Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm

High-dimensional longitudinal data is increasingly used in a wide range of scientific studies. However, there are few statistical methods for high-dimensional linear mixed models (LMMs), as most Bayesian variable selection or penalization methods are designed for independent observations. Additionally, the few available software packages for high-dimensional LMMs suffer from scalability issues. This work presents an efficient and accurate Bayesian framework for high-dimensional LMMs. We use empirical Bayes estimators of hyperparameters for increased flexibility and an Expectation-Conditional-Minimization (ECM) algorithm for computationally efficient maximum a posteriori probability (MAP) estimation of parameters. The novelty of the approach lies in its partitioning and parameter expansion as well as its fast and scalable computation. We illustrate Linear Mixed Modeling with PaRtitiOned empirical Bayes ECM (LMM-PROBE) in simulation studies evaluating fixed and random effects estimation along with computation time. A real-world example is provided using data from a study of lupus in children, where we identify genes and clinical factors associated with a new lupus biomarker and predict the biomarker over time

False discovery rate control for lesion-symptom mapping with heterogeneous data via weighted p-values

Statistic

False Discovery Rate Control for Lesion-Symptom Mapping with Heterogeneous data via Weighted P-values

Lesion-symptom mapping studies provide insight into what areas of the brain are involved in different aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is non-uniformly distributed with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Recent advancements in multiple testing methodologies allow researchers to weigh hypotheses according to side-information (e.g., information on power heterogeneity). In this paper, we propose the use of p-value weighting for voxel-based lesion-symptom mapping (VLSM) studies. The weights are created using the distribution of lesion status and spatial information to estimate different non-null prior probabilities for each hypothesis test through some common approaches. We provide a monotone minimum weight criterion which requires minimum a priori power information. Our methods are demonstrated on dependent simulated data and an aphasia study investigating which regions of the brain are associated with the severity of language impairment among stroke survivors. The results demonstrate that the proposed methods have robust error control and can increase power. Further, we showcase how weights can be used to identify regions that are inconclusive due to lack of power

Comparison of Methods Used to Estimate the Global Burden of Disease Related to Undernutrition and Suboptimal Breastfeeding

The Global Burden of Disease study (GBD) is an ambitious effort to estimate the disease burden attributable to various risk factors. The results from the GBD are used around the world to monitor the UN established Sustainable Development Goals, set health policies and research strategies, among others. The GBD along with other studies, such as those from the Maternal Child Epidemiology Estimation Group and the Lancet Breastfeeding Series Group, produce estimates of the nutrition-related global burden of disease that exhibit considerable differences. These differences are difficult to reconcile due to the estimation methods, which in recent years have substantially increased in complexity. In this paper, we give a detailed review of the methods used by GBD and other entities to estimate the global burden of disease that is attributable to undernutrition and suboptimal breastfeeding. Further, we compare the methods to determine causes for differences in estimates. We find that the main determinant of differences in estimates is what causes of death are linked to each risk factor. Methods used to estimate nutrition-related disease burden need to be more clearly documented to foster discussion and collaboration on the important assumptions required to produce estimates

Examining the Association Between Rurality and Positive Childhood Experiences Among a National Sample

Purpose The present study examines the association between rurality and positive childhood experiences (PCEs) among children and adolescents across all 50 states and the District of Columbia. Recent work has quantified the prevalence of PCEs at the national level, but these studies have been based on public use data files, which lack rurality information for 19 states. Methods Data for this cross-sectional analysis were drawn from 2016 to 2018 National Survey of Children\u27s Health (NSCH), using the full data set with restricted geographic data (n = 63,000). Descriptive statistics and bivariate analyses were used to calculate proportions and unadjusted associations. Multivariable regression models were used to examine the association between residence and the PCEs that were significant in the bivariate analyses. Findings Rural children were more likely than urban children to be reported as having PCEs: volunteering in their community (aOR 1.29; 95% CI 1.18-1.42), having a guiding mentor (aOR 1.75; 95% CI 1.45-2.10), residing in a safe neighborhood (aOR 1.97; 95% CI 1.54-2.53), and residing in a supportive neighborhood (aOR 1.10; 95% CI 1.01-1.20) than urban children. Conclusions The assessment of rural-urban differences in PCEs using the full NSCH is a unique opportunity to quantify exposure to PCEs. Given the higher baseline rate of PCEs in rural than urban children, programs to increase opportunities for PCEs in urban communities are warranted. Future research should delve further into whether these PCEs translate to better mental health outcomes in rural children

Challenges for Estimating the Global Prevalence of Micronutrient Deficiencies and Related Disease Burden: A Case Study of the Global Burden of Disease Study

Information on the prevalence of micronutrient deficiencies is needed to determine related disease burden; underpin evidence-based advocacy; and design, deliver, and monitor safe, effective interventions. Assessing the global prevalence of deficiency requires a valid micronutrient status biomarker with an appropriate cutoff to define deficiency and relevant data from representative surveys across multiple locations and years. The Global Burden of Disease Study includes prevalence estimates for iodine, iron, zinc, and vitamin A deficiencies, for which recommended biomarkers and appropriate deficiency cutoffs exist. Because representative survey data are lacking, only retinol concentration is used to model vitamin A deficiency, and proxy indicators are used for the other micronutrients (goiter for iodine, hemoglobin for iron, and dietary food adequacy for zinc). Because of data limitations, complex statistical modeling is required to produce current estimates, relying on assumptions and proxies that likely understate the extent of micronutrient deficiencies and the consequent global health burden

Linking Activity, Nutrition, and Child Health (Launch): Protocol for a Longitudinal Cohort Study of Children as They Develop From Infancy to Preschool Age

Background Physical activity is known to provide important health benefits in children ages 3 years and above, but little is known about the effects of physical activity on health in very young children under age 3. LAUNCH (Linking Activity, Nutrition, and Child Health) is a study designed to expand the body of knowledge on development of physical activity behavior and associations between physical activity and other health characteristics as children transition from infancy to preschool age. Methods Physical activity and sedentary behavior will be measured objectively in young children over a period of 30 months. Each child will complete a measurement protocol at 6, 12, 18, 24, 30 and 36 months of age. The following factors will be measured at each time point: physical activity, sedentary behavior, anthropometric characteristics, and motor developmental status. Objectively-measured sleep behavior will be included as an optional component of the protocol. Parents will provide information on demographic factors, parenting behaviors, home and childcare characteristics, and the child’s dietary and sleep behaviors. Discussion LAUNCH will employ a longitudinal study design and objective measures of physical activity, sedentary behavior and sleep in examining developmental trends for those characteristics in children between the ages of 6 and 36 months. Associations among physical activity, sedentary behavior, sleep, and weight status will be examined. Findings will inform public health guidance and intervention strategies for very young children

Finding a Disappearing Nontimber Forest Resource: Using Grounded Visualization to Explore Urbanization Impacts on Sweetgrass Basketmaking in Greater Mt. Pleasant, South Carolina

Despite growing interest in urbanization and its social and ecological impacts on formerly rural areas, empirical research remains limited. Extant studies largely focus either on issues of social exclusion and enclosure or ecological change. This article uses the case of sweetgrass basketmaking in Mt. Pleasant, South Carolina, to explore the implications of urbanization, including gentrification, for the distribution and accessibility of sweetgrass, an economically important nontimber forest product (NTFP) for historically African American communities, in this rapidly growing area. We explore the usefulness of grounded visualization for research efforts that are examining the existence of fringe ecologies associated with NTFP. Our findings highlight the importance of integrated qualitative and quantitative analyses for revealing the complex social and ecological changes that accompany both urbanization and rural gentrification