Estimating the Power of the Gehan Test

A bootstrap method suggested by COLLINGS and HAMILTON (1988) to estimate the power of the two-sample Wilcoxon test is adapted to use for estimating the power of the Gehan test. A Monte Carlo simulation study is done to determine how well the method works in this case.     

A Closed-form Jackknife Solution for the Behrens-Fisher Problem

A simple, closed-form jackknife estimate of the actual variance of the Mann-Whitney-Wilcoxon statistic, as opposed to the standard permutational variance under the test's null hypothesis has been derived which permits avoiding anticonservative performance in the presence of heterosce-dasticity. The formulation given allows modifications of the exponential scores test, of censored data tests by Gehan (1965), Peto & Peto (1977) and Prentice (1978), of tests for monotonic τ association by Kendall (1962) and for tests of ordered k-sample hypotheses. A Monte Carlo study supports recommendations for the jackknife procedures, but also shows their limited advantages in exponential scores and censored data versions. Thus, the paper extends results by Fligner & Policello (1981).     

Nonparametric Location Tests Based on the Empirical Distribution

We present a unified approach to the nonparametric comparison of locations in the one- and two sample case using the empirical distribution functions. This approach reveals how to define a simple test statistic for this comparison in the case of large sample sizes which is equivalent to the known Wilcoxon-Mann-Whitney statistics and is based on the grouping of the data. In the particular case of equal group sizes we find an extremely simple form for this location test.     

Estimation of a Secondary Parameter in a Group Sequential Clinical Trial

In this article, we investigate estimation of a secondary parameter in group sequential tests. We study the model in which the secondary parameter is the mean of the normal distribution in a subgroup of the subjects. The bias of the naive secondary parameter estimator is studied. It is shown that the sampling proportions of the subgroup have a crucial effect on the bias: As the sampling proportion of the subgroup at or just before the stopping time increases, the bias of the naive subgroup parameter estimator increases as well. An unbiased estimator for the subgroup parameter and an unbiased estimator for its variance are derived. Using simulations, we compare the mean squared error of the unbiased estimator to that of the naive estimator, and we show that the differences are negligible. As an example, the methods of estimation are applied to an actual group sequential clinical trial, The Beta-Blocker Heart Attack Trial.     

A Simple Method to Obtain all the Sample-Values and their Probabilities of the Wald-Wolfowitz Test Statistics in the Existence of Ties

In order to carry out non-conservative tests in the general two-sample problem with ties, we want to know all possible sample-values of the used test statistics and their occurrence probabilities as well. But this knowledge can be acquired only after very protracted attempts. In the present paper we depict a simple technique for obtaining that without any exertion in the case of the Wald-Wolfowitz test statistic. With that, we then are able to lead the Wald-Wolfowitz test easily and effortlessly in any manner conservative or non-conservative and in the existence of any number and any length of ties.     

Two-sample tests for comparing intra-individual genetic sequence diversity between populations

Consider a study of two groups of individuals infected with a population of a genetically related heterogeneous mixture of viruses, and multiple viral sequences are sampled from each person. Based on estimates of genetic distances between pairs of aligned viral sequences within individuals, we develop four new tests to compare intra-individual genetic sequence diversity between the two groups. This problem is complicated by two levels of dependency in the data structure: (i) Within an individual, any pairwise distances that share a common sequence are positively correlated; and (ii) for any two pairings of individuals which share a person, the two differences in intra-individual distances between the paired individuals are positively correlated. The first proposed test is based on the difference in mean intra-individual pairwise distances pooled over all individuals in each group, standardized by a variance estimate that corrects for the correlation structure using U-statistic theory. The second procedure is a nonparametric rank-based analog of the first test, and the third test contrasts the set of subject-specific average intra-individual pairwise distances between the groups. These tests are very easy to use and solve correlation problem (i). The fourth procedure is based on a linear combination of all possible U-statistics calculated on independent, identically distributed sequence subdatasets, over the two levels (i) and (ii) of dependencies in the data, and is more complicated than the other tests but can be more powerful. Although the proposed methods are empirical and do not fully utilize knowledge from population genetics, the tests reflect biology through the evolutionary models used to derive the pairwise sequence distances. The new tests are evaluated theoretically and in a simulation study, and are applied to a dataset of 200 HIV sequences sampled from 21 children.     

An Overview of Precedence-Type Tests for Censored Data

Precedence tests are simple yet robust nonparametric procedures useful for comparing two or more distributions. In this paper precedence-type tests are considered when the data contain some censored observations. Generalizing the precedence statistic for uncensored data, the precedence tests for censored data are based on the Kaplan-Meier estimators of the respective distribution functions and the corresponding quantile functions. The literature is reviewed for some two-sample as well as some multi-sample problems.     

Simple Nonparametric Inference for Monotonic Processes

A scoring system for possibly censored monotonic processes is presented which allows to analyze such processes by means of standard parametric or nonparametric tests. In either case the analyses are invariant to monotonic transformations of time. Such analyses are of relevance whenever changes in degree of function are observed rather than the single and 100% loss of function of survival analysis. An example of gradually increasing function after particular reconstructive surgery is supplied.     

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.