The influence of perceived shared risk in crisis communication: Elaborating the situational theory of publics

The study explored whether perceived shared experience with a media portrayal could influence various cognitions—such as concern, sense of personal involvement, and desire to learn more—that are important for behavior change. This research used the situational theory of publics in order to evaluate whether perceived shared risk is an antecedent to the factors leading to communication behavior. In general, the findings from this experiment supported the idea that shared risk experience with portrayals in news coverage has the potential to influence problem recognition, involvement recognition, and a new variable that combines information seeking and processing: that of information gaining.     

Two‐sample empirical likelihood ratio tests for medians in application to biomarker evaluations

The median is a commonly used parameter to characterize biomarker data. In particular, with two vastly different underlying distributions, comparing medians provides different information than comparing means; however, very few tests for medians are available. We propose a series of two‐sample median‐specific tests using empirical likelihood methodology and investigate their properties. We present the technical details of incorporating the relevant constraints into the empirical likelihood function for in‐depth median testing. An extensive Monte Carlo study shows that the proposed tests have excellent operating characteristics even under unfavourable occasions such as non‐exchangeability under the null hypothesis. We apply the proposed methods to analyze biomarker data from Western blot analysis to compare normal cells with bronchial epithelial cells from a case–control study. The Canadian Journal of Statistics 39: 671–689; 2011. © 2011 Statistical Society of Canada     

Why a Bayesian approach to safety analysis in pharmacovigilance is important

Large databases of routinely collected data are a valuable source of information for detecting potential associations between drugs and adverse events (AE). A pharmacovigilance system starts with a scan of these databases for potential signals of drug-AE associations that will subsequently be examined by experts to aid in regulatory decision-making. The signal generation process faces some key challenges: (1) an enormous volume of drug-AE combinations need to be tested (i.e. the problem of multiple testing); (2) the results are not in a format that allows the incorporation of accumulated experience and knowledge for future signal generation; and (3) the signal generation process ignores information captured from other processes in the pharmacovigilance system and does not allow feedback. Bayesian methods have been developed for signal generation in pharmacovigilance, although the full potential of these methods has not been realised. For instance, Bayesian hierarchical models will allow the incorporation of established medical and epidemiological knowledge into the priors for each drug-AE combination. Moreover, the outputs from this analysis can be incorporated into decision-making tools to help in signal validation and posterior actions to be taken by the regulators and companies. We discuss in this paper the apparent advantage of the Bayesian methods used in safety signal generation and the similarities and differences between the two widely used Bayesian methods. We will also propose the use of Bayesian hierarchical models to address the three key challenges and discuss the reasons why Bayesian methodology still have not been fully utilised in pharmacovigilance activities.     

特征分析、资源整合与中央国有企业可持续发展——中国建筑工程总公司案例

作为中央国有企业中不占有国家大量投资和自然资源,以建筑业和房地产业等完全竞争行业为核心业务而发展起来的中国建筑工程总公司具有较强市场竞争力,但在建筑行业已经全面开放的充分竞争市场条件下,中建的可持续发展面临中央国有企业特有的体制、产权、经营等层次的制约和障碍。本案例在实地调研的基础上,对中国建筑工程总公司的外部环境和内部要素进行详尽的分析,其目的在中建事关前途发展的关键点上,帮助其完成整体思考,通过内外部资源整合,确立走向未来的基本思路与主要对策。     

Marginal semiparametric multivariate accelerated failure time model with generalized estimating equations

The semiparametric accelerated failure time (AFT) model is not as widely used as the Cox relative risk model due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations for censored data provide promising tools to make the AFT models more attractive in practice. For multivariate AFT models, we propose a generalized estimating equations (GEE) approach, extending the GEE to censored data. The consistency of the regression coefficient estimator is robust to misspecification of working covariance, and the efficiency is higher when the working covariance structure is closer to the truth. The marginal error distributions and regression coefficients are allowed to be unique for each margin or partially shared across margins as needed. The initial estimator is a rank-based estimator with Gehan’s weight, but obtained from an induced smoothing approach with computational ease. The resulting estimator is consistent and asymptotically normal, with variance estimated through a multiplier resampling method. In a large scale simulation study, our estimator was up to three times as efficient as the estimateor that ignores the within-cluster dependence, especially when the within-cluster dependence was strong. The methods were applied to the bivariate failure times data from a diabetic retinopathy study.     

Trends of Income Inequality Among the Elderly in Korea

This study investigates the overall trend of inequality among the elderly in Korea and to examine what the most influential factor is for these changes. Although inequality among the elderly is far greater than it is for any other group, little attention has been given to how inequality changes over time. Used are data from the Urban Household Survey with five‐year intervals from 1986 to 2006. The results show that inequality within a cohort increases over time and that earnings and benefits from occupational retirement plans are the most influential factors on overall inequality changes. In addition, the impact of public transfers has gradually increased during the past twenty years. The results imply that our society needs to enact a social policy for lessening earnings inequality in the labor market and to increase public transfers for the elderly.     

An evaluation of the bootstrap for model validation in mixture models

Bootstrapping has been used as a diagnostic tool for validating model results for a wide array of statistical models. Here we evaluate the use of the non-parametric bootstrap for model validation in mixture models. We show that the bootstrap is problematic for validating the results of class enumeration and demonstrating the stability of parameter estimates in both finite mixture and regression mixture models. In only 44% of simulations did bootstrapping detect the correct number of classes in at least 90% of the bootstrap samples for a finite mixture model without any model violations. For regression mixture models and cases with violated model assumptions, the performance was even worse. Consequently, we cannot recommend the non-parametric bootstrap for validating mixture models.

The cause of the problem is that when resampling is used influential individual observations have a high likelihood of being sampled many times. The presence of multiple replications of even moderately extreme observations is shown to lead to additional latent classes being extracted. To verify that these replications cause the problems we show that leave-k-out cross-validation where sub-samples taken without replacement does not suffer from the same problem.     

Optimal Berry–Esseen bound for parameter estimation of SPDE with small noise

We investigate a rate of convergence on asymptotic normality of the maximum likelihood estimator (MLE) for parameter $\theta$ appearing in parabolic SPDEs of the form

$d{u}^{?}(t,x)=(A_0+\theta A_1)u^{?}(t,x)dt+?dW(t,x),$

where $A_0$ and$A_1$ are partial differential operators, $W$ is a cylindrical Brownian motion (CBM) and $?\downarrow 0$. We find an optimal Berry–Esseen bound for central limit theorem (CLT) of the MLE. It is proved by developing techniques based on combining Malliavin calculus and Stein’s method.     

Optimal consumption and portfolio selection with negative wealth constraints,subsistence consumption constraints,and CARA utility

We consider the optimal consumption and portfolio selection problem with constant absolute risk aversion (CARA) utility. The economic agent in this model receives constant labor income, and her economic behavior is restricted on consumption and wealth, which are called the subsistence consumption constraint and the negative wealth constraint. We use the convex duality method to derive the value function and the optimal policies in closed-form solutions. Also we illustrate some numerical examples.     

‘Keeping families and children in mind’: an evaluation of a web‐based workforce resource

This study outlines pilot evaluation data of the web‐based training resource ‘Keeping Families and Children in Mind’, designed for clinicians who work with families where a parent has a mental illness. The resource was developed from scoping existing workforce packages and in consultation with consumers, carers, researchers and mental‐health clinicians. Preliminary evaluation data were collected from an urban and a rural site in Australia via focus group interviews and pre‐ and post‐training questionnaires to ascertain the experiences of those who participated in the training. Additionally, training facilitators were invited to maintain journals in order to identify planning and implementation issues when using the resource. Post‐training, participants emphasized the need to work collaboratively with others, as well as the importance of acknowledging and working with the family members of consumers, especially children. Also, participants reported positive changes in knowledge, skill and confidence when working with families affected by parental mental illness. Facilitators highlighted technology issues and the need to work interactively with participants when using the resource. Recommendations regarding policy and future research conclude this paper.