Identifying regions of inhomogeneities in spatial processes via an M-RA and mixture priors.

Soils have been heralded as a hidden resource that can be leveraged to mitigate and address some of the major global environmental challenges. Specifically, the organic carbon stored in soils, called soil organic carbon (SOC), can, through proper soil management, help offset fuel emissions, increase food productivity, and improve water quality. As collecting data on SOC are costly and time-consuming, not much data on SOC are available, although understanding the spatial variability in SOC is of fundamental importance for effective soil management. In this manuscript, we propose a modeling framework that can be used to gain a better understanding of the dependence structure of a spatial process by identifying regions within a spatial domain where the process displays the same spatial correlation range. To achieve this goal, we propose a generalization of the multiresolution approximation (M-RA) modeling framework of Katzfuss originally introduced as a strategy to reduce the computational burden encountered when analyzing massive spatial datasets. To allow for the possibility that the correlation of a spatial process might be characterized by a different range in different subregions of a spatial domain, we provide the M-RA basis functions weights with a two-component mixture prior with one of the mixture components a shrinking prior. We call our approach the mixture M-RA. Application of the mixture M-RA model to both stationary and nonstationary data show that the mixture M-RA model can handle both types of data, can correctly establish the type of spatial dependence structure in the data (e.g., stationary versus not), and can identify regions of local stationarity.

New methods for multiple testing in permutation inference for the general linear model.

Permutation methods are commonly used to test the significance of regressors of interest in general linear models (GLMs) for functional (image) data sets, in particular for neuroimaging applications as they rely on mild assumptions. Permutation inference for GLMs typically consists of three parts: choosing a relevant test statistic, computing pointwise permutation tests, and applying a multiple testing correction. We propose new multiple testing methods as an alternative to the commonly used maximum value of test statistics across the image. The new methods improve power and robustness against inhomogeneity of the test statistic across its domain. The methods rely on sorting the permuted functional test statistics based on pointwise rank measures; still, they can be implemented even for large data. The performance of the methods is demonstrated through a designed simulation experiment and an example of brain imaging data. We developed the R package GET, which can be used for the computation of the proposed procedures.

A hierarchical integrative group least absolute shrinkage and selection operator for analyzing environmental mixtures.

Environmental health studies are increasingly measuring multiple pollutants to characterize the joint health effects attributable to exposure mixtures. However, the underlying dose-response relationship between toxicants and health outcomes of interest may be highly nonlinear, with possible nonlinear interaction effects. Existing penalized regression methods that account for exposure interactions either cannot accommodate nonlinear interactions while maintaining strong heredity or are computationally unstable in applications with limited sample size. In this paper, we propose a general shrinkage and selection framework to identify noteworthy nonlinear main and interaction effects among a set of exposures. We design hierarchical integrative group least absolute shrinkage and selection operator (HiGLASSO) to (a) impose strong heredity constraints on two-way interaction effects (hierarchical), (b) incorporate adaptive weights without necessitating initial coefficient estimates (integrative), and (c) induce sparsity for variable selection while respecting group structure (group LASSO). We prove sparsistency of the proposed method and apply HiGLASSO to an environmental toxicants dataset from the LIFECODES birth cohort, where the investigators are interested in understanding the joint effects of 21 urinary toxicant biomarkers on urinary 8-isoprostane, a measure of oxidative stress. An implementation of HiGLASSO is available in the higlasso R package, accessible through the Comprehensive R Archive Network.

Selection of nonlinear interactions by a forward stepwise algorithm: Application to identifying environmental chemical mixtures affecting health outcomes.

In this paper, we propose a stepwise forward selection algorithm for detecting the effects of a set of correlated exposures and their interactions on a health outcome of interest when the underlying relationship could potentially be nonlinear. Though the proposed method is very general, our application in this paper remains to be on analysis of multiple pollutants and their interactions. Simultaneous exposure to multiple environmental pollutants could affect human health in a multitude of complex ways. For understanding the health effects of multiple environmental exposures, it is often important to identify and estimate complex interactions among exposures. However, this issue becomes analytically challenging in the presence of potential nonlinearity in the outcome-exposure response surface and a set of correlated exposures. Through simulation studies and analyses of test datasets that were simulated as a part of a data challenge in multipollutant modeling organized by the National Institute of Environmental Health Sciences (http://www.niehs.nih.gov/about/events/pastmtg/2015/statistical/), we illustrate the advantages of our proposed method in comparison with existing alternative approaches. A particular strength of our method is that it demonstrates very low false positives across empirical studies. Our method is also used to analyze a dataset that was released from the Health Outcomes and Measurement of the Environment Study as a benchmark beta-tester dataset as a part of the same workshop.

Stability of an ERP-based measure of brain network activation (BNA) in athletes: A new electrophysiological assessment tool for concussion.

PRIMARY OBJECTIVE: To determine test-re-test reliabilities of novel Evoked Response Potential (ERP)-based Brain Network Activation (BNA) scores in healthy athletes. RESEARCH DESIGN: Observational, repeated-measures study. METHODS AND DESIGN: Forty-two healthy male and female high school and collegiate athletes completed auditory oddball and go/no-go ERP assessments at baseline, 1 week, 6 weeks and 1 year. The BNA algorithm was applied to the ERP data, considering electrode location, frequency band, peak latency and normalized amplitude to generate seven unique BNA scores for each testing session. MAIN OUTCOMES AND RESULTS: Mean BNA scores, intra-class correlation coefficient (ICC) values and reliable change (RC) values were calculated for each of the seven BNA networks. BNA scores ranged from 46.3 ± 34.9 to 69.9 ± 22.8, ICC values ranged from 0.46-0.65 and 95% RC values ranged from 38.3-68.1 across the seven networks. CONCLUSIONS: The wide range of BNA scores observed in this population of healthy athletes suggests that a single BNA score or set of BNA scores from a single after-injury test session may be difficult to interpret in isolation without knowledge of the athlete's own baseline BNA score(s) and/or the results of serial tests performed at additional time points. The stability of each BNA network should be considered when interpreting test-re-test BNA score changes.

Informing the uninformed: optimizing the consent message using a fractional factorial design.

IMPORTANCE: Research information should be presented in a manner that promotes understanding. However, many parents and research subjects have difficulty understanding and making informed decisions. OBJECTIVE: To examine the effect of different communication strategies on parental understanding of research information. DESIGN: Observational study from January 2010 to June 2012 using a fractional factorial design. SETTING: Large tertiary care children's hospital. PARTICIPANTS: Six hundred forty parents of children scheduled for elective surgery. INTERVENTIONS: Parents were randomized to receive information about a hypothetical pain trial presented in 1 of 16 consent documents containing different combinations of 5 selected communication strategies (ie, length, readability, processability [formatting], graphical display, and supplemental verbal disclosure). MAIN OUTCOME AND MEASURES: Parents were interviewed to determine their understanding of the study elements (eg, protocol and alternatives) and their gist (main point) and verbatim (actual) understanding of the risks and benefits. RESULTS: Main effects for understanding were found for processability, readability, message length, use of graphics, and verbal discussion. Consent documents with high processability, eighth-grade reading level, and graphics resulted in significantly greater gist and verbatim understanding compared with forms without these attributes (mean difference, 0.57; 95% CI, 0.26-0.88, number of correct responses of 7 and mean difference, 0.54; 95% CI,0.20-0.88, number of correct responses of 4 for gist and verbatim, respectively). CONCLUSIONS AND RELEVANCE: Results identified several communication strategy combinations that improved parents' understanding of research information. Adoption of these active strategies by investigators, clinicians, institutional review boards, and study sponsors represents a simple, practical, and inexpensive means to optimize the consent message and enhance parental, participant, and patient understanding.

A randomized trial of an avatar-hosted multiple behavior change intervention for young adult smokers.

BACKGROUND: Young adulthood is a critical transition period for the development of health behaviors. We present here the results of a randomized controlled trial of an online avatar-hosted personal health makeover program designed for young adult smokers. METHODS: We conducted a three-group randomized trial comparing delivery of general lifestyle content (Tx1), personally tailored health information (Tx2), and personally tailored health information plus online video-based peer coaching (Tx3) as part of a 6-week online health program. Participants were asked to set weekly goals around eating breakfast, exercise, alcohol use, and cigarette smoking. Eligibility criteria included age (18-30 years) and smoking status (any cigarette use in the previous 30 days). The primary outcome was self-reported 30-day abstinence measured 12 weeks postenrollment. RESULTS: Participant (n = 1698) characteristics were balanced across the groups (72% women, mean age 24, 26% nonwhite, 32% high school education or less, and 50% daily smokers). Considering intention to treat, 30-day smoking abstinence rates were statistically significantly higher in the intervention groups (Tx1 = 11%, Tx2 = 23%, Tx3 = 31%, P < .001). Participants in the intervention groups were also more likely to reduce their number of days spent on binge drinking and increase their number of days eating breakfast and exercising. Overall, intervention group participants were much more likely to make positive changes in at least three or four of the target behaviors (Tx1 = 19%, Tx2 = 39%, Tx3 = 41%, P < .001). CONCLUSIONS: This online avatar-hosted personal health makeover "show" increased smoking abstinence and induced positive changes in multiple related health behaviors. Addition of the online video-based peer coaching further improved behavioral outcomes.