEMISTS , 2015. Vol. 25, no 3, 311-319 p.
Keyword [en]
reference interval; resampling method; Microsoft Excel; bootstrap method; biostatistics
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-122546DOI: 10.11613/BM.2015.031ISI: 000362855400001OAI: oai:DiVA.org:liu-122546DiVA: diva2:867833
Available from: 2015-11-06 Created: 2015-11-06 Last updated: 2017-12-01

Open Access in DiVA

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