Quantile Regression Methods for Longitudinal Data with Drop-outs: Application to CD4 Cell Counts of Patients Infected with the Human Immunodeficiency Virus

Patients infected with the human immunodeficiency virus (HIV) generally experience a decline in their CD4 cell count (a count of certain white blood cells). We describe the use of quantile regression methods to analyse longitudinal data on CD4 cell counts from 1300 patients who participated in clinical trials that compared two therapeutic treatments: zidovudine and didanosine. It is of scientific interest to determine any treatment differences in the CD4 cell counts over a short treatment period. However, the analysis of the CD4 data is complicated by drop-outs: patients with lower CD4 cell counts at the base-line appear more likely to drop out at later measurement occasions. Motivated by this example, we describe the use of `weighted' estimating equations in quantile regression models for longitudinal data with drop-outs. In particular, the conventional estimating equations for the quantile regression parameters are weighted inversely proportionally to the probability of drop-out. This approach requires the process generating the missing data to be estimable but makes no assumptions about the distribution of the responses other than those imposed by the quantile regression model. This method yields consistent estimates of the quantile regression parameters provided that the model for drop-out has been correctly specified. The methodology proposed is applied to the CD4 cell count data and the results are compared with those obtained from an `unweighted' analysis. These results demonstrate how an analysis that fails to account for drop-outs can mislead.     

A semiparametric method of multiple imputation

In this paper, we describe how to use multiple imputation semiparametrically to obtain estimates of parameters and their standard errors when some individuals have missing data. The methods given require the investigator to know or be able to estimate the process generating the missing data but requires no full distributional form for the data. The method is especially useful for non-standard problems, such as estimating the median when data are missing.     

Sliced Average Variance Estimation for Censored Data

We consider a nonlinear censored regression problem with a vector of predictors. With censoring, high-dimensional regression analysis becomes much more complicated. Since censoring can cause severe bias in estimation, modification to adjust such bias is needed to be made. Based on the weight adjustment, we develop the modification of sliced average variance estimation for estimating the lifetime central subspace without requiring a prespecified parametric model. Our proposed method preserves as much regression information as possible. Simulation results are reported and comparisons are made with the sliced inverse regression of Li et al. (1999 Li , K. C. , Wang , J. L. , Chen , C. H. ( 1999 ). Dimension reduction for censored regression data . Ann. Statist. 27 : 1 – 23 . [Google Scholar]).     

SPECIAL ISSUE： ECOLOGICAL CIVILIZATION AND BEAUTIFUL CHINA Roadmap for the Construction of an Ecological Rule of Law in China

十八大报告在将建设社会主义法治国家作为治国理政总体方略的同时,把中国特色社会主义事业总体布局由经济建设、政治建设、文化建设、社会建设“四位一体”拓展为包括生态文明建设的‘五位一体’,勾画了中国生态法治建设的路线图。其目标是确立与生态文明相适应的生态环境法治系统,任务是将生态理性纳入法治运行轨道,路径是健全和完善生态法治的制度与体制机制。     

Pairwise directions estimation for multivariate response regression data

This article concerns the analysis of multivariate response data with multi-dimensional covariates. Based on local linear smoothing techniques, we propose an iteratively adaptive estimation method to reduce the dimensions of response variables and covariates. Two weighted estimation strategies are incorporated in our approach to provide initial estimates. Our proposal is also extended to curve response data for a data-adaptive basis function searching. Instead of focusing on goodness of fit, we shift the problem to reveal the data structure and basis patterns. Simulation studies with multivariate response and curve data are conducted for our pairwise directions estimation (PDE) approach in comparison with sliced inverse regression of Li et al. [Dimension reduction for multivariate response data. J Amer Statist Assoc. 2003;98:99–109]. The results demonstrate that the proposed PDE method is useful for data with responses approximating linear or bending structures. Illustrative applications to two real datasets are also presented.     

析埃及签署合格工业区协议

2004年12月14日,埃及、美国和以色列的贸易代表在开罗签署了"合格工业区"(Qualifying Industrial Zones,简称QIZs)协议.这份协议在埃及引起了强烈的反响,人们围绕协议可能带给埃及的经济利益、国内形势变化以及埃及在巴以和平进程中的地位和影响等议题展开了大范围的讨论.     

An Inverse‐regression Method of Dependent Variable Transformation for Dimension Reduction with Non‐linear Confounding

Many model‐free dimension reduction methods have been developed for high‐dimensional regression data but have not paid much attention on problems with non‐linear confounding. In this paper, we propose an inverse‐regression method of dependent variable transformation for detecting the presence of non‐linear confounding. The benefit of using geometrical information from our method is highlighted. A ratio estimation strategy is incorporated in our approach to enhance the interpretation of variable selection. This approach can be implemented not only in principal Hessian directions (PHD) but also in other recently developed dimension reduction methods. Several simulation examples that are reported for illustration and comparisons are made with sliced inverse regression and PHD in ignorance of non‐linear confounding. An illustrative application to one real data is also presented.     

第四方物流努力水平影响的物流分包激励机制研究

本文考虑了第四方物流在整合物流资源时所付出的努力水平对第三方物流产出的影响,运用委托代理理论,研究了第四方物流分包时对多个第三方物流的激励问题,得出了第四方物流能力越强,付出的努力水平越高;第三方物流能力越强,越会更努力的工作;第四方物流给予能力强的第三方物流更多的激励的结论。     

Estimating Regression Parameters in an Extended Proportional Odds Model

The proportional odds model may serve as a useful alternative to the Cox proportional hazards model to study association between covariates and their survival functions in medical studies. In this article, we study an extended proportional odds model that incorporates the so-called "external" time-varying covariates. In the extended model, regression parameters have a direct interpretation of comparing survival functions, without specifying the baseline survival odds function. Semiparametric and maximum likelihood estimation procedures are proposed to estimate the extended model. Our methods are demonstrated by Monte-Carlo simulations, and applied to a landmark randomized clinical trial of a short course Nevirapine (NVP) for mother-to-child transmission (MTCT) of human immunodeficiency virus type-1 (HIV-1). Additional application includes analysis of the well-known Veterans Administration (VA) Lung Cancer Trial.     

‘It feels as if time has come to a standstill’: Institutionalised everyday lives among youth with a mental illness

This paper focuses on the everyday lives of young people with a severe mental illness living temporarily at a social psychiatric housing facility in Denmark. In the paper we take a temporal approach to the analysis of this and we draw on Henri Lefebvre’s work on rhythm analysis to investigate the differences between the rhythms of everyday life within the institution and the rhythms of what is perceived as the everyday life of ‘ordinary’ youth. We also show how digital technologies play a central part in these institutionalised everyday lives by creating connections as well as disruptions between different time-spaces. Centrally, we point to the positive and negative consequences this has for the young peoples’ sense of self. Empirically, the paper is based on a four-month ethnographic fieldwork at the housing facility in 2014.