The importance of cross‐disciplinary scientific engagement in the development of quantitative procedures for aggregate safety assessments

Recent guidance on safety monitoring during drug development, issued by regulatory authorities in the United States and European Union, indicate a shift in focus towards aggregate safety monitoring and scientific evaluation of integrated safety data. The call for program‐level reviews of accumulating safety data, including from ongoing studies, provides an opportunity to leverage the scientific expertise and medical judgment of safety management teams with (a) a multidisciplinary approach, (b) quantitative frameworks to measure level of evidence, and (c) assessments that are product‐specific and driven by medical judgment. A multidisciplinary team, regularly reviewing aggregate safety data throughout the development program, is vital not only for early signal detection but also for generating a better understanding of the accumulating data and context needed for decreasing false alarms.     

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.     

Diagnostics of multiple group influential observations for logistic regression models

In this paper, two new multiple influential observation detection methods, GCD.GSPR and mCD*, are introduced for logistic regression. The proposed diagnostic measures are compared with the generalized difference in fits (GDFFITS) and the generalized squared difference in beta (GSDFBETA), which are multiple influential diagnostics. The simulation study is conducted with one, two and five independent variable logistic regression models. The performance of the diagnostic measures is examined for a single contaminated independent variable for each model and in the case where all the independent variables are contaminated with certain contamination rates and intensity. In addition, the performance of the diagnostic measures is compared in terms of the correct identification rate and swamping rate via a frequently referred to data set in the literature.     

The Eurasian Economic Union: Development Prospects and Possible Obstacles

This article analyzes the development prospects and problems for the Eurasian Economic Union (EAEU). It examines issues of integration within the EAEU, as well as interactions with other countries, both CIS (Commonwealth of Independent States) and non-CIS. The main problem of integration within the EAEU is the dominance of redistributive motives over productive ones. This article assesses the extent of Russian oil and gas transfers to EAEU partners and the impact of tax maneuvers on their size. It shows that the creation of mechanisms for redistributing profits within the EAEU will allow a positive economic effect to materialize from free trade agreements with non-CIS countries. The article assesses the risks for the EAEU related to Russia’s introduction of a unilateral ban on food imports from countries on the sanctions list and to the possible establishment of tariffs on trade with Ukraine.     

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.     

Fast and approximate exhaustive variable selection for generalised linear models with APES

We present APproximated Exhaustive Search (APES), which enables fast and approximated exhaustive variable selection in Generalised Linear Models (GLMs). While exhaustive variable selection remains as the gold standard in many model selection contexts, traditional exhaustive variable selection suffers from computational feasibility issues. More precisely, there is often a high cost associated with computing maximum likelihood estimates (MLE) for all subsets of GLMs. Efficient algorithms for exhaustive searches exist for linear models, most notably the leaps‐and‐bound algorithm and, more recently, the mixed integer optimisation (MIO) algorithm. The APES method learns from observational weights in a generalised linear regression super‐model and reformulates the GLM problem as a linear regression problem. In this way, APES can approximate a true exhaustive search in the original GLM space. Where exhaustive variable selection is not computationally feasible, we propose a best‐subset search, which also closely approximates a true exhaustive search. APES is made available in both as a standalone R package as well as part of the already existing mplot package.     

A Graphical Tool for Interpreting Regression Coefficients of Trinomial Logit Models

Multinomial logit (also termed multi-logit) models permit the analysis of the statistical relation between a categorical response variable and a set of explicative variables (called covariates or regressors). Although multinomial logit is widely used in both the social and economic sciences, the interpretation of regression coefficients may be tricky, as the effect of covariates on the probability distribution of the response variable is nonconstant and difficult to quantify. The ternary plots illustrated in this article aim at facilitating the interpretation of regression coefficients and permit the effect of covariates (either singularly or jointly considered) on the probability distribution of the dependent variable to be quantified. Ternary plots can be drawn both for ordered and for unordered categorical dependent variables, when the number of possible outcomes equals three (trinomial response variable); these plots allow not only to represent the covariate effects over the whole parameter space of the dependent variable but also to compare the covariate effects of any given individual profile. The method is illustrated and discussed through analysis of a dataset concerning the transition of master’s graduates of the University of Trento (Italy) from university to employment.     

浙江省城市绿色技术创新效率评价及影响因素分析

基于网络DEA模型和Malmquist指数模型，利用浙江省11个地级市的2011-2020年的数据，以降维后的污染指数为非期望产出对绿色技术创新效率进行测度和分析。发现:浙江各市总体上成果转化阶段的效率优于研发阶段，城市间发展差异较大；整体TFP指数均值为1.121，其提升主要源自技术进步；城镇化水平、产业结构优化、对外开放、政府干预和研发强度对绿色技术创新效率有促进作用，但经济发展水平与绿色技术创新效率的U型关系不显著。     

影子银行、地方政府债务及金融发展动态关系研究——基于省级面板VAR模型

基于2004—2017年中国省级面板数据,运用面板向量自回归(PVAR)模型,使用系统GMM估计、脉冲响应函数、方差分解以及格兰杰因果关系检验等方法分析了影子银行、地方政府债务及金融发展之间的动态关系.结果表明:影子银行、地方政府债务与金融发展水平三者之间存在动态耦合关系.在地方政府融资能力受到约束的情况下,影子银行为地方政府提供了多元的融资方式,在增加政府融资能力的同时提升了政府债务水平;而地方政府债务需求显著推动了影子银行规模的快速发展.同时,影子银行过度扩张危害了金融市场的健康发展,降低金融发展水平,继而使地方政府的融资渠道受到约束.但金融发展并不能有效约束影子银行规模,原因在于,政府融资需求是影子银行的主要动力,若不能控制地方政府的借贷行为则无法从源头解决问题.监管机构在去杠杆的过程中,应该综合考虑影子银行与地方政府债务、金融发展之间的动态关系,如此才能够实现预期的政策效果.