Social exclusion and neighborhood support: a case study of empty-nest elderly in urban shanghai

The phenomenon of empty-nest elderly (ENE) has become a significant social issue in China. In this research on ENEs in urban Shanghai, a coastal city in China, case studies were undertaken through in-depth semistructured interviews to examine the social exclusion risks ENEs may face and the influence of neighborhood support on such risks. The results show 5 aspects of social exclusion ENEs are experiencing. Moreover, the neighborhood has the potential for providing different forms of support to reduce social exclusion risks. The research and service implications of our findings for promoting the holistic well-being of ENEs in China are discussed.     

Knot selection for least-squares and penalized splines

Two new stochastic search methods are proposed for optimizing the knot locations and/or smoothing parameters for least-squares or penalized splines. One of the methods is a golden-section-augmented blind search, while the other is a continuous genetic algorithm. Monte Carlo experiments indicate that the algorithms are very successful at producing knot locations and/or smoothing parameters that are near optimal in a squared error sense. Both algorithms are amenable to parallelization and have been implemented in OpenMP and MPI. An adjusted GCV criterion is also considered for selecting both the number and location of knots. The method performed well relative to MARS in a small empirical comparison.     

Improved Estimation of the Pareto Location Parameter Under Squared Error Loss

Suppose two independent observations are drawn from Pareto distributions with known shape parameters and an order restriction on the unknown location parameters. An isotonic regression estimator of the smaller location parameter dominates a preferred marginal estimator under squared error loss, but fails to dominate under stochastic domination. The results expressed herein advance the theory of order restricted inference.     

When a Constant Classifier is as Good as Any Linear Classifier

A classifier is constant if it classifies all examples into just one class. Call a training data set “(linearly) indiscriminate” if a constant classifier minimizes, among all linear classifiers, the misclassification rate on the training data set. General sufficient conditions are presented for the probability of getting an indiscriminate data set to be positive. Similarly, general sufficient conditions are also presented for the probability of getting an indiscriminate data set to be 0.

A small simulation study examines how our results are reflected in the behavior of logistic regression.     

Under the Influence: The Interplay among Industry,Publishing, and Drug Regulation

The relationships among academe, publishing, and industry can facilitate commercial bias in how drug efficacy and safety data are obtained, interpreted, and presented to regulatory bodies and prescribers. Through a critique of published and unpublished trials submitted to the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for approval of a new antidepressant, vortioxetine, we present a case study of the “ghost management” of the information delivery process. We argue that currently accepted practices undermine regulatory safeguards aimed at protecting the public from unsafe or ineffective medicines. The economies of influence that may intentionally and unintentionally produce evidence-biased—rather than evidence-based—medicine are identified. This is not a simple story of author financial conflicts of interest, but rather a complex tale of ghost management of the entire process of bringing a drug to market. This case study shows how weak regulatory policies allow for design choices and reporting strategies that can make marginal products look novel, more effective, and safer than they are, and how the selective and imbalanced reporting of clinical trial data in medical journals results in the marketing of expensive “me-too” drugs with questionable risk/benefit profiles. We offer solutions for neutralizing these economies of influence.     

Youth and parent perceptions of aftercare supports at discharge from residential care

While much is known about youth struggles after reintegration into the home and community settings following stays in out‐of‐home care, little is known about appropriate supports for aftercare, or parent (see Note ¹ ) and youth perceptions during this critical transition period. This study seeks to begin to address these questions through surveys evaluating youth (n = 48) and parent (n = 48) perceptions of (i) transition planning, family preparedness and aftercare; (ii) youth preparedness for the transition period across major life domains (e.g. education, health care, relationships); and (iii) the importance of specific aftercare supports and services. Results indicate differences between youth and parents on perceptions of need for continued supports, likelihood of participation in aftercare programs; and youth preparedness for success in relationships, family and independent living domains. In contrast, similarities were found on youth and parent ratings regarding the importance of specific services and supports, with both groups indicating supports in education as most important and supports in mental health as least. Study limitations, future research and implications are provided.     

Incorporating diagnostic accuracy into the estimation of discrete survival function

Empirical distribution function (EDF) is a commonly used estimator of population cumulative distribution function. Survival function is estimated as the complement of EDF. However, clinical diagnosis of an event is often subjected to misclassification, by which the outcome is given with some uncertainty. In the presence of such errors, the true distribution of the time to first event is unknown. We develop a method to estimate the true survival distribution by incorporating negative predictive values and positive predictive values of the prediction process into a product-limit style construction. This will allow us to quantify the bias of the EDF estimates due to the presence of misclassified events in the observed data. We present an unbiased estimator of the true survival rates and its variance. Asymptotic properties of the proposed estimators are provided and these properties are examined through simulations. We evaluate our methods using data from the VIRAHEP-C study.     

Improving the Predictive Accuracy of Hurricane Power Outage Forecasts Using Generalized Additive Models

Electric power is a critical infrastructure service after hurricanes, and rapid restoration of electric power is important in order to minimize losses in the impacted areas. However, rapid restoration of electric power after a hurricane depends on obtaining the necessary resources, primarily repair crews and materials, before the hurricane makes landfall and then appropriately deploying these resources as soon as possible after the hurricane. This, in turn, depends on having sound estimates of both the overall severity of the storm and the relative risk of power outages in different areas. Past studies have developed statistical, regression-based approaches for estimating the number of power outages in advance of an approaching hurricane. However, these approaches have either not been applicable for future events or have had lower predictive accuracy than desired. This article shows that a different type of regression model, a generalized additive model (GAM), can outperform the types of models used previously. This is done by developing and validating a GAM based on power outage data during past hurricanes in the Gulf Coast region and comparing the results from this model to the previously used generalized linear models.     

Statistical considerations in bioequivalence of two area under the concentration–time curves obtained from serial sampling data

In this paper, we study the bioequivalence (BE) inference problem motivated by pharmacokinetic data that were collected using the serial sampling technique. In serial sampling designs, subjects are independently assigned to one of the two drugs; each subject can be sampled only once, and data are collected at K distinct timepoints from multiple subjects. We consider design and hypothesis testing for the parameter of interest: the area under the concentration–time curve (AUC). Decision rules in demonstrating BE were established using an equivalence test for either the ratio or logarithmic difference of two AUCs. The proposed t-test can deal with cases where two AUCs have unequal variances. To control for the type I error rate, the involved degrees-of-freedom were adjusted using Satterthwaite's approximation. A power formula was derived to allow the determination of necessary sample sizes. Simulation results show that, when the two AUCs have unequal variances, the type I error rate is better controlled by the proposed method compared with a method that only handles equal variances. We also propose an unequal subject allocation method that improves the power relative to that of the equal and symmetric allocation. The methods are illustrated using practical examples.