Evaluating bias correction in weighted proportional hazards regression

Often in observational studies of time to an event, the study population is a biased (i.e., unrepresentative) sample of the target population. In the presence of biased samples, it is common to weight subjects by the inverse of their respective selection probabilities. Pan and Schaubel (Can J Stat 36:111–127, 2008) recently proposed inference procedures for an inverse selection probability weighted (ISPW) Cox model, applicable when selection probabilities are not treated as fixed but estimated empirically. The proposed weighting procedure requires auxiliary data to estimate the weights and is computationally more intense than unweighted estimation. The ignorability of sample selection process in terms of parameter estimators and predictions is often of interest, from several perspectives: e.g., to determine if weighting makes a significant difference to the analysis at hand, which would in turn address whether the collection of auxiliary data is required in future studies; to evaluate previous studies which did not correct for selection bias. In this article, we propose methods to quantify the degree of bias corrected by the weighting procedure in the partial likelihood and Breslow-Aalen estimators. Asymptotic properties of the proposed test statistics are derived. The finite-sample significance level and power are evaluated through simulation. The proposed methods are then applied to data from a national organ failure registry to evaluate the bias in a post-kidney transplant survival model.     

一个拉直岁月的女人

正一个家庭,五口人五个姓。在常人的眼中,她所经历的岁月是曲折的,犹如苦情剧般令人揪心。然而,如此苦情剧般的曲折岁月硬是让她用坚强、爱心和孝心拉直,赢得了一个最美家庭的赞誉!——题记2017年8月22日,这一年多雨的辽西朝阳又是一个雾蒙蒙的闷热天。在北票市文明办领导的陪同下,去位于北票市西北部的泉巨永乡涌泉村采访一个省级文明家庭。在车上,与陪同的领导简单了解了一下情况。得知,女主人公叫黄文岩。她有过两次婚姻,一个家庭,五口人,五个     

东北曾流通过百余种货币

正货币是交换过程中的必然产物。中国东北地区历史悠久,物产丰富,在这一地区使用货币已有2000多年的历史。清朝末年,在中国沿用2000多年的方孔铜钱,逐步被银元、铜元和机器印制的纸币所取代,货币形式变得多样化。与此同时,外资新式银行,如华俄道胜银行、日本横滨正金银行、朝鲜银行也开始在东北地区推行卢布(俗称羌帖)、银钞、金票等。据统计,从清朝末年到中华人民共和国成立初期,东北地区流通过的货币多达百余     

Professors and Students in China during World WarⅡ:The Political Involvement of Intellectuals during and after the War

There were in the past two main approaches to discussion of the political orientation of the intellectual community and its relations with student unrest in the Republican period, especially on the upsurge of the student movement during the civil war between the Guomindang and the Communist Party of China (CPC) (1945-1949). One was to attribute this to the leadership of the CPC, seeing it as the "second front" opened up by the CPC when the two parties were at war. The other approach was straightforward discussion of how intellectual circles promoted democracy, struggled against autocracy and gradually became estranged from the     

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived "factors" as representing biological "subpathway" structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology.     

北爱尔兰历史

1920年,英国政府颁布爱尔兰政府法,规定爱尔兰南部和北部分别成立独立政府。1921年,北爱尔兰邦成立。在这个法令颁布以后的许多年里,爱尔兰为摆脱英国、争取独立而进行的斗争持续不断。早在1912年,英国政府试图解决爱尔兰问题。当时的英国首相赫伯特·阿斯奎斯向下院提出议案,准许爱尔兰地方自治。北爱尔兰几千名新教徒     

Semiparametric additive marginal regression models for multiple type recurrent events

Recurrent event data are often encountered in biomedical research, for example, recurrent infections or recurrent hospitalizations for patients after renal transplant. In many studies, there are more than one type of events of interest. Cai and Schaube (Lifetime Data Anal 10:121-138, 2004) advocated a proportional marginal rate model for multiple type recurrent event data. In this paper, we propose a general additive marginal rate regression model. Estimating equations approach is used to obtain the estimators of regression coefficients and baseline rate function. We prove the consistency and asymptotic normality of the proposed estimators. The finite sample properties of our estimators are demonstrated by simulations. The proposed methods are applied to the India renal transplant study to examine risk factors for bacterial, fungal and viral infections.     

Properties of preliminary test estimators and shrinkage estimators for evaluating multiple exposures - Application to questionnaire data from the SONIC study

Epidemiology studies increasingly examine multiple exposures in relation to disease by selecting the exposures of interest in a thematic manner. For example, sun exposure, sunburn, and sun protection behavior could be themes for an investigation of sun-related exposures. Several studies now use pre-defined linear combinations of the exposures pertaining to the themes to estimate the effects of the individual exposures. Such analyses may improve the precision of the exposure effects, but they can lead to inflated bias and type I errors when the linear combinations are inaccurate. We investigate preliminary test estimators and empirical Bayes type shrinkage estimators as alternative approaches when it is desirable to exploit the thematic choice of exposures, but the accuracy of the pre-defined linear combinations is unknown. We show that the two types of estimator are intimately related under certain assumptions. The shrinkage estimator derived under the assumption of an exchangeable prior distribution gives precise estimates and is robust to misspecifications of the user-defined linear combinations. The precision gains and robustness of the shrinkage estimation approach are illustrated using data from the SONIC study, where the exposures are the individual questionnaire items and the outcome is (log) total back nevus count.     

In the "best interest" of immigrant and refugee children: deliberating on their unique circumstances

Each year, state juvenile courts provide thousands of immigrant and refugee children with access to consistent and reliable caregiving and a stable environment. To examine how courts interpret "the best interests" of immigrant and refugee children, this article examines 24 cases in courts across the United States, which indicate they use a territorial approach when evaluating the best interests standard. Although legal status was not an issue, many related factors were. Consequently, the courts restricted immigrant parents' rights in caring, guiding, and visiting their children; increased the risk of wrongfully terminating parental rights; and intensified the unpredictability of immigrant and refugee children's welfare in the long run. This article suggests an approach that encourages communication between social workers and the courts to address the special needs and circumstances of immigrant and refugee children on three key topics: the material and moral welfare of the child, and social welfare for immigrant and refugee families.     

Flexible semiparametric analysis of longitudinal genetic studies by reduced rank smoothing

In family-based longitudinal genetic studies, investigators collect repeated measurements on a trait that changes with time along with genetic markers. Since repeated measurements are nested within subjects and subjects are nested within families, both the subject-level and measurement-level correlations must be taken into account in the statistical analysis to achieve more accurate estimation. In such studies, the primary interests include to test for quantitative trait locus (QTL) effect, and to estimate age-specific QTL effect and residual polygenic heritability function. We propose flexible semiparametric models along with their statistical estimation and hypothesis testing procedures for longitudinal genetic designs. We employ penalized splines to estimate nonparametric functions in the models. We find that misspecifying the baseline function or the genetic effect function in a parametric analysis may lead to substantially inflated or highly conservative type I error rate on testing and large mean squared error on estimation. We apply the proposed approaches to examine age-specific effects of genetic variants reported in a recent genome-wide association study of blood pressure collected in the Framingham Heart Study.