The bioequivalence problem is of practical importance because the approval of most generic drugs in the United States and the European Community (EC) requires the establishment of bioequivalence between the name brand drug and the proposed generic version. The problem is theoretically interesting because it has been recognized as one for which the desired inference, instead of the usual significant difference, is practical equivalence. The concept of intersection-union tests will be shown to clarify, simplify, and unify bioequivalence testing. A test more powerful than the one currently specified by the FDA and EC guidelines will be derived. The claim that the bioequivalence problem defined in terms of the ratio of parameters is more difficult than the problem defined in terms of the difference of parameters will be refuted. The misconception that size-ff bioequivalence tests generally correspond to 100(1 \Gamma 2ff)% confidence sets will be shown to lead to incorrect statistical pract...

The problem of assessing similarity of two cumulative distribution functions (c.d.f.'s) has been the topic of a previous paper by the authors (Munk & Czado (1995)). Here, we developed an asymptotic test based on a trimmed version of the Mallows distance (Mallows 1972) between two c.d.f.'s F and G. This allows to assess the similarity of two c.d.f.'s with respect to this distance at controlled type I error rate. In particular, this applies to bioequivalence testing within a purely nonparametric setting. In this paper, we investigate the finite sample behavior of this test. The effect of trimming and non equal sample size on the observed power and level is studied. Sample size driven recommendations for the choice of the trimming bounds are given in order to minimize the bias. Finally, assuming normality and homogeneous variances, we simulate the relative efficiency of the Mallows test to the (asymptotically optimal) standard equivalence t test, which reveals the Mallows test as a robust...

A completely nonparametric approach to population bioequivalence in crossover trials has been suggested by Munk and Czado (1999). It is based on the Mallows (1972) metric as a nonparametric distance measure which allows the comparison between the entire distribution functions of test and reference formulations. It was shown that a separation between carry-over and period effects is not possible in the nonparametric setting. However when carry-over effects can be excluded, treatment effects can be assessed when period effects are present or not. Munk and Czado (1999) proved bootstrap limit laws of the corresponding test statistics because estimation of the limiting variance of the test statistic is very cumbersome. The purpose of this paper is to investigate the small sample behavior of various bootstrap methods and to compare it with the asymptotic test obtained by estimation of the limiting variance. The percentile (PC) and bias corrected and accelerated (BCA) bootstrap were compared ...

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A standard 2x2 crossover design has been widely used in drug clinical trials, but it is not optimal because of some undesirable statistical properties such as the low power to test carry-ov er effect. Higher-order crossov er designs address this defficiency and, theref ore, have drawn more attention recently. Howev er, exact power calculation of higher-order crossov er designs poses difficulity f or users. It requires sophisticated numerical integration, and it is not available in statistical software packages. This paper presents a SAS Macro to compute the empirical power for higher-order designs in comparative bioavailability clinical trials. The power curve and tables for higher-order crossover design with 2 sequences and 3 periods are presented. Keywords: Higher-order crossov er design, empirical power, comparativ e bioav ailability

Water quality monitoring is being used in local, regional, and national scales to measure how water quality variables behave in the natural environment. A common problem, which arises from monitoring, is how to relate information contained in data to the information needed by water resource management for decision-making. This is generally attempted through statistical analysis of the monitoring data. However, how the selection of methods with which to routinely analyze the data affects the quality and comparability of information produced is not as well understood as may first appear. To help understand the connectivity between the selection of methods for routine data analysis and the information produced to support management, the following three tasks were performed. An examination of the methods that are currently being used to analyze water quality monitoring data, including published criticisms of them. An exploration of how the selection of methods to analyze water quality data can impact the comparability of information used for water quality management purposes. Development of options by which data analysis methods employed in water quality

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Efficacy, safety and lack of immunogenicity of insulin aspart compared with regular human insulin for women with gestational diabetes mellitus