A latent process model for dementia and psychometric tests

We jointly model longitudinal values of a psychometric test and diagnosis of dementia. The model is based on a continuous-time latent process representing cognitive ability. The link between the latent process and the observations is modeled in two phases. Intermediate variables are noisy observations of the latent process; scores of the psychometric test and diagnosis of dementia are obtained by categorizing these intermediate variables. We propose maximum likelihood inference for this model and we propose algorithms for performing this task. We estimated the parameters of such a model using the data of the 5 year follow-up of the PAQUID study. In particular this analysis yielded interesting results about the effect of educational level on both latent cognitive ability and specific performance in the mini mental test examination. The predictive ability of the model is illustrated by predicting diagnosis of dementia at the 8 year follow-up of the PAQUID study based on the information from the first 5 years.     

Treatment-competing events in dynamic regimes

A dynamic treatment regime is a sequence of decision rules for assigning treatment based on a patient’s current need for treatment. Dynamic regimes are viewed, by many, as a natural way of treating patients with chronic diseases; that is, treating patients with adaptive, complex, longitudinal treatment regimens. In developing dynamic treatment strategies, treatment-competing events may play an important role in the overall treatment strategy, and their effects on subsequent treatment decisions and eventual outcome should be considered. Treatment-competing events may be defined generally as patient-specific, random events which interrupt the ongoing treatment decision process in a dynamic regime. Treatment-competing events censor later treatment decisions that would otherwise be made on a particular dynamic treatment regime had the competing events not occurred. For example, in therapeutic studies of HIV, physicians may assign treatment based on a patient’s current level HIV1-RNA; this defines a treatment assignment rule. However, the presence of opportunistic infections or severe adverse events may preclude a strict adherence of the treatment assignment rule. In other contexts, the “censoring”-by-death phenomenon may be viewed as an example of a treatment-competing event for a particular dynamic treatment regime. Treatment-competing events can be built into the dynamic treatment regime framework and counting processes are a natural mechanism to facilitate this development. In this paper, we develop treatment-competing events in a dynamic infusion policy, a random dynamic treatment regime where multiple infusion treatments are initiated simultaneously and given continuously over time subject to the presence/absence of a treatment-competing event. We illustrate how our methodology may be used to suggest an estimator for a particular causal estimand of recent interest. Finally, we exemplify our methods in a recent study of patients undergoing coronary stent implantation.     

Efficient estimation for the proportional hazards model with bivariate current status data

We consider efficient estimation of regression and association parameters jointly for bivariate current status data with the marginal proportional hazards model. Current status data occur in many fields including demographical studies and tumorigenicity experiments and several approaches have been proposed for regression analysis of univariate current status data. We discuss bivariate current status data and propose an efficient score estimation approach for the problem. In the approach, the copula model is used for joint survival function with the survival times assumed to follow the proportional hazards model marginally. Simulation studies are performed to evaluate the proposed estimates and suggest that the approach works well in practical situations. A real life data application is provided for illustration.     

Bounds on Parameters in Panel Dynamic Discrete Choice Models

Identification of dynamic nonlinear panel data models is an important and delicate problem in econometrics. In this paper we provide insights that shed light on the identification of parameters of some commonly used models. Using these insights, we are able to show through simple calculations that point identification often fails in these models. On the other hand, these calculations also suggest that the model restricts the parameter to lie in a region that is very small in many cases, and the failure of point identification may, therefore, be of little practical importance in those cases. Although the emphasis is on identification, our techniques are constructive in that they can easily form the basis for consistent estimates of the identified sets.     

关系：一种新的分析单位

社会科学的定量研究中都涉及到“分析单位”的问题。分析单位是什么?对这个问题的回答,一方面要看研究的对象是什么;另一方面又要结合社会研究中的资料类型来分析。总的来说,社会科学研究中的数据有多种,因而对应着不同的分析单位。一、三种数据类型J·斯考特(J·Scott,1992:3)认为,社会科学数据可以分为“属性数据”(attribute data)、“关系数据”(relational data)和观念数据(ideational data)三类。属性数据指的是社会行动者1(social actor)自身拥有的态度、观点以及行为等方面的数据,它们一般被视为社会行动者内在的性质、特点,如一…     

How robust are tests for two independent samples?

Non-parametric procedures are sometimes in use even in cases where the corresponding parametric procedure is preferable. This is mainly due to the fact that in practical applications of statistical methods too much attention is paid to any violation of the normality assumption–normal distribution is, however, primarily supposed in order to easily derive the exact distribution of the statistic used within parametric approaches.     

国际贸易统计研究中的几个基本关系

为使国际贸易统计和贸易平衡的研究更为深入和更好地开展,本文建议在研究中要处理好以下三个基本关系：经济发展与统计体系建设的关系,中国贸易统计的国际接轨与促进国际统计制度变革的关系,贸易统计数据生产与贸易统计数据使用的关系。     

无定向测量附合导线网的数据处理

对工程测量中无定向导线网的平差原理、计算方法作了综合论述,提出了无定向附合导线点坐标求定公式及误差方程式,并对全网平差计算及精度评定进行了分析.建立了运用计算机进行数据处理的模块化功能结构,解决了无定向导线网的平差数据处理问题     

用Delphi实现程序间数据传递的其他途径

应用程序间的数据交换是Windows多任务环境的重要特征,作为一种基于Windows的开发工具,Delphi支持三种交换方式：剪切板、动态数据交换（DDE）和对象的链接与嵌入（OLE）。通过WM－COPYDATA消息处理和调用API函数访问全局原子表实现数据交换则是两种非常有效的底层技术。