Adjusting O'Brien's test to control type I error for the generalized nonparametric Behrens-Fisher problem

O'Brien (1984, Biometrics 40, 1079-1087) introduced a simple nonparametric test procedure for testing whether multiple outcomes in one treatment group have consistently larger values than outcomes in the other treatment group. We first explore the theoretical properties of O'Brien's test. We then extend it to the general nonparametric Behrens-Fisher hypothesis problem when no assumption is made regarding the shape of the distributions. We provide conditions when O'Brien's test controls its error probability asymptotically and when it fails. We also provide adjusted tests when the conditions do not hold. Throughout this article, we do not assume that all outcomes are continuous. Simulations are performed to compare the adjusted tests to O'Brien's test. The difference is also illustrated using data from a Parkinson's disease clinical trial.     

A Multivariate Goodness-of-Fit Test for Stochastically Ordered Distributions

There are a number of nonparametric procedures known for testing goodness-of-fit in the univariate case. Similar procedures can be derived for testing goodness-of-fit in the multivariate case through an application of the theory of statistically equivalent blocks (SEB). The SEB transforms the data into coverages which are distributed as spacings from a uniform distribution on [0,1], under the null hypothesis. In this paper, we present a multivariate nonparametric test of goodness-of-fit based on the SEB when the multivariate distributions under the null hypothesis and the alternative hypothesis are “weakly” ordered. Empirical results are given on the performance of the proposed test in an application to the problem of assessing the reliability of a p-component system.     

Testing for Poisson Zero Inflation in Disease Mapping

When analyzing mortality data due to rare diseases in small areas, it is common to find several health zones with no mortality cases. In these circumstances, the classical homogeneous model based on the Poisson distribution used to estimate the relative risks within each area may encounter lack of fit due to a disproportionately large frequency of zeros. To cope with these zeros, the zero inflated Poisson model can be used. In this paper, we propose a test for detecting zero inflation in the context of disease mapping which is based on bootstrap techniques. The test is illustrated using male mortality data due to brain cancer in Navarra, Spain. In addition, comparisons with other tests for Poisson zero inflation such as the score test and the likelihood ratio test are carried out in terms of empirical power and size using the brain cancer scenario. The proposed bootstrap test has good power and size and works well when detecting the excess of zeros in small area data sets. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Development of a validation protocol to determine the ability of screw-top containers to be watertight and to withstand impact damage from accidental dropping

Polypropylene screw-top containers are used to collect, transport and store a variety of tissues within tissue banks. These containers are validated for use by tissue banks but no standard validation protocol is used. We present here a protocol for testing screw-top containers for leakage, evaporation and the ability to withstand accidental impact damage. Three different containers were tested, MedFor S072, MedFor S277 and MacoPharma PROT0483. The validation can detect differences between different manufacturer’s containers and this protocol will be used in future validations of screw-top containers within National Blood Service Tissue Services.     

羟基磷灰石生物安全性试验研究

为了评价羟基磷灰石的生物安全性,依据相关的医疗器械行业标准和GB/T16886系列标准,对该材料进行了迟发型超敏反应试验、热原试验、急性全身毒性试验、细胞毒性试验等一系列生物安全性试验,并对试验数据进行了分析评价,结果表明,羟基磷灰石具有良好的安全性。     

Simultaneous Randomization Tests

Randomization analyses have been developed for testing main effects and interactions in standard experimental designs. However, exact multiple comparisons procedures for these randomization analyses have received little attention. This article proposes a general procedure for constructing simultaneous randomization tests that have prescribed type I error rates. An application of the procedure does provide for multiple comparisons in the randomization analyses of designed experiments. This application is made to data collected in a biopharmaceutical experiment.