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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Location tests for biomarker studies: a comparison using simulations for the two-sample case.

M O Scheinhardt1, A Ziegler

  • 1Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Ratzeburger Allee 160, Haus 24, 23562 Lübeck, Germany.

Methods of Information in Medicine
|July 24, 2013
PubMed
Summary
This summary is machine-generated.

Standard location tests struggle with non-normal gene expression data. Adaptive non-parametric tests offer better power for heavy-tailed or skewed data, but can fail with unequal variances.

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Area of Science:

  • Biostatistics
  • Genomics
  • Statistical modeling

Background:

  • Gene, protein, and metabolite expression data often exhibit non-normal distributions, heavy tails, and outliers.
  • Standard statistical location tests may yield unreliable results in these conditions.

Purpose of the Study:

  • To compare the type I error rates and empirical power of standard location tests against three adaptive tests.
  • To evaluate test performance across a range of data distributions, including varying tail lengths and skewness.

Main Methods:

  • Conducted three Monte Carlo simulation studies.
  • Utilized the g-and-k-distribution family to simulate two-sample scenarios with controlled skewness and tail length.
  • Assessed performance under conditions of both homogeneous and heterogeneous variances between groups.

Main Results:

  • All tested methods maintained type I error control when variances were equal.
  • The standard non-parametric U-test demonstrated robust performance across most scenarios.
  • Two non-parametric adaptive tests outperformed the U-test for heavy-tailed or skewed data with homogeneous variances.
  • Several tests failed to control type I error with skewed data and heterogeneous variances.

Conclusions:

  • The standard U-test is recommended for general use, except for highly skewed or heavy-tailed data.
  • Non-parametric adaptive tests are powerful for normal and non-normal data with equal variances.
  • Adaptive tests may not maintain type I error control when sample variances differ.
  • Parametric adaptive tests showed insufficient power for skewed and heavy-tailed distributions.