Neoplasia masquerading as physical abuse

We describe three children with organic disease presenting as possible physical abuse. The first child was an infant with an ‘unexplained’ transverse fibular fracture, who was initially placed on the child protection register. Subsequent progress and investigation revealed that the fracture was caused by a haemangioma arising from the surrounding tissues. Two further children presented with bruising, in one case due to acute lymphoblastic leukaemia and in the other neuroblastoma. In both, the correct diagnosis became evident following assessment by an experienced paediatrician. These cases demonstrate the importance of obtaining an accurate history and of performing a careful examination when considering physical abuse, but also the need to review that diagnosis if subsequent contradictory evidence comes to light. Copyright © 2000 John Wiley & Sons, Ltd.     

The perils with the misuse of predictive power

In early drug development, especially when studying new mechanisms of action or in new disease areas, little is known about the targeted or anticipated treatment effect or variability estimates. Adaptive designs that allow for early stopping but also use interim data to adapt the sample size have been proposed as a practical way of dealing with these uncertainties. Predictive power and conditional power are two commonly mentioned techniques that allow predictions of what will happen at the end of the trial based on the interim data. Decisions about stopping or continuing the trial can then be based on these predictions. However, unless the user of these statistics has a deep understanding of their characteristics important pitfalls may be encountered, especially with the use of predictive power. The aim of this paper is to highlight these potential pitfalls. It is critical that statisticians understand the fundamental differences between predictive power and conditional power as they can have dramatic effects on decision making at the interim stage, especially if used to re-evaluate the sample size. The use of predictive power can lead to much larger sample sizes than either conditional power or standard sample size calculations. One crucial difference is that predictive power takes account of all uncertainty, parts of which are ignored by standard sample size calculations and conditional power. By comparing the characteristics of each of these statistics we highlight important characteristics of predictive power that experimenters need to be aware of when using this approach.     

Statistical resource needs to be increased in the European regulatory agencies

The following viewpoint from PSI and EFSPI regarding the current level of statistical resource in the European regulatory agencies was first presented as a position paper to a meeting of the EU Heads of Agencies in July 2009, and was endorsed by EFPIA. Copyright © 2010 John Wiley & Sons, Ltd.     

Hong Kong: From Worker to Manager

Review article on: Made by Hong Kong. Edited by Suzanne Berger and Richard K. Lester The Hong Kong Advantage. By Michael T. enright, Edit E. Scott and David Dodwell     

The Urbanization Question: Deficient Evidence and Mistaken Diagnosis

Book reviewed in this article: The Living City: Towards a Sustainable Future. Edited by David Cadman and Geoffrey Payne The Urbanization of the Third World. Edited by Josef Gugler Housing Africa's Urban Poor. Edited by Philip Amis and Peter Lloyd Housing and Economic Adjustment. United Nations     

Building Bridges into the Community: Social Capital in a Volunteering Project for Care Leavers

Volunteering to address poor life outcomes often experienced by care leavers is emphasised in UK policy. Although volunteering is credited with the ability to generate social capital, there is limited research on the impact of volunteering on the social capital of care leavers. This article re‐examines data from an evaluation of a volunteering project for care leavers. It explores in what ways young people's participation in the project constitutes social capital. The findings support the importance of regular face‐to‐face contact and co‐production for young people to become creators of their own social capital.     

Black–White Marriages: The Moderating Role of Openness on Experience of Couple Discrimination and Marital Satisfaction

This study examined how open communication between spouses may buffer against discrimination experienced by Black–White couples. Results from 178 couples analyzed using a combination of common‐fate and actor–partner interdependence models, indicated that for Black partners, marital satisfaction was not contingent on the level of openness when experiences of couple discrimination were low. When experiences of couple discrimination were high, levels of marital satisfaction were maintained among partners who reported high openness and reduced among partners who reported low openness. Moderation effects were not found for White partners. The results suggest that because Black partners are more aware of and sensitized to discrimination, White partners’ abilities to attune to their partners’ needs for support could help enhance their relationships.     

Savings–age profiles in the UK

We used UK Family Expenditure Surveys to analyse the relationship between savings and age structure. We address two key problems: the sample selection bias when data refer to households and not individuals, and the treatment of pension income when drawing inferences from individuals' savings–age profiles about the relationship between an economy's savings and age structure. Our principal findings are that household data exaggerate savings rates of young adults and the elderly whilst underestimating those of 45- to 60-year-olds, and individual saving rates follow more closely the ‘hump shape’ of the life-cycle model, although the savings rates of the elderly remain positive for some ages.