An Application of the Bootstrap in Clinical Chemistry

Abstract

We apply the bootstrap to a one-dimensional nonparametric discrimination problem in clinical chemistry: the construction of a cut-off point (discrimination limit) for a quantitative diagnostic test on the basis of a sample of “diseased” and “non-diseased” individuals and the subsequent evaluation of the resulting decision rule in the same sample. When the cut-off point is selected to maximize some performance criterion, the sample estimate of maximal performance is known to systematically overestimate the unknown true performance of the test at the selected cut-off point. Bootstrap methods are proposed in the classical paper by EFRON [2] to reduce bias and to obtain confidence intervals. We apply these methods and investigate their performance by a simulation study.