On the evaluation of reliability, repeatability, and reproducibility of instrumental evaluation methods and measurement systems.

Validation of instrumental evaluation methods or measurement systems plays an important role in both pharmaceutical and cosmetic research and development. In practice, it is suggested that validation should be performed according to performance characteristics as described in the United States Pharmacopedia and National Formulary (USP/NF, 2000) for analytical methods validation. A validated method or measurement system is expected to achieve a certain degree of accuracy and reliability. However, it is a concern whether the test results obtained are repeatable (with similar test samples) and/or reproducible (under similar but slightly different experimental conditions). In this article, reliability and repeatability/reproducibility of a measurement system estimated within a mixed-effects nested design are monitored by relevant variability acceptance limits. A method based on the concept of empirical power (reproducibility) is used to determine these acceptance limits and thus ensure that there is a high probability of repeatability/reproducibility of the tests results. Formulas or procedures for sample size requirements for comparing the variabilities between products are derived. An example is presented to illustrate the use of the proposed method.

Segregation indices for disease clustering.

Spatial clustering has important implications in various fields. In particular, disease clustering is of major public concern in epidemiology. In this article, we propose the use of two distance-based segregation indices to test the significance of disease clustering among subjects whose locations are from a homogeneous or an inhomogeneous population. We derive the asymptotic distributions of the segregation indices and compare them with other distance-based disease clustering tests in terms of empirical size and power by extensive Monte Carlo simulations. The null pattern we consider is the random labeling (RL) of cases and controls to the given locations. Along this line, we investigate the sensitivity of the size of these tests to the underlying background pattern (e.g., clustered or homogenous) on which the RL is applied, the level of clustering and number of clusters, or to differences in relative abundances of the classes. We demonstrate that differences in relative abundances have the highest influence on the empirical sizes of the tests. We also propose various non-RL patterns as alternatives to the RL pattern and assess the empirical power performances of the tests under these alternatives. We observe that the empirical size of one of the indices is more robust to the differences in relative abundances, and this index performs comparable with the best performers in literature in terms of power. We illustrate the methods on two real-life examples from epidemiology.

A review of tests for exponentiality with Monte Carlo comparisons.

In this paper, 91 different tests for exponentiality are reviewed. Some of the tests are universally consistent while others are against some special classes of life distributions. Power performances of 40 of these different tests for exponentiality of datasets are compared through extensive Monte Carlo simulations. The comparisons are conducted for different sample sizes of 10, 25, 50 and 100 for different groups of distributions according to the shape of their hazard functions at 5 percent level of significance. Also, the techniques are applied to two real-world datasets and a measure of power is employed for the comparison of the tests. The results show that some tests which are very good under one group of alternative distributions are not so under another group. Also, some tests maintained relatively high power over all the groups of alternative distributions studied while some others maintained poor power performances over all the groups of alternative distributions. Again, the result obtained from real-world datasets agree completely with those of the simulation studies.