Assessment of Optimal Selected Prognostic Factors

The identification and assessment of prognostic factors is one of the major tasks in clinical research. The assessment of one single prognostic factor can be done by recently established methods for using optimal cutpoints. Here, we suggest a method to consider an optimal selected prognostic factor from a set of prognostic factors of interest. This can be viewed as a variable selection method and is the underlying decision problem at each node of various tree building algorithms. We propose to use maximally selected statistics where the selection is defined over the set of prognostic factors and over all cutpoints in each prognostic factor. We demonstrate that it is feasible to compute the approximate null distribution. We illustrate the new variable selection test with data of the German Breast Cancer Study Group and of a small study on patients with diffuse large B‐cell lymphoma. Using the null distribution for a p‐value adjusted regression trees algorithm, we adjust for the number of variables analysed at each node as well. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Conditional and Unconditional Simulation of Healthy Patients' Visual Fields

This paper describes a simulation problem, motivated by the study of glaucoma, a very serious and widespread ocular illness. To ascertain whether a patient suffers from glaucoma, a perimetric test is done, but the evolution of the disease is very slow, and large longitudinal sets of tests taken on the same patient are needed to study its evolution, to analyze the efficiency of existing methods to detect the progression of glaucoma and to develop new ones. Simulation can be a very useful procedure to get appropriate data sets to work with. Our aim in this work is to simulate several VFs in a healthy patient to reflect his evolution in time. We use a spatio‐temporal model to simulate from, taking into account the correlation existing between the observed (or simulated) values in space and time. Two different simulation procedures (unconditional and conditional) are studied, and applied to obtain the simulations we are interested in. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Reaction mechanism of surfactant‐sensitized chemiluminescence of bis(2,4,6‐trichlorophyenyl) oxalate and hydrogen peroxide induced by gold nanoparticles

The chemiluminescence (CL) of bis(2,4,6‐trichlorophyenyl) oxalate with hydrogen peroxide in the present of cationic surfactant and gold nanoparticles was studied. The CL emission was obviously enhanced in the presence of surfactant at a suitable concentration, with a synergetic catalysis effect exhibited. Different sizes of gold nanoparticles (15 and 50 nm) showed different effects on CL intensity. Mechanisms of the CL reaction and sensitization effect are discussed. Copyright © 2008 John Wiley & Sons, Ltd.     

Describing Ordinal Odds Ratios for Stratified r × c Tables

For an r × ctable with ordinal responses, odds ratios are commonly used to describe the relationship between the row and column variables. This article shows two types of ordinal odds ratios where local‐global odds ratios are used to compare several groups on a c‐category ordinal response and a global odds ratio is used to measure the global association between a pair of ordinal responses. When there is a stratification factor, we consider Mantel‐Haenszel (MH) type estimators of these odds ratios to summarize the association from several strata. Like the ordinary MH estimator of the common odds ratio for several 2 × 2 contingency tables, the estimators are used when the association is not expected to vary drastically among the strata. Also, the estimators are consistent under the ordinary asymptotic framework in which the number of strata is fixed and also under sparse asymptotics in which the number of strata grows with the sample size. Compared to the maximum likelihood estimators, simulations find that the MH type estimators perform better especially when each stratum has few observations. This article provides variances and covariances formulae for the local‐global odds ratios estimators and applies the bootstrap method to obtain a standard error for the global odds ratio estimator. At the end, we discuss possible ways of testing the homogeneity assumption.     

Introducing the Multivariate Dale Model in Population‐Based Genetic Association Studies

Until recently, the most common parametric approaches to study the combined effects of several genetic polymorphisms located within a gene or in a small genomic region are, at the genotype level, logistic regressions and at the haplotype level, haplotype analyses. An alternative modeling approach, based on the case/control principle, is to regard exposures (e.g., genetic data such as derived from Single Nucleotide Polymorphisms – SNPs) as random and disease status as fixed and to use a marginal multivariate model that accounts for inter‐relationships between exposures. One such model is the multivariate Dale model. This model is based on multiple logistic regressions. That is why the model, applied in a case/control setting, leads to straightforward interpretations that are similar to those drawn in a classical logistic modeling framework. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Gramicidin A-phospholipid model systems

Gramicidin A forms ion-conducting channels which can traverse the hydrocarbon core of lipid bilayer membranes. The structures formed by gramicidin A are among the best characterized of all membrane-bound polypeptides or proteins. In this review a brief summary is given of the occurrence, conformation, and synthesis of gramicidin A, and of its use as a model for ion transport and the interaction of proteins and lipids in biological membranes.     

A Markov approach for describing post-fire succession of vegetation

Vegetation dynamics in the coastal area of the Seto Inland Sea region in Japan, where wild fires occur frequently, were described using a stationary Markov model. In this region, vegetation types ofMiscanthus-Pleioblastus grassland,Lespedeza-Mallotus scrub,Pinus-Rhododendron forest andCrassocephalum-Erechtites community have been identified, and these show cyclic succession under the influence of fires. The model uses parameters determining fire frequency and rate of successional change to analyze the effect of variation in these parameters on the areal ratio of each vegetation type at equilibrium and on the time taken for one vegetation type to succeed another (elapsed successional time). The effect of fire frequency differs between hypothetical habitats with high and low productivity. A policy for vegetation management in areas of high and low productivity is proposed. The advantages and limitations of applying Markov models to studies of vegetation succession are also discussed.