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The bootstrap: a technique for data-driven statistics. Using computer-intensive analyses to explore experimental

A Ralph Henderson¹

¹Department of Biochemistry, University of Western Ontario, London, Ontario, Canada N6A 5C1. ahenders@uwo.ca

Clinica Chimica Acta; International Journal of Clinical Chemistry

|June 7, 2005

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Bootstrapping, a resampling technique, offers significant advantages for analyzing non-Gaussian and limited data in various scientific fields. Its application in biomedical research has grown substantially, demonstrating its utility beyond theoretical mathematics.

Area of Science:

Biostatistics
Computational Biology
Medical Informatics

Background:

Bootstrapping, a resampling technique, has been utilized for over 30 years, offering theoretical advantages for non-Gaussian data.
While much literature focuses on its mathematical aspects, bootstrapping is increasingly adopted in medical and other applied fields.
The technique is particularly valuable for analyzing small, expensive datasets with sparse prior information and unclear distributional assumptions.

Purpose of the Study:

To review the literature on the transfer of bootstrapping to biomedical research following a key 1994 publication.
To introduce the fundamental theory and applications of bootstrapping, jackknife, and permutation tests.
To illustrate the elementary uses of bootstrapping in statistical analysis.

Main Methods:

Related Experiment Videos

Literature review of publications assessing the transfer of bootstrapping to biomedical literature.
Analysis of citation counts in major medical journals and Medline database.
Introduction to the theoretical underpinnings of bootstrapping, jackknife, and permutation tests.

Main Results:

Over 1600 references to bootstrapping appeared in Medline within ten years of the 1994 paper.
The four major medical journals published a combined total of 196 citations to the technique in the same period.
Bootstrapping is applicable to estimating accuracy (standard error, confidence intervals, bias) and performing various analyses.

Conclusions:

Bootstrapping, jackknife, and permutation tests can enhance existing nonparametric statistical methods.
These resampling techniques should be considered for data processing where appropriate.
The growing body of literature and citations indicates increasing acceptance and utility in scientific research.