Hydrothermal contamination of public supply wells in Napa and Sonoma Valleys, California

USGS Publications Warehouse

Forrest, Matthew J.; Kulongoski, Justin T.; Edwards, Matthew S.; Farrar, Christopher D.; Belitz, Kenneth; Norris, Richard D.

2013-01-01

Groundwater chemistry and isotope data from 44 public supply wells in the Napa and Sonoma Valleys, California were determined to investigate mixing of relatively shallow groundwater with deeper hydrothermal fluids. Multivariate analyses including Cluster Analyses, Multidimensional Scaling (MDS), Principal Components Analyses (PCA), Analysis of Similarities (ANOSIM), and Similarity Percentage Analyses (SIMPER) were used to elucidate constituent distribution patterns, determine which constituents are significantly associated with these hydrothermal systems, and investigate hydrothermal contamination of local groundwater used for drinking water. Multivariate statistical analyses were essential to this study because traditional methods, such as mixing tests involving single species (e.g. Cl or SiO2) were incapable of quantifying component proportions due to mixing of multiple water types. Based on these analyses, water samples collected from the wells were broadly classified as fresh groundwater, saline waters, hydrothermal fluids, or mixed hydrothermal fluids/meteoric water wells. The Multivariate Mixing and Mass-balance (M3) model was applied in order to determine the proportion of hydrothermal fluids, saline water, and fresh groundwater in each sample. Major ions, isotopes, and physical parameters of the waters were used to characterize the hydrothermal fluids as Na–Cl type, with significant enrichment in the trace elements As, B, F and Li. Five of the wells from this study were classified as hydrothermal, 28 as fresh groundwater, two as saline water, and nine as mixed hydrothermal fluids/meteoric water wells. The M3 mixing-model results indicated that the nine mixed wells contained between 14% and 30% hydrothermal fluids. Further, the chemical analyses show that several of these mixed-water wells have concentrations of As, F and B that exceed drinking-water standards or notification levels due to contamination by hydrothermal fluids.

The multivariate egg: quantifying within- and among-clutch correlations between maternally derived yolk immunoglobulins and yolk androgens using multivariate mixed models.

PubMed

Postma, Erik; Siitari, Heli; Schwabl, Hubert; Richner, Heinz; Tschirren, Barbara

2014-03-01

Egg components are important mediators of prenatal maternal effects in birds and other oviparous species. Because different egg components can have opposite effects on offspring phenotype, selection is expected to favour their mutual adjustment, resulting in a significant covariation between egg components within and/or among clutches. Here we tested for such correlations between maternally derived yolk immunoglobulins and yolk androgens in great tit (Parus major) eggs using a multivariate mixed-model approach. We found no association between yolk immunoglobulins and yolk androgens within clutches, indicating that within clutches the two egg components are deposited independently. Across clutches, however, there was a significant negative relationship between yolk immunoglobulins and yolk androgens, suggesting that selection has co-adjusted their deposition. Furthermore, an experimental manipulation of ectoparasite load affected patterns of covariance among egg components. Yolk immunoglobulins are known to play an important role in nestling immune defence shortly after hatching, whereas yolk androgens, although having growth-enhancing effects under many environmental conditions, can be immunosuppressive. We therefore speculate that variation in the risk of parasitism may play an important role in shaping optimal egg composition and may lead to the observed pattern of yolk immunoglobulin and yolk androgen deposition across clutches. More generally, our case study exemplifies how multivariate mixed-model methodology presents a flexible tool to not only quantify, but also test patterns of (co)variation across different organisational levels and environments, allowing for powerful hypothesis testing in ecophysiology.

Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

Predicting the multi-domain progression of Parkinson's disease: a Bayesian multivariate generalized linear mixed-effect model.

PubMed

Wang, Ming; Li, Zheng; Lee, Eun Young; Lewis, Mechelle M; Zhang, Lijun; Sterling, Nicholas W; Wagner, Daymond; Eslinger, Paul; Du, Guangwei; Huang, Xuemei

2017-09-25

It is challenging for current statistical models to predict clinical progression of Parkinson's disease (PD) because of the involvement of multi-domains and longitudinal data. Past univariate longitudinal or multivariate analyses from cross-sectional trials have limited power to predict individual outcomes or a single moment. The multivariate generalized linear mixed-effect model (GLMM) under the Bayesian framework was proposed to study multi-domain longitudinal outcomes obtained at baseline, 18-, and 36-month. The outcomes included motor, non-motor, and postural instability scores from the MDS-UPDRS, and demographic and standardized clinical data were utilized as covariates. The dynamic prediction was performed for both internal and external subjects using the samples from the posterior distributions of the parameter estimates and random effects, and also the predictive accuracy was evaluated based on the root of mean square error (RMSE), absolute bias (AB) and the area under the receiver operating characteristic (ROC) curve. First, our prediction model identified clinical data that were differentially associated with motor, non-motor, and postural stability scores. Second, the predictive accuracy of our model for the training data was assessed, and improved prediction was gained in particularly for non-motor (RMSE and AB: 2.89 and 2.20) compared to univariate analysis (RMSE and AB: 3.04 and 2.35). Third, the individual-level predictions of longitudinal trajectories for the testing data were performed, with ~80% observed values falling within the 95% credible intervals. Multivariate general mixed models hold promise to predict clinical progression of individual outcomes in PD. The data was obtained from Dr. Xuemei Huang's NIH grant R01 NS060722 , part of NINDS PD Biomarker Program (PDBP). All data was entered within 24 h of collection to the Data Management Repository (DMR), which is publically available ( https://pdbp.ninds.nih.gov/data-management ).

A mixed model framework for teratology studies.

PubMed

Braeken, Johan; Tuerlinckx, Francis

2009-10-01

A mixed model framework is presented to model the characteristic multivariate binary anomaly data as provided in some teratology studies. The key features of the model are the incorporation of covariate effects, a flexible random effects distribution by means of a finite mixture, and the application of copula functions to better account for the relation structure of the anomalies. The framework is motivated by data of the Boston Anticonvulsant Teratogenesis study and offers an integrated approach to investigate substantive questions, concerning general and anomaly-specific exposure effects of covariates, interrelations between anomalies, and objective diagnostic measurement.

MANOVA vs nonlinear mixed effects modeling: The comparison of growth patterns of female and male quail

NASA Astrophysics Data System (ADS)

Gürcan, Eser Kemal

2017-04-01

The most commonly used methods for analyzing time-dependent data are multivariate analysis of variance (MANOVA) and nonlinear regression models. The aim of this study was to compare some MANOVA techniques and nonlinear mixed modeling approach for investigation of growth differentiation in female and male Japanese quail. Weekly individual body weight data of 352 male and 335 female quail from hatch to 8 weeks of age were used to perform analyses. It is possible to say that when all the analyses are evaluated, the nonlinear mixed modeling is superior to the other techniques because it also reveals the individual variation. In addition, the profile analysis also provides important information.

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

Implementing Restricted Maximum Likelihood Estimation in Structural Equation Models

ERIC Educational Resources Information Center

Cheung, Mike W.-L.

2013-01-01

Structural equation modeling (SEM) is now a generic modeling framework for many multivariate techniques applied in the social and behavioral sciences. Many statistical models can be considered either as special cases of SEM or as part of the latent variable modeling framework. One popular extension is the use of SEM to conduct linear mixed-effects…

Identifying Pleiotropic Genes in Genome-Wide Association Studies for Multivariate Phenotypes with Mixed Measurement Scales

PubMed Central

Williams, L. Keoki; Buu, Anne

2017-01-01

We propose a multivariate genome-wide association test for mixed continuous, binary, and ordinal phenotypes. A latent response model is used to estimate the correlation between phenotypes with different measurement scales so that the empirical distribution of the Fisher’s combination statistic under the null hypothesis is estimated efficiently. The simulation study shows that our proposed correlation estimation methods have high levels of accuracy. More importantly, our approach conservatively estimates the variance of the test statistic so that the type I error rate is controlled. The simulation also shows that the proposed test maintains the power at the level very close to that of the ideal analysis based on known latent phenotypes while controlling the type I error. In contrast, conventional approaches–dichotomizing all observed phenotypes or treating them as continuous variables–could either reduce the power or employ a linear regression model unfit for the data. Furthermore, the statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that conducting a multivariate test on multiple phenotypes can increase the power of identifying markers that may not be, otherwise, chosen using marginal tests. The proposed method also offers a new approach to analyzing the Fagerström Test for Nicotine Dependence as multivariate phenotypes in genome-wide association studies. PMID:28081206

Access disparities to Magnet hospitals for patients undergoing neurosurgical operations

PubMed Central

Missios, Symeon; Bekelis, Kimon

2017-01-01

Background Centers of excellence focusing on quality improvement have demonstrated superior outcomes for a variety of surgical interventions. We investigated the presence of access disparities to hospitals recognized by the Magnet Recognition Program of the American Nurses Credentialing Center (ANCC) for patients undergoing neurosurgical operations. Methods We performed a cohort study of all neurosurgery patients who were registered in the New York Statewide Planning and Research Cooperative System (SPARCS) database from 2009–2013. We examined the association of African-American race and lack of insurance with Magnet status hospitalization for neurosurgical procedures. A mixed effects propensity adjusted multivariable regression analysis was used to control for confounding. Results During the study period, 190,535 neurosurgical patients met the inclusion criteria. Using a multivariable logistic regression, we demonstrate that African-Americans had lower admission rates to Magnet institutions (OR 0.62; 95% CI, 0.58–0.67). This persisted in a mixed effects logistic regression model (OR 0.77; 95% CI, 0.70–0.83) to adjust for clustering at the patient county level, and a propensity score adjusted logistic regression model (OR 0.75; 95% CI, 0.69–0.82). Additionally, lack of insurance was associated with lower admission rates to Magnet institutions (OR 0.71; 95% CI, 0.68–0.73), in a multivariable logistic regression model. This persisted in a mixed effects logistic regression model (OR 0.72; 95% CI, 0.69–0.74), and a propensity score adjusted logistic regression model (OR 0.72; 95% CI, 0.69–0.75). Conclusions Using a comprehensive all-payer cohort of neurosurgery patients in New York State we identified an association of African-American race and lack of insurance with lower rates of admission to Magnet hospitals. PMID:28684152

Multi-disease analysis of maternal antibody decay using non-linear mixed models accounting for censoring.

PubMed

Goeyvaerts, Nele; Leuridan, Elke; Faes, Christel; Van Damme, Pierre; Hens, Niel

2015-09-10

Biomedical studies often generate repeated measures of multiple outcomes on a set of subjects. It may be of interest to develop a biologically intuitive model for the joint evolution of these outcomes while assessing inter-subject heterogeneity. Even though it is common for biological processes to entail non-linear relationships, examples of multivariate non-linear mixed models (MNMMs) are still fairly rare. We contribute to this area by jointly analyzing the maternal antibody decay for measles, mumps, rubella, and varicella, allowing for a different non-linear decay model for each infectious disease. We present a general modeling framework to analyze multivariate non-linear longitudinal profiles subject to censoring, by combining multivariate random effects, non-linear growth and Tobit regression. We explore the hypothesis of a common infant-specific mechanism underlying maternal immunity using a pairwise correlated random-effects approach and evaluating different correlation matrix structures. The implied marginal correlation between maternal antibody levels is estimated using simulations. The mean duration of passive immunity was less than 4 months for all diseases with substantial heterogeneity between infants. The maternal antibody levels against rubella and varicella were found to be positively correlated, while little to no correlation could be inferred for the other disease pairs. For some pairs, computational issues occurred with increasing correlation matrix complexity, which underlines the importance of further developing estimation methods for MNMMs. Copyright © 2015 John Wiley & Sons, Ltd.

Organizational Change, Absenteeism, and Welfare Dependency

ERIC Educational Resources Information Center

Roed, Knut; Fevang, Elisabeth

2007-01-01

Based on Norwegian register data, we set up a multivariate mixed proportional hazard model (MMPH) to analyze nurses' pattern of work, sickness absence, nonemployment, and social insurance dependency from 1992 to 2000, and how that pattern was affected by workplace characteristics. The model is estimated by means of the nonparametric…

Solving large mixed linear models using preconditioned conjugate gradient iteration.

PubMed

Strandén, I; Lidauer, M

1999-12-01

Continuous evaluation of dairy cattle with a random regression test-day model requires a fast solving method and algorithm. A new computing technique feasible in Jacobi and conjugate gradient based iterative methods using iteration on data is presented. In the new computing technique, the calculations in multiplication of a vector by a matrix were recorded to three steps instead of the commonly used two steps. The three-step method was implemented in a general mixed linear model program that used preconditioned conjugate gradient iteration. Performance of this program in comparison to other general solving programs was assessed via estimation of breeding values using univariate, multivariate, and random regression test-day models. Central processing unit time per iteration with the new three-step technique was, at best, one-third that needed with the old technique. Performance was best with the test-day model, which was the largest and most complex model used. The new program did well in comparison to other general software. Programs keeping the mixed model equations in random access memory required at least 20 and 435% more time to solve the univariate and multivariate animal models, respectively. Computations of the second best iteration on data took approximately three and five times longer for the animal and test-day models, respectively, than did the new program. Good performance was due to fast computing time per iteration and quick convergence to the final solutions. Use of preconditioned conjugate gradient based methods in solving large breeding value problems is supported by our findings.

Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Multivariate meta-analysis using individual participant data

PubMed Central

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2016-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484

An R2 statistic for fixed effects in the linear mixed model.

PubMed

Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D; Qaqish, Bahjat F; Schabenberger, Oliver

2008-12-20

Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R(2) statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R(2) statistic for the linear mixed model by using only a single model. The proposed R(2) statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R(2) statistic arises as a 1-1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R(2) statistic leads immediately to a natural definition of a partial R(2) statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R(2) , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study.

A flexible model for multivariate interval-censored survival times with complex correlation structure.

PubMed

Falcaro, Milena; Pickles, Andrew

2007-02-10

We focus on the analysis of multivariate survival times with highly structured interdependency and subject to interval censoring. Such data are common in developmental genetics and genetic epidemiology. We propose a flexible mixed probit model that deals naturally with complex but uninformative censoring. The recorded ages of onset are treated as possibly censored ordinal outcomes with the interval censoring mechanism seen as arising from a coarsened measurement of a continuous variable observed as falling between subject-specific thresholds. This bypasses the requirement for the failure times to be observed as falling into non-overlapping intervals. The assumption of a normal age-of-onset distribution of the standard probit model is relaxed by embedding within it a multivariate Box-Cox transformation whose parameters are jointly estimated with the other parameters of the model. Complex decompositions of the underlying multivariate normal covariance matrix of the transformed ages of onset become possible. The new methodology is here applied to a multivariate study of the ages of first use of tobacco and first consumption of alcohol without parental permission in twins. The proposed model allows estimation of the genetic and environmental effects that are shared by both of these risk behaviours as well as those that are specific. 2006 John Wiley & Sons, Ltd.

Determination of rice syrup adulterant concentration in honey using three-dimensional fluorescence spectra and multivariate calibrations

NASA Astrophysics Data System (ADS)

Chen, Quansheng; Qi, Shuai; Li, Huanhuan; Han, Xiaoyan; Ouyang, Qin; Zhao, Jiewen

2014-10-01

To rapidly and efficiently detect the presence of adulterants in honey, three-dimensional fluorescence spectroscopy (3DFS) technique was employed with the help of multivariate calibration. The data of 3D fluorescence spectra were compressed using characteristic extraction and the principal component analysis (PCA). Then, partial least squares (PLS) and back propagation neural network (BP-ANN) algorithms were used for modeling. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The results showed that BP-ANN model was superior to PLS models, and the optimum prediction results of the mixed group (sunflower ± longan ± buckwheat ± rape) model were achieved as follow: RMSEP = 0.0235 and R = 0.9787 in the prediction set. The study demonstrated that the 3D fluorescence spectroscopy technique combined with multivariate calibration has high potential in rapid, nondestructive, and accurate quantitative analysis of honey adulteration.

Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1984-01-01

The objective of this investigation is to develop a state-of-the-art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies. A three-dimensional multivariate O/I analysis scheme has been developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.

Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

The development of a state of the art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies was investigated. A three dimensional multivariate O/I analysis scheme was developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.

POWERLIB: SAS/IML Software for Computing Power in Multivariate Linear Models

PubMed Central

Johnson, Jacqueline L.; Muller, Keith E.; Slaughter, James C.; Gurka, Matthew J.; Gribbin, Matthew J.; Simpson, Sean L.

2014-01-01

The POWERLIB SAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors. The software includes the Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the “univariate” approach to repeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda tests in “multivariate” approach (MULTIREP), as well as a limited but useful range of mixed models. The familiar univariate linear model with Gaussian errors is an important special case. For estimated covariance, the software provides confidence limits for the resulting estimated power. All power and confidence limits values can be output to a SAS dataset, which can be used to easily produce plots and tables for manuscripts. PMID:25400516

Development of Pattern Recognition Techniques for the Evaluation of Toxicant Impacts to Multispecies Systems

DTIC Science & Technology

1993-06-18

the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and clustering methods...rule rather than the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and...experiments using two microcosm protocols. We use nonmetric clustering, a multivariate pattern recognition technique developed by Matthews and Heame (1991

Multivariate Analysis of Longitudinal Rates of Change

PubMed Central

Bryan, Matthew; Heagerty, Patrick J.

2016-01-01

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed by Roy and Lin [1]; Proust-Lima, Letenneur and Jacqmin-Gadda [2]; and Gray and Brookmeyer [3] among others. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, Gray and Brookmeyer [3] introduce an “accelerated time” method which assumes that covariates rescale time in longitudinal models for disease progression. In this manuscript we detail an alternative multivariate model formulation that directly structures longitudinal rates of change, and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. PMID:27417129

The "Nursing Home Compare" measure of urinary/fecal incontinence: cross-sectional variation, stability over time, and the impact of case mix.

PubMed

Li, Yue; Schnelle, John; Spector, William D; Glance, Laurent G; Mukamel, Dana B

2010-02-01

To assess the impact of facility case mix on cross-sectional variations and short-term stability of the "Nursing Home Compare" incontinence quality measure (QM) and to determine whether multivariate risk adjustment can minimize such impacts. Retrospective analyses of the 2005 national minimum data set (MDS) that included approximately 600,000 long-term care residents in over 10,000 facilities in each quarterly sample. Mixed logistic regression was used to construct the risk-adjusted QM (nonshrinkage estimator). Facility-level ordinary least-squares models and adjusted R(2) were used to estimate the impact of case mix on cross-sectional and short-term longitudinal variations of currently published and risk-adjusted QMs. At least 50 percent of the cross-sectional variation and 25 percent of the short-term longitudinal variation of the published QM are explained by facility case mix. In contrast, the cross-sectional and short-term longitudinal variations of the risk-adjusted QM are much less susceptible to case-mix variations (adjusted R(2)<0.10), even for facilities with more extreme or more unstable outcome. Current "Nursing Home Compare" incontinence QM reflects considerable case-mix variations across facilities and over time, and therefore it may be biased. This issue can be largely addressed by multivariate risk adjustment using risk factors available in the MDS.

The impact of covariance misspecification in multivariate Gaussian mixtures on estimation and inference: an application to longitudinal modeling.

PubMed

Heggeseth, Brianna C; Jewell, Nicholas P

2013-07-20

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence-that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.

Multivariate meta-analysis using individual participant data.

PubMed

Riley, R D; Price, M J; Jackson, D; Wardle, M; Gueyffier, F; Wang, J; Staessen, J A; White, I R

2015-06-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

[Influence of sample surface roughness on mathematical model of NIR quantitative analysis of wood density].

PubMed

Huang, An-Min; Fei, Ben-Hua; Jiang, Ze-Hui; Hse, Chung-Yun

2007-09-01

Near infrared spectroscopy is widely used as a quantitative method, and the main multivariate techniques consist of regression methods used to build prediction models, however, the accuracy of analysis results will be affected by many factors. In the present paper, the influence of different sample roughness on the mathematical model of NIR quantitative analysis of wood density was studied. The result of experiments showed that if the roughness of predicted samples was consistent with that of calibrated samples, the result was good, otherwise the error would be much higher. The roughness-mixed model was more flexible and adaptable to different sample roughness. The prediction ability of the roughness-mixed model was much better than that of the single-roughness model.

Use of collateral information to improve LANDSAT classification accuracies

NASA Technical Reports Server (NTRS)

Strahler, A. H. (Principal Investigator)

1981-01-01

Methods to improve LANDSAT classification accuracies were investigated including: (1) the use of prior probabilities in maximum likelihood classification as a methodology to integrate discrete collateral data with continuously measured image density variables; (2) the use of the logit classifier as an alternative to multivariate normal classification that permits mixing both continuous and categorical variables in a single model and fits empirical distributions of observations more closely than the multivariate normal density function; and (3) the use of collateral data in a geographic information system as exercised to model a desired output information layer as a function of input layers of raster format collateral and image data base layers.

Deconvolution of mixing time series on a graph

PubMed Central

Blocker, Alexander W.; Airoldi, Edoardo M.

2013-01-01

In many applications we are interested in making inference on latent time series from indirect measurements, which are often low-dimensional projections resulting from mixing or aggregation. Positron emission tomography, super-resolution, and network traffic monitoring are some examples. Inference in such settings requires solving a sequence of ill-posed inverse problems, yt = Axt, where the projection mechanism provides information on A. We consider problems in which A specifies mixing on a graph of times series that are bursty and sparse. We develop a multilevel state-space model for mixing times series and an efficient approach to inference. A simple model is used to calibrate regularization parameters that lead to efficient inference in the multilevel state-space model. We apply this method to the problem of estimating point-to-point traffic flows on a network from aggregate measurements. Our solution outperforms existing methods for this problem, and our two-stage approach suggests an efficient inference strategy for multilevel models of multivariate time series. PMID:25309135

Mapping eQTL Networks with Mixed Graphical Markov Models

PubMed Central

Tur, Inma; Roverato, Alberto; Castelo, Robert

2014-01-01

Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect effects spread throughout genes as a result of genetic, molecular, and environmental perturbations. From a multivariate perspective one would like to adjust for the effect of all of these factors to end up with a network of direct associations connecting the path from genotype to phenotype. In this article we approach this challenge with mixed graphical Markov models, higher-order conditional independences, and q-order correlation graphs. These models show that additive genetic effects propagate through the network as function of gene–gene correlations. Our estimation of the eQTL network underlying a well-studied yeast data set leads to a sparse structure with more direct genetic and regulatory associations that enable a straightforward comparison of the genetic control of gene expression across chromosomes. Interestingly, it also reveals that eQTLs explain most of the expression variability of network hub genes. PMID:25271303

Alternative High School Students: Prevalence and Correlates of Overweight

ERIC Educational Resources Information Center

Kubik, Martha Y.; Davey, Cynthia; Fulkerson, Jayne A.; Sirard, John; Story, Mary; Arcan, Chrisa

2009-01-01

Objective: To determine prevalence and correlates of overweight among adolescents attending alternative high schools (AHS). Methods: AHS students (n=145) from 6 schools completed surveys and anthropometric measures. Cross-sectional associations were assessed using mixed model multivariate logistic regression. Results: Among students, 42% were…

A multivariate multilevel Gaussian model with a mixed effects structure in the mean and covariance part.

PubMed

Li, Baoyue; Bruyneel, Luk; Lesaffre, Emmanuel

2014-05-20

A traditional Gaussian hierarchical model assumes a nested multilevel structure for the mean and a constant variance at each level. We propose a Bayesian multivariate multilevel factor model that assumes a multilevel structure for both the mean and the covariance matrix. That is, in addition to a multilevel structure for the mean we also assume that the covariance matrix depends on covariates and random effects. This allows to explore whether the covariance structure depends on the values of the higher levels and as such models heterogeneity in the variances and correlation structure of the multivariate outcome across the higher level values. The approach is applied to the three-dimensional vector of burnout measurements collected on nurses in a large European study to answer the research question whether the covariance matrix of the outcomes depends on recorded system-level features in the organization of nursing care, but also on not-recorded factors that vary with countries, hospitals, and nursing units. Simulations illustrate the performance of our modeling approach. Copyright © 2013 John Wiley & Sons, Ltd.

Groundwater flow processes and mixing in active volcanic systems: the case of Guadalajara (Mexico)

NASA Astrophysics Data System (ADS)

Hernández-Antonio, A.; Mahlknecht, J.; Tamez-Meléndez, C.; Ramos-Leal, J.; Ramírez-Orozco, A.; Parra, R.; Ornelas-Soto, N.; Eastoe, C. J.

2015-02-01

Groundwater chemistry and isotopic data from 40 production wells in the Atemajac and Toluquilla Valleys, located in and around the Guadalajara metropolitan area, were determined to develop a conceptual model of groundwater flow processes and mixing. Multivariate analysis including cluster analysis and principal component analysis were used to elucidate distribution patterns of constituents and factors controlling groundwater chemistry. Based on this analysis, groundwater was classified into four groups: cold groundwater, hydrothermal water, polluted groundwater and mixed groundwater. Cold groundwater is characterized by low temperature, salinity, and Cl and Na concentrations and is predominantly of Na-HCO3 type. It originates as recharge at Primavera caldera and is found predominantly in wells in the upper Atemajac Valley. Hydrothermal water is characterized by high salinity, temperature, Cl, Na, HCO3, and the presence of minor elements such as Li, Mn and F. It is a mixed HCO3 type found in wells from Toluquilla Valley and represents regional flow circulation through basaltic and andesitic rocks. Polluted groundwater is characterized by elevated nitrate and sulfate concentrations and is usually derived from urban water cycling and subordinately from agricultural practices. Mixed groundwaters between cold and hydrothermal components are predominantly found in the lower Atemajac Valley. Tritium method elucidated that practically all of the sampled groundwater contains at least a small fraction of modern water. The multivariate mixing model M3 indicates that the proportion of hydrothermal fluids in sampled well water is between 13 (local groundwater) and 87% (hydrothermal water), and the proportion of polluted water in wells ranges from 0 to 63%. This study may help local water authorities to identify and quantify groundwater contamination and act accordingly.

Using empirical Bayes predictors from generalized linear mixed models to test and visualize associations among longitudinal outcomes.

PubMed

Mikulich-Gilbertson, Susan K; Wagner, Brandie D; Grunwald, Gary K; Riggs, Paula D; Zerbe, Gary O

2018-01-01

Medical research is often designed to investigate changes in a collection of response variables that are measured repeatedly on the same subjects. The multivariate generalized linear mixed model (MGLMM) can be used to evaluate random coefficient associations (e.g. simple correlations, partial regression coefficients) among outcomes that may be non-normal and differently distributed by specifying a multivariate normal distribution for their random effects and then evaluating the latent relationship between them. Empirical Bayes predictors are readily available for each subject from any mixed model and are observable and hence, plotable. Here, we evaluate whether second-stage association analyses of empirical Bayes predictors from a MGLMM, provide a good approximation and visual representation of these latent association analyses using medical examples and simulations. Additionally, we compare these results with association analyses of empirical Bayes predictors generated from separate mixed models for each outcome, a procedure that could circumvent computational problems that arise when the dimension of the joint covariance matrix of random effects is large and prohibits estimation of latent associations. As has been shown in other analytic contexts, the p-values for all second-stage coefficients that were determined by naively assuming normality of empirical Bayes predictors provide a good approximation to p-values determined via permutation analysis. Analyzing outcomes that are interrelated with separate models in the first stage and then associating the resulting empirical Bayes predictors in a second stage results in different mean and covariance parameter estimates from the maximum likelihood estimates generated by a MGLMM. The potential for erroneous inference from using results from these separate models increases as the magnitude of the association among the outcomes increases. Thus if computable, scatterplots of the conditionally independent empirical Bayes predictors from a MGLMM are always preferable to scatterplots of empirical Bayes predictors generated by separate models, unless the true association between outcomes is zero.

Analysis of multivariate longitudinal kidney function outcomes using generalized linear mixed models.

PubMed

Jaffa, Miran A; Gebregziabher, Mulugeta; Jaffa, Ayad A

2015-06-14

Renal transplant patients are mandated to have continuous assessment of their kidney function over time to monitor disease progression determined by changes in blood urea nitrogen (BUN), serum creatinine (Cr), and estimated glomerular filtration rate (eGFR). Multivariate analysis of these outcomes that aims at identifying the differential factors that affect disease progression is of great clinical significance. Thus our study aims at demonstrating the application of different joint modeling approaches with random coefficients on a cohort of renal transplant patients and presenting a comparison of their performance through a pseudo-simulation study. The objective of this comparison is to identify the model with best performance and to determine whether accuracy compensates for complexity in the different multivariate joint models. We propose a novel application of multivariate Generalized Linear Mixed Models (mGLMM) to analyze multiple longitudinal kidney function outcomes collected over 3 years on a cohort of 110 renal transplantation patients. The correlated outcomes BUN, Cr, and eGFR and the effect of various covariates such patient's gender, age and race on these markers was determined holistically using different mGLMMs. The performance of the various mGLMMs that encompass shared random intercept (SHRI), shared random intercept and slope (SHRIS), separate random intercept (SPRI) and separate random intercept and slope (SPRIS) was assessed to identify the one that has the best fit and most accurate estimates. A bootstrap pseudo-simulation study was conducted to gauge the tradeoff between the complexity and accuracy of the models. Accuracy was determined using two measures; the mean of the differences between the estimates of the bootstrapped datasets and the true beta obtained from the application of each model on the renal dataset, and the mean of the square of these differences. The results showed that SPRI provided most accurate estimates and did not exhibit any computational or convergence problem. Higher accuracy was demonstrated when the level of complexity increased from shared random coefficient models to the separate random coefficient alternatives with SPRI showing to have the best fit and most accurate estimates.

Order-restricted inference for multivariate longitudinal data with applications to the natural history of hearing loss.

PubMed

Rosen, Sophia; Davidov, Ori

2012-07-20

Multivariate outcomes are often measured longitudinally. For example, in hearing loss studies, hearing thresholds for each subject are measured repeatedly over time at several frequencies. Thus, each patient is associated with a multivariate longitudinal outcome. The multivariate mixed-effects model is a useful tool for the analysis of such data. There are situations in which the parameters of the model are subject to some restrictions or constraints. For example, it is known that hearing thresholds, at every frequency, increase with age. Moreover, this age-related threshold elevation is monotone in frequency, that is, the higher the frequency, the higher, on average, is the rate of threshold elevation. This means that there is a natural ordering among the different frequencies in the rate of hearing loss. In practice, this amounts to imposing a set of constraints on the different frequencies' regression coefficients modeling the mean effect of time and age at entry to the study on hearing thresholds. The aforementioned constraints should be accounted for in the analysis. The result is a multivariate longitudinal model with restricted parameters. We propose estimation and testing procedures for such models. We show that ignoring the constraints may lead to misleading inferences regarding the direction and the magnitude of various effects. Moreover, simulations show that incorporating the constraints substantially improves the mean squared error of the estimates and the power of the tests. We used this methodology to analyze a real hearing loss study. Copyright © 2012 John Wiley & Sons, Ltd.

Multivariate longitudinal data analysis with mixed effects hidden Markov models.

PubMed

Raffa, Jesse D; Dubin, Joel A

2015-09-01

Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies. © 2015, The International Biometric Society.

Sediment fingerprinting experiments to test the sensitivity of multivariate mixing models

NASA Astrophysics Data System (ADS)

Gaspar, Leticia; Blake, Will; Smith, Hugh; Navas, Ana

2014-05-01

Sediment fingerprinting techniques provide insight into the dynamics of sediment transfer processes and support for catchment management decisions. As questions being asked of fingerprinting datasets become increasingly complex, validation of model output and sensitivity tests are increasingly important. This study adopts an experimental approach to explore the validity and sensitivity of mixing model outputs for materials with contrasting geochemical and particle size composition. The experiments reported here focused on (i) the sensitivity of model output to different fingerprint selection procedures and (ii) the influence of source material particle size distributions on model output. Five soils with significantly different geochemistry, soil organic matter and particle size distributions were selected as experimental source materials. A total of twelve sediment mixtures were prepared in the laboratory by combining different quantified proportions of the < 63 µm fraction of the five source soils i.e. assuming no fluvial sorting of the mixture. The geochemistry of all source and mixture samples (5 source soils and 12 mixed soils) were analysed using X-ray fluorescence (XRF). Tracer properties were selected from 18 elements for which mass concentrations were found to be significantly different between sources. Sets of fingerprint properties that discriminate target sources were selected using a range of different independent statistical approaches (e.g. Kruskal-Wallis test, Discriminant Function Analysis (DFA), Principal Component Analysis (PCA), or correlation matrix). Summary results for the use of the mixing model with the different sets of fingerprint properties for the twelve mixed soils were reasonably consistent with the initial mixing percentages initially known. Given the experimental nature of the work and dry mixing of materials, geochemical conservative behavior was assumed for all elements, even for those that might be disregarded in aquatic systems (e.g. P). In general, the best fits between actual and modeled proportions were found using a set of nine tracer properties (Sr, Rb, Fe, Ti, Ca, Al, P, Si, K, Si) that were derived using DFA coupled with a multivariate stepwise algorithm, with errors between real and estimated value that did not exceed 6.7 % and values of GOF above 94.5 %. The second set of experiments aimed to explore the sensitivity of model output to variability in the particle size of source materials assuming that a degree of fluvial sorting of the resulting mixture took place. Most particle size correction procedures assume grain size affects are consistent across sources and tracer properties which is not always the case. Consequently, the < 40 µm fraction of selected soil mixtures was analysed to simulate the effect of selective fluvial transport of finer particles and the results were compared to those for source materials. Preliminary findings from this experiment demonstrate the sensitivity of the numerical mixing model outputs to different particle size distributions of source material and the variable impact of fluvial sorting on end member signatures used in mixing models. The results suggest that particle size correction procedures require careful scrutiny in the context of variable source characteristics.

On the Bayesian Treed Multivariate Gaussian Process with Linear Model of Coregionalization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Konomi, Bledar A.; Karagiannis, Georgios; Lin, Guang

2015-02-01

The Bayesian treed Gaussian process (BTGP) has gained popularity in recent years because it provides a straightforward mechanism for modeling non-stationary data and can alleviate computational demands by fitting models to less data. The extension of BTGP to the multivariate setting requires us to model the cross-covariance and to propose efficient algorithms that can deal with trans-dimensional MCMC moves. In this paper we extend the cross-covariance of the Bayesian treed multivariate Gaussian process (BTMGP) to that of linear model of Coregionalization (LMC) cross-covariances. Different strategies have been developed to improve the MCMC mixing and invert smaller matrices in the Bayesianmore » inference. Moreover, we compare the proposed BTMGP with existing multiple BTGP and BTMGP in test cases and multiphase flow computer experiment in a full scale regenerator of a carbon capture unit. The use of the BTMGP with LMC cross-covariance helped to predict the computer experiments relatively better than existing competitors. The proposed model has a wide variety of applications, such as computer experiments and environmental data. In the case of computer experiments we also develop an adaptive sampling strategy for the BTMGP with LMC cross-covariance function.« less

Comparison of Two Procedures for Analyzing Small Sets of Repeated Measures Data

ERIC Educational Resources Information Center

Vallejo, Guillermo; Livacic-Rojas, Pablo

2005-01-01

This article compares two methods for analyzing small sets of repeated measures data under normal and non-normal heteroscedastic conditions: a mixed model approach with the Kenward-Roger correction and a multivariate extension of the modified Brown-Forsythe (BF) test. These procedures differ in their assumptions about the covariance structure of…

Groundwater flow processes and mixing in active volcanic systems: the case of Guadalajara (Mexico)

NASA Astrophysics Data System (ADS)

Hernández-Antonio, A.; Mahlknecht, J.; Tamez-Meléndez, C.; Ramos-Leal, J.; Ramírez-Orozco, A.; Parra, R.; Ornelas-Soto, N.; Eastoe, C. J.

2015-09-01

Groundwater chemistry and isotopic data from 40 production wells in the Atemajac and Toluquilla valleys, located in and around the Guadalajara metropolitan area, were determined to develop a conceptual model of groundwater flow processes and mixing. Stable water isotopes (δ2H, δ18O) were used to trace hydrological processes and tritium (3H) to evaluate the relative contribution of modern water in samples. Multivariate analysis including cluster analysis and principal component analysis were used to elucidate distribution patterns of constituents and factors controlling groundwater chemistry. Based on this analysis, groundwater was classified into four groups: cold groundwater, hydrothermal groundwater, polluted groundwater and mixed groundwater. Cold groundwater is characterized by low temperature, salinity, and Cl and Na concentrations and is predominantly of Na-HCO3-type. It originates as recharge at "La Primavera" caldera and is found predominantly in wells in the upper Atemajac Valley. Hydrothermal groundwater is characterized by high salinity, temperature, Cl, Na and HCO3, and the presence of minor elements such as Li, Mn and F. It is a mixed-HCO3 type found in wells from Toluquilla Valley and represents regional flow circulation through basaltic and andesitic rocks. Polluted groundwater is characterized by elevated nitrate and sulfate concentrations and is usually derived from urban water cycling and subordinately from agricultural return flow. Mixed groundwaters between cold and hydrothermal components are predominantly found in the lower Atemajac Valley. Twenty-seven groundwater samples contain at least a small fraction of modern water. The application of a multivariate mixing model allowed the mixing proportions of hydrothermal fluids, polluted waters and cold groundwater in sampled water to be evaluated. This study will help local water authorities to identify and dimension groundwater contamination, and act accordingly. It may be broadly applicable to other active volcanic systems on Earth.

Antimicrobial Drug Prescription and Neisseria gonorrhoeae Susceptibility, United States, 2005–2013

PubMed Central

Bartoces, Monina G.; Soge, Olusegun O.; Riedel, Stefan; Kubin, Grace; Del Rio, Carlos; Papp, John R.; Hook, Edward W.; Hicks, Lauri A.

2017-01-01

We investigated whether outpatient antimicrobial drug prescribing is associated with Neisseria gonorrhoeae antimicrobial drug susceptibility in the United States. Using susceptibility data from the Gonococcal Isolate Surveillance Project during 2005–2013 and QuintilesIMS data on outpatient cephalosporin, macrolide, and fluoroquinolone prescribing, we constructed multivariable linear mixed models for each antimicrobial agent with 1-year lagged annual prescribing per 1,000 persons as the exposure and geometric mean MIC as the outcome of interest. Multivariable models did not demonstrate associations between antimicrobial drug prescribing and N. gonorrhoeae susceptibility for any of the studied antimicrobial drugs during 2005–2013. Elucidation of epidemiologic factors contributing to resistance, including further investigation of the potential role of antimicrobial drug use, is needed. PMID:28930001

A Comparison of the Bootstrap-F, Improved General Approximation, and Brown-Forsythe Multivariate Approaches in a Mixed Repeated Measures Design

ERIC Educational Resources Information Center

Seco, Guillermo Vallejo; Izquierdo, Marcelino Cuesta; Garcia, M. Paula Fernandez; Diez, F. Javier Herrero

2006-01-01

The authors compare the operating characteristics of the bootstrap-F approach, a direct extension of the work of Berkovits, Hancock, and Nevitt, with Huynh's improved general approximation (IGA) and the Brown-Forsythe (BF) multivariate approach in a mixed repeated measures design when normality and multisample sphericity assumptions do not hold.…

Civic Purpose in Late Adolescence: Factors That Prevent Decline in Civic Engagement after High School

ERIC Educational Resources Information Center

Malin, Heather; Han, Hyemin; Liauw, Indrawati

2017-01-01

This study investigated the effects of internal and demographic variables on civic development in late adolescence using the construct "civic purpose." We conducted surveys on civic engagement with 480 high school seniors, and surveyed them again 2 years later. Using multivariate regression and linear mixed models, we tested the main…

Sedentary Activity and Body Composition of Middle School Girls: The Trial of Activity for Adolescent Girls

ERIC Educational Resources Information Center

Pratt, Charlotte; Webber, Larry S.; Baggett, Chris D.; Ward, Dianne; Pate, Russell R.; Murray, David; Lohman, Timothy; Lytle, Leslie; Elder, John P.

2008-01-01

This study describes the relationships between sedentary activity and body composition in 1,458 sixth-grade girls from 36 middle schools across the United States. Multivariate associations between sedentary activity and body composition were examined with regression analyses using general linear mixed models. Mean age, body mass index, and…

Self-declared stock ownership and association with positive trial outcome in randomized controlled trials with binary outcomes published in general medical journals: a cross-sectional study.

PubMed

Falk Delgado, Alberto; Falk Delgado, Anna

2017-07-26

Describe the prevalence and types of conflicts of interest (COI) in published randomized controlled trials (RCTs) in general medical journals with a binary primary outcome and assess the association between conflicts of interest and favorable outcome. Parallel-group RCTs with a binary primary outcome published in three general medical journals during 2013-2015 were identified. COI type, funding source, and outcome were extracted. Binomial logistic regression model was performed to assess association between COI and funding source with outcome. A total of 509 consecutive parallel-group RCTs were included in the study. COI was reported in 74% in mixed funded RCTs and in 99% in for-profit funded RCTs. Stock ownership was reported in none of the non-profit RCTs, in 7% of mixed funded RCTs, and in 50% of for-profit funded RCTs. Mixed-funded RCTs had employees from the funding company in 11% and for-profit RCTs in 76%. Multivariable logistic regression revealed that stock ownership in the funding company among any of the authors was associated with a favorable outcome (odds ratio = 3.53; 95% confidence interval = 1.59-7.86; p < 0.01). COI in for-profit funded RCTs is extensive, because the factors related to COI are not fully independent, a multivariable analysis should be cautiously interpreted. However, after multivariable adjustment only stock ownership from the funding company among authors is associated with a favorable outcome.

Interhospital differences and case-mix in a nationwide prevalence survey.

PubMed

Kanerva, M; Ollgren, J; Lyytikäinen, O

2010-10-01

A prevalence survey is a time-saving and useful tool for obtaining an overview of healthcare-associated infection (HCAI) either in a single hospital or nationally. Direct comparison of prevalence rates is difficult. We evaluated the impact of case-mix adjustment on hospital-specific prevalences. All five tertiary care, all 15 secondary care and 10 (25% of 40) other acute care hospitals took part in the first national prevalence survey in Finland in 2005. US Centers for Disease Control and Prevention criteria served to define HCAI. The information collected included demographic characteristics, severity of the underlying disease, use of catheters and a respirator, and previous surgery. Patients with HCAI related to another hospital were excluded. Case-mix-adjusted HCAI prevalences were calculated by using a multivariate logistic regression model for HCAI risk and an indirect standardisation method. Altogether, 587 (7.2%) of 8118 adult patients had at least one infection; hospital-specific prevalences ranged between 1.9% and 12.6%. Risk factors for HCAI that were previously known or identified by univariate analysis (age, male gender, intensive care, high Charlson comorbidity and McCabe indices, respirator, central venous or urinary catheters, and surgery during stay) were included in the multivariate analysis for standardisation. Case-mix-adjusted prevalences varied between 2.6% and 17.0%, and ranked the hospitals differently from the observed rates. In 11 (38%) hospitals, the observed prevalence rank was lower than predicted by the case-mix-adjusted figure. Case-mix should be taken into consideration in the interhospital comparison of prevalence rates. Copyright 2010 The Hospital Infection Society. Published by Elsevier Ltd. All rights reserved.

Detection and quantification of adulteration of sesame oils with vegetable oils using gas chromatography and multivariate data analysis.

PubMed

Peng, Dan; Bi, Yanlan; Ren, Xiaona; Yang, Guolong; Sun, Shangde; Wang, Xuede

2015-12-01

This study was performed to develop a hierarchical approach for detection and quantification of adulteration of sesame oil with vegetable oils using gas chromatography (GC). At first, a model was constructed to discriminate the difference between authentic sesame oils and adulterated sesame oils using support vector machine (SVM) algorithm. Then, another SVM-based model is developed to identify the type of adulterant in the mixed oil. At last, prediction models for sesame oil were built for each kind of oil using partial least square method. To validate this approach, 746 samples were prepared by mixing authentic sesame oils with five types of vegetable oil. The prediction results show that the detection limit for authentication is as low as 5% in mixing ratio and the root-mean-square errors for prediction range from 1.19% to 4.29%, meaning that this approach is a valuable tool to detect and quantify the adulteration of sesame oil. Copyright © 2015 Elsevier Ltd. All rights reserved.

Combination of near infrared spectroscopy and chemometrics for authentication of taro flour from wheat and sago flour

NASA Astrophysics Data System (ADS)

Rachmawati; Rohaeti, E.; Rafi, M.

2017-05-01

Taro flour on the market is usually sold at higher price than wheat and sago flour. This situation could be a cause for adulteration of taro flour from wheat and sago flour. For this reason, we will need an identification and authentication. Combination of near infrared (NIR) spectrum with multivariate analysis was used in this study to identify and authenticate taro flour from wheat and sago flour. The authentication model of taro flour was developed by using a mixture of 5%, 25%, and 50% of adulterated taro flour from wheat and sago flour. Before subjected to multivariate analysis, an initial preprocessing signal was used namely normalization and standard normal variate to the NIR spectrum. We used principal component analysis followed by discriminant analysis to make an identification and authentication model of taro flour. From the result obtained, about 90.48% of the taro flour mixed with wheat flour and 85% of taro flour mixed with sago flour were successfully classified into their groups. So the combination of NIR spectrum with chemometrics could be used for identification and authentication of taro flour from wheat and sago flour.

Multivariate longitudinal data analysis with censored and intermittent missing responses.

PubMed

Lin, Tsung-I; Lachos, Victor H; Wang, Wan-Lun

2018-05-08

The multivariate linear mixed model (MLMM) has emerged as an important analytical tool for longitudinal data with multiple outcomes. However, the analysis of multivariate longitudinal data could be complicated by the presence of censored measurements because of a detection limit of the assay in combination with unavoidable missing values arising when subjects miss some of their scheduled visits intermittently. This paper presents a generalization of the MLMM approach, called the MLMM-CM, for a joint analysis of the multivariate longitudinal data with censored and intermittent missing responses. A computationally feasible expectation maximization-based procedure is developed to carry out maximum likelihood estimation within the MLMM-CM framework. Moreover, the asymptotic standard errors of fixed effects are explicitly obtained via the information-based method. We illustrate our methodology by using simulated data and a case study from an AIDS clinical trial. Experimental results reveal that the proposed method is able to provide more satisfactory performance as compared with the traditional MLMM approach. Copyright © 2018 John Wiley & Sons, Ltd.

A Call for Conducting Multivariate Mixed Analyses

ERIC Educational Resources Information Center

Onwuegbuzie, Anthony J.

2016-01-01

Several authors have written methodological works that provide an introductory- and/or intermediate-level guide to conducting mixed analyses. Although these works have been useful for beginning and emergent mixed researchers, with very few exceptions, works are lacking that describe and illustrate advanced-level mixed analysis approaches. Thus,…

Factors associated with parasite dominance in fishes from Brazil.

PubMed

Amarante, Cristina Fernandes do; Tassinari, Wagner de Souza; Luque, Jose Luis; Pereira, Maria Julia Salim

2016-06-14

The present study used regression models to evaluate the existence of factors that may influence the numerical parasite dominance with an epidemiological approximation. A database including 3,746 fish specimens and their respective parasites were used to evaluate the relationship between parasite dominance and biotic characteristics inherent to the studied hosts and the parasite taxa. Multivariate, classical, and mixed effects linear regression models were fitted. The calculations were performed using R software (95% CI). In the fitting of the classical multiple linear regression model, freshwater and planktivorous fish species and body length, as well as the species of the taxa Trematoda, Monogenea, and Hirudinea, were associated with parasite dominance. However, the fitting of the mixed effects model showed that the body length of the host and the species of the taxa Nematoda, Trematoda, Monogenea, Hirudinea, and Crustacea were significantly associated with parasite dominance. Studies that consider specific biological aspects of the hosts and parasites should expand the knowledge regarding factors that influence the numerical dominance of fish in Brazil. The use of a mixed model shows, once again, the importance of the appropriate use of a model correlated with the characteristics of the data to obtain consistent results.

DasPy – Open Source Multivariate Land Data Assimilation Framework with High Performance Computing

NASA Astrophysics Data System (ADS)

Han, Xujun; Li, Xin; Montzka, Carsten; Kollet, Stefan; Vereecken, Harry; Hendricks Franssen, Harrie-Jan

2015-04-01

Data assimilation has become a popular method to integrate observations from multiple sources with land surface models to improve predictions of the water and energy cycles of the soil-vegetation-atmosphere continuum. In recent years, several land data assimilation systems have been developed in different research agencies. Because of the software availability or adaptability, these systems are not easy to apply for the purpose of multivariate land data assimilation research. Multivariate data assimilation refers to the simultaneous assimilation of observation data for multiple model state variables into a simulation model. Our main motivation was to develop an open source multivariate land data assimilation framework (DasPy) which is implemented using the Python script language mixed with C++ and Fortran language. This system has been evaluated in several soil moisture, L-band brightness temperature and land surface temperature assimilation studies. The implementation allows also parameter estimation (soil properties and/or leaf area index) on the basis of the joint state and parameter estimation approach. LETKF (Local Ensemble Transform Kalman Filter) is implemented as the main data assimilation algorithm, and uncertainties in the data assimilation can be represented by perturbed atmospheric forcings, perturbed soil and vegetation properties and model initial conditions. The CLM4.5 (Community Land Model) was integrated as the model operator. The CMEM (Community Microwave Emission Modelling Platform), COSMIC (COsmic-ray Soil Moisture Interaction Code) and the two source formulation were integrated as observation operators for assimilation of L-band passive microwave, cosmic-ray soil moisture probe and land surface temperature measurements, respectively. DasPy is parallelized using the hybrid MPI (Message Passing Interface) and OpenMP (Open Multi-Processing) techniques. All the input and output data flow is organized efficiently using the commonly used NetCDF file format. Online 1D and 2D visualization of data assimilation results is also implemented to facilitate the post simulation analysis. In summary, DasPy is a ready to use open source parallel multivariate land data assimilation framework.

Does investor ownership of nursing homes compromise the quality of care?

PubMed

Harrington, C; Woolhandler, S; Mullan, J; Carrillo, H; Himmelstein, D U

2001-09-01

Two thirds of nursing homes are investor owned. This study examined whether investor ownership affects quality. We analyzed 1998 data from state inspections of 13,693 nursing facilities. We used a multivariate model and controlled for case mix, facility characteristics, and location. Investor-owned facilities averaged 5.89 deficiencies per home, 46.5% higher than nonprofit facilities and 43.0% higher than public facilities. In multivariate analysis, investor ownership predicted 0.679 additional deficiencies per home; chain ownership predicted an additional 0.633 deficiencies. Nurse staffing was lower at investor-owned nursing homes. Investor-owned nursing homes provide worse care and less nursing care than do not-for-profit or public homes.

PharmML in Action: an Interoperable Language for Modeling and Simulation.

PubMed

Bizzotto, R; Comets, E; Smith, G; Yvon, F; Kristensen, N R; Swat, M J

2017-10-01

PharmML is an XML-based exchange format created with a focus on nonlinear mixed-effect (NLME) models used in pharmacometrics, but providing a very general framework that also allows describing mathematical and statistical models such as single-subject or nonlinear and multivariate regression models. This tutorial provides an overview of the structure of this language, brief suggestions on how to work with it, and use cases demonstrating its power and flexibility. © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Results of a survey to determine demographic and business management factors associated with size and growth rate of rural mixed-animal veterinary practices.

PubMed

Brusk, Amy M; White, Brad J; Goehl, Dan R; Dhuyvetter, Kevin C

2010-12-15

To determine potential associations between demographic and business management factors and practice size and growth rate in rural mixed-animal veterinary practices. Cross-sectional survey. 54 mixed-animal practitioners. A cross-sectional survey (96 questions) was electronically disseminated. Responses were collected, and outcomes (number of veterinarians [NV], growth in number of veterinarians [NVG], gross practice income [GPI], growth in gross practice income [GPIG], gross practice income per veterinarian [GPIV], and growth in gross practice income per veterinarian [GPIVG]) were calculated. Bivariate analyses were performed and multivariable models created to determine associations between survey responses and outcomes of interest. Survey respondents were from mixed-animal practices, and most (46/54 [85.2%]) practiced in small communities (< 25,000 people). Study practices had a median ± SD NV of 2.3 ± 1.9 veterinarians, median GPI of $704,547 ± 754,839, and median GPIV of $282,065 ± 182,344. Multivariable regression analysis revealed several factors related to practice size, including the number of associate veterinarians and veterinary technicians in the practice, service fee structure, and employment of a business manager. Typically, practices had positive mean growth in NVG (4.4%), GPIG (8.5%), and GPIVG (8.1%), but growth rate was highly variable among practices. Factors associated with growth rate included main species interest, frequency for adjusting prices, use of a marketing plan, service fee structure, and sending a client newsletter. Mixed-animal practices had a large range in size and growth rate. Economic indices were impacted by common business management practices.

Panic disorder and agoraphobia: A direct comparison of their multivariate comorbidity patterns.

PubMed

Greene, Ashley L; Eaton, Nicholas R

2016-01-15

Scientific debate has long surrounded whether agoraphobia is a severe consequence of panic disorder or a frequently comorbid diagnosis. Multivariate comorbidity investigations typically treat these diagnoses as fungible in structural models, assuming both are manifestations of the fear-subfactor in the internalizing-externalizing model. No studies have directly compared these disorders' multivariate associations, which could clarify their conceptualization in classification and comorbidity research. In a nationally representative sample (N=43,093), we examined the multivariate comorbidity of panic disorder (1) without agoraphobia, (2) with agoraphobia, and (3) regardless of agoraphobia; and (4) agoraphobia without panic. We conducted exploratory and confirmatory factor analyses of these and 10 other lifetime DSM-IV diagnoses in a nationally representative sample (N=43,093). Differing bivariate and multivariate relations were found. Panic disorder without agoraphobia was largely a distress disorder, related to emotional disorders. Agoraphobia without panic was largely a fear disorder, related to phobias. When considered jointly, concomitant agoraphobia and panic was a fear disorder, and when panic was assessed without regard to agoraphobia (some individuals had agoraphobia while others did not) it was a mixed distress and fear disorder. Diagnoses were obtained from comprehensively trained lay interviewers, not clinicians and analyses used DSM-IV diagnoses (rather than DSM-5). These findings support the conceptualization of agoraphobia as a distinct diagnostic entity and the independent classification of both disorders in DSM-5, suggesting future multivariate comorbidity studies should not assume various panic/agoraphobia diagnoses are invariably fear disorders. Copyright © 2015 Elsevier B.V. All rights reserved.

Analysis of the mixing processes in the subtropical Advancetown Lake, Australia

NASA Astrophysics Data System (ADS)

Bertone, Edoardo; Stewart, Rodney A.; Zhang, Hong; O'Halloran, Kelvin

2015-03-01

This paper presents an extensive investigation of the mixing processes occurring in the subtropical monomictic Advancetown Lake, which is the main water body supplying the Gold Coast City in Australia. Meteorological, chemical and physical data were collected from weather stations, laboratory analysis of grab samples and an in-situ Vertical Profiling System (VPS), for the period 2008-2012. This comprehensive, high frequency dataset was utilised to develop a one-dimensional model of the vertical transport and mixing processes occurring along the water column. Multivariate analysis revealed that air temperature and rain forecasts enabled a reliable prediction of the strength of the lake stratification. Vertical diffusion is the main process driving vertical mixing, particularly during winter circulation. However, a high reservoir volume and warm winters can limit the degree of winter mixing, causing only partial circulation to occur, as was the case in 2013. This research study provides a comprehensive approach for understanding and predicting mixing processes for similar lakes, whenever high-frequency data are available from VPS or other autonomous water monitoring systems.

A k-omega multivariate beta PDF for supersonic turbulent combustion

NASA Technical Reports Server (NTRS)

Alexopoulos, G. A.; Baurle, R. A.; Hassan, H. A.

1993-01-01

In a recent attempt by the authors at predicting measurements in coaxial supersonic turbulent reacting mixing layers involving H2 and air, a number of discrepancies involving the concentrations and their variances were noted. The turbulence model employed was a one-equation model based on the turbulent kinetic energy. This required the specification of a length scale. In an attempt at detecting the cause of the discrepancy, a coupled k-omega joint probability density function (PDF) is employed in conjunction with a Navier-Stokes solver. The results show that improvements resulting from a k-omega model are quite modest.

Quantitative assessment of the flow pattern in the southern Arava Valley (Israel) by environmental tracers and a mixing cell model

NASA Astrophysics Data System (ADS)

Adar, E. M.; Rosenthal, E.; Issar, A. S.; Batelaan, O.

1992-08-01

This paper demonstrates the implementation of a novel mathematical model to quantify subsurface inflows from various sources into the arid alluvial basin of the southern Arava Valley divided between Israel and Jordan. The model is based on spatial distribution of environmental tracers and is aimed for use on basins with complex hydrogeological structure and/or with scarce physical hydrologic information. However, a sufficient qualified number of wells and springs are required to allow water sampling for chemical and isotopic analyses. Environmental tracers are used in a multivariable cluster analysis to define potential sources of recharge, and also to delimit homogeneous mixing compartments within the modeled aquifer. Six mixing cells were identified based on 13 constituents. A quantitative assessment of 11 significant subsurface inflows was obtained. Results revealed that the total recharge into the southern Arava basin is around 12.52 × 10 6m3year-1. The major source of inflow into the alluvial aquifer is from the Nubian sandstone aquifer which comprises 65-75% of the total recharge. Only 19-24% of the recharge, but the most important source of fresh water, originates over the eastern Jordanian mountains and alluvial fans.

Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula

NASA Astrophysics Data System (ADS)

Sarhadi, Ali; Burn, Donald H.; Concepción Ausín, María.; Wiper, Michael P.

2016-03-01

A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments. The results demonstrate that the nature and the risk of extreme-climate multidimensional processes are changed over time under the impact of climate change, and accordingly the long-term decision making strategies should be updated based on the anomalies of the nonstationary environment.

Does Investor Ownership of Nursing Homes Compromise the Quality of Care?

PubMed Central

Harrington, Charlene; Woolhandler, Steffie; Mullan, Joseph; Carrillo, Helen; Himmelstein, David U.

2001-01-01

Objectives. Two thirds of nursing homes are investor owned. This study examined whether investor ownership affects quality. Methods. We analyzed 1998 data from state inspections of 13 693 nursing facilities. We used a multivariate model and controlled for case mix, facility characteristics, and location. Results. Investor-owned facilities averaged 5.89 deficiencies per home, 46.5% higher than nonprofit facilities and 43.0% higher than public facilities. In multivariate analysis, investor ownership predicted 0.679 additional deficiencies per home; chain ownership predicted an additional 0.633 deficiencies. Nurse staffing was lower at investor-owned nursing homes. Conclusions. Investor-owned nursing homes provide worse care and less nursing care than do not-for-profit or public homes. PMID:11527781

Predicting seasonal influenza transmission using functional regression models with temporal dependence.

PubMed

Oviedo de la Fuente, Manuel; Febrero-Bande, Manuel; Muñoz, María Pilar; Domínguez, Àngela

2018-01-01

This paper proposes a novel approach that uses meteorological information to predict the incidence of influenza in Galicia (Spain). It extends the Generalized Least Squares (GLS) methods in the multivariate framework to functional regression models with dependent errors. These kinds of models are useful when the recent history of the incidence of influenza are readily unavailable (for instance, by delays on the communication with health informants) and the prediction must be constructed by correcting the temporal dependence of the residuals and using more accessible variables. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model than they do with the classical models. They obtain extremely good results from the predictive point of view and are competitive with the classical time series approach for the incidence of influenza. An iterative version of the GLS estimator (called iGLS) was also proposed that can help to model complicated dependence structures. For constructing the model, the distance correlation measure [Formula: see text] was employed to select relevant information to predict influenza rate mixing multivariate and functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance to manage influenza epidemics.

DasPy 1.0 - the Open Source Multivariate Land Data Assimilation Framework in combination with the Community Land Model 4.5

NASA Astrophysics Data System (ADS)

Han, X.; Li, X.; He, G.; Kumbhar, P.; Montzka, C.; Kollet, S.; Miyoshi, T.; Rosolem, R.; Zhang, Y.; Vereecken, H.; Franssen, H.-J. H.

2015-08-01

Data assimilation has become a popular method to integrate observations from multiple sources with land surface models to improve predictions of the water and energy cycles of the soil-vegetation-atmosphere continuum. Multivariate data assimilation refers to the simultaneous assimilation of observation data from multiple model state variables into a simulation model. In recent years, several land data assimilation systems have been developed in different research agencies. Because of the software availability or adaptability, these systems are not easy to apply for the purpose of multivariate land data assimilation research. We developed an open source multivariate land data assimilation framework (DasPy) which is implemented using the Python script language mixed with the C++ and Fortran programming languages. LETKF (Local Ensemble Transform Kalman Filter) is implemented as the main data assimilation algorithm, and uncertainties in the data assimilation can be introduced by perturbed atmospheric forcing data, and represented by perturbed soil and vegetation parameters and model initial conditions. The Community Land Model (CLM) was integrated as the model operator. The implementation allows also parameter estimation (soil properties and/or leaf area index) on the basis of the joint state and parameter estimation approach. The Community Microwave Emission Modelling platform (CMEM), COsmic-ray Soil Moisture Interaction Code (COSMIC) and the Two-Source Formulation (TSF) were integrated as observation operators for the assimilation of L-band passive microwave, cosmic-ray soil moisture probe and land surface temperature measurements, respectively. DasPy has been evaluated in several assimilation studies of neutron count intensity (soil moisture), L-band brightness temperature and land surface temperature. DasPy is parallelized using the hybrid Message Passing Interface and Open Multi-Processing techniques. All the input and output data flows are organized efficiently using the commonly used NetCDF file format. Online 1-D and 2-D visualization of data assimilation results is also implemented to facilitate the post simulation analysis. In summary, DasPy is a ready to use open source parallel multivariate land data assimilation framework.

Survival advantage in black versus white men with CKD: effect of estimated GFR and case mix.

PubMed

Kovesdy, Csaba P; Quarles, L Darryl; Lott, Evan H; Lu, Jun Ling; Ma, Jennie Z; Molnar, Miklos Z; Kalantar-Zadeh, Kamyar

2013-08-01

Black dialysis patients have significantly lower mortality compared with white patients, in contradistinction to the higher mortality seen in blacks in the general population. It is unclear whether a similar paradox exists in patients with non-dialysis-dependent chronic kidney disease (CKD), and if it does, what its underlying reasons are. Historical cohort. 518,406 white and 52,402 black male US veterans with non-dialysis-dependent CKD stages 3-5. Black race. We examined overall and CKD stage-specific all-cause mortality using parametric survival models. The effect of sociodemographic characteristics, comorbid conditions, and laboratory characteristics on the observed differences was explored in multivariable models. During a median follow-up of 4.7 years, 172,093 patients died (mortality rate, 71.0 [95% CI, 70.6-71.3] per 1,000 patient-years). Black race was associated with significantly lower crude mortality (HR, 0.95; 95% CI, 0.94-0.97; P < 0.001). The survival advantage was attenuated after adjustment for age (HR, 1.14; 95% CI, 1.12-1.16), but was magnified after full multivariable adjustment (HR, 0.72; 95% CI, 0.70-0.73; P < 0.001). The unadjusted survival advantage of blacks was more prominent in those with more advanced stages of CKD, but CKD stage-specific differences were attenuated by multivariable adjustment. Exclusively male patients. Black patients with CKD have lower mortality compared with white patients. The survival advantage seen in blacks is accentuated in patients with more advanced stages of CKD, which may be explained by changes in case-mix and laboratory characteristics occurring during the course of kidney disease. Published by Elsevier Inc. on behalf of the National Kidney Foundation, Inc.

Survival Advantage in Black Versus White Men With CKD: Effect of Estimated GFR and Case Mix

PubMed Central

Kovesdy, Csaba P.; Quarles, L. Darryl; Lott, Evan H.; Lu, Jun Ling; Ma, Jennie Z.; Molnar, Miklos Z.; Kalantar-Zadeh, Kamyar

2013-01-01

Background Black dialysis patients have significantly lower mortality compared to white patients, in contradistinction to the higher mortality seen in blacks in the general population. It is unclear if a similar paradox exists in non–dialysis-dependent CKD, and if it does, what its underlying reasons are. Study Design Historical cohort. Setting & Participants 518,406 white and 52,402 black male US veterans with non-dialysis dependent CKD stages 3–5. Predictor Black race. Outcomes & Measurements We examined overall and CKD stage-specific all-cause mortality using parametric survival models. The effect of sociodemographic characteristics, comorbidities and laboratory characteristics on the observed differences was explored in multivariable models. Results Over a median follow-up of 4.7 years 172,093 patients died (mortality rate, 71.0 [95% CI, 70.6–71.3] per 1000 patient-years). Black race was associated with significantly lower crude mortality (HR, 0.95; 95% CI, 0.94–0.97; p<0.001). The survival advantage was attenuated after adjustment for age (HR, 1.14; 95% CI, 1.12–1.16), but was even magnified after full multivariable adjustment (HR, 0.72; 95% CI, 0.70–0.73; p<0.001). The unadjusted survival advantage of blacks was more prominent in those with more advanced stages of CKD, but CKD stage-specific differences were attenuated by multivariable adjustment. Limitations Exclusively male patients. Conclusions Black patients with CKD have lower mortality compared to white patients. The survival advantage seen in blacks is accentuated in patients with more advanced stages of CKD, which may be explained by changes in case mix and laboratory characteristics occurring during the course of kidney disease. PMID:23369826

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle.

PubMed

Belay, T K; Dagnachew, B S; Kowalski, Z M; Ådnøy, T

2017-08-01

Fourier transform mid-infrared (FT-MIR) spectra of milk are commonly used for phenotyping of traits of interest through links developed between the traits and milk FT-MIR spectra. Predicted traits are then used in genetic analysis for ultimate phenotypic prediction using a single-trait mixed model that account for cows' circumstances at a given test day. Here, this approach is referred to as indirect prediction (IP). Alternatively, FT-MIR spectral variable can be kept multivariate in the form of factor scores in REML and BLUP analyses. These BLUP predictions, including phenotype (predicted factor scores), were converted to single-trait through calibration outputs; this method is referred to as direct prediction (DP). The main aim of this study was to verify whether mixed modeling of milk spectra in the form of factors scores (DP) gives better prediction of blood β-hydroxybutyrate (BHB) than the univariate approach (IP). Models to predict blood BHB from milk spectra were also developed. Two data sets that contained milk FT-MIR spectra and other information on Polish dairy cattle were used in this study. Data set 1 (n = 826) also contained BHB measured in blood samples, whereas data set 2 (n = 158,028) did not contain measured blood values. Part of data set 1 was used to calibrate a prediction model (n = 496) and the remaining part of data set 1 (n = 330) was used to validate the calibration models, as well as to evaluate the DP and IP approaches. Dimensions of FT-MIR spectra in data set 2 were reduced either into 5 or 10 factor scores (DP) or into a single trait (IP) with calibration outputs. The REML estimates for these factor scores were found using WOMBAT. The BLUP values and predicted BHB for observations in the validation set were computed using the REML estimates. Blood BHB predicted from milk FT-MIR spectra by both approaches were regressed on reference blood BHB that had not been used in the model development. Coefficients of determination in cross-validation for untransformed blood BHB were from 0.21 to 0.32, whereas that for the log-transformed BHB were from 0.31 to 0.38. The corresponding estimates in validation were from 0.29 to 0.37 and 0.21 to 0.43, respectively, for untransformed and logarithmic BHB. Contrary to expectation, slightly better predictions of BHB were found when univariate variance structure was used (IP) than when multivariate covariance structures were used (DP). Conclusive remarks on the importance of keeping spectral data in multivariate form for prediction of phenotypes may be found in data sets where the trait of interest has strong relationships with spectral variables. The Authors. Published by the Federation of Animal Science Societies and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Modeling Multi-Variate Gaussian Distributions and Analysis of Higgs Boson Couplings with the ATLAS Detector

NASA Astrophysics Data System (ADS)

Krohn, Olivia; Armbruster, Aaron; Gao, Yongsheng; Atlas Collaboration

2017-01-01

Software tools developed for the purpose of modeling CERN LHC pp collision data to aid in its interpretation are presented. Some measurements are not adequately described by a Gaussian distribution; thus an interpretation assuming Gaussian uncertainties will inevitably introduce bias, necessitating analytical tools to recreate and evaluate non-Gaussian features. One example is the measurements of Higgs boson production rates in different decay channels, and the interpretation of these measurements. The ratios of data to Standard Model expectations (μ) for five arbitrary signals were modeled by building five Poisson distributions with mixed signal contributions such that the measured values of μ are correlated. Algorithms were designed to recreate probability distribution functions of μ as multi-variate Gaussians, where the standard deviation (σ) and correlation coefficients (ρ) are parametrized. There was good success with modeling 1-D likelihood contours of μ, and the multi-dimensional distributions were well modeled within 1- σ but the model began to diverge after 2- σ due to unmerited assumptions in developing ρ. Future plans to improve the algorithms and develop a user-friendly analysis package will also be discussed. NSF International Research Experiences for Students

Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies.

PubMed

Koerner, Tess K; Zhang, Yang

2017-02-27

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers.

Concurrent generation of multivariate mixed data with variables of dissimilar types.

PubMed

Amatya, Anup; Demirtas, Hakan

2016-01-01

Data sets originating from wide range of research studies are composed of multiple variables that are correlated and of dissimilar types, primarily of count, binary/ordinal and continuous attributes. The present paper builds on the previous works on multivariate data generation and develops a framework for generating multivariate mixed data with a pre-specified correlation matrix. The generated data consist of components that are marginally count, binary, ordinal and continuous, where the count and continuous variables follow the generalized Poisson and normal distributions, respectively. The use of the generalized Poisson distribution provides a flexible mechanism which allows under- and over-dispersed count variables generally encountered in practice. A step-by-step algorithm is provided and its performance is evaluated using simulated and real-data scenarios.

Variation in fistula use across dialysis facilities: is it explained by case-mix?

PubMed

Tangri, Navdeep; Moorthi, Ranjani; Tighiouhart, Hocine; Meyer, Klemens B; Miskulin, Dana C

2010-02-01

Arteriovenous fistulas (AVFs) remain the preferred vascular access for hemodialysis patients. Dialysis facilities that fail to meet Centers for Medicare & Medicaid Services goals cite patient case-mix as a reason for low AVF prevalence. This study aimed to determine the magnitude of the variability in AVF usage across dialysis facilities and the extent to which patient case-mix explains it. The vascular access used in 10,112 patients dialyzed at 173 Dialysis Clinic Inc. facilities from October 1 to December 31, 2004, was evaluated. The access in use was considered to be an AVF if it was used for >70% of hemodialysis treatments. Mixed-effects models with a random intercept for dialysis facilities evaluated the effect of facilities on AVF usage. Sequentially adjusted multivariate models measured the extent to which patient factors (case-mix) explain variation across facilities in AVF rates. 3787 patients (38%) were dialyzed using AVFs. There was a significant facility effect: 7.6% of variation in AVF use was attributable to facility. This was reduced to 7.1% after case-mix adjustment. There were no identified specific facility-level factors that explained the interfacility variation. AVF usage varies across dialysis facilities, and patient case-mix did not reduce this variation. In this study, 92% of the total variation in AVF usage was due to patient factors, but most were not measurable. A combination of patient factors and process indicators should be considered in adjudicating facility performance for this quality indicator.

Re-resection rates after breast-conserving surgery as a performance indicator: introduction of a case-mix model to allow comparison between Dutch hospitals.

PubMed

Talsma, A K; Reedijk, A M J; Damhuis, R A M; Westenend, P J; Vles, W J

2011-04-01

Re-resection rate after breast-conserving surgery (BCS) has been introduced as an indicator of quality of surgical treatment in international literature. The present study aims to develop a case-mix model for re-resection rates and to evaluate its performance in comparing results between hospitals. Electronic records of eligible patients diagnosed with in-situ and invasive breast cancer in 2006 and 2007 were derived from 16 hospitals in the Rotterdam Cancer Registry (RCR) (n = 961). A model was built in which prognostic factors for re-resections after BCS were identified and expected re-resection rate could be assessed for hospitals based on their case mix. To illustrate the opportunities of monitoring re-resections over time, after risk adjustment for patient profile, a VLAD chart was drawn for patients in one hospital. In general three out of every ten women had re-surgery; in about 50% this meant an additive mastectomy. Independent prognostic factors of re-resection after multivariate analysis were histological type, sublocalisation, tumour size, lymph node involvement and multifocal disease. After correction for case mix, one hospital was performing significantly less re-resections compared to the reference hospital. On the other hand, two were performing significantly more re-resections than was expected based on their patient mix. Our population-based study confirms earlier reports that re-resection is frequently required after an initial breast-conserving operation. Case-mix models such as the one we constructed can be used to correct for variation between hospitals performances. VLAD charts are valuable tools to monitor quality of care within individual hospitals. Copyright © 2011 Elsevier Ltd. All rights reserved.

Benchmarking antibiotic use in Finnish acute care hospitals using patient case-mix adjustment.

PubMed

Kanerva, Mari; Ollgren, Jukka; Lyytikäinen, Outi

2011-11-01

It is difficult to draw conclusions about the prudence of antibiotic use in different hospitals by directly comparing usage figures. We present a patient case-mix adjustment model of antibiotic use to rank hospitals while taking patient characteristics into account. Data on antibiotic use were collected during the national healthcare-associated infection (HAI) prevalence survey in 2005 in Finland in all 5 tertiary care, all 15 secondary care and 10 (25% of 40) other acute care hospitals. The use of antibiotics was measured using use-days/100 patient-days during a 7day period and the prevalence of patients receiving at least two antimicrobials during the study day. Case-mix-adjusted antibiotic use was calculated by using multivariate models and an indirect standardization method. Parameters in the model included age, sex, severity of underlying diseases, intensive care, haematology, preceding surgery, respirator, central venous and urinary catheters, community-associated infection, HAI and contact isolation due to methicillin-resistant Staphylococcus aureus. The ranking order changed one position in 12 (40%) hospitals and more than two positions in 13 (43%) hospitals when the case-mix-adjusted figures were compared with those observed. In 24 hospitals (80%), the antibiotic use density observed was lower than expected by the case-mix-adjusted use density. The patient case-mix adjustment of antibiotic use ranked the hospitals differently from the ranking according to observed use, and may be a useful tool for benchmarking hospital antibiotic use. However, the best set of easily and widely available parameters that would describe both patient material and hospital activities remains to be determined.

Changes in Case-Mix and Health Outcomes of Medicare Fee-for-Service Beneficiaries and Managed Care Enrollees During the Years 1992-2011.

PubMed

Koroukian, Siran M; Basu, Jayasree; Schiltz, Nicholas K; Navale, Suparna; Bakaki, Paul M; Warner, David F; Dor, Avi; Given, Charles W; Stange, Kurt C

2018-01-01

Recent studies suggest that managed care enrollees (MCEs) and fee-for-service beneficiaries (FFSBs) have become similar in case-mix over time; but comparisons of health outcomes have yielded mixed results. To examine changes in differentials between MCEs and FFSBs both in case-mix and health outcomes over time. Temporal study of the linked Health and Retirement Study (HRS) and Medicare data, comparing case-mix and health outcomes between MCEs and FFSBs across 3 time periods: 1992-1998, 1999-2004, and 2005-2011. We used multivariable analysis, stratified by, and pooled across the study periods. The unit of analysis was the person-wave (n=167,204). HRS participants who were also enrolled in Medicare. Outcome measures included self-reported fair/poor health, 2-year self-rated worse health, and 2-year mortality. Our main covariate was a composite measure of multimorbidity (MM), MM0-MM3, defined as the co-occurrence of chronic conditions, functional limitations, and/or geriatric syndromes. The case-mix differential between MCEs and FFSBs persisted over time. Results from multivariable models on the pooled data and incorporating interaction terms between managed care status and study period indicated that MCEs and FFSBs were as likely to die within 2 years from the HRS interview (P=0.073). This likelihood remained unchanged across the study periods. However, MCEs were more likely than FFSBs to report fair/poor health in the third study period (change in probability for the interaction term: 0.024, P=0.008), but less likely to rate their health worse in the last 2 years, albeit at borderline significance (change in probability: -0.021, P=0.059). Despite the persistence of selection bias, the differential in self-reported fair/poor status between MCEs and FFSBs seems to be closing over time.

Predictors of clinical outcome in pediatric oligodendroglioma: meta-analysis of individual patient data and multiple imputation.

PubMed

Wang, Kevin Yuqi; Vankov, Emilian R; Lin, Doris Da May

2018-02-01

OBJECTIVE Oligodendroglioma is a rare primary CNS neoplasm in the pediatric population, and only a limited number of studies in the literature have characterized this entity. Existing studies are limited by small sample sizes and discrepant interstudy findings in identified prognostic factors. In the present study, the authors aimed to increase the statistical power in evaluating for potential prognostic factors of pediatric oligodendrogliomas and sought to reconcile the discrepant findings present among existing studies by performing an individual-patient-data (IPD) meta-analysis and using multiple imputation to address data not directly available from existing studies. METHODS A systematic search was performed, and all studies found to be related to pediatric oligodendrogliomas and associated outcomes were screened for inclusion. Each study was searched for specific demographic and clinical characteristics of each patient and the duration of event-free survival (EFS) and overall survival (OS). Given that certain demographic and clinical information of each patient was not available within all studies, a multivariable imputation via chained equations model was used to impute missing data after the mechanism of missing data was determined. The primary end points of interest were hazard ratios for EFS and OS, as calculated by the Cox proportional-hazards model. Both univariate and multivariate analyses were performed. The multivariate model was adjusted for age, sex, tumor grade, mixed pathologies, extent of resection, chemotherapy, radiation therapy, tumor location, and initial presentation. A p value of less than 0.05 was considered statistically significant. RESULTS A systematic search identified 24 studies with both time-to-event and IPD characteristics available, and a total of 237 individual cases were available for analysis. A median of 19.4% of the values among clinical, demographic, and outcome variables in the compiled 237 cases were missing. Multivariate Cox regression analysis revealed subtotal resection (p = 0.007 [EFS] and 0.043 [OS]), initial presentation of headache (p = 0.006 [EFS] and 0.004 [OS]), mixed pathologies (p = 0.005 [EFS] and 0.049 [OS]), and location of the tumor in the parietal lobe (p = 0.044 [EFS] and 0.030 [OS]) to be significant predictors of tumor progression or recurrence and death. CONCLUSIONS The use of IPD meta-analysis provides a valuable means for increasing statistical power in investigations of disease entities with a very low incidence. Missing data are common in research, and multiple imputation is a flexible and valid approach for addressing this issue, when it is used conscientiously. Undergoing subtotal resection, having a parietal tumor, having tumors with mixed pathologies, and suffering headaches at the time of diagnosis portended a poorer prognosis in pediatric patients with oligodendroglioma.

Exploring the roles of cannot-link constraint in community detection via Multi-variance Mixed Gaussian Generative Model.

PubMed

Yang, Liang; Ge, Meng; Jin, Di; He, Dongxiao; Fu, Huazhu; Wang, Jing; Cao, Xiaochun

2017-01-01

Due to the demand for performance improvement and the existence of prior information, semi-supervised community detection with pairwise constraints becomes a hot topic. Most existing methods have been successfully encoding the must-link constraints, but neglect the opposite ones, i.e., the cannot-link constraints, which can force the exclusion between nodes. In this paper, we are interested in understanding the role of cannot-link constraints and effectively encoding pairwise constraints. Towards these goals, we define an integral generative process jointly considering the network topology, must-link and cannot-link constraints. We propose to characterize this process as a Multi-variance Mixed Gaussian Generative (MMGG) Model to address diverse degrees of confidences that exist in network topology and pairwise constraints and formulate it as a weighted nonnegative matrix factorization problem. The experiments on artificial and real-world networks not only illustrate the superiority of our proposed MMGG, but also, most importantly, reveal the roles of pairwise constraints. That is, though the must-link is more important than cannot-link when either of them is available, both must-link and cannot-link are equally important when both of them are available. To the best of our knowledge, this is the first work on discovering and exploring the importance of cannot-link constraints in semi-supervised community detection.

Exploring the roles of cannot-link constraint in community detection via Multi-variance Mixed Gaussian Generative Model

PubMed Central

Ge, Meng; Jin, Di; He, Dongxiao; Fu, Huazhu; Wang, Jing; Cao, Xiaochun

2017-01-01

Due to the demand for performance improvement and the existence of prior information, semi-supervised community detection with pairwise constraints becomes a hot topic. Most existing methods have been successfully encoding the must-link constraints, but neglect the opposite ones, i.e., the cannot-link constraints, which can force the exclusion between nodes. In this paper, we are interested in understanding the role of cannot-link constraints and effectively encoding pairwise constraints. Towards these goals, we define an integral generative process jointly considering the network topology, must-link and cannot-link constraints. We propose to characterize this process as a Multi-variance Mixed Gaussian Generative (MMGG) Model to address diverse degrees of confidences that exist in network topology and pairwise constraints and formulate it as a weighted nonnegative matrix factorization problem. The experiments on artificial and real-world networks not only illustrate the superiority of our proposed MMGG, but also, most importantly, reveal the roles of pairwise constraints. That is, though the must-link is more important than cannot-link when either of them is available, both must-link and cannot-link are equally important when both of them are available. To the best of our knowledge, this is the first work on discovering and exploring the importance of cannot-link constraints in semi-supervised community detection. PMID:28678864

Mouth opening in patients irradiated for head and neck cancer: a prospective repeated measures study.

PubMed

Kamstra, J I; Dijkstra, P U; van Leeuwen, M; Roodenburg, J L N; Langendijk, J A

2015-05-01

Aims of this prospective cohort study were (1) to analyze the course of mouth opening up to 48months post-radiotherapy (RT), (2) to assess risk factors predicting decrease in mouth opening, and (3) to develop a multivariable prediction model for change in mouth opening in a large sample of patients irradiated for head and neck cancer. Mouth opening was measured prior to RT (baseline) and at 6, 12, 18, 24, 36, and 48months post-RT. The primary outcome variable was mouth opening. Potential risk factors were entered into a linear mixed model analysis (manual backward-stepwise elimination) to create a multivariable prediction model. The interaction terms between time and risk factors that were significantly related to mouth opening were explored. The study population consisted of 641 patients: 70.4% male, mean age at baseline 62.3years (sd 12.5). Primary tumors were predominantly located in the oro- and nasopharynx (25.3%) and oral cavity (20.6%). Mean mouth opening at baseline was 38.7mm (sd 10.8). Six months post-RT, mean mouth opening was smallest, 36.7mm (sd 10.0). In the linear mixed model analysis, mouth opening was statistically predicted by the location of the tumor, natural logarithm of time post-RT in months (Ln (months)), gender, baseline mouth opening, and baseline age. All main effects interacted with Ln (months). The mean mouth opening decreased slightly over time. Mouth opening was predicted by tumor location, time, gender, baseline mouth opening, and age. The model can be used to predict mouth opening. Copyright © 2015 Elsevier Ltd. All rights reserved.

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.

PubMed

Pyne, Saumyadipta; Lee, Sharon X; Wang, Kui; Irish, Jonathan; Tamayo, Pablo; Nazaire, Marc-Danie; Duong, Tarn; Ng, Shu-Kay; Hafler, David; Levy, Ronald; Nolan, Garry P; Mesirov, Jill; McLachlan, Geoffrey J

2014-01-01

In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template--used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.

Sliding Mode Control of a Thermal Mixing Process

NASA Technical Reports Server (NTRS)

Richter, Hanz; Figueroa, Fernando

2004-01-01

In this paper we consider the robust control of a thermal mixer using multivariable Sliding Mode Control (SMC). The mixer consists of a mixing chamber, hot and cold fluid valves, and an exit valve. The commanded positions of the three valves are the available control inputs, while the controlled variables are total mass flow rate, chamber pressure and the density of the mixture inside the chamber. Unsteady thermodynamics and linear valve models are used in deriving a 5th order nonlinear system with three inputs and three outputs, An SMC controller is designed to achieve robust output tracking in the presence of unknown energy losses between the chamber and the environment. The usefulness of the technique is illustrated with a simulation.

Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population.

PubMed

Punzo, Antonio; Ingrassia, Salvatore; Maruotti, Antonello

2018-04-22

A time-varying latent variable model is proposed to jointly analyze multivariate mixed-support longitudinal data. The proposal can be viewed as an extension of hidden Markov regression models with fixed covariates (HMRMFCs), which is the state of the art for modelling longitudinal data, with a special focus on the underlying clustering structure. HMRMFCs are inadequate for applications in which a clustering structure can be identified in the distribution of the covariates, as the clustering is independent from the covariates distribution. Here, hidden Markov regression models with random covariates are introduced by explicitly specifying state-specific distributions for the covariates, with the aim of improving the recovering of the clusters in the data with respect to a fixed covariates paradigm. The hidden Markov regression models with random covariates class is defined focusing on the exponential family, in a generalized linear model framework. Model identifiability conditions are sketched, an expectation-maximization algorithm is outlined for parameter estimation, and various implementation and operational issues are discussed. Properties of the estimators of the regression coefficients, as well as of the hidden path parameters, are evaluated through simulation experiments and compared with those of HMRMFCs. The method is applied to physical activity data. Copyright © 2018 John Wiley & Sons, Ltd.

KMgene: a unified R package for gene-based association analysis for complex traits.

PubMed

Yan, Qi; Fang, Zhou; Chen, Wei; Stegle, Oliver

2018-02-09

In this report, we introduce an R package KMgene for performing gene-based association tests for familial, multivariate or longitudinal traits using kernel machine (KM) regression under a generalized linear mixed model (GLMM) framework. Extensive simulations were performed to evaluate the validity of the approaches implemented in KMgene. http://cran.r-project.org/web/packages/KMgene. qi.yan@chp.edu or wei.chen@chp.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2018. Published by Oxford University Press.

Hyperspectral imaging for detection of non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups on spread plates of mixed cultures

NASA Astrophysics Data System (ADS)

Yoon, Seung Chul; Windham, William R.; Ladely, Scott; Heitschmidt, Gerald W.; Lawrence, Kurt C.; Park, Bosoon; Narang, Neelam; Cray, William C.

2012-05-01

We investigated the feasibility of visible and near-infrared (VNIR) hyperspectral imaging for rapid presumptive-positive screening of six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) on spread plates of mixed cultures. Although the traditional culture method is still the "gold standard" for presumptive-positive pathogen screening, it is time-consuming, labor-intensive, not effective in testing large amount of food samples, and cannot completely prevent unwanted background microflora from growing together with target microorganisms on agar media. A previous study was performed using the data obtained from pure cultures individually inoculated on spot and/or spread plates in order to develop multivariate classification models differentiating each colony of the six non-O157 STEC serogroups and to optimize the models in terms of parameters. This study dealt with the validation of the trained and optimized models with a test set of new independent samples obtained from colonies on spread plates of mixed cultures. A new validation protocol appropriate to a hyperspectral imaging study for mixed cultures was developed. One imaging experiment with colonies obtained from two serial dilutions was performed. A total of six agar plates were prepared, where O45, O111 and O121 serogroups were inoculated into all six plates and each of O45, O103 and O145 serogroups was added into the mixture of the three common bacterial cultures. The number of colonies grown after 24-h incubation was 331 and the number of pixels associated with the grown colonies was 16,379. The best model found from this validation study was based on pre-processing with standard normal variate and detrending (SNVD), first derivative, spectral smoothing, and k-nearest neighbor classification (kNN, k=3) of scores in the principal component subspace spanned by 6 principal components. The independent testing results showed 95% overall detection accuracy at pixel level and 97% at colony level. The developed model was proven to be still valid even for the independent samples although the size of a test set was small and only one experiment was performed. This study was an important first step in validating and updating multivariate classification models for rapid screening of ground beef samples contaminated by non-O157 STEC pathogens using hyperspectral imaging.

EVENT-LEVEL ANALYSIS OF ANAL SEX ROLES AND SEX DRUG USE AMONG GAY AND BISEXUAL MEN IN VANCOUVER, BRITISH COLUMBIA, CANADA

PubMed Central

Rich, Ashleigh J; Lachowsky, Nathan J; Cui, Zishan; Sereda, Paul; Lal, Allan; Moore, David M; Hogg, Robert S; Roth, Eric A

2015-01-01

This study analyzed event-level partnership data from a computer-assisted survey of 719 gay and bisexual men (GBM) enrolled in the Momentum Health Study to delineate potential linkages between anal sex roles and so-called “sex drugs”, i.e. erectile dysfunction drugs (EDD), poppers and crystal methamphetamine. Univariable and multivariable analyses using generalized linear mixed models with logit link function with sexual encounters (n=2,514) as the unit of analysis tested four hypotheses: 1) EDD are significantly associated with insertive anal sex roles, 2) poppers are significantly associated with receptive anal sex, 3) both poppers and EDD are significantly associated with anal sexual versatility and, 4) crystal methamphetamine is significantly associated with all anal sex roles. Data for survey respondents and their sexual partners allowed testing these hypotheses for both anal sex partners in the same encounter. Multivariable results supported the first three hypotheses. Crystal methamphetamine was significantly associated with all anal sex roles in the univariable models, but not significant in any multivariable ones. Other multivariable significant variables included attending group sex events, venue where first met, and self-described sexual orientation. Results indicate that GBM sex-drug use behavior features rational decision-making strategies linked to anal sex roles. They also suggest that more research on anal sex roles, particularly versatility, is needed, and that sexual behavior research can benefit from partnership analysis. PMID:26525571

Event-Level Analysis of Anal Sex Roles and Sex Drug Use Among Gay and Bisexual Men in Vancouver, British Columbia, Canada.

PubMed

Rich, Ashleigh J; Lachowsky, Nathan J; Cui, Zishan; Sereda, Paul; Lal, Allan; Moore, David M; Hogg, Robert S; Roth, Eric A

2016-08-01

This study analyzed event-level partnership data from a computer-assisted survey of 719 gay and bisexual men (GBM) enrolled in the Momentum Health Study to delineate potential linkages between anal sex roles and the so-called "sex drugs," i.e., erectile dysfunction drugs (EDD), poppers, and crystal methamphetamine. Univariable and multivariable analyses using generalized linear mixed models with logit link function with sexual encounters (n = 2514) as the unit of analysis tested four hypotheses: (1) EDD are significantly associated with insertive anal sex roles, (2) poppers are significantly associated with receptive anal sex, (3) both poppers and EDD are significantly associated with anal sexual versatility, and (4) crystal methamphetamine is significantly associated with all anal sex roles. Data for survey respondents and their sexual partners allowed testing these hypotheses for both anal sex partners in the same encounter. Multivariable results supported the first three hypotheses. Crystal methamphetamine was significantly associated with all anal sex roles in the univariable models, but not significant in any multivariable ones. Other multivariable significant variables included attending group sex events, venue where first met, and self-described sexual orientation. Results indicate that GBM sex-drug use behavior features rational decision-making strategies linked to anal sex roles. They also suggest that more research on anal sex roles, particularly versatility, is needed, and that sexual behavior research can benefit from partnership analysis.

Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies

PubMed Central

Koerner, Tess K.; Zhang, Yang

2017-01-01

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers. PMID:28264422

Characteristics of Inpatient Units Associated With Sustained Hand Hygiene Compliance.

PubMed

Wolfe, Jonathan D; Domenico, Henry J; Hickson, Gerald B; Wang, Deede; Dubree, Marilyn; Feistritzer, Nancye; Wells, Nancy; Talbot, Thomas R

2018-04-20

Following institution of a hand hygiene (HH) program at an academic medical center, HH compliance increased from 58% to 92% for 3 years. Some inpatient units modeled early, sustained increases, and others exhibited protracted improvement rates. We examined the association between patterns of HH compliance improvement and unit characteristics. Adult inpatient units (N = 35) were categorized into the following three tiers based on their pattern of HH compliance: early adopters, nonsustained and late adopters, and laggards. Unit-based culture measures were collected, including nursing practice environment scores (National Database of Nursing Quality Indicators [NDNQI]), patient rated quality and teamwork (Hospital Consumer Assessment of Healthcare Provider and Systems), patient complaint rates, case mix index, staff turnover rates, and patient volume. Associations between variables and the binary outcome of laggard (n = 18) versus nonlaggard (n = 17) were tested using a Mann-Whitney U test. Multivariate analysis was performed using an ordinal regression model. In direct comparison, laggard units had clinically relevant differences in NDNQI scores, Hospital Consumer Assessment of Healthcare Provider and Systems scores, case mix index, patient complaints, patient volume, and staff turnover. The results were not statistically significant. In the multivariate model, the predictor variables explained a significant proportion of the variability associated with laggard status, (R = 0.35, P = 0.0481) and identified NDNQI scores and patient complaints as statistically significant. Uptake of an HH program was associated with factors related to a unit's safety culture. In particular, NDNQI scores and patient complaint rates might be used to assist in identifying units that may require additional attention during implementation of an HH quality improvement program.

What matters? Assessing and developing inquiry and multivariable reasoning skills in high school chemistry

NASA Astrophysics Data System (ADS)

Daftedar Abdelhadi, Raghda Mohamed

Although the Next Generation Science Standards (NGSS) present a detailed set of Science and Engineering Practices, a finer grained representation of the underlying skills is lacking in the standards document. Therefore, it has been reported that teachers are facing challenges deciphering and effectively implementing the standards, especially with regards to the Practices. This analytical study assessed the development of high school chemistry students' (N = 41) inquiry, multivariable causal reasoning skills, and metacognition as a mediator for their development. Inquiry tasks based on concepts of element properties of the periodic table as well as reaction kinetics required students to conduct controlled thought experiments, make inferences, and declare predictions of the level of the outcome variable by coordinating the effects of multiple variables. An embedded mixed methods design was utilized for depth and breadth of understanding. Various sources of data were collected including students' written artifacts, audio recordings of in-depth observational groups and interviews. Data analysis was informed by a conceptual framework formulated around the concepts of coordinating theory and evidence, metacognition, and mental models of multivariable causal reasoning. Results of the study indicated positive change towards conducting controlled experimentation, making valid inferences and justifications. Additionally, significant positive correlation between metastrategic and metacognitive competencies, and sophistication of experimental strategies, signified the central role metacognition played. Finally, lack of consistency in indicating effective variables during the multivariable prediction task pointed towards the fragile mental models of multivariable causal reasoning the students had. Implications for teacher education, science education policy as well as classroom research methods are discussed. Finally, recommendations for developing reform-based chemistry curricula based on the Practices are presented.

Modeling of Electrochemical Process for the Treatment of Wastewater Containing Organic Pollutants

NASA Astrophysics Data System (ADS)

Rodrigo, Manuel A.; Cañizares, Pablo; Lobato, Justo; Sáez, Cristina

Electrocoagulation and electrooxidation are promising electrochemical technologies that can be used to remove organic pollutants contained in wastewaters. To make these technologies competitive with the conventional technologies that are in use today, a better understanding of the processes involved must be achieved. In this context, the development of mathematical models that are consistent with the processes occurring in a physical system is a relevant advance, because such models can help to understand what is happening in the treatment process. In turn, a more detailed knowledge of the physical system can be obtained, and tools for a proper design of the processes, or for the analysis of operating problems, are attained. The modeling of these technologies can be carried out using single-variable or multivariable models. Likewise, the position dependence of the model species can be described with different approaches. In this work, a review of the basics of the modeling of these processes and a description of several representative models for electrochemical oxidation and coagulation are carried out. Regarding electrooxidation, two models are described: one which summarizes the pollution of a wastewater in only one model species and that considers a macroscopic approach to formulate the mass balances and other that considers more detailed profile of concentration to describe the time course of pollutants and intermediates through a mixed maximum gradient/macroscopic approach. On the topic of electrochemical coagulation, two different approaches are also described in this work: one that considers the hydrodynamic conditions as the main factor responsible for the electrochemical coagulation processes and the other that considers the chemical interaction of the reagents and the pollutants as the more significant processes in the description of the electrochemical coagulation of organic compounds. In addition, in this work it is also described a multivariable model for the electrodissolution of anodes (first stage in electrocoagulation processes). This later model use a mixed macroscopic/maximum gradient approach to describe the chemical and electrochemical processes and it also assumes that the rates of all processes are very high, and that they can be successfully modeled using pseudoequilibrium approaches.

Hydrogeochemistry of sodium-bicarbonate type bedrock groundwater in the Pocheon spa area, South Korea: water rock interaction and hydrologic mixing

NASA Astrophysics Data System (ADS)

Chae, Gi-Tak; Yun, Seong-Taek; Kim, Kangjoo; Mayer, Bernhard

2006-04-01

The Pocheon spa-land area, South Korea occurs in a topographically steep, fault-bounded basin and is characterized by a hydraulic upwelling flow zone of thermal water (up to 44 °C) in its central part. Hydrogeochemical and environmental isotope data for groundwater in the study area suggested the occurrence of two distinct water types, a Ca-HCO 3 type and a Na-HCO 3 type. The former water type is characterized by relatively high concentrations of Ca, SO 4 and NO 3, which show significant temporal variation indicating a strong influence by surface processes. In contrast, the Na-HCO 3 type waters have high and temporally constant temperature, pH, TDS, Na, Cl, HCO 3 and F, indicating the attainment of a chemical steady state with respect to the host rocks (granite and gneiss). Oxygen, hydrogen and tritium isotope data also indicate the differences in hydrologic conditions between the two groups: the relatively lower δ 18O, δD and tritium values for Na-HCO 3 type waters suggest that they recharged at higher elevations and have comparatively long mean residence times. Considering the geologic and hydrogeologic conditions of the study area, Na-HCO 3 type waters possibly have evolved from Ca-HCO 3 type waters. Mass balance modeling revealed that the chemistry of Na-HCO 3 type water was regulated by dissolution of silicates and carbonates and concurrent ion exchange. Particularly, low Ca concentrations in Na-HCO 3 water was mainly caused by cation exchange. Multivariate mixing and mass balance modeling (M3 modeling) was performed to evaluate the hydrologic mixing and mass transfer between discrete water masses occurring in the shallow peripheral part of the central spa-land area, where hydraulic upwelling occurs. Based on Q-mode factor analysis and mixing modeling using PHREEQC, an ideal mixing among three major water masses (surface water, shallow groundwater of Ca-HCO 3 type, deep groundwater of Na-HCO 3 type) was proposed. M3 modeling suggests that all the groundwaters in the spa area can be described as mixtures of these end-members. After mixing, the net mole transfer by geochemical reaction was less than that without mixing. Therefore, it is likely that in the hydraulic mixing zone geochemical reactions are of minor importance and, therefore, that mixing regulates the groundwater geochemistry.

Groundwater flow, quality (2007-10), and mixing in the Wind Cave National Park area, South Dakota

USGS Publications Warehouse

Long, Andrew J.; Ohms, Marc J.; McKaskey, Jonathan D.R.G.

2012-01-01

A study of groundwater flow, quality, and mixing in relation to Wind Cave National Park in western South Dakota was conducted during 2007-11 by the U.S. Geological Survey in cooperation with the National Park Service because of water-quality concerns and to determine possible sources of groundwater contamination in the Wind Cave National Park area. A large area surrounding Wind Cave National Park was included in this study because to understand groundwater in the park, a general understanding of groundwater in the surrounding southern Black Hills is necessary. Three aquifers are of particular importance for this purpose: the Minnelusa, Madison, and Precambrian aquifers. Multivariate methods applied to hydrochemical data, consisting of principal component analysis (PCA), cluster analysis, and an end-member mixing model, were applied to characterize groundwater flow and mixing. This provided a way to assess characteristics important for groundwater quality, including the differentiation of hydrogeologic domains within the study area, sources of groundwater to these domains, and groundwater mixing within these domains. Groundwater and surface-water samples collected for this study were analyzed for common ions (calcium, magnesium, sodium, bicarbonate, chloride, silica, and sulfate), arsenic, stable isotopes of oxygen and hydrogen, specific conductance, and pH. These 12 variables were used in all multivariate methods. A total of 100 samples were collected from 60 sites from 2007 to 2010 and included stream sinks, cave drip, cave water bodies, springs, and wells. In previous approaches that combined PCA with end-member mixing, extreme-value samples identified by PCA typically were assumed to represent end members. In this study, end members were not assumed to have been sampled but rather were estimated and constrained by prior hydrologic knowledge. Also, the end-member mixing model was quantified in relation to hydrogeologic domains, which focuses model results on major hydrologic processes. Finally, conservative tracers were weighted preferentially in model calibration, which distributed model errors of optimized values, or residuals, more appropriately than would otherwise be the case The latter item also provides an estimate of the relative effect of geochemical evolution along flow paths in comparison to mixing. The end-member mixing model estimated that Wind Cave sites received 38 percent of their groundwater inflow from local surface recharge, 34 percent from the upgradient Precambrian aquifer, 26 percent from surface recharge to the west, and 2 percent from regional flow. Artesian springs primarily received water from end members assumed to represent regional groundwater flow. Groundwater samples were collected and analyzed for chlorofluorocarbons, dissolved gasses (argon, carbon dioxide, methane, nitrogen, and oxygen), and tritium at selected sites and used to estimate groundwater age. Apparent ages, or model ages, for the Madison aquifer in the study area indicate that groundwater closest to surface recharge areas is youngest, with increasing age in a downgradient direction toward deeper parts of the aquifer. Arsenic concentrations in samples collected for this study ranged from 0.28 to 37.1 micrograms per liter (μg/L) with a median value of 6.4 μg/L, and 32 percent of these exceeded 10 μg/L. The highest arsenic concentrations in and near the study area are approximately coincident with the outcrop of the Minnelusa Formation and likely originated from arsenic in shale layers in this formation. Sample concentrations of nitrate plus nitrite were less than 2 milligrams per liter for 92 percent of samples collected, which is not a concern for drinking-water quality. Water samples were collected in the park and analyzed for five trace metals (chromium, copper, lithium, vanadium, and zinc), the concentrations of which did not correlate with arsenic. Dye tracing indicated hydraulic connection between three water bodies in Wind Cave.

A novel approach to mixing qualitative and quantitative methods in HIV and STI prevention research.

PubMed

Penman-Aguilar, Ana; Macaluso, Maurizio; Peacock, Nadine; Snead, M Christine; Posner, Samuel F

2014-04-01

Mixed-method designs are increasingly used in sexually transmitted infection (STI) and HIV prevention research. The authors designed a mixedmethod approach and applied it to estimate and evaluate a predictor of continued female condom use (6+ uses, among those who used it at least once) in a 6-month prospective cohort study. The analysis included 402 women who received an intervention promoting use of female and male condoms for STI prevention and completed monthly quantitative surveys; 33 also completed a semistructured qualitative interview. The authors identified a qualitative theme (couples' female condom enjoyment [CFCE]), applied discriminant analysis techniques to estimate CFCE for all participants, and added CFCE to a multivariable logistic regression model of continued female condom use. CFCE related to comfort, naturalness, pleasure, feeling protected, playfulness, ease of use, intimacy, and feeling in control of protection. CFCE was associated with continued female condom use (adjusted odds ratio: 2.8, 95% confidence interval: 1.4-5.6) and significantly improved model fit (p < .001). CFCE predicted continued female condom use. Mixed-method approaches for "scaling up" qualitative findings from small samples to larger numbers of participants can benefit HIV and STI prevention research.

Understanding Mixed Emotions: Paradigms and Measures

PubMed Central

Kreibig, Sylvia D.; Gross, James J.

2017-01-01

In this review, we examine the paradigms and measures available for experimentally studying mixed emotions in the laboratory. For eliciting mixed emotions, we describe a mixed emotions film library that allows for the repeated elicitation of a specific homogeneous mixed emotional state and appropriately matched pure positive, pure negative, and neutral emotional states. For assessing mixed emotions, we consider subjective and objective measures that fall into univariate, bivariate, and multivariate measurement categories. As paradigms and measures for objectively studying mixed emotions are still in their early stages, we conclude by outlining future directions that focus on the reliability, temporal dynamics, and response coherence of mixed emotions paradigms and measures. This research will build a strong foundation for future studies and significantly advance our understanding of mixed emotions. PMID:28804752

Understanding Mixed Emotions: Paradigms and Measures.

PubMed

Kreibig, Sylvia D; Gross, James J

2017-06-01

In this review, we examine the paradigms and measures available for experimentally studying mixed emotions in the laboratory. For eliciting mixed emotions, we describe a mixed emotions film library that allows for the repeated elicitation of a specific homogeneous mixed emotional state and appropriately matched pure positive, pure negative, and neutral emotional states. For assessing mixed emotions, we consider subjective and objective measures that fall into univariate, bivariate, and multivariate measurement categories. As paradigms and measures for objectively studying mixed emotions are still in their early stages, we conclude by outlining future directions that focus on the reliability, temporal dynamics, and response coherence of mixed emotions paradigms and measures. This research will build a strong foundation for future studies and significantly advance our understanding of mixed emotions.

U.S. Citizen Children of Undocumented Parents: The Link Between State Immigration Policy and the Health of Latino Children.

PubMed

Vargas, Edward D; Ybarra, Vickie D

2017-08-01

We examine Latino citizen children in mixed-status families and how their physical health status compares to their U.S. citizen, co-ethnic counterparts. We also examine Latino parents' perceptions of state immigration policy and its implications for child health status. Using the 2015 Latino National Health and Immigration Survey (n = 1493), we estimate a series of multivariate ordered logistic regression models with mixed-status family and perceptions of state immigration policy as primary predictors. We find that mixed-status families report worse physical health for their children as compared to their U.S. citizen co-ethnics. We also find that parental perceptions of their states' immigration status further exacerbate health disparities between families. These findings have implications for scholars and policy makers interested in immigrant health, family wellbeing, and health disparities in complex family structures. They contribute to the scholarship on Latino child health and on the erosion of the Latino immigrant health advantage.

U.S. Citizen Children of Undocumented Parents: The Link Between State Immigration Policy and the Health of Latino Children

PubMed Central

Vargas, Edward D.; Ybarra, Vickie D.

2016-01-01

Background We examine Latino citizen children in mixed-status families and how their physical health status compares to their U.S. citizen, co-ethnic counterparts. We also examine Latino parents’ perceptions of state immigration policy and its implications for child health status. Methods Using the 2015 Latino National Health and Immigration Survey (n=1493), we estimate a series of multivariate ordered logistic regression models with mixed-status family and perceptions of state immigration policy as primary predictors. Results We find that mixed-status families report worse physical health for their children as compared to their U.S. citizen co-ethnics. We also find that parental perceptions of their states’ immigration status further exacerbate health disparities between families. Discussion These findings have implications for scholars and policy makers interested in immigrant health, family wellbeing, and health disparities in complex family structures. They contribute to the scholarship on Latino child health and on the erosion of the Latino immigrant health advantage. PMID:27435476

Validation of Single-Item Screening Measures for Provider Burnout in a Rural Health Care Network.

PubMed

Waddimba, Anthony C; Scribani, Melissa; Nieves, Melinda A; Krupa, Nicole; May, John J; Jenkins, Paul

2016-06-01

We validated three single-item measures for emotional exhaustion (EE) and depersonalization (DP) among rural physician/nonphysician practitioners. We linked cross-sectional survey data (on provider demographics, satisfaction, resilience, and burnout) with administrative information from an integrated health care network (1 academic medical center, 6 community hospitals, 31 clinics, and 19 school-based health centers) in an eight-county underserved area of upstate New York. In total, 308 physicians and advanced-practice clinicians completed a self-administered, multi-instrument questionnaire (65.1% response rate). Significant proportions of respondents reported high EE (36.1%) and DP (9.9%). In multivariable linear mixed models, scores on EE/DP subscales of the Maslach Burnout Inventory were regressed on each single-item measure. The Physician Work-Life Study's single-item measure (classifying 32.8% of respondents as burning out/completely burned out) was correlated with EE and DP (Spearman's ρ = .72 and .41, p < .0001; Kruskal-Wallis χ(2) = 149.9 and 56.5, p < .0001, respectively). In multivariable models, it predicted high EE (but neither low EE nor low/high DP). EE/DP single items were correlated with parent subscales (Spearman's ρ = .89 and .81, p < .0001; Kruskal-Wallis χ(2) = 230.98 and 197.84, p < .0001, respectively). In multivariable models, the EE item predicted high/low EE, whereas the DP item predicted only low DP. Therefore, the three single-item measures tested varied in effectiveness as screeners for EE/DP dimensions of burnout. © The Author(s) 2015.

Variability in case-mix adjusted in-hospital cardiac arrest rates.

PubMed

Merchant, Raina M; Yang, Lin; Becker, Lance B; Berg, Robert A; Nadkarni, Vinay; Nichol, Graham; Carr, Brendan G; Mitra, Nandita; Bradley, Steven M; Abella, Benjamin S; Groeneveld, Peter W

2012-02-01

It is unknown how in-hospital cardiac arrest (IHCA) rates vary across hospitals and predictors of variability. Measure variability in IHCA across hospitals and determine if hospital-level factors predict differences in case-mix adjusted event rates. Get with the Guidelines Resuscitation (GWTG-R) (n=433 hospitals) was used to identify IHCA events between 2003 and 2007. The American Hospital Association survey, Medicare, and US Census were used to obtain detailed information about GWTG-R hospitals. Adult patients with IHCA. Case-mix-adjusted predicted IHCA rates were calculated for each hospital and variability across hospitals was compared. A regression model was used to predict case-mix adjusted event rates using hospital measures of volume, nurse-to-bed ratio, percent intensive care unit beds, palliative care services, urban designation, volume of black patients, income, trauma designation, academic designation, cardiac surgery capability, and a patient risk score. We evaluated 103,117 adult IHCAs at 433 US hospitals. The case-mix adjusted IHCA event rate was highly variable across hospitals, median 1/1000 bed days (interquartile range: 0.7 to 1.3 events/1000 bed days). In a multivariable regression model, case-mix adjusted IHCA event rates were highest in urban hospitals [rate ratio (RR), 1.1; 95% confidence interval (CI), 1.0-1.3; P=0.03] and hospitals with higher proportions of black patients (RR, 1.2; 95% CI, 1.0-1.3; P=0.01) and lower in larger hospitals (RR, 0.54; 95% CI, 0.45-0.66; P<0.0001). Case-mix adjusted IHCA event rates varied considerably across hospitals. Several hospital factors associated with higher IHCA event rates were consistent with factors often linked with lower hospital quality of care.

Differentiating Tumor Progression from Pseudoprogression in Patients with Glioblastomas Using Diffusion Tensor Imaging and Dynamic Susceptibility Contrast MRI.

PubMed

Wang, S; Martinez-Lage, M; Sakai, Y; Chawla, S; Kim, S G; Alonso-Basanta, M; Lustig, R A; Brem, S; Mohan, S; Wolf, R L; Desai, A; Poptani, H

2016-01-01

Early assessment of treatment response is critical in patients with glioblastomas. A combination of DTI and DSC perfusion imaging parameters was evaluated to distinguish glioblastomas with true progression from mixed response and pseudoprogression. Forty-one patients with glioblastomas exhibiting enhancing lesions within 6 months after completion of chemoradiation therapy were retrospectively studied. All patients underwent surgery after MR imaging and were histologically classified as having true progression (>75% tumor), mixed response (25%-75% tumor), or pseudoprogression (<25% tumor). Mean diffusivity, fractional anisotropy, linear anisotropy coefficient, planar anisotropy coefficient, spheric anisotropy coefficient, and maximum relative cerebral blood volume values were measured from the enhancing tissue. A multivariate logistic regression analysis was used to determine the best model for classification of true progression from mixed response or pseudoprogression. Significantly elevated maximum relative cerebral blood volume, fractional anisotropy, linear anisotropy coefficient, and planar anisotropy coefficient and decreased spheric anisotropy coefficient were observed in true progression compared with pseudoprogression (P < .05). There were also significant differences in maximum relative cerebral blood volume, fractional anisotropy, planar anisotropy coefficient, and spheric anisotropy coefficient measurements between mixed response and true progression groups. The best model to distinguish true progression from non-true progression (pseudoprogression and mixed) consisted of fractional anisotropy, linear anisotropy coefficient, and maximum relative cerebral blood volume, resulting in an area under the curve of 0.905. This model also differentiated true progression from mixed response with an area under the curve of 0.901. A combination of fractional anisotropy and maximum relative cerebral blood volume differentiated pseudoprogression from nonpseudoprogression (true progression and mixed) with an area under the curve of 0.807. DTI and DSC perfusion imaging can improve accuracy in assessing treatment response and may aid in individualized treatment of patients with glioblastomas. © 2016 by American Journal of Neuroradiology.

Poverty, hunger, education, and residential status impact survival in HIV.

PubMed

McMahon, James; Wanke, Christine; Terrin, Norma; Skinner, Sally; Knox, Tamsin

2011-10-01

Despite combination antiretroviral therapy (ART), HIV infected people have higher mortality than non-infected. Lower socioeconomic status (SES) predicts higher mortality in many chronic illnesses but data in people with HIV is limited. We evaluated 878 HIV infected individuals followed from 1995 to 2005. Cox proportional hazards for all-cause mortality were estimated for SES measures and other factors. Mixed effects analyses examined how SES impacts factors predicting death. The 200 who died were older, had lower CD4 counts, and higher viral loads (VL). Age, transmission category, education, albumin, CD4 counts, VL, hunger, and poverty predicted death in univariate analyses; age, CD4 counts, albumin, VL, and poverty in the multivariable model. Mixed models showed associations between (1) CD4 counts with education and hunger; (2) albumin with education, homelessness, and poverty; and (3) VL with education and hunger. SES contributes to mortality in HIV infected persons directly and indirectly, and should be a target of health policy in this population.

Experimental comparison of conventional and nonlinear model-based control of a mixing tank

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haeggblom, K.E.

1993-11-01

In this case study concerning control of a laboratory-scale mixing tank, conventional multiloop single-input single-output (SISO) control is compared with model-based'' control where the nonlinearity and multivariable characteristics of the process are explicitly taken into account. It is shown, especially if the operating range of the process is large, that the two outputs (level and temperature) cannot be adequately controlled by multiloop SISO control even if gain scheduling is used. By nonlinear multiple-input multiple-output (MIMO) control, on the other hand, very good control performance is obtained. The basic approach to nonlinear control used in this study is first to transformmore » the process into a globally linear and decoupled system, and then to design controllers for this system. Because of the properties of the resulting MIMO system, the controller design is very easy. Two nonlinear control system designs based on a steady-state and a dynamic model, respectively, are considered. In the dynamic case, both setpoint tracking and disturbance rejection can be addressed separately.« less

The impact of the National Denture Service on oral health-related quality of life among poor elders.

PubMed

Ha, J E; Heo, Y J; Jin, B H; Paik, D I; Bae, K H

2012-08-01

The objective of this study was to assess the effects of the Korean National Denture Service (NDS) for poor elderly people requiring dentures on oral health-related quality of life (OHRQOL). Data from follow-up studies were collected from 439 subjects at eight public health centres who answered every question of a questionnaire, and the OHRQOL was measured at the baseline and at 3-month follow-up after receiving the NDS according to the type of denture provision. The multivariate linear mixed model with a public health centre as a random effect for the score change of Oral Health Impact Profile (OHIP)-14K was carried out to confirm the factors related to the improvement in OHRQOL. The mean OHIP-14K was 28.60 at the baseline time points, and there was a decrease in the OHIP-14 scores to 21.14 ± 12.52 at the 3-month follow-up of the removable partial denture beneficiaries. The changes in OHIP-14K among complete denture beneficiaries were 21.53 ± 12.01 for previously dentate subjects and 22.54 ± 11.12 for edentate subjects. The multivariate linear mixed model of dentate subjects demonstrated that the improvement in the OHRQOL was associated with the number of remaining teeth, satisfaction with denture and self-reported oral health status after 3 months. In the case of the edentate model, satisfaction with denture was the only factor related to the improvement in OHRQOL. This study revealed considerable improvement in OHRQOL among poor elderly people after NDS. Satisfaction with provision of dentures was associated with improvement in the OHRQOL. © 2012 Blackwell Publishing Ltd.

Application of a sparseness constraint in multivariate curve resolution - Alternating least squares.

PubMed

Hugelier, Siewert; Piqueras, Sara; Bedia, Carmen; de Juan, Anna; Ruckebusch, Cyril

2018-02-13

The use of sparseness in chemometrics is a concept that has increased in popularity. The advantage is, above all, a better interpretability of the results obtained. In this work, sparseness is implemented as a constraint in multivariate curve resolution - alternating least squares (MCR-ALS), which aims at reproducing raw (mixed) data by a bilinear model of chemically meaningful profiles. In many cases, the mixed raw data analyzed are not sparse by nature, but their decomposition profiles can be, as it is the case in some instrumental responses, such as mass spectra, or in concentration profiles linked to scattered distribution maps of powdered samples in hyperspectral images. To induce sparseness in the constrained profiles, one-dimensional and/or two-dimensional numerical arrays can be fitted using a basis of Gaussian functions with a penalty on the coefficients. In this work, a least squares regression framework with L 0 -norm penalty is applied. This L 0 -norm penalty constrains the number of non-null coefficients in the fit of the array constrained without having an a priori on the number and their positions. It has been shown that the sparseness constraint induces the suppression of values linked to uninformative channels and noise in MS spectra and improves the location of scattered compounds in distribution maps, resulting in a better interpretability of the constrained profiles. An additional benefit of the sparseness constraint is a lower ambiguity in the bilinear model, since the major presence of null coefficients in the constrained profiles also helps to limit the solutions for the profiles in the counterpart matrix of the MCR bilinear model. Copyright © 2017 Elsevier B.V. All rights reserved.

Multitrophic functional diversity predicts ecosystem functioning in experimental assemblages of estuarine consumers.

PubMed

Lefcheck, Jonathan S; Duffy, J Emmett

2015-11-01

The use of functional traits to explain how biodiversity affects ecosystem functioning has attracted intense interest, yet few studies have a priori altered functional diversity, especially in multitrophic communities. Here, we manipulated multivariate functional diversity of estuarine grazers and predators within multiple levels of species richness to test how species richness and functional diversity predicted ecosystem functioning in a multitrophic food web. Community functional diversity was a better predictor than species richness for the majority of ecosystem properties, based on generalized linear mixed-effects models. Combining inferences from eight traits into a single multivariate index increased prediction accuracy of these models relative to any individual trait. Structural equation modeling revealed that functional diversity of both grazers and predators was important in driving final biomass within trophic levels, with stronger effects observed for predators. We also show that different species drove different ecosystem responses, with evidence for both sampling effects and complementarity. Our study extends experimental investigations of functional trait diversity to a multilevel food web, and demonstrates that functional diversity can be more accurate and effective than species richness in predicting community biomass in a food web context.

Open-target sparse sensing of biological agents using DNA microarray

PubMed Central

2011-01-01

Background Current biosensors are designed to target and react to specific nucleic acid sequences or structural epitopes. These 'target-specific' platforms require creation of new physical capture reagents when new organisms are targeted. An 'open-target' approach to DNA microarray biosensing is proposed and substantiated using laboratory generated data. The microarray consisted of 12,900 25 bp oligonucleotide capture probes derived from a statistical model trained on randomly selected genomic segments of pathogenic prokaryotic organisms. Open-target detection of organisms was accomplished using a reference library of hybridization patterns for three test organisms whose DNA sequences were not included in the design of the microarray probes. Results A multivariate mathematical model based on the partial least squares regression (PLSR) was developed to detect the presence of three test organisms in mixed samples. When all 12,900 probes were used, the model correctly detected the signature of three test organisms in all mixed samples (mean(R2)) = 0.76, CI = 0.95), with a 6% false positive rate. A sampling algorithm was then developed to sparsely sample the probe space for a minimal number of probes required to capture the hybridization imprints of the test organisms. The PLSR detection model was capable of correctly identifying the presence of the three test organisms in all mixed samples using only 47 probes (mean(R2)) = 0.77, CI = 0.95) with nearly 100% specificity. Conclusions We conceived an 'open-target' approach to biosensing, and hypothesized that a relatively small, non-specifically designed, DNA microarray is capable of identifying the presence of multiple organisms in mixed samples. Coupled with a mathematical model applied to laboratory generated data, and sparse sampling of capture probes, the prototype microarray platform was able to capture the signature of each organism in all mixed samples with high sensitivity and specificity. It was demonstrated that this new approach to biosensing closely follows the principles of sparse sensing. PMID:21801424

Development of a robust framework for controlling high performance turbofan engines

NASA Astrophysics Data System (ADS)

Miklosovic, Robert

This research involves the development of a robust framework for controlling complex and uncertain multivariable systems. Where mathematical modeling is often tedious or inaccurate, the new method uses an extended state observer (ESO) to estimate and cancel dynamic information in real time and dynamically decouple the system. As a result, controller design and tuning become transparent as the number of required model parameters is reduced. Much research has been devoted towards the application of modern multivariable control techniques on aircraft engines. However, few, if any, have been implemented on an operational aircraft, partially due to the difficulty in tuning the controller for satisfactory performance. The new technique is applied to a modern two-spool, high-pressure ratio, low-bypass turbofan with mixed-flow afterburning. A realistic Modular Aero-Propulsion System Simulation (MAPSS) package, developed by NASA, is used to demonstrate the new design process and compare its performance with that of a supplied nominal controller. This approach is expected to reduce gain scheduling over the full operating envelope of the engine and allow a controller to be tuned for engine-to-engine variations.

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Multivariate Quantitative Chemical Analysis

NASA Technical Reports Server (NTRS)

Kinchen, David G.; Capezza, Mary

1995-01-01

Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.

Genetic correlations between wellbeing, depression and anxiety symptoms and behavioral responses to the emotional faces task in healthy twins.

PubMed

Routledge, Kylie M; Williams, Leanne M; Harris, Anthony W F; Schofield, Peter R; Clark, C Richard; Gatt, Justine M

2018-06-01

Currently there is a very limited understanding of how mental wellbeing versus anxiety and depression symptoms are associated with emotion processing behaviour. For the first time, we examined these associations using a behavioural emotion task of positive and negative facial expressions in 1668 healthy adult twins. Linear mixed model results suggested faster reaction times to happy facial expressions was associated with higher wellbeing scores, and slower reaction times with higher depression and anxiety scores. Multivariate twin modelling identified a significant genetic correlation between depression and anxiety symptoms and reaction time to happy facial expressions, in the absence of any significant correlations with wellbeing. We also found a significant negative phenotypic relationship between depression and anxiety symptoms and accuracy for identifying neutral emotions, although the genetic or environment correlations were not significant in the multivariate model. Overall, the phenotypic relationships between speed of identifying happy facial expressions and wellbeing on the one hand, versus depression and anxiety symptoms on the other, were in opposing directions. Twin modelling revealed a small common genetic correlation between response to happy faces and depression and anxiety symptoms alone, suggesting that wellbeing and depression and anxiety symptoms show largely independent relationships with emotion processing at the behavioral level. Copyright © 2018 Elsevier B.V. All rights reserved.

Pooled Analysis of Individual Patient Data on Concurrent Chemoradiotherapy for Stage III Non-Small-Cell Lung Cancer in Elderly Patients Compared With Younger Patients Who Participated in US National Cancer Institute Cooperative Group Studies.

PubMed

Stinchcombe, Thomas E; Zhang, Ying; Vokes, Everett E; Schiller, Joan H; Bradley, Jeffrey D; Kelly, Karen; Curran, Walter J; Schild, Steven E; Movsas, Benjamin; Clamon, Gerald; Govindan, Ramaswamy; Blumenschein, George R; Socinski, Mark A; Ready, Neal E; Akerley, Wallace L; Cohen, Harvey J; Pang, Herbert H; Wang, Xiaofei

2017-09-01

Purpose Concurrent chemoradiotherapy is standard treatment for patients with stage III non-small-cell lung cancer. Elderly patients may experience increased rates of adverse events (AEs) or less benefit from concurrent chemoradiotherapy. Patients and Methods Individual patient data were collected from 16 phase II or III trials conducted by US National Cancer Institute-supported cooperative groups of concurrent chemoradiotherapy alone or with consolidation or induction chemotherapy for stage III non-small-cell lung cancer from 1990 to 2012. Overall survival (OS), progression-free survival, and AEs were compared between patients age ≥ 70 (elderly) and those younger than 70 years (younger). Unadjusted and adjusted hazard ratios (HRs) for survival time and CIs were estimated by single-predictor and multivariable frailty Cox models. Unadjusted and adjusted odds ratio (ORs) for AEs and CIs were obtained from single-predictor and multivariable generalized linear mixed-effect models. Results A total of 2,768 patients were classified as younger and 832 as elderly. In unadjusted and multivariable models, elderly patients had worse OS (HR, 1.20; 95% CI, 1.09 to 1.31 and HR, 1.17; 95% CI, 1.07 to 1.29, respectively). In unadjusted and multivariable models, elderly and younger patients had similar progression-free survival (HR, 1.01; 95% CI, 0.93 to 1.10 and HR, 1.00; 95% CI, 0.91 to 1.09, respectively). Elderly patients had a higher rate of grade ≥ 3 AEs in unadjusted and multivariable models (OR, 1.35; 95% CI, 1.07 to 1.70 and OR, 1.38; 95% CI, 1.10 to 1.74, respectively). Grade 5 AEs were significantly higher in elderly compared with younger patients (9% v 4%; P < .01). Fewer elderly compared with younger patients completed treatment (47% v 57%; P < .01), and more discontinued treatment because of AEs (20% v 13%; P < .01), died during treatment (7.8% v 2.9%; P < .01), and refused further treatment (5.8% v 3.9%; P = .02). Conclusion Elderly patients in concurrent chemoradiotherapy trials experienced worse OS, more toxicity, and had a higher rate of death during treatment than younger patients.

Adjusted adaptive Lasso for covariate model-building in nonlinear mixed-effect pharmacokinetic models.

PubMed

Haem, Elham; Harling, Kajsa; Ayatollahi, Seyyed Mohammad Taghi; Zare, Najaf; Karlsson, Mats O

2017-02-01

One important aim in population pharmacokinetics (PK) and pharmacodynamics is identification and quantification of the relationships between the parameters and covariates. Lasso has been suggested as a technique for simultaneous estimation and covariate selection. In linear regression, it has been shown that Lasso possesses no oracle properties, which means it asymptotically performs as though the true underlying model was given in advance. Adaptive Lasso (ALasso) with appropriate initial weights is claimed to possess oracle properties; however, it can lead to poor predictive performance when there is multicollinearity between covariates. This simulation study implemented a new version of ALasso, called adjusted ALasso (AALasso), to take into account the ratio of the standard error of the maximum likelihood (ML) estimator to the ML coefficient as the initial weight in ALasso to deal with multicollinearity in non-linear mixed-effect models. The performance of AALasso was compared with that of ALasso and Lasso. PK data was simulated in four set-ups from a one-compartment bolus input model. Covariates were created by sampling from a multivariate standard normal distribution with no, low (0.2), moderate (0.5) or high (0.7) correlation. The true covariates influenced only clearance at different magnitudes. AALasso, ALasso and Lasso were compared in terms of mean absolute prediction error and error of the estimated covariate coefficient. The results show that AALasso performed better in small data sets, even in those in which a high correlation existed between covariates. This makes AALasso a promising method for covariate selection in nonlinear mixed-effect models.

Using 3D geological modelling and geochemical mixing models to characterise alluvial aquifer recharge sources in the upper Condamine River catchment, Queensland, Australia.

PubMed

Martinez, Jorge L; Raiber, Matthias; Cendón, Dioni I

2017-01-01

The influence of mountain front recharge on the water balance of alluvial valley aquifers located in upland catchments of the Condamine River basin in Queensland, Australia, is investigated through the development of an integrated hydrogeological framework. A combination of three-dimensional (3D) geological modelling, hydraulic gradient maps, multivariate statistical analyses and hydrochemical mixing calculations is proposed for the identification of hydrochemical end-members and quantification of the relative contributions of each end-member to alluvial aquifer recharge. The recognised end-members correspond to diffuse recharge and lateral groundwater inflows from three hydrostratigraphic units directly connected to the alluvial aquifer. This approach allows mapping zones of potential inter-aquifer connectivity and areas of groundwater mixing between underlying units and the alluvium. Mixing calculations using samples collected under baseflow conditions reveal that lateral contribution from a regional volcanic aquifer system represents the majority (41%) of inflows to the alluvial aquifer. Diffuse recharge contribution (35%) and inflow from two sedimentary bedrock hydrostratigraphic units (collectively 24%) comprise the remainder of major recharge sources. A detailed geochemical assessment of alluvial groundwater evolution along a selected flowpath of a representative subcatchment of the Condamine River basin confirms mixing as a key process responsible for observed spatial variations in hydrochemistry. Dissolution of basalt-related minerals and dolomite, CO 2 uptake, ion-exchange, precipitation of clay minerals, and evapotranspiration further contribute to the hydrochemical evolution of groundwater in the upland alluvial aquifer. This study highlights the benefits of undertaking an integrated approach that combines multiple independent lines of evidence. The proposed methods can be applied to investigate processes associated with inter-aquifer mixing, including groundwater contamination resulting from depressurisation of underlying geological units hydraulically connected to the shallower water reservoirs. Copyright © 2016 Elsevier B.V. All rights reserved.

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

Baldwin, Scott A.; Imel, Zac E.; Braithwaite, Scott R.; Atkins, David C.

2014-01-01

Objective Multilevel models have become a standard data analysis approach in intervention research. Although the vast majority of intervention studies involve multiple outcome measures, few studies use multivariate analysis methods. The authors discuss multivariate extensions to the multilevel model that can be used by psychotherapy researchers. Method and Results Using simulated longitudinal treatment data, the authors show how multivariate models extend common univariate growth models and how the multivariate model can be used to examine multivariate hypotheses involving fixed effects (e.g., does the size of the treatment effect differ across outcomes?) and random effects (e.g., is change in one outcome related to change in the other?). An online supplemental appendix provides annotated computer code and simulated example data for implementing a multivariate model. Conclusions Multivariate multilevel models are flexible, powerful models that can enhance clinical research. PMID:24491071

A multivariate model exploring the predictive value of demographic, adolescent, and family factors on glycemic control in adolescents with type 1 diabetes.

PubMed

Agarwal, Shivani; Jawad, Abbas F; Miller, Victoria A

2016-11-01

The current study examined how a comprehensive set of variables from multiple domains, including at the adolescent and family level, were predictive of glycemic control in adolescents with type 1 diabetes (T1D). Participants included 100 adolescents with T1D ages 10-16 yrs and their parents. Participants were enrolled in a longitudinal study about youth decision-making involvement in chronic illness management of which the baseline data were available for analysis. Bivariate associations with glycemic control (HbA1C) were tested. Hierarchical linear regression was implemented to inform the predictive model. In bivariate analyses, race, family structure, household income, insulin regimen, adolescent-reported adherence to diabetes self-management, cognitive development, adolescent responsibility for T1D management, and parent behavior during the illness management discussion were associated with HbA1c. In the multivariate model, the only significant predictors of HbA1c were race and insulin regimen, accounting for 17% of the variance. Caucasians had better glycemic control than other racial groups. Participants using pre-mixed insulin therapy and basal-bolus insulin had worse glycemic control than those on insulin pumps. This study shows that despite associations of adolescent and family-level variables with glycemic control at the bivariate level, only race and insulin regimen are predictive of glycemic control in hierarchical multivariate analyses. This model offers an alternative way to examine the relationship of demographic and psychosocial factors on glycemic control in adolescents with T1D. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Homotopy Algorithm for Fixed Order Mixed H2/H(infinity) Design

NASA Technical Reports Server (NTRS)

Whorton, Mark; Buschek, Harald; Calise, Anthony J.

1996-01-01

Recent developments in the field of robust multivariable control have merged the theories of H-infinity and H-2 control. This mixed H-2/H-infinity compensator formulation allows design for nominal performance by H-2 norm minimization while guaranteeing robust stability to unstructured uncertainties by constraining the H-infinity norm. A key difficulty associated with mixed H-2/H-infinity compensation is compensator synthesis. A homotopy algorithm is presented for synthesis of fixed order mixed H-2/H-infinity compensators. Numerical results are presented for a four disk flexible structure to evaluate the efficiency of the algorithm.

A New Approach to Identifying the Drivers of Regulation Compliance Using Multivariate Behavioural Models

PubMed Central

Thomas, Alyssa S.; Milfont, Taciano L.; Gavin, Michael C.

2016-01-01

Non-compliance with fishing regulations can undermine management effectiveness. Previous bivariate approaches were unable to untangle the complex mix of factors that may influence fishers’ compliance decisions, including enforcement, moral norms, perceived legitimacy of regulations and the behaviour of others. We compared seven multivariate behavioural models of fisher compliance decisions using structural equation modeling. An online survey of over 300 recreational fishers tested the ability of each model to best predict their compliance with two fishing regulations (daily and size limits). The best fitting model for both regulations was composed solely of psycho-social factors, with social norms having the greatest influence on fishers’ compliance behaviour. Fishers’ attitude also directly affected compliance with size limit, but to a lesser extent. On the basis of these findings, we suggest behavioural interventions to target social norms instead of increasing enforcement for the focal regulations in the recreational blue cod fishery in the Marlborough Sounds, New Zealand. These interventions could include articles in local newspapers and fishing magazines highlighting the extent of regulation compliance as well as using respected local fishers to emphasize the benefits of compliance through public meetings or letters to the editor. Our methodological approach can be broadly applied by natural resource managers as an effective tool to identify drivers of compliance that can then guide the design of interventions to decrease illegal resource use. PMID:27727292

Employment Status and Income Generation among Homeless Young Adults: Results from a Five-City, Mixed-Methods Study

ERIC Educational Resources Information Center

Ferguson, Kristin M.; Bender, Kimberly; Thompson, Sanna J.; Maccio, Elaine M.; Pollio, David

2012-01-01

This mixed-methods study identified correlates of unemployment among homeless young adults in five cities. Two hundred thirty-eight homeless young people from Los Angeles (n = 50), Austin (n = 50), Denver (n = 50), New Orleans (n = 50), and St. Louis (n = 38) were recruited using comparable sampling strategies. Multivariate logistic regression…

Multivariate test power approximations for balanced linear mixed models in studies with missing data.

PubMed

Ringham, Brandy M; Kreidler, Sarah M; Muller, Keith E; Glueck, Deborah H

2016-07-30

Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

A Methodology for Conducting Integrative Mixed Methods Research and Data Analyses

PubMed Central

Castro, Felipe González; Kellison, Joshua G.; Boyd, Stephen J.; Kopak, Albert

2011-01-01

Mixed methods research has gained visibility within the last few years, although limitations persist regarding the scientific caliber of certain mixed methods research designs and methods. The need exists for rigorous mixed methods designs that integrate various data analytic procedures for a seamless transfer of evidence across qualitative and quantitative modalities. Such designs can offer the strength of confirmatory results drawn from quantitative multivariate analyses, along with “deep structure” explanatory descriptions as drawn from qualitative analyses. This article presents evidence generated from over a decade of pilot research in developing an integrative mixed methods methodology. It presents a conceptual framework and methodological and data analytic procedures for conducting mixed methods research studies, and it also presents illustrative examples from the authors' ongoing integrative mixed methods research studies. PMID:22167325

Hospital variation and the impact of postoperative complications on the use of perioperative chemo(radio)therapy in resectable gastric cancer. Results from the Dutch Upper GI Cancer Audit.

PubMed

Schouwenburg, M G; Busweiler, L A D; Beck, N; Henneman, D; Amodio, S; van Berge Henegouwen, M I; Cats, A; van Hillegersberg, R; van Sandick, J W; Wijnhoven, B P L; Wouters, M W J; Nieuwenhuijzen, G A P

2018-04-01

Dutch national guidelines on the diagnosis and treatment of gastric cancer recommend the use of perioperative chemotherapy in patients with resectable gastric cancer. However, adjuvant chemotherapy is often not administered. The aim of this study was to evaluate hospital variation on the probability to receive adjuvant chemotherapy and to identify associated factors with special attention to postoperative complications. All patients who received neoadjuvant chemotherapy and underwent an elective surgical resection for stage IB-IVa (M0) gastric adenocarcinoma between 2011 and 2015 were identified from a national database (Dutch Upper GI Cancer Audit). A multivariable linear mixed model was used to evaluate case-mix adjusted hospital variation and to identify factors associated with adjuvant therapy. Of all surgically treated gastric cancer patients who received neoadjuvant chemotherapy (n = 882), 68% received adjuvant chemo(radio)therapy. After adjusting for case-mix and random variation, a large hospital variation in the administration rates for adjuvant was observed (OR range 0.31-7.1). In multivariable analysis, weight loss, a poor health status and failure of neoadjuvant chemotherapy completion were strongly associated with an increased likelihood of adjuvant therapy omission. Patients with severe postoperative complications had a threefold increased likelihood of adjuvant therapy omission (OR 3.07 95% CI 2.04-4.65). Despite national guidelines, considerable hospital variation was observed in the probability of receiving adjuvant chemo(radio)therapy. Postoperative complications were strongly associated with adjuvant chemo(radio)therapy omission, underlining the need to further reduce perioperative morbidity in gastric cancer surgery. Copyright © 2018 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

Neighbourhood walkability, leisure-time and transport-related physical activity in a mixed urban-rural area.

PubMed

de Sa, Eric; Ardern, Chris I

2014-01-01

Objectives. To develop a walkability index specific to mixed rural/suburban areas, and to explore the relationship between walkability scores and leisure time physical activity. Methods. Respondents were geocoded with 500 m and 1,000 m buffer zones around each address. A walkability index was derived from intersections, residential density, and land-use mix according to built environment measures. Multivariable logistic regression models were used to quantify the association between the index and physical activity levels. Analyses used cross-sectional data from the 2007-2008 Canadian Community Health Survey (n = 1158; ≥18 y). Results. Respondents living in highly walkable 500 m buffer zones (upper quartiles of the walkability index) were more likely to walk or cycle for leisure than those living in low-walkable buffer zones (quartile 1). When a 1,000 m buffer zone was applied, respondents in more walkable neighbourhoods were more likely to walk or cycle for both leisure-time and transport-related purposes. Conclusion. Developing a walkability index can assist in exploring the associations between measures of the built environment and physical activity to prioritize neighborhood change.

Log-gamma linear-mixed effects models for multiple outcomes with application to a longitudinal glaucoma study

PubMed Central

Zhang, Peng; Luo, Dandan; Li, Pengfei; Sharpsten, Lucie; Medeiros, Felipe A.

2015-01-01

Glaucoma is a progressive disease due to damage in the optic nerve with associated functional losses. Although the relationship between structural and functional progression in glaucoma is well established, there is disagreement on how this association evolves over time. In addressing this issue, we propose a new class of non-Gaussian linear-mixed models to estimate the correlations among subject-specific effects in multivariate longitudinal studies with a skewed distribution of random effects, to be used in a study of glaucoma. This class provides an efficient estimation of subject-specific effects by modeling the skewed random effects through the log-gamma distribution. It also provides more reliable estimates of the correlations between the random effects. To validate the log-gamma assumption against the usual normality assumption of the random effects, we propose a lack-of-fit test using the profile likelihood function of the shape parameter. We apply this method to data from a prospective observation study, the Diagnostic Innovations in Glaucoma Study, to present a statistically significant association between structural and functional change rates that leads to a better understanding of the progression of glaucoma over time. PMID:26075565

The role of change in the relationship between commitment and turnover: a latent growth modeling approach.

PubMed

Bentein, Kathleen; Vandenberghe, Christian; Vandenberg, Robert; Stinglhamber, Florence

2005-05-01

Through the use of affective, normative, and continuance commitment in a multivariate 2nd-order factor latent growth modeling approach, the authors observed linear negative trajectories that characterized the changes in individuals across time in both affective and normative commitment. In turn, an individual's intention to quit the organization was characterized by a positive trajectory. A significant association was also found between the change trajectories such that the steeper the decline in an individual's affective and normative commitments across time, the greater the rate of increase in that individual's intention to quit, and, further, the greater the likelihood that the person actually left the organization over the next 9 months. Findings regarding continuance commitment and its components were mixed.

Robust Nonlinear Feedback Control of Aircraft Propulsion Systems

NASA Technical Reports Server (NTRS)

Garrard, William L.; Balas, Gary J.; Litt, Jonathan (Technical Monitor)

2001-01-01

This is the final report on the research performed under NASA Glen grant NASA/NAG-3-1975 concerning feedback control of the Pratt & Whitney (PW) STF 952, a twin spool, mixed flow, after burning turbofan engine. The research focussed on the design of linear and gain-scheduled, multivariable inner-loop controllers for the PW turbofan engine using H-infinity and linear, parameter-varying (LPV) control techniques. The nonlinear turbofan engine simulation was provided by PW within the NASA Rocket Engine Transient Simulator (ROCETS) simulation software environment. ROCETS was used to generate linearized models of the turbofan engine for control design and analysis as well as the simulation environment to evaluate the performance and robustness of the controllers. Comparison between the H-infinity, and LPV controllers are made with the baseline multivariable controller and developed by Pratt & Whitney engineers included in the ROCETS simulation. Simulation results indicate that H-infinity and LPV techniques effectively achieve desired response characteristics with minimal cross coupling between commanded values and are very robust to unmodeled dynamics and sensor noise.

Insurance Coverage for Rehabilitation Therapies and Association with Social Participation Outcomes among Low-Income Children.

PubMed

Mirza, Mansha; Kim, Yoonsang

2016-01-01

(1) To profile children's health insurance coverage rates for specific rehabilitation therapies; (2) to determine whether coverage for rehabilitation therapies is associated with social participation outcomes after adjusting for child and household characteristics; (3) to assess whether rehabilitation insurance differentially affects social participation of children with and without disabilities. We conducted a cross-sectional analysis of secondary survey data on 756 children (ages 3-17) from 370 households living in low-income neighborhoods in a Midwestern U.S. city. Multivariate mixed effects logistic regression models were estimated. Significantly higher proportions of children with disabilities had coverage for physical therapy, occupational therapy, and speech and language pathology, yet gaps in coverage were noted. Multivariate analysis indicated that rehabilitation insurance coverage was significantly associated with social participation (OR = 1.67, 95% CI: 1.013-2.75). This trend was sustained in subgroup analysis. Findings support the need for comprehensive coverage of all essential services under children's health insurance programs.

Unified theory for stochastic modelling of hydroclimatic processes: Preserving marginal distributions, correlation structures, and intermittency

NASA Astrophysics Data System (ADS)

Papalexiou, Simon Michael

2018-05-01

Hydroclimatic processes come in all "shapes and sizes". They are characterized by different spatiotemporal correlation structures and probability distributions that can be continuous, mixed-type, discrete or even binary. Simulating such processes by reproducing precisely their marginal distribution and linear correlation structure, including features like intermittency, can greatly improve hydrological analysis and design. Traditionally, modelling schemes are case specific and typically attempt to preserve few statistical moments providing inadequate and potentially risky distribution approximations. Here, a single framework is proposed that unifies, extends, and improves a general-purpose modelling strategy, based on the assumption that any process can emerge by transforming a specific "parent" Gaussian process. A novel mathematical representation of this scheme, introducing parametric correlation transformation functions, enables straightforward estimation of the parent-Gaussian process yielding the target process after the marginal back transformation, while it provides a general description that supersedes previous specific parameterizations, offering a simple, fast and efficient simulation procedure for every stationary process at any spatiotemporal scale. This framework, also applicable for cyclostationary and multivariate modelling, is augmented with flexible parametric correlation structures that parsimoniously describe observed correlations. Real-world simulations of various hydroclimatic processes with different correlation structures and marginals, such as precipitation, river discharge, wind speed, humidity, extreme events per year, etc., as well as a multivariate example, highlight the flexibility, advantages, and complete generality of the method.

Terminal hospitalizations of nursing home residents: does facility increasing the rate of do not resuscitate orders reduce them?

PubMed

Teno, Joan M; Gozalo, Pedro; Mitchell, Susan L; Bynum, Julie P W; Dosa, David; Mor, Vincent

2011-06-01

Terminal hospitalizations are costly and often avoidable with appropriate advance care planning. This study examined the association between advance care planning, as measured by facility rate of do not resuscitate (DNR) orders in U.S. nursing homes (NHs) and changes in terminal hospitalization rates. Retrospective cohort study of the changing prevalence of DNR orders in U.S. NHs. Using a fixed effect multivariate model, we examined whether increasing facility rate of DNR orders correlates with reductions in terminal hospitalizations in the last week of life, controlling for changes in facility characteristics (staffing, use of NP/PA, case mix of nursing residents, admission volume, racial composition, payer mix). The average facility rate of terminal hospitalizations was 15.5%, fluctuating between 1999 (15.0%) and 2007 (14.8%). NHs starting with low rates of DNR orders that increased their rates had fewer terminal hospital admissions in 2007 (11.2%) than facilities with continuously low DNR usage. Even after applying a multivariate fixed effect model, the effect of changes in facility DNR order rate on terminal hospitalization was -0.056 (95% confidence interval: -0.061, -0.050), indicating that for every 10% increase in DNR orders there was 0.56% decrease in terminal hospitalizations. This rate can be compared with the increase of 0.70% in the terminal hospitalization rate when an NH became disproportionately dependent on Medicaid funding or the 0.40% decrease in terminal hospitalization rate associated with adding a nurse practitioner to the clinical staff complement. NHs that changed their culture of decision making by increasing their facility rate of DNR orders decreased their rate of terminal hospitalizations. Copyright © 2011 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved.

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

PubMed

Liu, Yin; Tian, Guo-Liang; Tang, Man-Lai; Yuen, Kam Chuen

2018-05-25

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, ) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Estimating correlation between multivariate longitudinal data in the presence of heterogeneity.

PubMed

Gao, Feng; Philip Miller, J; Xiong, Chengjie; Luo, Jingqin; Beiser, Julia A; Chen, Ling; Gordon, Mae O

2017-08-17

Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time. Bivariate linear mixed model (BLMM) provides a versatile tool with regard to assessing correlation. However, BLMMs often assume that all individuals are drawn from a single homogenous population where the individual trajectories are distributed smoothly around population average. Using longitudinal mean deviation (MD) and visual acuity (VA) from the Ocular Hypertension Treatment Study (OHTS), we demonstrated strategies to better understand the correlation between multivariate longitudinal data in the presence of potential heterogeneity. Conditional correlation (i.e., marginal correlation given random effects) was calculated to describe how the association between longitudinal outcomes evolved over time within specific subpopulation. The impact of heterogeneity on correlation was also assessed by simulated data. There was a significant positive correlation in both random intercepts (ρ = 0.278, 95% CI: 0.121-0.420) and random slopes (ρ = 0.579, 95% CI: 0.349-0.810) between longitudinal MD and VA, and the strength of correlation constantly increased over time. However, conditional correlation and simulation studies revealed that the correlation was induced primarily by participants with rapid deteriorating MD who only accounted for a small fraction of total samples. Conditional correlation given random effects provides a robust estimate to describe the correlation between multivariate longitudinal data in the presence of unobserved heterogeneity (NCT00000125).

Analysis of crash proportion by vehicle type at traffic analysis zone level: A mixed fractional split multinomial logit modeling approach with spatial effects.

PubMed

Lee, Jaeyoung; Yasmin, Shamsunnahar; Eluru, Naveen; Abdel-Aty, Mohamed; Cai, Qing

2018-02-01

In traffic safety literature, crash frequency variables are analyzed using univariate count models or multivariate count models. In this study, we propose an alternative approach to modeling multiple crash frequency dependent variables. Instead of modeling the frequency of crashes we propose to analyze the proportion of crashes by vehicle type. A flexible mixed multinomial logit fractional split model is employed for analyzing the proportions of crashes by vehicle type at the macro-level. In this model, the proportion allocated to an alternative is probabilistically determined based on the alternative propensity as well as the propensity of all other alternatives. Thus, exogenous variables directly affect all alternatives. The approach is well suited to accommodate for large number of alternatives without a sizable increase in computational burden. The model was estimated using crash data at Traffic Analysis Zone (TAZ) level from Florida. The modeling results clearly illustrate the applicability of the proposed framework for crash proportion analysis. Further, the Excess Predicted Proportion (EPP)-a screening performance measure analogous to Highway Safety Manual (HSM), Excess Predicted Average Crash Frequency is proposed for hot zone identification. Using EPP, a statewide screening exercise by the various vehicle types considered in our analysis was undertaken. The screening results revealed that the spatial pattern of hot zones is substantially different across the various vehicle types considered. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spectroscopy and multivariate analyses applications related to solid rocket nozzle bondline

NASA Technical Reports Server (NTRS)

Arendale, W. F.; Hatcher, Richard; Benson, Brian; Workman, Gary L.

1991-01-01

Chemical composition and molecular orientation define the properties of materials. Information related to chemical composition and molecular configuration is obtained by various forms of spectroscopy. Software algorithms developed for multivariate analyses, expert systems, and Artificial Intelligence (AI) are used to conduct repetitive operations. The techniques are believed to be of particular significance toward achieving TQM objectives. The objective was to obtain information related to the quality of the bondline in the solid rocket motor, SRM, nozzle. Hysol 934 NA, a room temperature curing epoxide resin, is used as the bonding agent. A good bond requires that the adhesive be placed on a properly prepared metal surface, the adhesives Part A and B be mixed in appropriate ratio from material within shelf life specifications. Spectroscopic data was obtained for surfaces prepared according to specifications, contaminated metal surfaces, samples of the epoxide adhesive at times that represent shelf aging from 3 months to 2 years, several mix ratio of A to B, and curing material. Temperature was found to be a significant factor. The study concentrated on pot life and mix ratio.

The disconnected values model improves mental well-being and fitness in an employee wellness program.

PubMed

Anshel, Mark H; Brinthaupt, Thomas M; Kang, Minsoo

2010-01-01

This study examined the effect of a 10-week wellness program on changes in physical fitness and mental well-being. The conceptual framework for this study was the Disconnected Values Model (DVM). According to the DVM, detecting the inconsistencies between negative habits and values (e.g., health, family, faith, character) and concluding that these "disconnects" are unacceptable promotes the need for health behavior change. Participants were 164 full-time employees at a university in the southeastern U.S. The program included fitness coaching and a 90-minute orientation based on the DVM. Multivariate Mixed Model analyses indicated significantly improved scores from pre- to post-intervention on selected measures of physical fitness and mental well-being. The results suggest that the Disconnected Values Model provides an effective cognitive-behavioral approach to generating health behavior change in a 10-week workplace wellness program.

Genetic parameters for growth characteristics of free-range chickens under univariate random regression models.

PubMed

Rovadoscki, Gregori A; Petrini, Juliana; Ramirez-Diaz, Johanna; Pertile, Simone F N; Pertille, Fábio; Salvian, Mayara; Iung, Laiza H S; Rodriguez, Mary Ana P; Zampar, Aline; Gaya, Leila G; Carvalho, Rachel S B; Coelho, Antonio A D; Savino, Vicente J M; Coutinho, Luiz L; Mourão, Gerson B

2016-09-01

Repeated measures from the same individual have been analyzed by using repeatability and finite dimension models under univariate or multivariate analyses. However, in the last decade, the use of random regression models for genetic studies with longitudinal data have become more common. Thus, the aim of this research was to estimate genetic parameters for body weight of four experimental chicken lines by using univariate random regression models. Body weight data from hatching to 84 days of age (n = 34,730) from four experimental free-range chicken lines (7P, Caipirão da ESALQ, Caipirinha da ESALQ and Carijó Barbado) were used. The analysis model included the fixed effects of contemporary group (gender and rearing system), fixed regression coefficients for age at measurement, and random regression coefficients for permanent environmental effects and additive genetic effects. Heterogeneous variances for residual effects were considered, and one residual variance was assigned for each of six subclasses of age at measurement. Random regression curves were modeled by using Legendre polynomials of the second and third orders, with the best model chosen based on the Akaike Information Criterion, Bayesian Information Criterion, and restricted maximum likelihood. Multivariate analyses under the same animal mixed model were also performed for the validation of the random regression models. The Legendre polynomials of second order were better for describing the growth curves of the lines studied. Moderate to high heritabilities (h(2) = 0.15 to 0.98) were estimated for body weight between one and 84 days of age, suggesting that selection for body weight at all ages can be used as a selection criteria. Genetic correlations among body weight records obtained through multivariate analyses ranged from 0.18 to 0.96, 0.12 to 0.89, 0.06 to 0.96, and 0.28 to 0.96 in 7P, Caipirão da ESALQ, Caipirinha da ESALQ, and Carijó Barbado chicken lines, respectively. Results indicate that genetic gain for body weight can be achieved by selection. Also, selection for body weight at 42 days of age can be maintained as a selection criterion. © 2016 Poultry Science Association Inc.

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing.

PubMed

Van Hertem, T; Bahr, C; Schlageter Tello, A; Viazzi, S; Steensels, M; Romanini, C E B; Lokhorst, C; Maltz, E; Halachmi, I; Berckmans, D

2016-09-01

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

Comparison of mixed effects models of antimicrobial resistance metrics of livestock and poultry Salmonella isolates from a national monitoring system.

PubMed

Bjork, K E; Kopral, C A; Wagner, B A; Dargatz, D A

2015-12-01

Antimicrobial use in agriculture is considered a pathway for the selection and dissemination of resistance determinants among animal and human populations. From 1997 through 2003 the U.S. National Antimicrobial Resistance Monitoring System (NARMS) tested clinical Salmonella isolates from multiple animal and environmental sources throughout the United States for resistance to panels of 16-19 antimicrobials. In this study we applied two mixed effects models, the generalized linear mixed model (GLMM) and accelerated failure time frailty (AFT-frailty) model, to susceptible/resistant and interval-censored minimum inhibitory concentration (MIC) metrics, respectively, from Salmonella enterica subspecies enterica serovar Typhimurium isolates from livestock and poultry. Objectives were to compare characteristics of the two models and to examine the effects of time, species, and multidrug resistance (MDR) on the resistance of isolates to individual antimicrobials, as revealed by the models. Fixed effects were year of sample collection, isolate source species and MDR indicators; laboratory study site was included as a random effect. MDR indicators were significant for every antimicrobial and were dominant effects in multivariable models. Temporal trends and source species influences varied by antimicrobial. In GLMMs, the intra-class correlation coefficient ranged up to 0.8, indicating that the proportion of variance accounted for by laboratory study site could be high. AFT models tended to be more sensitive, detecting more curvilinear temporal trends and species differences; however, high levels of left- or right-censoring made some models unstable and results uninterpretable. Results from GLMMs may be biased by cutoff criteria used to collapse MIC data into binary categories, and may miss signaling important trends or shifts if the series of antibiotic dilutions tested does not span a resistance threshold. Our findings demonstrate the challenges of measuring the AMR ecosystem and the complexity of interacting factors, and have implications for future monitoring. We include suggestions for future data collection and analyses, including alternative modeling approaches. Published by Elsevier B.V.

Fractional Brownian motion and multivariate-t models for longitudinal biomedical data, with application to CD4 counts in HIV-positive patients.

PubMed

Stirrup, Oliver T; Babiker, Abdel G; Carpenter, James R; Copas, Andrew J

2016-04-30

Longitudinal data are widely analysed using linear mixed models, with 'random slopes' models particularly common. However, when modelling, for example, longitudinal pre-treatment CD4 cell counts in HIV-positive patients, the incorporation of non-stationary stochastic processes such as Brownian motion has been shown to lead to a more biologically plausible model and a substantial improvement in model fit. In this article, we propose two further extensions. Firstly, we propose the addition of a fractional Brownian motion component, and secondly, we generalise the model to follow a multivariate-t distribution. These extensions are biologically plausible, and each demonstrated substantially improved fit on application to example data from the Concerted Action on SeroConversion to AIDS and Death in Europe study. We also propose novel procedures for residual diagnostic plots that allow such models to be assessed. Cohorts of patients were simulated from the previously reported and newly developed models in order to evaluate differences in predictions made for the timing of treatment initiation under different clinical management strategies. A further simulation study was performed to demonstrate the substantial biases in parameter estimates of the mean slope of CD4 decline with time that can occur when random slopes models are applied in the presence of censoring because of treatment initiation, with the degree of bias found to depend strongly on the treatment initiation rule applied. Our findings indicate that researchers should consider more complex and flexible models for the analysis of longitudinal biomarker data, particularly when there are substantial missing data, and that the parameter estimates from random slopes models must be interpreted with caution. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Evaluation of third-degree and fourth-degree laceration rates as quality indicators.

PubMed

Friedman, Alexander M; Ananth, Cande V; Prendergast, Eri; D'Alton, Mary E; Wright, Jason D

2015-04-01

To examine the patterns and predictors of third-degree and fourth-degree laceration in women undergoing vaginal delivery. We identified a population-based cohort of women in the United States who underwent a vaginal delivery between 1998 and 2010 using the Nationwide Inpatient Sample. Multivariable log-linear regression models were developed to account for patient, obstetric, and hospital factors related to lacerations. Between-hospital variability of laceration rates was calculated using generalized log-linear mixed models. Among 7,096,056 women who underwent vaginal delivery in 3,070 hospitals, 3.3% (n=232,762) had a third-degree laceration and 1.1% (n=76,347) had a fourth-degree laceration. In an adjusted model for fourth-degree lacerations, important risk factors included shoulder dystocia and forceps and vacuum deliveries with and without episiotomy. Other demographic, obstetric, medical, and hospital variables, although statistically significant, were not major determinants of lacerations. Risk factors in a multivariable model for third-degree lacerations were similar to those in the fourth-degree model. Regression analysis of hospital rates (n=3,070) of lacerations demonstrated limited between-hospital variation. Risk of third-degree and fourth-degree laceration was most strongly related to operative delivery and shoulder dystocia. Between-hospital variation was limited. Given these findings and that the most modifiable practice related to lacerations would be reduction in operative vaginal deliveries (and a possible increase in cesarean delivery), third-degree and fourth-degree laceration rates may be a quality metric of limited utility.

Patient out-of-pocket spending in cranial neurosurgery: single-institution analysis of 6569 consecutive cases and literature review.

PubMed

Yoon, Seungwon; Mooney, Michael A; Bohl, Michael A; Sheehy, John P; Nakaji, Peter; Little, Andrew S; Lawton, Michael T

2018-05-01

OBJECTIVE With drastic changes to the health insurance market, patient cost sharing has significantly increased in recent years. However, the patient financial burden, or out-of-pocket (OOP) costs, for surgical procedures is poorly understood. The goal of this study was to analyze patient OOP spending in cranial neurosurgery and identify drivers of OOP spending growth. METHODS For 6569 consecutive patients who underwent cranial neurosurgery from 2013 to 2016 at the authors' institution, the authors created univariate and multivariate mixed-effects models to investigate the effect of patient demographic and clinical factors on patient OOP spending. The authors examined OOP payments stratified into 10 subsets of case categories and created a generalized linear model to study the growth of OOP spending over time. RESULTS In the multivariate model, case categories (craniotomy for pain, tumor, and vascular lesions), commercial insurance, and out-of-network plans were significant predictors of higher OOP payments for patients (all p < 0.05). Patient spending varied substantially across procedure types, with patients undergoing craniotomy for pain ($1151 ± $209) having the highest mean OOP payments. On average, commercially insured patients spent nearly twice as much in OOP payments as the overall population. From 2013 to 2016, the mean patient OOP spending increased 17%, from $598 to $698 per patient encounter. Commercially insured patients experienced more significant growth in OOP spending, with a cumulative rate of growth of 42% ($991 in 2013 to $1403 in 2016). CONCLUSIONS Even after controlling for inflation, case-mix differences, and partial fiscal periods, OOP spending for cranial neurosurgery patients significantly increased from 2013 to 2016. The mean OOP spending for commercially insured neurosurgical patients exceeded $1400 in 2016, with an average annual growth rate of 13%. As patient cost sharing in health insurance plans becomes more prevalent, patients and providers must consider the potential financial burden for patients receiving specialized neurosurgical care.

Time from first detectable PSA following radical prostatectomy to biochemical recurrence: A competing risk analysis.

PubMed

de Boo, Leonora; Pintilie, Melania; Yip, Paul; Baniel, Jack; Fleshner, Neil; Margel, David

2015-01-01

In this study, we estimated the time from first detectable prostate-specific antigen (PSA) following radical prostatectomy (RP) to commonly used definitions of biochemical recurrence (BCR). We also identified the predictors of time to BCR. We identified subjects who underwent a RP and had an undetectable PSA after surgery followed by at least 1 detectable PSA between 2000 and 2011. The primary outcome was time to BCR (PSA ≥0.2 and successive PSA ≥0.2) and prediction of the rate of PSA rise. Outcomes were calculated using a competing risk analysis, with univariable and multivariable Fine and Grey models. We employed a mixed effect model to test clinical predictors that are associated with the rate of PSA rise. The cohort included 376 patients. The median follow-up from surgery was 60.5 months (interquartile range [IQR] 40.8-91.5) and from detectable PSA was 18 months (IQR 11-32). Only 45.74% (n = 172) had PSA values ≥0.2 ng/mL, while 15.16% (n = 57) reached the PSA level of ≥0.4 ng/mL and rising. On multivariable analysis, the values of the first detectable PSA and pathologic Gleason grade 8 or higher were consistently independent predictors of time to BCR. In the mixed effect model rate, the PSA rise was associated with time from surgery to first detectable PSA, Gleason score, and prostate volume. The main limitation of this study is the large proportion of patients that received treatment without reaching BCR. It is plausible that shorter estimated median times would occur at a centre that does not use salvage therapy at such an early state. The time from first detectable PSA to BCR may be lengthy. Our analyses of the predictors of the rate of PSA rise can help determine a personalized approach for patients with a detectable PSA after surgery.

Time from first detectable PSA following radical prostatectomy to biochemical recurrence: A competing risk analysis

PubMed Central

de Boo, Leonora; Pintilie, Melania; Yip, Paul; Baniel, Jack; Fleshner, Neil; Margel, David

2015-01-01

Introduction: In this study, we estimated the time from first detectable prostate-specific antigen (PSA) following radical prostatectomy (RP) to commonly used definitions of biochemical recurrence (BCR). We also identified the predictors of time to BCR. Methods: We identified subjects who underwent a RP and had an undetectable PSA after surgery followed by at least 1 detectable PSA between 2000 and 2011. The primary outcome was time to BCR (PSA ≥0.2 and successive PSA ≥0.2) and prediction of the rate of PSA rise. Outcomes were calculated using a competing risk analysis, with univariable and multivariable Fine and Grey models. We employed a mixed effect model to test clinical predictors that are associated with the rate of PSA rise. Results: The cohort included 376 patients. The median follow-up from surgery was 60.5 months (interquartile range [IQR] 40.8–91.5) and from detectable PSA was 18 months (IQR 11–32). Only 45.74% (n = 172) had PSA values ≥0.2 ng/mL, while 15.16% (n = 57) reached the PSA level of ≥0.4 ng/mL and rising. On multivariable analysis, the values of the first detectable PSA and pathologic Gleason grade 8 or higher were consistently independent predictors of time to BCR. In the mixed effect model rate, the PSA rise was associated with time from surgery to first detectable PSA, Gleason score, and prostate volume. The main limitation of this study is the large proportion of patients that received treatment without reaching BCR. It is plausible that shorter estimated median times would occur at a centre that does not use salvage therapy at such an early state. Conclusion: The time from first detectable PSA to BCR may be lengthy. Our analyses of the predictors of the rate of PSA rise can help determine a personalized approach for patients with a detectable PSA after surgery. PMID:25624961

Anticonvulsants and suicide attempts in bipolar I disorders.

PubMed

Bellivier, F; Belzeaux, R; Scott, J; Courtet, P; Golmard, J-L; Azorin, J-M

2017-05-01

To identify risk factors for suicide attempts (SA) in individuals commencing treatment for a manic or mixed episode. A total of 3390 manic or mixed cases with bipolar disorder (BD) type I recruited from 14 European countries were included in a prospective, 2-year observational study. Poisson regression models were used to identify individual and treatment factors associated with new SA events. Two multivariate models were built, stratified for the presence or absence of prior SA. A total of 302 SA were recorded prospectively; the peak incidence was 0-12 weeks after commencing treatment. In cases with a prior history of SA, risk of SA repetition was associated with younger age of first manic episode (P = 0.03), rapid cycling (P < 0.001), history of alcohol and/or substance use disorder (P < 0.001), number of psychotropic drugs prescribed (P < 0.001) and initiation of an anticonvulsant at study entry (P < 0.001). In cases with no previous SA, the first SA event was associated with rapid cycling (P = 0.02), lifetime history of alcohol use disorder (P = 0.02) and initiation of an anticonvulsant at study entry (P = 0.002). The introduction of anticonvulsants for a recent-onset manic or mixed episode may be associated with an increased risk of SA. Further BD studies must determine whether this link is causal. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

Investigating College and Graduate Students' Multivariable Reasoning in Computational Modeling

ERIC Educational Resources Information Center

Wu, Hsin-Kai; Wu, Pai-Hsing; Zhang, Wen-Xin; Hsu, Ying-Shao

2013-01-01

Drawing upon the literature in computational modeling, multivariable reasoning, and causal attribution, this study aims at characterizing multivariable reasoning practices in computational modeling and revealing the nature of understanding about multivariable causality. We recruited two freshmen, two sophomores, two juniors, two seniors, four…

The occipitofrontal circumference: reliable prediction of the intracranial volume in children with syndromic and complex craniosynostosis.

PubMed

Rijken, Bianca Francisca Maria; den Ottelander, Bianca Kelly; van Veelen, Marie-Lise Charlotte; Lequin, Maarten Hans; Mathijssen, Irene Margreet Jacqueline

2015-05-01

OBJECT Patients with syndromic and complex craniosynostosis are characterized by the premature fusion of one or more cranial sutures. These patients are at risk for developing elevated intracranial pressure (ICP). There are several factors known to contribute to elevated ICP in these patients, including craniocerebral disproportion, hydrocephalus, venous hypertension, and obstructive sleep apnea. However, the causal mechanism is unknown, and patients develop elevated ICP even after skull surgery. In clinical practice, the occipitofrontal circumference (OFC) is used as an indirect measure for intracranial volume (ICV), to evaluate skull growth. However, it remains unknown whether OFC is a reliable predictor of ICV in patients with a severe skull deformity. Therefore, in this study the authors evaluated the relation between ICV and OFC. METHODS Eighty-four CT scans obtained in 69 patients with syndromic and complex craniosynostosis treated at the Erasmus University Medical Center-Sophia Children's Hospital were included. The ICV was calculated based on CT scans by using autosegmentation with an HU threshold < 150. The OFC was collected from electronic patient files. The CT scans and OFC measurements were matched based on a maximum amount of the time that was allowed between these examinations, which was dependent on age. A Pearson correlation coefficient was calculated to evaluate the correlations between OFC and ICV. The predictive value of OFC, age, and sex on ICV was then further evaluated using a univariate linear mixed model. The significant factors in the univariate analysis were subsequently entered in a multivariate mixed model. RESULTS The correlations found between OFC and ICV were r = 0.908 for the total group (p < 0.001), r = 0.981 for Apert (p < 0.001), r = 0.867 for Crouzon-Pfeiffer (p < 0.001), r = 0.989 for Muenke (p < 0.001), r = 0.858 for Saethre- Chotzen syndrome (p = 0.001), and r = 0.917 for complex craniosynostosis (p < 0.001). Age and OFC were significant predictors of ICV in the univariate linear mixed model (p < 0.001 for both factors). The OFC was the only predictor that remained significant in the multivariate analysis (p < 0.001). CONCLUSIONS The OFC is a significant predictor of ICV in patients with syndromic and complex craniosynostosis. Therefore, measuring the OFC during clinical practice is very useful in determining which patients are at risk for impaired skull growth.

A Multivariate Model for the Study of Parental Acceptance-Rejection and Child Abuse.

ERIC Educational Resources Information Center

Rohner, Ronald P.; Rohner, Evelyn C.

This paper proposes a multivariate strategy for the study of parental acceptance-rejection and child abuse and describes a research study on parental rejection and child abuse which illustrates the advantages of using a multivariate, (rather than a simple-model) approach. The multivariate model is a combination of three simple models used to study…

Trabecular Meshwork Height in Primary Open-Angle Glaucoma Versus Primary Angle-Closure Glaucoma.

PubMed

Masis, Marisse; Chen, Rebecca; Porco, Travis; Lin, Shan C

2017-11-01

To determine if trabecular meshwork (TM) height differs between primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG) eyes. Prospective, cross-sectional clinical study. Adult patients were consecutively recruited from glaucoma clinics at the University of California, San Francisco, from January 2012 to July 2015. Images were obtained from spectral-domain optical coherence tomography (Cirrus OCT; Carl Zeiss Meditec, Inc, Dublin, California, USA). Univariate and multivariate linear mixed models comparing TM height and glaucoma type were performed to assess the relationship between TM height and glaucoma subtype. Mixed-effects regression was used to adjust for the use of both eyes in some subjects. The study included 260 eyes from 161 subjects, composed of 61 men and 100 women. Mean age was 70 years (SD 11.77). There were 199 eyes (123 patients) in the POAG group and 61 eyes (38 patients) in the PACG group. Mean TM heights in the POAG and PACG groups were 812 ± 13 μm and 732 ± 27 μm, respectively, and the difference was significant in univariate analysis (P = .004) and in multivariate analysis (β = -88.7 [24.05-153.5]; P = .008). In this clinic-based population, trabecular meshwork height is shorter in PACG patients compared to POAG patients. This finding may provide insight into the pathophysiology of angle closure and provide assistance in future diagnosis, prevention, and management of the angle-closure spectrum of disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

Quantitation of active pharmaceutical ingredients and excipients in powder blends using designed multivariate calibration models by near-infrared spectroscopy.

PubMed

Li, Weiyong; Worosila, Gregory D

2005-05-13

This research note demonstrates the simultaneous quantitation of a pharmaceutical active ingredient and three excipients in a simulated powder blend containing acetaminophen, Prosolv and Crospovidone. An experimental design approach was used in generating a 5-level (%, w/w) calibration sample set that included 125 samples. The samples were prepared by weighing suitable amount of powders into separate 20-mL scintillation vials and were mixed manually. Partial least squares (PLS) regression was used in calibration model development. The models generated accurate results for quantitation of Crospovidone (at 5%, w/w) and magnesium stearate (at 0.5%, w/w). Further testing of the models demonstrated that the 2-level models were as effective as the 5-level ones, which reduced the calibration sample number to 50. The models had a small bias for quantitation of acetaminophen (at 30%, w/w) and Prosolv (at 64.5%, w/w) in the blend. The implication of the bias is discussed.

A multivariate fall risk assessment model for VHA nursing homes using the minimum data set.

PubMed

French, Dustin D; Werner, Dennis C; Campbell, Robert R; Powell-Cope, Gail M; Nelson, Audrey L; Rubenstein, Laurence Z; Bulat, Tatjana; Spehar, Andrea M

2007-02-01

The purpose of this study was to develop a multivariate fall risk assessment model beyond the current fall Resident Assessment Protocol (RAP) triggers for nursing home residents using the Minimum Data Set (MDS). Retrospective, clustered secondary data analysis. National Veterans Health Administration (VHA) long-term care nursing homes (N = 136). The study population consisted of 6577 national VHA nursing home residents who had an annual assessment during FY 2005, identified from the MDS, as well as an earlier annual or admission assessment within a 1-year look-back period. A dichotomous multivariate model of nursing home residents coded with a fall on selected fall risk characteristics from the MDS, estimated with general estimation equations (GEE). There were 17 170 assessments corresponding to 6577 long-term care nursing home residents. The increased odds ratio (OR) of being classified as a faller relative to the omitted "dependent" category of activities of daily living (ADL) ranged from OR = 1.35 for "limited" ADL category up to OR = 1.57 for "extensive-2" ADL (P < .0001). Unsteady gait more than doubles the odds of being a faller (OR = 2.63, P < .0001). The use of assistive devices such as canes, walkers, or crutches, or the use of wheelchairs increases the odds of being a faller (OR = 1.17, P < .0005) or (OR = 1.19, P < .0002), respectively. Foot problems may also increase the odds of being a faller (OR = 1.26, P < .0016). Alzheimer's or other dementias also increase the odds of being classified as a faller (OR = 1.18, P < .0219) or (OR=1.22, P < .0001), respectively. In addition, anger (OR = 1.19, P < .0065); wandering (OR = 1.53, P < .0001); or use of antipsychotic medications (OR = 1.15, P < .0039), antianxiety medications (OR = 1.13, P < .0323), or antidepressant medications (OR = 1.39, P < .0001) was also associated with the odds of being a faller. This national study in one of the largest managed healthcare systems in the United States has empirically confirmed the relative importance of certain risk factors for falls in long-term care settings. The model incorporated an ADL index and adjusted for case mix by including only long-term care nursing home residents. The study offers clinicians practical estimates by combining multiple univariate MDS elements in an empirically based, multivariate fall risk assessment model.

Rural Hospital Ownership: Medical Service Provision, Market Mix, and Spillover Effects

PubMed Central

Horwitz, Jill R; Nichols, Austin

2011-01-01

Objective To test whether nonprofit, for-profit, or government hospital ownership affects medical service provision in rural hospital markets, either directly or through the spillover effects of ownership mix. Data Sources/Study Setting Data are from the American Hospital Association, U.S. Census, CMS Healthcare Cost Report Information System and Prospective Payment System Minimum Data File, and primary data collection for geographic coordinates. The sample includes all nonfederal, general medical, and surgical hospitals located outside of metropolitan statistical areas and within the continental United States from 1988 to 2005. Study Design We estimate multivariate regression models to examine the effects of (1) hospital ownership and (2) hospital ownership mix within rural hospital markets on profitable versus unprofitable medical service offerings. Principal Findings Rural nonprofit hospitals are more likely than for-profit hospitals to offer unprofitable services, many of which are underprovided services. Nonprofits respond less than for-profits to changes in service profitability. Nonprofits with more for-profit competitors offer more profitable services and fewer unprofitable services than those with fewer for-profit competitors. Conclusions Rural hospital ownership affects medical service provision at the hospital and market levels. Nonprofit hospital regulation should reflect both the direct and spillover effects of ownership. PMID:21639860

Rural hospital ownership: medical service provision, market mix, and spillover effects.

PubMed

Horwitz, Jill R; Nichols, Austin

2011-10-01

To test whether nonprofit, for-profit, or government hospital ownership affects medical service provision in rural hospital markets, either directly or through the spillover effects of ownership mix. Data are from the American Hospital Association, U.S. Census, CMS Healthcare Cost Report Information System and Prospective Payment System Minimum Data File, and primary data collection for geographic coordinates. The sample includes all nonfederal, general medical, and surgical hospitals located outside of metropolitan statistical areas and within the continental United States from 1988 to 2005. We estimate multivariate regression models to examine the effects of (1) hospital ownership and (2) hospital ownership mix within rural hospital markets on profitable versus unprofitable medical service offerings. Rural nonprofit hospitals are more likely than for-profit hospitals to offer unprofitable services, many of which are underprovided services. Nonprofits respond less than for-profits to changes in service profitability. Nonprofits with more for-profit competitors offer more profitable services and fewer unprofitable services than those with fewer for-profit competitors. Rural hospital ownership affects medical service provision at the hospital and market levels. Nonprofit hospital regulation should reflect both the direct and spillover effects of ownership. © Health Research and Educational Trust.

Extensions to Multivariate Space Time Mixture Modeling of Small Area Cancer Data.

PubMed

Carroll, Rachel; Lawson, Andrew B; Faes, Christel; Kirby, Russell S; Aregay, Mehreteab; Watjou, Kevin

2017-05-09

Oral cavity and pharynx cancer, even when considered together, is a fairly rare disease. Implementation of multivariate modeling with lung and bronchus cancer, as well as melanoma cancer of the skin, could lead to better inference for oral cavity and pharynx cancer. The multivariate structure of these models is accomplished via the use of shared random effects, as well as other multivariate prior distributions. The results in this paper indicate that care should be taken when executing these types of models, and that multivariate mixture models may not always be the ideal option, depending on the data of interest.

Restructuring in response to case mix reimbursement in nursing homes: A contingency approach

PubMed Central

Zinn, Jacqueline; Feng, Zhanlian; Mor, Vincent; Intrator, Orna; Grabowski, David

2013-01-01

Background Resident-based case mix reimbursement has become the dominant mechanism for publicly funded nursing home care. In 1998 skilled nursing facility reimbursement changed from cost-based to case mix adjusted payments under the Medicare Prospective Payment System for the costs of all skilled nursing facility care provided to Medicare recipients. In addition, as of 2004, 35 state Medicaid programs had implemented some form of case mix reimbursement. Purpose The purpose of the study is to determine if the implementation of Medicare and Medicaid case mix reimbursement increased the administrative burden on nursing homes, as evidenced by increased levels of nurses in administrative functions. Methodology/Approach The primary data for this study come from the Centers for Medicare and Medicaid Services Online Survey Certification and Reporting database from 1997 through 2004, a national nursing home database containing aggregated facility-level information, including staffing, organizational characteristics and resident conditions, on all Medicare/Medicaid certified nursing facilities in the country. We conducted multivariate regression analyses using a facility fixed-effects model to examine the effects of the implementation of Medicaid case mix reimbursement and Medicare Prospective Payment System on changes in the level of total administrative nurse staffing in nursing homes. Findings Both Medicaid case mix reimbursement and Medicare Prospective Payment System increased the level of administrative nurse staffing, on average by 5.5% and 4.0% respectively. However, lack of evidence for a substitution effect suggests that any decline in direct care staffing after the introduction of case mix reimbursement is not attributable to a shift from clinical nursing resources to administrative functions. Practice Implications Our findings indicate that the administrative burden posed by case mix reimbursement has resource implications for all freestanding facilities. At the margin, the increased administrative burden imposed by case mix may become a factor influencing a range of decisions, including resident admission and staff hiring. PMID:18360162

Restructuring in response to case mix reimbursement in nursing homes: a contingency approach.

PubMed

Zinn, Jacqueline; Feng, Zhanlian; Mor, Vincent; Intrator, Orna; Grabowski, David

2008-01-01

Resident-based case mix reimbursement has become the dominant mechanism for publicly funded nursing home care. In 1998 skilled nursing facility reimbursement changed from cost-based to case mix adjusted payments under the Medicare Prospective Payment System for the costs of all skilled nursing facility care provided to Medicare recipients. In addition, as of 2004, 35 state Medicaid programs had implemented some form of case mix reimbursement. The purpose of the study is to determine if the implementation of Medicare and Medicaid case mix reimbursement increased the administrative burden on nursing homes, as evidenced by increased levels of nurses in administrative functions. The primary data for this study come from the Centers for Medicare and Medicaid Services Online Survey Certification and Reporting database from 1997 through 2004, a national nursing home database containing aggregated facility-level information, including staffing, organizational characteristics and resident conditions, on all Medicare/Medicaid certified nursing facilities in the country. We conducted multivariate regression analyses using a facility fixed-effects model to examine the effects of the implementation of Medicaid case mix reimbursement and Medicare Prospective Payment System on changes in the level of total administrative nurse staffing in nursing homes. Both Medicaid case mix reimbursement and Medicare Prospective Payment System increased the level of administrative nurse staffing, on average by 5.5% and 4.0% respectively. However, lack of evidence for a substitution effect suggests that any decline in direct care staffing after the introduction of case mix reimbursement is not attributable to a shift from clinical nursing resources to administrative functions. Our findings indicate that the administrative burden posed by case mix reimbursement has resource implications for all freestanding facilities. At the margin, the increased administrative burden imposed by case mix may become a factor influencing a range of decisions, including resident admission and staff hiring.

Child Maltreatment and Delinquency Onset Among African American Adolescent Males

PubMed Central

Williams, James Herbert; Van Dorn, Richard A.; Bright, Charlotte Lyn; Jonson-Reid, Melissa; Nebbitt, Von E.

2013-01-01

Child welfare and criminology research have increasingly sought to better understand factors that increase the likelihood that abused and neglected children will become involved in the juvenile justice system. However, few studies have addressed this relationship among African American male adolescents. The current study examines the relationship between child maltreatment (i.e., neglect, physical abuse, sexual abuse, and other/mixed abuse) and the likelihood of a delinquency petition using a sample of African American males (N = 2,335) born before 1990. Multivariable logistic regression models compared those with a delinquency-based juvenile justice petition to those without. Results indicate that African American males with a history of neglect, physical abuse, or other/mixed abuse were more likely to be involved in the juvenile justice system than those without any child maltreatment. Additionally, multiple maltreatment reports, a prior history of mental health treatment, victimization, and having a parent who did not complete high school also increased the likelihood of a delinquency petition. Implications for intervention and prevention are discussed. PMID:23730121

Child Maltreatment and Delinquency Onset Among African American Adolescent Males.

PubMed

Williams, James Herbert; Van Dorn, Richard A; Bright, Charlotte Lyn; Jonson-Reid, Melissa; Nebbitt, Von E

2010-05-01

Child welfare and criminology research have increasingly sought to better understand factors that increase the likelihood that abused and neglected children will become involved in the juvenile justice system. However, few studies have addressed this relationship among African American male adolescents. The current study examines the relationship between child maltreatment (i.e., neglect, physical abuse, sexual abuse, and other/mixed abuse) and the likelihood of a delinquency petition using a sample of African American males ( N = 2,335) born before 1990. Multivariable logistic regression models compared those with a delinquency-based juvenile justice petition to those without. Results indicate that African American males with a history of neglect, physical abuse, or other/mixed abuse were more likely to be involved in the juvenile justice system than those without any child maltreatment. Additionally, multiple maltreatment reports, a prior history of mental health treatment, victimization, and having a parent who did not complete high school also increased the likelihood of a delinquency petition. Implications for intervention and prevention are discussed.

A Polyhedral Outer-approximation, Dynamic-discretization optimization solver, 1.x

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bent, Rusell; Nagarajan, Harsha; Sundar, Kaarthik

2017-09-25

In this software, we implement an adaptive, multivariate partitioning algorithm for solving mixed-integer nonlinear programs (MINLP) to global optimality. The algorithm combines ideas that exploit the structure of convex relaxations to MINLPs and bound tightening procedures

Modeling genetic and nongenetic variation of feed efficiency and its partial relationships between component traits as a function of management and environmental factors.

PubMed

Lu, Y; Vandehaar, M J; Spurlock, D M; Weigel, K A; Armentano, L E; Staples, C R; Connor, E E; Wang, Z; Coffey, M; Veerkamp, R F; de Haas, Y; Tempelman, R J

2017-01-01

Feed efficiency (FE), characterized as the fraction of feed nutrients converted into salable milk or meat, is of increasing economic importance in the dairy industry. We conjecture that FE is a complex trait whose variation and relationships or partial efficiencies (PE) involving the conversion of dry matter intake to milk energy and metabolic body weight may be highly heterogeneous across environments or management scenarios. In this study, a hierarchical Bayesian multivariate mixed model was proposed to jointly infer upon such heterogeneity at both genetic and nongenetic levels on PE and variance components (VC). The heterogeneity was modeled by embedding mixed effects specifications on PE and VC in addition to those directly specified on the component traits. We validated the model by simulation and applied it to a joint analysis of a dairy FE consortium data set with 5,088 Holstein cows from 13 research stations in Canada, the Netherlands, the United Kingdom, and the United States. Although no differences were detected among research stations for PE at the genetic level, some evidence was found of heterogeneity in residual PE. Furthermore, substantial heterogeneity in VC across stations, parities, and ration was observed with heritability estimates of FE ranging from 0.16 to 0.46 across stations. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Multivariate statistical analysis and hydrogeochemical modelling of seawater-freshwater mixing along selected flow paths: Case of Korba coastal aquifer Tunisia

NASA Astrophysics Data System (ADS)

Slama, Fairouz; Bouhlila, Rachida

2017-11-01

Groundwater sampling and piezometric measurements were carried out along two flow paths (corresponding to two transects) in Korba coastal plain (Northeast of Tunisia). The study aims to identify hydrochemical processes occurring when seawater and freshwater mix. Those processes can be used as indicators of seawater intrusion progression and freshwater flushing into seawater accompanying Submarine Groundwater Discharge (SGD). Seawater fractions in the groundwater were calculated using the chloride concentration. Hierarchical cluster analysis (HCA) was applied to isolate wells potentially affected by seawater. In addition, PHREEQC was used to simulate the theoretical mixing between two end members: seawater and a fresh-brackish groundwater sample. Geochemical conventional diagrams showed that the groundwater chemistry is explained by a mixing process between two end members. Results also revealed the presence of other geochemical processes, correlated to the hydrodynamic flow paths. Direct cation exchange was linked to seawater intrusion, and reverse cation exchange was associated to the freshwater flushing into seawater. The presence of these processes indicated that seawater intrusion was in progress. An excess of Ca, that could not be explained by only cation exchange processes, was observed in both transects. Dedolomitization combined to gypsum leaching is the possible explanation of the groundwater Ca enrichment. Finally, redox processes were also found to contribute to the groundwater composition along flow paths.

Strong consistency of nonparametric Bayes density estimation on compact metric spaces with applications to specific manifolds

PubMed Central

Bhattacharya, Abhishek; Dunson, David B.

2012-01-01

This article considers a broad class of kernel mixture density models on compact metric spaces and manifolds. Following a Bayesian approach with a nonparametric prior on the location mixing distribution, sufficient conditions are obtained on the kernel, prior and the underlying space for strong posterior consistency at any continuous density. The prior is also allowed to depend on the sample size n and sufficient conditions are obtained for weak and strong consistency. These conditions are verified on compact Euclidean spaces using multivariate Gaussian kernels, on the hypersphere using a von Mises-Fisher kernel and on the planar shape space using complex Watson kernels. PMID:22984295

The impact of undergraduate clinical teaching models on the perceptions of work-readiness among new graduate nurses: A cross sectional study.

PubMed

Patterson, Emma E B; Boyd, Leanne; Mnatzaganian, George

2017-08-01

Clinical Placements are an essential component of bridging the gap between academic theory and nursing practice. There are multiple clinical models designed to ease the transition from student to professional, yet there has been little exploration of such models and their impact on graduates' perceptions of work-readiness. This cross sectional study examined perceptions of work-readiness of new graduate nurses who attended one of the following clinical teaching models: the University Fellowship Program (UFP), the Traditional Multi-facility Clinical Model (TMCPM), and the Mixed Program (MP). Three groups of first year graduate nurses (UFP, TMCPM, and MP) were compared using the Work-readiness Scale, a validated and reliable tool, which assessed nurses' perceptions of work-readiness in four domains: organizational acumen, personal work characteristics, social intelligence, and work competence. A multivariable Generalized Estimating Equations regression investigated socio-demographic and teaching-modelrelated factors associated with work-readiness. Of 43 nurses approached, 28 completed the survey (65% response rate) of whom 6 were UFP attendants, 8 attended the TMCPM and 14 the MP. Those who had attended the UFP scored higher than the other two in all four domains; however, the crude between-group comparisons did not yield statistically significant results. Only after accounting for age, gender, teaching setting and prior work experience, the multivariable model showed that undertaking the UFP was likely to increase perceptions of work-readiness by 1.4 points (95% CI 0.11-2.69), P=0.03). The UFP was superior to the other two placement models. The study suggests that the UFP may enhance graduate nurses' perceptions of work readiness. Copyright © 2017 Elsevier Ltd. All rights reserved.

A multivariate time series approach to modeling and forecasting demand in the emergency department.

PubMed

Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L

2009-02-01

The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

Comparison of Multidimensional Item Response Models: Multivariate Normal Ability Distributions versus Multivariate Polytomous Ability Distributions. Research Report. ETS RR-08-45

ERIC Educational Resources Information Center

Haberman, Shelby J.; von Davier, Matthias; Lee, Yi-Hsuan

2008-01-01

Multidimensional item response models can be based on multivariate normal ability distributions or on multivariate polytomous ability distributions. For the case of simple structure in which each item corresponds to a unique dimension of the ability vector, some applications of the two-parameter logistic model to empirical data are employed to…

Predicting the scope of practice of family physicians.

PubMed

Wong, Eric; Stewart, Moira

2010-06-01

To identify factors that are associated with the scope of practice of FPs and GPs who have office-based practices. Secondary univariable and multivariable analyses of cross-sectional data from the 2001 National Family Physician Workforce Survey conducted by the College of Family Physicians of Canada. Canada. General community of FPs and GPs who spent most of their clinical time in office settings. Demographic characteristics and scope of practice score (SPS), which was the number of 12 selected medical services provided by office-based FPs and GPs. The multivariable model explained 35.1% of the variation in the SPS among participants. Geographic factors of provincial division and whether or not the population served was rural explained 30.5% of the variation in the SPS. Male physician sex, younger physician age, being in group practice, greater access to hospital beds, less access to specialists, main practice setting of an academic teaching unit, mixed method physician payment, additional structured postresidency training, and greater number of different types of allied health professionals in the main practice setting were also associated with higher SPSs. Geographic factors were the strongest determinants of scope of practice; physician-related factors, availability of health care resources to the main practice setting, and practice organization factors were weaker determinants. It is important to understand how and why geographic factors influence scope of practice, and whether a broad scope of practice independent of population needs benefits the population. This study supports primary care renewal efforts that use mixed payment systems, incorporate allied health care professionals into family and general practices, and foster group practices.

Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loveday, D.L.; Craggs, C.

Box-Jenkins-based multivariate stochastic modeling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures - at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modeling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable techniquemore » for building thermal analysis. Application to multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modeling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimize energy savings. An approach to system simulation is suggested.« less

Quantification of proportions of different water sources in a mining operation.

PubMed

Scheiber, Laura; Ayora, Carlos; Vázquez-Suñé, Enric

2018-04-01

The water drained in mining operations (galleries, shafts, open pits) usually comes from different sources. Evaluating the contribution of these sources is very often necessary for water management. To determine mixing ratios, a conventional mass balance is often used. However, the presence of more than two sources creates uncertainties in mass balance applications. Moreover, the composition of the end-members is not commonly known with certainty and/or can vary in space and time. In this paper, we propose a powerful tool for solving such problems and managing groundwater in mining sites based on multivariate statistical analysis. This approach was applied to the Cobre Las Cruces mining complex, the largest copper mine in Europe. There, the open pit water is a mixture of three end-members: runoff (RO), basal Miocene (Mb) and Paleozoic (PZ) groundwater. The volume of water drained from the Miocene base aquifer must be determined and compensated via artificial recharging to comply with current regulations. Through multivariate statistical analysis of samples from a regional field campaign, the compositions of PZ and Mb end-members were firstly estimated, and then used for mixing calculations at the open pit scale. The runoff end-member was directly determined from samples collected in interception trenches inside the open pit. The application of multivariate statistical methods allowed the estimation of mixing ratios for the hydrological years 2014-2015 and 2015-2016. Open pit water proportions have changed from 15% to 7%, 41% to 36%, and 44% to 57% for runoff, Mb and PZ end-members, respectively. An independent estimation of runoff based on the curve method yielded comparable results. Copyright © 2017 Elsevier B.V. All rights reserved.

Epidemiology of Mixed, Stress & Urgency Urinary Incontinence in Mid-Aged/Older Women: Importance of Incontinence History

PubMed Central

Komesu, Yuko M.; Schrader, Ronald M.; Ketai, Loren H.; Rogers, Rebecca G.; Dunivan, Gena C.

2016-01-01

Introduction & Hypothesis Urinary incontinence (UI) is common and the relationship between its subtypes is complex. Our objective was to describe the natural history and predictors of incontinence subtypes, Stress, Urgency and Mixed, in mid-aged and older U.S. women. We hypothesized that past UI subtype history predicted future UI subtype status and sought to determine the extent to which this occurred. Methods We analyzed longitudinal urinary incontinence data in 10,572 community-dwelling women ≥50 in the 2004–2010 Health and Retirement Study database. Mixed, Stress, Urgency incontinence prevalence (2004,2006,2008,2010) and 2-year cumulative incidence and remissions (2004–6,2006–8 2008–10) were estimated. Patient characteristics and incontinence subtype status 2004–2008 were entered into a multivariable model to determine predictors for incontinence subtype occurrence in 2010. Results Prevalence of each subtype in this population (median age 63–66) was 2.6%–8.9%. Subtype incidence equaled 2.1–3.5% and remissions for each varied between 22.3–48.7%. Incontinence subtype incidence predictors included ethnicity/race, age, body mass index, functional limitations. Compared to White women, Black women had decreased odds of incident Stress Incontinence, Hispanic women had increased odds of Stress Incontinence remission. Age 80–90 and severe obesity predicted incident Mixed Incontinence. Functional limitations predicted Mixed and Urgency Incontinence. The strongest predictor of incontinence subtypes was incontinence subtype history. Presence of the respective incontinence subtypes in 2004 and 2006 strongly predicted 2010 recurrence [Odds Ratio (OR) Stress Incontinence=30.7, Urgency OR=47.4, Mixed OR=42.1]. Conclusions Although remissions were high, prior history of incontinence subtypes predicted recurrence. Incontinence status is dynamic but tends to recur over the longer term. PMID:26670573

Clustering of Variables for Mixed Data

NASA Astrophysics Data System (ADS)

Saracco, J.; Chavent, M.

2016-05-01

This chapter presents clustering of variables which aim is to lump together strongly related variables. The proposed approach works on a mixed data set, i.e. on a data set which contains numerical variables and categorical variables. Two algorithms of clustering of variables are described: a hierarchical clustering and a k-means type clustering. A brief description of PCAmix method (that is a principal component analysis for mixed data) is provided, since the calculus of the synthetic variables summarizing the obtained clusters of variables is based on this multivariate method. Finally, the R packages ClustOfVar and PCAmixdata are illustrated on real mixed data. The PCAmix and ClustOfVar approaches are first used for dimension reduction (step 1) before applying in step 2 a standard clustering method to obtain groups of individuals.

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Understanding the trends in HIV and hepatitis C prevalence amongst injecting drug users in different settings--implications for intervention impact.

PubMed

Vickerman, Peter; Martin, Natasha K; Hickman, Matthew

2012-06-01

A recent systematic review observed that HIV prevalence amongst injectors is negligible (<1%) below a threshold HCV prevalence of 30%, but thereafter increases with HCV prevalence. We explore whether a model can reproduce these trends, what determines different epidemiological profiles and how this affects intervention impact. An HIV/HCV transmission model was developed. Univariate sensitivity analyses determined whether the model projected a HCV prevalence threshold below which HIV is negligible, and how different behavioural and epidemiological factors affect the threshold. Multivariate uncertainty analyses considered whether the model could reproduce the observed breadth of HIV/HCV epidemics, how specific behavioural patterns produce different epidemic profiles, and how this affects an intervention's impact (reduces injecting risk by 30%). The model projected a HCV prevalence threshold, which varied depending on the heterogeneity in risk, mixing, and injecting duration in a setting. Multivariate uncertainty analyses showed the model could produce the same range of observed HIV/HCV epidemics. Variability in injecting transmission risk, degree of heterogeneity and injecting duration mainly determined different epidemic profiles. The intervention resulted in 50%/28% reduction in HIV incidence/prevalence and 37%/10% reduction in HCV incidence/prevalence over five years. For either infection, greater impact occurred in settings with lower prevalence of that infection and higher prevalence of the other infection. There are threshold levels of HCV prevalence below which HIV risk is negligible but these thresholds are likely to vary by setting. A setting's HIV and HCV prevalence may give insights into IDU risk behaviour and intervention impact. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Mixed-location cerebral hemorrhage/microbleeds: Underlying microangiopathy and recurrence risk.

PubMed

Pasi, Marco; Charidimou, Andreas; Boulouis, Gregoire; Auriel, Eitan; Ayres, Alison; Schwab, Kristin M; Goldstein, Joshua N; Rosand, Jonathan; Viswanathan, Anand; Pantoni, Leonardo; Greenberg, Steven M; Gurol, M Edip

2018-01-09

To assess the predominant type of cerebral small vessel disease (SVD) and recurrence risk in patients who present with a combination of lobar and deep intracerebral hemorrhage (ICH)/microbleed locations (mixed ICH). Of 391 consecutive patients with primary ICH enrolled in a prospective registry, 75 (19%) had mixed ICH. Their demographics, clinical/laboratory features, and SVD neuroimaging markers were compared to those of 191 patients with probable cerebral amyloid angiopathy (CAA-ICH) and 125 with hypertensive strictly deep microbleeds and ICH (HTN-ICH). ICH recurrence and case fatality were also analyzed. Patients with mixed ICH showed a higher burden of vascular risk factors reflected by a higher rate of left ventricular hypertrophy, higher creatinine values, and more lacunes and severe basal ganglia (BG) enlarged perivascular spaces (EPVS) than patients with CAA-ICH (all p < 0.05). In multivariable models mixed ICH diagnosis was associated with higher creatinine levels (odds ratio [OR] 2.5, 95% confidence interval [CI] 1.2-5.0, p = 0.010), more lacunes (OR 3.4, 95% CI 1.7-6.8), and more severe BG EPVS (OR 5.8, 95% CI 1.7-19.7) than patients with CAA-ICH. Conversely, when patients with mixed ICH were compared to patients with HTN-ICH, they were independently associated with older age (OR 1.03, 95% CI 1.02-1.1), more lacunes (OR 2.4, 95% CI 1.1-5.3), and higher microbleed count (OR 1.6, 95% CI 1.3-2.0). Among 90-day survivors, adjusted case fatality rates were similar for all 3 categories. Annual risk of ICH recurrence was 5.1% for mixed ICH, higher than for HTN-ICH but lower than for CAA-ICH (1.6% and 10.4%, respectively). Mixed ICH, commonly seen on MRI obtained during etiologic workup, appears to be driven mostly by vascular risk factors similar to HTN-ICH but demonstrates more severe parenchymal damage and higher ICH recurrence risk. Copyright © 2017 American Academy of Neurology.

Exposure to lithium through drinking water and calcium homeostasis during pregnancy: A longitudinal study.

PubMed

Harari, Florencia; Åkesson, Agneta; Casimiro, Esperanza; Lu, Ying; Vahter, Marie

2016-05-01

There is increasing evidence of adverse health effects due to elevated lithium exposure through drinking water but the impact on calcium homeostasis is unknown. This study aimed at elucidating if lithium exposure through drinking water during pregnancy may impair the maternal calcium homeostasis. In a population-based mother-child cohort in the Argentinean Andes (n=178), with elevated lithium concentrations in the drinking water (5-1660μg/L), blood lithium concentrations (correlating significantly with lithium in water, urine and plasma) were measured repeatedly during pregnancy by inductively coupled plasma mass spectrometry and used as exposure biomarker. Markers of calcium homeostasis included: plasma 25-hydroxyvitamin D3, serum parathyroid hormone (PTH), and calcium, phosphorus and magnesium concentrations in serum and urine. The median maternal blood lithium concentration was 25μg/L (range 1.9-145). In multivariable-adjusted mixed-effects linear regression models, blood lithium was inversely associated with 25-hydroxyvitamin D3 (-6.1nmol/L [95%CI -9.5; -2.6] for a 25μg/L increment in blood lithium). The estimate increased markedly with increasing percentiles of 25-hydroxyvitamin D3. In multivariable-adjusted mixed-effects logistic regression models, the odds ratio of having 25-hydroxyvitamin D3<30nmol/L (19% of the women) was 4.6 (95%CI 1.1; 19.3) for a 25μg/L increment in blood lithium. Blood lithium was also positively associated with serum magnesium, but not with serum calcium and PTH, and inversely associated with urinary calcium and magnesium. In conclusion, our study suggests that lithium exposure through drinking water during pregnancy may impair the calcium homeostasis, particularly vitamin D. The results reinforce the need for better control of lithium in drinking water, including bottled water. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Volume of Plasma Expansion and Functional Outcomes in Stroke.

PubMed

Miller, Joseph B; Lewandowski, Christopher; Wira, Charles R; Taylor, Andrew; Burmeister, Charlotte; Welch, Robert

2017-04-01

Plasma expansion in acute ischemic stroke has potential to improve cerebral perfusion, but the long-term effects on functional outcome are mixed in prior trials. The goal of this study was to evaluate how the magnitude of plasma expansion affects neurological recovery in acute stroke. This was a secondary analysis of data from the Albumin in Acute Stroke Part 2 trial investigating the relationship between the magnitude of overall intravenous volume infusion (crystalloid and colloid) to clinical outcome. The data were inclusive of 841 patients with a mean age of 64 years and a median National Institutes of Health Stroke Scale (NIHSS) of 11. In a multivariable-adjusted logistic regression model, this analysis tested the volume of plasma expansion over the first 48 h of hospitalization as a predictor of favorable outcome, defined as either a modified Rankin Scale score of 0 or 1 or a NIHSS score of 0 or 1 at 90 days. This model included all study patients, irrespective of albumin or isotonic saline treatment. Patients that received higher volumes of plasma expansion more frequently had large vessel ischemic stroke and higher NIHSS scores. The multivariable-adjusted model revealed that there was decreased odds of a favorable outcome for every 250 ml additional volume plasma expansion over the first 48 h (OR 0.91, 95 % CI, 0.88-0.94). The present study demonstrates an association between greater volume of plasma expansion and worse neurological recovery.

A Semi-parametric Multivariate Gap-filling Model for Eddy Covariance Latent Heat Flux

NASA Astrophysics Data System (ADS)

Li, M.; Chen, Y.

2010-12-01

Quantitative descriptions of latent heat fluxes are important to study the water and energy exchanges between terrestrial ecosystems and the atmosphere. The eddy covariance approaches have been recognized as the most reliable technique for measuring surface fluxes over time scales ranging from hours to years. However, unfavorable micrometeorological conditions, instrument failures, and applicable measurement limitations may cause inevitable flux gaps in time series data. Development and application of suitable gap-filling techniques are crucial to estimate long term fluxes. In this study, a semi-parametric multivariate gap-filling model was developed to fill latent heat flux gaps for eddy covariance measurements. Our approach combines the advantages of a multivariate statistical analysis (principal component analysis, PCA) and a nonlinear interpolation technique (K-nearest-neighbors, KNN). The PCA method was first used to resolve the multicollinearity relationships among various hydrometeorological factors, such as radiation, soil moisture deficit, LAI, and wind speed. The KNN method was then applied as a nonlinear interpolation tool to estimate the flux gaps as the weighted sum latent heat fluxes with the K-nearest distances in the PCs’ domain. Two years, 2008 and 2009, of eddy covariance and hydrometeorological data from a subtropical mixed evergreen forest (the Lien-Hua-Chih Site) were collected to calibrate and validate the proposed approach with artificial gaps after standard QC/QA procedures. The optimal K values and weighting factors were determined by the maximum likelihood test. The results of gap-filled latent heat fluxes conclude that developed model successful preserving energy balances of daily, monthly, and yearly time scales. Annual amounts of evapotranspiration from this study forest were 747 mm and 708 mm for 2008 and 2009, respectively. Nocturnal evapotranspiration was estimated with filled gaps and results are comparable with other studies. Seasonal and daily variability of latent heat fluxes were also discussed.

Characterization of Urinary Phthalate Metabolites Among Custodians

PubMed Central

Cavallari, Jennifer M.; Simcox, Nancy J.; Wakai, Sara; Lu, Chensheng; Garza, Jennifer L.; Cherniack, Martin

2015-01-01

Phthalates, a ubiquitous class of chemicals found in consumer, personal care, and cleaning products, have been linked to adverse health effects. Our goal was to characterize urinary phthalate metabolite concentrations and to identify work and nonwork sources among custodians using traditional cleaning chemicals and ‘green’ or environmentally preferable products (EPP). Sixty-eight custodians provided four urine samples on a workday (first void, before shift, end of shift, and before bedtime) and trained observers recorded cleaning tasks and types of products used (traditional, EPP, or disinfectant) hourly over the work shifts. Questionnaires were used to assess personal care product use. Four different phthalate metabolites [monoethyl phthalate (MEP), monomethyl phthalate (MMP), mono (2-ethylhexyl) phthalate (MEHP), and monobenzyl phthalate (MBzP)] were quantified using liquid chromatography mass spectrometry. Geometric means (GM) and 95% confidence intervals (95% CI) were calculated for creatinine-adjusted urinary phthalate concentrations. Mixed effects univariate and multivariate modeling, using a random intercept for each individual, was performed to identify predictors of phthalate metabolites including demographics, workplace factors, and personal care product use. Creatinine-adjusted urinary concentrations [GM (95% CI)] of MEP, MMP, MEHP, and MBzP were 107 (91.0–126), 2.69 (2.18–3.30), 6.93 (6.00–7.99), 8.79 (7.84–9.86) µg g−1, respectively. An increasing trend in phthalate concentrations from before to after shift was not observed. Creatinine-adjusted urinary MEP was significantly associated with frequency of traditional cleaning chemical intensity in the multivariate model after adjusting for potential confounding by demographics, workplace factors, and personal care product use. While numerous demographics, workplace factors, and personal care products were statistically significant univariate predictors of MMP, MEHP, and MBzP, few associations persisted in multivariate models. In summary, among this population of custodians, we identified both occupational and nonoccupational predictors of phthalate exposures. Identification of phthalates as ingredients in cleaning chemicals and consumer products would allow workers and consumers to avoid phthalate exposure. PMID:26240196

Frequency Doubling Technology Perimetry and Changes in Quality of Life of Glaucoma Patients: A Longitudinal Study.

PubMed

Abe, Ricardo Y; Gracitelli, Carolina P B; Diniz-Filho, Alberto; Zangwill, Linda M; Weinreb, Robert N; Medeiros, Felipe A

2015-07-01

To evaluate the relationship between rates of change on frequency doubling technology (FDT) perimetry and longitudinal changes in quality of life (QoL) of glaucoma patients. Prospective observational cohort study. One hundred fifty-two subjects (127 glaucoma and 25 healthy) were followed for an average of 3.2 ± 1.1 years. All subjects were evaluated with National Eye Institute Visual Function Questionnaire (NEI VFQ-25), FDT, and standard automated perimetry (SAP). Glaucoma patients had a median of 3 NEI VFQ-25, 8 FDT, and 8 SAP tests during follow-up. Mean sensitivities of the integrated binocular visual fields were estimated for FDT and SAP and used to calculate rates of change. A joint longitudinal multivariable mixed model was used to investigate the association between change in binocular mean sensitivities and change in NEI VFQ-25 Rasch-calibrated scores. There was a statistically significant correlation between change in binocular mean sensitivity for FDT and change in NEI VFQ-25 scores during follow-up in the glaucoma group. In multivariable analysis with the confounding factors, each 1 dB/year change in binocular FDT mean sensitivity corresponded to a change of 0.8 units per year in the NEI VFQ-25 scores (P = .001). For binocular SAP mean sensitivity, each 1 dB/year change was associated with 2.4 units per year change in NEI VFQ-25 scores (P < .001). The multivariable model containing baseline and rate of change information from SAP had stronger ability to predict change in NEI VFQ-25 scores compared to the equivalent model for FDT (R(2) of 50% and 30%, respectively; P = .001). SAP performed significantly better than FDT in predicting change in NEI VFQ-25 scores in our population, suggesting that it may still be the preferable perimetric technique for predicting risk of disability from the disease. Copyright © 2015 Elsevier Inc. All rights reserved.

Hyperedge bundling: Data, source code, and precautions to modeling-accuracy bias to synchrony estimates.

PubMed

Wang, Sheng H; Lobier, Muriel; Siebenhühner, Felix; Puoliväli, Tuomas; Palva, Satu; Palva, J Matias

2018-06-01

It has not been well documented that MEG/EEG functional connectivity graphs estimated with zero-lag-free interaction metrics are severely confounded by a multitude of spurious interactions (SI), i.e., the false-positive "ghosts" of true interactions [1], [2]. These SI are caused by the multivariate linear mixing between sources, and thus they pose a severe challenge to the validity of connectivity analysis. Due to the complex nature of signal mixing and the SI problem, there is a need to intuitively demonstrate how the SI are discovered and how they can be attenuated using a novel approach that we termed hyperedge bundling. Here we provide a dataset with software with which the readers can perform simulations in order to better understand the theory and the solution to SI. We include the supplementary material of [1] that is not directly relevant to the hyperedge bundling per se but reflects important properties of the MEG source model and the functional connectivity graphs. For example, the gyri of dorsal-lateral cortices are the most accurately modeled areas; the sulci of inferior temporal, frontal and the insula have the least modeling accuracy. Importantly, we found the interaction estimates are heavily biased by the modeling accuracy between regions, which means the estimates cannot be straightforwardly interpreted as the coupling between brain regions. This raise a red flag that the conventional method of thresholding graphs by estimate values is rather suboptimal: because the measured topology of the graph reflects the geometric property of source-model instead of the cortical interactions under investigation.

Additive genetic variation and evolvability of a multivariate trait can be increased by epistatic gene action.

PubMed

Griswold, Cortland K

2015-12-21

Epistatic gene action occurs when mutations or alleles interact to produce a phenotype. Theoretically and empirically it is of interest to know whether gene interactions can facilitate the evolution of diversity. In this paper, we explore how epistatic gene action affects the additive genetic component or heritable component of multivariate trait variation, as well as how epistatic gene action affects the evolvability of multivariate traits. The analysis involves a sexually reproducing and recombining population. Our results indicate that under stabilizing selection conditions a population with a mixed additive and epistatic genetic architecture can have greater multivariate additive genetic variation and evolvability than a population with a purely additive genetic architecture. That greater multivariate additive genetic variation can occur with epistasis is in contrast to previous theory that indicated univariate additive genetic variation is decreased with epistasis under stabilizing selection conditions. In a multivariate setting, epistasis leads to less relative covariance among individuals in their genotypic, as well as their breeding values, which facilitates the maintenance of additive genetic variation and increases a population׳s evolvability. Our analysis involves linking the combinatorial nature of epistatic genetic effects to the ancestral graph structure of a population to provide insight into the consequences of epistasis on multivariate trait variation and evolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

[Impact of care pathway on the delay for initiation of antituberculosis treatment in Conakry, Guinea].

PubMed

Camara, A; Bah-Sow, O Y; Baldé, N M; Camara, L M; Barry, I S; Bah, B; Diallo, M; Chaperon, J; Riou, F

2009-06-01

Complex care pathways can result in detrimental treatment delay particularly in tuberculosis patients. The purpose of this retrospective study was to assess the care pathways followed by tuberculosis patients prior to diagnosis and to assess impact on the delay for initiation of treatment in Conakry, Guinea. A total of 112 patients were interviewed at the time of first admission for pulmonary tuberculosis with positive bacilloscopy. Based on interview data, pathways were classified as conventional (use of health care facilities only) and mixed (use of health care facilities, self-medication, and traditional medicine). The correlation between patient characteristics and type of pathway was assessed by univariate and multivariate analysis and the two groups, i.e., conventional vs. mixed, were compared with regard to delay for initiation of treatment. The care pathway was classified as mixed in two out of three patients. Multivariate analysis showed that this type of pathway was only correlated with schooling (p=0.02). The mean delay for treatment was similar, i.e., 13.4 and 12.8 weeks for conventional and mixed pathways respectively (p<0.68). The percentage of pathways including three consultations at health care facilities was significantly higher in the conventional than mixed group (72% vs. 30%, p<0.001). The main reasons given for delayed use of health care facilities were poor knowledge of tuberculosis symptoms (26%) and high cost of care (12%). The findings of this study indicate that tuberculosis patients follow a variety of care pathways that can lead to delayed treatment. An information campaign is needed to increase awareness among the population and care providers.

No causal impact of serum vascular endothelial growth factor level on temporal changes in body mass index in Japanese male workers: a five-year longitudinal study.

PubMed

Imatoh, Takuya; Kamimura, Seiichiro; Miyazaki, Motonobu

2017-03-01

It has been reported that adipocytes secrete vascular endothelial growth factor. Therefore, we conducted a 5-year longitudinal epidemiological study to further elucidate the association between vascular endothelial growth factor levels and temporal changes in body mass index. Our study subjects were Japanese male workers, who had regular health check-ups. Vascular endothelial growth factor levels were measured at baseline. To examine the association between vascular endothelial growth factor levels and overweight, we calculated the odds ratio using a multivariate logistic regression model. Moreover, linear mixed effect models were used to assess the association between vascular endothelial growth factor level and temporal changes in body mass index during the 5-year follow-up period. Vascular endothelial growth factor levels were marginally higher in subjects with a body mass index greater than 25 kg/m 2 compared with in those with a body mass index less than 25 kg/m 2 (505.4 vs. 465.5 pg/mL, P = 0.1) and were weakly correlated with leptin levels (β: 0.05, P = 0.07). In multivariate logistic regression, subjects in the highest vascular endothelial growth factor quantile were significantly associated with an increased risk for overweight compared with those in the lowest quantile (odds ratio 1.65, 95 % confidential interval: 1.10-2.50). Moreover P for trend was significant (P for trend = 0.003). However, the linear mixed effect model revealed that vascular endothelial growth factor levels were not associated with changes in body mass index over a 5-year period (quantile 2, β: 0.06, P = 0.46; quantile 3, β: -0.06, P = 0.45; quantile 4, β: -0.10, P = 0.22; quantile 1 as reference). Our results suggested that high vascular endothelial growth factor levels were significantly associated with overweight in Japanese males but high vascular endothelial growth factor levels did not necessarily cause obesity.

Estimation of genetic parameters of the productive and reproductive traits in Ethiopian Holstein using multi-trait models.

PubMed

Ayalew, Wondossen; Aliy, Mohammed; Negussie, Enyew

2017-11-01

This study estimated the genetic parameters for productive and reproductive traits. The data included production and reproduction records of animals that have calved between 1979 and 2013. The genetic parameters were estimated using multivariate mixed models (DMU) package, fitting univariate and multivariate mixed models with average information restricted maximum likelihood algorithm. The estimates of heritability for milk production traits from the first three lactation records were 0.03±0.03 for lactation length (LL), 0.17±0.04 for lactation milk yield (LMY), and 0.15±0.04 for 305 days milk yield (305-d MY). For reproductive traits the heritability estimates were, 0.09±0.03 for days open (DO), 0.11±0.04 for calving interval (CI), and 0.47±0.06 for age at first calving (AFC). The repeatability estimates for production traits were 0.12±0.02, for LL, 0.39±0.02 for LMY, and 0.25±0.02 for 305-d MY. For reproductive traits the estimates of repeatability were 0.19±0.02 for DO, and to 0.23±0.02 for CI. The phenotypic correlations between production and reproduction traits ranged from 0.08±0.04 for LL and AFC to 0.42±0.02 for LL and DO. The genetic correlation among production traits were generally high (>0.7) and between reproductive traits the estimates ranged from 0.06±0.13 for AFC and DO to 0.99±0.01 between CI and DO. Genetic correlations of productive traits with reproductive traits were ranged from -0.02 to 0.99. The high heritability estimates observed for AFC indicated that reasonable genetic improvement for this trait might be possible through selection. The h2 and r estimates for reproductive traits were slightly different from single versus multi-trait analyses of reproductive traits with production traits. As single-trait method is biased due to selection on milk yield, a multi-trait evaluation of fertility with milk yield is recommended.

The spatial pattern of suicide in the US in relation to deprivation, fragmentation and rurality.

PubMed

Congdon, Peter

2011-01-01

Analysis of geographical patterns of suicide and psychiatric morbidity has demonstrated the impact of latent ecological variables (such as deprivation, rurality). Such latent variables may be derived by conventional multivariate techniques from sets of observed indices (for example, by principal components), by composite variable methods or by methods which explicitly consider the spatial framework of areas and, in particular, the spatial clustering of latent risks and outcomes. This article considers a latent random variable approach to explaining geographical contrasts in suicide in the US; and it develops a spatial structural equation model incorporating deprivation, social fragmentation and rurality. The approach allows for such latent spatial constructs to be correlated both within and between areas. Potential effects of area ethnic mix are also included. The model is applied to male and female suicide deaths over 2002–06 in 3142 US counties.

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

ERIC Educational Resources Information Center

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Status quo of German-speaking medical students' attitudes toward and knowledge about central aspects of forensic psychiatry across four European countries.

PubMed

Warnke, Ingeborg; Gamma, Alex; Buadze, Anna; Schleifer, Roman; Canela, Carlos; Rüsch, Nicolas; Rössler, Wulf; Strebel, Bernd; Tényi, Tamás; Liebrenz, Michael

While forensic psychiatry is of increasing importance in mental health care, limited available evidence shows that attitudes toward the discipline are contradictory and that knowledge about it seems to be limited in medical students. We aimed to shed light on this subject by analyzing medical students' central attitudes toward and their association with knowledge about forensic psychiatry as well as with socio-demographic and education-specific predictor variables. We recruited N = 1345 medical students from 45 universities with a German language curriculum across four European countries (Germany, Switzerland, Austria and Hungary) by using an innovative approach, namely snowball sampling via Facebook. Students completed an online questionnaire, and data were analyzed descriptively and multivariably by linear mixed effects models and multinomial regression. The results showed overall neutral to positive attitudes toward forensic psychiatry, with indifferent attitudes toward the treatment of sex offenders, and forensic psychiatrists' expertise in the media. Whereas medical students knew about the term 'forensic psychiatry', they showed a lack of specific medico-legal knowledge. Multivariable models on predictor variables revealed statistically significant findings with, however, small estimates and variance explanation. Therefore, further research is required along with the development of a refined assessment instrument for medical students to explore both attitudes and knowledge in forensic psychiatry. Copyright © 2018 Elsevier Ltd. All rights reserved.

Quality control for quantitative PCR based on amplification compatibility test.

PubMed

Tichopad, Ales; Bar, Tzachi; Pecen, Ladislav; Kitchen, Robert R; Kubista, Mikael; Pfaffl, Michael W

2010-04-01

Quantitative qPCR is a routinely used method for the accurate quantification of nucleic acids. Yet it may generate erroneous results if the amplification process is obscured by inhibition or generation of aberrant side-products such as primer dimers. Several methods have been established to control for pre-processing performance that rely on the introduction of a co-amplified reference sequence, however there is currently no method to allow for reliable control of the amplification process without directly modifying the sample mix. Herein we present a statistical approach based on multivariate analysis of the amplification response data generated in real-time. The amplification trajectory in its most resolved and dynamic phase is fitted with a suitable model. Two parameters of this model, related to amplification efficiency, are then used for calculation of the Z-score statistics. Each studied sample is compared to a predefined reference set of reactions, typically calibration reactions. A probabilistic decision for each individual Z-score is then used to identify the majority of inhibited reactions in our experiments. We compare this approach to univariate methods using only the sample specific amplification efficiency as reporter of the compatibility. We demonstrate improved identification performance using the multivariate approach compared to the univariate approach. Finally we stress that the performance of the amplification compatibility test as a quality control procedure depends on the quality of the reference set. Copyright 2010 Elsevier Inc. All rights reserved.

Determinants of Viraemia Copy-Years in People with HIV/AIDS Following Initiation of Antiretroviral Therapy

PubMed Central

Wright, Stephen T; Hoy, Jennifer; Mulhall, Brian; O’Connor, Catherine C; Petoumenos, Kathy; Read, Timothy; Smith, Don; Woolley, Ian; Boyd, Mark A

2014-01-01

Background Recent studies suggest higher cumulative HIV viraemia exposure measured as viraemia copy-years (VCY) is associated with increased all-cause mortality. The objectives of this study are (a) report the association between VCY and all-cause mortality, and (b) assess associations between common patient characteristics and VCY. Methods Analyses were based on patients recruited to the Australian HIV Observational Database (AHOD) who had received ≥ 24 weeks of antiretroviral therapy (ART). We established VCY after 1, 3, 5 and 10 years of ART by calculating the area under the plasma viral load time-series. We used survival methods to determine the association between high VCY and all-cause mortality. We used multivariable mixed-effect models to determine predictors of VCY. We compared a baseline information model with a time-updated model to evaluate discrimination of patients with high VCY. Results Of the 3021 AHOD participants that initiated ART, 2073(69%), 1667(55%), 1267(42%) and 638(21%) were eligible for analysis at 1, 3, 5, 10 years of ART respectively. Multivariable adjusted hazard ratio (HR) association between all-cause mortality and high VCY was statistically significant, HR 1.52(1.09, 2.13), p-value=0.01. Predicting high VCY after one-year of ART for a time-updated model compared to a baseline information only model, the area under the sensitivity/specificity curve (AUC) was 0.92 vs. 0.84; and at 10 years of ART, AUC: 0.87 vs. 0.61 respectively. Conclusion A high cumulative measure of viral load after initiating ART is associated with increased risk of all-cause mortality. Identifying patients with high VCY is improved by incorporating time-updated information. PMID:24463783

Robust, Adaptive Functional Regression in Functional Mixed Model Framework.

PubMed

Zhu, Hongxiao; Brown, Philip J; Morris, Jeffrey S

2011-09-01

Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be sensitive to outlying curves and outlying regions of curves, so is not robust. In this paper, we introduce a new Bayesian method, robust functional mixed models (R-FMM), for performing robust functional regression within the general functional mixed model framework, which includes multiple continuous or categorical predictors and random effect functions accommodating potential between-function correlation induced by the experimental design. The underlying model involves a hierarchical scale mixture model for the fixed effects, random effect and residual error functions. These modeling assumptions across curves result in robust nonparametric estimators of the fixed and random effect functions which down-weight outlying curves and regions of curves, and produce statistics that can be used to flag global and local outliers. These assumptions also lead to distributions across wavelet coefficients that have outstanding sparsity and adaptive shrinkage properties, with great flexibility for the data to determine the sparsity and the heaviness of the tails. Together with the down-weighting of outliers, these within-curve properties lead to fixed and random effect function estimates that appear in our simulations to be remarkably adaptive in their ability to remove spurious features yet retain true features of the functions. We have developed general code to implement this fully Bayesian method that is automatic, requiring the user to only provide the functional data and design matrices. It is efficient enough to handle large data sets, and yields posterior samples of all model parameters that can be used to perform desired Bayesian estimation and inference. Although we present details for a specific implementation of the R-FMM using specific distributional choices in the hierarchical model, 1D functions, and wavelet transforms, the method can be applied more generally using other heavy-tailed distributions, higher dimensional functions (e.g. images), and using other invertible transformations as alternatives to wavelets.

Robust, Adaptive Functional Regression in Functional Mixed Model Framework

PubMed Central

Zhu, Hongxiao; Brown, Philip J.; Morris, Jeffrey S.

2012-01-01

Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be sensitive to outlying curves and outlying regions of curves, so is not robust. In this paper, we introduce a new Bayesian method, robust functional mixed models (R-FMM), for performing robust functional regression within the general functional mixed model framework, which includes multiple continuous or categorical predictors and random effect functions accommodating potential between-function correlation induced by the experimental design. The underlying model involves a hierarchical scale mixture model for the fixed effects, random effect and residual error functions. These modeling assumptions across curves result in robust nonparametric estimators of the fixed and random effect functions which down-weight outlying curves and regions of curves, and produce statistics that can be used to flag global and local outliers. These assumptions also lead to distributions across wavelet coefficients that have outstanding sparsity and adaptive shrinkage properties, with great flexibility for the data to determine the sparsity and the heaviness of the tails. Together with the down-weighting of outliers, these within-curve properties lead to fixed and random effect function estimates that appear in our simulations to be remarkably adaptive in their ability to remove spurious features yet retain true features of the functions. We have developed general code to implement this fully Bayesian method that is automatic, requiring the user to only provide the functional data and design matrices. It is efficient enough to handle large data sets, and yields posterior samples of all model parameters that can be used to perform desired Bayesian estimation and inference. Although we present details for a specific implementation of the R-FMM using specific distributional choices in the hierarchical model, 1D functions, and wavelet transforms, the method can be applied more generally using other heavy-tailed distributions, higher dimensional functions (e.g. images), and using other invertible transformations as alternatives to wavelets. PMID:22308015

Determination of boiling point of petrochemicals by gas chromatography-mass spectrometry and multivariate regression analysis of structural activity relationship.

PubMed

Fakayode, Sayo O; Mitchell, Breanna S; Pollard, David A

2014-08-01

Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries. Copyright © 2014 Elsevier B.V. All rights reserved.

Growth in stature in fragile X families: A mixed longitudinal study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loesch, D.Z.; Huggins, R.M.; Hoang, N.H.

1995-09-11

The effect of fragile X on growth in stature was estimated in individuals aged 5-20 years from 50 fragile X families. The multivariate normal model for pedigree analysis was applied to the mixed longitudinal data, which varied with regard to intervals between the measurements and their number in individual subjects, totalling 349 measurement data points from fragile X families, and 292 data points from unrelated normal subjects. The results of genetic and regression analysis showed that, in fragile X boys and girls, total pubertal height gain is impaired, whereas the rate of growth during the preadolescent period is increased, comparedmore » with the growth rate of nonfragile X subjects. Moreover, the growth parameters in fragile X males were found to be correlated with the size of CGG trinucleotide expansion. The hypothesis of premature activation of the hypothalamo-pituitary gonadal axis is postulated as the cause of growth impairment in fragile X boys and girls, which should be verified by data on the timing of pubertal stages, hormone levels, and bone maturation. 33 refs., 2 figs., 3 tabs.« less

Posterior propriety for hierarchical models with log-likelihoods that have norm bounds

DOE PAGES

Michalak, Sarah E.; Morris, Carl N.

2015-07-17

Statisticians often use improper priors to express ignorance or to provide good frequency properties, requiring that posterior propriety be verified. Our paper addresses generalized linear mixed models, GLMMs, when Level I parameters have Normal distributions, with many commonly-used hyperpriors. It provides easy-to-verify sufficient posterior propriety conditions based on dimensions, matrix ranks, and exponentiated norm bounds, ENBs, for the Level I likelihood. Since many familiar likelihoods have ENBs, which is often verifiable via log-concavity and MLE finiteness, our novel use of ENBs permits unification of posterior propriety results and posterior MGF/moment results for many useful Level I distributions, including those commonlymore » used with multilevel generalized linear models, e.g., GLMMs and hierarchical generalized linear models, HGLMs. Furthermore, those who need to verify existence of posterior distributions or of posterior MGFs/moments for a multilevel generalized linear model given a proper or improper multivariate F prior as in Section 1 should find the required results in Sections 1 and 2 and Theorem 3 (GLMMs), Theorem 4 (HGLMs), or Theorem 5 (posterior MGFs/moments).« less

Multivariate Models of Parent-Late Adolescent Gender Dyads: The Importance of Parenting Processes in Predicting Adjustment

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2008-01-01

Although parent-adolescent interactions have been examined, relevant variables have not been integrated into a multivariate model. As a result, this study examined a multivariate model of parent-late adolescent gender dyads in an attempt to capture important predictors in late adolescents' important and unique transition to adulthood. The sample…

Determining sources of elevated salinity in pre-hydraulic fracturing water quality data using a multivariate discriminant analysis model

NASA Astrophysics Data System (ADS)

Lautz, L. K.; Hoke, G. D.; Lu, Z.; Siegel, D. I.

2013-12-01

Hydraulic fracturing has the potential to introduce saline water into the environment due to migration of deep formation water to shallow aquifers and/or discharge of flowback water to the environment during transport and disposal. It is challenging to definitively identify whether elevated salinity is associated with hydraulic fracturing, in part, due to the real possibility of other anthropogenic sources of salinity in the human-impacted watersheds in which drilling is taking place and some formation water present naturally in shallow groundwater aquifers. We combined new and published chemistry data for private drinking water wells sampled across five southern New York (NY) counties overlying the Marcellus Shale (Broome, Chemung, Chenango, Steuben, and Tioga). Measurements include Cl, Na, Br, I, Ca, Mg, Ba, SO4, and Sr. We compared this baseline groundwater quality data in NY, now under a moratorium on hydraulic fracturing, with published chemistry data for 6 different potential sources of elevated salinity in shallow groundwater, including Appalachian Basin formation water, road salt runoff, septic effluent, landfill leachate, animal waste, and water softeners. A multivariate random number generator was used to create a synthetic, low salinity (< 20 mg/L Cl) groundwater data set (n=1000) based on the statistical properties of the observed low salinity groundwater. The synthetic, low salinity groundwater was then artificially mixed with variable proportions of different potential sources of salinity to explore chemical differences between groundwater impacted by formation water, road salt runoff, septic effluent, landfill leachate, animal waste, and water softeners. We then trained a multivariate, discriminant analysis model on the resulting data set to classify observed high salinity groundwater (> 20 mg/L Cl) as being affected by formation water, road salt, septic effluent, landfill leachate, animal waste, or water softeners. Single elements or pairs of elements (e.g. Cl and Br) were not effective at discriminating between sources of salinity, indicating multivariate methods are needed. The discriminant analysis model classified most accurately samples affected by formation water and landfill leachate, whereas those contaminated by road salt, animal waste, and water softeners were more likely to be discriminated as contaminated by a different source. Using this approach, no shallow groundwater samples from NY appear to be affected by formation water, suggesting the source of salinity pre-hydraulic fracturing is primarily a combination of road salt, septic effluent, landfill leachate, and animal waste.

Landscape characteristics influencing the genetic structure of greater sage-grouse within the stronghold of their range: a holistic modeling approach

USGS Publications Warehouse

Row, Jeff R; Oyler-McCance, Sara J.; Fike, Jennifer; O'Donnell, Michael; Doherty, Kevin E.; Aldridge, Cameron L.; Bowen, Zachary H.; Fedy, Brad C.

2015-01-01

Given the significance of animal dispersal to population dynamics and geographic variability, understanding how dispersal is impacted by landscape patterns has major ecological and conservation importance. Speaking to the importance of dispersal, the use of linear mixed models to compare genetic differentiation with pairwise resistance derived from landscape resistance surfaces has presented new opportunities to disentangle the menagerie of factors behind effective dispersal across a given landscape. Here, we combine these approaches with novel resistance surface parameterization to determine how the distribution of high- and low-quality seasonal habitat and individual landscape components shape patterns of gene flow for the greater sage-grouse (Centrocercus urophasianus) across Wyoming. We found that pairwise resistance derived from the distribution of low-quality nesting and winter, but not summer, seasonal habitat had the strongest correlation with genetic differentiation. Although the patterns were not as strong as with habitat distribution, multivariate models with sagebrush cover and landscape ruggedness or forest cover and ruggedness similarly had a much stronger fit with genetic differentiation than an undifferentiated landscape. In most cases, landscape resistance surfaces transformed with 17.33-km-diameter moving windows were preferred, suggesting small-scale differences in habitat were unimportant at this large spatial extent. Despite the emergence of these overall patterns, there were differences in the selection of top models depending on the model selection criteria, suggesting research into the most appropriate criteria for landscape genetics is required. Overall, our results highlight the importance of differences in seasonal habitat preferences to patterns of gene flow and suggest the combination of habitat suitability modeling and linear mixed models with our resistance parameterization is a powerful approach to discerning the effects of landscape on gene flow.

Short leukocyte telomere length is associated with obesity in American Indians: the Strong Heart Family study.

PubMed

Chen, Shufeng; Yeh, Fawn; Lin, Jue; Matsuguchi, Tet; Blackburn, Elizabeth; Lee, Elisa T; Howard, Barbara V; Zhao, Jinying

2014-05-01

Shorter leukocyte telomere length (LTL) has been associated with a wide range of age-related disorders including cardiovascular disease (CVD) and diabetes. Obesity is an important risk factor for CVD and diabetes. The association of LTL with obesity is not well understood. This study for the first time examines the association of LTL with obesity indices including body mass index, waist circumference, percent body fat, waist-to-hip ratio, and waist-to-height ratio in 3,256 American Indians (14-93 years old, 60% women) participating in the Strong Heart Family Study. Association of LTL with each adiposity index was examined using multivariate generalized linear mixed model, adjusting for chronological age, sex, study center, education, lifestyle (smoking, alcohol consumption, and total energy intake), high-sensitivity C-reactive protein, hypertension and diabetes. Results show that obese participants had significantly shorter LTL than non-obese individuals (age-adjusted P=0.0002). Multivariate analyses demonstrate that LTL was significantly and inversely associated with all of the studied obesity parameters. Our results may shed light on the potential role of biological aging in pathogenesis of obesity and its comorbidities.

Prematurity and fetal lung response after tracheal occlusion in fetuses with severe congenital diaphragmatic hernia.

PubMed

Sananes, Nicolas; Rodo, Carlota; Peiro, Jose Luis; Britto, Ingrid Schwach Werneck; Sangi-Haghpeykar, Haleh; Favre, Romain; Joal, Arnaud; Gaudineau, Adrien; Silva, Marcos Marques da; Tannuri, Uenis; Zugaib, Marcelo; Carreras, Elena; Ruano, Rodrigo

2016-09-01

To evaluate the independent association of fetal pulmonary response and prematurity to postnatal outcomes after fetal tracheal occlusion for congenital diaphragmatic hernia. Fetal pulmonary response, prematurity (<37 weeks at delivery) and extreme prematurity (<32 weeks at delivery) were evaluated and compared between survivors and non-survivors at 6 months of life. Multivariable analysis was conducted with generalized linear mixed models for variables significantly associated with survival in univariate analysis. Eighty-four infants were included, of whom 40 survived (47.6%) and 44 died (52.4%). Univariate analysis demonstrated that survival was associated with greater lung response (p=0.006), and the absence of extreme preterm delivery (p=0.044). In multivariable analysis, greater pulmonary response after FETO was an independent predictor of survival (aOR 1.87, 95% CI 1.08-3.33, p=0.023), whereas the presence of extreme prematurity was not statistically associated with mortality after controlling for fetal pulmonary response (aOR 0.52, 95% CI 0.12-2.30, p=0.367). Fetal pulmonary response after FETO is the most important factor associated with survival, independently from the gestational age at delivery.

Utility and Clinical Profile of Dexmedetomidine in Pediatric Cardiac Catheterization Procedures: A Matched Controlled Analysis.

PubMed

Riveros, Ricardo; Makarova, Natalya; Riveros-Perez, Efrain; Chodavarapu, Praneeta; Saasouh, Wael; Yılmaz, Hüseyin Oğuz; Cuko, Evis; Babazade, Rovnat; Kimatian, Stephen; Turan, Alparslan

2017-12-01

Dexmedetomidine is increasingly used in children undergoing cardiac catheterization procedures. We compared the percentage of surgical time with hemodynamic instability and the incidence of postoperative agitation between pediatric cardiac catheterization patients who received dexmedetomidine infusion and those who did not and the incidence of postoperative agitation. We matched 653 pediatric patients scheduled for cardiac catheterization. Two separate multivariable linear mixed models were used to assess the association between dexmedetomidine use and intraoperative blood pressure and heart rate instability. A multivariate logistic regression was used for relationship between dexmedetomidine and postoperative agitation. No difference between the study groups was found in the duration of MAP ( P = .867) or heart rate (HR) instabilities ( P = .224). The relationship between dexmedetomidine use and the duration of negative hemodynamic effects does not depend on any of the considered CHD types (all P > .001) or intervention ( P = .453 for MAP and P = .023 for HR). No difference in postoperative agitation was found between the study groups ( P = .590). Our study demonstrated no benefit in using dexmedetomidine infusion compared with other general anesthesia techniques to maintain hemodynamic stability or decrease agitation in pediatric patients undergoing cardiac catheterization procedures.

Musculoskeletal ultrasonography delineates ankle symptoms in rheumatoid arthritis.

PubMed

Toyota, Yukihiro; Tamura, Maasa; Kirino, Yohei; Sugiyama, Yumiko; Tsuchida, Naomi; Kunishita, Yosuke; Kishimoto, Daiga; Kamiyama, Reikou; Miura, Yasushi; Minegishi, Kaoru; Yoshimi, Ryusuke; Ueda, Atsuhisa; Nakajima, Hideaki

2017-05-01

To clarify the use of musculoskeletal ultrasonography (US) of ankle joints in rheumatoid arthritis (RA). Consecutive RA patients with or without ankle symptoms participated in the study. The US, clinical examination (CE), and patients' visual analog scale for pain (pVAS) for ankles were assessed. Prevalence of tibiotalar joint synovitis and tenosynovitis were assessed by grayscale (GS) and power Doppler (PD) US using a semi-quantitative grading (0-3). The positive US and CE findings were defined as GS score ≥2 and/or PD score ≥1, and joint swelling and/or tenderness, respectively. Multivariate analysis with the generalized linear mixed model was performed by assigning ankle pVAS as a dependent variable. Among a total of 120 ankles from 60 RA patients, positive ankle US findings were found in 21 (35.0%) patients. The concordance rate of CE and US was moderate (kappa 0.57). Of the 88 CE negative ankles, US detected positive findings in 9 (10.2%) joints. Multivariate analysis revealed that ankle US, clinical disease activity index, and foot Health Assessment Questionnaire, but not CE, was independently associated with ankle pVAS. US examination is useful to illustrate RA ankle involvement, especially for patients who complain ankle pain but lack CE findings.

A multivariate model and statistical method for validating tree grade lumber yield equations

Treesearch

Donald W. Seegrist

1975-01-01

Lumber yields within lumber grades can be described by a multivariate linear model. A method for validating lumber yield prediction equations when there are several tree grades is presented. The method is based on multivariate simultaneous test procedures.

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data

PubMed Central

Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan; Keles, Sunduz; Wang, Sijian

2015-01-01

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies. PMID:26609213

The multiplicative effect of combining alcohol with energy drinks on adolescent gambling.

PubMed

Vieno, Alessio; Canale, Natale; Potente, Roberta; Scalese, Marco; Griffiths, Mark D; Molinaro, Sabrina

2018-07-01

There has been increased concern about the negative effects of adolescents consuming a combination of alcohol mixed with energy drinks (AmED). To date, few studies have focused on AmED use and gambling. The present study analyzed the multiplicative effect of AmED consumption, compared to alcohol alone, on the likelihood of at-risk or problem gambling during adolescence. Data from the ESPAD®Italia 2015 study, a cross-sectional survey conducted in a nationally representative sample of students (ages 15 to 19years) were used to examine the association between self-reported AmED use (≥6 times,≥10 times, and ≥20 times during the last month) and self-reported gambling severity. Multivariate models were used to calculate adjusted prevalence ratios to evaluate the association between alcohol use, AmED use, and gambling among a representative sample of adolescents who reported gambling in the last year and completed a gambling severity scale (n=4495). Among the 19% students classed as at-risk and problem gamblers, 43.9% were classed as AmED consumers, while 23.6% were classed as alcohol consumers (i.e. did not mix alcohol with energy drinks). In multivariate analyses that controlled for covariates, AmED consumers were three times more likely to be at-risk and problem gamblers (OR=3.05) compared to non-consuming adolescents, while the effect became less pronounced with considering those who consumed alcohol without the addition of energy drinks (OR=1.37). The present study clearly established that consuming AmED might pose a significantly greater risk of experiencing gambling-related problems among adolescents. Copyright © 2018 Elsevier Ltd. All rights reserved.

Epidemiology of antibiotic-resistant wound infections from six countries in Africa

PubMed Central

Bebell, Lisa M; Meney, Carron; Valeri, Linda

2017-01-01

Introduction Little is known about the antimicrobial susceptibility of common bacteria responsible for wound infections from many countries in sub-Saharan Africa. Methods We performed a retrospective review of microbial isolates collected based on clinical suspicion of wound infection between 2004 and 2016 from Mercy Ships, a non-governmental organisation operating a single mobile surgical unit in Benin, Congo, Liberia, Madagascar, Sierra Leone and Togo. Antimicrobial resistant organisms of interest were defined as methicillin-resistant Staphylococcus aureus (MRSA) or Enterobacteriaceae resistant to third-generation cephalosporins. Generalised mixed-effects models accounting for repeated isolates in a patient, potential clustering by case mix for each field service, age, gender and country were used to test the hypothesis that rates of antimicrobial resistance differed between countries. Results 3145 isolates from repeated field services in six countries were reviewed. In univariate analyses, the highest proportion of MRSA was found in Benin (34.6%) and Congo (31.9%), while the lowest proportion was found in Togo (14.3%) and Madagascar (14.5%); country remained a significant predictor in multivariate analyses (P=0.002). In univariate analyses, the highest proportion of third-generation cephalosporin-resistant Enterobacteriaceae was found in Benin (35.8%) and lowest in Togo (14.3%) and Madagascar (16.3%). Country remained a significant predictor for antimicrobial-resistant isolates in multivariate analyses (P=0.009). Conclusion A significant proportion of isolates from wound cultures were resistant to first-line antimicrobials in each country. Though antimicrobial resistance isolates were not verified in a reference laboratory and these data may not be representative of all regions of the countries studied, differences in the proportion of antimicrobial-resistant isolates and resistance profiles between countries suggest site-specific surveillance should be a priority and local antimicrobial resistance profiles should be used to guide empiric antibiotic selection. PMID:29588863

An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan.

PubMed

Chang, Hsien-Yen; Weiner, Jonathan P

2010-01-18

Diagnosis-based risk adjustment is becoming an important issue globally as a result of its implications for payment, high-risk predictive modelling and provider performance assessment. The Taiwanese National Health Insurance (NHI) programme provides universal coverage and maintains a single national computerized claims database, which enables the application of diagnosis-based risk adjustment. However, research regarding risk adjustment is limited. This study aims to examine the performance of the Adjusted Clinical Group (ACG) case-mix system using claims-based diagnosis information from the Taiwanese NHI programme. A random sample of NHI enrollees was selected. Those continuously enrolled in 2002 were included for concurrent analyses (n = 173,234), while those in both 2002 and 2003 were included for prospective analyses (n = 164,562). Health status measures derived from 2002 diagnoses were used to explain the 2002 and 2003 health expenditure. A multivariate linear regression model was adopted after comparing the performance of seven different statistical models. Split-validation was performed in order to avoid overfitting. The performance measures were adjusted R2 and mean absolute prediction error of five types of expenditure at individual level, and predictive ratio of total expenditure at group level. The more comprehensive models performed better when used for explaining resource utilization. Adjusted R2 of total expenditure in concurrent/prospective analyses were 4.2%/4.4% in the demographic model, 15%/10% in the ACGs or ADGs (Aggregated Diagnosis Group) model, and 40%/22% in the models containing EDCs (Expanded Diagnosis Cluster). When predicting expenditure for groups based on expenditure quintiles, all models underpredicted the highest expenditure group and overpredicted the four other groups. For groups based on morbidity burden, the ACGs model had the best performance overall. Given the widespread availability of claims data and the superior explanatory power of claims-based risk adjustment models over demographics-only models, Taiwan's government should consider using claims-based models for policy-relevant applications. The performance of the ACG case-mix system in Taiwan was comparable to that found in other countries. This suggested that the ACG system could be applied to Taiwan's NHI even though it was originally developed in the USA. Many of the findings in this paper are likely to be relevant to other diagnosis-based risk adjustment methodologies.

Impact of Case Mix Severity on Quality Improvement in a Patient-centered Medical Home (PCMH) in the Maryland Multi-Payor Program.

PubMed

Khanna, Niharika; Shaya, Fadia T; Chirikov, Viktor V; Sharp, David; Steffen, Ben

2016-01-01

We present data on quality of care (QC) improvement in 35 of 45 National Quality Forum metrics reported annually by 52 primary care practices recognized as patient-centered medical homes (PCMHs) that participated in the Maryland Multi-Payor Program from 2011 to 2013. We assigned QC metrics to (1) chronic, (2) preventive, and (3) mental health care domains. The study used a panel data design with no control group. Using longitudinal fixed-effects regressions, we modeled QC and case mix severity in a PCMH. Overall, 35 of 45 quality metrics reported by 52 PCMHs demonstrated improvement over 3 years, and case mix severity did not affect the achievement of quality improvement. From 2011 to 2012, QC increased by 0.14 (P < .01) for chronic, 0.15 (P < .01) for preventive, and 0.34 (P < .01) for mental health care domains; from 2012 to 2013 these domains increased by 0.03 (P = .06), 0.04 (P = .05), and 0.07 (P = .12), respectively. In univariate analyses, lower National Commission on Quality Assurance PCMH level was associated with higher QC for the mental health care domain, whereas case mix severity did not correlate with QC. In multivariate analyses, higher QC correlated with larger practices, greater proportion of older patients, and readmission visits. Rural practices had higher proportions of Medicaid patients, lower QC, and higher QC improvement in interaction analyses with time. The gains in QC in the chronic disease domain, the preventive care domain, and, most significantly, the mental health care domain were observed over time regardless of patient case mix severity. QC improvement was generally not modified by practice characteristics, except for rurality. © Copyright 2016 by the American Board of Family Medicine.

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

PubMed Central

Galván-Tejada, Carlos E.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L.

2017-01-01

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions. PMID:28216571

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis.

PubMed

Galván-Tejada, Carlos E; Zanella-Calzada, Laura A; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L

2017-02-14

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

Performance of 21 HPV vaccination programs implemented in low and middle-income countries, 2009–2013

PubMed Central

2014-01-01

Background Cervical cancer is the third most common cancer in women worldwide, with high incidence in lowest income countries. Vaccination against Human Papilloma Virus (HPV) may help to reduce the incidence of cervical cancer. The aim of the study was to analyze HPV vaccination programs performance implemented in low and middle-income countries. Methods The Gardasil Access Program provides HPV vaccine at no cost to help national institutions gain experience implementing HPV vaccination. Data on vaccine delivery model, number of girls vaccinated, number of girls completing the three-dose campaign, duration of vaccination program, community involvement and sensitization strategies were collected from each program upon completion. Vaccine Uptake Rate (VUR) and Vaccine Adherence between the first and third doses (VA) rate were calculated. Multivariate linear regressions analyses were fitted. Results Twenty-one programs were included in 14 low and middle-income countries. Managing institutions were non-governmental organizations (NGOs) (n = 8) or Ministries of Health (n = 13). Twelve programs were school-based, five were health clinic-based and four utilized a mixed model. A total of 217,786 girls received a full course of vaccination. Mean VUR was 88.7% (SD = 10.5) and VA was 90.8% (SD = 7.3). The mean total number of girls vaccinated per program-month was 2,426.8 (SD = 2,826.6) in school model, 335.1 (SD = 202.5) in the health clinic and 544.7 (SD = 369.2) in the mixed models (p = 0.15). Community involvement in the follow-up of girls participating in the vaccination campaign was significantly associated with VUR. Multivariate analyses identified school-based (β = 13.35, p = 0.001) and health clinic (β = 13.51, p = 0.03) models, NGO management (β = 14.58, p < 10-3) and duration of program vaccination (β = -1.37, p = 0.03) as significant factors associated with VUR. Conclusion School and health clinic-based models appeared as predictive factors for vaccination coverage, as was management by an NGO; program duration could play a role in the program’s effectiveness. Results suggest that HPV vaccine campaigns tailored to meet the needs of communities can be effective. These results may be useful in the development of national HPV vaccination policies in low and middle-income countries. PMID:24981818

Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

PubMed

Aguero-Valverde, Jonathan

2013-10-01

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Head and facial anthropometry of mixed-race US Army male soldiers for military design and sizing: a pilot study.

PubMed

Yokota, Miyo

2005-05-01

In the United States, the biologically admixed population is increasing. Such demographic changes may affect the distribution of anthropometric characteristics, which are incorporated into the design of equipment and clothing for the US Army and other large organizations. The purpose of this study was to examine multivariate craniofacial anthropometric distributions between biologically admixed male populations and single racial groups of Black and White males. Multivariate statistical results suggested that nose breadth and lip length were different between Blacks and Whites. Such differences may be considered for adjustments to respirators and chemical-biological protective masks. However, based on this pilot study, multivariate anthropometric distributions of admixed individuals were within the distributions of single racial groups. Based on the sample reported, sizing and designing for the admixed groups are not necessary if anthropometric distributions of single racial groups comprising admixed groups are known.

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

PubMed Central

Lv, Yong; Song, Gangbing

2018-01-01

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal. PMID:29659510

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition.

PubMed

Yuan, Rui; Lv, Yong; Song, Gangbing

2018-04-16

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal.

A multidimensional approach to case mix for home health services

PubMed Central

Manton, Kenneth G.; Hausner, Tony

1987-01-01

Developing a case-mix methodology for home health services is more difficult than developing one for hospitalization and acute health services, because the determinants of need for home health care are more complex and because of the difficulty in defining episodes of care. To evaluate home health service case mix, a multivariate grouping methodology was applied to records from the 1982 National Long-Term Care Survey linked to Medicare records on home health reimbursements. Using this method, six distinct health and functional status dimensions were identified. These dimensions, combined with factors describing informal care resources and local market conditions, were used to explain significant proportions of the variance (r2 = .45) of individual differences in Medicare home health reimbursements and numbers of visits. Though the data were not collected for that purpose, the high level of prediction strongly suggests the feasibility of developing case-mix strategies for home health services. PMID:10312187

A multidimensional approach to case mix for home health services.

PubMed

Manton, K G; Hausner, T

1987-01-01

Developing a case-mix methodology for home health services is more difficult than developing one for hospitalization and acute health services, because the determinants of need for home health care are more complex and because of the difficulty in defining episodes of care. To evaluate home health service case mix, a multivariate grouping methodology was applied to records from the 1982 National Long-Term Care Survey linked to Medicare records on home health reimbursements. Using this method, six distinct health and functional status dimensions were identified. These dimensions, combined with factors describing informal care resources and local market conditions, were used to explain significant proportions of the variance (r2 = .45) of individual differences in Medicare home health reimbursements and numbers of visits. Though the data were not collected for that purpose, the high level of prediction strongly suggests the feasibility of developing case-mix strategies for home health services.

Does the Presence of Blood in the Catheter or the Degree of Difficulty of Embryo Transfer Affect Live Birth?

PubMed

Plowden, Torie C; Hill, Micah J; Miles, Shana M; Hoyt, Benjamin; Yauger, Belinda; Segars, James H; Csokmay, John M; Chason, Rebecca J

2017-05-01

The technique used for embryo transfer (ET) can affect implantation. Prior research that evaluated the effect of postprocedural blood of the transfer catheter tip have yielded mixed results, and it is unclear whether this is actually a marker of difficulty of the transfer. Our objective was to estimate the effect of blood at the time of ET and the difficulty of ET on live birth rates (LBR). This retrospective cohort study utilized generalized estimating equations (GEEs) with nesting for repeated cycles for all analyses. Univariate modeling was performed and a final multivariate (adjusted) GEE model accounted for all significant confounders. Embryo transfers were subjectively graded (easy, medium, or hard) by a physician at the time of transfer. The presence of blood at ET was associated with more difficult ETs, retained embryos, and presence of mucous in the catheter. In the univariate analysis, ET with blood was not associated with live birth, while the degree of difficulty for ET had a negative impact on LBR. In the final multivariate GEE model, which accounts for repeated cycles from a patient, the only factors associated with an increased LBR were the degree of difficulty of the ET, female age, and blastocyst transfer. After controlling for confounding variables, the presence of blood in the transfer catheter was not associated with the likelihood of pregnancy and thus was not an independent predictor of cycle outcome. This indicates that the difficulty of the transfer itself was a strong negative predictor of pregnancy.

HIV-1 disease progression during highly active antiretroviral therapy: an application using population-level data in British Columbia: 1996-2011.

PubMed

Nosyk, Bohdan; Min, Jeong; Lima, Viviane D; Yip, Benita; Hogg, Robert S; Montaner, Julio S G

2013-08-15

Accurately estimating rates of disease progression is of central importance in developing mathematical models used to project outcomes and guide resource allocation decisions. Our objective was to specify a multivariate regression model to estimate changes in disease progression among individuals on highly active antiretroviral treatment in British Columbia, Canada, 1996-2011. We used population-level data on disease progression and antiretroviral treatment utilization from the BC HIV Drug Treatment Program. Disease progression was captured using longitudinal CD4 and plasma viral load testing data, linked with data on antiretroviral treatment. The study outcome was categorized into (CD4 count ≥ 500, 500-350, 350-200, <200 cells/mm, and mortality). A 5-state continuous-time Markov model was used to estimate covariate-specific probabilities of CD4 progression, focusing on temporal changes during the study period. A total of 210,083 CD4 measurements among 7421 individuals with HIV/AIDS were included in the study. Results of the multivariate model suggested that current highly active antiretroviral treatment at baseline, lower baseline CD4 (<200 cells/mm), and extended durations of elevated plasma viral load were each associated with accelerated progression. Immunological improvement was accelerated significantly from 2004 onward, with 23% and 46% increases in the probability of CD4 improvement from the fourth CD4 stratum (CD4 < 200) in 2004-2008 and 2008-2011, respectively. Our results demonstrate the impact of innovations in antiretroviral treatment and treatment delivery at the population level. These results can be used to estimate a transition probability matrix flexible to changes in the observed mix of clients in different clinical stages and treatment regimens over time.

Effects of Numeric Representation of Women on Interest in Engineering as a Career

ERIC Educational Resources Information Center

Creamer, Elizabeth G.

2012-01-01

Little is known about how the presence of women influences undergraduates' experiences in engineering. This paper presents results from a mixed methods, multivariate, and multi-institutional study to determine the impact of the numeric representation of women on the intent to be employed in engineering following graduation. Results from the…

Income and Education in Turkey: A Multivariate Analysis

ERIC Educational Resources Information Center

Sari, Ramazan; Soytas, Ugur

2006-01-01

Although the role of education in an economy is emphasized in theoretical studies, empirical literature finds mixed results for the relationship between growth and education. We examine the relationship between Gross Domestic Product (GDP) and enrollments in primary, secondary, and high schools, as well as universities in Turkey for 1937-1996, in…

A Robust Bayesian Approach for Structural Equation Models with Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum; Xia, Ye-Mao

2008-01-01

In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear…

Materialist and post-materialist values and cannabis smoking among young adults: a population-based study in southern Sweden.

PubMed

Lindström, Martin

2007-04-01

The association between materialist, mixed and post-materialist values, and the experience of cannabis smoking among young adults was investigated. The 2004 public health survey in Skåne, southern Sweden, is a cross-sectional study with a 59% response rate. The 6787 persons aged 18-34 years included in this study answered a postal questionnaire. A logistic regression model was used to investigate the association between materialist, mixed and post-materialist values and ever having experienced cannabis smoking. The multivariate analysis was performed to investigate the importance of possible confounders (age and education) on the differences in ever having experienced cannabis smoking according to materialist, mixed and post-materialist values. 28% of the men and 17% of the women had ever experienced cannabis smoking. The experience of cannabis smoking was significantly and positively associated with post-materialist values among both men and women. The odds ratios were 2.4 (1.8-3.1) for men with post-materialist values compared to men with materialist values, and 3.1 (2.4-4.0) for women with post-materialist values compared to women with materialist values. This study suggests that post-materialist values are positively associated with the risk of ever smoking cannabis. Because this is a cross-sectional study, the direction of causality remains to be investigated.

Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills.

PubMed

Brandstätter, Christian; Laner, David; Prantl, Roman; Fellner, Johann

2014-12-01

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed. This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables. The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills. Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Comparison of Three Multivariate Models for Estimating Test Battery Reliability.

ERIC Educational Resources Information Center

Wood, Terry M.; Safrit, Margaret J.

1987-01-01

A comparison of three multivariate models (canonical reliability model, maximum generalizability model, canonical correlation model) for estimating test battery reliability indicated that the maximum generalizability model showed the least degree of bias, smallest errors in estimation, and the greatest relative efficiency across all experimental…

Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

NASA Astrophysics Data System (ADS)

Schwartz, Craig R.; Thelen, Brian J.; Kenton, Arthur C.

1995-06-01

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

PubMed

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

Dual-mixed HIV-1 Coreceptor Tropism and HIV-Associated Neurocognitive Deficits

PubMed Central

Morris, Sheldon R.; Woods, Steven Paul; Deutsch, Reena; Little, Susan J.; Wagner, Gabriel; Morgan, Erin E.; Heaton, Robert K.; Letendre, Scott L.; Grant, Igor; Smith, Davey M.

2014-01-01

Background HIV coreceptor usage of CXCR4 (X4) is associated with decreased CD4+ T-cell counts and accelerated disease progression, but the role of X4 tropism in HIV-associated neurocognitive disorders (HAND) has not previously been described. Methods This longitudinal study evaluated data on 197 visits from 72 recently HIV-infected persons who had undergone up to 4 sequential neurocognitive assessments over a median of 160 days (IQR 138–192). Phenotypic tropism testing (Trofile ES, Monogram, Biosciences) was performed on stored blood samples. Multivariable mixed model repeated measures regression was used to determine the association between HAND and dual-mixed (DM) viral tropism, estimated duration of infection (EDI), HIV RNA, CD4 count and problematic methamphetamine use. Results Six subjects (8.3%) had dual mixed tropism (DM) at their first neurocognitive assessment and four converted to DM in subsequent sampling (for total of 10 DM) at a median EDI of 10.1 months (IQR 7.2–12.2). There were 44 (61.1%) subjects who demonstrated HAND on at least one study visit. HAND was associated with DM tropism (odds ratio 4.4, 95% CI 0.9–20.5) and shorter EDI (odds ratio 1.1 per month earlier, 95% CI 1.0–1.2). Conclusion This study found that recency of HIV-1 infection and the development of DM tropism may be associated with HAND in the relatively early stage of infection. Together these data suggest that viral interaction with cellular receptors may play an important role in the early manifestation of HAND. PMID:24078557

On the Choice of Variable for Atmospheric Moisture Analysis

NASA Technical Reports Server (NTRS)

Dee, Dick P.; DaSilva, Arlindo M.; Atlas, Robert (Technical Monitor)

2002-01-01

The implications of using different control variables for the analysis of moisture observations in a global atmospheric data assimilation system are investigated. A moisture analysis based on either mixing ratio or specific humidity is prone to large extrapolation errors, due to the high variability in space and time of these parameters and to the difficulties in modeling their error covariances. Using the logarithm of specific humidity does not alleviate these problems, and has the further disadvantage that very dry background estimates cannot be effectively corrected by observations. Relative humidity is a better choice from a statistical point of view, because this field is spatially and temporally more coherent and error statistics are therefore easier to obtain. If, however, the analysis is designed to preserve relative humidity in the absence of moisture observations, then the analyzed specific humidity field depends entirely on analyzed temperature changes. If the model has a cool bias in the stratosphere this will lead to an unstable accumulation of excess moisture there. A pseudo-relative humidity can be defined by scaling the mixing ratio by the background saturation mixing ratio. A univariate pseudo-relative humidity analysis will preserve the specific humidity field in the absence of moisture observations. A pseudorelative humidity analysis is shown to be equivalent to a mixing ratio analysis with flow-dependent covariances. In the presence of multivariate (temperature-moisture) observations it produces analyzed relative humidity values that are nearly identical to those produced by a relative humidity analysis. Based on a time series analysis of radiosonde observed-minus-background differences it appears to be more justifiable to neglect specific humidity-temperature correlations (in a univariate pseudo-relative humidity analysis) than to neglect relative humidity-temperature correlations (in a univariate relative humidity analysis). A pseudo-relative humidity analysis is easily implemented in an existing moisture analysis system, by simply scaling observed-minus background moisture residuals prior to solving the analysis equation, and rescaling the analyzed increments afterward.

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

PubMed Central

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin

2013-01-01

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs.

PubMed

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin

2013-10-15

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis

ERIC Educational Resources Information Center

Ansari, Asim; Iyengar, Raghuram

2006-01-01

We develop semiparametric Bayesian Thurstonian models for analyzing repeated choice decisions involving multinomial, multivariate binary or multivariate ordinal data. Our modeling framework has multiple components that together yield considerable flexibility in modeling preference utilities, cross-sectional heterogeneity and parameter-driven…

Pharmacodynamics and effectiveness of topical nitroglycerin at lowering blood pressure during autonomic dysreflexia.

PubMed

Solinsky, R; Bunnell, A E; Linsenmeyer, T A; Svircev, J N; Engle, A; Burns, S P

2017-10-01

Secondary analysis of prospectively collected observational data assessing the safety of an autonomic dysreflexia (AD) management protocol. To estimate the time to onset of action, time to full clinical effect (sustained systolic blood pressure (SBP) <160 mm Hg) and effectiveness of nitroglycerin ointment at lowering blood pressure for patients with spinal cord injuries experiencing AD. US Veterans Affairs inpatient spinal cord injury (SCI) unit. Episodes of AD recalcitrant to nonpharmacologic interventions that were given one to two inches of 2% topical nitroglycerin ointment were recorded. Pharmacodynamics as above and predictive characteristics (through a mixed multivariate logistic regression model) were calculated. A total of 260 episodes of pharmacologically managed AD were recorded in 56 individuals. Time to onset of action for nitroglycerin ointment was 9-11 min. Time to full clinical effect was 14-20 min. Topical nitroglycerin controlled SBP <160 mm Hg in 77.3% of pharmacologically treated AD episodes with the remainder requiring additional antihypertensive medications. A multivariate logistic regression model was unable to identify statistically significant factors to predict which patients would respond to nitroglycerin ointment (odds ratios 95% confidence intervals 0.29-4.93). The adverse event rate, entirely attributed to hypotension, was 3.6% with seven of the eight events resolving with close observation alone and one episode requiring normal saline. Nitroglycerin ointment has a rapid onset of action and time to full clinical effect with high efficacy and relatively low adverse event rate for patients with SCI experiencing AD.

The impact of new vaccine introduction on the coverage of existing vaccines: a cross-national, multivariable analysis.

PubMed

Shearer, Jessica C; Walker, Damian G; Risko, Nicholas; Levine, Orin S

2012-12-14

A surge of new and underutilized vaccine introductions into national immunization programmes has called into question the effect of new vaccine introduction on immunization and health systems. In particular, countries deciding whether to introduce a new or underutilized vaccine into their routine immunization programme may query possible effects on the delivery and coverage of existing vaccines. Using coverage of diphtheria-tetanus-pertussis (DTP) vaccine as a proxy for immunization system performance, this study aims to test whether new vaccine introduction into national immunization programs was associated with changes in coverage of three doses of DTP vaccine among infants. DTP3 vaccine coverage was analyzed in 187 countries during 1999-2009 using multivariable cross-national mixed-effect longitudinal models. Controlling for other possible determinants of DTP3 coverage at the national level these models found minimal association between the introduction of Hepatitis-, Haemophilus influenzae type b-, and rotavirus-containing vaccines and DTP3 coverage. Instead, frequent and sometimes large fluctuations in coverage are associated with other development and health systems variables, including the presence of armed conflict, coverage of antenatal care services, infant mortality, the percent of health expenditures that are private and total health expenditures per capita. Introductions of new vaccines did not affect national coverage of DTP3 vaccine in the countries studied. Introductions of other new vaccines and multiple vaccine introductions should be monitored for immunization and health systems impacts. Copyright © 2012 Elsevier Ltd. All rights reserved.

Nitrate source identification in groundwater of multiple land-use areas by combining isotopes and multivariate statistical analysis: A case study of Asopos basin (Central Greece).

PubMed

Matiatos, Ioannis

2016-01-15

Nitrate (NO3) is one of the most common contaminants in aquatic environments and groundwater. Nitrate concentrations and environmental isotope data (δ(15)N-NO3 and δ(18)O-NO3) from groundwater of Asopos basin, which has different land-use types, i.e., a large number of industries (e.g., textile, metal processing, food, fertilizers, paint), urban and agricultural areas and livestock breeding facilities, were analyzed to identify the nitrate sources of water contamination and N-biogeochemical transformations. A Bayesian isotope mixing model (SIAR) and multivariate statistical analysis of hydrochemical data were used to estimate the proportional contribution of different NO3 sources and to identify the dominant factors controlling the nitrate content of the groundwater in the region. The comparison of SIAR and Principal Component Analysis showed that wastes originating from urban and industrial zones of the basin are mainly responsible for nitrate contamination of groundwater in these areas. Agricultural fertilizers and manure likely contribute to groundwater contamination away from urban fabric and industrial land-use areas. Soil contribution to nitrate contamination due to organic matter is higher in the south-western part of the area far from the industries and the urban settlements. The present study aims to highlight the use of environmental isotopes combined with multivariate statistical analysis in locating sources of nitrate contamination in groundwater leading to a more effective planning of environmental measures and remediation strategies in river basins and water bodies as defined by the European Water Frame Directive (Directive 2000/60/EC).

Disability and the Built Environment: An Investigation of Community and Neighborhood Land Uses and Participation for Physically Impaired Adults

PubMed Central

Botticello, Amanda L.; Rohrbach, Tanya; Cobbold, Nicolette

2014-01-01

Purpose There is a need for empirical support of the association between the built environment and disability-related outcomes. This study explores the associations between community and neighborhood land uses and community participation among adults with acquired physical disability. Methods Cross-sectional data from 508 community-living, chronically disabled adults in New Jersey were obtained from among participants in national Spinal Cord Injury Model Systems database. Participants’ residential addresses were geocoded to link individual survey data with Geographic Information Systems (GIS) data on land use and destinations. The influence of residential density, land use mix, destination counts, and open space on four domains of participation were modeled at two geographic scales—the neighborhood (i.e., half mile buffer) and community (i.e., five mile) using multivariate logistic regression. All analyses were adjusted for demographic and impairment-related differences. Results Living in communities with greater land use mix and more destinations was associated with a decreased likelihood of reporting optimum social and physical activity. Conversely, living in neighborhoods with large portions of open space was positively associated with the likelihood of reporting full physical, occupational, and social participation. Conclusions These findings suggest that the overall living conditions of the built environment may be relevant to social inclusion for persons with physical disabilities. PMID:24935467

Inferring network structure in non-normal and mixed discrete-continuous genomic data.

PubMed

Bhadra, Anindya; Rao, Arvind; Baladandayuthapani, Veerabhadran

2018-03-01

Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are potentially associated with cancer. Traditionally, conditional independence has been studied using sparse Gaussian graphical models for continuous data and sparse Ising models for discrete data. However, there are two clear situations when these approaches are inadequate. The first occurs when the data are continuous but display non-normal marginal behavior such as heavy tails or skewness, rendering an assumption of normality inappropriate. The second occurs when a part of the data is ordinal or discrete (e.g., presence or absence of a mutation) and the other part is continuous (e.g., expression levels of genes or proteins). In this case, the existing Bayesian approaches typically employ a latent variable framework for the discrete part that precludes inferring conditional independence among the data that are actually observed. The current article overcomes these two challenges in a unified framework using Gaussian scale mixtures. Our framework is able to handle continuous data that are not normal and data that are of mixed continuous and discrete nature, while still being able to infer a sparse conditional sign independence structure among the observed data. Extensive performance comparison in simulations with alternative techniques and an analysis of a real cancer genomics data set demonstrate the effectiveness of the proposed approach. © 2017, The International Biometric Society.

Inferring network structure in non-normal and mixed discrete-continuous genomic data

PubMed Central

Bhadra, Anindya; Rao, Arvind; Baladandayuthapani, Veerabhadran

2017-01-01

Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are potentially associated with cancer. Traditionally, conditional independence has been studied using sparse Gaussian graphical models for continuous data and sparse Ising models for discrete data. However, there are two clear situations when these approaches are inadequate. The first occurs when the data are continuous but display non-normal marginal behavior such as heavy tails or skewness, rendering an assumption of normality inappropriate. The second occurs when a part of the data is ordinal or discrete (e.g., presence or absence of a mutation) and the other part is continuous (e.g., expression levels of genes or proteins). In this case, the existing Bayesian approaches typically employ a latent variable framework for the discrete part that precludes inferring conditional independence among the data that are actually observed. The current article overcomes these two challenges in a unified framework using Gaussian scale mixtures. Our framework is able to handle continuous data that are not normal and data that are of mixed continuous and discrete nature, while still being able to infer a sparse conditional sign independence structure among the observed data. Extensive performance comparison in simulations with alternative techniques and an analysis of a real cancer genomics data set demonstrate the effectiveness of the proposed approach. PMID:28437848

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Effects of Covariance Heterogeneity on Three Procedures for Analyzing Multivariate Repeated Measures Designs.

ERIC Educational Resources Information Center

Vallejo, Guillermo; Fidalgo, Angel; Fernandez, Paula

2001-01-01

Estimated empirical Type I error rate and power rate for three procedures for analyzing multivariate repeated measures designs: (1) the doubly multivariate model; (2) the Welch-James multivariate solution (H. Keselman, M. Carriere, a nd L. Lix, 1993); and (3) the multivariate version of the modified Brown-Forsythe procedure (M. Brown and A.…

Hospital costs by cost center of inpatient hospitalization for medicare patients undergoing major abdominal surgery.

PubMed

Stey, Anne M; Brook, Robert H; Needleman, Jack; Hall, Bruce L; Zingmond, David S; Lawson, Elise H; Ko, Clifford Y

2015-02-01

This study aims to describe the magnitude of hospital costs among patients undergoing elective colectomy, cholecystectomy, and pancreatectomy, determine whether these costs relate as expected to duration of care, patient case-mix severity and comorbidities, and whether risk-adjusted costs vary significantly by hospital. Correctly estimating the cost of production of surgical care may help decision makers design mechanisms to improve the efficiency of surgical care. Patient data from 202 hospitals in the ACS-NSQIP were linked to Medicare inpatient claims. Patient charges were mapped to cost center cost-to-charge ratios in the Medicare cost reports to estimate costs. The association of patient case-mix severity and comorbidities with cost was analyzed using mixed effects multivariate regression. Cost variation among hospitals was quantified by estimating risk-adjusted hospital cost ratios and 95% confidence intervals from the mixed effects multivariate regression. There were 21,923 patients from 202 hospitals who underwent an elective colectomy (n = 13,945), cholecystectomy (n = 5,569), or pancreatectomy (n = 2,409). Median cost was lowest for cholecystectomy ($15,651) and highest for pancreatectomy ($37,745). Room and board costs accounted for the largest proportion (49%) of costs and were correlated with length of stay, R = 0.89, p < 0.001. The patient case-mix severity and comorbidity variables most associated with cost were American Society of Anesthesiologists (ASA) class IV (estimate 1.72, 95% CI 1.57 to 1.87) and fully dependent functional status (estimate 1.63, 95% CI 1.53 to 1.74). After risk-adjustment, 66 hospitals had significantly lower costs than the average hospital and 57 hospitals had significantly higher costs. The hospital costs estimates appear to be consistent with clinical expectations of hospital resource use and differ significantly among 202 hospitals after risk-adjustment for preoperative patient characteristics and procedure type. Copyright © 2015 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Assisted living and nursing homes: apples and oranges?

PubMed

Zimmerman, Sheryl; Gruber-Baldini, Ann L; Sloane, Philip D; Eckert, J Kevin; Hebel, J Richard; Morgan, Leslie A; Stearns, Sally C; Wildfire, Judith; Magaziner, Jay; Chen, Cory; Konrad, Thomas R

2003-04-01

The goals of this study are to describe the current state of residential care/assisted living (RC/AL) care and residents in comparison with nursing home (NH) care and residents, identify different types of RC/AL care and residents, and consider how variation in RC/AL case-mix reflects differences in care provision and/or consumer preference. Data were derived from the Collaborative Studies of Long-Term Care, a four-state study of 193 RC/AL facilities and 40 NHs. Multivariate analyses examined differences in ten process of care measures between RC/AL facilities with less than 16 beds; traditional RC/AL with 16 or more beds; new-model RC/AL; and NHs. Generalized estimating equation models determined differences in resident case-mix across RC/AL facilities using data for 2,078 residents. NHs report provision of significantly more health services and have significantly more lenient admission policies than RC/AL facilities, but provide less privacy. They do not differ from larger RC/AL facilities in policy clarity or resident control. Differences within RC/AL types are evident, with smaller and for-profit facilities scoring lower than other facilities across multiple process measures, including those related to individual freedom and institutional order. Resident impairment is substantial in both NHs and RC/AL settings, but differs by RC/AL facility characteristics. Differences in process of care and resident characteristics by facility type highlight the importance of considering: (1) the adequacy of existing process measures for evaluating smaller facilities; (2) resident case-mix when comparing facility types and outcomes; and (3) the complexity of understanding the implication of the process of care, given the importance of person-environment fit. Work is continuing to clarify the role of RC/AL vis-à-vis NHs in our nation's system of residential long-term care.

Frequency and Determinants of a Short-Interval Follow-up Recommendation After an Abnormal Screening Mammogram.

PubMed

Pelletier, Eric; Daigle, Jean-Marc; Defay, Fannie; Major, Diane; Guertin, Marie-Hélène; Brisson, Jacques

2016-11-01

After imaging assessment of an abnormal screening mammogram, a follow-up examination 6 months later is recommended to some women. Our aim was to identify which characteristics of lesions, women, and physicians are associated to such short-interval follow-up recommendation in the Quebec Breast Cancer Screening Program. Between 1998 and 2008, 1,839,396 screening mammograms were performed and a total of 114,781 abnormal screens were assessed by imaging only. Multivariate analysis was done with multilevel Poisson regression models with robust variance and generalized linear mixed models. A short-interval follow-up was recommended in 26.7% of assessments with imaging only, representing 2.3% of all screens. Case-mix adjusted proportion of short-interval follow-up recommendations varied substantially across physicians (range: 4%-64%). Radiologists with high recall rates (≥15%) had a high proportion of short-interval follow-up recommendation (risk ratio: 1.82; 95% confidence interval: 1.35-2.45) compared to radiologists with low recall rates (<5%). The adjusted proportion of short-interval follow-up was high (22.8%) even when a previous mammogram was usually available. Short-interval follow-up recommendation at assessment is frequent in this Canadian screening program, even when a previous mammogram is available. Characteristics related to radiologists appear to be key determinants of short-interval follow-up recommendation, rather than characteristics of lesions or patient mix. Given that it can cause anxiety to women and adds pressure on the health system, it appears important to record and report short-interval follow-up and to identify ways to reduce its frequency. Short-interval follow-up recommendations should be considered when assessing the burden of mammography screening. Copyright © 2016 Canadian Association of Radiologists. Published by Elsevier Inc. All rights reserved.

Students' Conceptions of the Nature of Science: Perspectives from Canadian and Korean Middle School Students

ERIC Educational Resources Information Center

Park, Hyeran; Nielsen, Wendy; Woodruff, Earl

2014-01-01

This study examined and compared students' understanding of nature of science (NOS) with 521 Grade 8 Canadian and Korean students using a mixed methods approach. The concepts of NOS were measured using a survey that had both quantitative and qualitative elements. Descriptive statistics and one-way multivariate analysis of variances examined the…

Effects of Kindergarten Retention on Children's Social-Emotional Development: An Application of Propensity Score Method to Multivariate, Multilevel Data

ERIC Educational Resources Information Center

Hong, Guanglei; Yu, Bing

2008-01-01

This study examines the effects of kindergarten retention on children's social-emotional development in the early, middle, and late elementary years. Previous studies have generated mixed results partly due to some major methodological challenges, including selection bias, measurement error, and divergent perceptions of multiple respondents in…

On the Numerical Formulation of Parametric Linear Fractional Transformation (LFT) Uncertainty Models for Multivariate Matrix Polynomial Problems

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.

1998-01-01

Robust control system analysis and design is based on an uncertainty description, called a linear fractional transformation (LFT), which separates the uncertain (or varying) part of the system from the nominal system. These models are also useful in the design of gain-scheduled control systems based on Linear Parameter Varying (LPV) methods. Low-order LFT models are difficult to form for problems involving nonlinear parameter variations. This paper presents a numerical computational method for constructing and LFT model for a given LPV model. The method is developed for multivariate polynomial problems, and uses simple matrix computations to obtain an exact low-order LFT representation of the given LPV system without the use of model reduction. Although the method is developed for multivariate polynomial problems, multivariate rational problems can also be solved using this method by reformulating the rational problem into a polynomial form.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.

PubMed

Meeker, Daniella; Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D'Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila

2015-11-01

Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

MULTIVARIATE RECEPTOR MODELS AND MODEL UNCERTAINTY. (R825173)

EPA Science Inventory

Abstract

Estimation of the number of major pollution sources, the source composition profiles, and the source contributions are the main interests in multivariate receptor modeling. Due to lack of identifiability of the receptor model, however, the estimation cannot be...

Assessment of Platelet Function in Traumatic Brain Injury-A Retrospective Observational Study in the Neuro-Critical Care Setting.

PubMed

Lindblad, Caroline; Thelin, Eric Peter; Nekludov, Michael; Frostell, Arvid; Nelson, David W; Svensson, Mikael; Bellander, Bo-Michael

2018-01-01

Despite seemingly functional coagulation, hemorrhagic lesion progression is a common and devastating condition following traumatic brain injury (TBI), stressing the need for new diagnostic techniques. Multiple electrode aggregometry (MEA) measures platelet function and could aid in coagulopathy assessment following TBI. The aims of this study were to evaluate MEA temporal dynamics, influence of concomitant therapy, and its capabilities to predict lesion progression and clinical outcome in a TBI cohort. Adult TBI patients in a neurointensive care unit that underwent MEA sampling were retrospectively included. MEA was sampled if the patient was treated with antiplatelet therapy, bled heavily during surgery, or had abnormal baseline coagulation values. We assessed platelet activation pathways involving the arachidonic acid receptor (ASPI), P2Y 12 receptor, and thrombin receptor (TRAP). ASPI was the primary focus of analysis. If several samples were obtained, they were included. Retrospective data were extracted from hospital charts. Outcome variables were radiologic hemorrhagic progression and Glasgow Outcome Scale assessed prospectively at 12 months posttrauma. MEA levels were compared between patients on antiplatelet therapy. Linear mixed effect models and uni-/multivariable regression models were used to study longitudinal dynamics, hemorrhagic progression and outcome, respectively. In total, 178 patients were included (48% unfavorable outcome). ASPI levels increased from initially low values in a time-dependent fashion ( p  < 0.001). Patients on cyclooxygenase inhibitors demonstrated low ASPI levels ( p  < 0.001), while platelet transfusion increased them ( p  < 0.001). The first ASPI ( p  = 0.039) and TRAP ( p  = 0.009) were significant predictors of outcome, but not lesion progression, in univariate analyses. In multivariable analysis, MEA values were not independently correlated with outcome. A general longitudinal trend of MEA is identified in this TBI cohort, even in patients without known antiplatelet therapies. Values appear also affected by platelet inhibitory treatment and by platelet transfusions. While significant in univariate models to predict outcome, MEA values did not independently correlate to outcome or lesion progression in multivariable analyses. Further prospective studies to monitor coagulation in TBI patients are warranted, in particular the interpretation of pathological MEA values in patients without antiplatelet therapies.

Evaluating the impact of a mandatory pre-abortion ultrasound viewing law: A mixed methods study.

PubMed

Upadhyay, Ushma D; Kimport, Katrina; Belusa, Elise K O; Johns, Nicole E; Laube, Douglas W; Roberts, Sarah C M

2017-01-01

Since mid-2013, Wisconsin abortion providers have been legally required to display and describe pre-abortion ultrasound images. We aimed to understand the impact of this law. We used a mixed-methods study design at an abortion facility in Wisconsin. We abstracted data from medical charts one year before the law to one year after and used multivariable models, mediation/moderation analysis, and interrupted time series to assess the impact of the law, viewing, and decision certainty on likelihood of continuing the pregnancy. We conducted in-depth interviews with women in the post-law period about their ultrasound experience and analyzed them using elaborative and modified grounded theory. A total of 5342 charts were abstracted; 8.7% continued their pregnancies pre-law and 11.2% post-law (p = 0.002). A multivariable model confirmed the law was associated with higher odds of continuing pregnancy (aOR = 1.23, 95% CI: 1.01-1.50). Decision certainty (aOR = 6.39, 95% CI: 4.72-8.64) and having to pay fully out of pocket (aOR = 4.98, 95% CI: 3.86-6.41) were most strongly associated with continuing pregnancy. Ultrasound viewing fully mediated the relationship between the law and continuing pregnancy. Interrupted time series analyses found no significant effect of the law but may have been underpowered to detect such a small effect. Nineteen of twenty-three women interviewed viewed their ultrasound image. Most reported no impact on their abortion decision; five reported a temporary emotional impact or increased certainty about choosing abortion. Two women reported that viewing helped them decide to continue the pregnancy; both also described preexisting decision uncertainty. This law caused an increase in viewing rates and a statistically significant but small increase in continuing pregnancy rates. However, the majority of women were certain of their abortion decision and the law did not change their decision. Other factors were more significant in women's decision-making, suggesting evaluations of restrictive laws should take account of the broader social environment.

Evaluating the impact of a mandatory pre-abortion ultrasound viewing law: A mixed methods study

PubMed Central

Kimport, Katrina; Belusa, Elise K. O.; Johns, Nicole E.; Laube, Douglas W.; Roberts, Sarah C. M.

2017-01-01

Background Since mid-2013, Wisconsin abortion providers have been legally required to display and describe pre-abortion ultrasound images. We aimed to understand the impact of this law. Methods We used a mixed-methods study design at an abortion facility in Wisconsin. We abstracted data from medical charts one year before the law to one year after and used multivariable models, mediation/moderation analysis, and interrupted time series to assess the impact of the law, viewing, and decision certainty on likelihood of continuing the pregnancy. We conducted in-depth interviews with women in the post-law period about their ultrasound experience and analyzed them using elaborative and modified grounded theory. Results A total of 5342 charts were abstracted; 8.7% continued their pregnancies pre-law and 11.2% post-law (p = 0.002). A multivariable model confirmed the law was associated with higher odds of continuing pregnancy (aOR = 1.23, 95% CI: 1.01–1.50). Decision certainty (aOR = 6.39, 95% CI: 4.72–8.64) and having to pay fully out of pocket (aOR = 4.98, 95% CI: 3.86–6.41) were most strongly associated with continuing pregnancy. Ultrasound viewing fully mediated the relationship between the law and continuing pregnancy. Interrupted time series analyses found no significant effect of the law but may have been underpowered to detect such a small effect. Nineteen of twenty-three women interviewed viewed their ultrasound image. Most reported no impact on their abortion decision; five reported a temporary emotional impact or increased certainty about choosing abortion. Two women reported that viewing helped them decide to continue the pregnancy; both also described preexisting decision uncertainty. Conclusions This law caused an increase in viewing rates and a statistically significant but small increase in continuing pregnancy rates. However, the majority of women were certain of their abortion decision and the law did not change their decision. Other factors were more significant in women’s decision-making, suggesting evaluations of restrictive laws should take account of the broader social environment. PMID:28746377

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution

PubMed Central

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section. PMID:28983398

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Schröter, Kai; Merz, Bruno

2016-05-01

Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB).In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.

A statistical human rib cage geometry model accounting for variations by age, sex, stature and body mass index.

PubMed

Shi, Xiangnan; Cao, Libo; Reed, Matthew P; Rupp, Jonathan D; Hoff, Carrie N; Hu, Jingwen

2014-07-18

In this study, we developed a statistical rib cage geometry model accounting for variations by age, sex, stature and body mass index (BMI). Thorax CT scans were obtained from 89 subjects approximately evenly distributed among 8 age groups and both sexes. Threshold-based CT image segmentation was performed to extract the rib geometries, and a total of 464 landmarks on the left side of each subject׳s ribcage were collected to describe the size and shape of the rib cage as well as the cross-sectional geometry of each rib. Principal component analysis and multivariate regression analysis were conducted to predict rib cage geometry as a function of age, sex, stature, and BMI, all of which showed strong effects on rib cage geometry. Except for BMI, all parameters also showed significant effects on rib cross-sectional area using a linear mixed model. This statistical rib cage geometry model can serve as a geometric basis for developing a parametric human thorax finite element model for quantifying effects from different human attributes on thoracic injury risks. Copyright © 2014 Elsevier Ltd. All rights reserved.

An error bound for a discrete reduced order model of a linear multivariable system

NASA Technical Reports Server (NTRS)

Al-Saggaf, Ubaid M.; Franklin, Gene F.

1987-01-01

The design of feasible controllers for high dimension multivariable systems can be greatly aided by a method of model reduction. In order for the design based on the order reduction to include a guarantee of stability, it is sufficient to have a bound on the model error. Previous work has provided such a bound for continuous-time systems for algorithms based on balancing. In this note an L-infinity bound is derived for model error for a method of order reduction of discrete linear multivariable systems based on balancing.

Impact of a function-based payment model on the financial performance of acute inpatient medical rehabilitation providers: a simulation analysis.

PubMed

Sutton, J P; DeJong, G; Song, H; Wilkerson, D

1997-12-01

To operationalize research findings about a medical rehabilitation classification and payment model by building a prototype of a prospective payment system, and to determine whether this prototype model promotes payment equity. This latter objective is accomplished by identifying whether any facility or payment model characteristics are systematically associated with financial performance. This study was conducted in two phases. In Phase 1 the components of a diagnosis-related group (DRG)-like payment system, including a base rate, function-related group (FRG) weights, and adjusters, were identified and estimated using hospital cost functions. Phase 2 consisted of a simulation analysis in which each facility's financial performance was modeled, based on its 1990-1991 case mix. A multivariate regression equation was conducted to assess the extent to which characteristics of 42 rehabilitation facilities contribute toward determining financial performance under the present Medicare payment system as well as under the hypothetical model developed. Phase 1 (model development) included 61 rehabilitation hospitals. Approximately 59% were rehabilitation units within a general hospital and 48% were teaching facilities. The number of rehabilitation beds averaged 52. Phase 2 of the stimulation analysis included 42 rehabilitation facilities, subscribers to UDS in 1990-1991. Of these, 69% were rehabilitation units and 52% were teaching facilities. The number of rehabilitation beds averaged 48. Financial performance, as measured by the ratio of reimbursement to average costs. Case-mix index is the primary determinant of financial performance under the present Medicare payment system. None of the facility characteristics included in this analysis were associated with financial performance under the hypothetical FRG payment model. The most notable impact of an FRG-based payment model would be to create a stronger link between resource intensity and level of reimbursement, resulting in greater equity in the reimbursement of inpatient medical rehabilitation hospitals.

How important are venue-based HIV risks among male clients of female sex workers? A mixed methods analysis of the risk environment in nightlife venues in Tijuana, Mexico

PubMed Central

Goldenberg, Shira; Strathdee, Steffanie A.; Gallardo, Manuel; Nguyen, Lucie; Lozada, Remedios; Semple, Shirley J.; Patterson, Thomas L.

2011-01-01

In 2008, 400 males ≥ 18 years old who paid or traded for sex with a female sex worker (FSW) in Tijuana, Mexico, in the past 4 months completed surveys and HIV/STI testing; 30 also completed qualitative interviews. To analyze environmental HIV vulnerability among male clients of FSWs in Tijuana, Mexico, we used mixed methods to investigate correlates of clients who met FSWs in nightlife venues and clients’ perspectives on venue-based risks. Logistic regression identified micro-level correlates of meeting FSWs in nightlife venues, which were triangulated with clients’ narratives regarding macro-level influences. In a multivariate model, offering increased pay for unprotected sex and binge drinking were micro-level factors that were independently associated with meeting FSWs in nightlife venues versus other places. In qualitative interviews, clients characterized nightlife venues as high risk due to the following macro-level features: social norms dictating heavy alcohol consumption; economic exploitation by establishment owners; and poor enforcement of sex work regulations in nightlife venues. Structural interventions in nightlife venues are needed to address venue-based risks. PMID:21396875

How important are venue-based HIV risks among male clients of female sex workers? A mixed methods analysis of the risk environment in nightlife venues in Tijuana, Mexico.

PubMed

Goldenberg, Shira M; Strathdee, Steffanie A; Gallardo, Manuel; Nguyen, Lucie; Lozada, Remedios; Semple, Shirley J; Patterson, Thomas L

2011-05-01

In 2008, 400 males ≥18 years old who paid or traded for sex with a female sex worker (FSW) in Tijuana, Mexico, in the past 4 months completed surveys and HIV/STI testing; 30 also completed qualitative interviews. To analyze environmental sources of HIV vulnerability among male clients of FSWs in Tijuana, we used mixed methods to investigate correlates of clients who met FSWs in nightlife venues and clients' perspectives on venue-based HIV risk. Logistic regression identified micro-level correlates of meeting FSWs in nightlife venues, which were triangulated with clients' narratives regarding macro-level influences. In a multivariate model, offering increased pay for unprotected sex and binge drinking were micro-level factors that were independently associated with meeting FSWs in nightlife venues versus other places. In qualitative interviews, clients characterized nightlife venues as high risk due to the following macro-level features: social norms dictating heavy alcohol consumption; economic exploitation by establishment owners; and poor enforcement of sex work regulations in nightlife venues. Structural interventions in nightlife venues are needed to address venue-based risks. Copyright © 2011 Elsevier Ltd. All rights reserved.

Patterns of Unit and Item Nonresponse in the CAHPS® Hospital Survey

PubMed Central

Elliott, Marc N; Edwards, Carol; Angeles, January; Hambarsoomians, Katrin; Hays, Ron D

2005-01-01

Objective To examine the predictors of unit and item nonresponse, the magnitude of nonresponse bias, and the need for nonresponse weights in the Consumer Assessment of Health Care Providers and Systems (CAHPS®) Hospital Survey. Methods A common set of 11 administrative variables (41 degrees of freedom) was used to predict unit nonresponse and the rate of item nonresponse in multivariate models. Descriptive statistics were used to examine the impact of nonresponse on CAHPS Hospital Survey ratings and reports. Results Unit nonresponse was highest for younger patients and patients other than non-Hispanic whites (p<.001); item nonresponse increased steadily with age (p<.001). Fourteen of 20 reports of ratings of care had significant (p<.05) but small negative correlations with nonresponse weights (median −0.06; maximum −0.09). Nonresponse weights do not improve overall precision below sample sizes of 300–1,000, and are unlikely to improve the precision of hospital comparisons. In some contexts, case-mix adjustment eliminates most observed nonresponse bias. Conclusions Nonresponse weights should not be used for between-hospital comparisons of the CAHPS Hospital Survey, but may make small contributions to overall estimates or demographic comparisons, especially in the absence of case-mix adjustment. PMID:16316440

Occupational safety and HIV risk among female sex workers in China: A mixed-methods analysis of sex-work harms and mommies

PubMed Central

Yi, Huso; Zheng, Tiantian; Wan, Yanhai; Mantell, Joanne E.; Park, Minah; Csete, Joanne

2013-01-01

Female sex workers (FSWs) in China are exposed to multiple work-related harms that increase HIV vulnerability. Using mixed-methods, we explored the social-ecological aspects of sexual risk among 348 FSWs in Beijing. Sex-work harms were assessed by property stolen, being underpaid or not paid at all, verbal and sexual abuse, forced drinking; and forced sex more than once. The majority (90%) reported at least one type of harm, 38% received harm protection from ‘mommies’ (i.e., managers) and 32% reported unprotected sex with clients. In multivariate models, unprotected sex was significantly associated with longer involvement in sex work, greater exposure to harms, and no protection from mommies. Mommies’ protection moderated the effect of sex-work harms on unprotected sex with clients. Our ethnography indicated that mommies played a core role in sex-work networks. Such networks provide a basis for social capital; they are not only profitable economically, but also protect FSWs from sex-work harms. Effective HIV prevention interventions for FSWs in China must address the occupational safety and health of FSWs by facilitating social capital and protection agency (e.g., mommies) in the sex-work industry. PMID:22375698

Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI

PubMed Central

Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Treviño, Victor; Tamez-Peña, José G.

2015-01-01

In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain. PMID:26504490

A spatial analysis of the spread of mumps: the importance of college students and their spring-break-associated travel.

PubMed

Polgreen, P M; Bohnett, L C; Yang, M; Pentella, M A; Cavanaugh, J E

2010-03-01

To characterize the association between county-level risk factors and the incidence of mumps in the 2006 Iowa outbreak, we used generalized linear mixed models with the number of mumps cases per county as the dependent variable. To assess the impact of spring-break travel, we tested for differences in the proportions of mumps cases in three different age groups. In the final multivariable model, the proportion of Iowa's college students per county was positively associated (P<0.0001) with mumps cases, but the number of colleges was negatively associated with cases (P=0.0002). Thus, if the college students in a county were spread among more campuses, this was associated with fewer mumps cases. Finally, we found the proportion of mumps cases in both older and younger persons increased after 1 April (P=0.0029), suggesting that spring-break college travel was associated with the spread of mumps to other age groups.

Bio hydrogen production from cassava starch by anaerobic mixed cultures: Multivariate statistical modeling

NASA Astrophysics Data System (ADS)

Tien, Hai Minh; Le, Kien Anh; Le, Phung Thi Kim

2017-09-01

Bio hydrogen is a sustainable energy resource due to its potentially higher efficiency of conversion to usable power, high energy efficiency and non-polluting nature resource. In this work, the experiments have been carried out to indicate the possibility of generating bio hydrogen as well as identifying effective factors and the optimum conditions from cassava starch. Experimental design was used to investigate the effect of operating temperature (37-43 °C), pH (6-7), and inoculums ratio (6-10 %) to the yield hydrogen production, the COD reduction and the ratio of volume of hydrogen production to COD reduction. The statistical analysis of the experiment indicated that the significant effects for the fermentation yield were the main effect of temperature, pH and inoculums ratio. The interaction effects between them seem not significant. The central composite design showed that the polynomial regression models were in good agreement with the experimental results. This result will be applied to enhance the process of cassava starch processing wastewater treatment.

Fingerprinting of bed sediment in the Tay Estuary, Scotland: an environmental magnetism approach

NASA Astrophysics Data System (ADS)

Jenkins, Pierre A.; Duck, Rob W.; Rowan, John S.; Walden, John

Sediment fingerprinting is commonly used for sediment provenance studies in lakes, rivers and reservoirs and on hillslopes and floodplains. This investigation explores the mixing of terrestrial and marine-derived sediment in the Tay Estuary, Scotland, using mineral magnetic attributes for fingerprinting. Samples representative of the estuary sediments and of four sources (end-members) were subjected to a suite of magnetic susceptibility and remanence measurements. Sediment samples from the beds of the Rivers Tay and Earn represented fluvial inputs while samples from the Angus and Fife coasts represented marine input. Multivariate discriminant and factor analysis showed that the sources could be separated on the basis of six magnetic parameters in a simple multivariate unmixing model to identify source contributions to estuarine bed sediments. Multi-domain magnetite signatures, characteristic of unweathered bedrock, dominate the magnetic measurements. Overall contributions of 3% from the River Earn, 17% from the River Tay, 29% from the Angus coast and 51% from the Fife coast source end-members, demonstrated the present-day regime of marine sediment derivation in the Tay Estuary. However, this conceals considerable spatial variability both along-estuary and in terms of sub-environments, with small-scale variations in sediment provenance reflecting local morphology, particularly areas of channel convergence.

Does investor-ownership of nursing homes compromise the quality of care?

PubMed

Harrington, Charlene; Woolhandler, Steffie; Mullan, Joseph; Carrillo, Helen; Himmelstein, David U

2002-01-01

Quality problems have long plagued the nursing home industry. While two-thirds of U.S. nursing homes are investor-owned, few studies have examined the impact of investor-ownership on the quality of care. The authors analyzed 1998 data from inspections of 13,693 nursing facilities representing virtually all U.S. nursing homes. They grouped deficiency citations issued by inspectors into three categories ("quality of care," "quality of life," and "other") and compared deficiency rates in investor-owned, nonprofit, and public nursing homes. A multivariate model was used to control for case mix, percentage of residents covered by Medicaid, whether the facility was hospital-based, whether it was a skilled nursing facility for Medicare only, chain ownership, and location by state. The study also assessed nurse staffing. The authors found that investor-owned nursing homes provide worse care and less nursing care than nonprofit or public homes. Investor-owned facilities averaged 5.89 deficiencies per home, 46.5 percent higher than nonprofit and 43.0 percent higher than public facilities, and also had more of each category of deficiency. In the multivariate analysis, investor-ownership predicted 0.679 additional deficiencies per home; chain-ownership predicted an additional 0.633 deficiencies per home. Nurse staffing ratios were markedly lower at investor-owned homes.

Perceived racism in relation to weight change in the Black Women's Health Study.

PubMed

Cozier, Yvette C; Wise, Lauren A; Palmer, Julie R; Rosenberg, Lynn

2009-06-01

Obesity is more common in black women than in white women. Racial discrimination is a form of chronic stress that may influence weight. We assessed the association of perceived racism with weight change between 1997 and 2005 in 43,103 women from the Black Women's Health Study, a prospective follow-up of U.S. black women aged 21-69 years at entry in 1995. Eight questions about perceptions and experiences of racism were asked in 1997 from which two summary variables were created: everyday racism (e.g., how often do people act "as if you are not intelligent?"), and lifetime racism (e.g., unfair treatment due to race "on the job"). Mixed linear regression models were used to calculate the multivariate adjusted means for changes in body weight across categories of perceived racism. Weight gain increased as levels of everyday and lifetime racism increased. The mean multivariable-adjusted difference in weight change between the highest and the lowest quartile of everyday racism was 0.56 kg. The mean difference comparing the highest category of lifetime racism to the lowest was 0.48 kg. These prospective data suggest that experiences of racism may contribute to the excess burden of obesity in U.S. black women.

Preliminary Multi-Variable Parametric Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Hendrichs, Todd

2010-01-01

This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.

Difficulties in the differential diagnosis of vaginismus, dyspareunia and mixed sexual pain disorder.

PubMed

van Lankveld, J J; Brewaeys, A M; Ter Kuile, M M; Weijenborg, P T

1995-12-01

This retrospective study was undertaken to investigate predictors of vaginismus, dyspareunia and mixed sexual pain disorder in respect of symptom profile and treatment history variables of female patients and their partners. The study sample consisted of 147 female patients attending a university hospital outpatient clinic for Psychosomatic Gynecology and Sexology. All patients met the DSM-III-R criteria of the diagnoses of vaginismus (n = 50), dyspareunia (n = 46), or of both diagnoses (n = 51). No univariate differences were found between members of the three groups or between their partners. It was not possible to make a multivariate prediction of group membership.

Performance of fast-setting impression materials in the reproduction of subgingival tooth surfaces without soft tissue retraction.

PubMed

Rudolph, Heike; Röhl, Andreas; Walter, Michael H; Luthardt, Ralph G; Quaas, Sebastian

2014-01-01

Fast-setting impression materials may be prone to inaccuracies due to accidental divergence from the recommended mixing protocol. This prospective randomized clinical trial aimed to assess three-dimensional (3D) deviations in the reproduction of subgingival tooth surfaces and to determine the effect of either following or purposely diverging from the recommended mixing procedure for a fast-setting addition-curing silicone (AS) and fast-setting polyether (PE). After three impressions each were taken from 96 participants, sawcut gypsum casts were fabricated with a standardized procedure and then optically digitized. Data were assessed with a computer-aided 3D analysis. For AS impressions, multivariate analysis of variance revealed a significant influence of the individual tooth and the degree to which the recommended mixing protocol was violated. For PE impressions, the ambient air temperature and individual tooth showed significant effects, while divergence from the recommended mixing protocol was not of significance. The fast-setting PE material was not affected by changes in the recommended mixing protocol. For the two fast-setting materials examined, no divergences from the recommended mixing protocol of less than 2 minutes led to failures in the reproduction of the subgingival tooth surfaces.

Nutrition therapy cost analysis in the US: pre-mixed multi-chamber bag vs compounded parenteral nutrition.

PubMed

Turpin, Robin S; Canada, Todd; Liu, Frank Xiaoqing; Mercaldi, Catherine J; Pontes-Arruda, Alessandro; Wischmeyer, Paul

2011-09-01

Bloodstream infections (BSI) occur in up to 350 000 inpatient admissions each year in the US, with BSI rates among patients receiving parenteral nutrition (PN) varying from 1.3% to 39%. BSI-attributable costs were estimated to approximate $US12 000 per episode in 2000. While previous studies have compared the cost of different PN preparation methods, this analysis evaluates both the direct costs of PN and the treatment costs for BSI associated with different PN delivery methods to determine whether compounded or manufactured pre-mixed PN has lower overall costs. The purpose of this study was to compare costs in the US associated with compounded PN versus pre-mixed multi-chamber bag (MCB) PN based on underlying infection risk. Using claims information from the Premier Perspective™ database, multivariate logistic regression was used to estimate the risk of infection. A total of 44 358 hospitalized patients aged ≥18 years who received PN between 1 January 2005 and 31 December 2007 were included in the analyses. A total of 3256 patients received MCB PN and 41 102 received compounded PN. The PN-associated costs and length of stay were analysed using multivariate ordinary least squares regression models constructed to measure the impact of infectious events on total hospital costs after controlling for baseline and clinical patient characteristics. There were 7.3 additional hospital days attributable to BSI. After adjustment for baseline variables, the probability of developing a BSI was 30% higher in patients receiving compounded PN than in those receiving MCB PN (16.1% vs 11.3%; odds ratio = 1.56; 95% CI 1.37, 1.79; p < 0.0001), demonstrating 2172 potentially avoidable infections. The observed daily mean PN acquisition cost for patients receiving MCB PN was $US164 (including all additives and fees) compared with $US239 for patients receiving compounded PN (all differences p < 0.001). With a mean cost attributable to BSI of $US16 141, the total per-patient savings (including avoided BSI and PN costs) was $US1545. In this analysis of real-world PN use, MCB PN is associated with lower costs than compounded PN with regards to both PN acquisition and potential avoidance of BSI. Our base case indicates that $US1545 per PN patient may be saved; even if as few as 50% of PN patients are candidates for standardized pre-mix formulations, a potential savings of $US773 per patient may be realized.

Physiology-Based Modeling May Predict Surgical Treatment Outcome for Obstructive Sleep Apnea

PubMed Central

Li, Yanru; Ye, Jingying; Han, Demin; Cao, Xin; Ding, Xiu; Zhang, Yuhuan; Xu, Wen; Orr, Jeremy; Jen, Rachel; Sands, Scott; Malhotra, Atul; Owens, Robert

2017-01-01

Study Objectives: To test whether the integration of both anatomical and nonanatomical parameters (ventilatory control, arousal threshold, muscle responsiveness) in a physiology-based model will improve the ability to predict outcomes after upper airway surgery for obstructive sleep apnea (OSA). Methods: In 31 patients who underwent upper airway surgery for OSA, loop gain and arousal threshold were calculated from preoperative polysomnography (PSG). Three models were compared: (1) a multiple regression based on an extensive list of PSG parameters alone; (2) a multivariate regression using PSG parameters plus PSG-derived estimates of loop gain, arousal threshold, and other trait surrogates; (3) a physiological model incorporating selected variables as surrogates of anatomical and nonanatomical traits important for OSA pathogenesis. Results: Although preoperative loop gain was positively correlated with postoperative apnea-hypopnea index (AHI) (P = .008) and arousal threshold was negatively correlated (P = .011), in both model 1 and 2, the only significant variable was preoperative AHI, which explained 42% of the variance in postoperative AHI. In contrast, the physiological model (model 3), which included AHIREM (anatomy term), fraction of events that were hypopnea (arousal term), the ratio of AHIREM and AHINREM (muscle responsiveness term), loop gain, and central/mixed apnea index (control of breathing terms), was able to explain 61% of the variance in postoperative AHI. Conclusions: Although loop gain and arousal threshold are associated with residual AHI after surgery, only preoperative AHI was predictive using multivariate regression modeling. Instead, incorporating selected surrogates of physiological traits on the basis of OSA pathophysiology created a model that has more association with actual residual AHI. Commentary: A commentary on this article appears in this issue on page 1023. Clinical Trial Registration: ClinicalTrials.Gov; Title: The Impact of Sleep Apnea Treatment on Physiology Traits in Chinese Patients With Obstructive Sleep Apnea; Identifier: NCT02696629; URL: https://clinicaltrials.gov/show/NCT02696629 Citation: Li Y, Ye J, Han D, Cao X, Ding X, Zhang Y, Xu W, Orr J, Jen R, Sands S, Malhotra A, Owens R. Physiology-based modeling may predict surgical treatment outcome for obstructive sleep apnea. J Clin Sleep Med. 2017;13(9):1029–1037. PMID:28818154

Aging Trajectories in Different Body Systems Share Common Environmental Etiology: The Healthy Aging Twin Study (HATS).

PubMed

Moayyeri, Alireza; Hart, Deborah J; Snieder, Harold; Hammond, Christopher J; Spector, Timothy D; Steves, Claire J

2016-02-01

Little is known about the extent to which aging trajectories of different body systems share common sources of variance. We here present a large twin study investigating the trajectories of change in five systems: cardiovascular, respiratory, skeletal, morphometric, and metabolic. Longitudinal clinical data were collected on 3,508 female twins in the TwinsUK registry (complete pairs:740 monozygotic (MZ), 986 dizygotic (DZ), mean age at entry 48.9 ± 10.4, range 18-75 years; mean follow-up 10.2 ± 2.8 years, range 4-17.8 years). Panel data on multiple age-related variables were used to estimate biological ages for each individual at each time point, in linear mixed effects models. A weighted average approach was used to combine variables within predefined body system groups. Aging trajectories for each system in each individual were then constructed using linear modeling. Multivariate structural equation modeling of these aging trajectories showed low genetic effects (heritability), ranging from 2% in metabolic aging to 22% in cardiovascular aging. However, we found a significant effect of shared environmental factors on the variations in aging trajectories in cardiovascular (54%), skeletal (34%), morphometric (53%), and metabolic systems (53%). The remainder was due to environmental factors unique to each individual plus error. Multivariate Cholesky decomposition showed that among aging trajectories for various body systems there were significant and substantial correlations between the unique environmental latent factors as well as shared environmental factors. However, there was no evidence for a single common factor for aging. This study, the first of its kind in aging, suggests that diverse organ systems share non-genetic sources of variance for aging trajectories. Confirmatory studies are needed using population-based twin cohorts and alternative methods of handling missing data.

MRI criteria differentiating asymptomatic PML from new MS lesions during natalizumab pharmacovigilance.

PubMed

Wijburg, Martijn T; Witte, Birgit I; Vennegoor, Anke; Roosendaal, Stefan D; Sanchez, Esther; Liu, Yaou; Martins Jarnalo, Carine O; Uitdehaag, Bernard Mj; Barkhof, Frederik; Killestein, Joep; Wattjes, Mike P

2016-10-01

Differentiation between progressive multifocal leukoencephalopathy (PML) and new multiple sclerosis (MS) lesions on brain MRI during natalizumab pharmacovigilance in the absence of clinical signs and symptoms is challenging but is of substantial clinical relevance. We aim to define MRI characteristics that can aid in this differentiation. Reference and follow-up brain MRIs of natalizumab-treated patients with MS with asymptomatic PML (n=21), or asymptomatic new MS lesions (n=20) were evaluated with respect to characteristics of newly detected lesions by four blinded raters. We tested the association with PML for each characteristic and constructed a multivariable prediction model which we analysed using a receiver operating characteristic (ROC) curve. Presence of punctate T2 lesions, cortical grey matter involvement, juxtacortical white matter involvement, ill-defined and mixed lesion borders towards both grey and white matter, lesion size of >3 cm, and contrast enhancement were all associated with PML. Focal lesion appearance and periventricular localisation were associated with new MS lesions. In the multivariable model, punctate T2 lesions and cortical grey matter involvement predict for PML, while focal lesion appearance and periventricular localisation predict for new MS lesions (area under the curve: 0.988, 95% CI 0.977 to 1.0, sensitivity: 100%, specificity: 80.6%). The MRI characteristics of asymptomatic natalizumab-associated PML lesions proved to differ from new MS lesions. This led to a prediction model with a high discriminating power. Careful assessment of the presence of punctate T2 lesions, cortical grey matter involvement, focal lesion appearance and periventricular localisation allows for an early diagnosis of PML. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

High versus low-dose rate brachytherapy for cervical cancer.

PubMed

Patankar, Sonali S; Tergas, Ana I; Deutsch, Israel; Burke, William M; Hou, June Y; Ananth, Cande V; Huang, Yongmei; Neugut, Alfred I; Hershman, Dawn L; Wright, Jason D

2015-03-01

Brachytherapy plays an important role in the treatment of cervical cancer. While small trials have shown comparable survival outcomes between high (HDR) and low-dose rate (LDR) brachytherapy, little data is available in the US. We examined the utilization of HDR brachytherapy and analyzed the impact of type of brachytherapy on survival for cervical cancer. Women with stages IB2-IVA cervical cancer treated with primary (external beam and brachytherapy) radiotherapy between 2003-2011 and recorded in the National Cancer Database (NCDB) were analyzed. Generalized linear mixed models and Cox proportional hazards regression were used to examine predictors of HDR brachytherapy use and the association between HDR use and survival. A total of 10,564 women including 2681 (25.4%) who received LDR and 7883 (74.6%) that received HDR were identified. Use of HDR increased from 50.2% in 2003 to 83.9% in 2011 (P<0.0001). In a multivariable model, year of diagnosis was the strongest predictor of use of HDR. While patients in the Northeast were more likely to receive HDR therapy, there were no other clinical or socioeconomic characteristics associated with receipt of HDR. In a multivariable Cox model, survival was similar between the HDR and LDR groups (HR=0.93; 95% CI 0.83-1.03). Similar findings were noted in analyses stratified by stage and histology. Kaplan-Meier analyses demonstrated no difference in survival based on type of brachytherapy for stage IIB (P=0.68), IIIB (P=0.17), or IVA (P=0.16) tumors. The use of HDR therapy has increased rapidly. Overall survival is similar for LDR and HDR brachytherapy. Copyright © 2015 Elsevier Inc. All rights reserved.

High versus Low-Dose Rate Brachytherapy for Cervical Cancer

PubMed Central

Patankar, Sonali S.; Tergas, Ana I.; Deutsch, Israel; Burke, William M.; Hou, June Y.; Ananth, Cande V.; Huang, Yongmei; Neugut, Alfred I.; Hershman, Dawn L.; Wright, Jason D.

2015-01-01

Objectives Brachytherapy plays an important role in the treatment of cervical cancer. While small trials have shown comparable survival outcomes between high (HDR) and low-dose rate (LDR) brachytherapy, little data is available in the US. We examined the utilization of HDR brachytherapy and analyzed the impact of type of brachytherapy on survival for cervical cancer. Methods Women with stage IB2–IVA cervical cancer treated with primary (external beam and brachytherapy) radiotherapy between 2003–2011 and recorded in the National Cancer Database (NCDB) were analyzed. Generalized linear mixed models and Cox proportional hazards regression were used to examine predictors of HDR brachytherapy use and the association between HDR use and survival. Results A total of 10,564 women including 2681 (25.4%) who received LDR and 7883 (74.6%) that received HDR were identified. Use of HDR increased from 50.2% in 2003 to 83.9% in 2011 (P<0.0001). In a multivariable model, year of diagnosis was the strongest predictor of use of HDR. While patients in the Northeast were more likely to receive HDR therapy, there were no other clinical or socioeconomic characteristics associated with receipt of HDR. In a multivariable Cox model, survival was similar between the HDR and LDR groups (HR=0.93; 95% 0.83–1.03). Similar findings were noted in analyses stratified by stage and histology. Kaplan-Meier analyses demonstrated no difference in survival based on type of brachytherapy for stage IIB (P=0.68), IIIB (P=0.17), or IVA (P=0.16) tumors. Conclusions The use of HDR therapy has increased rapidly. Overall survival is similar for LDR and HDR brachytherapy. PMID:25575481

Quality of life after lacunar stroke: the Secondary Prevention of Small Subcortical Strokes study.

PubMed

Dhamoon, Mandip S; McClure, Leslie A; White, Carole L; Lau, Helena; Benavente, Oscar; Elkind, Mitchell S V

2014-01-01

We sought to describe the course and predictors of quality of life (QOL) after lacunar stroke. We hypothesized that there is a decline in QOL after recovery from lacunar stroke. The Secondary Prevention of Small Subcortical Strokes is a clinical trial in lacunar stroke patients with annual assessments of QOL with the stroke-specific QOL score. The overall score was used and analyzed as a continuous variable (range 0-5). We fit linear mixed models to assess the trend in QOL over time, assuming linearity of time, and adjusted for demographics, medical risk factors, cognitive factors, and functional status in univariable and multivariable models. Among 2870 participants, mean age was 63.4 years (SD 10.7), 63% were men, 51% White, 32% Hispanic, 36% had college education, 36% had diabetes, 89% had hypertension, and 10% had prior stroke. Mean poststroke Barthel Index (BI) score was 95.4 (assessed on average 6 months after stroke). In the final multivariable model, there was an average increase in QOL of .6% per year, and factors associated with decline in QOL over time included age (-.0003 per year, P < .0001), any college education (-.0013 per year, .01), prior stroke (-.004 per year, P < .0001), and BI (-.0002 per year, P < .0001). In this clinical trial of lacunar stroke patients, there was a slight annual increase in QOL overall, and age, level of education, and prior stroke were associated with changes in QOL over time. Multiple strokes may cause decline in QOL over time in the absence of recurrent events. Copyright © 2014 National Stroke Association. Published by Elsevier Inc. All rights reserved.

A Novel Approach to Detect Accelerated Aged and Surface-Mediated Degradation in Explosives by UPLC-ESI-MS.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beppler, Christina L

2015-12-01

A new approach was created for studying energetic material degradation. This approach involved detecting and tentatively identifying non-volatile chemical species by liquid chromatography-mass spectrometry (LC-MS) with multivariate statistical data analysis that form as the CL-20 energetic material thermally degraded. Multivariate data analysis showed clear separation and clustering of samples based on sample group: either pristine or aged material. Further analysis showed counter-clockwise trends in the principal components analysis (PCA), a type of multivariate data analysis, Scores plots. These trends may indicate that there was a discrete shift in the chemical markers as the went from pristine to aged material, andmore » then again when the aged CL-20 mixed with a potentially incompatible material was thermally aged for 4, 6, or 9 months. This new approach to studying energetic material degradation should provide greater knowledge of potential degradation markers in these materials.« less

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

NASA Astrophysics Data System (ADS)

Cannon, Alex J.

2018-01-01

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

Partial Least Squares Calibration Modeling Towards the Multivariate Limit of Detection for Enriched Isotopic Mixtures via Laser Ablation Molecular Isotopic Spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harris, Candace; Profeta, Luisa; Akpovo, Codjo

The psuedo univariate limit of detection was calculated to compare to the multivariate interval. ompared with results from the psuedounivariate LOD, the multivariate LOD includes other factors (i.e. signal uncertainties) and the reveals the significance in creating models that not only use the analyte’s emission line but also its entire molecular spectra.

Multiple imputation for handling missing outcome data when estimating the relative risk.

PubMed

Sullivan, Thomas R; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-09-06

Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

A simplified parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.

Introduction of functionality, selection of topology, and enhancement of gas adsorption in multivariate metal-organic framework-177.

PubMed

Zhang, Yue-Biao; Furukawa, Hiroyasu; Ko, Nakeun; Nie, Weixuan; Park, Hye Jeong; Okajima, Satoshi; Cordova, Kyle E; Deng, Hexiang; Kim, Jaheon; Yaghi, Omar M

2015-02-25

Metal-organic framework-177 (MOF-177) is one of the most porous materials whose structure is composed of octahedral Zn4O(-COO)6 and triangular 1,3,5-benzenetribenzoate (BTB) units to make a three-dimensional extended network based on the qom topology. This topology violates a long-standing thesis where highly symmetric building units are expected to yield highly symmetric networks. In the case of octahedron and triangle combinations, MOFs based on pyrite (pyr) and rutile (rtl) nets were expected instead of qom. In this study, we have made 24 MOF-177 structures with different functional groups on the triangular BTB linker, having one or more functionalities. We find that the position of the functional groups on the BTB unit allows the selection for a specific net (qom, pyr, and rtl), and that mixing of functionalities (-H, -NH2, and -C4H4) is an important strategy for the incorporation of a specific functionality (-NO2) into MOF-177 where otherwise incorporation of such functionality would be difficult. Such mixing of functionalities to make multivariate MOF-177 structures leads to enhancement of hydrogen uptake by 25%.

Introduction of Functionality, Selection of Topology, and Enhancement of Gas Adsorption in Multivariate Metal–Organic Framework-177

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Yue-Biao; Furukawa, Hiroyasu; Ko, Nakeun

2015-02-25

Metal–organic framework-177 (MOF-177) is one of the most porous materials whose structure is composed of octahedral Zn 4O(-COO) 6 and triangular 1,3,5-benzenetribenzoate (BTB) units to make a three-dimensional extended network based on the qom topology. This topology violates a long-standing thesis where highly symmetric building units are expected to yield highly symmetric networks. In the case of octahedron and triangle combinations, MOFs based on pyrite (pyr) and rutile (rtl) nets were expected instead of qom. In this study, we have made 24 MOF-177 structures with different functional groups on the triangular BTB linker, having one or more functionalities. We findmore » that the position of the functional groups on the BTB unit allows the selection for a specific net (qom, pyr, and rtl), and that mixing of functionalities (-H, -NH 2, and -C 4H 4) is an important strategy for the incorporation of a specific functionality (-NO 2) into MOF-177 where otherwise incorporation of such functionality would be difficult. Such mixing of functionalities to make multivariate MOF-177 structures leads to enhancement of hydrogen uptake by 25%.« less

Hydration Status, Kidney Function, and Kidney Injury in Florida Agricultural Workers.

PubMed

Mix, Jacqueline; Elon, Lisa; Vi Thien Mac, Valerie; Flocks, Joan; Economos, Eugenia; Tovar-Aguilar, Antonio J; Stover Hertzberg, Vicki; McCauley, Linda A

2018-05-01

Recent findings suggest that laboring in hot occupational environments is related to kidney damage in agricultural workers. We examined hydration status and kidney function in 192 Florida agricultural workers. Blood and urine samples were collected over 555 workdays during the summers of 2015 and 2016. Urine-specific gravity (USG), serum creatinine, and other kidney function markers were examined pre- and post-shift on each workday. Multivariable mixed modeling was used to examine the association of risk factors with hydration status and acute kidney injury (AKI). Approximately 53% of workers were dehydrated (USG ≥1.020) pre-shift and 81% post-shift; 33% of participants had AKI on at least one workday. The odds of AKI increased 47% for each 5-degree (°F) increase in heat index. A strikingly high prevalence of dehydration and AKI exists in Florida agricultural workers.

HIV-Negative Partnered Men's Willingness to Use Pre-Exposure Prophylaxis and Associated Factors Among an Internet Sample of U.S. HIV-Negative and HIV-Discordant Male Couples

PubMed Central

Stephenson, Rob

2015-01-01

Abstract Purpose: Research on male couples' willingness to use pre-exposure prophylaxis (PrEP) is critically lacking. Methods: A cross-sectional 2011 Internet survey collected dyadic data from 275 HIV-negative and 58 HIV-discordant male couples to describe 631 HIV-negative partnered mens' willingness to use PrEP and associated couple-level demographic and behavioral factors with multivariate multilevel modeling. Results: Fifty-three percent were very to extremely likely to use PrEP. Willingness was positively associated with being in a mixed race and behaviorally non-monogamous relationship, and with amyl nitrate use with sex outside the relationship. Willingness was negatively associated with having a college education. Conclusion: Prevention efforts should educate male couples about the potential benefits of PrEP. PMID:26790016

An empirical study of rape in the context of multiple murder.

PubMed

DeLisi, Matt

2014-03-01

In recent years, multiple homicide offending has received increased research attention from criminologists; however, there is mixed evidence about the role of rape toward the perpetration of multiple murder. Drawing on criminal career data from a nonprobability sample of 618 confined male homicide offenders selected from eight U.S. states, the current study examines the role of rape as a predictor of multiple homicide offending. Bivariate analyses indicated a significant association between rape and murder charges. Multivariate path regression models indicated that rape had a significant and robust association with multiple murder. This relationship withstood the confounding effects of kidnapping, prior prison confinement, and prior murder, rape, and kidnapping. These results provide evidence that rape potentially serves as a gateway to multiple murder for some serious offenders. Suggestions for future research are proffered.

Piecewise multivariate modelling of sequential metabolic profiling data.

PubMed

Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

2008-02-19

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

Fatty acid content, vitamins and selenium in bulk tank milk from organic and conventional Swedish dairy herds during the indoor season.

PubMed

Fall, Nils; Emanuelson, Ulf

2011-08-01

Fatty acids, vitamins and minerals in milk are important for the human consumer, the calf and the cow. Studies indicate that milk from organic and conventional dairy herds may differ in these aspects. The aim of this study was therefore to investigate whether there are differences in the fatty acid composition and concentration of vitamins and selenium in milk between organic and conventional herds in Sweden. Bulk tank milk was sampled in 18 organic and 19 conventional dairy herds on three occasions during the indoor season 2005-2006. Herd characteristics were collected by questionnaires and from the official milk recording scheme. Multivariable linear mixed models were used to evaluate the associations between milk composition and type of herd, while adjusting for potential confounders and the repeated observations within herd. In addition to management type, variables included in the initial models were housing type, milk fat content, herd size, average milk yield and time on pasture during summer. The median concentration of conjugated linoleic fatty acids (CLA) was 0·63% in organic compared with 0·48% in conventional herds, the content of total n-3 fatty acids was 1·44% and 1·04% in organic and conventional milk, respectively, and the content of total n-6 fatty acids was 2·72% and 2·20% in organic and conventional milk, respectively. The multivariable regression models indicated significantly higher concentrations of CLA, total n-3 and n-6 fatty acids in organic milk and a more desirable ratio of n-6 to n-3 fatty acids, for the human consumer, in organic milk. The multivariable models did not demonstrate any differences in retinol, α-tocopherol, β-carotene or selenium concentrations between systems. Median concentrations of α-tocopherol were 0·80 μg/ml in organic and 0·88 μg/ml in conventional milk, while for β-carotene the median concentrations were 0·19 and 0·18 μg/ml, respectively; for retinol, the median concentration was 0·32 μg/ml in both groups; the median concentrations of selenium were 13·0 and 13·5 μg/kg, respectively, for organic and conventional systems.

Not accounting for interindividual variability can mask habitat selection patterns: a case study on black bears.

PubMed

Lesmerises, Rémi; St-Laurent, Martin-Hugues

2017-11-01

Habitat selection studies conducted at the population scale commonly aim to describe general patterns that could improve our understanding of the limiting factors in species-habitat relationships. Researchers often consider interindividual variation in selection patterns to control for its effects and avoid pseudoreplication by using mixed-effect models that include individuals as random factors. Here, we highlight common pitfalls and possible misinterpretations of this strategy by describing habitat selection of 21 black bears Ursus americanus. We used Bayesian mixed-effect models and compared results obtained when using random intercept (i.e., population level) versus calculating individual coefficients for each independent variable (i.e., individual level). We then related interindividual variability to individual characteristics (i.e., age, sex, reproductive status, body condition) in a multivariate analysis. The assumption of comparable behavior among individuals was verified only in 40% of the cases in our seasonal best models. Indeed, we found strong and opposite responses among sampled bears and individual coefficients were linked to individual characteristics. For some covariates, contrasted responses canceled each other out at the population level. In other cases, interindividual variability was concealed by the composition of our sample, with the majority of the bears (e.g., old individuals and bears in good physical condition) driving the population response (e.g., selection of young forest cuts). Our results stress the need to consider interindividual variability to avoid misinterpretation and uninformative results, especially for a flexible and opportunistic species. This study helps to identify some ecological drivers of interindividual variability in bear habitat selection patterns.

A tridiagonal parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a tridiagonal parsimonious higher-order multivariate Markov chain model (TPHOMMCM). Moreover, estimation method of the parameters in TPHOMMCM is give. Numerical experiments illustrate the effectiveness of TPHOMMCM.

Bidirectional influence: A longitudinal analysis of size of drug network and depression among inner-city residents in Baltimore, Maryland

PubMed Central

Yang, Jingyan; Latkin, Carl A.; Davey-Rothwell, Melissa

2015-01-01

BACKGROUND The prevalence of depression among drug users is high. It has been recognized that drug use behaviors can be influenced and spread through social networks. OBJECTIVES We investigated the directional relationship between social network factors and depressive symptoms among a sample of inner-city residents in Baltimore, MD. METHODS We performed a longitudinal study of four-wave data collected from a network-based HIV/STI prevention intervention for women and network members, consisting of both men and women. Our primary outcome and exposure were depression using CESD scale and social network characteristics, respectively. Linear mixed model with clustering adjustment was used to account for both repeated measurement and network design. RESULTS Of the 746 participants, those who had high levels of depression tended to be female, less educated, homeless, smokers, and did not have a main partner. In the univariate longitudinal model, larger size of drug network was significantly associated with depression (OR=1.38, p<0.001). This relationship held after controlling for age, gender, homeless in the past six months, college education, having a main partner, cigarette smoking, perceived health, and social support network (aOR=1.19, p=0.001). In the univariate mixed model using depression to predict size of drug network, the data suggested that depression was associated with larger size of drug network (coef.=1.23, p<0.001) and the same relation held in multivariate model (adjusted coef.=1.08, p=0.001). CONCLUSIONS The results suggest that larger size of drug network is a risk factor for depression, and vice versa. Further intervention strategies to reduce depression should address social networks factors. PMID:26584046

Coordination of size-control, reproduction and generational memory in freshwater planarians

NASA Astrophysics Data System (ADS)

Yang, Xingbo; Kaj, Kelson J.; Schwab, David J.; Collins, Eva-Maria S.

2017-06-01

Uncovering the mechanisms that control size, growth, and division rates of organisms reproducing through binary division means understanding basic principles of their life cycle. Recent work has focused on how division rates are regulated in bacteria and yeast, but this question has not yet been addressed in more complex, multicellular organisms. We have, over the course of several years, assembled a unique large-scale data set on the growth and asexual reproduction of two freshwater planarian species, Dugesia japonica and Girardia tigrina, which reproduce by transverse fission and succeeding regeneration of head and tail pieces into new planarians. We show that generation-dependent memory effects in planarian reproduction need to be taken into account to accurately capture the experimental data. To achieve this, we developed a new additive model that mixes multiple size control strategies based on planarian size, growth, and time between divisions. Our model quantifies the proportions of each strategy in the mixed dynamics, revealing the ability of the two planarian species to utilize different strategies in a coordinated manner for size control. Additionally, we found that head and tail offspring of both species employ different mechanisms to monitor and trigger their reproduction cycles. Thus, we find a diversity of strategies not only between species but between heads and tails within species. Our additive model provides two advantages over existing 2D models that fit a multivariable splitting rate function to the data for size control: firstly, it can be fit to relatively small data sets and can thus be applied to systems where available data is limited. Secondly, it enables new biological insights because it explicitly shows the contributions of different size control strategies for each offspring type.

Coordination of size-control, reproduction and generational memory in freshwater planarians.

PubMed

Yang, Xingbo; Kaj, Kelson J; Schwab, David J; Collins, Eva-Maria S

2017-05-23

Uncovering the mechanisms that control size, growth, and division rates of organisms reproducing through binary division means understanding basic principles of their life cycle. Recent work has focused on how division rates are regulated in bacteria and yeast, but this question has not yet been addressed in more complex, multicellular organisms. We have, over the course of several years, assembled a unique large-scale data set on the growth and asexual reproduction of two freshwater planarian species, Dugesia japonica and Girardia tigrina, which reproduce by transverse fission and succeeding regeneration of head and tail pieces into new planarians. We show that generation-dependent memory effects in planarian reproduction need to be taken into account to accurately capture the experimental data. To achieve this, we developed a new additive model that mixes multiple size control strategies based on planarian size, growth, and time between divisions. Our model quantifies the proportions of each strategy in the mixed dynamics, revealing the ability of the two planarian species to utilize different strategies in a coordinated manner for size control. Additionally, we found that head and tail offspring of both species employ different mechanisms to monitor and trigger their reproduction cycles. Thus, we find a diversity of strategies not only between species but between heads and tails within species. Our additive model provides two advantages over existing 2D models that fit a multivariable splitting rate function to the data for size control: firstly, it can be fit to relatively small data sets and can thus be applied to systems where available data is limited. Secondly, it enables new biological insights because it explicitly shows the contributions of different size control strategies for each offspring type.

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Comparing Within-Person Effects from Multivariate Longitudinal Models

ERIC Educational Resources Information Center

Bainter, Sierra A.; Howard, Andrea L.

2016-01-01

Several multivariate models are motivated to answer similar developmental questions regarding within-person (intraindividual) effects between 2 or more constructs over time, yet the within-person effects tested by each model are distinct. In this article, the authors clarify the types of within-person inferences that can be made from each model.…

Applying the multivariate time-rescaling theorem to neural population models

PubMed Central

Gerhard, Felipe; Haslinger, Robert; Pipa, Gordon

2011-01-01

Statistical models of neural activity are integral to modern neuroscience. Recently, interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based upon the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models which neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem, and provide a practical step-by-step procedure for applying it towards testing the sufficiency of neural population models. Using several simple analytically tractable models and also more complex simulated and real data sets, we demonstrate that important features of the population activity can only be detected using the multivariate extension of the test. PMID:21395436

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Heavy charged scalars from c\\overline{s} fusion: a generic search strategy applied to a 3HDM with U(1) × U(1) family symmetry

NASA Astrophysics Data System (ADS)

Camargo-Molina, José Eliel; Mandal, Tanumoy; Pasechnik, Roman; Wessén, Jonas

2018-03-01

We describe a class of three Higgs doublet models (3HDMs) with a softly broken U(1) × U(1) family symmetry that enforces a Cabibbo-like quark mixing while forbidding tree-level flavour changing neutral currents. The hierarchy in the observed quark masses is partly explained by a softer hierarchy in the vacuum expectation values of the three Higgs doublets. As a consequence, the physical scalar spectrum contains a Standard Model (SM) like Higgs boson h 125 while exotic scalars couple the strongest to the second quark family, leading to rather unconventional discovery channels that could be probed at the Large Hadron Collider. In particular, we describe a search strategy for the lightest charged Higgs boson H ±, through the process c\\overline{s}\\to {H}+\\to {W}+{h}_{125} , using a multivariate analysis that leads to an excellent discriminatory power against the SM background. Although the analysis is applied to the proposed class of 3HDMs, we employ a model-independent formulation such that it can be applied to any other model with the same discovery channel.

Analytical Problems and Suggestions in the Analysis of Behavioral Economic Demand Curves.

PubMed

Yu, Jihnhee; Liu, Liu; Collins, R Lorraine; Vincent, Paula C; Epstein, Leonard H

2014-01-01

Behavioral economic demand curves (Hursh, Raslear, Shurtleff, Bauman, & Simmons, 1988) are innovative approaches to characterize the relationships between consumption of a substance and its price. In this article, we investigate common analytical issues in the use of behavioral economic demand curves, which can cause inconsistent interpretations of demand curves, and then we provide methodological suggestions to address those analytical issues. We first demonstrate that log transformation with different added values for handling zeros changes model parameter estimates dramatically. Second, demand curves are often analyzed using an overparameterized model that results in an inefficient use of the available data and a lack of assessment of the variability among individuals. To address these issues, we apply a nonlinear mixed effects model based on multivariate error structures that has not been used previously to analyze behavioral economic demand curves in the literature. We also propose analytical formulas for the relevant standard errors of derived values such as P max, O max, and elasticity. The proposed model stabilizes the derived values regardless of using different added increments and provides substantially smaller standard errors. We illustrate the data analysis procedure using data from a relative reinforcement efficacy study of simulated marijuana purchasing.

Meta-Analytic Structural Equation Modeling (MASEM): Comparison of the Multivariate Methods

ERIC Educational Resources Information Center

Zhang, Ying

2011-01-01

Meta-analytic Structural Equation Modeling (MASEM) has drawn interest from many researchers recently. In doing MASEM, researchers usually first synthesize correlation matrices across studies using meta-analysis techniques and then analyze the pooled correlation matrix using structural equation modeling techniques. Several multivariate methods of…

MULTIVARIATE RECEPTOR MODELS-CURRENT PRACTICE AND FUTURE TRENDS. (R826238)

EPA Science Inventory

Multivariate receptor models have been applied to the analysis of air quality data for sometime. However, solving the general mixture problem is important in several other fields. This paper looks at the panoply of these models with a view of identifying common challenges and ...

A Nonlinear Mixed Effects Approach for Modeling the Cell-To-Cell Variability of Mig1 Dynamics in Yeast

PubMed Central

Almquist, Joachim; Bendrioua, Loubna; Adiels, Caroline Beck; Goksör, Mattias; Hohmann, Stefan; Jirstrand, Mats

2015-01-01

The last decade has seen a rapid development of experimental techniques that allow data collection from individual cells. These techniques have enabled the discovery and characterization of variability within a population of genetically identical cells. Nonlinear mixed effects (NLME) modeling is an established framework for studying variability between individuals in a population, frequently used in pharmacokinetics and pharmacodynamics, but its potential for studies of cell-to-cell variability in molecular cell biology is yet to be exploited. Here we take advantage of this novel application of NLME modeling to study cell-to-cell variability in the dynamic behavior of the yeast transcription repressor Mig1. In particular, we investigate a recently discovered phenomenon where Mig1 during a short and transient period exits the nucleus when cells experience a shift from high to intermediate levels of extracellular glucose. A phenomenological model based on ordinary differential equations describing the transient dynamics of nuclear Mig1 is introduced, and according to the NLME methodology the parameters of this model are in turn modeled by a multivariate probability distribution. Using time-lapse microscopy data from nearly 200 cells, we estimate this parameter distribution according to the approach of maximizing the population likelihood. Based on the estimated distribution, parameter values for individual cells are furthermore characterized and the resulting Mig1 dynamics are compared to the single cell times-series data. The proposed NLME framework is also compared to the intuitive but limited standard two-stage (STS) approach. We demonstrate that the latter may overestimate variabilities by up to almost five fold. Finally, Monte Carlo simulations of the inferred population model are used to predict the distribution of key characteristics of the Mig1 transient response. We find that with decreasing levels of post-shift glucose, the transient response of Mig1 tend to be faster, more extended, and displays an increased cell-to-cell variability. PMID:25893847

Regression Models For Multivariate Count Data

PubMed Central

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2016-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data. PMID:28348500

Regression Models For Multivariate Count Data.

PubMed

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

Menopause and Risk of Kidney Stones.

PubMed

Prochaska, Megan; Taylor, Eric N; Curhan, Gary

2018-05-03

Metabolic changes due to menopause may alter urine composition and kidney stone risk but results from prior work on this association have been mixed. We examined menopause and risk of incident kidney stones and changes in 24-hour urine composition in the Nurses' Health Study II. We conducted a prospective analysis of 108,639 Nurses' Health Study II participants who provided information on menopause and kidney stones. We used multivariate adjusted Cox proportional hazards models. We also analyzed 24-hour urine collections from 658 participants who performed a collection while pre-menopausal and a repeat collection after menopause. During 22 years of follow-up, there were 3,456 incident kidney stones. The multivariate adjusted relative risk for an incident kidney stone for post-menopausal participants compared with pre-menopause was 1.27 (95% CI 1.08 to 1.46). In a stratified analysis, compared with pre-menopause, the multivariate adjusted relative risk of natural menopause was 1.27 (95% CI 1.09 to 1.48) and surgically induced menopause was 1.43 (95% CI 1.19 to 1.73). Among 74,505 post-menopausal participants, there were 1,041 incident stone events. Compared with no hormone therapy use, neither current nor past use was significantly associated with kidney stone risk. Compared with pre-menopause, the post-menopausal urine collections had lower mean calcium, citrate, phosphorus, and uric acid, and higher mean volume. Post-menopausal status is associated with higher risk of incident kidney stone. Natural and surgical menopause are each independently associated with higher risk. There are small but significant differences in urine composition between pre- and post-menopausal urine collections. Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Association of Low-Grade Albuminuria with Adverse Cardiac Mechanics: Findings from the HyperGEN Study

PubMed Central

Katz, Daniel H.; Selvaraj, Senthil; Aguilar, Frank G.; Martinez, Eva E.; Beussink, Lauren; Kim, Kwang-Youn A.; Peng, Jie; Sha, Jin; Irvin, Marguerite R.; Eckfeldt, John H.; Turner, Stephen T.; Freedman, Barry I.; Arnett, Donna K.; Shah, Sanjiv J.

2013-01-01

Introduction Albuminuria is a marker of endothelial dysfunction and has been associated with adverse cardiovascular outcomes. The reasons for this association are unclear, but may be due to the relationship between endothelial dysfunction and intrinsic myocardial dysfunction. Methods and Results In the HyperGEN study, a population- and family-based study of hypertension, we examined the relationship between urine albumin-to-creatinine ratio (UACR) and cardiac mechanics (N=1894, all of whom had normal left ventricular ejection fraction and wall motion). We performed speckle-tracking echocardiographic analysis to quantify global longitudinal, circumferential, and radial strain (GLS, GCS, and GRS, respectively), and early diastolic (e′) tissue velocities. We used E/e′ ratio as a marker of increased LV filling pressures. We used multivariable-adjusted linear mixed effect models to determine independent associations between UACR and cardiac mechanics. The mean age was 50±14 years, 59% were female, and 46% were African-American. Comorbidities were increasingly prevalent among higher UACR quartiles. Albuminuria was associated with GLS, GCS, GRS, e′ velocity, and E/e′ ratio on unadjusted analyses. After adjustment for covariates, UACR was independently associated with lower absolute GLS (multivariable-adjusted mean GLS [95% CI] for UACR Quartile 1 = 15.3 [15.0–15.5]% vs. UACR Q4 = 14.6 [14.3–14.9]%, P for trend <0.001) and increased E/e′ ratio (Q1 = 25.3 [23.5–27.1] vs. Q4 = 29.0 [27.0–31.0], P= 0.003). The association between UACR and GLS was present even in participants with UACR < 30 mg/g (P<0.001 after multivariable adjustment). Conclusions Albuminuria, even at low levels, is associated with adverse cardiac mechanics and higher E/e′ ratio. PMID:24077169

Concentration-Dependent Antagonism and Culture Conversion in Pulmonary Tuberculosis

PubMed Central

Pasipanodya, Jotam G.; Denti, Paolo; Sirgel, Frederick; Lesosky, Maia; Gumbo, Tawanda; Meintjes, Graeme; McIlleron, Helen; Wilkinson, Robert J.

2017-01-01

Abstract Background. There is scant evidence to support target drug exposures for optimal tuberculosis outcomes. We therefore assessed whether pharmacokinetic/pharmacodynamic (PK/PD) parameters could predict 2-month culture conversion. Methods. One hundred patients with pulmonary tuberculosis (65% human immunodeficiency virus coinfected) were intensively sampled to determine rifampicin, isoniazid, and pyrazinamide plasma concentrations after 7–8 weeks of therapy, and PK parameters determined using nonlinear mixed-effects models. Detailed clinical data and sputum for culture were collected at baseline, 2 months, and 5–6 months. Minimum inhibitory concentrations (MICs) were determined on baseline isolates. Multivariate logistic regression and the assumption-free multivariate adaptive regression splines (MARS) were used to identify clinical and PK/PD predictors of 2-month culture conversion. Potential PK/PD predictors included 0- to 24-hour area under the curve (AUC0-24), maximum concentration (Cmax), AUC0-24/MIC, Cmax/MIC, and percentage of time that concentrations persisted above the MIC (%TMIC). Results. Twenty-six percent of patients had Cmax of rifampicin <8 mg/L, pyrazinamide <35 mg/L, and isoniazid <3 mg/L. No relationship was found between PK exposures and 2-month culture conversion using multivariate logistic regression after adjusting for MIC. However, MARS identified negative interactions between isoniazid Cmax and rifampicin Cmax/MIC ratio on 2-month culture conversion. If isoniazid Cmax was <4.6 mg/L and rifampicin Cmax/MIC <28, the isoniazid concentration had an antagonistic effect on culture conversion. For patients with isoniazid Cmax >4.6 mg/L, higher isoniazid exposures were associated with improved rates of culture conversion. Conclusions. PK/PD analyses using MARS identified isoniazid Cmax and rifampicin Cmax/MIC thresholds below which there is concentration-dependent antagonism that reduces 2-month sputum culture conversion. PMID:28205671

A mixing timescale model for TPDF simulations of turbulent premixed flames

DOE PAGES

Kuron, Michael; Ren, Zhuyin; Hawkes, Evatt R.; ...

2017-02-06

Transported probability density function (TPDF) methods are an attractive modeling approach for turbulent flames as chemical reactions appear in closed form. However, molecular micro-mixing needs to be modeled and this modeling is considered a primary challenge for TPDF methods. In the present study, a new algebraic mixing rate model for TPDF simulations of turbulent premixed flames is proposed, which is a key ingredient in commonly used molecular mixing models. The new model aims to properly account for the transition in reactive scalar mixing rate behavior from the limit of turbulence-dominated mixing to molecular mixing behavior in flamelets. An a priorimore » assessment of the new model is performed using direct numerical simulation (DNS) data of a lean premixed hydrogen–air jet flame. The new model accurately captures the mixing timescale behavior in the DNS and is found to be a significant improvement over the commonly used constant mechanical-to-scalar mixing timescale ratio model. An a posteriori TPDF study is then performed using the same DNS data as a numerical test bed. The DNS provides the initial conditions and time-varying input quantities, including the mean velocity, turbulent diffusion coefficient, and modeled scalar mixing rate for the TPDF simulations, thus allowing an exclusive focus on the mixing model. Here, the new mixing timescale model is compared with the constant mechanical-to-scalar mixing timescale ratio coupled with the Euclidean Minimum Spanning Tree (EMST) mixing model, as well as a laminar flamelet closure. It is found that the laminar flamelet closure is unable to properly capture the mixing behavior in the thin reaction zones regime while the constant mechanical-to-scalar mixing timescale model under-predicts the flame speed. Furthermore, the EMST model coupled with the new mixing timescale model provides the best prediction of the flame structure and flame propagation among the models tested, as the dynamics of reactive scalar mixing across different flame regimes are appropriately accounted for.« less

A mixing timescale model for TPDF simulations of turbulent premixed flames

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kuron, Michael; Ren, Zhuyin; Hawkes, Evatt R.

Transported probability density function (TPDF) methods are an attractive modeling approach for turbulent flames as chemical reactions appear in closed form. However, molecular micro-mixing needs to be modeled and this modeling is considered a primary challenge for TPDF methods. In the present study, a new algebraic mixing rate model for TPDF simulations of turbulent premixed flames is proposed, which is a key ingredient in commonly used molecular mixing models. The new model aims to properly account for the transition in reactive scalar mixing rate behavior from the limit of turbulence-dominated mixing to molecular mixing behavior in flamelets. An a priorimore » assessment of the new model is performed using direct numerical simulation (DNS) data of a lean premixed hydrogen–air jet flame. The new model accurately captures the mixing timescale behavior in the DNS and is found to be a significant improvement over the commonly used constant mechanical-to-scalar mixing timescale ratio model. An a posteriori TPDF study is then performed using the same DNS data as a numerical test bed. The DNS provides the initial conditions and time-varying input quantities, including the mean velocity, turbulent diffusion coefficient, and modeled scalar mixing rate for the TPDF simulations, thus allowing an exclusive focus on the mixing model. Here, the new mixing timescale model is compared with the constant mechanical-to-scalar mixing timescale ratio coupled with the Euclidean Minimum Spanning Tree (EMST) mixing model, as well as a laminar flamelet closure. It is found that the laminar flamelet closure is unable to properly capture the mixing behavior in the thin reaction zones regime while the constant mechanical-to-scalar mixing timescale model under-predicts the flame speed. Furthermore, the EMST model coupled with the new mixing timescale model provides the best prediction of the flame structure and flame propagation among the models tested, as the dynamics of reactive scalar mixing across different flame regimes are appropriately accounted for.« less

Penalized spline estimation for functional coefficient regression models.

PubMed

Cao, Yanrong; Lin, Haiqun; Wu, Tracy Z; Yu, Yan

2010-04-01

The functional coefficient regression models assume that the regression coefficients vary with some "threshold" variable, providing appreciable flexibility in capturing the underlying dynamics in data and avoiding the so-called "curse of dimensionality" in multivariate nonparametric estimation. We first investigate the estimation, inference, and forecasting for the functional coefficient regression models with dependent observations via penalized splines. The P-spline approach, as a direct ridge regression shrinkage type global smoothing method, is computationally efficient and stable. With established fixed-knot asymptotics, inference is readily available. Exact inference can be obtained for fixed smoothing parameter λ, which is most appealing for finite samples. Our penalized spline approach gives an explicit model expression, which also enables multi-step-ahead forecasting via simulations. Furthermore, we examine different methods of choosing the important smoothing parameter λ: modified multi-fold cross-validation (MCV), generalized cross-validation (GCV), and an extension of empirical bias bandwidth selection (EBBS) to P-splines. In addition, we implement smoothing parameter selection using mixed model framework through restricted maximum likelihood (REML) for P-spline functional coefficient regression models with independent observations. The P-spline approach also easily allows different smoothness for different functional coefficients, which is enabled by assigning different penalty λ accordingly. We demonstrate the proposed approach by both simulation examples and a real data application.

A "Model" Multivariable Calculus Course.

ERIC Educational Resources Information Center

Beckmann, Charlene E.; Schlicker, Steven J.

1999-01-01

Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series

ERIC Educational Resources Information Center

Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.

2011-01-01

Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

[Heart failure in primary care: Attitudes, knowledge and self-care].

PubMed

Salvadó-Hernández, Cristina; Cosculluela-Torres, Pilar; Blanes-Monllor, Carmen; Parellada-Esquius, Neus; Méndez-Galeano, Carmen; Maroto-Villanova, Neus; García-Cerdán, Rosa Maria; Núñez-Manrique, M Pilar; Barrio-Ruiz, Carmen; Salvador-González, Betlem

2018-04-01

To determine the attitudes, knowledge, and self-care practices in patients with heart failure (HF) in Primary Care, as well as to identify factors associated with better self-care. Cross-sectional and multicentre study. Primary Care. Subjects over 18 years old with HF diagnosis, attended in 10 Primary Health Care Centres in the Metropolitan Area of Barcelona. Self-care was measured using the European Heart Failure Self-Care Behaviour Scale. Sociodemographic and clinical characteristics, tests on attitudes (Self-efficacy Managing Chronic Disease Scale), knowledge (Patient Knowledge Questionnaire), level of autonomy (Barthel), and anxiety and depression screening (Goldberg Test), were also gathered in an interview. A multivariate mixed model stratified by centre was used to analyse the adjusted association of covariates with self-care. A total of 295 subjects (77.6%) agreed to participate, with a mean age of 75.6 years (SD: 11), 56.6% women, and 62% with no primary education. The mean self-care score was 28.65 (SD: 8.22), with 25% of patients scoring lower than 21 points. In the final stratified multivariate model (n=282; R 2 conditional=0.3382), better self-care was associated with higher knowledge (coefficient, 95% confidence interval: -1.37; -1.85 to -0.90), and coronary heart disease diagnosis (-2.41; -4.36: -0.46). Self-care was moderate. The correlation of better self-care with higher knowledge highlights the opportunity to implement strategies to improve self-care, which should consider the characteristics of heart failure patients attended in Primary Care. Copyright © 2017 Elsevier España, S.L.U. All rights reserved.

Multiple modality biomarker prediction of cognitive impairment in prospectively followed de novo Parkinson disease.

PubMed

Caspell-Garcia, Chelsea; Simuni, Tanya; Tosun-Turgut, Duygu; Wu, I-Wei; Zhang, Yu; Nalls, Mike; Singleton, Andrew; Shaw, Leslie A; Kang, Ju-Hee; Trojanowski, John Q; Siderowf, Andrew; Coffey, Christopher; Lasch, Shirley; Aarsland, Dag; Burn, David; Chahine, Lana M; Espay, Alberto J; Foster, Eric D; Hawkins, Keith A; Litvan, Irene; Richard, Irene; Weintraub, Daniel

2017-01-01

To assess the neurobiological substrate of initial cognitive decline in Parkinson's disease (PD) to inform patient management, clinical trial design, and development of treatments. We longitudinally assessed, up to 3 years, 423 newly diagnosed patients with idiopathic PD, untreated at baseline, from 33 international movement disorder centers. Study outcomes were four determinations of cognitive impairment or decline, and biomarker predictors were baseline dopamine transporter (DAT) single photon emission computed tomography (SPECT) scan, structural magnetic resonance imaging (MRI; volume and thickness), diffusion tensor imaging (mean diffusivity and fractional anisotropy), cerebrospinal fluid (CSF; amyloid beta [Aβ], tau and alpha synuclein), and 11 single nucleotide polymorphisms (SNPs) previously associated with PD cognition. Additionally, longitudinal structural MRI and DAT scan data were included. Univariate analyses were run initially, with false discovery rate = 0.2, to select biomarker variables for inclusion in multivariable longitudinal mixed-effect models. By year 3, cognitive impairment was diagnosed in 15-38% participants depending on the criteria applied. Biomarkers, some longitudinal, predicting cognitive impairment in multivariable models were: (1) dopamine deficiency (decreased caudate and putamen DAT availability); (2) diffuse, cortical decreased brain volume or thickness (frontal, temporal, parietal, and occipital lobe regions); (3) co-morbid Alzheimer's disease Aβ amyloid pathology (lower CSF Aβ 1-42); and (4) genes (COMT val/val and BDNF val/val genotypes). Cognitive impairment in PD increases in frequency 50-200% in the first several years of disease, and is independently predicted by biomarker changes related to nigrostriatal or cortical dopaminergic deficits, global atrophy due to possible widespread effects of neurodegenerative disease, co-morbid Alzheimer's disease plaque pathology, and genetic factors.

Evidence-Based Medicine Curriculum Improves Pediatric Emergency Fellows' Scores on In-Training Examinations.

PubMed

Tavarez, Melissa M; Kenkre, Tanya S; Zuckerbraun, Noel

2017-05-30

The aim of this study was to determine if implementation of our evidence-based medicine (EBM) curriculum had an effect on pediatric emergency medicine fellows' scores on the relevant section of the in-training examination (ITE). We obtained deidentified subscores for 22 fellows over 6 academic years for the Core Knowledge in Scholarly Activities (SA) and, as a balance measure, Emergencies Treated Medically sections. We divided the subscores into the following 3 instruction periods: "baseline" for academic years before our current EBM curriculum, "transition" for academic years with use of a research method curriculum with some overlapping EBM content, and "EBM" for academic years with our current EBM curriculum. We analyzed data using the Kruskal-Wallis test, the Mann-Whitney U test, and multivariate mixed-effects linear models. The SA subscore median was higher during the EBM period in comparison with the baseline and transition periods. In contrast, the Emergencies Treated Medically subscore median was similar across instruction periods. Multivariate modeling demonstrated that our EBM curriculum had the following independent effects on the fellows' SA subscore: (1) in comparison with the transition period, the fellows' SA subscore was 21 percentage points higher (P = 0.005); and (2) in comparison to the baseline period, the fellows' SA subscore was 28 percentage points higher during the EBM curriculum instruction period (P < 0.001). Our EBM curriculum was associated with significantly higher scores on the SA section of the ITE. Pediatric emergency medicine educators could consider using fellows' scores on this section of the ITE to assess the effect of their EBM curricula.

Load compensation in a lean burn natural gas vehicle

NASA Astrophysics Data System (ADS)

Gangopadhyay, Anupam

A new multivariable PI tuning technique is developed in this research that is primarily developed for regulation purposes. Design guidelines are developed based on closed-loop stability. The new multivariable design is applied in a natural gas vehicle to combine idle and A/F ratio control loops. This results in better recovery during low idle operation of a vehicle under external step torques. A powertrain model of a natural gas engine is developed and validated for steady-state and transient operation. The nonlinear model has three states: engine speed, intake manifold pressure and fuel fraction in the intake manifold. The model includes the effect of fuel partial pressure in the intake manifold filling and emptying dynamics. Due to the inclusion of fuel fraction as a state, fuel flow rate into the cylinders is also accurately modeled. A linear system identification is performed on the nonlinear model. The linear model structure is predicted analytically from the nonlinear model and the coefficients of the predicted transfer function are shown to be functions of key physical parameters in the plant. Simulations of linear system and model parameter identification is shown to converge to the predicted values of the model coefficients. The multivariable controller developed in this research could be designed in an algebraic fashion once the plant model is known. It is thus possible to implement the multivariable PI design in an adaptive fashion combining the controller with identified plant model on-line. This will result in a self-tuning regulator (STR) type controller where the underlying design criteria is the multivariable tuning technique designed in this research.

Practical robustness measures in multivariable control system analysis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Lehtomaki, N. A.

1981-01-01

The robustness of the stability of multivariable linear time invariant feedback control systems with respect to model uncertainty is considered using frequency domain criteria. Available robustness tests are unified under a common framework based on the nature and structure of model errors. These results are derived using a multivariable version of Nyquist's stability theorem in which the minimum singular value of the return difference transfer matrix is shown to be the multivariable generalization of the distance to the critical point on a single input, single output Nyquist diagram. Using the return difference transfer matrix, a very general robustness theorem is presented from which all of the robustness tests dealing with specific model errors may be derived. The robustness tests that explicitly utilized model error structure are able to guarantee feedback system stability in the face of model errors of larger magnitude than those robustness tests that do not. The robustness of linear quadratic Gaussian control systems are analyzed.

Prevalence, Risk Factors and Consequent Effect of Dystocia in Holstein Dairy Cows in Iran

PubMed Central

Atashi, Hadi; Abdolmohammadi, Alireza; Dadpasand, Mohammad; Asaadi, Anise

2012-01-01

The objective of this research was to determine the prevalence, risk factors and consequent effect of dystocia on lactation performance in Holstein dairy cows in Iran. The data set consisted of 55,577 calving records on 30,879 Holstein cows in 30 dairy herds for the period March 2000 to April 2009. Factors affecting dystocia were analyzed using multivariable logistic regression models through the maximum likelihood method in the GENMOD procedure. The effect of dystocia on lactation performance and factors affecting calf birth weight were analyzed using mixed linear model in the MIXED procedure. The average incidence of dystocia was 10.8% and the mean (SD) calf birth weight was 42.13 (5.42) kg. Primiparous cows had calves with lower body weight and were more likely to require assistance at parturition (p<0.05). Female calves had lower body weight, and had a lower odds ratio for dystocia than male calves (p<0.05). Twins had lower birth weight, and had a higher odds ratio for dystocia than singletons (p<0.05). Cows which gave birth to a calf with higher weight at birth experienced more calving difficulty (OR (95% CI) = 1.1(1.08–1.11). Total 305-d milk, fat and protein yield was 135 (23), 3.16 (0.80) and 6.52 (1.01) kg less, in cows that experienced dystocia at calving compared with those that did not (p<0.05). PMID:25049584

Measuring utilization and impact of home care services: a systems model approach for cost effectiveness.

PubMed

Day, S R

1984-01-01

The relevance of home care research to policy questions is discussed as framework for study on "effects" (precursors and sequelae) of home care. This study used a large, multi-service agency's longitudinal (8-year) case records (N = 2436) to examine a system model for relationships among entry characteristics, utilization of services, and need for services upon discharge from home care. Deducing case-mix from utilization patterns, pay plan at entry was identified as best of the available predictors of both duration and intensity (using multivariate analysis). Duration and intensity, dual contributors to "total visits," were found to vary inversely and were predicted by different entering pay plans. While 1/3 of all cases were discharged to informal or self care, that was the most prevalent exit status of the clients (49%) who entered directly from hospital care. The methods used in disaggregating and analyzing these retrospectively-coded case records suggest that home services research: 1. distinguish type, intensity, and duration as components of "total visits" which combine to account for costs of care; 2. find concomitants of functional level (such as pay plan) which are accessible for designating case mix for purpose of projecting service use; 3. measure effectiveness in terms relevant to stated objectives of the long term care system, which need to acknowledge mortality and to separate service needs at entry room those at exist from the series of formal and informal providers on a continuum of care.

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Some rules for polydimensional squeezing

NASA Technical Reports Server (NTRS)

Manko, Vladimir I.

1994-01-01

The review of the following results is presented: For mixed state light of N-mode electromagnetic field described by Wigner function which has generic Gaussian form, the photon distribution function is obtained and expressed explicitly in terms of Hermite polynomials of 2N-variables. The momenta of this distribution are calculated and expressed as functions of matrix invariants of the dispersion matrix. The role of new uncertainty relation depending on photon state mixing parameter is elucidated. New sum rules for Hermite polynomials of several variables are found. The photon statistics of polymode even and odd coherent light and squeezed polymode Schroedinger cat light is given explicitly. Photon distribution for polymode squeezed number states expressed in terms of multivariable Hermite polynomials is discussed.

Describing the Elephant: Structure and Function in Multivariate Data.

ERIC Educational Resources Information Center

McDonald, Roderick P.

1986-01-01

There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family of models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain. (Author)

Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models.

PubMed

Chen, Zewei; Zhang, Xin; Zhang, Zhuoyong

2016-12-01

Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring. Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD. For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models. Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.

Cole-Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass.

PubMed

Dabros, Michal; Dennewald, Danielle; Currie, David J; Lee, Mark H; Todd, Robert W; Marison, Ian W; von Stockar, Urs

2009-02-01

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole-Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole-Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole-Cole and PLS models, the latter technique giving more satisfactory results.

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Power of Models in Longitudinal Study: Findings from a Full-Crossed Simulation Design

ERIC Educational Resources Information Center

Fang, Hua; Brooks, Gordon P.; Rizzo, Maria L.; Espy, Kimberly Andrews; Barcikowski, Robert S.

2009-01-01

Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3…

Species distribution modelling for plant communities: Stacked single species or multivariate modelling approaches?

Treesearch

Emilie B. Henderson; Janet L. Ohmann; Matthew J. Gregory; Heather M. Roberts; Harold S.J. Zald

2014-01-01

Landscape management and conservation planning require maps of vegetation composition and structure over large regions. Species distribution models (SDMs) are often used for individual species, but projects mapping multiple species are rarer. We compare maps of plant community composition assembled by stacking results from many SDMs with multivariate maps constructed...

IRT-ZIP Modeling for Multivariate Zero-Inflated Count Data

ERIC Educational Resources Information Center

Wang, Lijuan

2010-01-01

This study introduces an item response theory-zero-inflated Poisson (IRT-ZIP) model to investigate psychometric properties of multiple items and predict individuals' latent trait scores for multivariate zero-inflated count data. In the model, two link functions are used to capture two processes of the zero-inflated count data. Item parameters are…

Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

NASA Astrophysics Data System (ADS)

Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.

2017-03-01

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were < 0.05 and all AUC > 0.60.

Southern Ocean Mixed-Layer Seasonal and Interannual Variations From Combined Satellite and In Situ Data

NASA Astrophysics Data System (ADS)

Buongiorno Nardelli, B.; Guinehut, S.; Verbrugge, N.; Cotroneo, Y.; Zambianchi, E.; Iudicone, D.

2017-12-01

The depth of the upper ocean mixed layer provides fundamental information on the amount of seawater that directly interacts with the atmosphere. Its space-time variability modulates water mass formation and carbon sequestration processes related to both the physical and biological pumps. These processes are particularly relevant in the Southern Ocean, where surface mixed-layer depth estimates are generally obtained either as climatological fields derived from in situ observations or through numerical simulations. Here we demonstrate that weekly observation-based reconstructions can be used to describe the variations of the mixed-layer depth in the upper ocean over a range of space and time scales. We compare and validate four different products obtained by combining satellite measurements of the sea surface temperature, salinity, and dynamic topography and in situ Argo profiles. We also compute an ensemble mean and use the corresponding spread to estimate mixed-layer depth uncertainties and to identify the more reliable products. The analysis points out the advantage of synergistic approaches that include in input the sea surface salinity observations obtained through a multivariate optimal interpolation. Corresponding data allow to assess mixed-layer depth seasonal and interannual variability. Specifically, the maximum correlations between mixed-layer anomalies and the Southern Annular Mode are found at different time lags, related to distinct summer/winter responses in the Antarctic Intermediate Water and Sub-Antarctic Mode Waters main formation areas.

Cycling Empirical Antibiotic Therapy in Hospitals: Meta-Analysis and Models

PubMed Central

Abel, Sören; Viechtbauer, Wolfgang; Bonhoeffer, Sebastian

2014-01-01

The rise of resistance together with the shortage of new broad-spectrum antibiotics underlines the urgency of optimizing the use of available drugs to minimize disease burden. Theoretical studies suggest that coordinating empirical usage of antibiotics in a hospital ward can contain the spread of resistance. However, theoretical and clinical studies came to different conclusions regarding the usefulness of rotating first-line therapy (cycling). Here, we performed a quantitative pathogen-specific meta-analysis of clinical studies comparing cycling to standard practice. We searched PubMed and Google Scholar and identified 46 clinical studies addressing the effect of cycling on nosocomial infections, of which 11 met our selection criteria. We employed a method for multivariate meta-analysis using incidence rates as endpoints and find that cycling reduced the incidence rate/1000 patient days of both total infections by 4.95 [9.43–0.48] and resistant infections by 7.2 [14.00–0.44]. This positive effect was observed in most pathogens despite a large variance between individual species. Our findings remain robust in uni- and multivariate metaregressions. We used theoretical models that reflect various infections and hospital settings to compare cycling to random assignment to different drugs (mixing). We make the realistic assumption that therapy is changed when first line treatment is ineffective, which we call “adjustable cycling/mixing”. In concordance with earlier theoretical studies, we find that in strict regimens, cycling is detrimental. However, in adjustable regimens single resistance is suppressed and cycling is successful in most settings. Both a meta-regression and our theoretical model indicate that “adjustable cycling” is especially useful to suppress emergence of multiple resistance. While our model predicts that cycling periods of one month perform well, we expect that too long cycling periods are detrimental. Our results suggest that “adjustable cycling” suppresses multiple resistance and warrants further investigations that allow comparing various diseases and hospital settings. PMID:24968123

Energy Drink and Alcohol mixed Energy Drink use among high school adolescents: Association with risk taking behavior, social characteristics.

PubMed

Scalese, Marco; Denoth, Francesca; Siciliano, Valeria; Bastiani, Luca; Cotichini, Rodolfo; Cutilli, Arianna; Molinaro, Sabrina

2017-09-01

The aims of the study were to: a) examine the prevalence of energy drink (ED) and alcohol mixed with energy drink (AmED) consumption; b) investigate the relationships between ED and AmED with alcohol, binge drinking and drugs accounting for at risk behaviors among a representative sample of Italian adolescents. A representative sample of 30,588 Italian high school students, aged 15-19years, was studied. Binary and multivariate logistic regression analyses were performed to determine the independent association of the potential predictors' characteristics with the ED and AmED drinking during the last year. Respectively 41.4% and 23.2% of respondents reported drinking EDs and AmEDs in the last year. Multivariate analysis revealed that consumption of EDs and AmEDs during the last year were significantly associated with daily smoking, binge drinking, use of cannabis and other psychotropic drugs. Among life habits and risky behaviors the following were positively associated: going out with friends for fun, participating in sports, experiencing physical fights/accidents or injury, engaging in sexual intercourse without protection and being involved in accidents while driving. This study demonstrates the popularity of ED and AmED consumption among the Italian school population aged 15-19years old: 4 out of 10 students consumed EDs in the last year and 2 out of 10 AmED. Multivariate analysis highlighted the association with illicit drug consumption and harming behaviors, confirming that consumption of EDs and AmEDs is a compelling issue especially during adolescence, as it can effect health as well as risk taking behaviors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging

PubMed Central

Naylor, Melissa G.; Cardenas, Valerie A.; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2015-01-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. PMID:23408378

Preliminary Multivariable Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored

Processes and subdivisions in diogenites, a multivariate statistical analysis

NASA Technical Reports Server (NTRS)

Harriott, T. A.; Hewins, R. H.

1984-01-01

Multivariate statistical techniques used on diogenite orthopyroxene analyses show the relationships that occur within diogenites and the two orthopyroxenite components (class I and II) in the polymict diogenite Garland. Cluster analysis shows that only Peckelsheim is similar to Garland class I (Fe-rich) and the other diogenites resemble Garland class II. The unique diogenite Y 75032 may be related to type I by fractionation. Factor analysis confirms the subdivision and shows that Fe does not correlate with the weakly incompatible elements across the entire pyroxene composition range, indicating that igneous fractionation is not the process controlling total diogenite composition variation. The occurrence of two groups of diogenites is interpreted as the result of sampling or mixing of two main sequences of orthopyroxene cumulates with slightly different compositions.

The Impact of Parent Care on Marital Quality and Well-Being in Adult Daughters and Sons

PubMed Central

2009-01-01

This study prospectively examined the long-term impact of providing parent care using data from a probability-based U.S. sample of adult daughters and sons who had varying parent care experiences over time (N = 716). Parent care × Gender × Time mixed multivariate analyses of covariance using marital quality and well-being indicators as outcomes showed that, on average, experienced caregivers reported less marital happiness, more marital role inequity, and greater hostility than recent adult child caregivers. Significant three-way interactions indicated that experienced and recent caregiving daughters, respectively, showed an increase over time in depressive symptomatology and long-term depression, whereas their male counterparts showed a decline over the same period. Findings are discussed in terms of gender differences in the relative applicability of the wear-and-tear versus adaptation models of caregiving outcomes. PMID:19359594

Pediatric Inflammatory Bowel Diseases: Should We Be Looking for Kidney Abnormalities?

PubMed

Lauritzen, Didde; Andreassen, Bente Utoft; Heegaard, Niels Henrik H; Klinge, Lone Gabriels; Walsted, Anne-Mette; Neland, Mette; Nielsen, Rasmus Gaardskær; Wittenhagen, Per

2018-04-26

Kidney disease has been reported in adults with inflammatory bowel disease (IBD) and is regarded an extraintestinal manifestation or more rarely a side effect of the medical treatment. In this cross-sectional study we describe the extent of kidney pathology in a cohort of 56 children with IBD. Blood and urine samples were analyzed for markers of kidney disease and ultrasonography was performed to evaluate pole-to-pole kidney length. We found that 25% of the patients had either previously reported kidney disease or ultrasonographic signs of chronic kidney disease. The median kidney size compared with normal children was significantly reduced. In a multivariate linear mixed model, small kidneys significantly correlated with the use of infliximab, whereas the use of enteral nutritional therapy was associated with larger kidneys. Children with IBD are at risk of chronic kidney disease, and the risk seems to be increased with the severity of the disease.

[Theory, method and application of method R on estimation of (co)variance components].

PubMed

Liu, Wen-Zhong

2004-07-01

Theory, method and application of Method R on estimation of (co)variance components were reviewed in order to make the method be reasonably used. Estimation requires R values,which are regressions of predicted random effects that are calculated using complete dataset on predicted random effects that are calculated using random subsets of the same data. By using multivariate iteration algorithm based on a transformation matrix,and combining with the preconditioned conjugate gradient to solve the mixed model equations, the computation efficiency of Method R is much improved. Method R is computationally inexpensive,and the sampling errors and approximate credible intervals of estimates can be obtained. Disadvantages of Method R include a larger sampling variance than other methods for the same data,and biased estimates in small datasets. As an alternative method, Method R can be used in larger datasets. It is necessary to study its theoretical properties and broaden its application range further.

Factors associated with the serological prevalence of Salmonella enterica in Greek finishing swineherds.

PubMed Central

Leontides, L. S.; Grafanakis, E.; Genigeorgis, C.

2003-01-01

Blood samples were taken from 50 finishing pigs at 90-105 kg in each of 59 randomly selected farrow-to-finish herds. The sera were tested for antibodies to Salmonella enterica by the Danish mix-ELISA. Samples with an optical density of > 10% were considered to be positive. Associations between the odds of seropositivity of pigs and possible risk factors were evaluated in multivariable logistic regression models. The results of the analysis indicated that pigs fed non-pelleted dry or wet ration had 11 (P = 0.0004) or 9 (P = 0.02) times, respectively, lower odds of seropositivity than those fed pelleted ration. The risk of seropositivity was 4 (P = 0.0006) times higher in pigs fed a combination of chlortetracycline, procaine penicillin and sulphamethazine during fattening than in those fed an approved growth promotor or a probiotic. PMID:12948357

DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)

EPA Science Inventory

Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...

Variations in hospitalization rates among nursing home residents: the role of discretionary hospitalizations.

PubMed

Carter, Mary W

2003-08-01

To examine variations in hospitalization rates among nursing home residents associated with discretionary hospitalization practices. Quarterly Medicaid case-mix reimbursement data from the state of Massachusetts served as the core data source for this study, which was linked with data from the Medicare Provider Analysis and Review file (MEDPAR) to specify hospitalization status, nursing facility attribute data from the state of Massachusetts to specify facility-level organizational and structural attributes, and data from the Area Resource File (ARF) to specify area market-level attributes. Data spans three years (1991-1993) to produce a longitudinal analytical file containing 72,319 person-quarter-level observations. Two-step, multivariate logistic regression models were estimated for highly discretionary hospitalizations versus those containing less discretion, and low discretionary hospitalizations versus those containing greater amounts of physician discretion. Findings indicate that facility case-mix levels and area hospital bed supply levels contribute to variations in hospitalization rates among nursing home residents. Highly discretionary hospitalizations appear to be most sensitive to patient diagnoses best described as chronic, ambulatory care sensitive conditions. Findings suggest that defining hospitalizations simply in terms of whether an event occurs versus otherwise may obscure valuable information regarding the contribution of various risk factors to highly discretionary versus low discretionary hospitalization rates.

Multivariate modelling of endophenotypes associated with the metabolic syndrome in Chinese twins.

PubMed

Pang, Z; Zhang, D; Li, S; Duan, H; Hjelmborg, J; Kruse, T A; Kyvik, K O; Christensen, K; Tan, Q

2010-12-01

The common genetic and environmental effects on endophenotypes related to the metabolic syndrome have been investigated using bivariate and multivariate twin models. This paper extends the pairwise analysis approach by introducing independent and common pathway models to Chinese twin data. The aim was to explore the common genetic architecture in the development of these phenotypes in the Chinese population. Three multivariate models including the full saturated Cholesky decomposition model, the common factor independent pathway model and the common factor common pathway model were fitted to 695 pairs of Chinese twins representing six phenotypes including BMI, total cholesterol, total triacylglycerol, fasting glucose, HDL and LDL. Performances of the nested models were compared with that of the full Cholesky model. Cross-phenotype correlation coefficients gave clear indication of common genetic or environmental backgrounds in the phenotypes. Decomposition of phenotypic correlation by the Cholesky model revealed that the observed phenotypic correlation among lipid phenotypes had genetic and unique environmental backgrounds. Both pathway models suggest a common genetic architecture for lipid phenotypes, which is distinct from that of the non-lipid phenotypes. The declining performance with model restriction indicates biological heterogeneity in development among some of these phenotypes. Our multivariate analyses revealed common genetic and environmental backgrounds for the studied lipid phenotypes in Chinese twins. Model performance showed that physiologically distinct endophenotypes may follow different genetic regulations.

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data.

PubMed

Vial, Flavie; Wei, Wei; Held, Leonhard

2016-12-20

In an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues' two-component model, to two multivariate animal health datasets from Switzerland. In our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities. Stochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

Carbon dioxide emissions, GDP, energy use, and population growth: a multivariate and causality analysis for Ghana, 1971-2013.

PubMed

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2016-07-01

In this study, the relationship between carbon dioxide emissions, GDP, energy use, and population growth in Ghana was investigated from 1971 to 2013 by comparing the vector error correction model (VECM) and the autoregressive distributed lag (ARDL). Prior to testing for Granger causality based on VECM, the study tested for unit roots, Johansen's multivariate co-integration and performed a variance decomposition analysis using Cholesky's technique. Evidence from the variance decomposition shows that 21 % of future shocks in carbon dioxide emissions are due to fluctuations in energy use, 8 % of future shocks are due to fluctuations in GDP, and 6 % of future shocks are due to fluctuations in population. There was evidence of bidirectional causality running from energy use to GDP and a unidirectional causality running from carbon dioxide emissions to energy use, carbon dioxide emissions to GDP, carbon dioxide emissions to population, and population to energy use. Evidence from the long-run elasticities shows that a 1 % increase in population in Ghana will increase carbon dioxide emissions by 1.72 %. There was evidence of short-run equilibrium relationship running from energy use to carbon dioxide emissions and GDP to carbon dioxide emissions. As a policy implication, the addition of renewable energy and clean energy technologies into Ghana's energy mix can help mitigate climate change and its impact in the future.

Chemometric and multivariate statistical analysis of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulfides.

PubMed

Kalegowda, Yogesh; Harmer, Sarah L

2012-03-20

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of mineral samples are complex, comprised of large mass ranges and many peaks. Consequently, characterization and classification analysis of these systems is challenging. In this study, different chemometric and statistical data evaluation methods, based on monolayer sensitive TOF-SIMS data, have been tested for the characterization and classification of copper-iron sulfide minerals (chalcopyrite, chalcocite, bornite, and pyrite) at different flotation pulp conditions (feed, conditioned feed, and Eh modified). The complex mass spectral data sets were analyzed using the following chemometric and statistical techniques: principal component analysis (PCA); principal component-discriminant functional analysis (PC-DFA); soft independent modeling of class analogy (SIMCA); and k-Nearest Neighbor (k-NN) classification. PCA was found to be an important first step in multivariate analysis, providing insight into both the relative grouping of samples and the elemental/molecular basis for those groupings. For samples exposed to oxidative conditions (at Eh ~430 mV), each technique (PCA, PC-DFA, SIMCA, and k-NN) was found to produce excellent classification. For samples at reductive conditions (at Eh ~ -200 mV SHE), k-NN and SIMCA produced the most accurate classification. Phase identification of particles that contain the same elements but a different crystal structure in a mixed multimetal mineral system has been achieved.

Power analysis to detect treatment effects in longitudinal clinical trials for Alzheimer's disease.

PubMed

Huang, Zhiyue; Muniz-Terrera, Graciela; Tom, Brian D M

2017-09-01

Assessing cognitive and functional changes at the early stage of Alzheimer's disease (AD) and detecting treatment effects in clinical trials for early AD are challenging. Under the assumption that transformed versions of the Mini-Mental State Examination, the Clinical Dementia Rating Scale-Sum of Boxes, and the Alzheimer's Disease Assessment Scale-Cognitive Subscale tests'/components' scores are from a multivariate linear mixed-effects model, we calculated the sample sizes required to detect treatment effects on the annual rates of change in these three components in clinical trials for participants with mild cognitive impairment. Our results suggest that a large number of participants would be required to detect a clinically meaningful treatment effect in a population with preclinical or prodromal Alzheimer's disease. We found that the transformed Mini-Mental State Examination is more sensitive for detecting treatment effects in early AD than the transformed Clinical Dementia Rating Scale-Sum of Boxes and Alzheimer's Disease Assessment Scale-Cognitive Subscale. The use of optimal weights to construct powerful test statistics or sensitive composite scores/endpoints can reduce the required sample sizes needed for clinical trials. Consideration of the multivariate/joint distribution of components' scores rather than the distribution of a single composite score when designing clinical trials can lead to an increase in power and reduced sample sizes for detecting treatment effects in clinical trials for early AD.

Similar survival of patients with multiple versus single primary melanomas based on Utah Surveillance, Epidemiology, and End Results data (1973-2011).

PubMed

Grossman, Douglas; Farnham, James M; Hyngstrom, John; Klapperich, Marki E; Secrest, Aaron M; Empey, Sarah; Bowen, Glen M; Wada, David; Andtbacka, Robert H I; Grossmann, Kenneth; Bowles, Tawnya L; Cannon-Albright, Lisa A

2018-03-01

Survival data are mixed comparing patients with multiple primary melanomas (MPM) to those with single primary melanomas (SPM). We compared MPM versus SPM patient survival using a matching method that avoids potential biases associated with other analytic approaches. Records of 14,138 individuals obtained from the Surveillance, Epidemiology, and End Results registry of all melanomas diagnosed or treated in Utah between 1973 and 2011 were reviewed. A single matched control patient was selected randomly from the SPM cohort for each MPM patient, with the restriction that they survived at least as long as the interval between the first and second diagnoses for the matched MPM patient. Survival curves (n = 887 for both MPM and SPM groups) without covariates showed a significant survival disadvantage for MPM patients (chi-squared 39.29, P < .001). However, a multivariate Cox proportional hazards model showed no significant survival difference (hazard ratio 1.07, P = .55). Restricting the multivariate analysis to invasive melanomas also showed no significant survival difference (hazard ratio 0.99, P = .96). Breslow depth, ulceration status, and specific cause of death were not available for all patients. Patients with MPM had similar survival times as patients with SPM. Copyright © 2018 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

PubMed

Dankers, Frank; Wijsman, Robin; Troost, Esther G C; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L

2017-05-07

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

NASA Astrophysics Data System (ADS)

Dankers, Frank; Wijsman, Robin; Troost, Esther G. C.; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L.

2017-05-01

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Multivariate meta-analysis: potential and promise.

PubMed

Jackson, Dan; Riley, Richard; White, Ian R

2011-09-10

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Multivariate meta-analysis: Potential and promise

PubMed Central

Jackson, Dan; Riley, Richard; White, Ian R

2011-01-01

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

Stress and Personal Resource as Predictors of the Adjustment of Parents to Autistic Children: A Multivariate Model

ERIC Educational Resources Information Center

Siman-Tov, Ayelet; Kaniel, Shlomo

2011-01-01

The research validates a multivariate model that predicts parental adjustment to coping successfully with an autistic child. The model comprises four elements: parental stress, parental resources, parental adjustment and the child's autism symptoms. 176 parents of children aged between 6 to 16 diagnosed with PDD answered several questionnaires…

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

Treesearch

Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome Chave

2014-01-01

We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...

The potential of kiwifruit puree as a clean label ingredient to stabilize high pressure pasteurized cloudy apple juice during storage.

PubMed

Yi, Junjie; Kebede, Biniam; Kristiani, Kristiani; Buvé, Carolien; Van Loey, Ann; Grauwet, Tara; Hendrickx, Marc

2018-07-30

In the fruit juice industry, high pressure (HP) processing has become a commercial success. However, enzymatic browning, cloud loss, and flavor changes during storage remain challenges. The aim of this study is to combine kiwifruit puree and HP pasteurization (600 MPa/3 min) to stabilize cloudy apple juice during storage at 4 °C. A wide range of targeted and untargeted quality characteristics was evaluated using a multivariate approach. Due to high ascorbic acid content and high viscosity, kiwifruit puree allowed to prevent enzymatic browning and phase separation of an apple-kiwifruit mixed juice. Besides, no clear changes in organic acids, viscosity, and particle size distribution were detected in mixed juice during storage. Sucrose of apple and mixed juices decreased with glucose and fructose increasing during storage. The volatile changes of both juices behaved similar, mainly esters being degraded. Sensory evaluation demonstrated consumer preferred the aroma of mixed juice compared to apple juice. Copyright © 2018 Elsevier Ltd. All rights reserved.

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis.

PubMed

Collins, Tony J; Ylanko, Jarkko; Geng, Fei; Andrews, David W

2015-11-01

A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose-response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds.

A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis

PubMed Central

Collins, Tony J.; Ylanko, Jarkko; Geng, Fei

2015-01-01

Abstract A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose–response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds. PMID:26422066

Application of multivariate analysis to investigate the trace element contamination in top soil of coal mining district in Jorong, South Kalimantan, Indonesia

NASA Astrophysics Data System (ADS)

Pujiwati, Arie; Nakamura, K.; Watanabe, N.; Komai, T.

2018-02-01

Multivariate analysis is applied to investigate geochemistry of several trace elements in top soils and their relation with the contamination source as the influence of coal mines in Jorong, South Kalimantan. Total concentration of Cd, V, Co, Ni, Cr, Zn, As, Pb, Sb, Cu and Ba was determined in 20 soil samples by the bulk analysis. Pearson correlation is applied to specify the linear correlation among the elements. Principal Component Analysis (PCA) and Cluster Analysis (CA) were applied to observe the classification of trace elements and contamination sources. The results suggest that contamination loading is contributed by Cr, Cu, Ni, Zn, As, and Pb. The elemental loading mostly affects the non-coal mining area, for instances the area near settlement and agricultural land use. Moreover, the contamination source is classified into the areas that are influenced by the coal mining activity, the agricultural types, and the river mixing zone. Multivariate analysis could elucidate the elemental loading and the contamination sources of trace elements in the vicinity of coal mine area.

How to engage occupational physicians in recruitment of research participants: a mixed-methods study of challenges and opportunities.

PubMed

Arends, Iris; Bültmann, Ute; Shaw, William S; van Rhenen, Willem; Roelen, Corné; Nielsen, Karina; van der Klink, Jac J L

2014-03-01

To investigate barriers and facilitators for research participant recruitment by occupational physicians (OPs). A mixed-methods approach was used. Focus groups and interviews were conducted with OPs to explore perceived barriers and facilitators for recruitment. Based on data of a cluster-randomised controlled trial (cluster-RCT), univariate and multivariate analyses were conducted to investigate associations between OPs' personal and work characteristics and the number of recruited participants for the cluster-RCT per OP. Perceived barriers and facilitators for recruitment were categorised into: study characteristics (e.g. concise inclusion criteria); study population characteristics; OP's attention; OP's workload; context (e.g. working at different locations); and OP's characteristics (e.g. motivated to help). Important facilitators were encouragement by colleagues and reminders by information technology tools. Multivariate analyses showed that the number of OPs within the clinical unit who recruited participants was positively associated with the number of recruited participants per OP [rate ratio of 1.43, 95 % confidence interval 1.24-1.64]. When mobilising OPs for participant recruitment, researchers need to engage entire clinical units rather than approach OPs on an individual basis. OPs consider regular communication, especially face-to-face contact and information technology tools serving as reminders, as helpful.

MixSIAR: advanced stable isotope mixing models in R

EPA Science Inventory

Background/Question/Methods The development of stable isotope mixing models has coincided with modeling products (e.g. IsoSource, MixSIR, SIAR), where methodological advances are published in parity with software packages. However, while mixing model theory has recently been ex...

Adjustment of automatic control systems of production facilities at coal processing plants using multivariant physico- mathematical models

NASA Astrophysics Data System (ADS)

Evtushenko, V. F.; Myshlyaev, L. P.; Makarov, G. V.; Ivushkin, K. A.; Burkova, E. V.

2016-10-01

The structure of multi-variant physical and mathematical models of control system is offered as well as its application for adjustment of automatic control system (ACS) of production facilities on the example of coal processing plant.

A simplified parsimonious higher order multivariate Markov chain model with new convergence condition

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. (TPHOMMCM-NCC). Moreover, estimation method of the parameters in TPHOMMCM-NCC is give. Numerical experiments illustrate the effectiveness of TPHOMMCM-NCC.

Various forms of indexing HDMR for modelling multivariate classification problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aksu, Çağrı; Tunga, M. Alper

2014-12-10

The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled.more » In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.« less

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Insights on multivariate updates of physical and biogeochemical ocean variables using an Ensemble Kalman Filter and an idealized model of upwelling

NASA Astrophysics Data System (ADS)

Yu, Liuqian; Fennel, Katja; Bertino, Laurent; Gharamti, Mohamad El; Thompson, Keith R.

2018-06-01

Effective data assimilation methods for incorporating observations into marine biogeochemical models are required to improve hindcasts, nowcasts and forecasts of the ocean's biogeochemical state. Recent assimilation efforts have shown that updating model physics alone can degrade biogeochemical fields while only updating biogeochemical variables may not improve a model's predictive skill when the physical fields are inaccurate. Here we systematically investigate whether multivariate updates of physical and biogeochemical model states are superior to only updating either physical or biogeochemical variables. We conducted a series of twin experiments in an idealized ocean channel that experiences wind-driven upwelling. The forecast model was forced with biased wind stress and perturbed biogeochemical model parameters compared to the model run representing the "truth". Taking advantage of the multivariate nature of the deterministic Ensemble Kalman Filter (DEnKF), we assimilated different combinations of synthetic physical (sea surface height, sea surface temperature and temperature profiles) and biogeochemical (surface chlorophyll and nitrate profiles) observations. We show that when biogeochemical and physical properties are highly correlated (e.g., thermocline and nutricline), multivariate updates of both are essential for improving model skill and can be accomplished by assimilating either physical (e.g., temperature profiles) or biogeochemical (e.g., nutrient profiles) observations. In our idealized domain, the improvement is largely due to a better representation of nutrient upwelling, which results in a more accurate nutrient input into the euphotic zone. In contrast, assimilating surface chlorophyll improves the model state only slightly, because surface chlorophyll contains little information about the vertical density structure. We also show that a degradation of the correlation between observed subsurface temperature and nutrient fields, which has been an issue in several previous assimilation studies, can be reduced by multivariate updates of physical and biogeochemical fields.

Factors associated with degree of atopy in Latino children in a nationwide pediatric sample: The GALA II Study

PubMed Central

Kumar, Rajesh; Nguyen, Elizabeth A; Roth, Lindsey A; Oh, Sam S; Gignoux, Christopher R.; Huntsman, Scott; Eng, Celeste; Moreno-Estrada, Andres; Sandoval, Karla; Peñaloza-Espinosa, Rosenda; López-López, Marisol; Avila, Pedro C.; Farber, Harold J.; Tcheurekdjian, Haig; Rodriguez-Cintron, William; Rodriguez-Santana, Jose R; Serebrisky, Denise; Thyne, Shannon M.; Williams, L. Keoki; Winkler, Cheryl; Bustamante, Carlos D.; Pérez-Stable, Eliseo J.; Borrell, Luisa N.; Burchard, Esteban G

2013-01-01

Background Atopy varies by ethnicity even within Latino groups. This variation may be due to environmental, socio-cultural or genetic factors. Objective To examine risk factors for atopy within a nationwide study of U.S. Latino children with and without asthma. Methods Aeroallergen skin test repsonse was analyzed in 1830 US latino subjects. Key determinants of atopy included: country / region of origin, generation in the U.S., acculturation, genetic ancestry and site to which individuals migrated. Serial multivariate zero inflated negative binomial regressions, stratified by asthma status, examined the association of each key determinant variable with the number of positive skin tests. In addition, the independent effect of each key variable was determined by including all key variables in the final models. Results In baseline analyses, African ancestry was associated with 3 times as many positive skin tests in participants with asthma (95% CI:1.62–5.57) and 3.26 times as many positive skin tests in control participants (95% CI: 1.02–10.39). Generation and recruitment site were also associated with atopy in crude models. In final models adjusted for key variables, Puerto Rican [exp(β) (95%CI): 1.31(1.02–1.69)] and mixed ethnicity [exp(β) (95%CI):1.27(1.03–1.56)] asthmatics had a greater probability of positive skin tests compared to Mexican asthmatics. Ancestry associations were abrogated by recruitment site, but not region of origin. Conclusions Puerto Rican ethnicity and mixed origin were associated with degree of atopy within U.S. Latino children with asthma. African ancestry was not associated with degree of atopy after adjusting for recruitment site. Local environment variation, represented by site, was associated with degree of sensitization. PMID:23684070

Idiopathic Pulmonary Fibrosis: Data-driven Textural Analysis of Extent of Fibrosis at Baseline and 15-Month Follow-up.

PubMed

Humphries, Stephen M; Yagihashi, Kunihiro; Huckleberry, Jason; Rho, Byung-Hak; Schroeder, Joyce D; Strand, Matthew; Schwarz, Marvin I; Flaherty, Kevin R; Kazerooni, Ella A; van Beek, Edwin J R; Lynch, David A

2017-10-01

Purpose To evaluate associations between pulmonary function and both quantitative analysis and visual assessment of thin-section computed tomography (CT) images at baseline and at 15-month follow-up in subjects with idiopathic pulmonary fibrosis (IPF). Materials and Methods This retrospective analysis of preexisting anonymized data, collected prospectively between 2007 and 2013 in a HIPAA-compliant study, was exempt from additional institutional review board approval. The extent of lung fibrosis at baseline inspiratory chest CT in 280 subjects enrolled in the IPF Network was evaluated. Visual analysis was performed by using a semiquantitative scoring system. Computer-based quantitative analysis included CT histogram-based measurements and a data-driven textural analysis (DTA). Follow-up CT images in 72 of these subjects were also analyzed. Univariate comparisons were performed by using Spearman rank correlation. Multivariate and longitudinal analyses were performed by using a linear mixed model approach, in which models were compared by using asymptotic χ 2 tests. Results At baseline, all CT-derived measures showed moderate significant correlation (P < .001) with pulmonary function. At follow-up CT, changes in DTA scores showed significant correlation with changes in both forced vital capacity percentage predicted (ρ = -0.41, P < .001) and diffusing capacity for carbon monoxide percentage predicted (ρ = -0.40, P < .001). Asymptotic χ 2 tests showed that inclusion of DTA score significantly improved fit of both baseline and longitudinal linear mixed models in the prediction of pulmonary function (P < .001 for both). Conclusion When compared with semiquantitative visual assessment and CT histogram-based measurements, DTA score provides additional information that can be used to predict diminished function. Automatic quantification of lung fibrosis at CT yields an index of severity that correlates with visual assessment and functional change in subjects with IPF. © RSNA, 2017.

Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

PubMed Central

2013-01-01

Background Recently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls. Methods In this study, we propose multivariate GMDR, an extension of GMDR for multivariate phenotypes. Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions. We construct generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models. Using the score vectors from GEE we discriminate high-risk from low-risk groups. We applied the multivariate GMDR method to the blood pressure data of the 7,546 subjects from the Korean Association Resource study: systolic blood pressure (SBP) and diastolic blood pressure (DBP). We compare the results of multivariate GMDR for SBP and DBP to the results from separate univariate GMDR for SBP and DBP, respectively. We also applied the multivariate GMDR method to the repeatedly measured hypertension status from 5,466 subjects and compared its result with those of univariate GMDR at each time point. Results Results from the univariate GMDR and multivariate GMDR in two-locus model with both blood pressures and hypertension phenotypes indicate best combinations of SNPs whose interaction has significant association with risk for high blood pressures or hypertension. Although the test balanced accuracy (BA) of multivariate analysis was not always greater than that of univariate analysis, the multivariate BAs were more stable with smaller standard deviations. Conclusions In this study, we have developed multivariate GMDR method using GEE approach. It is useful to use multivariate GMDR with correlated multiple phenotypes of interests. PMID:24565370

Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices

PubMed Central

Meyer, Karin; Kirkpatrick, Mark

2005-01-01

Principal component analysis is a widely used 'dimension reduction' technique, albeit generally at a phenotypic level. It is shown that we can estimate genetic principal components directly through a simple reparameterisation of the usual linear, mixed model. This is applicable to any analysis fitting multiple, correlated genetic effects, whether effects for individual traits or sets of random regression coefficients to model trajectories. Depending on the magnitude of genetic correlation, a subset of the principal component generally suffices to capture the bulk of genetic variation. Corresponding estimates of genetic covariance matrices are more parsimonious, have reduced rank and are smoothed, with the number of parameters required to model the dispersion structure reduced from k(k + 1)/2 to m(2k - m + 1)/2 for k effects and m principal components. Estimation of these parameters, the largest eigenvalues and pertaining eigenvectors of the genetic covariance matrix, via restricted maximum likelihood using derivatives of the likelihood, is described. It is shown that reduced rank estimation can reduce computational requirements of multivariate analyses substantially. An application to the analysis of eight traits recorded via live ultrasound scanning of beef cattle is given. PMID:15588566

A multivariate quadrature based moment method for LES based modeling of supersonic combustion

NASA Astrophysics Data System (ADS)

Donde, Pratik; Koo, Heeseok; Raman, Venkat

2012-07-01

The transported probability density function (PDF) approach is a powerful technique for large eddy simulation (LES) based modeling of scramjet combustors. In this approach, a high-dimensional transport equation for the joint composition-enthalpy PDF needs to be solved. Quadrature based approaches provide deterministic Eulerian methods for solving the joint-PDF transport equation. In this work, it is first demonstrated that the numerical errors associated with LES require special care in the development of PDF solution algorithms. The direct quadrature method of moments (DQMOM) is one quadrature-based approach developed for supersonic combustion modeling. This approach is shown to generate inconsistent evolution of the scalar moments. Further, gradient-based source terms that appear in the DQMOM transport equations are severely underpredicted in LES leading to artificial mixing of fuel and oxidizer. To overcome these numerical issues, a semi-discrete quadrature method of moments (SeQMOM) is formulated. The performance of the new technique is compared with the DQMOM approach in canonical flow configurations as well as a three-dimensional supersonic cavity stabilized flame configuration. The SeQMOM approach is shown to predict subfilter statistics accurately compared to the DQMOM approach.

Stability of binary and ternary model oil-field particle suspensions: a multivariate analysis approach.

PubMed

Dudásová, Dorota; Rune Flåten, Geir; Sjöblom, Johan; Øye, Gisle

2009-09-15

The transmission profiles of one- to three-component particle suspension mixtures were analyzed by multivariate methods such as principal component analysis (PCA) and partial least-squares regression (PLS). The particles mimic the solids present in oil-field-produced water. Kaolin and silica represent solids of reservoir origin, whereas FeS is the product of bacterial metabolic activities, and Fe(3)O(4) corrosion product (e.g., from pipelines). All particles were coated with crude oil surface active components to imitate particles in real systems. The effects of different variables (concentration, temperature, and coating) on the suspension stability were studied with Turbiscan LAb(Expert). The transmission profiles over 75 min represent the overall water quality, while the transmission during the first 15.5 min gives information for suspension behavior during a representative time period for the hold time in the separator. The behavior of the mixed particle suspensions was compared to that of the single particle suspensions and models describing the systems were built. The findings are summarized as follows: silica seems to dominate the mixture properties in the binary suspensions toward enhanced separation. For 75 min, temperature and concentration are the most significant, while for 15.5 min, concentration is the only significant variable. Models for prediction of transmission spectra from run parameters as well as particle type from transmission profiles (inverse calibration) give a reasonable description of the relationships. In ternary particle mixtures, silica is not dominant and for 75 min, the significant variables for mixture (temperature and coating) are more similar to single kaolin and FeS/Fe(3)O(4). On the other hand, for 15.5 min, the coating is the most significant and this is similar to one for silica (at 15.5 min). The model for prediction of transmission spectra from run parameters gives good estimates of the transmission profiles. Although the model for prediction of particle type from transmission parameters is able to predict some particles, further improvement is required before all particles are consistently correctly classified. Cross-validation was done for both models and estimation errors are reported.

Estimation and model selection of semiparametric multivariate survival functions under general censorship.

PubMed

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2010-07-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root- n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.

Estimation and model selection of semiparametric multivariate survival functions under general censorship

PubMed Central

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2013-01-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided. PMID:24790286

Usual Dietary Intakes: SAS Macros for Fitting Multivariate Measurement Error Models & Estimating Multivariate Usual Intake Distributions

Cancer.gov

The following SAS macros can be used to create a multivariate usual intake distribution for multiple dietary components that are consumed nearly every day or episodically. A SAS macro for performing balanced repeated replication (BRR) variance estimation is also included.

Dual-mixed HIV-1 coreceptor tropism and HIV-associated neurocognitive deficits.

PubMed

Morris, Sheldon R; Woods, Steven Paul; Deutsch, Reena; Little, Susan J; Wagner, Gabriel; Morgan, Erin E; Heaton, Robert K; Letendre, Scott L; Grant, Igor; Smith, Davey M

2013-10-01

HIV coreceptor usage of CXCR4 (X4) is associated with decreased CD4+ T-cell counts and accelerated disease progression, but the role of X4 tropism in HIV-associated neurocognitive disorders (HAND) has not previously been described. This longitudinal study evaluated data on 197 visits from 72 recently HIV-infected persons who had undergone up to four sequential neurocognitive assessments over a median of 160 days (IQR, 138–192). Phenotypic tropism testing (Trofile ES, Monogram, Biosciences) was performed on stored blood samples. Multivariable mixed model repeated measures regression was used to determine the association between HAND and dual-mixed (DM) viral tropism, estimated duration of infection (EDI), HIV RNA, CD4 count, and problematic methamphetamine use. Six subjects (8.3 %) had DM at their first neurocognitive assessment and four converted to DM in subsequent sampling (for total of 10 DM) at a median EDI of 10.1 months (IQR, 7.2–12.2). There were 44 (61.1 %) subjects who demonstrated HAND on at least one study visit. HAND was associated with DM tropism (odds ratio, 4.4; 95 % CI, 0.9–20.5) and shorter EDI (odds ratio 1.1 per month earlier; 95 % CI, 1.0–1.2). This study found that recency of HIV-1 infection and the development of DM tropism may be associated with HAND in the relatively early stage of infection. Together, these data suggest that viral interaction with cellular receptors may play an important role in the early manifestation of HAND.

Sildenafil (Viagra) and club drug use in gay and bisexual men: the role of drug combinations and context.

PubMed

Halkitis, Perry N; Green, Kelly A

2007-06-01

Data ascertained in a study of club drug use among 450 gay and bisexual men indicate that at least one class of PDE-5 (phosphodiesterase type 5 inhibitor, sildenafil [Viagra]) is used frequently in combination with club drugs such as methamphetamine, MDMA (3,4 methylenedioxymethamphetamine [ecstasy]), ketamine, cocaine, and GHB (gamma hydroxy butyrate). Patterns of sildenafil use in combination with each of the club drugs differ among key demographics including race and age. Multivariate models, controlling for demographic factors, suggest that contextual factors are key to understanding why men mix sildenafil with club drugs, although age may still be an important issue to consider. Of particular importance is the fact that use of club drugs in combination with sildenafil is strongly associated with circuit and sex parties, where a centerpiece of these environments focuses on sexual exchange. These models imply interplay between person-level and contextual factors in explaining drug use patterns and further indicate that interventions aimed at addressing illicit substance use must carefully consider the role of environmental factors in explaining behavior.

Critical elements on fitting the Bayesian multivariate Poisson Lognormal model

NASA Astrophysics Data System (ADS)

Zamzuri, Zamira Hasanah binti

2015-10-01

Motivated by a problem on fitting multivariate models to traffic accident data, a detailed discussion of the Multivariate Poisson Lognormal (MPL) model is presented. This paper reveals three critical elements on fitting the MPL model: the setting of initial estimates, hyperparameters and tuning parameters. These issues have not been highlighted in the literature. Based on simulation studies conducted, we have shown that to use the Univariate Poisson Model (UPM) estimates as starting values, at least 20,000 iterations are needed to obtain reliable final estimates. We also illustrated the sensitivity of the specific hyperparameter, which if it is not given extra attention, may affect the final estimates. The last issue is regarding the tuning parameters where they depend on the acceptance rate. Finally, a heuristic algorithm to fit the MPL model is presented. This acts as a guide to ensure that the model works satisfactorily given any data set.

Multivariate Bayesian modeling of known and unknown causes of events--an application to biosurveillance.

PubMed

Shen, Yanna; Cooper, Gregory F

2012-09-01

This paper investigates Bayesian modeling of known and unknown causes of events in the context of disease-outbreak detection. We introduce a multivariate Bayesian approach that models multiple evidential features of every person in the population. This approach models and detects (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities. We report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A contribution of this paper is that it introduces a multivariate Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has general applicability in domains where the space of known causes is incomplete. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

FACTOR ANALYTIC MODELS OF CLUSTERED MULTIVARIATE DATA WITH INFORMATIVE CENSORING

EPA Science Inventory

This paper describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censorin...

Using Qualitative Metasummary to Synthesize Qualitative and Quantitative Descriptive Findings

PubMed Central

Sandelowski, Margarete; Barroso, Julie; Voils, Corrine I.

2008-01-01

The new imperative in the health disciplines to be more methodologically inclusive has generated a growing interest in mixed research synthesis, or the integration of qualitative and quantitative research findings. Qualitative metasummary is a quantitatively oriented aggregation of qualitative findings originally developed to accommodate the distinctive features of qualitative surveys. Yet these findings are similar in form and mode of production to the descriptive findings researchers often present in addition to the results of bivariate and multivariable analyses. Qualitative metasummary, which includes the extraction, grouping, and formatting of findings, and the calculation of frequency and intensity effect sizes, can be used to produce mixed research syntheses and to conduct a posteriori analyses of the relationship between reports and findings. PMID:17243111

Exposure to lithium through drinking water and calcium homeostasis during pregnancy: A longitudinal study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harari, Florencia; Åkesson, Agneta; Casimiro, Esperanza

There is increasing evidence of adverse health effects due to elevated lithium exposure through drinking water but the impact on calcium homeostasis is unknown. This study aimed at elucidating if lithium exposure through drinking water during pregnancy may impair the maternal calcium homeostasis. In a population-based mother-child cohort in the Argentinean Andes (n=178), with elevated lithium concentrations in the drinking water (5–1660 μg/L), blood lithium concentrations (correlating significantly with lithium in water, urine and plasma) were measured repeatedly during pregnancy by inductively coupled plasma mass spectrometry and used as exposure biomarker. Markers of calcium homeostasis included: plasma 25-hydroxyvitamin D{sub 3},more » serum parathyroid hormone (PTH), and calcium, phosphorus and magnesium concentrations in serum and urine. The median maternal blood lithium concentration was 25 μg/L (range 1.9–145). In multivariable-adjusted mixed-effects linear regression models, blood lithium was inversely associated with 25-hydroxyvitamin D{sub 3} (−6.1 nmol/L [95%CI −9.5; −2.6] for a 25 μg/L increment in blood lithium). The estimate increased markedly with increasing percentiles of 25-hydroxyvitamin D{sub 3}. In multivariable-adjusted mixed-effects logistic regression models, the odds ratio of having 25-hydroxyvitamin D3<30 nmol/L (19% of the women) was 4.6 (95%CI 1.1; 19.3) for a 25 μg/L increment in blood lithium. Blood lithium was also positively associated with serum magnesium, but not with serum calcium and PTH, and inversely associated with urinary calcium and magnesium. In conclusion, our study suggests that lithium exposure through drinking water during pregnancy may impair the calcium homeostasis, particularly vitamin D. The results reinforce the need for better control of lithium in drinking water, including bottled water. - Highlights: • Elevated drinking water lithium (Li) concentrations are increasingly reported. • We studied a Li-exposed population-based mother-child cohort in northern Argentina. • Li exposure during pregnancy affected maternal calcium homeostasis. • Blood Li was consistently inversely associated with maternal plasma vitamin D{sub 3}. • Associations were independent of season of sampling and lifestyle.« less

An Examination of the Domain of Multivariable Functions Using the Pirie-Kieren Model

ERIC Educational Resources Information Center

Sengul, Sare; Yildiz, Sevda Goktepe

2016-01-01

The aim of this study is to employ the Pirie-Kieren model so as to examine the understandings relating to the domain of multivariable functions held by primary school mathematics preservice teachers. The data obtained was categorized according to Pirie-Kieren model and demonstrated visually in tables and bar charts. The study group consisted of…

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

A Multivariate Model for the Meta-Analysis of Study Level Survival Data at Multiple Times

ERIC Educational Resources Information Center

Jackson, Dan; Rollins, Katie; Coughlin, Patrick

2014-01-01

Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which we model simultaneously, and…

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

PubMed

Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

2015-01-01

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site. Copyright © 2014 Elsevier Ltd. All rights reserved.

The creation and evaluation of a model to simulate the probability of conception in seasonal-calving pasture-based dairy heifers.

PubMed

Fenlon, Caroline; O'Grady, Luke; Butler, Stephen; Doherty, Michael L; Dunnion, John

2017-01-01

Herd fertility in pasture-based dairy farms is a key driver of farm economics. Models for predicting nulliparous reproductive outcomes are rare, but age, genetics, weight, and BCS have been identified as factors influencing heifer conception. The aim of this study was to create a simulation model of heifer conception to service with thorough evaluation. Artificial Insemination service records from two research herds and ten commercial herds were provided to build and evaluate the models. All were managed as spring-calving pasture-based systems. The factors studied were related to age, genetics, and time of service. The data were split into training and testing sets and bootstrapping was used to train the models. Logistic regression (with and without random effects) and generalised additive modelling were selected as the model-building techniques. Two types of evaluation were used to test the predictive ability of the models: discrimination and calibration. Discrimination, which includes sensitivity, specificity, accuracy and ROC analysis, measures a model's ability to distinguish between positive and negative outcomes. Calibration measures the accuracy of the predicted probabilities with the Hosmer-Lemeshow goodness-of-fit, calibration plot and calibration error. After data cleaning and the removal of services with missing values, 1396 services remained to train the models and 597 were left for testing. Age, breed, genetic predicted transmitting ability for calving interval, month and year were significant in the multivariate models. The regression models also included an interaction between age and month. Year within herd was a random effect in the mixed regression model. Overall prediction accuracy was between 77.1% and 78.9%. All three models had very high sensitivity, but low specificity. The two regression models were very well-calibrated. The mean absolute calibration errors were all below 4%. Because the models were not adept at identifying unsuccessful services, they are not suggested for use in predicting the outcome of individual heifer services. Instead, they are useful for the comparison of services with different covariate values or as sub-models in whole-farm simulations. The mixed regression model was identified as the best model for prediction, as the random effects can be ignored and the other variables can be easily obtained or simulated.

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China.

PubMed

Pei, Ling-Ling; Li, Qin; Wang, Zheng-Xin

2018-03-08

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China's pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N )) model based on the nonlinear least square (NLS) method. The Gauss-Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N ) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N ) and the NLS-based TNGM (1, N ) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO₂ and dust, alongside GDP per capita in China during the period 1996-2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N ) model presents greater precision when forecasting WDPC, SO₂ emissions and dust emissions per capita, compared to the traditional GM (1, N ) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO₂ and dust reduce accordingly.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

PubMed

Naylor, Melissa G; Cardenas, Valerie A; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2014-03-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. Copyright © 2013 Wiley Periodicals, Inc.

A Multivariate Descriptive Model of Motivation for Orthodontic Treatment.

ERIC Educational Resources Information Center

Hackett, Paul M. W.; And Others

1993-01-01

Motivation for receiving orthodontic treatment was studied among 109 young adults, and a multivariate model of the process is proposed. The combination of smallest scale analysis and Partial Order Scalogram Analysis by base Coordinates (POSAC) illustrates an interesting methodology for health treatment studies and explores motivation for dental…

Mathematical Formulation of Multivariate Euclidean Models for Discrimination Methods.

ERIC Educational Resources Information Center

Mullen, Kenneth; Ennis, Daniel M.

1987-01-01

Multivariate models for the triangular and duo-trio methods are described, and theoretical methods are compared to a Monte Carlo simulation. Implications are discussed for a new theory of multidimensional scaling which challenges the traditional assumption that proximity measures and perceptual distances are monotonically related. (Author/GDC)

A Multivariate Model of Parent-Adolescent Relationship Variables in Early Adolescence

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2011-01-01

Given the importance of predicting outcomes for early adolescents, this study examines a multivariate model of parent-adolescent relationship variables, including parenting, family environment, and conflict. Participants, who completed measures assessing these variables, included 710 culturally diverse 11-14-year-olds who were attending a middle…

Classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.

2002-01-01

An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

An Efficient Alternative Mixed Randomized Response Procedure

ERIC Educational Resources Information Center

Singh, Housila P.; Tarray, Tanveer A.

2015-01-01

In this article, we have suggested a new modified mixed randomized response (RR) model and studied its properties. It is shown that the proposed mixed RR model is always more efficient than the Kim and Warde's mixed RR model. The proposed mixed RR model has also been extended to stratified sampling. Numerical illustrations and graphical…

Impact of social inequalities at birth on the longevity of children born 1914-1916: A cohort study.

PubMed

Todd, Nicolas; Le Fur, Sophie; Bougnères, Pierre; Valleron, Alain-Jacques

2017-01-01

Testing whether familial socioeconomic status (SES) in childhood is a predictor of mortality has rarely been done on historical cohorts. The birth certificates of 4,805 individuals born 1914-1916 in 16 districts of the Paris region were retrieved. The handwritten information provided the occupation of parents, the legitimacy status, life events (e.g. marriage, divorce), and the precise date of death when after 1945 (i.e. age 31 years (y) in the cohort). We used the median age at death (MAD) as a global measure of mortality, then studied separately survival to and after 31 y. Multivariate Imputation by Chained Equations (MICE), Generalized Additive Models (GAMs) and mixed effect Cox models were used. MAD showed large variations according to paternal occupation. The lowest MAD in both sexes was that of workers' children: it was 56.3 y (95% CI: [48.6-62.7]) in men and 67.4 y (95% CI: [60.8-72.7]) in women, respectively (95% CI: 13.4 y [5.7-21.3]) and 12.3 y (95% CI: [4.0-19.2]) below the highest MAD attained. MAD experienced by illegitimate children was 18.9 y (95% CI: [13.3-32.3]) shorter than of legitimate children. The multivariate analysis revealed that in both sexes survival to age 31 y was predicted independently by legitimacy and paternal occupation. Paternal occupation was found significantly associated with mortality after age 31 y in females only: accordingly difference in life expectancy at age 31 y was 4.4 y (95% CI: [1.2-7.6]) between upper class and workers' daughters. Paternal occupation and legitimacy status were strong predictors of offspring longevity in this one-century historical cohort born during World War One.

High-intensity cannabis use associated with lower plasma human immunodeficiency virus-1 RNA viral load among recently infected people who use injection drugs.

PubMed

Milloy, M-J; Marshall, Brandon; Kerr, Thomas; Richardson, Lindsey; Hogg, Robert; Guillemi, Silvia; Montaner, Julio S G; Wood, Evan

2015-03-01

Cannabis use is common among people who are living with human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS). While there is growing pre-clinical evidence of the immunomodulatory and anti-viral effects of cannabinoids, their possible effects on HIV disease parameters in humans are largely unknown. Thus, we sought to investigate the possible effects of cannabis use on plasma HIV-1 RNA viral loads (pVLs) among recently seroconverted illicit drug users. We used data from two linked longitudinal observational cohorts of people who use injection drugs. Using multivariable linear mixed-effects modelling, we analysed the relationship between pVL and high-intensity cannabis use among participants who seroconverted following recruitment. Between May 1996 and March 2012, 88 individuals seroconverted after recruitment and were included in these analyses. Median pVL in the first 365 days among all seroconverters was 4.66 log10 c mL(-1) . In a multivariable model, at least daily cannabis use was associated with 0.51 log10 c mL(-1) lower pVL (β = -0.51, standard error = 0.170, P value = 0.003). Consistent with the findings from recent in vitro and in vivo studies, including one conducted among lentiviral-infected primates, we observed a strong association between cannabis use and lower pVL following seroconversion among illicit drug-using participants. Our findings support the further investigation of the immunomodulatory or antiviral effects of cannabinoids among individuals living with HIV/AIDS. © 2014 Australasian Professional Society on Alcohol and other Drugs.

Prospective Analysis of Primary Breast Augmentation on Body Image Using the BREAST-Q: Results from a Nationwide Study

PubMed Central

Pusic, Andrea; Murphy, Diane K.

2016-01-01

Background: The Breast Implant Follow-up Study is a large, ongoing observational study of women who received Natrelle round silicone-filled or saline-filled breast implants. This analysis describes patient-reported outcomes in the cohort who underwent breast augmentation. Methods: Subjects prospectively completed two validated scales of the BREAST-Q (satisfaction with breasts and psychosocial well-being) preoperatively and at 1 and 4 years postoperatively. Effect size and z tests were used to compare differences between preoperative versus postoperative scores; multivariate mixed models were used to compare differences in scores between silicone-filled and saline-filled implants. Results: Of 17,899 subjects completing the BREAST-Q preoperatively, 14,514 (81.1 percent) completed the postoperative questionnaire (12,726 received silicone-filled implants and 1788 received saline-filled implants). Overall, satisfaction with breasts and psychosocial well-being increased significantly at postoperative year 1 (p < 0.0001 for both), and the improvement was sustained at year 4 (p < 0.0001 for both). Large effect sizes were observed for satisfaction with breasts (2.0 at year 1; 1.8 at year 4) and psychosocial well-being (1.2 at year 1; 1.0 at year 4). In the multivariate model, silicone-filled implants were associated with significantly greater improvement compared with saline-filled implants for satisfaction with breasts and psychosocial well-being at year 1 (p < 0.0001 for both) and year 4 (p < 0.0001 and p < 0.0019, respectively). Conclusions: Breast implants are effective in improving women’s quality of life. The authors found significant and sustained improvements in satisfaction and psychosocial well-being in women undergoing breast augmentation with Natrelle silicone-filled or saline-filled implants. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV. PMID:27219264

Predictors of CD34+ cell mobilization and collection in adult men with germ cell tumors: implications for the salvage treatment strategy.

PubMed

Necchi, Andrea; Miceli, Rosalba; Pedrazzoli, Paolo; Giannatempo, Patrizia; Secondino, Simona; Di Nicola, Massimo; Farè, Elena; Raggi, Daniele; Magni, Michele; Matteucci, Paola; Longoni, Paolo; Milanesi, Marco; Paternò, Emanuela; Ravagnani, Fernando; Arienti, Flavio; Nicolai, Nicola; Salvioni, Roberto; Carlo-Stella, Carmelo; Gianni, Alessandro M

2014-06-01

High-dose chemotherapy with tandem or triple carboplatin and etoposide course is currently the first curative choice for relapsing GCT. The collection of an adequate amount of hematopoietic (CD34(+)) stem cells is a priority. We analyzed data of patients who underwent HDCT at 2 referral institutions. Chemotherapy followed by myeloid growth factors was applied in all cases. Uni- and multivariable models were used to evaluate the association between 2 prespecified variables and mobilization parameters. Analyses included only the first mobilizing course of chemotherapy and mobilization failures. A total of 116 consecutive patients underwent a mobilization attempt from December 1995 to November 2012. Mobilizing regimens included cyclophosphamide (CTX) 7 gr/m(2) (n = 39), cisplatin, etoposide, and ifosfamide (PEI) (n = 42), paclitaxel, cisplatin, and gemcitabine (TPG) (n = 11), and mixed regimens (n = 24). Thirty-seven percent were treated in first-line, 50% (n = 58) in second-line, 9.5% (n = 11) and 3.4% (n = 4) in third- and fourth-line settings, respectively. Six patients did not undergo HDCT because they were poor mobilizers, 2 in first- and second-line (1.9%), and 4 beyond the second-line (26.7%). In the multivariable model, third-line or later setting was associated with a lower CD34(+) cell peak/μL (P = .028) and a lower total CD34(+)/kg collected (P = .008). The latter was also influenced by the type of mobilizing regimen (P < .001). A decline in significant mobilization parameters was found, primarily depending on the pretreatment load. Results lend support to the role of CD34(+) cell mobilization in the therapeutic algorithm of relapsing GCT, for whom multiple HDCT courses are still an option, and potentially a cure. Copyright © 2014 Elsevier Inc. All rights reserved.

Comparison of Patient Outcomes in 3725 Overlapping vs 3633 Nonoverlapping Neurosurgical Procedures Using a Single Institution's Clinical and Administrative Database.

PubMed

Zygourakis, Corinna C; Keefe, Malla; Lee, Janelle; Barba, Julio; McDermott, Michael W; Mummaneni, Praveen V; Lawton, Michael T

2017-02-01

Overlapping surgery is a common practice to improve surgical efficiency, but there are limited data on its safety. To analyze the patient outcomes of overlapping vs nonoverlapping surgeries performed by multiple neurosurgeons. Retrospective review of 7358 neurosurgical procedures, 2012 to 2015, at an urban academic hospital. Collected variables: patient age, gender, insurance, American Society of Anesthesiologists score, severity of illness, mortality risk, admission type, transfer source, procedure type, surgery date, number of cosurgeons, presence of neurosurgery resident/fellow/another attending, and overlapping vs nonoverlapping surgery. Outcomes: procedure time, length of stay, estimated blood loss, discharge location, 30-day mortality, 30-day readmission, return to operating room, acute respiratory failure, and severe sepsis. Statistics: univariate, then multivariate mixed-effect models. Overlapping surgery patients (n = 3725) were younger and had lower American Society of Anesthesiologists scores, severity of illness, and mortality risk (P < .0001) than nonoverlapping surgery patients (n = 3633). Overlapping surgeries had longer procedure times (214 vs 172 min; P < .0001), but shorter length of stay (7.3 vs 7.9 d; P = .010) and lower estimated blood loss (312 vs 363 mL’s; P = .003). Overlapping surgery patients were more likely to be discharged home (73.6% vs 66.2%; P < .0001), and had lower mortality rates (1.3% vs 2.5%; P = .0005) and acute respiratory failure (1.8% vs 2.6%; P = .021). In multivariate models, there was no significant difference between overlapping and nonoverlapping surgeries for any patient outcomes, except for procedure duration, which was longer in overlapping surgery (estimate = 23.03; P < .001). When planned appropriately, overlapping surgery can be performed safely within the infrastructure at our academic institution. Copyright © 2017 by the Congress of Neurological Surgeons

Multiple modality biomarker prediction of cognitive impairment in prospectively followed de novo Parkinson disease

PubMed Central

Caspell-Garcia, Chelsea; Simuni, Tanya; Tosun-Turgut, Duygu; Wu, I-Wei; Zhang, Yu; Nalls, Mike; Singleton, Andrew; Shaw, Leslie A.; Kang, Ju-Hee; Trojanowski, John Q.; Siderowf, Andrew; Coffey, Christopher; Lasch, Shirley; Aarsland, Dag; Burn, David; Chahine, Lana M.; Espay, Alberto J.; Foster, Eric D.; Hawkins, Keith A.; Litvan, Irene; Richard, Irene; Weintraub, Daniel

2017-01-01

Objectives To assess the neurobiological substrate of initial cognitive decline in Parkinson’s disease (PD) to inform patient management, clinical trial design, and development of treatments. Methods We longitudinally assessed, up to 3 years, 423 newly diagnosed patients with idiopathic PD, untreated at baseline, from 33 international movement disorder centers. Study outcomes were four determinations of cognitive impairment or decline, and biomarker predictors were baseline dopamine transporter (DAT) single photon emission computed tomography (SPECT) scan, structural magnetic resonance imaging (MRI; volume and thickness), diffusion tensor imaging (mean diffusivity and fractional anisotropy), cerebrospinal fluid (CSF; amyloid beta [Aβ], tau and alpha synuclein), and 11 single nucleotide polymorphisms (SNPs) previously associated with PD cognition. Additionally, longitudinal structural MRI and DAT scan data were included. Univariate analyses were run initially, with false discovery rate = 0.2, to select biomarker variables for inclusion in multivariable longitudinal mixed-effect models. Results By year 3, cognitive impairment was diagnosed in 15–38% participants depending on the criteria applied. Biomarkers, some longitudinal, predicting cognitive impairment in multivariable models were: (1) dopamine deficiency (decreased caudate and putamen DAT availability); (2) diffuse, cortical decreased brain volume or thickness (frontal, temporal, parietal, and occipital lobe regions); (3) co-morbid Alzheimer’s disease Aβ amyloid pathology (lower CSF Aβ 1–42); and (4) genes (COMT val/val and BDNF val/val genotypes). Conclusions Cognitive impairment in PD increases in frequency 50–200% in the first several years of disease, and is independently predicted by biomarker changes related to nigrostriatal or cortical dopaminergic deficits, global atrophy due to possible widespread effects of neurodegenerative disease, co-morbid Alzheimer’s disease plaque pathology, and genetic factors. PMID:28520803

Hospital-level Variation in Utilization of Surgery for Clinical Stage I-II Pancreatic Adenocarcinoma.

PubMed

Swords, Douglas S; Mulvihill, Sean J; Skarda, David E; Finlayson, Samuel R G; Stoddard, Gregory J; Ott, Mark J; Firpo, Matthew A; Scaife, Courtney L

2017-07-11

To (1) evaluate rates of surgery for clinical stage I-II pancreatic ductal adenocarcinoma (PDAC), (2) identify predictors of not undergoing surgery, (3) quantify the degree to which patient- and hospital-level factors explain differences in hospital surgery rates, and (4) evaluate the association between adjusted hospital-specific surgery rates and overall survival (OS) of patients treated at different hospitals. Curative-intent surgery for potentially resectable PDAC is underutilized in the United States. Retrospective cohort study of patients ≤85 years with clinical stage I-II PDAC in the 2004 to 2014 National Cancer Database. Mixed effects multivariable models were used to characterize hospital-level variation across quintiles of hospital surgery rates. Multivariable Cox proportional hazards models were used to estimate the effect of adjusted hospital surgery rates on OS. Of 58,553 patients without contraindications or refusal of surgery, 63.8% underwent surgery, and the rate decreased from 2299/3528 (65.2%) in 2004 to 4412/7092 (62.2%) in 2014 (P < 0.001). Adjusted hospital rates of surgery varied 6-fold (11.4%-70.9%). Patients treated at hospitals with higher rates of surgery had better unadjusted OS (median OS 10.2, 13.3, 14.2, 16.5, and 18.4 months in quintiles 1-5, respectively, P < 0.001, log-rank). Treatment at hospitals in lower surgery rate quintiles 1-3 was independently associated with mortality [Hazard ratio (HR) 1.10 (1.01, 1.21), HR 1.08 (1.02, 1.15), and HR 1.09 (1.04, 1.14) for quintiles 1-3, respectively, compared with quintile 5] after adjusting for patient factors, hospital type, and hospital volume. Quality improvement efforts are needed to help hospitals with low rates of surgery ensure that their patients have access to appropriate surgery.

A Longitudinal Analysis of Daily Pill Burden and Likelihood of Optimal Adherence to Antiretroviral Therapy Among People Living With HIV Who Use Drugs.

PubMed

Mohd Salleh, Nur Afiqah; Richardson, Lindsey; Kerr, Thomas; Shoveller, Jean; Montaner, Julio; Kamarulzaman, Adeeba; Milloy, M-J

2018-03-07

Among people living with HIV (PLWH), high levels of adherence to prescribed antiretroviral therapy (ART) is required to achieve optimal treatment outcomes. However, little is known about the effects of daily pill burden on adherence amongst PLWH who use drugs. We sought to investigate the association between daily pill burden and adherence to ART among members of this key population in Vancouver, Canada. We used data from the AIDS Care Cohort to Evaluate Exposure to Survival Services study, a long-running community-recruited cohort of PLWH who use illicit drugs linked to comprehensive HIV clinical records. The longitudinal relationship between daily pill burden and the odds of ≥95% adherence to ART among ART-exposed individuals was analyzed using multivariable generalized linear mixed-effects modeling, adjusting for sociodemographic, behavioural, and structural factors linked to adherence. Between December 2005 and May 2014, the study enrolled 770 ART-exposed participants, including 257 (34%) women, with a median age of 43 years. At baseline, 437 (56.7%) participants achieved ≥95% adherence in the previous 180 days. Among all interview periods, the median adherence was 100% (interquartile range 71%-100%). In a multivariable model, a greater number of pills per day was negatively associated with ≥95% adherence (adjusted odds ratio [AOR] 0.87 per pill, 95% confidence interval [CI] 0.84-0.91). Further analysis showed that once-a-day ART regimens were positively associated with optimal adherence (AOR 1.39, 95% CI 1.07-1.80). In conclusion, simpler dosing demands (ie, fewer pills and once-a-day single tablet regimens) promoted optimal adherence among PLWH who use drugs. Our findings highlight the need for simpler dosing to be encouraged explicitly for PWUD with multiple adherence barriers.

A Skew-t space-varying regression model for the spectral analysis of resting state brain activity.

PubMed

Ismail, Salimah; Sun, Wenqi; Nathoo, Farouk S; Babul, Arif; Moiseev, Alexader; Beg, Mirza Faisal; Virji-Babul, Naznin

2013-08-01

It is known that in many neurological disorders such as Down syndrome, main brain rhythms shift their frequencies slightly, and characterizing the spatial distribution of these shifts is of interest. This article reports on the development of a Skew-t mixed model for the spatial analysis of resting state brain activity in healthy controls and individuals with Down syndrome. Time series of oscillatory brain activity are recorded using magnetoencephalography, and spectral summaries are examined at multiple sensor locations across the scalp. We focus on the mean frequency of the power spectral density, and use space-varying regression to examine associations with age, gender and Down syndrome across several scalp regions. Spatial smoothing priors are incorporated based on a multivariate Markov random field, and the markedly non-Gaussian nature of the spectral response variable is accommodated by the use of a Skew-t distribution. A range of models representing different assumptions on the association structure and response distribution are examined, and we conduct model selection using the deviance information criterion. (1) Our analysis suggests region-specific differences between healthy controls and individuals with Down syndrome, particularly in the left and right temporal regions, and produces smoothed maps indicating the scalp topography of the estimated differences.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Multivariate missing data in hydrology - Review and applications

NASA Astrophysics Data System (ADS)

Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

2017-12-01

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

Quantifying the effect of mixing on the mean age of air in CCMVal-2 and CCMI-1 models

NASA Astrophysics Data System (ADS)

Dietmüller, Simone; Eichinger, Roland; Garny, Hella; Birner, Thomas; Boenisch, Harald; Pitari, Giovanni; Mancini, Eva; Visioni, Daniele; Stenke, Andrea; Revell, Laura; Rozanov, Eugene; Plummer, David A.; Scinocca, John; Jöckel, Patrick; Oman, Luke; Deushi, Makoto; Kiyotaka, Shibata; Kinnison, Douglas E.; Garcia, Rolando; Morgenstern, Olaf; Zeng, Guang; Stone, Kane Adam; Schofield, Robyn

2018-05-01

The stratospheric age of air (AoA) is a useful measure of the overall capabilities of a general circulation model (GCM) to simulate stratospheric transport. Previous studies have reported a large spread in the simulation of AoA by GCMs and coupled chemistry-climate models (CCMs). Compared to observational estimates, simulated AoA is mostly too low. Here we attempt to untangle the processes that lead to the AoA differences between the models and between models and observations. AoA is influenced by both mean transport by the residual circulation and two-way mixing; we quantify the effects of these processes using data from the CCM inter-comparison projects CCMVal-2 (Chemistry-Climate Model Validation Activity 2) and CCMI-1 (Chemistry-Climate Model Initiative, phase 1). Transport along the residual circulation is measured by the residual circulation transit time (RCTT). We interpret the difference between AoA and RCTT as additional aging by mixing. Aging by mixing thus includes mixing on both the resolved and subgrid scale. We find that the spread in AoA between the models is primarily caused by differences in the effects of mixing and only to some extent by differences in residual circulation strength. These effects are quantified by the mixing efficiency, a measure of the relative increase in AoA by mixing. The mixing efficiency varies strongly between the models from 0.24 to 1.02. We show that the mixing efficiency is not only controlled by horizontal mixing, but by vertical mixing and vertical diffusion as well. Possible causes for the differences in the models' mixing efficiencies are discussed. Differences in subgrid-scale mixing (including differences in advection schemes and model resolutions) likely contribute to the differences in mixing efficiency. However, differences in the relative contribution of resolved versus parameterized wave forcing do not appear to be related to differences in mixing efficiency or AoA.

Immunohistochemistry Analysis of CD44, EGFR, and p16 in Oral Cavity and Oropharyngeal Squamous Cell Carcinoma.

PubMed

Cohen, Erin R; Reis, Isildinha M; Gomez, Carmen; Pereira, Lutecia; Freiser, Monika E; Hoosien, Gia; Franzmann, Elizabeth J

2017-08-01

Objectives We analyze the relationship between CD44, epidermal growth factor receptor (EGFR), and p16 expression in oral cavity and oropharyngeal cancers in a diverse population. We also describe whether particular patterns of staining are associated with progression-free survival and overall survival. Study Design Prospective study, single-blind to pathologist and laboratory technologist. Setting Hospital based. Subjects and Methods Immunohistochemistry, comprising gross staining and cellular expression, was performed and interpreted in a blinded fashion on 24 lip/oral cavity and 40 oropharyngeal cancer specimens collected between 2007 and 2012 from participants of a larger study. Information on overall survival and progression-free survival was obtained from medical records. Results Nineteen cases were clinically p16 positive, 16 of which were oropharyngeal. Oral cavity lesions were more likely to exhibit strong CD44 membrane staining ( P = .0002). Strong CD44 membrane and strong EGFR membrane and/or cytoplasmic staining were more common in p16-negative cancers ( P = .006). Peripheral/mixed gross p16 staining pattern was associated with worse survival than the universal staining on univariate and multivariate analyses ( P = .006, P = .030). This held true when combining gross and cellular localization for p16. For CD44, universal gross staining demonstrated poorer overall survival compared with the peripheral/mixed group ( P = .039). CD44 peripheral/mixed group alone and when combined with universal p16 demonstrated the best survival on multivariate analysis ( P = .010). Conclusion In a diverse population, systematic analysis applying p16, CD44, and EGFR gross staining and cellular localization on immunohistochemistry demonstrates distinct patterns that may have prognostic potential exceeding current methods. Larger studies are warranted to investigate these findings further.

A multivariable assessment quantifying effects of cohort-level factors associated with combined mortality and culling risk in cohorts of U.S. commercial feedlot cattle.

PubMed

Babcock, A H; Cernicchiaro, N; White, B J; Dubnicka, S R; Thomson, D U; Ives, S E; Scott, H M; Milliken, G A; Renter, D G

2013-01-01

Economic losses due to cattle mortality and culling have a substantial impact on the feedlot industry. Since criteria for culling may vary and may affect measures of cumulative mortality within cattle cohorts, it is important to assess both mortality and culling when evaluating cattle losses over time and among feedlots. To date, there are no published multivariable assessments of factors associated with combined mortality and culling risk. Our objective was to evaluate combined mortality and culling losses in feedlot cattle cohorts and quantify effects of commonly measured cohort-level risk factors (weight at feedlot arrival, gender, and month of feedlot arrival) using data routinely collected by commercial feedlots. We used retrospective data representing 8,904,965 animals in 54,416 cohorts from 16 U.S. feedlots from 2000 to 2007. The sum of mortality and culling counts for each cohort (given the number of cattle at risk) was used to generate the outcome of interest, the cumulative incidence of combined mortality and culling. Associations between this outcome variable and cohort-level risk factors were evaluated using a mixed effects multivariable negative binomial regression model with random effects for feedlot, year, month and week of arrival. Mean arrival weight of the cohort, gender, and arrival month and a three-way interaction (and corresponding two-way interactions) among arrival weight, gender and month were significantly (P<0.05) associated with the outcome. Results showed that as the mean arrival weight of the cohort increased, mortality and culling risk decreased, but effects of arrival weight were modified both by the gender of the cohort and the month of feedlot arrival. There was a seasonal pattern in combined mortality and culling risk for light and middle-weight male and female cohorts, with a significantly (P<0.05) higher risk for cattle arriving at the feedlot in spring and summer (March-September) than in cattle arriving during fall, and winter months (November-February). Our results quantified effects of covariate patterns that have been heretofore difficult to fully evaluate in smaller scale studies; in addition, they illustrated the importance of utilizing multivariable approaches when quantifying risk factors in heterogeneous feedlot populations. Estimated effects from our model could be useful for managing financial risks associated with adverse health events based on data that are routinely available. Copyright © 2012 Elsevier B.V. All rights reserved.

Effect of selected gastrointestinal parasites and viral agents on fecal S100A12 concentrations in puppies as a potential comparative model.

PubMed

Heilmann, Romy M; Grellet, Aurélien; Grützner, Niels; Cranford, Shannon M; Suchodolski, Jan S; Chastant-Maillard, Sylvie; Steiner, Jörg M

2018-04-17

Previous data suggest that fecal S100A12 has clinical utility as a biomarker of chronic gastrointestinal inflammation (idiopathic inflammatory bowel disease) in both people and dogs, but the effect of gastrointestinal pathogens on fecal S100A12 concentrations is largely unknown. The role of S100A12 in parasite and viral infections is also difficult to study in traditional animal models due to the lack of S100A12 expression in rodents. Thus, the aim of this study was to evaluate fecal S100A12 concentrations in a cohort of puppies with intestinal parasites (Cystoisospora spp., Toxocara canis, Giardia sp.) and viral agents that are frequently encountered and known to cause gastrointestinal signs in dogs (coronavirus, parvovirus) as a comparative model. Spot fecal samples were collected from 307 puppies [median age (range): 7 (4-13) weeks; 29 different breeds] in French breeding kennels, and fecal scores (semiquantitative system; scores 1-13) were assigned. Fecal samples were tested for Cystoisospora spp. (C. canis and C. ohioensis), Toxocara canis, Giardia sp., as well as canine coronavirus (CCV) and parvovirus (CPV). S100A12 concentrations were measured in all fecal samples using an in-house radioimmunoassay. Statistical analyses were performed using non-parametric 2-group or multiple-group comparisons, non-parametric correlation analysis, association testing between nominal variables, and construction of a multivariate mixed model. Fecal S100A12 concentrations ranged from < 24-14,363 ng/g. Univariate analysis only showed increased fecal S100A12 concentrations in dogs shedding Cystoisospora spp. (P = 0.0384) and in dogs infected with parvovirus (P = 0.0277), whereas dogs infected with coronavirus had decreased fecal S100A12 concentrations (P = 0.0345). However, shedding of any single enteropathogen did not affect fecal S100A12 concentrations in multivariate analysis (all P > 0.05) in this study. Only fecal score and breed size had an effect on fecal S100A12 concentrations in multivariate analysis (P < 0.0001). An infection with any single enteropathogen tested in this study is unlikely to alter fecal S100A12 concentrations, and these preliminary data are important for further studies evaluating fecal S100A12 concentrations in dogs or when using fecal S100A12 concentrations as a biomarker in patients with chronic idiopathic gastrointestinal inflammation.

A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores

PubMed Central

Neelon, Brian; Gelfand, Alan E.; Miranda, Marie Lynn

2013-01-01

Summary Researchers in the health and social sciences often wish to examine joint spatial patterns for two or more related outcomes. Examples include infant birth weight and gestational length, psychosocial and behavioral indices, and educational test scores from different cognitive domains. We propose a multivariate spatial mixture model for the joint analysis of continuous individual-level outcomes that are referenced to areal units. The responses are modeled as a finite mixture of multivariate normals, which accommodates a wide range of marginal response distributions and allows investigators to examine covariate effects within subpopulations of interest. The model has a hierarchical structure built at the individual level (i.e., individuals are nested within areal units), and thus incorporates both individual- and areal-level predictors as well as spatial random effects for each mixture component. Conditional autoregressive (CAR) priors on the random effects provide spatial smoothing and allow the shape of the multivariate distribution to vary flexibly across geographic regions. We adopt a Bayesian modeling approach and develop an efficient Markov chain Monte Carlo model fitting algorithm that relies primarily on closed-form full conditionals. We use the model to explore geographic patterns in end-of-grade math and reading test scores among school-age children in North Carolina. PMID:26401059

Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record

NASA Astrophysics Data System (ADS)

Relan, Rishi; Tiels, Koen; Marconato, Anna; Dreesen, Philippe; Schoukens, Johan

2018-05-01

Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper, a methodology to identify a parsimonious discrete-time nonlinear state space model (NLSS) for the nonlinear dynamical system with relatively short data record is proposed. The capability of the NLSS model structure is demonstrated by introducing two different initialisation schemes, one of them using multivariate polynomials. In addition, a method using first-order information of the multivariate polynomials and tensor decomposition is employed to obtain the parsimonious decoupled representation of the set of multivariate real polynomials estimated during the identification of NLSS model. Finally, the experimental verification of the model structure is done on the cascaded water-benchmark identification problem.

[Primary branch size of Pinus koraiensis plantation: a prediction based on linear mixed effect model].

PubMed

Dong, Ling-Bo; Liu, Zhao-Gang; Li, Feng-Ri; Jiang, Li-Chun

2013-09-01

By using the branch analysis data of 955 standard branches from 60 sampled trees in 12 sampling plots of Pinus koraiensis plantation in Mengjiagang Forest Farm in Heilongjiang Province of Northeast China, and based on the linear mixed-effect model theory and methods, the models for predicting branch variables, including primary branch diameter, length, and angle, were developed. Considering tree effect, the MIXED module of SAS software was used to fit the prediction models. The results indicated that the fitting precision of the models could be improved by choosing appropriate random-effect parameters and variance-covariance structure. Then, the correlation structures including complex symmetry structure (CS), first-order autoregressive structure [AR(1)], and first-order autoregressive and moving average structure [ARMA(1,1)] were added to the optimal branch size mixed-effect model. The AR(1) improved the fitting precision of branch diameter and length mixed-effect model significantly, but all the three structures didn't improve the precision of branch angle mixed-effect model. In order to describe the heteroscedasticity during building mixed-effect model, the CF1 and CF2 functions were added to the branch mixed-effect model. CF1 function improved the fitting effect of branch angle mixed model significantly, whereas CF2 function improved the fitting effect of branch diameter and length mixed model significantly. Model validation confirmed that the mixed-effect model could improve the precision of prediction, as compare to the traditional regression model for the branch size prediction of Pinus koraiensis plantation.

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

PubMed

Snell, Kym I E; Hua, Harry; Debray, Thomas P A; Ensor, Joie; Look, Maxime P; Moons, Karel G M; Riley, Richard D

2016-01-01

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Multivariate Formation Pressure Prediction with Seismic-derived Petrophysical Properties from Prestack AVO inversion and Poststack Seismic Motion Inversion

NASA Astrophysics Data System (ADS)

Yu, H.; Gu, H.

2017-12-01

A novel multivariate seismic formation pressure prediction methodology is presented, which incorporates high-resolution seismic velocity data from prestack AVO inversion, and petrophysical data (porosity and shale volume) derived from poststack seismic motion inversion. In contrast to traditional seismic formation prediction methods, the proposed methodology is based on a multivariate pressure prediction model and utilizes a trace-by-trace multivariate regression analysis on seismic-derived petrophysical properties to calibrate model parameters in order to make accurate predictions with higher resolution in both vertical and lateral directions. With prestack time migration velocity as initial velocity model, an AVO inversion was first applied to prestack dataset to obtain high-resolution seismic velocity with higher frequency that is to be used as the velocity input for seismic pressure prediction, and the density dataset to calculate accurate Overburden Pressure (OBP). Seismic Motion Inversion (SMI) is an inversion technique based on Markov Chain Monte Carlo simulation. Both structural variability and similarity of seismic waveform are used to incorporate well log data to characterize the variability of the property to be obtained. In this research, porosity and shale volume are first interpreted on well logs, and then combined with poststack seismic data using SMI to build porosity and shale volume datasets for seismic pressure prediction. A multivariate effective stress model is used to convert velocity, porosity and shale volume datasets to effective stress. After a thorough study of the regional stratigraphic and sedimentary characteristics, a regional normally compacted interval model is built, and then the coefficients in the multivariate prediction model are determined in a trace-by-trace multivariate regression analysis on the petrophysical data. The coefficients are used to convert velocity, porosity and shale volume datasets to effective stress and then to calculate formation pressure with OBP. Application of the proposed methodology to a research area in East China Sea has proved that the method can bridge the gap between seismic and well log pressure prediction and give predicted pressure values close to pressure meassurements from well testing.

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

Determining the Relationship Between Moral Waivers and Marine Corps Unsuitability Attrition

DTIC Science & Technology

2008-03-01

observed characteristics. However, econometric research indicates that the magnitude of interaction effects estimated via probit or logit models may...1997 to 2005. Multivariate probit models were used to analyze the effects of moral waivers on unsatisfactory service separations. 15. NUMBER OF...files from fiscal years 1997 to 2005. Multivariate probit models were used to analyze the effects of moral waivers on unsatisfactory service

Selective Thinning of the Perifoveal Inner Retina as an Early Sign of Hydroxychloroquine Retinal Toxicity

PubMed Central

Pasadhika, Sirichai; Fishman, Gerald A; Choi, Dongseok; Shahidi, Mahnaz

2013-01-01

Purpose To evaluate macular thickness profiles using spectral-domain optical coherence tomography (SDOCT) and image segmentation in patients with chronic exposure to hydroxychloroquine. Methods This study included 8 patients with chronic exposure to hydroxychloroquine (Group 1) and 8 controls (Group 2). Group 1 patients had no clinically-evident retinal toxicity. All subjects underwent SDOCT imaging of the macula. An image segmentation technique was used to measure thickness of 6 retinal layers at 200 µm intervals. A mixed-effects model was used for multivariate analysis. Results By measuring total retinal thickness either at the central macular (2800 µm in diameter), the perifoveal region 1200-µm-width ring surrounding the central macula), or the overall macular area (5200 µm in diameter), there were no significant differences in the thickness between Groups 1 and 2. On an image segmentation analysis, selective thinning of the inner plexiform + ganglion cell layers (p=0.021) was observed only in the perifoveal area of the patients in Group 1 compared to that of Group 2 by using the mixed-effects model analysis. Conclusions Our results suggest that chronic exposure to hydroxychloroquine is associated with thinning of the perifoveal inner retinal layers, especially in the ganglion cell and inner plexiform layers, even in the absence of functional or structural clinical changes involving the photoreceptor or retinal pigment epithelial cell layers. This may be a contributing factor as the reason most patients who have early detectable signs of drug toxicity present with paracentral or pericentral scotomas. PMID:20395978

Correlation of hospital magnet status with the quality of physicians performing neurosurgical procedures in New York State.

PubMed

Bekelis, Kimon; Missios, Symeon; MacKenzie, Todd A

2018-01-24

The quality of physicians practicing in hospitals recognized for nursing excellence by the American Nurses Credentialing Center has not been studied before. We investigated whether Magnet hospital recognition is associated with higher quality of physicians performing neurosurgical procedures. We performed a cohort study of patients undergoing neurosurgical procedures from 2009-2013, who were registered in the New York Statewide Planning and Research Cooperative System (SPARCS) database. Propensity score adjusted multivariable regression models were used to adjust for known confounders, with mixed effects methods to control for clustering at the facility level. An instrumental variable analysis was used to control for unmeasured confounding and simulate the effect of a randomized trial. During the study period, 185,277 patients underwent neurosurgical procedures, and met the inclusion criteria. Of these, 66,607 (35.6%) were hospitalized in Magnet hospitals, and 118,670 (64.4%) in non-Magnet institutions. Instrumental variable analysis demonstrated that undergoing neurosurgical operations in Magnet hospitals was associated with a 13.6% higher chance of being treated by a physician with superior performance in terms of mortality (95% CI, 13.2% to 14.1%), and a 4.3% higher chance of being treated by a physician with superior performance in terms of length-of-stay (LOS) (95% CI, 3.8% to 4.7%) in comparison to non-Magnet institutions. The same associations were present in propensity score adjusted mixed effects models. Using a comprehensive all-payer cohort of neurosurgical patients in New York State we identified an association of Magnet hospital recognition with superior physician performance.

The Vineyard Yeast Microbiome, a Mixed Model Microbial Map

PubMed Central

Setati, Mathabatha Evodia; Jacobson, Daniel; Andong, Ursula-Claire; Bauer, Florian

2012-01-01

Vineyards harbour a wide variety of microorganisms that play a pivotal role in pre- and post-harvest grape quality and will contribute significantly to the final aromatic properties of wine. The aim of the current study was to investigate the spatial distribution of microbial communities within and between individual vineyard management units. For the first time in such a study, we applied the Theory of Sampling (TOS) to sample gapes from adjacent and well established commercial vineyards within the same terroir unit and from several sampling points within each individual vineyard. Cultivation-based and molecular data sets were generated to capture the spatial heterogeneity in microbial populations within and between vineyards and analysed with novel mixed-model networks, which combine sample correlations and microbial community distribution probabilities. The data demonstrate that farming systems have a significant impact on fungal diversity but more importantly that there is significant species heterogeneity between samples in the same vineyard. Cultivation-based methods confirmed that while the same oxidative yeast species dominated in all vineyards, the least treated vineyard displayed significantly higher species richness, including many yeasts with biocontrol potential. The cultivatable yeast population was not fully representative of the more complex populations seen with molecular methods, and only the molecular data allowed discrimination amongst farming practices with multivariate and network analysis methods. Importantly, yeast species distribution is subject to significant intra-vineyard spatial fluctuations and the frequently reported heterogeneity of tank samples of grapes harvested from single vineyards at the same stage of ripeness might therefore, at least in part, be due to the differing microbiota in different sections of the vineyard. PMID:23300721

Measurements of multi-scalar mixing in a turbulent coaxial jet

NASA Astrophysics Data System (ADS)

Hewes, Alais; Mydlarski, Laurent

2017-11-01

There are relatively few studies of turbulent multi-scalar mixing, despite the occurrence of this phenomenon in common processes (e.g. chemically reacting flows, oceanic mixing). In the present work, we simultaneously measure the evolution of two passive scalars (temperature and helium concentration) and velocity in a coaxial jet. Such a flow is particularly relevant, as coaxial jets are regularly employed in applications of turbulent non-premixed combustion, which relies on multi-scalar mixing. The coaxial jet used in the current experiment is based on the work of Cai et al. (J. Fluid Mech., 2011), and consists of a vertically oriented central jet of helium and air, surrounded by an annular flow of (unheated) pure air, emanating into a slow co-flow of (pure) heated air. The simultaneous two-scalar and velocity measurements are made using a 3-wire hot-wire anemometry probe. The first two wires of this probe form an interference (or Way-Libby) probe, and measure velocity and concentration. The third wire, a hot-wire operating at a low overheat ratio, measures temperature. The 3-wire probe is used to obtain concurrent velocity, concentration, and temperature statistics to characterize the mixing process by way of single and multivariable/joint statistics. Supported by the Natural Sciences and Engineering Research Council of Canada (Grant 217184).

A continuous mixing model for pdf simulations and its applications to combusting shear flows

NASA Technical Reports Server (NTRS)

Hsu, A. T.; Chen, J.-Y.

1991-01-01

The problem of time discontinuity (or jump condition) in the coalescence/dispersion (C/D) mixing model is addressed in this work. A C/D mixing model continuous in time is introduced. With the continuous mixing model, the process of chemical reaction can be fully coupled with mixing. In the case of homogeneous turbulence decay, the new model predicts a pdf very close to a Gaussian distribution, with finite higher moments also close to that of a Gaussian distribution. Results from the continuous mixing model are compared with both experimental data and numerical results from conventional C/D models.

A General Multivariate Latent Growth Model with Applications to Student Achievement

ERIC Educational Resources Information Center

Bianconcini, Silvia; Cagnone, Silvia

2012-01-01

The evaluation of the formative process in the University system has been assuming an ever increasing importance in the European countries. Within this context, the analysis of student performance and capabilities plays a fundamental role. In this work, the authors propose a multivariate latent growth model for studying the performances of a…

Bayesian Estimation of Random Coefficient Dynamic Factor Models

ERIC Educational Resources Information Center

Song, Hairong; Ferrer, Emilio

2012-01-01

Dynamic factor models (DFMs) have typically been applied to multivariate time series data collected from a single unit of study, such as a single individual or dyad. The goal of DFMs application is to capture dynamics of multivariate systems. When multiple units are available, however, DFMs are not suited to capture variations in dynamics across…

Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.

ERIC Educational Resources Information Center

Molenaar, Peter C. M.; Nesselroade, John R.

2001-01-01

Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…

Modeling Associations among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach

ERIC Educational Resources Information Center

Tchumtchoua, Sylvie; Dey, Dipak K.

2012-01-01

This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the…

Parametric Cost Models for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

A study is in-process to develop a multivariable parametric cost model for space telescopes. Cost and engineering parametric data has been collected on 30 different space telescopes. Statistical correlations have been developed between 19 variables of 59 variables sampled. Single Variable and Multi-Variable Cost Estimating Relationships have been developed. Results are being published.

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

PubMed Central

Avalappampatty Sivasamy, Aneetha; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

PubMed

Sivasamy, Aneetha Avalappampatty; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

Predictive model for falling in Parkinson disease patients.

PubMed

Custodio, Nilton; Lira, David; Herrera-Perez, Eder; Montesinos, Rosa; Castro-Suarez, Sheila; Cuenca-Alfaro, Jose; Cortijo, Patricia

2016-12-01

Falls are a common complication of advancing Parkinson's disease (PD). Although numerous risk factors are known, reliable predictors of future falls are still lacking. The aim of this study was to develop a multivariate model to predict falling in PD patients. Prospective cohort with forty-nine PD patients. The area under the receiver-operating characteristic curve (AUC) was calculated to evaluate predictive performance of the purposed multivariate model. The median of PD duration and UPDRS-III score in the cohort was 6 years and 24 points, respectively. Falls occurred in 18 PD patients (30%). Predictive factors for falling identified by univariate analysis were age, PD duration, physical activity, and scores of UPDRS motor, FOG, ACE, IFS, PFAQ and GDS ( p -value < 0.001), as well as fear of falling score ( p -value = 0.04). The final multivariate model (PD duration, FOG, ACE, and physical activity) showed an AUC = 0.9282 (correctly classified = 89.83%; sensitivity = 92.68%; specificity = 83.33%). This study showed that our multivariate model have a high performance to predict falling in a sample of PD patients.

The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

PubMed

Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L

2015-12-30

Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Application of zero-inflated poisson mixed models in prognostic factors of hepatitis C.

PubMed

Akbarzadeh Baghban, Alireza; Pourhoseingholi, Asma; Zayeri, Farid; Jafari, Ali Akbar; Alavian, Seyed Moayed

2013-01-01

In recent years, hepatitis C virus (HCV) infection represents a major public health problem. Evaluation of risk factors is one of the solutions which help protect people from the infection. This study aims to employ zero-inflated Poisson mixed models to evaluate prognostic factors of hepatitis C. The data was collected from a longitudinal study during 2005-2010. First, mixed Poisson regression (PR) model was fitted to the data. Then, a mixed zero-inflated Poisson model was fitted with compound Poisson random effects. For evaluating the performance of the proposed mixed model, standard errors of estimators were compared. The results obtained from mixed PR showed that genotype 3 and treatment protocol were statistically significant. Results of zero-inflated Poisson mixed model showed that age, sex, genotypes 2 and 3, the treatment protocol, and having risk factors had significant effects on viral load of HCV patients. Of these two models, the estimators of zero-inflated Poisson mixed model had the minimum standard errors. The results showed that a mixed zero-inflated Poisson model was the almost best fit. The proposed model can capture serial dependence, additional overdispersion, and excess zeros in the longitudinal count data.

Studying Resist Stochastics with the Multivariate Poisson Propagation Model

DOE PAGES

Naulleau, Patrick; Anderson, Christopher; Chao, Weilun; ...

2014-01-01

Progress in the ultimate performance of extreme ultraviolet resist has arguably decelerated in recent years suggesting an approach to stochastic limits both in photon counts and material parameters. Here we report on the performance of a variety of leading extreme ultraviolet resist both with and without chemical amplification. The measured performance is compared to stochastic modeling results using the Multivariate Poisson Propagation Model. The results show that the best materials are indeed nearing modeled performance limits.

Multivariable Parametric Cost Model for Ground Optical Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2005-01-01

A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.

Clinical, polysomnographic, and CPAP titration features of obstructive sleep apnea: Mixed versus purely obstructive type.

PubMed

Lee, Sang-Ahm; Lee, Gha-Hyun; Chung, Yoo-Sam; Kim, Woo Sung

2015-08-15

To determine whether obstructive sleep apnea syndrome (OSAS) patients with mixed sleep apnea (MSA) have different clinical, polysomnographic, and continuous positive airway pressure (CPAP) titration findings compared to OSAS patients without MSA. We retrospectively reviewed the records of OSAS patients who had undergone CPAP titration and categorized them into pure-OSA and mixed-OSA groups. Demographic features, daytime sleepiness, and apnea severity were compared between the two groups using univariate and multivariate analyses. CPAP titration findings were also compared between the two groups. One hundred and ninety-five subjects (n=126 pure-OSA; n=69 mixed-OSA) were included in the analysis. Compared to the pure-OSA group, the mixed-OSA group had a higher percentage of males (p=0.003) and a higher body mass index (p=0.044), Epworth Sleepiness Scale score (p=0.028), and apnea-hypopnea index (AHI) (p<0.001). In logistic regression analysis, older age, male sex, and higher body mass index were independently associated with mixed-OSA before PSG study. When using AHI as a covariable, the higher AHI with older age, male sex, and daytime sleepiness was independently related to mixed-OSA. The mixed-OSA group had a higher percentage of patients with complex sleep apnea, a lower percentage of patients with optimal titration, and a higher titrated pressure than the pure-OSA group. Severe OSA, older age, male sex, obesity, and daytime sleepiness were related to mixed-OSA. Complex sleep apnea, less optimal titration, and a higher titrated CPAP were also associated with MSA in OSAS patients. Copyright © 2015 Elsevier B.V. All rights reserved.

A time dependent mixing model to close PDF equations for transport in heterogeneous aquifers

NASA Astrophysics Data System (ADS)

Schüler, L.; Suciu, N.; Knabner, P.; Attinger, S.

2016-10-01

Probability density function (PDF) methods are a promising alternative to predicting the transport of solutes in groundwater under uncertainty. They make it possible to derive the evolution equations of the mean concentration and the concentration variance, used in moment methods. The mixing model, describing the transport of the PDF in concentration space, is essential for both methods. Finding a satisfactory mixing model is still an open question and due to the rather elaborate PDF methods, a difficult undertaking. Both the PDF equation and the concentration variance equation depend on the same mixing model. This connection is used to find and test an improved mixing model for the much easier to handle concentration variance. Subsequently, this mixing model is transferred to the PDF equation and tested. The newly proposed mixing model yields significantly improved results for both variance modelling and PDF modelling.

Unifying error structures in commonly used biotracer mixing models.

PubMed

Stock, Brian C; Semmens, Brice X

2016-10-01

Mixing models are statistical tools that use biotracers to probabilistically estimate the contribution of multiple sources to a mixture. These biotracers may include contaminants, fatty acids, or stable isotopes, the latter of which are widely used in trophic ecology to estimate the mixed diet of consumers. Bayesian implementations of mixing models using stable isotopes (e.g., MixSIR, SIAR) are regularly used by ecologists for this purpose, but basic questions remain about when each is most appropriate. In this study, we describe the structural differences between common mixing model error formulations in terms of their assumptions about the predation process. We then introduce a new parameterization that unifies these mixing model error structures, as well as implicitly estimates the rate at which consumers sample from source populations (i.e., consumption rate). Using simulations and previously published mixing model datasets, we demonstrate that the new error parameterization outperforms existing models and provides an estimate of consumption. Our results suggest that the error structure introduced here will improve future mixing model estimates of animal diet. © 2016 by the Ecological Society of America.

Lagrangian mixed layer modeling of the western equatorial Pacific

NASA Technical Reports Server (NTRS)

Shinoda, Toshiaki; Lukas, Roger

1995-01-01

Processes that control the upper ocean thermohaline structure in the western equatorial Pacific are examined using a Lagrangian mixed layer model. The one-dimensional bulk mixed layer model of Garwood (1977) is integrated along the trajectories derived from a nonlinear 1 1/2 layer reduced gravity model forced with actual wind fields. The Global Precipitation Climatology Project (GPCP) data are used to estimate surface freshwater fluxes for the mixed layer model. The wind stress data which forced the 1 1/2 layer model are used for the mixed layer model. The model was run for the period 1987-1988. This simple model is able to simulate the isothermal layer below the mixed layer in the western Pacific warm pool and its variation. The subduction mechanism hypothesized by Lukas and Lindstrom (1991) is evident in the model results. During periods of strong South Equatorial Current, the warm and salty mixed layer waters in the central Pacific are subducted below the fresh shallow mixed layer in the western Pacific. However, this subduction mechanism is not evident when upwelling Rossby waves reach the western equatorial Pacific or when a prominent deepening of the mixed layer occurs in the western equatorial Pacific or when a prominent deepening of the mixed layer occurs in the western equatorial Pacific due to episodes of strong wind and light precipitation associated with the El Nino-Southern Oscillation. Comparison of the results between the Lagrangian mixed layer model and a locally forced Eulerian mixed layer model indicated that horizontal advection of salty waters from the central Pacific strongly affects the upper ocean salinity variation in the western Pacific, and that this advection is necessary to maintain the upper ocean thermohaline structure in this region.

Seeking and Receiving Social Support on Facebook for Surgery

PubMed Central

2015-01-01

Social networking sites such as Facebook provide a new way to seek and receive social support, a factor widely recognized as important for one's health. However, few studies have used actual conversations from social networking sites to study social support for health related matters. We studied 3,899 Facebook users, among a sample of 33,326 monitored adults, who initiated a conversation that referred to surgery on their Facebook Wall during a six-month period to explore predictors of social support as measured by number of response posts from “friends.” Among our sample, we identified 8,343 Facebook conversation threads with the term “surgery” in the initial post with, on average, 5.7 response posts (SD 6.2). We used a variant of latent semantic analysis to explore the relationship between specific words in the posts that allowed us to develop three thematic categories of words related to family, immediacy of the surgery, and prayer. We used generalized linear mixed models to examine the association between characteristics of the Facebook user as well as the thematic categories on the likelihood of receiving response posts following the announcement of a surgery. Words from the three thematic categories were used in 32.5% (family), 39.5 (immediacy), and 50.7% (prayer) of root posts. Surprisingly, few user characteristics were associated with response in multivariate models [rate ratios, RR, 1.08 (95% CI 1.01,1.15) for married/living with partner; 1.10 (95% CI 1.03,1.19) for annual income ≥ $75,000]. In multivariate models adjusted for Facebook user characteristics and network size, use of family and prayer words were associated with significantly higher number of response posts, RR 1.40 (95% CI 1.37,1.43) and 2.07 (95% CI 2.02,2.12) respectively. We found some evidence of social support on Facebook for surgery and that the language used in the initial post of a conversation thread is predictive of overall response. PMID:25753284

Imaging muscle as a potential biomarker of denervation in motor neuron disease

PubMed Central

Jenkins, Thomas M; Alix, James J P; David, Charlotte; Pearson, Eilish; Rao, D Ganesh; Hoggard, Nigel; O’Brien, Eoghan; Baster, Kathleen; Bradburn, Michael; Bigley, Julia; McDermott, Christopher J; Wilkinson, Iain D; Shaw, Pamela J

2018-01-01

Objective To assess clinical, electrophysiological and whole-body muscle MRI measurements of progression in patients with motor neuron disease (MND), as tools for future clinical trials, and to probe pathophysiological mechanisms in vivo. Methods A prospective, longitudinal, observational, clinicoelectrophysiological and radiological cohort study was performed. Twenty-nine patients with MND and 22 age-matched and gender-matched healthy controls were assessed with clinical measures, electrophysiological motor unit number index (MUNIX) and T2-weighted whole-body muscle MRI, at first clinical presentation and 4 months later. Between-group differences and associations were assessed using age-adjusted and gender-adjusted multivariable regression models. Within-subject longitudinal changes were assessed using paired t-tests. Patterns of disease spread were modelled using mixed-effects multivariable regression, assessing associations between muscle relative T2 signal and anatomical adjacency to site of clinical onset. Results Patients with MND had 30% higher relative T2 muscle signal than controls at baseline (all regions mean, 95% CI 15% to 45%, p<0.001). Higher T2 signal was associated with greater overall disability (coefficient −0.009, 95% CI −0.017 to –0.001, p=0.023) and with clinical weakness and lower MUNIX in multiple individual muscles. Relative T2 signal in bilateral tibialis anterior increased over 4 months in patients with MND (right: 10.2%, 95% CI 2.0% to 18.4%, p=0.017; left: 14.1%, 95% CI 3.4% to 24.9%, p=0.013). Anatomically, contiguous disease spread on MRI was not apparent in this model. Conclusions Whole-body muscle MRI offers a new approach to objective assessment of denervation over short timescales in MND and enables investigation of patterns of disease spread in vivo. Muscles inaccessible to conventional clinical and electrophysiological assessment may be investigated using this methodology. PMID:29089397

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China

PubMed Central

Pei, Ling-Ling; Li, Qin

2018-01-01

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China’s pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N)) model based on the nonlinear least square (NLS) method. The Gauss–Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N) and the NLS-based TNGM (1, N) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO2 and dust, alongside GDP per capita in China during the period 1996–2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N) model presents greater precision when forecasting WDPC, SO2 emissions and dust emissions per capita, compared to the traditional GM (1, N) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO2 and dust reduce accordingly. PMID:29517985

Space-time variation of respiratory cancers in South Carolina: a flexible multivariate mixture modeling approach to risk estimation.

PubMed

Carroll, Rachel; Lawson, Andrew B; Kirby, Russell S; Faes, Christel; Aregay, Mehreteab; Watjou, Kevin

2017-01-01

Many types of cancer have an underlying spatiotemporal distribution. Spatiotemporal mixture modeling can offer a flexible approach to risk estimation via the inclusion of latent variables. In this article, we examine the application and benefits of using four different spatiotemporal mixture modeling methods in the modeling of cancer of the lung and bronchus as well as "other" respiratory cancer incidences in the state of South Carolina. Of the methods tested, no single method outperforms the other methods; which method is best depends on the cancer under consideration. The lung and bronchus cancer incidence outcome is best described by the univariate modeling formulation, whereas the "other" respiratory cancer incidence outcome is best described by the multivariate modeling formulation. Spatiotemporal multivariate mixture methods can aid in the modeling of cancers with small and sparse incidences when including information from a related, more common type of cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Multivariate Time Series Decomposition into Oscillation Components.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Study of sediment movement in an irrigated maize-cotton system combining rainfall simulations, sediment tracers and soil erosion models

NASA Astrophysics Data System (ADS)

Guzmán, Gema; Laguna, Ana; Cañasveras, Juan Carlos; Boulal, Hakim; Barrón, Vidal; Gómez-Macpherson, Helena; Giráldez, Juan Vicente; Gómez, José Alfonso

2015-05-01

Although soil erosion is one of the main threats to agriculture sustainability in many areas of the world, its processes are difficult to measure and still need a better characterization. The use of iron oxides as sediment tracers, combined with erosion and mixing models opens up a pathway for improving the knowledge of the erosion and redistribution of soil, determining sediment sources and sinks. In this study, magnetite and a multivariate mixing model were used in rainfall simulations at the micro-plot scale to determine the source of the sediment at different stages of a furrow-ridge system both with (+T) and without (-T) wheel tracks. At a plot scale, magnetite, hematite and goethite combined with two soil erosion models based on the kinematic wave approach were used in a sprinkler irrigation test to study trends in sediment transport and tracer dynamics along furrow lengths under a wide range of scenarios. In the absence of any stubble cover, sediment contribution from the ridges was larger than the furrow bed one, almost 90%, while an opposite trend was observed with stubble, with a smaller contribution from the ridge (32%) than that of the bed, at the micro-plot trials. Furthermore, at a plot scale, the tracer concentration analysis showed an exponentially decreasing trend with the downstream distance both for sediment detachment along furrows and soil source contribution from tagged segments. The parameters of the distributed model KINEROS2 have been estimated using the PEST Model to obtain a more accurate evaluation. Afterwards, this model was used to simulate a broad range of common scenarios of topography and rainfall from commercial farms in southern Spain. Higher slopes had a significant influence on sediment yields while long furrow distances allowed a more efficient water use. For the control of runoff, and therefore soil loss, an equilibrium between irrigation design (intensity, duration, water pattern) and hydric needs of the crops should be defined in order to establish a sustainable management strategy.

Miscibility and Thermodynamics of Mixing of Different Models of Formamide and Water in Computer Simulation.

PubMed

Kiss, Bálint; Fábián, Balázs; Idrissi, Abdenacer; Szőri, Milán; Jedlovszky, Pál

2017-07-27

The thermodynamic changes that occur upon mixing five models of formamide and three models of water, including the miscibility of these model combinations itself, is studied by performing Monte Carlo computer simulations using an appropriately chosen thermodynamic cycle and the method of thermodynamic integration. The results show that the mixing of these two components is close to the ideal mixing, as both the energy and entropy of mixing turn out to be rather close to the ideal term in the entire composition range. Concerning the energy of mixing, the OPLS/AA_mod model of formamide behaves in a qualitatively different way than the other models considered. Thus, this model results in negative, while the other ones in positive energy of mixing values in combination with all three water models considered. Experimental data supports this latter behavior. Although the Helmholtz free energy of mixing always turns out to be negative in the entire composition range, the majority of the model combinations tested either show limited miscibility, or, at least, approach the miscibility limit very closely in certain compositions. Concerning both the miscibility and the energy of mixing of these model combinations, we recommend the use of the combination of the CHARMM formamide and TIP4P water models in simulations of water-formamide mixtures.

Probability of identification: adulteration of American Ginseng with Asian Ginseng.

PubMed

Harnly, James; Chen, Pei; Harrington, Peter De B

2013-01-01

The AOAC INTERNATIONAL guidelines for validation of botanical identification methods were applied to the detection of Asian Ginseng [Panax ginseng (PG)] as an adulterant for American Ginseng [P. quinquefolius (PQ)] using spectral fingerprints obtained by flow injection mass spectrometry (FIMS). Samples of 100% PQ and 100% PG were physically mixed to provide 90, 80, and 50% PQ. The multivariate FIMS fingerprint data were analyzed using soft independent modeling of class analogy (SIMCA) based on 100% PQ. The Q statistic, a measure of the degree of non-fit of the test samples with the calibration model, was used as the analytical parameter. FIMS was able to discriminate between 100% PQ and 100% PG, and between 100% PQ and 90, 80, and 50% PQ. The probability of identification (POI) curve was estimated based on the SD of 90% PQ. A digital model of adulteration, obtained by mathematically summing the experimentally acquired spectra of 100% PQ and 100% PG in the desired ratios, agreed well with the physical data and provided an easy and more accurate method for constructing the POI curve. Two chemometric modeling methods, SIMCA and fuzzy optimal associative memories, and two classification methods, partial least squares-discriminant analysis and fuzzy rule-building expert systems, were applied to the data. The modeling methods correctly identified the adulterated samples; the classification methods did not.

University of South Carolina CB Support, Basic Research in Materials and Techniques for Optical Computing Standoff Sensors

DTIC Science & Technology

2004-10-01

chloroform-soaked swab prior to making electrical contact with directly related to the oxidation and reduction potential of the an alligator clip. In...other cases, no cleaning protocol was used emitting layers.’.’ Wrighton et al.𔃺 examined the cyclic and a direct connection via an alligator clip was...applied to optical spectra of complex mix- samples requires techniques of simple multivariate patterntame (gasoline, blood , environmental samples

A flavor symmetry model for bilarge leptonic mixing and the lepton masses

NASA Astrophysics Data System (ADS)

Ohlsson, Tommy; Seidl, Gerhart

2002-11-01

We present a model for leptonic mixing and the lepton masses based on flavor symmetries and higher-dimensional mass operators. The model predicts bilarge leptonic mixing (i.e., the mixing angles θ12 and θ23 are large and the mixing angle θ13 is small) and an inverted hierarchical neutrino mass spectrum. Furthermore, it approximately yields the experimental hierarchical mass spectrum of the charged leptons. The obtained values for the leptonic mixing parameters and the neutrino mass squared differences are all in agreement with atmospheric neutrino data, the Mikheyev-Smirnov-Wolfenstein large mixing angle solution of the solar neutrino problem, and consistent with the upper bound on the reactor mixing angle. Thus, we have a large, but not close to maximal, solar mixing angle θ12, a nearly maximal atmospheric mixing angle θ23, and a small reactor mixing angle θ13. In addition, the model predicts θ 12≃ {π}/{4}-θ 13.

Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature were examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter were derived.

A Multivariate Multilevel Approach to the Modeling of Accuracy and Speed of Test Takers

ERIC Educational Resources Information Center

Klein Entink, R. H.; Fox, J. P.; van der Linden, W. J.

2009-01-01

Response times on test items are easily collected in modern computerized testing. When collecting both (binary) responses and (continuous) response times on test items, it is possible to measure the accuracy and speed of test takers. To study the relationships between these two constructs, the model is extended with a multivariate multilevel…

Multivariate regression model for partitioning tree volume of white oak into round-product classes

Treesearch

Daniel A. Yaussy; David L. Sonderman

1984-01-01

Describes the development of multivariate equations that predict the expected cubic volume of four round-product classes from independent variables composed of individual tree-quality characteristics. Although the model has limited application at this time, it does demonstrate the feasibility of partitioning total tree cubic volume into round-product classes based on...

The Dirichlet-Multinomial Model for Multivariate Randomized Response Data and Small Samples

ERIC Educational Resources Information Center

Avetisyan, Marianna; Fox, Jean-Paul

2012-01-01

In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The beta-binomial model for binary RR data will be generalized…

Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms

ERIC Educational Resources Information Center

Anderson, John R.

2012-01-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…

Four Families of Multi-Variant Issues in Graduate-Level Asynchronous Online Courses

ERIC Educational Resources Information Center

Gisburne, Jaclyn M.; Fairchild, Patricia J.

2004-01-01

This is the first of several papers developed from a faculty and student perspective describing a new distance learning (DL) model. Integral to the model are four interrelated families of multi-variant issues, referred to here as (a) the academic divide, (b) student misalignment, (c) administrative influences, and (d) the use of student…

Assessing Reliability of Student Ratings of Advisor: A Comparison of Univariate and Multivariate Generalizability Approaches.

ERIC Educational Resources Information Center

Sun, Anji; Valiga, Michael J.

In this study, the reliability of the American College Testing (ACT) Program's "Survey of Academic Advising" (SAA) was examined using both univariate and multivariate generalizability theory approaches. The primary purpose of the study was to compare the results of three generalizability theory models (a random univariate model, a mixed…

Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicolas

2011-01-01

This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting…

Multivariate Generalizations of Student's t-Distribution. ONR Technical Report. [Biometric Lab Report No. 90-3.

ERIC Educational Resources Information Center

Gibbons, Robert D.; And Others

In the process of developing a conditionally-dependent item response theory (IRT) model, the problem arose of modeling an underlying multivariate normal (MVN) response process with general correlation among the items. Without the assumption of conditional independence, for which the underlying MVN cdf takes on comparatively simple forms and can be…

Bias and Precision of Measures of Association for a Fixed-Effect Multivariate Analysis of Variance Model

ERIC Educational Resources Information Center

Kim, Soyoung; Olejnik, Stephen

2005-01-01

The sampling distributions of five popular measures of association with and without two bias adjusting methods were examined for the single factor fixed-effects multivariate analysis of variance model. The number of groups, sample sizes, number of outcomes, and the strength of association were manipulated. The results indicate that all five…

Mixed Membership Distributions with Applications to Modeling Multiple Strategy Usage

ERIC Educational Resources Information Center

Galyardt, April

2012-01-01

This dissertation examines two related questions. "How do mixed membership models work?" and "Can mixed membership be used to model how students use multiple strategies to solve problems?". Mixed membership models have been used in thousands of applications from text and image processing to genetic microarray analysis. Yet…

MULTIVARIATE ANALYSES (CONONICAL CORRELATION AND PARTIAL LEAST SQUARE, PLS) TO MODEL AND ASSESS THE ASSOCIATION OF LANDSCAPE METRICS TO SURFACE WATER CHEMICAL AND BIOLOGICAL PROPERTIES USING SAVANNAH RIVER BASIN DATA.

EPA Science Inventory

Many multivariate methods are used in describing and predicting relation; each has its unique usage of categorical and non-categorical data. In multivariate analysis of variance (MANOVA), many response variables (y's) are related to many independent variables that are categorical...

Effects of Precipitation on Ocean Mixed-Layer Temperature and Salinity as Simulated in a 2-D Coupled Ocean-Cloud Resolving Atmosphere Model

NASA Technical Reports Server (NTRS)

Li, Xiaofan; Sui, C.-H.; Lau, K-M.; Adamec, D.

1999-01-01

A two-dimensional coupled ocean-cloud resolving atmosphere model is used to investigate possible roles of convective scale ocean disturbances induced by atmospheric precipitation on ocean mixed-layer heat and salt budgets. The model couples a cloud resolving model with an embedded mixed layer-ocean circulation model. Five experiment are performed under imposed large-scale atmospheric forcing in terms of vertical velocity derived from the TOGA COARE observations during a selected seven-day period. The dominant variability of mixed-layer temperature and salinity are simulated by the coupled model with imposed large-scale forcing. The mixed-layer temperatures in the coupled experiments with 1-D and 2-D ocean models show similar variations when salinity effects are not included. When salinity effects are included, however, differences in the domain-mean mixed-layer salinity and temperature between coupled experiments with 1-D and 2-D ocean models could be as large as 0.3 PSU and 0.4 C respectively. Without fresh water effects, the nocturnal heat loss over ocean surface causes deep mixed layers and weak cooling rates so that the nocturnal mixed-layer temperatures tend to be horizontally-uniform. The fresh water flux, however, causes shallow mixed layers over convective areas while the nocturnal heat loss causes deep mixed layer over convection-free areas so that the mixed-layer temperatures have large horizontal fluctuations. Furthermore, fresh water flux exhibits larger spatial fluctuations than surface heat flux because heavy rainfall occurs over convective areas embedded in broad non-convective or clear areas, whereas diurnal signals over whole model areas yield high spatial correlation of surface heat flux. As a result, mixed-layer salinities contribute more to the density differences than do mixed-layer temperatures.

Prediction of stock markets by the evolutionary mix-game model

NASA Astrophysics Data System (ADS)

Chen, Fang; Gou, Chengling; Guo, Xiaoqian; Gao, Jieping

2008-06-01

This paper presents the efforts of using the evolutionary mix-game model, which is a modified form of the agent-based mix-game model, to predict financial time series. Here, we have carried out three methods to improve the original mix-game model by adding the abilities of strategy evolution to agents, and then applying the new model referred to as the evolutionary mix-game model to forecast the Shanghai Stock Exchange Composite Index. The results show that these modifications can improve the accuracy of prediction greatly when proper parameters are chosen.

Prognosis of metastatic gastric and gastroesophageal junction cancer by HER2 status: a European and USA International collaborative analysis.

PubMed

Janjigian, Y Y; Werner, D; Pauligk, C; Steinmetz, K; Kelsen, D P; Jäger, E; Altmannsberger, H-M; Robinson, E; Tafe, L J; Tang, L H; Shah, M A; Al-Batran, S-E

2012-10-01

To determine whether human epidermal growth factor receptor 2 (HER2) status is an independent prognostic factor in metastatic gastric and gastroesophageal junction (GEJ) adenocarcinoma. Formalin-fixed paraffin-embedded tumor samples from 381 metastatic gastric/GEJ cancer patients enrolled at Krankenhaus Nordwest and Memorial Sloan-Kettering Cancer Centers on six first-line trials of chemotherapy without trastuzumab were examined for HER2 by immunohistochemistry (IHC) and in situ hybridization (ISH). IHC 3+ or ISH-positive tumors were considered HER2 positive. Seventy-eight of 381 patients (20%) had HER2-positive disease. In the multivariate logistic model, there were significantly higher rates of HER2 positivity in patients with liver metastasis (liver metastasis 31%; no liver metastasis 11%; P = 0.025) and intestinal histology (intestinal 33%; diffuse/mixed 8%; P = 0.001). No significant differences in HER2 positivity were found between resections and biopsies or primaries and metastases. Patients with HER2-positive gastric cancer had longer median overall survival compared with HER2-negative gastric cancer patients (13.9 versus 11.4 months, P = 0.047), but multivariate analysis indicated that HER2 status was not an independent prognostic factor (hazard ratio 0.79; 0.44-1.14; P = 0.194). Approximately 20% of Western patients with metastatic gastric cancer are HER2 positive. Unlike breast cancer, HER2 positivity is not independently prognostic of patient outcome in metastatic gastric or GEJ.

Quality of life improvement in HIV-1 patients treated with raltegravir in a real-life observational study: RACING.

PubMed

Spire, Bruno; Nait-Ighil, Lella; Pugliese, Pascal; Poizot-Martin, Isabelle; Jullien, Vincent; Marcelin, Anne-Geneviève; Billaud, Eric

2017-01-01

Good efficacy and safety of raltegravir in person living with HIV was demonstrated in clinical trials over five years, but real-life data, particularly about quality of life (QoL), are lacking. QoL was evaluated over time in adult patients first treated or switched to regimens containing raltegravir in an observational cohort study. Patient QoL was evaluated using the Fatigue Impact Scale (FIS) and the HIV Symptom Index (HSI). Data were collected at baseline and at 1, 3, 6, 12, 18, and 24 months. Baseline FIS and HSI subscores were compared with the scores at each visit using the paired Wilcoxon test. The impact of time, sociodemographic and medical variables upon patient-perceived fatigue and symptoms was also assessed using mixed multivariate models. From baseline, all FIS and HSI subscores improved significantly after one month of treatment. In addition, psychosocial FIS subscores and both the frequency of bothersome symptoms and HSI subscores improved significantly at each visit. Physical FIS subscores also improved significantly, except at month 18, whereas both cognitive and total FIS subscores improved only after 6 months and 24 months, respectively. In multivariate analysis, employment was independently associated over time with improved improvement in both FIS and HSI subscores. Patient QoL improved significantly over a 24-month period of treatment with a raltegravir-containing regimen. FIS and HSI are sensitive tools to measure the impact of new antiretroviral combinations on a patient's perception of QoL.

Factors associated with school-aged children's body mass index in Korean American families.

PubMed

Jang, Myoungock; Grey, Margaret; Sadler, Lois; Jeon, Sangchoon; Nam, Soohyun; Song, Hee-Jung; Whittemore, Robin

2017-08-01

To examine factors associated with children's body mass index and obesity-risk behaviours in Korean American families. Limited data are available about family factors related to overweight and obesity in Korean American children. A cross-sectional study. Convenient sampling was employed to recruit Korean American families in the Northeast of the United States between August 2014 and January 2015. Child, family and societal/demographic/community factors were measured with self-report questionnaires completed by mothers and children. Height and weight were measured to calculate body mass index. Data were analyzed using mixed effects models incorporating within-group correlation in siblings. The sample included 170 Korean American children and 137 mothers. In bivariate analyses, more child screen time, number of children in the household, greater parental underestimation of child's weight and children's participation in the school lunch program were significantly associated with higher child body mass index. In multivariate analyses that included variables showing significant bivariate relationship, no variable was associated with child body mass index. There were no child, family and societal/demographic/community factors related to child body mass index in Korean American families in the multivariate analysis, which is contrary to research in other racial/ethnic groups. In bivariate analyses, there is evidence that some factors were significantly related to child body mass index. Further research is needed to understand the unique behavioural, social and cultural features that contribute to childhood obesity in Korean American families. © 2017 John Wiley & Sons Ltd.

Quantifying athlete self-talk.

PubMed

Hardy, James; Hall, Craig R; Hardy, Lew

2005-09-01

Two studies were conducted. The aims of Study 1 were (a) to generate quantitative data on the content of athletes' self-talk and (b) to examine differences in the use of self-talk in general as well as the functions of self-talk in practice and competition settings. Differences in self-talk between the sexes, sport types and skill levels were also assessed. Athletes (n = 295, mean age = 21.9 years) from a variety of sports and competitive levels completed the Self-Talk Use Questionnaire (STUQ), which was developed specifically for the study. In Study 1, single-factor between-group multivariate analyses of variance revealed significant differences across sex and sport type for the content of self-talk. Mixed-model multivariate analyses of variance revealed overall greater use of self-talk, as well as increased use of the functions of self-talk, in competition compared with practice. Moreover, individual sport athletes reported greater use of self-talk, as well as the functions of self-talk, than their team sport counterparts. In Study 2, recreational volleyball players (n = 164, mean age = 21.5 years) completed a situationally modified STUQ. The results were very similar to those of Study 1. That the content of athlete self-talk was generally positive, covert and abbreviated lends support to the application of Vygotsky's (1986) verbal self-regulation theory to the study of self-talk in sport. Researchers are encouraged to examine the effectiveness of self-talk in future studies.

Three novel approaches to structural identifiability analysis in mixed-effects models.

PubMed

Janzén, David L I; Jirstrand, Mats; Chappell, Michael J; Evans, Neil D

2016-05-06

Structural identifiability is a concept that considers whether the structure of a model together with a set of input-output relations uniquely determines the model parameters. In the mathematical modelling of biological systems, structural identifiability is an important concept since biological interpretations are typically made from the parameter estimates. For a system defined by ordinary differential equations, several methods have been developed to analyse whether the model is structurally identifiable or otherwise. Another well-used modelling framework, which is particularly useful when the experimental data are sparsely sampled and the population variance is of interest, is mixed-effects modelling. However, established identifiability analysis techniques for ordinary differential equations are not directly applicable to such models. In this paper, we present and apply three different methods that can be used to study structural identifiability in mixed-effects models. The first method, called the repeated measurement approach, is based on applying a set of previously established statistical theorems. The second method, called the augmented system approach, is based on augmenting the mixed-effects model to an extended state-space form. The third method, called the Laplace transform mixed-effects extension, is based on considering the moment invariants of the systems transfer function as functions of random variables. To illustrate, compare and contrast the application of the three methods, they are applied to a set of mixed-effects models. Three structural identifiability analysis methods applicable to mixed-effects models have been presented in this paper. As method development of structural identifiability techniques for mixed-effects models has been given very little attention, despite mixed-effects models being widely used, the methods presented in this paper provides a way of handling structural identifiability in mixed-effects models previously not possible. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Within-individual correlations reveal link between a behavioral syndrome, condition and cortisol in free-ranging Belding's ground squirrels

PubMed Central

Brooks, Katherine C.; Mateo, Jill. M.

2014-01-01

Animals often exhibit consistent individual differences in behavior (i.e. animal personality) and correlations between behaviors (i.e. behavioral syndromes), yet the causes of those patterns of behavioral variation remain insufficiently understood. Many authors hypothesize that state-dependent behavior produces animal personality and behavioral syndromes. However, empirical studies assessing patterns of covariation among behavioral traits and state variables have produced mixed results. New statistical methods that partition correlations into between-individual and residual within-individual correlations offer an opportunity to more sufficiently quantify relationships among behaviors and state variables to assess hypotheses of animal personality and behavioral syndromes. In a population of wild Belding's ground squirrels (Urocitellus beldingi) we repeatedly measured activity, exploration, and response to restraint behaviors alongside glucocorticoids and nutritional condition. We used multivariate mixed models to determine whether between-individual or within-individual correlations drive phenotypic relationships among traits. Squirrels had consistent individual differences for all five traits. At the between-individual level, activity and exploration were positively correlated whereas both traits negatively correlated with response to restraint, demonstrating a behavioral syndrome. At the within-individual level, condition negatively correlated with cortisol, activity and exploration. Importantly, this indicates that although behavior is state-dependent, which may play a role in animal personality and behavioral syndromes, feedback mechanisms between condition and behavior appear not to produce consistent individual differences in behavior and correlations between them. PMID:25598565

Chiral micellar electrokinetic chromatography (CMEKC)-atmospheric pressure photoionization of benzoin derivatives using mixed molecular micelles

PubMed Central

He, Jun; Shamsi, Shahab A.

2012-01-01

In the present work we report, for the first time, the successful on-line coupling of chiral micellar electrokinetic chromatography (CMEKC) to atmospheric pressure photo-ionization mass spectrometry (APPI-MS). Four structurally similar neutral test solutes (e.g., benzoin derivatives) were successfully ionized by APPI-MS. The mass spectra in the positive ion mode showed that the protonated molecular ions of benzoins are not the most abundant fragment ions. Simultaneous enantioseparation by CMEKC and on-line APPI-MS detection of four photoinitiators: hydrobenzoin (HBNZ), benzoin (BNZ), benzoin methyl ether (BME), benzoin ethyl ether (BEE), were achieved using an optimized molar ratio of mixed molecular micelle of two polymeric chiral surfactants (polysodium N-undecenoxy carbonyl-L-leucinate and polysodium N-undecenoyl-L,L-leucylvalinate). The CMEKC conditions, such as voltage, chiral polymeric surfactant concentration, buffer pH, and BGE concentration, were optimized using a multivariate central composite design (CCD). The sheath liquid composition (involving % v/v methanol, dopant concentration, electrolyte additive concentration, and flow rate) and spray chamber parameters (drying gas flow rate, drying gas temperature, and vaporizer temperature) were also optimized with CCD. Models built based on the CCD results and response surface method was used to analyze the interactions between factors and their effects on the responses. The final overall optimum conditions for CMEKC-APPI-MS were also predicted and found in agreement with the experimentally optimized parameters. PMID:21500208

Exploring breast cancer preventive lifestyle and social support of Iranian women: a study protocol for a mixed-methods approach.

PubMed

Khazaee-Pool, Maryam; Pashaei, Tahereh; Jahangiry, Leila; Ponnet, Koen; Gholami, Ali

2017-06-07

It is widely accepted that a healthy lifestyle may decrease the probability of developing cancer. This study aimed to describe a study protocol that makes it possible to explore preventive health lifestyles of Iranian women and their received social support for the purpose of developing cultural strategies to increase breast cancer prevention. A mixed-methods study will be accomplished in two sequential parts. First, a cross-sectional study will be conducted in which 2,250 Iranian women are recruited by using a random multistage cluster sampling of 20 health care centers. Structured face-to-face interviews will be conducted to obtain information on the participants' health lifestyle and perceived social support. Data will be analyzed using both multivariate regression and structural equation modeling techniques. Then, a qualitative study will be conducted among employed women using a purposive sampling design. Data will be collected by means of focus groups and semi-structured interviews and will be analyzed using a conventional content analysis approach. The results of the quantitative and qualitative study will be used to develop breast cancer preventive strategies. Researchers need to acquire knowledge regarding the lifestyle and perceived social support of Iranian women that will foster culturally competent approaches to promote healthy lifestyles to develop breast cancer preventive strategies. Examining breast cancer preventive lifestyles provides valuable information for designing applicable intervention programs for improving women's health.

Motivations for genetic testing for lung cancer risk among young smokers.

PubMed

O'Neill, Suzanne C; Lipkus, Isaac M; Sanderson, Saskia C; Shepperd, James; Docherty, Sharron; McBride, Colleen M

2013-11-01

To examine why young people might want to undergo genetic susceptibility testing for lung cancer despite knowing that tested gene variants are associated with small increases in disease risk. The authors used a mixed-method approach to evaluate motives for and against genetic testing and the association between these motivations and testing intentions in 128 college students who smoke. Exploratory factor analysis yielded four reliable factors: Test Scepticism, Test Optimism, Knowledge Enhancement and Smoking Optimism. Test Optimism and Knowledge Enhancement correlated positively with intentions to test in bivariate and multivariate analyses (ps<0.001). Test Scepticism correlated negatively with testing intentions in multivariate analyses (p<0.05). Open-ended questions assessing testing motivations generally replicated themes of the quantitative survey. In addition to learning about health risks, young people may be motivated to seek genetic testing for reasons, such as gaining knowledge about new genetic technologies more broadly.

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data.

PubMed

Zhou, Hua; Blangero, John; Dyer, Thomas D; Chan, Kei-Hang K; Lange, Kenneth; Sobel, Eric M

2017-04-01

Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper, we reexamine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (six CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1,388 individuals in 124 pedigrees) takes less than 2 min and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 min and 1.5 GB of memory. The algorithm is implemented as the Ped-GWAS Analysis (Option 29) in the Mendel statistical genetics package, which is freely available for Macintosh, Linux, and Windows platforms from http://genetics.ucla.edu/software/mendel. © 2016 WILEY PERIODICALS, INC.

Hydrochemical evolution and groundwater flow processes in the Galilee and Eromanga basins, Great Artesian Basin, Australia: a multivariate statistical approach.

PubMed

Moya, Claudio E; Raiber, Matthias; Taulis, Mauricio; Cox, Malcolm E

2015-03-01

The Galilee and Eromanga basins are sub-basins of the Great Artesian Basin (GAB). In this study, a multivariate statistical approach (hierarchical cluster analysis, principal component analysis and factor analysis) is carried out to identify hydrochemical patterns and assess the processes that control hydrochemical evolution within key aquifers of the GAB in these basins. The results of the hydrochemical assessment are integrated into a 3D geological model (previously developed) to support the analysis of spatial patterns of hydrochemistry, and to identify the hydrochemical and hydrological processes that control hydrochemical variability. In this area of the GAB, the hydrochemical evolution of groundwater is dominated by evapotranspiration near the recharge area resulting in a dominance of the Na-Cl water types. This is shown conceptually using two selected cross-sections which represent discrete groundwater flow paths from the recharge areas to the deeper parts of the basins. With increasing distance from the recharge area, a shift towards a dominance of carbonate (e.g. Na-HCO3 water type) has been observed. The assessment of hydrochemical changes along groundwater flow paths highlights how aquifers are separated in some areas, and how mixing between groundwater from different aquifers occurs elsewhere controlled by geological structures, including between GAB aquifers and coal bearing strata of the Galilee Basin. The results of this study suggest that distinct hydrochemical differences can be observed within the previously defined Early Cretaceous-Jurassic aquifer sequence of the GAB. A revision of the two previously recognised hydrochemical sequences is being proposed, resulting in three hydrochemical sequences based on systematic differences in hydrochemistry, salinity and dominant hydrochemical processes. The integrated approach presented in this study which combines different complementary multivariate statistical techniques with a detailed assessment of the geological framework of these sedimentary basins, can be adopted in other complex multi-aquifer systems to assess hydrochemical evolution and its geological controls. Copyright © 2014 Elsevier B.V. All rights reserved.

Alcohol mixed with energy drinks: Associations with risky drinking and functioning in high school.

PubMed

Tucker, Joan S; Troxel, Wendy M; Ewing, Brett A; D'Amico, Elizabeth J

2016-10-01

Mixing alcohol with energy drinks is associated with heavier drinking and related problems among college students. However, little is known about how high school drinkers who mix alcohol with energy drinks (AmED) compare to those who do not (AwoED). This study compares high school AmED and AwoED users on their alcohol use during middle and high school, as well as key domains of functioning in high school. Two surveys were conducted three years apart in adolescents initially recruited from 16 middle schools in Southern California. The analytic sample consists of 696 past month drinkers. Multivariable models compared AmED and AwoED users on alcohol use, mental health, social functioning, academic orientation, delinquency and other substance use at age 17, and on their alcohol use and related cognitions at age 14. AmED was reported by 13% of past month drinkers. AmED and AwoED users did not differ on alcohol use or cognitions in middle school, but AmED users drank more often, more heavily, and reported more negative consequences in high school. AmED users were also more likely to report poor grades, delinquent behavior, substance use-related unsafe driving, public intoxication, and drug use than AwoED users in high school. Group differences were not found on mental health, social functioning, or academic aspirations. AmED use is common among high school drinkers. The higher risk behavioral profile of these young AmED users, which includes drug use and substance use-related unsafe driving, is a significant cause for concern and warrants further attention. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Serious gaming during multidisciplinary rehabilitation for patients with complex chronic pain or fatigue complaints: study protocol for a controlled trial and process evaluation

PubMed Central

Joosen, Margot C W; Mert, Agali; Zedlitz, Aglaia; Vrijhoef, Hubertus J M

2017-01-01

Introduction Many individuals suffer from chronic pain or functional somatic syndromes and face boundaries for diminishing functional limitations by means of biopsychosocial interventions. Serious gaming could complement multidisciplinary interventions through enjoyment and independent accessibility. A study protocol is presented for studying whether, how, for which patients and under what circumstances, serious gaming improves patient health outcomes during regular multidisciplinary rehabilitation. Methods and analysis A mixed-methods design is described that prioritises a two-armed naturalistic quasi-experiment. An experimental group is composed of patients who follow serious gaming during an outpatient multidisciplinary programme at two sites of a Dutch rehabilitation centre. Control group patients follow the same programme without serious gaming in two similar sites. Multivariate mixed-modelling analysis is planned for assessing how much variance in 250 patient records of routinely monitored pain intensity, pain coping and cognition, fatigue and psychopathology outcomes is attributable to serious gaming. Embedded qualitative methods include unobtrusive collection and analyses of stakeholder focus group interviews, participant feedback and semistructured patient interviews. Process analyses are carried out by a systematic approach of mixing qualitative and quantitative methods at various stages of the research. Ethics and dissemination The Ethics Committee of the Tilburg School of Social and Behavioural Sciences approved the research after reviewing the protocol for the protection of patients’ interests in conformity to the letter and rationale of the applicable laws and research practice (EC 2016.25t). Findings will be presented in research articles and international scientific conferences. Trial registration number A prospective research protocol for the naturalistic quasi-experimental outcome evaluation was entered in the Dutch trial register (registration number: NTR6020; Pre-results). PMID:28600377