A Bayesian model for ranking hazardous road sites

Summary.  Road safety has recently become a major concern in most modern societies. The identification of sites that are more dangerous than others (black spots) can help in better scheduling road safety policies. This paper proposes a methodology for ranking sites according to their level of hazard. The model is innovative in at least two respects. Firstly, it makes use of all relevant information per accident location, including the total number of accidents and the number of fatalities, as well as the number of slight and serious injuries. Secondly, the model includes the use of a cost function to rank the sites with respect to their total expected cost to society. Bayesian estimation for the model via a Markov chain Monte Carlo approach is proposed. Accident data from 519 intersections in Leuven (Belgium) are used to illustrate the methodology proposed. Furthermore, different cost functions are used to show the effect of the proposed method on the use of different costs per type of injury.     

Drinking motives, alcohol expectancies, self-efficacy, and drinking patterns

The current study focused on the associations between drinking motives, alcohol expectancies, self-efficacy, and drinking behavior in a representative sample of 553 Dutch adolescents and adults. Data were gathered by means of self-report questionnaires and a 14-days drinking diary. A model was postulated in which negative expectancies and self-efficacy were directly associated with drinking, and in which drinking motives mediated the associations between positive expectancies, and drinking. The findings of multivariate analyses showed that drinking motives were related to general indicators of drinking and to drinking levels in specific situations. Furthermore, self-efficacy was moderately related to all drinking variables. Negative expectancies were related to general drinking variables but hardly to drinking in specific situations. Positive expectancies were hardly related to drinking in multivariate analyses and therefore mediation models could not be tested. No systematic moderator effects were apparent for age and gender on the associations between drinking motives, alcohol expectancies, self-efficacy, and drinking.     

Toward Less Division of Labor? New Production Concepts in the Automotive, Chemical, Clothing, and Machine Tool Industries

In this contribution, we focus on the results ofthe Belgian Trend Study. The intention of this study wasto examine the prevalence of new production conceptswithin the widest possible range of companies in the automotive, the machine tool, thechemical, and the clothing industries. The Trend Studyaimed to answer the following questions: is theTaylorist division of labor a thing of the past? Whatare the alternatives? Are shifts in the division oflabor accompanied by another type of personnel policy,and do traditional industrial relations have to make wayfor this new approach? The methodological concept used had to guarantee that the findingsat the level of each industry could be generalized.Though the picture emerging from the empirical datacollected in the four industrial sectors is inevitablydiverse, the data make it possible merely to suggest aneo- rather than a post-Taylorist or -Fordistconcept.     

“Positive Feelings”: Group Support for Children of HIV-Infected Mothers

This paper describes a support group for children, ages 9–13, whose mothers are living with the Human Immunodeficiency Virus (HIV). Beginning with identification of the need for, and purpose of the group, the paper describes the group recruitment, screening and intake process; group composition; group content and process; barriers to service and issues specific to facilitation of the group. The group provided support to children by increasing knowledge of HIV and Acquired Immunodeficiency Syndrome (AIDS) issues, providing a forum for expression of feelings and concerns regarding their parent's HIV status, improving communication skills, identifying and expanding coping strategies, and facilitating peer support for normalization and validation of fears and concerns.     

The Singularity of the Information Matrix of the Mixed Proportional Hazard Model

This paper presents new identification conditions for the mixed proportional hazard model. In particular, the baseline hazard is assumed to be bounded away from 0 and ∞ near t = 0. These conditions ensure that the information matrix is nonsingular. The paper also presents an estimator for the mixed proportional hazard model that converges at rate N^−1/2.     

Strategies for handling missing data in longitudinal studies with questionnaires

Missing data methods, maximum likelihood estimation (MLE) and multiple imputation (MI), for longitudinal questionnaire data were investigated via simulation. Predictive mean matching (PMM) was applied at both item and scale levels, logistic regression at item level and multivariate normal imputation at scale level. We investigated a hybrid approach which is combination of MLE and MI, i.e. scales from the imputed data are eliminated if all underlying items were originally missing. Bias and mean square error (MSE) for parameter estimates were examined. ML seemed to provide occasionally the best results in terms of bias, but hardly ever on MSE. All imputation methods at the scale level and logistic regression at item level hardly ever showed the best performance. The hybrid approach is similar or better than its original MI. The PMM-hybrid approach at item level demonstrated the best MSE for most settings and in some cases also the smallest bias.     

Model Averaging Using Fractional Polynomials to Estimate a Safe Level of Exposure

Quantitative risk assessment involves the determination of a safe level of exposure. Recent techniques use the estimated dose-response curve to estimate such a safe dose level. Although such methods have attractive features, a low-dose extrapolation is highly dependent on the model choice. Fractional polynomials, basically being a set of (generalized) linear models, are a nice extension of classical polynomials, providing the necessary flexibility to estimate the dose-response curve. Typically, one selects the best-fitting model in this set of polynomials and proceeds as if no model selection were carried out. We show that model averaging using a set of fractional polynomials reduces bias and has better precision in estimating a safe level of exposure (say, the benchmark dose), as compared to an estimator from the selected best model. To estimate a lower limit of this benchmark dose, an approximation of the variance of the model-averaged estimator, as proposed by Burnham and Anderson, can be used. However, this is a conservative method, often resulting in unrealistically low safe doses. Therefore, a bootstrap-based method to more accurately estimate the variance of the model averaged parameter is proposed.     

Teacher ethnicity,student ethnicity,and student outcomes

A review of the empirical literature was conducted to establish the relation between teacher and student ethnicity, and cognitive and noncognitive student outcomes. It was hypothesized that ethnic teacher–student congruence results in more favorable outcomes for especially minority students. A total of 24 quantitative studies focusing on primary and secondary education in the United States were reviewed. The results show that there is as yet little unambiguous empirical evidence that a stronger degree of ethnic match be it in the form of a one-to-one coupling of a teacher to students with the same ethnic background, or a larger share of ethnic minority teachers at an ethnically mixed school, leads to predominantly positive results. Insofar positive effects were found, they apply to a greater extent to subjective teacher evaluations than to objective achievement outcome measures.     

Impact of selection bias on the evaluation of clusters of chemical compounds in the drug discovery process

Expert opinion plays an important role when selecting promising clusters of chemical compounds in the drug discovery process. Indeed, experts can qualitatively assess the potential of each cluster, and with appropriate statistical methods, these qualitative assessments can be quantified into a success probability for each of them. However, one crucial element often overlooked is the procedure by which the clusters are assigned to/selected by the experts for evaluation. In the present work, the impact such a procedure may have on the statistical analysis and the entire evaluation process is studied. It has been shown that some implementations of the selection procedure may seriously compromise the validity of the evaluation even when the rating and selection processes are independent. Consequently, the fully random allocation of the clusters to the experts is strongly advocated. Copyright © 2014 John Wiley & Sons, Ltd.     

Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data

Summary.  A common objective in longitudinal studies is the joint modelling of a longitudinal response with a time-to-event outcome. Random effects are typically used in the joint modelling framework to explain the interrelationships between these two processes. However, estimation in the presence of random effects involves intractable integrals requiring numerical integration. We propose a new computational approach for fitting such models that is based on the Laplace method for integrals that makes the consideration of high dimensional random-effects structures feasible. Contrary to the standard Laplace approximation, our method requires much fewer repeated measurements per individual to produce reliable results.