Every child matters: a legal perspective

Recent legal and policy changes, including the introduction of the Human Rights Act, provide the context for the new child welfare and protection policies proposed for England. These proposals are discussed from a legal perspective in relation to early and coordinated intervention, sharing information between agencies and with children and families, partnership with children, information management, and structural and legal change. Consideration is also given to issues overlooked by the Green Paper, such as immigration and asylum, youth justice and the whole family justice system. The discussion concludes by arguing for the need for integrating cultural and structural change and for leadership (and investment of resources) across government to achieve such goals. Copyright © 2004 John Wiley & Sons, Ltd.     

Families and family study in international perspective

Many changes are occurring in the world's families. Some observers feel that the changes are destructive, whereas others see them as leading to new opportunities and understanding. Issues in international family studies include regional limitations and the various aspects of doing research cross‐culturally. Knowledge regarding certain categories of families, inheritance, and the social psychology of families is incomplete. There are, however, some universals and universal or worldwide changes, including movement toward individual partner choice, more divorces, lower fertility, and greater opportunities for women.     

Robust estimation for linear regression with asymmetric errors

The authors propose a new class of robust estimators for the parameters of a regression model in which the distribution of the error terms belongs to a class of exponential families including the log‐gamma distribution. These estimates, which are a natural extension of the MM‐estimates for ordinary regression, may combine simultaneously high asymptotic efficiency and a high breakdown point. The authors prove the consistency and derive the asymptotic normal distribution of these estimates. A Monte Carlo study allows them to assess the efficiency and robustness of these estimates for finite samples.     

Stability in Center Day Care: Relations with Children's Well‐being and Problem Behavior in Day Care1

Mothers and primary professional caregivers of 186 children, aged 6–30 months, participated in this study in which a new measure for daily stability in center day care was developed, describing staffing, grouping, and program features. Relative contributions of infants’ daily experiences of care stability, quality of care, and mother's daily stress to the child's adjustment to day care were analyzed with hierarchical regressions. The child's adjustment was assessed with the Child Behavior Checklist ( Achenbach, 1997 ) and with the Leiden Inventory for the Child's Well‐being in Day Care. Children enrolled in fewer care arrangements showed less internalizing behavior and more well‐being in the day care setting. Children had higher well‐being scores when trusted caregivers were more available. Mother's family management stress was related to more internalizing problem behavior. Interactions between mother's daily stress and stability in care were found to affect the child's adjustment to day care.     

On a class of bivariate mixed Sarmanov distributions

Multivariate distributions are more and more used to model the dependence encountered in many fields. However, classical multivariate distributions can be restrictive by their nature, while Sarmanov's multivariate distribution, by joining different marginals in a flexible and tractable dependence structure, often provides a valuable alternative. In this paper, we introduce some bivariate mixed Sarmanov distributions with the purpose to extend the class of bivariate Sarmanov distributions and to obtain new dependency structures. Special attention is paid to the bivariate mixed Sarmanov distribution with Poisson marginals and, in particular, to the resulting bivariate Sarmanov distributions with negative binomial and with Poisson‐inverse Gaussian marginals; these particular types of mixed distributions have possible applications in, for example modelling bivariate count data. The extension to higher dimensions is also discussed. Moreover, concerning the dependency structure, we also present some correlation formulas.     

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.