Quantile contours and allometric modelling for risk classification of abnormal ratios with an application to asymmetric growth-restriction in preterm infants

We develop an approach to risk classification based on quantile contours and allometric modelling of multivariate anthropometric measurements. We propose the definition of allometric direction tangent to the directional quantile envelope, which divides ratios of measurements into half-spaces. This in turn provides an operational definition of directional quantile that can be used as cutoff for risk assessment. We show the application of the proposed approach using a large dataset from the Vermont Oxford Network containing observations of birthweight (BW) and head circumference (HC) for more than 150,000 preterm infants. Our analysis suggests that disproportionately growth-restricted infants with a larger HC-to-BW ratio are at increased mortality risk as compared to proportionately growth-restricted infants. The role of maternal hypertension is also investigated.Comment: 31 pages, 3 figures, 8 table

Dosing pole recommendations for lymphatic filariasis elimination: A height-weight quantile regression modeling approach

BACKGROUND: The World Health Organization (WHO) currently recommends height or age-based dosing as alternatives to weight-based dosing for mass drug administration lymphatic filariasis (LF) elimination programs. The goals of our study were to compare these alternative dosing strategies to weight-based dosing and to develop and evaluate new height-based dosing pole scenarios. METHODOLOGY/PRINCIPAL FINDINGS: Age, height and weight data were collected from \u3e26,000 individuals in five countries during a cluster randomized LF clinical trial. Weight-based dosing for diethylcarbamazine (DEC; 6 mg/kg) and ivermectin (IVM; 200 ug/kg) with tablet numbers derived from a table of weight intervals was treated as the gold standard for this study. Following WHO recommended age-based dosing of DEC and height-based dosing of IVM would have resulted in 32% and 27% of individuals receiving treatment doses below those recommended by weight-based dosing for DEC and IVM, respectively. Underdosing would have been especially common in adult males, who tend to have the highest LF prevalence in many endemic areas. We used a 3-step modeling approach to develop and evaluate new dosing pole cutoffs. First, we analyzed the clinical trial data using quantile regression to predict weight from height. We then used weight predictions to develop new dosing pole cutoff values. Finally, we compared different dosing pole cutoffs and age and height-based WHO dosing recommendations to weight-based dosing. We considered hundreds of scenarios including country- and sex-specific dosing poles. A simple dosing pole with a 6-tablet maximum for both DEC and IVM reduced the underdosing rate by 30% and 21%, respectively, and was nearly as effective as more complex pole combinations for reducing underdosing. CONCLUSIONS/SIGNIFICANCE: Using a novel modeling approach, we developed a simple dosing pole that would markedly reduce underdosing for DEC and IVM in MDA programs compared to current WHO recommended height or age-based dosing

The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective

Several market and macro-level variables influence the evolution of equity risk in addition to the well-known volatility persistence. However, the impact of those covariates might change depending on the risk level, being different between low and high volatility states. By combining equity risk estimates, obtained from the Realized Range Volatility, corrected for microstructure noise and jumps, and quantile regression methods, we evaluate the forecasting implications of the equity risk determinants in different volatility states and, without distributional assumptions on the realized range innovations, we recover both the points and the conditional distribution forecasts. In addition, we analyse how the the relationships among the involved variables evolve over time, through a rolling window procedure. The results show evidence of the selected variables\u2019 relevant impacts and, particularly during periods of market stress, highlight heterogeneous effects across quantiles

Non-linear regression models for Approximate Bayesian Computation

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.Comment: 4 figures; version 3 minor changes; to appear in Statistics and Computin

Fish Habitat Utilization Patterns and Evaluation of the Efficacy of Marine Protected Areas in Hawaii: Integration of NOAA Digital Benthic Habitat Mapping and Coral Reef Ecological Studies

Over the past four decades, the state of Hawaii has developed a system of eleven Marine Life Conservation Districts (MLCDs) to conserve and replenish marine resources around the state. Initially established to provide opportunities for public interaction with the marine environment, these MLCDs vary in size, habitat quality, and management regimes, providing an excellent opportunity to test hypotheses concerning marine protected area (MPA) design and function using multiple discreet sampling units. NOAA/NOS/NCCOS/Center for Coastal Monitoring and Assessment’s Biogeography Team developed digital benthic habitat maps for all MLCD and adjacent habitats. These maps were used to evaluate the efficacy of existing MLCDs for biodiversity conservation and fisheries replenishment, using a spatially explicit stratified random sampling design. Coupling the distribution of habitats and species habitat affinities using GIS technology elucidates species habitat utilization patterns at scales that are commensurate with ecosystem processes and is useful in defining essential fish habitat and biologically relevant boundaries for MPAs. Analysis of benthic cover validated the a priori classification of habitat types and provided justification for using these habitat strata to conduct stratified random sampling and analyses of fish habitat utilization patterns. Results showed that the abundance and distribution of species and assemblages exhibited strong correlations with habitat types. Fish assemblages in the colonized and uncolonized hardbottom habitats were found to be most similar among all of the habitat types. Much of the macroalgae habitat sampled was macroalgae growing on hard substrate, and as a result showed similarities with the other hardbottom assemblages. The fish assemblages in the sand habitats were highly variable but distinct from the other habitat types. Management regime also played an important role in the abundance and distribution of fish assemblages. MLCDs had higher values for most fish assemblage characteristics (e.g. biomass, size, diversity) compared with adjacent fished areas and Fisheries Management Areas (FMAs) across all habitat types. In addition, apex predators and other targeted resources species were more abundant and larger in the MLCDs, illustrating the effectiveness of these closures in conserving fish populations. Habitat complexity, quality, size and level of protection from fishing were important determinates of MLCD effectiveness with respect to their associated fish assemblages. (PDF contains 217 pages