Biomedical subjects
Publications and source records attributed to Stan Altan.
We consider the problem of constructing tolerance limits in the context of a one-way random effects model The usual parametric method for calculating tolerance intervals is based on the normality assumption. However, in practice, we frequently observe nonnormally distributed data. We propose the use of the double bootstrap (or nested bootstrap) method to estimate tolerance limits, which allows us to relax the normality assumption.
Serially balanced designs are useful in applications in which repeated measurements of multiple samples (treatments) are being taken according to an ordered sequence. The design permits the estimation of direct treatment and carryover effects. When the samples comprise two groups, where the sequence requires samples from one group to be separated by samples from another group (e.g., when a wash step has to be performed between treated samples), then the sequence has to be arranged in such a way as to accommodate this requirement. The solution to the construction of such a serially balanced sequence is given according to the construction method given by Altan et al. (2004) for the first group of treatments in combination with the use of an F-square to dictate placement of the second group of treatments.
In this note we discuss the relationship between the underlying kinetic model and the statistical (or analytic) model used to study degradation. For small degradation rates, the zeroth, first, and second order statistical models give approximately the same fits and predictions on either the original assay scale or the percent of label claim scale. However, It is shown that the zeroth and second order statistical models artificially induce differential degradation rates across strengths when the percent of label claim response data are analyzed and poolability is not allowed across strengths. The first order model is free of this problem when the true degradation kinetics are first order. We make some recommendations in pooling stability data across strengths.