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Resampling methods in Microsoft Excel® for estimating reference intervals.

Elvar Theodorsson1

  • 1Department of Clinical Chemistry and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden.

Biochemia Medica
|November 4, 2015
PubMed
Summary
This summary is machine-generated.

Computer-intensive resampling methods are effective for calculating reference intervals from small or non-Gaussian sample data. Microsoft Excel 2010 and later versions offer functions suitable for this statistical estimation.

Keywords:
Microsoft Excelbiostatisticsbootstrap methodreference intervalresampling method

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Area of Science:

  • Biostatistics
  • Laboratory Medicine
  • Computational Biology

Background:

  • Calculating reference intervals is crucial for interpreting laboratory test results.
  • Non-Gaussian or small sample sizes pose challenges for traditional parametric methods.
  • Computer-intensive methods offer alternative approaches for statistical analysis.

Purpose of the Study:

  • To introduce resampling estimation techniques for reference interval calculation.
  • To demonstrate the application of Microsoft Excel® 2010 for these methods.
  • To provide guidance on selecting appropriate statistical approaches based on data characteristics.

Main Methods:

  • Utilizing computer-intensive resampling (bootstrap) methods.
  • Employing Microsoft Excel® 2010 functions for interpolation and percentile estimation (2.5th and 97.5th).
  • Generating 500-1000 random samples with replacement from reference sample measurements.

Main Results:

  • Resampling methods are feasible for non-Gaussian or small reference samples.
  • Parametric methods are preferred for Gaussian data, even with <120 samples.
  • Resampling is appropriate for non-Gaussian data with approximately 40 samples.

Conclusions:

  • Resampling techniques provide a viable alternative for reference interval estimation when parametric assumptions are not met.
  • Microsoft Excel® 2010 facilitates the practical implementation of these resampling methods.
  • The choice between parametric and resampling methods depends on the distribution and size of the reference sample data.