Guidelines for selecting among different types of bootstraps

Abstract

Background: The bootstrap has become very popular in health economics. Its success lies in the ease of estimating sampling distribution, standard error and confidence intervals with few or no assumptions about the distribution of the underlying population.

Objective: The purpose of this paper is three-fold: (1) to provide an overview of four common bootstrap techniques for readers who have little or no statistical background; (2) to suggest a guideline for selecting the most applicable bootstrap technique for your data; and (3) to connect guidelines with a real world example, to illustrate how different bootstraps behave in one model, or in different models.

Results: The assumptions of homoscedasticity and normality are key to selecting the best bootstrapping technique. These assumptions should be tested before applying any bootstrapping technique. If homoscedasticity and normality hold, then parametric bootstrapping is consistent and efficient. Paired and wild bootstrapping are consistent under heteroscedasticity and non-normality assumptions.

Conclusion: Selecting the correct type of bootstrapping is crucial for arriving at efficient estimators. Our example illustrates that if we selected an inconsistent bootstrapping technique, results could be misleading. An insignificant effect of controller treatment on total health expenditures among asthma patients would have been found significant and negative by an improperly chosen bootstrapping technique, regardless of the type of model chosen.