Regression analysis of case-cohort studies in the presence of dependent interval censoring

The case-cohort design is widely used as a means of reducing the cost in large cohort studies, especially when the disease rate is low and covariate measurements may be expensive, and has been discussed by many authors. In this paper, we discuss regression analysis of case-cohort studies that produce interval-censored failure time with dependent censoring, a situation for which there does not seem to exist an established approach. For inference, a sieve inverse probability weighting estimation procedure is developed with the use of Bernstein polynomials to approximate the unknown baseline cumulative hazard functions. The proposed estimators are shown to be consistent and the asymptotic normality of the resulting regression parameter estimators is established. A simulation study is conducted to assess the finite sample properties of the proposed approach and indicates that it works well in practical situations. The proposed method is applied to an HIV/AIDS case-cohort study that motivated this investigation.     

Estimation of the additive hazards model with interval-censored data and missing covariates

The additive hazards model is one of the most commonly used regression models in the analysis of failure time data and many methods have been developed for its inference in various situations. However, no established estimation procedure exists when there are covariates with missing values and the observed responses are interval-censored; both types of complications arise in various settings including demographic, epidemiological, financial, medical and sociological studies. To address this deficiency, we propose several inverse probability weight-based and reweighting-based estimation procedures for the situation where covariate values are missing at random. The resulting estimators of regression model parameters are shown to be consistent and asymptotically normal. The numerical results that we report from a simulation study suggest that the proposed methods work well in practical situations. An application to a childhood cancer survival study is provided. The Canadian Journal of Statistics 48: 499–517; 2020 © 2020 Statistical Society of Canada     

Working and disability expectancies at older ages: The role of childhood circumstances and education

The ability to work at older ages depends on health and education. Both accumulate starting very early in life. We assess how childhood disadvantages combine with education to affect working and health trajectories. Applying multistate period life tables to data from the Health and Retirement Study (HRS) for the period 2008–2014, we estimate how the residual life expectancy at age 50 is distributed in number of years of work and disability, by number of childhood disadvantages, gender, and race/ethnicity. Our findings indicate that number of childhood disadvantages is negatively associated with work and positively with disability, irrespective of gender and race/ethnicity. Childhood disadvantages intersect with low education resulting in shorter lives, and redistributing life years from work to disability. Among the highly educated, health and work differences between groups of childhood disadvantage are small. Combining multistate models and inverse probability weighting, we show that the return of high education is greater among the most disadvantaged.     

Enriching Surveys with Supplementary Data and its Application to Studying Wage Regression

We consider the problem of supplementing survey data with additional information from a population. The framework we use is very general; examples are missing data problems, measurement error models and combining data from multiple surveys. We do not require the survey data to be a simple random sample of the population of interest. The key assumption we make is that there exists a set of common variables between the survey and the supplementary data. Thus, the supplementary data serve the dual role of providing adjustments to the survey data for model consistencies and also enriching the survey data for improved efficiency. We propose a semi‐parametric approach using empirical likelihood to combine data from the two sources. The method possesses favourable large and moderate sample properties. We use the method to investigate wage regression using data from the National Longitudinal Survey of Youth Study.     

PCA likelihood ratio test approach for attributed social networks monitoring

Abstract

One of the most important factors in building and changing communication mechanisms in social networks is considering features of the members of social networks. Most of the existing methods in network monitoring don’t consider effects of features in network formation mechanisms and others don’t lead to reliable results when the features abound or when there are correlations among them. In this article, we combined two methods principal component analysis (PCA) and likelihood method to monitor the underlying network model when the features of individuals abound and when some of them have high correlations with each other.     

基于组合赋权UM的煤炭企业人员流失风险评价

针对煤炭企业人员流失风险的复杂性和不确定性,建立了基于组合赋权未确知测度(UM)的综合评价模型。在应用AHM-信息熵确定风险评价因素权重的基础上,采用未确知测度方法对煤炭企业内不同职能的人力资源进行分析,评价出风险等级。最后运用实例证明了组合赋权的有效性和评价模型的可靠性,为企业的风险管理提供决策依据。     

Probabilistic population projections for countries with generalized HIV/AIDS epidemics

In 2015, the United Nations (UN) issued probabilistic population projections for all countries up to 2100, by simulating future levels of total fertility and life expectancy and combining the results using a standard cohort component projection method. For the 40 countries with generalized HIV/AIDS epidemics, the mortality projections used the Spectrum/Estimation and Projection Package (EPP) model, a complex, multistate model designed for short-term projections of policy-relevant quantities for the epidemic. We propose a simpler approach that is more compatible with existing UN projection methods for other countries. Changes in life expectancy are projected probabilistically using a simple time series regression and then converted to age- and sex-specific mortality rates using model life tables designed for countries with HIV/AIDS epidemics. These are then input to the cohort component method, as for other countries. The method performed well in an out-of-sample cross-validation experiment. It gives similar short-run projections to Spectrum/EPP, while being simpler and avoiding multistate modelling.     

Analysis of dependently truncated data in Cox framework

Truncation is a known feature of bone marrow transplant (BMT) registry data, for which the survival time of a leukemia patient is left truncated by the waiting time to transplant. It was recently noted that a longer waiting time was linked to poorer survival. A straightforward solution is a Cox model on the survival time with the waiting time as both truncation variable and covariate. The Cox model should also include other recognized risk factors as covariates. In this article, we focus on estimating the distribution function of waiting time and the probability of selection under the aforementioned Cox model.     

全域旅游视角下的天府旅游名县竞争力研究——以四川省县域城市为例

天府旅游名县创建是按照全域旅游发展理念开展的，以建设世界重要旅游目的地为目标的建设工程。文章在系统整理旅游目的地竞争力主要研究成果基础上，建立15个天府旅游名县竞争力评价指标，基于可量化数据，采用主成分和聚类分析方法，对进入天府旅游名县的40个县(市、区）进行实证研究。结果显示，旅游核心竞争力、旅游发展规模、旅游综合实力、非优势资源品牌创建能力等因素是构成天府旅游名县评判的主要因子；从空间结构上看，呈现成都平原最强，川西、川北居中，川南最弱的局面；从创建动力上看，不应仅关注创建结果，而应通过创建工作，促进县域经济综合发展水平和城市发展潜力提升。     

新时代人的全面发展的制约因素与实现路径

实现人的全面发展是马克思主义的最高价值目标,是中国共产党人和中国人民的不懈追求。新时代中国的发展取得了显著成就,在经济、政治、文化、社会以及生态五个方面为人的全面发展提供了有利条件。但是,人民日益增长的美好生活需要和不平衡不充分的发展之间的矛盾制约了人的全面发展。为更好地推动人的全面发展,需要实现经济高质量发展,建设社会主义民主政治,繁荣社会主义文化,保障和改善民生,建设社会主义生态文明。这对加快中国特色社会主义建设、实现社会全面进步和促进人的全面发展具有重要意义。