Application of the bootstrap procedure provides an alternative to standardstatistical procedures in the estimation of the vitamin B-6 requirement

The bootstrap procedure is a versatile statistical tool for the estimation of standard errors and confidence intervals. It is useful when standard sta tistical methods are not available or are poorly behaved, e.g., for nonline ar functions or when assumptions of a statistical model have been violated. Inverse regression estimation is an example of a statistical tool with a w ide application in human nutrition. In a recent study, inverse regression w as used to estimate the vitamin B-6 requirement of young women. In the pres ent statistical application, both standard statistical methods and the boot strap technique were used to estimate the mean vitamin B-6 requirement, sta ndard errors and 95% confidence intervals for the mean. The bootstrap proce dure produced standard error estimates and confidence intervals that were s imilar to those calculated by using standard statistical estimators. In a M onte Carlo simulation exploring the behavior of the inverse regression esti mators, bootstrap standard errors were found to be nearly unbiased, even wh en the basic assumptions of the regression model were violated. On the othe r hand, the standard asymptotic estimator was found to behave well when the assumptions of the regression model were met, but behaved poorly when the assumptions were violated. In human metabolic studies. which are often rest ricted to small sample sizes, or when statistical methods are not available or are poorly behaved, bootstrap estimates for calculating standard errors and confidence intervals may be preferred. Investigators in human nutritio n may find that the bootstrap procedure is superior to standard statistical procedures in cases similar to the examples presented in this paper.