On Graphing Paired Data

Techniques for graphing paired data are considered. A conventional method frequently used when reporting scientific results, particularly in medical journals, involves representing pairs of measurements by straight line segments titled at different angles related to the differences between the components of the data pairs. However when the sample size is only moderately large, this type of display can become cluttered and thus uninformative due to overlap. It is suggested that a new graph that combines features of ANOVA plots and multivalued dot charts may provide a more effective visual display of paired data. A designed experiment in data perception using simulated data provides some evidence in favor of this parallel line plot.     

Non-parametric Tests for the Comparison of Point Processes Based on Incomplete Data

We consider the comparison of point processes in a discrete observation situation in which each subject is observed only at discrete time points and no history information between observation times is available. A class of non-parametric test statistics for the comparison of point processes based on this kind of data is presented and their asymptotic distributions are derived. The proposed tests are generalizations of the corresponding tests for continuous observations. Some results from a simulation study for evaluating the proposed tests are presented and an illustrative example from a clinical trial is discussed.     

中国消费价格指数季节变动的函数性数据分析

运用函数性数据分析方法对中国从1990年1月到2009年12月期间的消费价格指数的季节变动特征进行了分析,结果发现中国消费价格指数存在季节性,一般年份对应的相平面图呈现相似的几何结构;基于傅里叶基函数组合给出的拟合函数较好地刻画了消费价格指数的季节变动特征。另外,中国物价水平具有惯性特征,其在受到一个结构性冲击的影响偏离正常水平之后,大约在一个季度回复到正常水平。     

A critical review of graphics for subgroup analyses in clinical trials

Subgroup analyses are a routine part of clinical trials to investigate whether treatment effects are homogeneous across the study population. Graphical approaches play a key role in subgroup analyses to visualise effect sizes of subgroups, to aid the identification of groups that respond differentially, and to communicate the results to a wider audience. Many existing approaches do not capture the core information and are prone to lead to a misinterpretation of the subgroup effects. In this work, we critically appraise existing visualisation techniques, propose useful extensions to increase their utility and attempt to develop an effective visualisation approach. We focus on forest plots, UpSet plots, Galbraith plots, subpopulation treatment effect pattern plot, and contour plots, and comment on other approaches whose utility is more limited. We illustrate the methods using data from a prostate cancer study.     

从外部监督入手解决统计数据质量问题的努力

 以企业会计信息披露为镜子可以发现,要保证政府统计数据的质量,特别需要外部监督,并且需要就整个统计数据生产过程进行外部监督。为此,需要依法监督,并要结合统计数据生产的特点,将统计执法落到实处。《统计违法违纪行为处分规定》的意义正在于此。     

基于随机森林模型的分类数据缺失值插补

缺失数据是影响调查问卷数据质量的重要因素,对调查问卷中的缺失值进行插补可以显著提高调查数据的质量。调查问卷的数据类型多以分类型数据为主,数据挖掘技术中的分类算法是处理属性分类问题的常用方法,随机森林模型是众多分类算法中精度较高的方法之一。将随机森林模型引入调查问卷缺失数据的插补研究中,提出了基于随机森林模型的分类数据缺失值插补方法,并根据不同的缺失模式探讨了相应的插补步骤。通过与其它方法的实证模拟比较,表明随机森林插补法得到的插补值准确度更优、可信度更高。     

Estimating Incidence Rate on Current Status Data with Application to a Phase IV Cancer Trial

New drug discovery in the pediatrics has dramatically improved survival, but with long- term adverse events. This motivates the examination of adverse outcomes such as long-term toxicity in a phase IV trial. An ideal approach to monitor long-term toxicity is to systematically follow the survivors, which is generally not feasible. Instead, cross-sectional surveys are conducted in Hudson et al. (2007 Hudson , M. M. , Rai , S. N. , Nunez , C. , Merchant , T. E. , Marina , N. M. , Zalamea , N. , Cox , C. , Phipps , S. , Pompeu , R. , Rosenthal , D. ( 2007 ). Noninvasive evaluation of late anthracycline cardiac toxicity in childhood cancer survivors . J. Clin. Oncol. 25 : 3635 – 3643 .[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]), with one of the objectives to estimate the cumulative incidence rates along with specific interest in fixed-term (5 or 10 year) rates. We present inference procedures based on current status data to our motivating example with very interesting findings.     

提高统计数据质量的探讨

一、提高数据质量是当务之急 准确可靠的统计数据,是科学决策的依据,也是搞好统计服务和监督的基础。统计数据的准确性至关重要,如果说有些物质产品质量不合格,还可能当“处理品”降格使用,统计数据质量低劣,则不但没有使用价值,而且可能导致决策的失误,造成难以估量的损失。历史上,我们曾经有过沉痛的教训。 党的十一届三中全会以来,随着党的实事求是思想路线的重新确定,为恢复和发展统计工作提供了良好条件,统计数据质量也有所提高。目前,就全国总体说来,统计数据基本上是符合实际的,能够比较灵敏准确地反映国民经济运行的动态。但是,某些地区、部门、单     

Examining Associations between Occupation and Health by using Routinely Collected Data

When examining a large number of associations simultaneously, as happens when routinely collected data are used to assess associations between occupation and health, it is not obvious how best to identify associations requiring further investigation since some risks may be high, or low, by chance alone. We have developed an approach to deal with this problem which is relatively easy to apply and appropriate to applications where data are not too sparse. Observed to expected ratios are estimated using an empirical Bayes procedure. Anomalous associations can be seen as outliers in a normal probability plot of the log-ratios. The method is illustrated in the analysis of 252 000 cancers registered in men in England during 1981–87.     

“大数据”背景下利用扫描数据编制中国CPI问题研究

扫描数据为政府统计源头数据信息化改革与宏观经济测度提供了新的技术范式。基于对世界各国利用扫描数据编制CPI的现状进行梳理研究,并针对中国扫描数据的现状和政府价格统计的特点,提出了一种利用扫描数据编制中国CPI的思路,力图为基于"大数据"的政府统计源头数据信息化改革提供理论和实践参考。     

Functional boxplots based on epigraphs and hypographs

Functional boxplot is an attractive technique to visualize data that come from functions. We propose an alternative to the functional boxplot based on depth measures. Our proposal generalizes the usual construction of the box-plot in one dimension related to the down-upward orderings of the data by considering two intuitive pre-orders in the functional context. These orderings are based on the epigraphs and hypographs of the data that allow a new definition of functional quartiles which is more robust to shape outliers. Simulated and real examples show that this proposal provides a convenient visualization technique with a great potential for analyzing functional data and illustrate its usefulness to detect outliers that other procedures do not detect.     

对政府统计数据质量成本的探讨

本文在界定统计数据质量成本含义基础上,对统计数据质量成本构成进行了分析,给出了统计数据质量成本要素表及核算方法,以及统计数据质量成本分析、预测、计划和控制的内容,并对统计数据质量成本和统计数据质量之间关系进行了探讨。     

A comparison of analysis procedures for correlated binary data in dedicated multi‐rater imaging trials

In this paper, three analysis procedures for repeated correlated binary data with no a priori ordering of the measurements are described and subsequently investigated. Examples for correlated binary data could be the binary assessments of subjects obtained by several raters in the framework of a clinical trial. This topic is especially of relevance when success criteria have to be defined for dedicated imaging trials involving several raters conducted for regulatory purposes. First, an analytical result on the expectation of the ‘Majority rater’ is presented when only the marginal distributions of the single raters are given. The paper provides a simulation study where all three analysis procedures are compared for a particular setting. It turns out that in many cases, ‘Average rater’ is associated with a gain in power. Settings were identified where ‘Majority significant’ has favorable properties. ‘Majority rater’ is in many cases difficult to interpret. Copyright © 2014 John Wiley & Sons, Ltd.     

社会核算矩阵编制中的数据衔接问题研究

由于数据来源不同,所以在编制社会核算矩阵时会出现大量数据不衔接的问题.目前国内外学者多数采用RAS、CE等方法进行平衡处理,而这些方法都属于纯数学线性调整和平衡方法,并没有考虑具体数据的现实经济意义.鉴此,设计一种“项目对应平衡法”,以矩阵表中元素的具体经济含义为依据,对所有不平衡项目进行逐项处理,以达到整个矩阵的总体平衡.同时采用这种平衡方法,实际编制了2007年中国社会核算矩阵.     

European Federation of Statisticians in the Pharmaceutical Industry's position on access to clinical trial data

The European Federation of Statisticians in the Pharmaceutical Industry (EFSPI) believes access to clinical trial data should be implemented in a way that supports good research, avoids misuse of such data, lies within the scope of the original informed consent and fully protects patient confidentiality. In principle, EFSPI supports responsible data sharing. EFSPI acknowledges it is in the interest of patients that their data are handled in a strictly confidential manner to avoid misuse under all possible circumstances. It is also in the interest of the altruistic nature of patients participating in trials that such data will be used for further development of science as much as possible applying good statistical principles. This paper summarises EFSPI's position on access to clinical trial data. The position was developed during the European Medicines Agency (EMA) advisory process and before the draft EMA policy on publication and access to clinical trial data was released for consultation; however, the EFSPI's position remains unchanged following the release of the draft policy. Finally, EFSPI supports a need for further guidance to be provided on important technical aspects relating to re‐analyses and additional analyses of clinical trial data, for example, multiplicity, meta‐analysis, subgroup analyses and publication bias. Copyright © 2013 John Wiley & Sons, Ltd.     

Estimation for Location-Scale Models with Censored Data

This article considers a class of estimators for the location and scale parameters in the location-scale model based on ‘synthetic data’ when the observations are randomly censored on the right. The asymptotic normality of the estimators is established using counting process and martingale techniques when the censoring distribution is known and unknown, respectively. In the case when the censoring distribution is known, we show that the asymptotic variances of this class of estimators depend on the data transformation and have a lower bound which is not achievable by this class of estimators. However, in the case that the censoring distribution is unknown and estimated by the Kaplan–Meier estimator, this class of estimators has the same asymptotic variance and attains the lower bound for variance for the case of known censoring distribution. This is different from censored regression analysis, where asymptotic variances depend on the data transformation. Our method has three valuable advantages over the method of maximum likelihood estimation. First, our estimators are available in a closed form and do not require an iterative algorithm. Second, simulation studies show that our estimators being moment-based are comparable to maximum likelihood estimators and outperform them when sample size is small and censoring rate is high. Third, our estimators are more robust to model misspecification than maximum likelihood estimators. Therefore, our method can serve as a competitive alternative to the method of maximum likelihood in estimation for location-scale models with censored data. A numerical example is presented to illustrate the proposed method.     

Inference for Bivariate Survival Data by Copula Models Adjusted for the Boundary Effect

Copula models describe the dependence structure of two random variables separately from their marginal distributions and hence are particularly useful in studying the association for bivariate survival data. Semiparametric inference for bivariate survival data based on copula models has been studied for various types of data, including complete data, right-censored data, and current status data. This article discusses the boundary effect on these inference procedures, a problem that has been neglected in the previous literature. Specifically, asymptotic distribution of the association estimator on the boundary of parameter space is derived for one-dimensional copula models. The boundary properties are applied to test independence and to study the estimation efficiency. Simulation study is conducted for the bivariate right-censored data and current status data.     

Nonparametric Predictive Inference for Ordinal Data

Nonparametric predictive inference (NPI) is a powerful frequentist statistical framework based only on an exchangeability assumption for future and past observations, made possible by the use of lower and upper probabilities. In this article, NPI is presented for ordinal data, which are categorical data with an ordering of the categories. The method uses a latent variable representation of the observations and categories on the real line. Lower and upper probabilities for events involving the next observation are presented, and briefly compared to NPI for non ordered categorical data. As application, the comparison of multiple groups of ordinal data is presented.     

Simplified Maximum Likelihood Inference Based on the Likelihood Decomposition for Missing Data

In this paper, we propose an estimation method when sample data are incomplete. We decompose the likelihood according to missing patterns and combine the estimators based on each likelihood weighting by the Fisher information ratio. This approach provides a simple way of estimating parameters, especially for non‐monotone missing data. Numerical examples are presented to illustrate this method.