Nonparametric testing of melatonin characteristics as potential markers of breast cancer risk.

On the average, the circadian A of ML is larger in clinically healthy women at high vs. low RDBC. The original finding was made without numerical sampling on data from a relatively small group of women sampled systematically around the clock and the year. It was hence of further interest to see whether the risk-related difference can be corroborated by numerical resampling, i.e., by BS, a fairly general, usually computerintensive technique used for estimating the sampling distribution of an estimator or statistic (i.e., a quantity computed from the data). For BS of circannual parameters, one here assumes that 1) circadian As (obtained by the fit of a 24-hour cosine curve by linear least squares) are correlated for any given subject from season to season; and 2) deviations of each circadian A from the average are random. The method then estimates the characteristics of harmonic components, ordered by P-values obtained by linear least-squares analysis involving a test of the assumption of zero A for each special component. Results from BS validate differences in circadian A of ML between the low and high RDBC groups throughout the year. The use of BS serves to introduce inferential considerations into discriminant analysis and to test results obtained with very limited samples sizes, before embarking upon the actual labor-intensive, costly resampling required in circannual and circadian work. BS is a procedure recommended for broad routine use in non-parametric hypothesis testing and biomedical signal simulation. The re-evaluation of ML data by BS provides further inferential statistical validation of the finding as a first complement, not substitute, for additional sampling in order to assess a possibly important component of a test battery for breast cancer risk.