The generalized estimating equation (GEE) method is a popular method for analyzing longitudinal data. An inappropriate specification of the working correlation structure reduces the effciency of the GEE estimation. Pan (2001a) and Hin and Wang (2009) proposed a quasi-likelihood under the independence model criterion (QIC) and a correlation information criterion (CIC) for selecting a proper working correlation structure, respectively. In this study, we proposed modifications to the QIC and CIC using the variance estimators of the GEE with improved small-sample properties. In a simulation study, the performance of the modified QIC and CIC was better than that of the original QIC and CIC. The modified methods were illustrated using the data for an air pollution study.
As clinical trials with “positive” results are more likely to be published, a meta-analysis of only published trials may be biased toward positive results (referred to as “publication bias”). A number of statistical tests have been proposed to detect publication bias. However, they have undesirable properties, particularly, the inflation of type I error and low power. A primordial countermeasure has been launched. In September 2004, the International Committee of Medical Journal Editors announced that they would no longer publish trials that were not registered in a public registry in advance. They embraced the WHO trial registration set consisting of 20 items including target sample size, which is related to the publication of results. The aim of this paper is to propose a new approach with a higher statistical power for detecting publication bias by using information on the sample sizes of all trials, including unpublished trials from the registry. We compared the proposed method to commonly used methods via simulations. The proposed method was found to have a higher power than the other methods in many situations. It will be useful for detecting publication bias because clinical trial registration will be more widespread in the near future.
Under the assumptions of the continuous distribution and equality of variance, we consider multiple comparisons tests for the differences among mean responses in k samples. Closed testing procedures are superior to the Tukey-Kramar type multiple tests which are single-step procedures in many cases. The closed testing procedure more powerful than the Tukey-Welsch test and the REGW test is proposed under the usual normality. Furthermore the nonparametric closed testing procedure is discussed based on ranks. Last the proposed nonparametric closed testing procedure is illustrated with data regarding sarcoidosis from the assay of serum angiotensin-converting enzyme (SACE).
At the present day, it becomes imperative to develop appropriate statistical methods for high-dimensional and small sample data analysis because data formats in the biological or medical fields have been dramatically changed. Especially, it will be common in the near future to analyze clinical data together with genomic data. In this review paper, we introduce several current approaches to the analysis relating to genomic and proteomic data, and describe some limitations or problems in the statistical performance. In the former part of this paper, we explain a problem of p»n, which is the fundamental challenge in data analysis in bioinformatics. In particular, we consider a typical problem of p»n in prediction of treatment effects using microarray data as feature vectors. Then, we introduce some new boosting methods based on the area under the ROC curve. After showing some applications of the boosting methods, we summarize the present problems and refer to outlook for the future.