We apply a specialized Bayesian method that helps us deal with the methodological challenge of unobservedheterogeneity among immigrant voters. Our approach is based on \emphgeneralized linear mixed Dirichlet models (GLMDM) whererandom effects are specified semiparametrically using a Dirichlet process mixture prior that has been shown to account forunobserved grouping in the data. Such models are drawn from Bayesian nonparametrics to help overcome objections handling latenteffects with strongly informed prior distributions. Using 2009 German voting data of immigrants, we show that for difficultproblems of missing key covariates and unexplained heterogeneity this approach provides (1) overall improved model fit, (2)smaller standard errors on average, and (3) less bias from omitted variables. As a result, the GLMDM changed our substantiveunderstanding of the factors affecting immigrants’ turnout and vote choice. Once we account for unobserved heterogeneity amongimmigrant voters, whether a voter belongs to the first immigrant generation or not is much less important than the extantliterature suggests. When looking at vote choice we also found that an immigrant’s degree of structural integration does notaffect the vote in favor of the CDU/CSU, a party which is traditionally associated with restrictive immigration policy.
Background: The National Cancer Institute’s Transdisciplinary Research in Energetics and Cancer initiative is in its second round of funding. Despite increasing agreement that trans-disciplinary team-based research is valuable in addressing complex problems like energy balance and cancer, methods for constructing and maintaining transdisciplinary teams is lacking. Purpose: We articulate a method for assessing trans-disciplinary teams that relies on social network analysis and using this knowledge to improve their functioning. Methods: Using data from the Washington University TREC site in 2011 and 2013, we demonstrate the use of social network analysis to assess and provide feedback on team functioning. Results: We portray broker functioning in both years. By 2013, the director and co-director had begun to share broker functions with other members. Some brokers fostered communication with less central network members. Conclusions: The information obtained can help to train a new generation of investigators to optimally participate on transdisciplinary research teams.A Social Network Analysis Approach to... (PDF Download Available). Available from: https://www.researchgate.net/publication/279790188_A_Social_Network_Analysis_Approach_to_Diagnosing_and_Improving_the_Functioning_of_Transdisciplinary_Teams_in_Public_Health
Identification of modifiable risk factors is gravely needed to prevent adverse prostate health outcomes. We previously developed a murine precancer model in which exposure to maternal obesity stimulated prostate hyperplasia in offspring. Here, we used generalized linear modeling to evaluate the influence of additional environmental covariates on prostate hyperplasia. As expected from our previous work, the model revealed that aging and maternal diet-induced obesity (DIO) each correlated with prostate hyperplasia. However, prostate hyperplasia was not correlated with the length of maternal DIO. Cage density positively associated with both prostate hyperplasia and offspring body weight. Expression of the glucocorticoid receptor in prostates also positively correlated with cage density and negatively correlated with age of the animal. Together, these findings suggest that prostate tissue was adversely patterned during early life by maternal overnutrition and was susceptible to alteration by environmental factors such as cage density. Additionally, prostate hyperplasia may be acutely influenced by exposure to DIO, rather than occurring as a response to worsening obesity and comorbidities experienced by the mother. Finally, cage density correlated with both corticosteroid receptor abundance and prostate hyperplasia, suggesting that overcrowding influenced offspring prostate hyperplasia. These results emphasize the need for multivariate regression models to evaluate the influence of coordinated variables in complicated animal systems. Keywords prostate hyperplasia, cage overcrowding, maternal obesity, developmental programming, generalized linear modeling
Purpose Recognition of the complex, multidimensional relationship between excess adiposity and cancer control outcomes has motivated the scientific community to seek new research models and paradigms. Methods The National Cancer Institute developed an innovative concept to establish a center grant mechanism in nutrition, energetics, and physical activity, referred to as the Transdisciplinary Research on Energetics and Cancer (TREC) Initiative. This paper gives an overview of the 2011–2016 TREC Collaborative Network and the 15 research projects being conducted at the centers. Results Four academic institutions were awarded TREC center grants in 2011: Harvard University, University of California San Diego, University of Pennsylvania, and Washington University in St. Louis. The Fred Hutchinson Cancer Research Center is the Coordination Center. The TREC research portfolio includes three animal studies, three cohort studies, four randomized clinical trials, one cross-sectional study, and two modeling studies. Disciplines represented by TREC investigators include basic science, endocrinology, epidemiology, biostatistics, behavior, medicine, nutrition, physical activity, genetics, engineering, health economics, and computer science. Approximately 41,000 participants will be involved in these studies, including children, healthy adults, and breast and prostate cancer survivors. Outcomes include biomarkers of cancer risk, changes in weight and physical activity, persistent adverse treatment effects (e.g., lymphedema, urinary and sexual function), and breast and prostate cancer mortality. Conclusion The NIH Science of Team Science group will evaluate the value added by this collaborative science. However, the most important outcome will be whether this transdisciplinary initiative improves the health of Americans at risk of cancer as well as cancer survivors.
There is some confusion in political science, and the social sciences in general, about the meaning and interpretation ofinteraction effects in models with non-interval, non-normal outcome variables. Often these terms are casually thrown into a modelspecification without observing that their presence fundamentally changes the interpretation of the resulting coefficients. Thework here explains the conditional nature of reported coefficients and their standard errors in models with interactions, definingthe necessarily different interpretation required by generalized linear models, and providing a general analytical method forcorrectly calculating coefficient standard errors in models with second-order or higher interactions. Methodological issues areillustrated with an application of generalized linear models with interactions applied to voter information structured byelectoral systems and resulting legislative representation in comparative politics.
Background Outcomes after traumatic brain injury are worsened by secondary insults; modern intensive-care units address such challenges through use of best-practice pathways. Organisation of intensive-care units has an important role in pathway effectiveness. We aimed to assess the effect of a paediatric neurocritical care programme (PNCP) on outcomes for children with severe traumatic brain injury. Methods