The Use of Lay Assessors

This paper reports on some findings from a research study conductedby the authors for the Commission for Social Care Inspection.It examines the reasons for the involvement of lay assessorsand perceptions of the effectiveness of lay assessors in specificregulatory functions.     

Helping parents to manage infant crying and sleeping: a review of the evidence and its implications for services

Infant crying and night waking are common concerns for parents, costly problems for health services and may trigger infant abuse or lead to serious child disturbances. Parents are given contradictory advice on how to manage infant crying and sleeping, indicating the need for evidence‐based guidance. This review of recent research draws distinctions between infant crying and sleeping problems, between the problem identified by parents and the infant behaviour underlying the problem, between different types of crying behaviour and their causes, and between the types of cases which present at different ages. It proposes that the two main approaches to parenting advocated by baby‐care experts, ‘infant‐demand’ and ‘structured’ parenting, have different benefits, and costs. Comparative studies have found that infant‐demand parenting is associated with low amounts of fussing and crying in the first three months of age, but with night waking which continues beyond three months. Randomised controlled trials have provided evidence that structured parenting leads to more overall fussing and crying during the first three months, but reduced night waking and crying after that. The findings are translated into recommendations for preventing and treating infant crying and sleeping problems, for policy debate, and for further research. Copyright © 2007 John Wiley & Sons, Ltd.     

Futures forecasting for strategic planning at general electric

Without in any way denying that there are forces for stability, I want to state, as strongly as I can, my conviction that change is now more rapid, more complex and more pervasive than ever before in human history. There are forces loose in the world, particularly in the ‘post-industrial society’ of the United States, that go far beyond the youth movement in creating change. As a result, it is utterly unrealistic to pretend that business can continue to plan its strategies in the traditional mode.     

Methods for exploring treatment effect heterogeneity in subgroup analysis: an application to global clinical trials

Multi‐country randomised clinical trials (MRCTs) are common in the medical literature, and their interpretation has been the subject of extensive recent discussion. In many MRCTs, an evaluation of treatment effect homogeneity across countries or regions is conducted. Subgroup analysis principles require a significant test of interaction in order to claim heterogeneity of treatment effect across subgroups, such as countries in an MRCT. As clinical trials are typically underpowered for tests of interaction, overly optimistic expectations of treatment effect homogeneity can lead researchers, regulators and other stakeholders to over‐interpret apparent differences between subgroups even when heterogeneity tests are insignificant. In this paper, we consider some exploratory analysis tools to address this issue. We present three measures derived using the theory of order statistics, which can be used to understand the magnitude and the nature of the variation in treatment effects that can arise merely as an artefact of chance. These measures are not intended to replace a formal test of interaction but instead provide non‐inferential visual aids, which allow comparison of the observed and expected differences between regions or other subgroups and are a useful supplement to a formal test of interaction. We discuss how our methodology differs from recently published methods addressing the same issue. A case study of our approach is presented using data from the Study of Platelet Inhibition and Patient Outcomes (PLATO), which was a large cardiovascular MRCT that has been the subject of controversy in the literature. An R package is available that implements the proposed methods. Copyright © 2014 John Wiley & Sons, Ltd.     

Relative Risk Regression for Binary Outcomes: Methods and Recommendations

Relative risks are often considered preferable to odds ratios for quantifying the association between a predictor and a binary outcome. Relative risk regression is an alternative to logistic regression where the parameters are relative risks rather than odds ratios. It uses a log link binomial generalised linear model, or log‐binomial model, which requires parameter constraints to prevent probabilities from exceeding 1. This leads to numerical problems with standard approaches for finding the maximum likelihood estimate (MLE), such as Fisher scoring, and has motivated various non‐MLE approaches. In this paper we discuss the roles of the MLE and its main competitors for relative risk regression. It is argued that reliable alternatives to Fisher scoring mean that numerical issues are no longer a motivation for non‐MLE methods. Nonetheless, non‐MLE methods may be worthwhile for other reasons and we evaluate this possibility for alternatives within a class of quasi‐likelihood methods. The MLE obtained using a reliable computational method is recommended, but this approach requires bootstrapping when estimates are on the parameter space boundary. If convenience is paramount, then quasi‐likelihood estimation can be a good alternative, although parameter constraints may be violated. Sensitivity to model misspecification and outliers is also discussed along with recommendations and priorities for future research.     

Can ranking techniques elicit robust values?

This paper reports two experiments which examine the use of ranking methods to elicit ‘certainty equivalent’ values. It investigates whether such methods are able to eliminate the disparities between choice and value which constitute the ‘preference reversal phenomenon’ and which thereby pose serious problems for both theory and policy application. The results show that ranking methods are vulnerable to distorting effects of their own, but that when such effects are controlled for, the preference reversal phenomenon, previously so strong and striking, is very considerably attenuated.

PREDICTORS OF STRESS AMONG AUSTRALIAN FARMERS

Australian farms continue to suffer economic pressure which directly and indirectly produces stress among farm family members. This study uses qualitative and quantitative data to isolate factors which predict stress, Financial condition, perception of what is at stake, the combination of on-farm and off-farm obligations and the general frustrations of farm life are found to predict stress among both men and women. Gender relations and attachment to farm life emerge as stronger predictors for women, while youth is a stronger predictor for men. The study concludes that attempts to develop and preserve the family farm may be creating stressful situations which threaten the family relations upon which the farming system is based.     

Post-Fordism,union strategy and the rhetoric of restructuring: The case of Australia, 1980–1996

Theory and Society -     

Locally quadratic log likelihood and data-based transformations

The investigation of multi-parameter likelihood functions is simplified if the log likelihood is quadratic near the maximum, as then normal approximations to the likelihood can be accurately used to obtain quantities such as likelihood regions. This paper proposes that data-based transformations of the parameters can be employed to make the log likelihood more quadratic, and illustrates the method with one of the simplest bivariate likelihoods, the normal two-parameter likelihood.     

A clustering approach to interpretable principal components

A new method for constructing interpretable principal components is proposed. The method first clusters the variables, and then interpretable (sparse) components are constructed from the correlation matrices of the clustered variables. For the first step of the method, a new weighted-variances method for clustering variables is proposed. It reflects the nature of the problem that the interpretable components should maximize the explained variance and thus provide sparse dimension reduction. An important feature of the new clustering procedure is that the optimal number of clusters (and components) can be determined in a non-subjective manner. The new method is illustrated using well-known simulated and real data sets. It clearly outperforms many existing methods for sparse principal component analysis in terms of both explained variance and sparseness.