Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms

Competing risks occur in a time-to-event analysis in which a patient can experience one of several types of events. Traditional methods for handling competing risks data presuppose one censoring process, which is assumed to be independent. In a controlled clinical trial, censoring can occur for several reasons: some independent, others dependent. We propose an estimator of the cumulative incidence function in the presence of both independent and dependent censoring mechanisms. We rely on semi-parametric theory to derive an augmented inverse probability of censoring weighted (AIPCW) estimator. We demonstrate the efficiency gained when using the AIPCW estimator compared to a non-augmented estimator via simulations. We then apply our method to evaluate the safety and efficacy of three anti-HIV regimens in a randomized trial conducted by the AIDS Clinical Trial Group, ACTG A5095.     

Bioaccessibility and Risk of Exposure to Metals and SVOCs in Artificial Turf Field Fill Materials and Fibers

To reduce maintenance costs, municipalities and schools are starting to replace natural grass fields with a new generation synthetic turf. Unlike Astro‐Turf, which was first introduced in the 1960s, synthetic field turf provides more cushioning to athletes. Part of this cushioning comes from materials like crumb rubber infill, which is manufactured from recycled tires and may contain a variety of chemicals. The goal of this study was to evaluate potential exposures from playing on artificial turf fields and associated risks to trace metals, semi‐volatile organic compounds (SVOCs), and polycyclic aromatic hydrocarbons (PAHs) by examining typical artificial turf fibers (n = 8), different types of infill (n = 8), and samples from actual fields (n = 7). Three artificial biofluids were prepared, which included: lung, sweat, and digestive fluids. Artificial biofluids were hypothesized to yield a more representative estimation of dose than the levels obtained from total extraction methods. PAHs were routinely below the limit of detection across all three biofluids, precluding completion of a meaningful risk assessment. No SVOCs were identified at quantifiable levels in any extracts based on a match of their mass spectrum to compounds that are regulated in soil. The metals were measurable but at concentrations for which human health risk was estimated to be low. The study demonstrated that for the products and fields we tested, exposure to infill and artificial turf was generally considered de minimus, with the possible exception of lead for some fields and materials.     

College students with congenital heart disease: A critical time for transition

As medical and surgical advances improve, more young adults with congenital heart disease (CHD) are attending college. This case study illustrates some of the issues that these young adults may face as they attend college and discusses the role that college health practitioners can play in easing that transition. Participants: A case of a male with CHD presenting to the college health clinic with a new onset headache. Methods: The authors discuss some of the unique challenges that college health practitioners may face when caring for students with CHD. In addition, they make recommendations on how best to care for these patients and how best to coordinate care with CHD students other care providers. Results: This student with a history of coarctation of the aorta presented with new onset headaches and was found to have high blood pressure. He was diagnosed with recurrent coarctation, underwent percutaneous treatment with stenting and quickly resumed classes. Conclusions: As more students with CHD enter college, college health providers will need to understand some of the health risks that CHD students face. In addition, understanding some of the optimal ways to coordinate care with CHD providers can ease the transition that CHD students face as they enter college.     

The Inability of Self-affirmations to Decrease Defensive Bias Toward an Alcohol-Related Risk Message Among High-Risk College Students

Abstract

Objective: To examine the efficacy of a self-affirmation task in deterring college alcohol misuse and the importance of preexisting beliefs in predicting subsequent behavior change. Participants: Heavy-drinking undergraduates (N = 110) participated during the 2011–2012 academic year. Methods: Participants were randomized to complete an affirmation or control task before reading an alcohol risk message. Alcohol-related beliefs and behaviors were assessed. Participants completed a 2-week online follow-up assessing alcohol-related behaviors. Results: Both groups reported increased perceived problem importance, but neither group displayed changes in personal risk. Follow-up assessment revealed similar, significant declines in peak consumption in both groups, with no significant between-group differences. Preexisting beliefs accounted for 5% to 10% of variance in drinking outcomes. Conclusions: An affirmation task does not seem to decrease defensive processing or alter high-risk drinking behaviors among college students and should not be utilized in lieu of more effective strategies.     

Supply Chain Consequences of Subsidies for Corporate Social Responsibility

The use of government incentives tied to market prices as means of boosting corporate social responsibility (CSR) has expanded notably in recent decades. Enhanced business tax deductions for charitable donations and credits for conservation easements are notable cases. While providing incentives for socially desirable behavior to achieve legislative goals has intuitive appeal, the broader economic consequences are not always fully understood. In this study, we examine such wider consequences for supply chains when subsidies for CSR are offered. One effect we identify is that since incentives are typically tied to market value, firms have not only an added incentive to achieve societal objectives (say by donating inventory) but also an incentive to raise output (retail) market prices. A second consequence is that since firms forgo potential revenues by engaging in socially desired behavior, they become increasingly sensitive to supplier pricing; in an uncoordinated supply chain this leads to input (wholesale) price concessions. Among other things, the results underscore that incentives put in place to meet broader societal objectives also have notable ramifications for suppliers, retailers, and consumers in primary markets.     

Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.