Bootstrapping is a non-parametric technique used to estimate the distribution of an important statistic such as an incremental cost-effectiveness ratio (ICER) from a population sample such as a clinical trial. Random samples of the same size as the original sample are drawn with replacement from the data source. The statistic of interest is calculated from each of these resamples, and these estimates are stored and collated to build up an empirical distribution for the statistic, for which measures of central tendency (mean, median) and spread (confidence intervals) are obtained. Typically, 1000 or more bootstrap samples are required. In the case of ICERs generated from clinical trial or observational data it is important to generate pairs of values (for costs and effects) for each treatment alternative in the same re-sample. The term ‘bootstrapping’ refers to the apparently impossible achievement of pulling oneself up by ones own bootstraps: ‘parametric’ equations for sampling distributions, which may be difficult to estimate (for example for ICERs), are not required and instead, the data replies on its own observations. The central and important assumption is that the study sample is an accurate representation of the full population. A number of methods (for example: ‘percentile, ‘bias corrected’) have been developed to estimate confidence intervals from bootstrapped samples in different circumstances, including meta-analyses from more than one dataset.

How to cite: Bootstrapping [online]. (2016). York; York Health Economics Consortium; 2016.