Empirical bayes estimation of the binomial parameter n

Estimation of the prior distribution of the binomial parameter nbased on a system of orthogonal polynomials, the Poisson-Charlier polynomials, is studied. It is shown that the resulting estimator is mean squared consistent with rate O(N ^ε-1), where Nis the sample size and ε> 0 is arbitrarily small.     

ESTIMATING THE NUMBER OF VISITS TO THE DOCTOR

The frequency of doctor consultations has direct consequences for health care budgets, yet little statistical analysis of the determinants of doctor visits has been reported. We consider the distribution of the number of visits to the doctor and, in particular, we model its dependence on a number of demographic factors. Examination of the Australian 1995 National Health Survey data reveals that generalized linear Poisson or negative binomial models are inadequate for modelling the mean as a function of covariates, because of excessive zero counts, and a mean‐variance relationship that varies enormously over covariate values. A negative binomial model is used, with parameter values estimated in subgroups according to the discrete combinations of the covariate values. Smoothing splines are then used to smooth and interpolate the parameter values. In effect the mean and the shape parameters are each modelled as (different) functions of gender, age and geographical factors. The estimated regressions for the mean have simple and intuitive interpretations. However, the dependence of the (negative binomial) shape parameter on the covariates is more difficult to interpret and is subject to influence by extreme observations. We illustrate the use of the model by estimating the distribution of the number of doctor consultations in the Statistical Local Area of Ryde, based on population numbers from the 1996 census.     

Bootstrap goodness-of-fit test for the beta-binomial model

A common question in the analysis of binary data is how to deal with overdispersion. One widely advocated sampling distribution for overdispersed binary data is the beta-binomial model. For example, this distribution is often used to model litter effects in toxicological experiments. Testing the null hypothesis of a beta-binomial distribution against all other distributions is difficult, however, when the litter sizes vary greatly. Herein, we propose a test statistic based on combining Pearson statistics from individual litter sizes, and estimate the p-value using bootstrap techniques. A Monte Carlo study confirms the accuracy and power of the test against a beta-binomial distribution contaminated with a few outliers. The method is applied to data from environmental toxicity studies.     

How Effect Size (Practical Significance) Misleads Clinical Practice: The Case for Switching to Practical Benefit to Assess Applied Research Findings

ABSTRACT

Relying on effect size as a measure of practical significance is turning out to be just as misleading as using p-values to determine the effectiveness of interventions for improving clinical practice in complex organizations such as schools. This article explains how effect sizes have misdirected practice in education and other disciplines. Even when effect size is incorporated into RCT research the recommendations of whether interventions are effective are misleading and generally useless to practitioners. As a result, a new criterion of practical benefit is recommended for evaluating research findings about the effectiveness of interventions in complex organizations where benchmarks of existing performance exist. Practical benefit exists when the unadjusted performance of an experimental group provides a noticeable advantage over an existing benchmark. Some basic principles for determining practical benefit are provided. Practical benefit is more intuitive and is expected to enable leaders to make more accurate assessments as to whether published research findings are likely to produce noticeable improvements in their organizations. In addition, practical benefit is used routinely as the research criterion for the alternative scientific methodology of improvement science that has an established track record of being a more efficient way to develop new interventions that improve practice dramatically than RCT research. Finally, the problems with practical significance suggest that the research community should seek different inferential methods for research designed to improve clinical performance in complex organizations, as compared to methods for testing theories and medicines.     

Risk Analysis of PCB Exposure via the Soil–Food Crop Pathway, and Alternatives for Remediation at Serpukhov, Russian Federation

A risk assessment was conducted to determine the likelihood of certain health risks resulting from exposure to soils and food crops contaminated with polychlorinated biphenyl (PCBs). PCBs have contaminated soils, river sediments, and air in the past as a result of industrial activities at a capacitor plant located in the City of Serpukhov, Russian Federation. This risk assessment and suggestions for remediation are designed to aid in decision-making efforts by a joint Russian–American research team developing a community, national, and international response to industrial contamination. Bobovnikova et al. ( The Science of the Total Environment 139/140 , 357–364, [1993]) have reported that PCBs are elevated in soils and sediments, breast milk, and locally grown foods in the Serpukhov area. Data from these and other investigators have been used in this risk assessment to calculate a potential cancer risk resulting from exposure to PCBs. Our assessment indicates that members of the local population may be at increased risk of cancer, and possibly other adverse health effects, as a result of PCB contamination of their environment. Because previously unassessed environmental contamination is a common problem in the former Soviet Republics, as well as many other areas of the world, we believe this type of evaluation, using known methods, can serve as a model for assessment efforts in other parts of the globe and result in remediative efforts in regions constrained by faltering economies.     

Class and gender inequalities in different types of welfare states: the Social Citizenship Indicator Program (SCIP)

Korpi W. Class and gender inequalities in different types of welfare states: the Social Citizenship Indicator Program (SCIP) Int J Soc Welfare 2010: ??: ??–??© 2010 The Author(s), Journal compilation © 2010 Blackwell Publishing Ltd and International Journal of Social Welfare. This article considers the role of legislated welfare state institutions as mediators of effects of political and structural forces on citizens' levels of living of relevance to inequalities in health and mortality. The focus is on institutional structures of welfare state programmes relevant to class inequality, as indicated by income inequalities, and to gender inequality, conceived of as differences in agency. I introduce the Social Citizenship Indicator Program, a database providing quantitative and qualitative information on structures of main social insurance programmes in 18 countries from 1930 to 2000, on about 300,000 data points. It is used to delineate types of distributive institutions of relevance for income inequality. Institutions relevant for gendered agency inequality affect choices by women, especially mothers, between unpaid and paid work. Driving forces behind the emergence of differences in distributive institutions are discussed, and patterns of class and gender inequalities are outlined.     

A different kind of holding environment: a case study of group work with pediatric staff

This paper reports a nearly two-year intervention with staff at a major urban pediatric facility. The authors worked as trainers and consultants for two groups-the Emergency Department (ED) and Chronic Care units (CC). Following two days of training about grief and loss, highlighting skill building for work with traumatized and grieving children and parents, the consultants worked to develop teams which could provide a Winnicottian holding environment for staff. The efficacy of this staff training is analyzed and support needs of staff members are identified. Findings include varied willingness to make use of group support. Chronic care participants are more likely to express willingness to engage in on-going support for one another, while ED staff members seem to prefer structured, educational training and episodic support. The holding environment of the processing groups must be adapted to fit the needs of the medical sub-culture.