Research Subject Advocate: A New Protector of Research Participants

In 2001, the National Center for Research Resources (NCRR) directed the 78 General Clinical Research Centers (GCRC) to develop a Research Subject Advocate (RSA) position. The RSA would report directly to the Principal Investigator (PI) of each GCRC and assure compliance of studies conducted on the GCRC with federal regulations and policies. Seven RSAs agreed to be interviewed about their new role. Website documents, electronic correspondence, and presentations at the first annual national meeting of RSAs were scrutinized using discursive analysis to shed light on this new organizational form and its potential for increased protection of human research participants. The RSA role actualizes the ethical principles of respect for persons, justice, and beneficence that are the foundation of the protection of research participants. The results also reveal the regulatory, institutional, collegial, and personal resources and barriers that assist the RSA in the successful implementation of the RSA role. In addition, issues important to the RSAs are described.     

A Critical Look at Some Analyses of Major League Baseball Salaries

At a data analysis exposition sponsored by the Section on Statistical Graphics of the ASA in 1988, 15 groups of statisticians analyzed the same data about salaries of major league baseball players. By examining what they did, what worked, and what failed, we can begin to learn about the relative strengths and weaknesses of different approaches to analyzing data. The data are rich in difficulties. They require reexpression, contain errors and outliers, and exhibit nonlinear relationships. They thus pose a realistic challenge to the variety of data analysis techniques used. The analysis groups chose a wide range of model-fitting methods, including regression, principal components, factor analysis, time series, and CART. We thus have an effective framework for comparing these approaches so that we can learn more about them. Our examination shows that approaches commonly identified with Exploratory Data Analysis are substantially more effective at revealing the underlying patterns in the data and at building parsimonious, understandable models that fit the data well. We also find that common data displays, when applied carefully, are often sufficient for even complex analyses such as this.     

A simulation study of the nearest neighbour analysis method of papadakis

The relative performances of randomised block, balanced lattice squares and Papadakis nearest neighbour analyses were compared on two simulated fields whose soil heterogeneity profiles were generated, one with a few evenly spaced contours and the other with many unevenly spaced contours. Four levels of random error were generated to simulate different proportions of random error and soil heterogeneity. Dummy treatments, corresponding to 7 x 7 and 11 x 11 balanced lattice squares were applied to the fields. The results from simulated experiments showed an interaction of error mean square (EMS) between size of experiment (7 x 7, 11 x 11) and levels of soil heterogeneity in the lattice analyses, but no such interaction in the Papadakis analyses. The Papadakis EMS decreased as random error decreased but at a rate depending on the map andthe ratio of soil heterogeneity to random error.     

A study of DEA models without explicit inputs

In performance evaluations, data without explicit inputs (such as index data, pure output data) are widely used. To directly use such data, this paper presents a study on building DEA models without explicit inputs, so-called DEA-WEI models, which are applicable to the evaluation applications where inputs are not directly considered. We provide an axiom foundation of these kinds of models, and further discuss how to incorporate value judgments of decision makers into these DEA-WEI models. Several such models are derived. Finally, applications of the DEA-WEI models are presented.     

对CBA职业联赛赛区、俱乐部受处罚的情况分析与应对措施简

近几个赛季篮协对CBA职业联赛各赛区、俱乐部开出的罚单就一直没有减少,赛场秩序的混乱已经严重干扰了联赛的正常进行。现对2011-2012赛季CBA职业联赛赛区、俱乐部受处罚的原因、类型进行分析研究,提出一些解决问题的方法和措施,减少比赛中教练员、裁判员之间的矛盾,降低现场观众暴力行为的发生率,但愿能对CBA职业联赛的发展起到推波助澜的作用。     

Incorporating quality into data envelopment analysis of nursing home performance: A case study

When using data envelopment analysis (DEA) as a benchmarking technique for nursing homes, it is essential to include measures of the quality of care. We survey applications where quality has been incorporated into DEA models and consider the concerns that arise when the results show that quality measures have been effectively ignored. Three modeling techniques are identified that address these concerns. Each of these techniques requires some input from management as to the proper emphasis to be placed on the quality aspect of performance. We report the results of a case study in which we apply these techniques to a DEA model of nursing home performance. We examine in depth not only the resulting efficiency scores, but also the benchmark sets and the weights given to the input and output measures. We find that two of the techniques are effective in insuring that DEA results discriminate between high and low quality performance.     

中央苏区调查统计工作的内容和成效 ——中央苏区调查统计工作的光辉实践与有益启示（二）

 中央苏区制发了众多的统计报表全面反映战争和建设情况，采取多种调查统计方法确保数据真实可靠，并利用丰富的调查统计资料积极开展统计分析，及时发布统计资料，为革命战争和苏区经济建设服务。     

中国与荷兰曲棍球后备人才培养现状对比分析

曲棍球后备人才是一个国家曲棍球竞技水平的标志。荷兰是曲棍球后备人才发展较好的国家,他之所以长盛不衰,一个重要的因素在于有一个雄厚的群众基础和一个科学并行之有效的后备人才培养体系。目前中国后备人才培养方面与荷兰相比存在着问题和不足,这些对中国曲棍球项目的可持续发展是非常不利的。针对上述状况,本文提出了中国曲棍球管理部门应加强和完善管理体制,建立符合中国具体国情的曲棍球后备人才培养机制。     

A pattern-mixture odds ratio model for incomplete categorical data

Most models for incomplete data are formulated within the selection model framework. Pattern-mixture models are increasingly seen as a viable alternative, both from an interpretational as well as from a computational point of view (Little 1993, Hogan and Laird 1997, Ekholm and Skinner 1998). Whereas most applications are either for continuous normally distributed data or for simplified categorical settings such as contingency tables, we show how a multivariate odds ratio model (Molenberghs and Lesaffre 1994, 1998) can be used to fit pattern-mixture models to repeated binary outcomes with continuous covariates. Apart from point estimation, useful methods for interval estimation are presented and data from a clinical study are analyzed to illustrate the methods.     

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