Tackling the problem of teenage pregnancy in looked‐after children: a peer mentoring approach

Teenage pregnancy is associated with adverse health and social outcomes, even after adjusting for prior disadvantage, and is recognized as a major public health issue. Rates of teenage pregnancy in the UK are among the highest in Europe. Interventions introduced in the past decade to address the problem, such as improved sex and relationships education in schools, have been accompanied by a fall in teenage pregnancy rates in the UK. However, this decline has not been mirrored among looked‐after children. In this paper, we discuss why this may be the case. We suggest that a system of peer mentoring, involving a young person, whose experience of life post‐care has been positive, may be an effective approach to tackling the problem of pregnancy in this group. Peer mentoring has the potential to assist young people in developing self‐esteem, confidence and in making choices regarding their education, personal development and relationships.     

Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease

In this paper, the goal of identifying disease subgroups based on differences in observed symptom profile is considered. Commonly referred to as phenotype identification, solutions to this task often involve the application of unsupervised clustering techniques. In this paper, we investigate the application of a Dirichlet process mixture model for this task. This model is defined by the placement of the Dirichlet process on the unknown components of a mixture model, allowing for the expression of uncertainty about the partitioning of observed data into homogeneous subgroups. To exemplify this approach, an application to phenotype identification in Parkinson's disease is considered, with symptom profiles collected using the Unified Parkinson's Disease Rating Scale.     

Missing data: Discussion points from the PSI missing data expert group

The Points to Consider Document on Missing Data was adopted by the Committee of Health and Medicinal Products (CHMP) in December 2001. In September 2007 the CHMP issued a recommendation to review the document, with particular emphasis on summarizing and critically appraising the pattern of drop‐outs, explaining the role and limitations of the ‘last observation carried forward’ method and describing the CHMP's cautionary stance on the use of mixed models. In preparation for the release of the updated guidance document, statisticians in the Pharmaceutical Industry held a one‐day expert group meeting in September 2008. Topics that were debated included minimizing the extent of missing data and understanding the missing data mechanism, defining the principles for handling missing data and understanding the assumptions underlying different analysis methods. A clear message from the meeting was that at present, biostatisticians tend only to react to missing data. Limited pro‐active planning is undertaken when designing clinical trials. Missing data mechanisms for a trial need to be considered during the planning phase and the impact on the objectives assessed. Another area for improvement is in the understanding of the pattern of missing data observed during a trial and thus the missing data mechanism via the plotting of data; for example, use of Kaplan–Meier curves looking at time to withdrawal. Copyright © 2009 John Wiley & Sons, Ltd.     

Intermediate Performance Impacts of Advanced Manufacturing Technology Systems: An Empirical Investigation*

A large-scale random sample is used to empirically examine the relationships between implementation of Advanced Manufacturing Technology (AMT) and three organization-level measures that have historically been attributed to AMT, but not fully tested along the AMT spectrum: market-oriented flexibility of the production process, organizational integration of production processes, and administrative intensity of the organization. Results indicate that as an organization moves along the technology scale from stand-alone AMT (e.g., CNC machines) through functionally oriented AMT (FMS and CAM) toward CIM, not only do its production processes become more integrated with each other, but those processes become more integrated with other functional systems of the organization, and the quality and timeliness of production information increase. Furthermore, this relationship becomes stronger as companies increase their level of implementation for the latter two technologies. Conversely, market-oriented flexibility decreases and administrative intensity is not observed to change as companies move along the technology spectrum. Future research should examine how organizational redesign and implementation strategies that accompany AMT implementation can concomitantly enhance organizational integration of the production process and market-oriented flexibility.     

A Reality Check for Data Snooping

Data snooping occurs when a given set of data is used more than once for purposes of inference or model selection. When such data reuse occurs, there is always the possibility that any satisfactory results obtained may simply be due to chance rather than to any merit inherent in the method yielding the results. This problem is practically unavoidable in the analysis of time‐series data, as typically only a single history measuring a given phenomenon of interest is available for analysis. It is widely acknowledged by empirical researchers that data snooping is a dangerous practice to be avoided, but in fact it is endemic. The main problem has been a lack of sufficiently simple practical methods capable of assessing the potential dangers of data snooping in a given situation. Our purpose here is to provide such methods by specifying a straightforward procedure for testing the null hypothesis that the best model encountered in a specification search has no predictive superiority over a given benchmark model. This permits data snooping to be undertaken with some degree of confidence that one will not mistake results that could have been generated by chance for genuinely good results.     

Asymptotic Distribution Theory for Nonparametric Entropy Measures of Serial Dependence

Entropy is a classical statistical concept with appealing properties. Establishing asymptotic distribution theory for smoothed nonparametric entropy measures of dependence has so far proved challenging. In this paper, we develop an asymptotic theory for a class of kernel‐based smoothed nonparametric entropy measures of serial dependence in a time‐series context. We use this theory to derive the limiting distribution of Granger and Lin's (1994) normalized entropy measure of serial dependence, which was previously not available in the literature. We also apply our theory to construct a new entropy‐based test for serial dependence, providing an alternative to Robinson's (1991) approach. To obtain accurate inferences, we propose and justify a consistent smoothed bootstrap procedure. The naive bootstrap is not consistent for our test. Our test is useful in, for example, testing the random walk hypothesis, evaluating density forecasts, and identifying important lags of a time series. It is asymptotically locally more powerful than Robinson's (1991) test, as is confirmed in our simulation. An application to the daily S&P 500 stock price index illustrates our approach.     

Unemployment Propensity and Unemployment Duration

ABSTRACT: This paper analyses the factors affecting the duration of an unemployment spell. The results indicate the absence of statistically significant ‘‘duration dependence‘‘ effects for men. Person-specific unemployment propensity is found to have a significant effect on re-employment probabilities. The latter implies an unequal distribution of unemployment burden among the population.