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Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method.

Alok Kumar Dwivedi1,2, Indika Mallawaarachchi2, Luis A Alvarado2

  • 1Division of Biostatistics and Epidemiology, Department of Biomedical Sciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas, U.S.A.

Statistics in Medicine
|March 10, 2017
PubMed
Summary
This summary is machine-generated.

For small sample sizes in biomedical research, a nonparametric pooled bootstrap test offers robust hypothesis testing. This method demonstrates equal or greater power than traditional tests for comparing means, especially with non-normal data.

Keywords:
bootstrap testexperimental studiesnonparametric testparametric testresampling methodsmall sample size

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Area of Science:

  • Biostatistics
  • Biomedical Research Methodology

Background:

  • Small sample sizes in biomedical research present analytical challenges, leading to conflicting recommendations for parametric vs. nonparametric statistical tests.
  • Existing methods like bootstrap tests also face limitations with small sample sizes.
  • Nonparametric tests are sometimes considered too conservative and less powerful than parametric alternatives.

Purpose of the Study:

  • To evaluate a nonparametric bootstrap test with a pooled resampling method to address small sample size limitations in hypothesis testing.
  • To compare the performance of the nonparametric pooled bootstrap test against parametric, nonparametric, and permutation tests.
  • To assess the utility of this method for comparing means and validating analysis of variance (ANOVA) in small, non-normal datasets.

Main Methods:

  • Extensive simulations were conducted under various conditions using both simulated and real data.
  • The nonparametric pooled bootstrap t-test was compared with unpaired t-test, Welch t-test, Wilcoxon rank sum test, and permutation tests.
  • Performance was evaluated based on statistical power and type I error probability.

Main Results:

  • The nonparametric pooled bootstrap t-test showed equal or greater power than standard tests for comparing two means across most conditions.
  • It maintained type I error probability, except for Cauchy and extreme variable lognormal distributions, where an exact Wilcoxon rank sum test is suggested.
  • The nonparametric bootstrap paired t-test outperformed alternatives, and the nonparametric bootstrap test offered benefits over the Kruskal-Wallis test.

Conclusions:

  • The nonparametric bootstrap test with pooled resampling is recommended for comparing paired or unpaired means in small sample size studies with non-normal data.
  • This method is also suggested for validating one-way analysis of variance (ANOVA) results when data are non-normal and sample sizes are small.
  • For specific distributions (Cauchy, extreme lognormal), exact Wilcoxon rank sum tests are advised.