Parental divorce, sibship size, family resources, and children’s academic performance

Using data from 19,839 adolescents from the National Education Longitudinal Study, this study investigates whether the effects of parental divorce on adolescents’ academic test performance vary by sibship size. Analyses show that the negative effect of divorce on adolescent performance attenuates as sibship size increases. On the other side of the interaction, the inverse relationship between sibship size and test performance is weaker in disrupted than in two-biological-parent families. Trends of such interactions are evident when sibship size is examined either as a continuous or a categorical measure. Finally, the observed interactions on adolescents’ academic performance are completely explained by variations in parental financial, human, cultural, and social resources. In sum, this study underlines the importance of treating the effect of parental divorce as a variable and calls for more research to identify child and family features that may change the magnitude of such an effect.     

A simple two-stage design for quantitative responses with application to a study in diabetic neuropathic pain

Two-stage designs offer substantial advantages for early phase II studies. The interim analysis following the first stage allows the study to be stopped for futility, or more positively, it might lead to early progression to the trials needed for late phase II and phase III. If the study is to continue to its second stage, then there is an opportunity for a revision of the total sample size. Two-stage designs have been implemented widely in oncology studies in which there is a single treatment arm and patient responses are binary. In this paper the case of two-arm comparative studies in which responses are quantitative is considered. This setting is common in therapeutic areas other than oncology. It will be assumed that observations are normally distributed, but that there is some doubt concerning their standard deviation, motivating the need for sample size review. The work reported has been motivated by a study in diabetic neuropathic pain, and the development of the design for that trial is described in detail.     

Consultants' forum: should post hoc sample size calculations be done?

Pre‐study sample size calculations for clinical trial research protocols are now mandatory. When an investigator is designing a study to compare the outcomes of an intervention, an essential step is the calculation of sample sizes that will allow a reasonable chance (power) of detecting a pre‐determined difference (effect size) in the outcome variable, at a given level of statistical significance. Frequently studies will recruit fewer patients than the initial pre‐study sample size calculation suggested. Investigators are faced with the fact that their study may be inadequately powered to detect the pre‐specified treatment effect and the statistical analysis of the collected outcome data may or may not report a statistically significant result. If the data produces a “non‐statistically significant result” then investigators are frequently tempted to ask the question “Given the actual final study size, what is the power of the study, now, to detect a treatment effect or difference?” The aim of this article is to debate whether or not it is desirable to answer this question and to undertake a power calculation, after the data have been collected and analysed. Copyright © 2008 John Wiley & Sons, Ltd.     

Quantile dispersion graphs to compare the efficiencies of cluster randomized designs

The purpose of this article is to compare efficiencies of several cluster randomized designs using the method of quantile dispersion graphs (QDGs). A cluster randomized design is considered whenever subjects are randomized at a group level but analyzed at the individual level. A prior knowledge of the correlation existing between subjects within the same cluster is necessary to design these cluster randomized trials. Using the QDG approach, we are able to compare several cluster randomized designs without requiring any information on the intracluster correlation. For a given design, several quantiles of the power function, which are directly related to the effect size, are obtained for several effect sizes. The quantiles depend on the intracluster correlation present in the model. The dispersion of these quantiles over the space of the unknown intracluster correlation is determined, and then depicted by the QDGs. Two applications of the proposed methodology are presented.     

Estimation in capture–recapture models when covariates are subject to measurement errors and missing data

For capture–recapture models when covariates are subject to measurement errors and missing data, a set of estimating equations is constructed to estimate population size and relevant parameters. These estimating equations can be solved by an algorithm similar to the EM algorithm. The proposed method is also applicable to the situation when covariates with no measurement errors have missing data. Simulation studies are used to assess the performance of the proposed estimator. The estimator is also applied to a capture–recapture experiment on the bird species Prinia flaviventris in Hong Kong. The Canadian Journal of Statistics 37: 645–658; 2009 © 2009 Statistical Society of Canada     

Assessing NGO Empowerment in Housing Development Frameworks: Discourse and Practice in the Philippines

Amid the rise of democratization movements since the 1980s, many governments in developing countries have proactively introduced the ideas of decentralization and devolution into the policy arena. In the pursuit of democratic planning processes where civil society is encouraged to be a part of the formal decision-making system, articles advocating the empowerment of non-governmental organizations (NGOs) have been included into a series of legislations. This shift was soon reflected in housing development frameworks for the poor, leading to the formulation of a new approach called "enablement." The Philippines' experience for the last two decades follows this line of development. Filipino NGOs are now prescribed as a catalyst to add further dynamism to the improvement of substandard living environments. Empirical findings of this study exemplify that NGO empowerment has been increasingly accelerated by the combination of NGOs' self-endeavors and governments' initiatives. By utilizing the paradigm shift under the enablement approach, NGOs are engaged in the attempt of alliance-formation and network-building to expand their influence. Contrary to ideals, however, NGO empowerment may reveal some adverse effects such as excessive dependency on NGOs and distorted representation by NGOs. Moreover, the professionalization of NGOs is an additional factor which generates hierarchization at the grass-roots level. This study suggests that further research analyze the depth and width to which NGO empowerment has been penetrated in order to precisely capture the processes and consequences resulting in disempowerment.     

Housing interventions for homeless,pregnant/parenting women with addictions: a systematic review

Family homelessness is a growing problem in North America with most of these families headed by single women. Homeless women also experience high rates of pregnancy and addiction (drugs and alcohol). Housing interventions have been identified as key to addressing the complex needs of pregnant/early parenting, homeless women with addictions. The aim of this systematic review is to determine what housing models and programs for this population yield the best outcomes. We systematically searched 10 databases and retrieved eight articles describing four distinct studies that met the inclusion criteria for this review. Overall, improved outcomes were found for all intervention groups with the most recent and rigorous studies favoring models combining Housing First and case management. However, methodological limitations, study quality, and varied outcomes made comparison across studies difficult. Further research must be done in this area using standardized outcomes and rigorous designs to develop evidence-based best practice guidelines to address the unique needs of this population.     

Statistical modeling for Bayesian extrapolation of adult clinical trial information in pediatric drug evaluation

Children represent a large underserved population of “therapeutic orphans,” as an estimated 80% of children are treated off‐label. However, pediatric drug development often faces substantial challenges, including economic, logistical, technical, and ethical barriers, among others. Among many efforts trying to remove these barriers, increased recent attention has been paid to extrapolation; that is, the leveraging of available data from adults or older age groups to draw conclusions for the pediatric population. The Bayesian statistical paradigm is natural in this setting, as it permits the combining (or “borrowing”) of information across disparate sources, such as the adult and pediatric data. In this paper, authored by the pediatric subteam of the Drug Information Association Bayesian Scientific Working Group and Adaptive Design Working Group, we develop, illustrate, and provide suggestions on Bayesian statistical methods that could be used to design improved pediatric development programs that use all available information in the most efficient manner. A variety of relevant Bayesian approaches are described, several of which are illustrated through 2 case studies: extrapolating adult efficacy data to expand the labeling for Remicade to include pediatric ulcerative colitis and extrapolating adult exposure‐response information for antiepileptic drugs to pediatrics.     

Sample size estimation for comparing rates of change in K-group repeated count outcomes

Sample size estimation for comparing the rates of change in two-arm repeated measurements has been investigated by many investigators. In contrast, the literature has paid relatively less attention to sample size estimation for studies with multi-arm repeated measurements where the design and data analysis can be more complex than two-arm trials. For continuous outcomes, Jung and Ahn (2004 Jung, S., Ahn, C. (2004). K-sample test and sample size calculation for comparing slopes in data with repeated measurements. Biometrical J. 46(5):554–564.[Crossref], [Web of Science ®] , [Google Scholar]) and Zhang and Ahn (2013 Zhang, S., Ahn, C. (2013). Sample size calculation for comparing time-averaged responses in k-group repeated measurement studies. Comput. Stat. Data Anal. 58:283–291.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) have presented sample size formulas to compare the rates of change and time-averaged responses in multi-arm trials, using the generalized estimating equation (GEE) approach. To our knowledge, there has been no corresponding development for multi-arm trials with count outcomes. We present a sample size formula for comparing the rates of change in multi-arm repeated count outcomes using the GEE approach that accommodates various correlation structures, missing data patterns, and unbalanced designs. We conduct simulation studies to assess the performance of the proposed sample size formula under a wide range of designing configurations. Simulation results suggest that empirical type I error and power are maintained close to their nominal levels. The proposed method is illustrated using an epileptic clinical trial example.     

Alone and adrift: The association between mass school shootings,school size,and student support

Background

School shootings have approached epidemic levels in recent years. While mental illness is undoubtedly involved in nearly all cases of mass school violence, we sought to determine how environmental context may exacerbate preexisting personal factors. The present study investigated the associations between mass school shootings, school enrollment size, student–teacher ratios, and student transitions.