Joint GEEs for multivariate correlated data with incomplete binary outcomes

This study considers a fully-parametric but uncongenial multiple imputation (MI) inference to jointly analyze incomplete binary response variables observed in a correlated data settings. Multiple imputation model is specified as a fully-parametric model based on a multivariate extension of mixed-effects models. Dichotomized imputed datasets are then analyzed using joint GEE models where covariates are associated with the marginal mean of responses with response-specific regression coefficients and a Kronecker product is accommodated for cluster-specific correlation structure for a given response variable and correlation structure between multiple response variables. The validity of the proposed MI-based JGEE (MI-JGEE) approach is assessed through a Monte Carlo simulation study under different scenarios. The simulation results, which are evaluated in terms of bias, mean-squared error, and coverage rate, show that MI-JGEE has promising inferential properties even when the underlying multiple imputation is misspecified. Finally, Adolescent Alcohol Prevention Trial data are used for illustration.     

Marginal models for the association structure of hierarchical binary responses

Clustered binary responses are often found in ecological studies. Data analysis may include modeling the marginal probability response. However, when the association is the main scientific focus, modeling the correlation structure between pairs of responses is the key part of the analysis. Second-order generalized estimating equations (GEE) are established in the literature. Some of them are more efficient in computational terms, especially facing large clusters. Alternating logistic regression (ALR) and orthogonalized residual (ORTH) GEE methods are presented and compared in this paper. Simulation results show a slightly superiority of ALR over ORTH. Marginal probabilities and odds ratios are also estimated and compared in a real ecological study involving a three-level hierarchical clustering. ALR and ORTH models are useful for modeling complex association structure with large cluster sizes.     

Tests for Structural Changes in Time Series of Counts

We propose methods for detecting structural changes in time series with discrete‐valued observations. The detector statistics come in familiar L2‐type formulations incorporating the empirical probability generating function. Special emphasis is given to the popular models of integer autoregression and Poisson autoregression. For both models, we study mainly structural changes due to a change in distribution, but we also comment for the classical problem of parameter change. The asymptotic properties of the proposed test statistics are studied under the null hypothesis as well as under alternatives. A Monte Carlo power study on bootstrap versions of the new methods is also included along with a real data example.     

A general long-term aging model with different underlying activation mechanisms: Modeling,Bayesian estimation,and case influence diagnostics

In this paper we propose a general cure rate aging model. Our approach enables different underlying activation mechanisms which lead to the event of interest. The number of competing causes of the event of interest is assumed to follow a logarithmic distribution. The model is parameterized in terms of the cured fraction which is then linked to covariates. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis for the proposed model. Moreover, some discussions on the model selection to compare the fitted models are given, as well as case deletion influence diagnostics are developed for the joint posterior distribution based on the ψ-divergence, which has several divergence measures as particular cases, such as the Kullback–Leibler (K-L), J-distance, L₁ norm, and χ²-square divergence measures. Simulation studies are performed and experimental results are illustrated based on a real malignant melanoma data.     

Slashed generalized Rayleigh distribution

We introduce a new family of distributions suitable for fitting positive data sets with high kurtosis which is called the Slashed Generalized Rayleigh Distribution. This distribution arises as the quotient of two independent random variables, one being a generalized Rayleigh distribution in the numerator and the other a power of the uniform distribution in the denominator. We present properties and carry out estimation of the model parameters by moment and maximum likelihood (ML) methods. Finally, we conduct a small simulation study to evaluate the performance of ML estimators and analyze real data sets to illustrate the usefulness of the new model.     

The effects of under‐skilling on need for recovery,losing employment and retirement intentions among older office workers: A prospective cohort study

Based on a sample of older workers from the Maastricht Cohort Study, the authors investigate the prevalence and dynamics of self‐reported under‐skilling and its effects on workers' need for recovery, their risk of losing employment and their retirement intentions over a two‐year period. They find that being under‐skilled is associated both with higher levels of need for recovery over time and with the risk of losing employment, but not with early retirement intentions. To achieve sustainable employment, they argue, the course of under‐skilling should be monitored throughout workers' careers, enabling timely interventions to avoid its negative consequences.     

Characteristics of Couples Who Attend Family Services Counselor Visits in Relationship Education

Case management services for low-income couples receiving relationship education have been required by federal funding agencies. Studies examining relationship education outcomes for low-income couples have yet to address participant characteristics of those who attend case management services (e.g., distressed vs. nondistressed). Thus, this study examined the influence of baseline relationship satisfaction and demographics on attendance at case management visits for 2,628 low-income, partnered participants who attended relationship education. Findings indicated that participants who reported higher baseline relationship distress attended more family services counselor (FSC) visits. However, when we added demographics to the model, relationship distress no longer predicted FSC visits, whereas income served as a predictor for men and women, with those reporting greater monthly income attending more FSC visits.     

Subjective Recognition of Emotional Expressions in Face Transplant Patients

One of the most prevalent problems in face transplant patients is an inability to generate facial expression of emotions. The purpose of this study was to measure the subjective recognition of patients’ emotional expressions by other people. We examined facial expression of six emotions in two facial transplant patients (patient A = partial, patient B = full) and one healthy control using video clips to evoke emotions. We recorded target subjects’ facial expressions with a video camera while they were watching the clips. These were then shown to a panel of 130 viewers and rated in terms of degree of emotional expressiveness on a 7-point Likert scale. The scores for emotional expressiveness were higher for the healthy control than they were for patients A and B, and these varied as a function of emotion. The most recognizable emotion was happiness. The least recognizable emotions in Patient A were fear, surprise, and anger. The expressions of Patient B scored lower than those of Patient A and the healthy control. The findings show that partial and full-face transplant patients may have difficulties in generating facial expression of emotions even if they can feel those emotions, and different parts of the face seem to play critical roles in different emotional expressions.     

Personal reflection and experience of a Fulbright Scholar: teaching a mental health course in Vietnam’s changing culture

This paper documents the experience of developing and teaching a course on mental health for undergraduate social worker students during a semester in Vietnam. Social work is a profession at the early stages of development in Vietnam, requiring much curriculum development. There are over 40 universities with undergraduate social work education programs, but few of the faculty have had training or experience in practicing social work. The paper will summarize the main reasons for developing such a course given the current state of mental health need and services in a changing culture, as well as the current state of professional social work education and the newly established profession of social work. The author describes the contexts underlying the course—the status of social work and social work education in Vietnam, the context of mental health and mental illness in Vietnam, and curriculum adaptation to the cultural context. From this experience, the lessons learned about the role of cross-cultural critical thinking, cultural knowledge, and other strategies will be discussed about teaching a subject like mental health internationally.