Analysis of Crossover Designs with a Categorical Response

In the analysis of two-period crossover designs, one frequently must consider the case of a categorical response with binary as a special case. These circumstances are considered in this paper and the proposed method—an extension of GART'S tests for the binary case—is similar to that of PIKE, CASAGRANDE and SMITH for the analysis of pair-matched case-control studies. The results are illustrated with a numerical example.     

Estimation of Parameters in Binomial Crossover Designs

Maximum Likelihood Estimation of order and treatment effects are derived for Binomial Crossover Designs. All estimators and estimated covariance and variances have closed forms and can be easily evaluated. These results are useful for use in multi-center clinical trials.     

Searching for Optimal Block Designs when Block Effects are Random

An algorithm that searches for optimal block designs when block effects are random is described. Some situations in which such an algorithm will prove useful are considered. An illustrative example and some discussion are given.     

Experiments with Three-Treatment Three-Period Crossover Design: Analysis Through the General Linear Model

In this paper we consider balanced three-treatment three-period crossover designs. Using a strategy similar to that applied in the analysis of split-plot experiments we describe both the within and the between sample units models, as well as the corresponding Analysis of Variance. We illustrate these procedures with a numerical example and discuss their implementation through computer programs designed for the analysis of the general linear model.     

A Nonparametrical Evaluation of Anova and MANOVA Designs Using Interaction Structure Analysis

The interaction structure analysis (ISA) is proposed as a nonparametric procedure for the evaluation of uni- and multivariate analysis of variance models. Main effects and interactions of (independent) treatment variables are replaced by interaction-types where types are defined as those treatment-response combinations which occur significantly more often than expected under a null hypothesis (H_o) of no treatment effects. The application of ISA and the typological interpretation of ISA results are illustrated for an ANOVA design from toxicology and for a MANOVA design from psychopharmacology.     

Testing association between species abundance and a continuous variable with Kolmogorov-Smirnov statistics

Association of species abundance with a continuous environmental variable is frequently tested with regression or correlation analyses. However, because these methods ignore the range and frequency distribution of levels of the variable occurring in the study area, they may generate misleading results. We give examples to illustrate the argument. A better approach to test the association between species abundance and a continuous variable should compare levels of the variable in the study area to levels of the variable occurring in sites occupied by the species. If a particular species abundance is not associated with a given continuous variable, then the frequency distribution of levels of this variable measured where individuals of the species occur should mirror the frequency distribution of levels of the variable measured over the study area. We explain how to use the one- and two-sample Kolmogorov-Smirnov statistics to compare the cumulative relative frequencies of levels of the variable where individuals are present with points in the study area. We discuss the statistics, assumptions, limitations, and advantages of these tests.     

Interaction Biases in Two-Period Crossover Studies: A Modified Analysis to Test with More Sensitivity

The two-period crossover trial is considered the most powerful means of determining the efficacy of new drugs. However, this study design is frequently invalidated by treatment-by-period interaction. If, for example, the effect of the first treatment period carries on into the next one, then it influences the response to the latter period (carryover effect). A second problem is, that the standard approach (Hills-Armitage analysis) for interaction bias has a low statistical sensitivity. The author recently described an alternative method entitled the clinical approach because it looks at the clinical performance of the separate treatment groups and not, as the standard approach, at the means of the groups. It may be hypothesized that this alternative approach is statistically more sensitive than the standard in situations where there is interaction in just one of the treatment groups. The present study uses two examples and a mathematical model. It shows that in case of single-group interaction the clinical approach can, indeed, detect carryover effect at a 30% lower level than the standard. On the other hand, however, the standard approach does so even at a 40% lower level than the clinical in case of two-group interaction. I conclude that one approach supplements the other and that they be used in future studies simultaneously.     

一类具有随机周期移民扰动的非线性人口发展方程随机周期解的存在唯一性

本文给出了一类具有随机周期移民扰动的非线性m增生人口发展方程随机周期解的存在性和唯一性结论。     

Combining dependent tests for linkage or association across multiple phenotypic traits

A robust statistical method to detect linkage or association between a genetic marker and a set of distinct phenotypic traits is to combine univariate trait-specific test statistics for a more powerful overall test. This procedure does not need complex modeling assumptions, can easily handle the problem with partially missing trait values, and is applicable to the case with a mixture of qualitative and quantitative traits. In this note, we propose a simple test procedure along this line, and show its advantages over the standard combination tests for linkage or association in the literature through a data set from Genetic Analysis Workshop 12 (GAW12) and an extensive simulation study.     

On Testing for Equicorrelation in Random Coefficient Models

Recently BHATTI (1993) considered an efficient estimation of random coefficient model based on survey data. The main objective of this paper is to construct one sided test for testing equicorrelation coefficient in presence of random coefficients using optimal testing procedure. The test statistic is a ratio of quadratic forms in normal variables which is most powerful and point optimal invariant.     

Unbalanced Repeated Measures with Random Coefficients

In a random coefficient repeated measures model, the regression coefficients relating the observations to some underlying variable, such as time, are themselves taken to be random distributed over experimental units. In this paper, a general approach to repeated measures analysis is extended to this wider model. In the model three specific error structures for the random regression coefficients have been studied, viz, the random coefficients variance matrix is considered to be (i) diagonal, (ii) proportional to the identity matrix and (iii) completely general. An example will be analyzed to illustrate the procedure.     

Assay of Random RNA Oligomerisation in Buffers with High Concentrations of Divalent Metal Ions

Certain conditions, such as high concentrations of divalent metal ions in the reaction buffer or low pH, can cause aggregation and precipitation of RNA species with complementary sequences. If oligomerisation has gone unnoticed, some sequences from the pool of random RNA may be underrepresented or even lost at the very beginning of the selection experiment. Two simple assays for RNA oligomerisation are suggested. One is based on electrophoresis in non‐denaturing gels, and the other uses gel‐filtration.