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Guidelines for selecting among different types of bootstraps.

Onur Baser1, William H Crown, Christine Pollicino

  • 1Outcomes Research and Econometrics, Thomson-Medstat Group Inc, Ann Arbor, MI, USA. onur.baser@thomson.com

Current Medical Research and Opinion
|May 11, 2006
PubMed
Summary

Choosing the right bootstrap method in health economics is crucial. Incorrect selection can lead to misleading results, impacting the interpretation of treatment effects on health expenditures.

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Area of Science:

  • Health Economics
  • Statistical Methods

Background:

  • The bootstrap method is widely used in health economics for its flexibility in estimating statistical measures.
  • It allows for the calculation of sampling distributions, standard errors, and confidence intervals with minimal distributional assumptions.

Purpose of the Study:

  • To offer an overview of four common bootstrap techniques for non-statisticians.
  • To provide a guideline for selecting the most appropriate bootstrap method.
  • To demonstrate the behavior of different bootstrap methods using a real-world example.

Main Methods:

  • Review of four common bootstrap techniques.
  • Guideline development for bootstrap method selection based on data assumptions.
  • Application and comparison of methods in a health economics model.

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Main Results:

  • Homoscedasticity and normality assumptions are critical for selecting bootstrap techniques.
  • Parametric bootstrapping is efficient under homoscedasticity and normality.
  • Paired and wild bootstrapping are suitable for heteroscedastic and non-normal data.

Conclusions:

  • Correct bootstrap technique selection is vital for efficient and reliable estimation.
  • Inconsistent bootstrap methods can yield misleading findings, altering the interpretation of results.
  • An inappropriate bootstrap choice could misrepresent the significance and direction of treatment effects.