Statistical testing strategies for assessing treatment efficacy and marker accuracy in phase III trials

When a candidate predictive marker is available, but evidence on its predictive ability is not sufficiently reliable, all‐comers trials with marker stratification are frequently conducted. We propose a framework for planning and evaluating prospective testing strategies in confirmatory, phase III marker‐stratified clinical trials based on a natural assumption on heterogeneity of treatment effects across marker‐defined subpopulations, where weak rather than strong control is permitted for multiple population tests. For phase III marker‐stratified trials, it is expected that treatment efficacy is established in a particular patient population, possibly in a marker‐defined subpopulation, and that the marker accuracy is assessed when the marker is used to restrict the indication or labelling of the treatment to a marker‐based subpopulation, ie, assessment of the clinical validity of the marker. In this paper, we develop statistical testing strategies based on criteria that are explicitly designated to the marker assessment, including those examining treatment effects in marker‐negative patients. As existing and developed statistical testing strategies can assert treatment efficacy for either the overall patient population or the marker‐positive subpopulation, we also develop criteria for evaluating the operating characteristics of the statistical testing strategies based on the probabilities of asserting treatment efficacy across marker subpopulations. Numerical evaluations to compare the statistical testing strategies based on the developed criteria are provided.     

大数据时代项目评审中社会伦理问题的对抗性设计

通过对抗性设计方法解决项目评审中的社会伦理问题，为专家、申请者、大众提供争论和表达异议的民主环境。项目自动评价平台完成民主环境的构建，呈现专家、申请者、平台的争胜性关系，揭示霸权，旨在重塑自由、平等、公平、正义、特权等的项目评审中的社会伦理。     

Uniform consistency rate of kNN regression estimation for functional time series data

In this paper, we investigate the k-nearest neighbours (kNN) estimation of nonparametric regression model for strong mixing functional time series data. More precisely, we establish the uniform almost complete convergence rate of the kNN estimator under some mild conditions. Furthermore, a simulation study and an empirical application to the real data analysis of sea surface temperature (SST) are carried out to illustrate the finite sample performances and the usefulness of the kNN approach.     

Divorce and parity progression following the death of a child: A register-based study from Finland

Most studies that have examined whether a child’s death influences parental relationship stability have used small-scale data sets and their results are inconclusive. A likely reason is that child loss affects not only the risk of parental separation, but also the risk of having another child. Hence parity progression and separation must be treated as two competing events in relation to child loss. The analysis in this paper used Finnish register data from 1971 to 2003, covering over 100,000 married couples whose durations of both first marriage and parenthood could be observed. We ran parity-specific Cox regressions in which process time started from the birth of each additional child. All marriages included women of childbearing age, none of whom had experienced any child death on entering the analysis. We find that child loss only modestly influences the divorce risk, whereas its effect on the risk of parity progression is considerable.     

A Qualitative Case Study on Recovery and Personal Growth in Korean Drug Addicts

ABSTRACT

In recent years, crimes related to drug smuggling and production have gradually increased in Korea. This qualitative case study explores the experiences of Koreans recovering from drug addiction and identifies specific details and mechanisms of recovery. For this purpose, participants were selected based on criteria such as engaging in social activities after recovery from addiction for 3 years or more. Next, one-on-one in-depth interviews were conducted with five voluntarily consenting participants. Data were analyzed using the within-/cross-case analysis suggested by Creswell. Substantial personal growth took place in the recovery process for drug addicts. Nineteen themes were identified as manifestations of personal growth through within-case analysis. Four common themes were then derived through cross-case analysis. These four common themes were “reconnection with reality,” “embracing the essence of one's own existence,” “reconstructing oneself in social relations,” and “self-love.” The study's results suggest that a recovery program needs to be developed wherein the program not only enables drug addicts to reflect on their current existence but also allows them to develop their specific coping skills. Future research should be broadened to consider the social support mechanism and viewpoints of people including family and local community members in close contact with drug addicts.     

数据赋能驱动制造业企业实现敏捷制造案例研究

  世界经济的发展呼唤先进制造理念和制造技术，敏捷制造受到学者的广泛关注。制造业企业通过了解实现敏捷制造的过程机理，可以使其灵活地运用各种先进生产技术促成敏捷制造。然而，已有对制造业企业实现敏捷制造过程的研究缺少契合当前情景的视角以及完整、清晰的探究。         鉴于此，引入赋能概念，从数据驱动的视角对青岛酷特智能公司的研发、生产和营销3个阶段进行深入分析，结合扎根理论的3级编码技术，构建制造业企业通过数据赋能实现敏捷制造的过程模型。在已有研究基础上，构建敏捷制造的开放式概念框架，通过调研访谈、实地考察等方式获得分析数据，分析并构建制造业企业实现敏捷制造的过程模型，结合已有研究对该模型进行进一步讨论和完善。研究过程严格遵循理论构建的理论-实践-理论研究逻辑。         研究结果表明，企业的敏捷制造能力可以通过高效对接消费者需求与资源以及提高中间对接环节的效率来打造。从数据驱动视角看，数据化、标准化和联网化是制造业企业实现敏捷制造的核心所在。一方面，数据化和标准化可以提高数据的可接入性和流通性，减少制造加工环节的延缓；另一方面，联网化可以让需求数据直接对接企业的资源和制造过程的设备，进而实现资源的自动整合，打造企业高响应速度、高质量、低成本的供给。         从数据驱动视角构建的敏捷制造过程模型系统详细地解释了制造业企业通过数据赋能实现敏捷制造的过程，完善和拓展了关于敏捷制造的研究，为未来敏捷制造实证研究提供了一定的研究基础和启示。此外，为了给企业实践提供更加详细、明确的指导，给出基于数据赋能驱动制造业企业实现敏捷制造的实践指导步骤。     

Bibliometrics of Big Data Research Literature in Social Science

[Purpose/Significance] This article provides a bibliometric study of the big data research literature in social science to explore its features and patterns both in domestic and international area, quantitatively and qualitatively. [Method/Process] This article collects the data from academic databases, then makes statistical analysis on significant characteristics to evaluate the regularity of document increase, distribution of subject categories, most prolific and impactful institutions, and then discusses the relations between research front and intellectual base via citation and co-citation analysis, finally concludes research features and patterns of big data. [Result/Conclusion] (1) Different subject categories show the different level of research interest while big data research has been one of the most popular topics in social science. The most documents are from information science and library science. (2) Besides journals of high output subject categories, computer science, telecommunications and multidisciplinary journals are common intellectual bases of big data research in social science. (3) Renmin University of China in domestic and Harvard University in the world are top institutions according to the number of fruits and influence of their publications of big data research in social science. (4) For China, publication quantity in social science makes apparent growth while international academic influence is still limited.