A folded model for compositional data analysis

A folded type model is developed for analysing compositional data. The proposed model involves an extension of the α‐transformation for compositional data and provides a new and flexible class of distributions for modelling data defined on the simplex sample space. Despite its rather seemingly complex structure, employment of the EM algorithm guarantees efficient parameter estimation. The model is validated through simulation studies and examples which illustrate that the proposed model performs better in terms of capturing the data structure, when compared to the popular logistic normal distribution, and can be advantageous over a similar model without folding.     

Estimation of group means in generalized linear mixed models

In this study, we investigate the concept of the mean response for a treatment group mean as well as its estimation and prediction for generalized linear models with a subject‐wise random effect. Generalized linear models are commonly used to analyze categorical data. The model‐based mean for a treatment group usually estimates the response at the mean covariate. However, the mean response for the treatment group for studied population is at least equally important in the context of clinical trials. New methods were proposed to estimate such a mean response in generalized linear models; however, this has only been done when there are no random effects in the model. We suggest that, in a generalized linear mixed model (GLMM), there are at least two possible definitions of a treatment group mean response that can serve as estimation/prediction targets. The estimation of these treatment group means is important for healthcare professionals to be able to understand the absolute benefit vs risk. For both of these treatment group means, we propose a new set of methods that suggests how to estimate/predict both of them in a GLMMs with a univariate subject‐wise random effect. Our methods also suggest an easy way of constructing corresponding confidence and prediction intervals for both possible treatment group means. Simulations show that proposed confidence and prediction intervals provide correct empirical coverage probability under most circumstances. Proposed methods have also been applied to analyze hypoglycemia data from diabetes clinical trials.     

TITE‐BOIN‐ET: Time‐to‐event Bayesian optimal interval design to accelerate dose‐finding based on both efficacy and toxicity outcomes

One of the primary purposes of an oncology dose‐finding trial is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent clinical trials. In addition, it is quite important to accelerate early stage trials to shorten the entire period of drug development. However, it is often challenging to make adaptive decisions of dose escalation and de‐escalation in a timely manner because of the fast accrual rate, the difference of outcome evaluation periods for efficacy and toxicity and the late‐onset outcomes. To solve these issues, we propose the time‐to‐event Bayesian optimal interval design to accelerate dose‐finding based on cumulative and pending data of both efficacy and toxicity. The new design, named “TITE‐BOIN‐ET” design, is nonparametric and a model‐assisted design. Thus, it is robust, much simpler, and easier to implement in actual oncology dose‐finding trials compared with the model‐based approaches. These characteristics are quite useful from a practical point of view. A simulation study shows that the TITE‐BOIN‐ET design has advantages compared with the model‐based approaches in both the percentage of correct OD selection and the average number of patients allocated to the ODs across a variety of realistic settings. In addition, the TITE‐BOIN‐ET design significantly shortens the trial duration compared with the designs without sequential enrollment and therefore has the potential to accelerate early stage dose‐finding trials.     

Understanding the Self Through Siblings: Self‐awareness Mediates the Sibling Effect on Social Understanding

This study addresses the role of sibling influence on social understanding in the second year of life, in a sample of families living in New Zealand who identify as Pacific Island (N = 43). We tested toddlers at 20 and 26 months on social understanding tasks, as well as their levels of self‐awareness on the Stipek self‐concept questionnaire. We hypothesized that the presence of siblings provides a rich resource from which to learn about the mind. There were significant differences in children's social understanding and self‐awareness as a function of having older siblings. Further analyses revealed that the relation between older siblings and social understanding was mediated by toddlers' level of self‐awareness.     

Peach Trees Grow in the Spaces Between People: Counselling Practices in the Territory of Violence

In this paper the authors reflect on how therapeutic practices wove a community of care and concern with a young man in South Africa who was at risk of anger ruling his life. A counselor and outsider witnesses participated in responding to the problems the young man and his family faced. The authors discuss the steps they took in the therapeutic journey and describe how their personal and professional lives were shaped by this collaboration. This work inspires and sustains their enthusiasm in seeking to understand how to best respond when positioned to counsel.     

Immigrant Children: Their Experience of Violence at School and Community in Host Country

This article describes our qualitative sociological study of immigrant children's life experiences of violence. We conducted interviews with 42 first‐generation immigrant children from any country, aged 9–13 years old, living in the Quebec City region (Canada). Results from three main themes are presented: representations of violence and concrete violent acts experienced; perceived effects of violence on children health and well‐being; and reactions and coping strategies. Overall, the narratives show that they may experience racist peer violence in school that leads to suffering situations, and they consequently have to develop strategies to maintain their well‐being. Social implications are discussed.     

Label‐ and Level‐Invariant Graphical Log‐Linear Models

We introduce two types of graphical log‐linear models: label‐ and level‐invariant models for triangle‐free graphs. These models generalise symmetry concepts in graphical log‐linear models and provide a tool with which to model symmetry in the discrete case. A label‐invariant model is category‐invariant and is preserved after permuting some of the vertices according to transformations that maintain the graph, whereas a level‐invariant model equates expected frequencies according to a given set of permutations. These new models can both be seen as instances of a new type of graphical log‐linear model termed the restricted graphical log‐linear model, or RGLL, in which equality restrictions on subsets of main effects and first‐order interactions are imposed. Their likelihood equations and graphical representation can be obtained from those derived for the RGLL models.     

A moral vision: human dignity in the eyes of the founders of sociology

The case argued in this paper is not that the concerns of the founders of sociology are uniformly and in every particular still our own (Runciman, 2008), but that the concepts and methods used to address just one of their concerns were both ground breaking and of enduring value (Shilling and Mellor, 2001, 2011, for example, make a similar claim). Such a concern focused on the kind of morality grounded in a capitalist social order and, by implication, how it might be theorized. This generated in the process the uniquely sociological operationalization of what had seemed hitherto a philosophical concept: human dignity, along with the freedom and autonomy that attend it. Certainly, the priorities differed in each of the contributions to this endeavour but, in coming at the problem from different standpoints, the concept of dignity came to appear more rounded, more substantive and more relevant to the human condition in all its historical specificity. Quite crucially, there is also in these sources from the classical period of sociology an intimation of method: both the way in which human dignity is to be ‘perceived’ within an inter‐personal dialectic at a micro‐level and, at a macro‐level, how we can discern that dignity transcends artificial confinement by any one aspect of life (be it economic, political or cultural).¹     

Social Contagion of Risk Perceptions in Environmental Management Networks

An important requisite for improving risk communication practice related to contentious environmental issues is having a better theoretical understanding of how risk perceptions function in real‐world social systems. Our study applied Scherer and Cho's social network contagion theory of risk perception (SNCTRP) to cormorant management (a contentious environmental management issue) in the Great Lakes Basin to: (1) assess contagion effects on cormorant‐related risk perceptions and individual factors believed to influence those perceptions and (2) explore the extent of social contagion in a full network (consisting of interactions between and among experts and laypeople) and three “isolated” models separating different types of interactions from the full network (i.e., expert‐to‐expert, layperson‐to‐layperson, and expert‐to‐layperson). We conducted interviews and administered questionnaires with experts (e.g., natural resource professionals) and laypeople (e.g., recreational and commercial anglers, business owners, bird enthusiasts) engaged in cormorant management in northern Lake Huron (n = 115). Our findings generally support the SNCTRP; however, the scope and scale of social contagion varied considerably based on the variables (e.g., individual risk perception factors), actors (i.e., experts or laypeople), and interactions of interest. Contagion effects were identified more frequently, and were stronger, in the models containing interactions between experts and laypeople than in those models containing only interactions among experts or laypeople.     

Contingencies in Mother–Child Teaching Interactions and Behavioral Regulation and Dysregulation in Early Childhood

Predictable patterns in early parent–child interactions may help lay the foundation for how children learn to self‐regulate. The present study examined contingencies between maternal teaching and directives and child compliance in mother–child problem‐solving interactions at age 3.5 and whether they predicted children's behavioral regulation and dysregulation (inhibitory control and externalizing behaviors) as rated by mothers, fathers, and teachers at a four‐month follow‐up (N = 100). The predictive utility of mother‐ and child‐initiated contingencies was also compared with that of frequencies of individual mother and child behaviors. Structural equation models revealed that a higher probability that maternal directives were followed by child compliance predicted better child behavioral regulation, whereas the reverse pattern and the overall frequency of maternal directives did not. For teaching, stronger mother‐ and child‐initiated contingencies and the overall frequency of maternal teaching all showed evidence for predicting better behavioral regulation. Findings depended on which caregiver was rating child outcomes. We conclude that dyadic measures are useful for understanding how parent–child interactions impact children's burgeoning regulatory abilities in early childhood.