提高深圳出生人口素质的理论与思考

本文通过全面回顾深圳市在提高出生人口素质,预防出生缺陷方面的机构设置、工作方法,分析了深圳提高出生人口素质面临的挑战．并从政策分析的角度提出了提高出生人口素质的政策建议。     

Depict a better mood

In the language of contemporary painting, the conceptualization of a gradual replacement of the art itself seems to be reduced to an unprecedented bottom line requirements for color, shape and composition of fi ne art and other art forms the boundary is increasingly blurred. In such the air environment, in order to     

论“主体建构”的认识论来源及理论意义

本文着力解决的是主体建构在社会科学诸多范式与取向中的合法性问题。主体建构是一种视角、一个解释工具,也是一种方法论取向,同时也可以被理解为一种认识论倾向。主体建构以现象学关于主体意向性与意义建构的论断为认识论基础,以理解社会学为理论源泉,试图理解关于同一社会现象在不同主体的视角下的不同的意义。主体建构的目的是对这些不同的意义进行差异性理解、过程性理解与情境性理解,并以此解构垄断意义与固化意义,寻求社会现象的多元意义。     

医疗需求与中国医疗费用增长——基于城乡老年医疗支出差异的视角

在人口老龄化和老年医疗支出城乡差异凸显的背景下,中国医疗费用增长部分源于合理的健康需求。利用中国营养与健康调查1991—2011年的八轮数据,构造出生组跟踪样本,分别考察城乡居民医疗支出的年龄效应,估算城乡老年医疗需求导致的费用上涨,可以发现:城市居民的人均医疗支出随年龄显著增加,但农村居民的人均医疗支出随年龄增长的趋势并不明显。而忽略出生组效应,会低估城乡老年医疗支出的差距。城乡老年医疗支出差距缩小,将导致医疗费用在2010—2030年年均实际增长约5.2%。在控制医疗总费用上涨的同时,医疗保险制度需根据老年医疗需求,调整医疗资源配置结构。     

西尔科的小说《典仪》的生态批评解读

西尔科的小说《典仪》从自然生态、社会生态和精神生态三个维度反映了造成人类生态危机的思想文化根源,表达了印第安文化中蕴含的和谐生态智慧。这不仅是处于困境中的印第安民族的出路,更是人类恢复生态、自我救赎的救助策略。     

社会转型期的师德继承与发展问题的探索

社会转型期使人们的思想方法、价值观念、道德取向都会产生变化,师德建设怎样与社会发展相适应就成为一个值得探索的新问题了,这是因为教育是改造社会的重要力量之一,教师是通过塑造新人来实现对未来社会的改造的。正因为教师这种在整个社会发展、经济建设、文化建设中的重要性及教师职业的特殊性,决定了在社会主义市场经济条件下加强师德建设的重要性。本文对这个古老而常新的问题提出自己的看法     

中心村规划——广州农村可持续发展战略

一种既非农村,又非城市的新的生态群落———被称之为“都市里的村庄”已成为广州城市化进程的严重障碍,因此,城市化进程应该包括改造旧村庄和规划建设新村庄,尤其要重视中心村规划建设。中心村规划,从规划区域上操作,包括“点”与“面”两个方面,即把中心村（点）按城市居住单元（或小区）经济技术指标规划成一个集镇,把中心村管辖范围规划成几个功能区;从规划结构上操作,要完成“六图一书”从规划体系上操作,要在上级区域规划指导下进行     

Composite quantile regression for massive datasets

Analysis of massive datasets is challenging owing to limitations of computer primary memory. Composite quantile regression (CQR) is a robust and efficient estimation method. In this paper, we extend CQR to massive datasets and propose a divide-and-conquer CQR method. The basic idea is to split the entire dataset into several blocks, applying the CQR method for data in each block, and finally combining these regression results via weighted average. The proposed approach significantly reduces the required amount of primary memory, and the resulting estimate will be as efficient as if the entire data set is analysed simultaneously. Moreover, to improve the efficiency of CQR, we propose a weighted CQR estimation approach. To achieve sparsity with high-dimensional covariates, we develop a variable selection procedure to select significant parametric components and prove the method possessing the oracle property. Both simulations and data analysis are conducted to illustrate the finite sample performance of the proposed methods.     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

An additive–multiplicative mean model for panel count data with dependent observation and dropout processes

This paper discusses regression analysis of panel count data with dependent observation and dropout processes. For the problem, a general mean model is presented that can allow both additive and multiplicative effects of covariates on the underlying point process. In addition, the proportional rates model and the accelerated failure time model are employed to describe possible covariate effects on the observation process and the dropout or follow‐up process, respectively. For estimation of regression parameters, some estimating equation‐based procedures are developed and the asymptotic properties of the proposed estimators are established. In addition, a resampling approach is proposed for estimating a covariance matrix of the proposed estimator and a model checking procedure is also provided. Results from an extensive simulation study indicate that the proposed methodology works well for practical situations, and it is applied to a motivating set of real data.