Idealized models of the joint probability distribution of wind speeds

NASA Astrophysics Data System (ADS)

Monahan, Adam H.

2018-05-01

The joint probability distribution of wind speeds at two separate locations in space or points in time completely characterizes the statistical dependence of these two quantities, providing more information than linear measures such as correlation. In this study, we consider two models of the joint distribution of wind speeds obtained from idealized models of the dependence structure of the horizontal wind velocity components. The bivariate Rice distribution follows from assuming that the wind components have Gaussian and isotropic fluctuations. The bivariate Weibull distribution arises from power law transformations of wind speeds corresponding to vector components with Gaussian, isotropic, mean-zero variability. Maximum likelihood estimates of these distributions are compared using wind speed data from the mid-troposphere, from different altitudes at the Cabauw tower in the Netherlands, and from scatterometer observations over the sea surface. While the bivariate Rice distribution is more flexible and can represent a broader class of dependence structures, the bivariate Weibull distribution is mathematically simpler and may be more convenient in many applications. The complexity of the mathematical expressions obtained for the joint distributions suggests that the development of explicit functional forms for multivariate speed distributions from distributions of the components will not be practical for more complicated dependence structure or more than two speed variables.

[Comparison of simple pooling and bivariate model used in meta-analyses of diagnostic test accuracy published in Chinese journals].

PubMed

Huang, Yuan-sheng; Yang, Zhi-rong; Zhan, Si-yan

2015-06-18

To investigate the use of simple pooling and bivariate model in meta-analyses of diagnostic test accuracy (DTA) published in Chinese journals (January to November, 2014), compare the differences of results from these two models, and explore the impact of between-study variability of sensitivity and specificity on the differences. DTA meta-analyses were searched through Chinese Biomedical Literature Database (January to November, 2014). Details in models and data for fourfold table were extracted. Descriptive analysis was conducted to investigate the prevalence of the use of simple pooling method and bivariate model in the included literature. Data were re-analyzed with the two models respectively. Differences in the results were examined by Wilcoxon signed rank test. How the results differences were affected by between-study variability of sensitivity and specificity, expressed by I2, was explored. The 55 systematic reviews, containing 58 DTA meta-analyses, were included and 25 DTA meta-analyses were eligible for re-analysis. Simple pooling was used in 50 (90.9%) systematic reviews and bivariate model in 1 (1.8%). The remaining 4 (7.3%) articles used other models pooling sensitivity and specificity or pooled neither of them. Of the reviews simply pooling sensitivity and specificity, 41(82.0%) were at the risk of wrongly using Meta-disc software. The differences in medians of sensitivity and specificity between two models were both 0.011 (P<0.001, P=0.031 respectively). Greater differences could be found as I2 of sensitivity or specificity became larger, especially when I2>75%. Most DTA meta-analyses published in Chinese journals(January to November, 2014) combine the sensitivity and specificity by simple pooling. Meta-disc software can pool the sensitivity and specificity only through fixed-effect model, but a high proportion of authors think it can implement random-effect model. Simple pooling tends to underestimate the results compared with bivariate model. The greater the between-study variance is, the more likely the simple pooling has larger deviation. It is necessary to increase the knowledge level of statistical methods and software for meta-analyses of DTA data.

Moving Average Models with Bivariate Exponential and Geometric Distributions.

DTIC Science & Technology

1985-03-01

ordinary time series and of point processes. Developments in Statistics, Vol. 1, P.R. Krishnaiah , ed. Academic Press, New York. [9] Esary, J.D. and...valued and discrete - valued time series with ARMA correlation structure. Multivariate Analysis V, P.R. Krishnaiah , ed. North-Holland. 151-166. [28

Vector wind and vector wind shear models 0 to 27 km altitude for Cape Kennedy, Florida, and Vandenberg AFB, California

NASA Technical Reports Server (NTRS)

Smith, O. E.

1976-01-01

The techniques are presented to derive several statistical wind models. The techniques are from the properties of the multivariate normal probability function. Assuming that the winds can be considered as bivariate normally distributed, then (1) the wind components and conditional wind components are univariate normally distributed, (2) the wind speed is Rayleigh distributed, (3) the conditional distribution of wind speed given a wind direction is Rayleigh distributed, and (4) the frequency of wind direction can be derived. All of these distributions are derived from the 5-sample parameter of wind for the bivariate normal distribution. By further assuming that the winds at two altitudes are quadravariate normally distributed, then the vector wind shear is bivariate normally distributed and the modulus of the vector wind shear is Rayleigh distributed. The conditional probability of wind component shears given a wind component is normally distributed. Examples of these and other properties of the multivariate normal probability distribution function as applied to Cape Kennedy, Florida, and Vandenberg AFB, California, wind data samples are given. A technique to develop a synthetic vector wind profile model of interest to aerospace vehicle applications is presented.

Developing a bivariate spatial association measure: An integration of Pearson's r and Moran's I

NASA Astrophysics Data System (ADS)

Lee, Sang-Il

This research is concerned with developing a bivariate spatial association measure or spatial correlation coefficient, which is intended to capture spatial association among observations in terms of their point-to-point relationships across two spatial patterns. The need for parameterization of the bivariate spatial dependence is precipitated by the realization that aspatial bivariate association measures, such as Pearson's correlation coefficient, do not recognize spatial distributional aspects of data sets. This study devises an L statistic by integrating Pearson's r as an aspatial bivariate association measure and Moran's I as a univariate spatial association measure. The concept of a spatial smoothing scalar (SSS) plays a pivotal role in this task.

Shared genetic factors underlie migraine and depression

PubMed Central

Yang, Yuanhao; Zhao, Huiying; Heath, Andrew C; Madden, Pamela AF; Martin, Nicholas G; Nyholt, Dale R

2017-01-01

Migraine frequently co-occurs with depression. Using a large sample of Australian twin pairs, we aimed to characterise the extent to which shared genetic factors underlie these two disorders. Migraine was classified using three diagnostic measures, including self-reported migraine, the ID migraine™ screening tool, or migraine without aura (MO) and migraine with aura (MA) based on International Headache Society (IHS) diagnostic criteria. Major depressive disorder (MDD) and minor depressive disorder (MiDD) were classified using the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria. Univariate and bivariate twin models, with and without sex-limitation, were constructed to estimate the univariate and bivariate variance components and genetic correlation for migraine and depression. The univariate heritability of broad migraine (self-reported, ID migraine or IHS MO/MA) and broad depression (MiDD or MDD) was estimated at 56% (95% confidence interval [CI]: 53–60%) and 42% (95% CI: 37–46%), respectively. A significant additive genetic correlation (rG=0.36, 95% CI: 0.29–0.43) and bivariate heritability (h2=5.5%, 95% CI: 3.6–7.8%) was observed between broad migraine and depression using the bivariate Cholesky model. Notably, both the bivariate h2 (13.3%, 95% CI: 7.0–24.5%) and rG (0.51, 95% CI: 0.37–0.69) estimates significantly increased when analysing the more narrow clinically-accepted diagnoses of IHS MO/MA and MDD. Our results indicate that for both broad and narrow definitions, the observed comorbidity between migraine and depression can be explained almost entirely by shared underlying genetically determined disease mechanisms. PMID:27302564

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain.

PubMed

Rabbani, Hossein; Sonka, Milan; Abramoff, Michael D

2013-01-01

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR.

An Agent-Based Modeling Approach to Integrate Tsunami Science, Human Behavior, and Unplanned Network Disruptions for Nearfield Tsunami Evacuation

NASA Astrophysics Data System (ADS)

Cox, D. T.; Wang, H.; Cramer, L.; Mostafizi, A.; Park, H.

2016-12-01

A 2015 heatwave in Pakistan is blamed for over a thousand deaths. This event consisted of several days of very high temperatures and unusually high humidity for this region. However, none of these days exceeded the threshold for "extreme danger" in terms of the heat index. The heat index is a univariate function of both temperature and humidity which is universally applied at all locations regardless of local climate. Understanding extremes which arise from multiple factors is challenging. In this paper we will present a tool for examining bivariate extreme behavior. The tool, developed in the statistical software R, draws isolines of equal exceedance probability. These isolines can be understood as bivariate "return levels". The tool is based on a dependence framework specific for extremes, is semiparametric, and is able to extrapolate isolines beyond the range of the data. We illustrate this tool using the Pakistan heat wave data and other bivariate data.

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain

PubMed Central

Sonka, Milan; Abramoff, Michael D.

2013-01-01

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR. PMID:24222760

Reliability Implications in Wood Systems of a Bivariate Gaussian-Weibull Distribution and the Associated Univariate Pseudo-truncated Weibull

Treesearch

Steve P. Verrill; James W. Evans; David E. Kretschmann; Cherilyn A. Hatfield

2014-01-01

Two important wood properties are the modulus of elasticity (MOE) and the modulus of rupture (MOR). In the past, the statistical distribution of the MOE has often been modeled as Gaussian, and that of the MOR as lognormal or as a two- or three-parameter Weibull distribution. It is well known that MOE and MOR are positively correlated. To model the simultaneous behavior...

Beyond Reading Alone: The Relationship Between Aural Literacy And Asthma Management

PubMed Central

Rosenfeld, Lindsay; Rudd, Rima; Emmons, Karen M.; Acevedo-García, Dolores; Martin, Laurie; Buka, Stephen

2010-01-01

Objectives To examine the relationship between literacy and asthma management with a focus on the oral exchange. Methods Study participants, all of whom reported asthma, were drawn from the New England Family Study (NEFS), an examination of links between education and health. NEFS data included reading, oral (speaking), and aural (listening) literacy measures. An additional survey was conducted with this group of study participants related to asthma issues, particularly asthma management. Data analysis focused on bivariate and multivariable logistic regression. Results In bivariate logistic regression models exploring aural literacy, there was a statistically significant association between those participants with lower aural literacy skills and less successful asthma management (OR:4.37, 95%CI:1.11, 17.32). In multivariable logistic regression analyses, controlling for gender, income, and race in separate models (one-at-a-time), there remained a statistically significant association between those participants with lower aural literacy skills and less successful asthma management. Conclusion Lower aural literacy skills seem to complicate asthma management capabilities. Practice Implications Greater attention to the oral exchange, in particular the listening skills highlighted by aural literacy, as well as other related literacy skills may help us develop strategies for clear communication related to asthma management. PMID:20399060

Statistical analysis of multivariate atmospheric variables. [cloud cover

NASA Technical Reports Server (NTRS)

Tubbs, J. D.

1979-01-01

Topics covered include: (1) estimation in discrete multivariate distributions; (2) a procedure to predict cloud cover frequencies in the bivariate case; (3) a program to compute conditional bivariate normal parameters; (4) the transformation of nonnormal multivariate to near-normal; (5) test of fit for the extreme value distribution based upon the generalized minimum chi-square; (6) test of fit for continuous distributions based upon the generalized minimum chi-square; (7) effect of correlated observations on confidence sets based upon chi-square statistics; and (8) generation of random variates from specified distributions.

Creating Realistic Data Sets with Specified Properties via Simulation

ERIC Educational Resources Information Center

Goldman, Robert N.; McKenzie, John D. Jr.

2009-01-01

We explain how to simulate both univariate and bivariate raw data sets having specified values for common summary statistics. The first example illustrates how to "construct" a data set having prescribed values for the mean and the standard deviation--for a one-sample t test with a specified outcome. The second shows how to create a bivariate data…

Probabilistic forecasting of extreme weather events based on extreme value theory

NASA Astrophysics Data System (ADS)

Van De Vyver, Hans; Van Schaeybroeck, Bert

2016-04-01

Extreme events in weather and climate such as high wind gusts, heavy precipitation or extreme temperatures are commonly associated with high impacts on both environment and society. Forecasting extreme weather events is difficult, and very high-resolution models are needed to describe explicitly extreme weather phenomena. A prediction system for such events should therefore preferably be probabilistic in nature. Probabilistic forecasts and state estimations are nowadays common in the numerical weather prediction community. In this work, we develop a new probabilistic framework based on extreme value theory that aims to provide early warnings up to several days in advance. We consider the combined events when an observation variable Y (for instance wind speed) exceeds a high threshold y and its corresponding deterministic forecasts X also exceeds a high forecast threshold y. More specifically two problems are addressed:} We consider pairs (X,Y) of extreme events where X represents a deterministic forecast, and Y the observation variable (for instance wind speed). More specifically two problems are addressed: Given a high forecast X=x_0, what is the probability that Y>y? In other words: provide inference on the conditional probability: [ Pr{Y>y|X=x_0}. ] Given a probabilistic model for Problem 1, what is the impact on the verification analysis of extreme events. These problems can be solved with bivariate extremes (Coles, 2001), and the verification analysis in (Ferro, 2007). We apply the Ramos and Ledford (2009) parametric model for bivariate tail estimation of the pair (X,Y). The model accommodates different types of extremal dependence and asymmetry within a parsimonious representation. Results are presented using the ensemble reforecast system of the European Centre of Weather Forecasts (Hagedorn, 2008). Coles, S. (2001) An Introduction to Statistical modelling of Extreme Values. Springer-Verlag.Ferro, C.A.T. (2007) A probability model for verifying deterministic forecasts of extreme events. Wea. Forecasting {22}, 1089-1100.Hagedorn, R. (2008) Using the ECMWF reforecast dataset to calibrate EPS forecasts. ECMWF Newsletter, {117}, 8-13.Ramos, A., Ledford, A. (2009) A new class of models for bivariate joint tails. J.R. Statist. Soc. B {71}, 219-241.

Model-based methods for case definitions from administrative health data: application to rheumatoid arthritis

PubMed Central

Kroeker, Kristine; Widdifield, Jessica; Muthukumarana, Saman; Jiang, Depeng; Lix, Lisa M

2017-01-01

Objective This research proposes a model-based method to facilitate the selection of disease case definitions from validation studies for administrative health data. The method is demonstrated for a rheumatoid arthritis (RA) validation study. Study design and setting Data were from 148 definitions to ascertain cases of RA in hospital, physician and prescription medication administrative data. We considered: (A) separate univariate models for sensitivity and specificity, (B) univariate model for Youden’s summary index and (C) bivariate (ie, joint) mixed-effects model for sensitivity and specificity. Model covariates included the number of diagnoses in physician, hospital and emergency department records, physician diagnosis observation time, duration of time between physician diagnoses and number of RA-related prescription medication records. Results The most common case definition attributes were: 1+ hospital diagnosis (65%), 2+ physician diagnoses (43%), 1+ specialist physician diagnosis (51%) and 2+ years of physician diagnosis observation time (27%). Statistically significant improvements in sensitivity and/or specificity for separate univariate models were associated with (all p values <0.01): 2+ and 3+ physician diagnoses, unlimited physician diagnosis observation time, 1+ specialist physician diagnosis and 1+ RA-related prescription medication records (65+ years only). The bivariate model produced similar results. Youden’s index was associated with these same case definition criteria, except for the length of the physician diagnosis observation time. Conclusion A model-based method provides valuable empirical evidence to aid in selecting a definition(s) for ascertaining diagnosed disease cases from administrative health data. The choice between univariate and bivariate models depends on the goals of the validation study and number of case definitions. PMID:28645978

Some bivariate distributions for modeling the strength properties of lumber

Treesearch

Richard A. Johnson; James W. Evans; David W. Green

Accurate modeling of the joint stochastic nature of the strength properties of dimension lumber is essential to the determination of reliability-based design safety factors. This report reviews the major techniques for obtaining bivariate distributions and then discusses bivariate distributions whose marginal distributions suggest they might be useful for modeling the...

FUNSTAT and statistical image representations

NASA Technical Reports Server (NTRS)

Parzen, E.

1983-01-01

General ideas of functional statistical inference analysis of one sample and two samples, univariate and bivariate are outlined. ONESAM program is applied to analyze the univariate probability distributions of multi-spectral image data.

Multivariate carbon and nitrogen stable isotope model for the reconstruction of prehistoric human diet.

PubMed

Froehle, A W; Kellner, C M; Schoeninger, M J

2012-03-01

Using a sample of published archaeological data, we expand on an earlier bivariate carbon model for diet reconstruction by adding bone collagen nitrogen stable isotope values (δ(15) N), which provide information on trophic level and consumption of terrestrial vs. marine protein. The bivariate carbon model (δ(13) C(apatite) vs. δ(13) C(collagen) ) provides detailed information on the isotopic signatures of whole diet and dietary protein, but is limited in its ability to distinguish between C(4) and marine protein. Here, using cluster analysis and discriminant function analysis, we generate a multivariate diet reconstruction model that incorporates δ(13) C(apatite) , δ(13) C(collagen) , and δ(15) N holistically. Inclusion of the δ(15) N data proves useful in resolving protein-related limitations of the bivariate carbon model, and splits the sample into five distinct dietary clusters. Two significant discriminant functions account for 98.8% of the sample variance, providing a multivariate model for diet reconstruction. Both carbon variables dominate the first function, while δ(15) N most strongly influences the second. Independent support for the functions' ability to accurately classify individuals according to diet comes from a small sample of experimental rats, which cluster as expected from their diets. The new model also provides a statistical basis for distinguishing between food sources with similar isotopic signatures, as in a previously analyzed archaeological population from Saipan (see Ambrose et al.: AJPA 104(1997) 343-361). Our model suggests that the Saipan islanders' (13) C-enriched signal derives mainly from sugarcane, not seaweed. Further development and application of this model can similarly improve dietary reconstructions in archaeological, paleontological, and primatological contexts. Copyright © 2011 Wiley Periodicals, Inc.

Sexual Harassment Retaliation Climate DEOCS 4.1 Construct Validity Summary

DTIC Science & Technology

2017-08-01

exploratory factor analysis, and bivariate correlations (sample 1) 2) To determine the factor structure of the remaining (final) questions via...statistics, reliability analysis, exploratory factor analysis, and bivariate correlations of the prospective Sexual Harassment Retaliation Climate...reported by the survey requester). For information regarding the composition of sample, refer to Table 1. Table 1. Sample 1 Demographics n

Associations of Social Support, Friends Only Known Through the Internet, and Health-Related Quality of Life with Internet Gaming Disorder in Adolescence.

PubMed

Wartberg, Lutz; Kriston, Levente; Kammerl, Rudolf

2017-07-01

Internet Gaming Disorder (IGD) has been included in the current edition of the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5). In the present study, the relationship among social support, friends only known through the Internet, health-related quality of life, and IGD in adolescence was explored for the first time. For this purpose, 1,095 adolescents aged from 12 to 14 years were surveyed with a standardized questionnaire concerning IGD, self-perceived social support, proportion of friends only known through the Internet, and health-related quality of life. The authors conducted unpaired t-tests, a chi-square test, as well as correlation and logistic regression analyses. According to the statistical analyses, adolescents with IGD reported lower self-perceived social support, more friends only known through the Internet, and a lower health-related quality of life compared with the group without IGD. Both in bivariate and multivariate logistic regression models, statistically significant associations between IGD and male gender, a higher proportion of friends only known through the Internet, and a lower health-related quality of life (multivariate model: Nagelkerke's R 2  = 0.37) were revealed. Lower self-perceived social support was related to IGD in the bivariate model only. In summary, quality of life and social aspects seem to be important factors for IGD in adolescence and therefore should be incorporated in further (longitudinal) studies. The findings of the present survey may provide starting points for the development of prevention and intervention programs for adolescents affected by IGD.

Fisher information for two gamma frailty bivariate Weibull models.

PubMed

Bjarnason, H; Hougaard, P

2000-03-01

The asymptotic properties of frailty models for multivariate survival data are not well understood. To study this aspect, the Fisher information is derived in the standard bivariate gamma frailty model, where the survival distribution is of Weibull form conditional on the frailty. For comparison, the Fisher information is also derived in the bivariate gamma frailty model, where the marginal distribution is of Weibull form.

Multiple imputation methods for bivariate outcomes in cluster randomised trials.

PubMed

DiazOrdaz, K; Kenward, M G; Gomes, M; Grieve, R

2016-09-10

Missing observations are common in cluster randomised trials. The problem is exacerbated when modelling bivariate outcomes jointly, as the proportion of complete cases is often considerably smaller than the proportion having either of the outcomes fully observed. Approaches taken to handling such missing data include the following: complete case analysis, single-level multiple imputation that ignores the clustering, multiple imputation with a fixed effect for each cluster and multilevel multiple imputation. We contrasted the alternative approaches to handling missing data in a cost-effectiveness analysis that uses data from a cluster randomised trial to evaluate an exercise intervention for care home residents. We then conducted a simulation study to assess the performance of these approaches on bivariate continuous outcomes, in terms of confidence interval coverage and empirical bias in the estimated treatment effects. Missing-at-random clustered data scenarios were simulated following a full-factorial design. Across all the missing data mechanisms considered, the multiple imputation methods provided estimators with negligible bias, while complete case analysis resulted in biased treatment effect estimates in scenarios where the randomised treatment arm was associated with missingness. Confidence interval coverage was generally in excess of nominal levels (up to 99.8%) following fixed-effects multiple imputation and too low following single-level multiple imputation. Multilevel multiple imputation led to coverage levels of approximately 95% throughout. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Bivariate categorical data analysis using normal linear conditional multinomial probability model.

PubMed

Sun, Bingrui; Sutradhar, Brajendra

2015-02-10

Bivariate multinomial data such as the left and right eyes retinopathy status data are analyzed either by using a joint bivariate probability model or by exploiting certain odds ratio-based association models. However, the joint bivariate probability model yields marginal probabilities, which are complicated functions of marginal and association parameters for both variables, and the odds ratio-based association model treats the odds ratios involved in the joint probabilities as 'working' parameters, which are consequently estimated through certain arbitrary 'working' regression models. Also, this later odds ratio-based model does not provide any easy interpretations of the correlations between two categorical variables. On the basis of pre-specified marginal probabilities, in this paper, we develop a bivariate normal type linear conditional multinomial probability model to understand the correlations between two categorical variables. The parameters involved in the model are consistently estimated using the optimal likelihood and generalized quasi-likelihood approaches. The proposed model and the inferences are illustrated through an intensive simulation study as well as an analysis of the well-known Wisconsin Diabetic Retinopathy status data. Copyright © 2014 John Wiley & Sons, Ltd.

From Weakly Chaotic Dynamics to Deterministic Subdiffusion via Copula Modeling

NASA Astrophysics Data System (ADS)

Nazé, Pierre

2018-03-01

Copula modeling consists in finding a probabilistic distribution, called copula, whereby its coupling with the marginal distributions of a set of random variables produces their joint distribution. The present work aims to use this technique to connect the statistical distributions of weakly chaotic dynamics and deterministic subdiffusion. More precisely, we decompose the jumps distribution of Geisel-Thomae map into a bivariate one and determine the marginal and copula distributions respectively by infinite ergodic theory and statistical inference techniques. We verify therefore that the characteristic tail distribution of subdiffusion is an extreme value copula coupling Mittag-Leffler distributions. We also present a method to calculate the exact copula and joint distributions in the case where weakly chaotic dynamics and deterministic subdiffusion statistical distributions are already known. Numerical simulations and consistency with the dynamical aspects of the map support our results.

Random matrix theory for transition strengths: Applications and open questions

NASA Astrophysics Data System (ADS)

Kota, V. K. B.

2017-12-01

Embedded random matrix ensembles are generic models for describing statistical properties of finite isolated interacting quantum many-particle systems. A finite quantum system, induced by a transition operator, makes transitions from its states to the states of the same system or to those of another system. Examples are electromagnetic transitions (then the initial and final systems are same), nuclear beta and double beta decay (then the initial and final systems are different) and so on. Using embedded ensembles (EE), there are efforts to derive a good statistical theory for transition strengths. With m fermions (or bosons) in N mean-field single particle levels and interacting via two-body forces, we have with GOE embedding, the so called EGOE(1+2). Now, the transition strength density (transition strength multiplied by the density of states at the initial and final energies) is a convolution of the density generated by the mean-field one-body part with a bivariate spreading function due to the two-body interaction. Using the embedding U(N) algebra, it is established, for a variety of transition operators, that the spreading function, for sufficiently strong interactions, is close to a bivariate Gaussian. Also, as the interaction strength increases, the spreading function exhibits a transition from bivariate Breit-Wigner to bivariate Gaussian form. In appropriate limits, this EE theory reduces to the polynomial theory of Draayer, French and Wong on one hand and to the theory due to Flambaum and Izrailev for one-body transition operators on the other. Using spin-cutoff factors for projecting angular momentum, the theory is applied to nuclear matrix elements for neutrinoless double beta decay (NDBD). In this paper we will describe: (i) various developments in the EE theory for transition strengths; (ii) results for nuclear matrix elements for 130Te and 136Xe NDBD; (iii) important open questions in the current form of the EE theory.

Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach

PubMed Central

Chen, Yong; Hong, Chuan; Ning, Yang; Su, Xiao

2018-01-01

When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-binomial model for the meta-analysis of studies with binary outcomes. This model is based on the composite likelihood approach, and has several attractive features compared to the existing models such as bivariate generalized linear mixed model (Chu and Cole, 2006) and Sarmanov beta-binomial model (Chen et al., 2012). The advantages of the proposed marginal model include modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, since the marginal beta-binomial model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-binomial model with the bivariate generalized linear mixed model and the Sarmanov beta-binomial model by simulation studies. Interestingly, the results show that the marginal beta-binomial model performs better than the Sarmanov beta-binomial model, whether or not the true model is Sarmanov beta-binomial, and the marginal beta-binomial model is more robust than the bivariate generalized linear mixed model under model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration. PMID:26303591

A Statistical Tool for Examining Heat Waves and Other Extreme Phenomena Arising from Multiple Factors

NASA Astrophysics Data System (ADS)

Cooley, D. S.; Castillo, F.; Thibaud, E.

2017-12-01

A 2015 heatwave in Pakistan is blamed for over a thousand deaths. This event consisted of several days of very high temperatures and unusually high humidity for this region. However, none of these days exceeded the threshold for "extreme danger" in terms of the heat index. The heat index is a univariate function of both temperature and humidity which is universally applied at all locations regardless of local climate. Understanding extremes which arise from multiple factors is challenging. In this paper we will present a tool for examining bivariate extreme behavior. The tool, developed in the statistical software R, draws isolines of equal exceedance probability. These isolines can be understood as bivariate "return levels". The tool is based on a dependence framework specific for extremes, is semiparametric, and is able to extrapolate isolines beyond the range of the data. We illustrate this tool using the Pakistan heat wave data and other bivariate data.

Assessment of Coastal and Urban Flooding Hazards Applying Extreme Value Analysis and Multivariate Statistical Techniques: A Case Study in Elwood, Australia

NASA Astrophysics Data System (ADS)

Guimarães Nobre, Gabriela; Arnbjerg-Nielsen, Karsten; Rosbjerg, Dan; Madsen, Henrik

2016-04-01

Traditionally, flood risk assessment studies have been carried out from a univariate frequency analysis perspective. However, statistical dependence between hydrological variables, such as extreme rainfall and extreme sea surge, is plausible to exist, since both variables to some extent are driven by common meteorological conditions. Aiming to overcome this limitation, multivariate statistical techniques has the potential to combine different sources of flooding in the investigation. The aim of this study was to apply a range of statistical methodologies for analyzing combined extreme hydrological variables that can lead to coastal and urban flooding. The study area is the Elwood Catchment, which is a highly urbanized catchment located in the city of Port Phillip, Melbourne, Australia. The first part of the investigation dealt with the marginal extreme value distributions. Two approaches to extract extreme value series were applied (Annual Maximum and Partial Duration Series), and different probability distribution functions were fit to the observed sample. Results obtained by using the Generalized Pareto distribution demonstrate the ability of the Pareto family to model the extreme events. Advancing into multivariate extreme value analysis, first an investigation regarding the asymptotic properties of extremal dependence was carried out. As a weak positive asymptotic dependence between the bivariate extreme pairs was found, the Conditional method proposed by Heffernan and Tawn (2004) was chosen. This approach is suitable to model bivariate extreme values, which are relatively unlikely to occur together. The results show that the probability of an extreme sea surge occurring during a one-hour intensity extreme precipitation event (or vice versa) can be twice as great as what would occur when assuming independent events. Therefore, presuming independence between these two variables would result in severe underestimation of the flooding risk in the study area.

Modeling animal-vehicle collisions using diagonal inflated bivariate Poisson regression.

PubMed

Lao, Yunteng; Wu, Yao-Jan; Corey, Jonathan; Wang, Yinhai

2011-01-01

Two types of animal-vehicle collision (AVC) data are commonly adopted for AVC-related risk analysis research: reported AVC data and carcass removal data. One issue with these two data sets is that they were found to have significant discrepancies by previous studies. In order to model these two types of data together and provide a better understanding of highway AVCs, this study adopts a diagonal inflated bivariate Poisson regression method, an inflated version of bivariate Poisson regression model, to fit the reported AVC and carcass removal data sets collected in Washington State during 2002-2006. The diagonal inflated bivariate Poisson model not only can model paired data with correlation, but also handle under- or over-dispersed data sets as well. Compared with three other types of models, double Poisson, bivariate Poisson, and zero-inflated double Poisson, the diagonal inflated bivariate Poisson model demonstrates its capability of fitting two data sets with remarkable overlapping portions resulting from the same stochastic process. Therefore, the diagonal inflated bivariate Poisson model provides researchers a new approach to investigating AVCs from a different perspective involving the three distribution parameters (λ(1), λ(2) and λ(3)). The modeling results show the impacts of traffic elements, geometric design and geographic characteristics on the occurrences of both reported AVC and carcass removal data. It is found that the increase of some associated factors, such as speed limit, annual average daily traffic, and shoulder width, will increase the numbers of reported AVCs and carcass removals. Conversely, the presence of some geometric factors, such as rolling and mountainous terrain, will decrease the number of reported AVCs. Published by Elsevier Ltd.

Methods of Information Geometry to model complex shapes

NASA Astrophysics Data System (ADS)

De Sanctis, A.; Gattone, S. A.

2016-09-01

In this paper, a new statistical method to model patterns emerging in complex systems is proposed. A framework for shape analysis of 2- dimensional landmark data is introduced, in which each landmark is represented by a bivariate Gaussian distribution. From Information Geometry we know that Fisher-Rao metric endows the statistical manifold of parameters of a family of probability distributions with a Riemannian metric. Thus this approach allows to reconstruct the intermediate steps in the evolution between observed shapes by computing the geodesic, with respect to the Fisher-Rao metric, between the corresponding distributions. Furthermore, the geodesic path can be used for shape predictions. As application, we study the evolution of the rat skull shape. A future application in Ophthalmology is introduced.

Predicting the Size of Sunspot Cycle 24 on the Basis of Single- and Bi-Variate Geomagnetic Precursor Methods

NASA Technical Reports Server (NTRS)

Wilson, Robert M.; Hathaway, David H.

2009-01-01

Examined are single- and bi-variate geomagnetic precursors for predicting the maximum amplitude (RM) of a sunspot cycle several years in advance. The best single-variate fit is one based on the average of the ap index 36 mo prior to cycle minimum occurrence (E(Rm)), having a coefficient of correlation (r) equal to 0.97 and a standard error of estimate (se) equal to 9.3. Presuming cycle 24 not to be a statistical outlier and its minimum in March 2008, the fit suggests cycle 24 s RM to be about 69 +/- 20 (the 90% prediction interval). The weighted mean prediction of 11 statistically important single-variate fits is 116 +/- 34. The best bi-variate fit is one based on the maximum and minimum values of the 12-mma of the ap index; i.e., APM# and APm*, where # means the value post-E(RM) for the preceding cycle and * means the value in the vicinity of cycle minimum, having r = 0.98 and se = 8.2. It predicts cycle 24 s RM to be about 92 +/- 27. The weighted mean prediction of 22 statistically important bi-variate fits is 112 32. Thus, cycle 24's RM is expected to lie somewhere within the range of about 82 to 144. Also examined are the late-cycle 23 behaviors of geomagnetic indices and solar wind velocity in comparison to the mean behaviors of cycles 2023 and the geomagnetic indices of cycle 14 (RM = 64.2), the weakest sunspot cycle of the modern era.

Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach.

PubMed

Chen, Yong; Hong, Chuan; Ning, Yang; Su, Xiao

2016-01-15

When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-binomial model for the meta-analysis of studies with binary outcomes. This model is based on the composite likelihood approach and has several attractive features compared with the existing models such as bivariate generalized linear mixed model (Chu and Cole, 2006) and Sarmanov beta-binomial model (Chen et al., 2012). The advantages of the proposed marginal model include modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, because the marginal beta-binomial model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-binomial model with the bivariate generalized linear mixed model and the Sarmanov beta-binomial model by simulation studies. Interestingly, the results show that the marginal beta-binomial model performs better than the Sarmanov beta-binomial model, whether or not the true model is Sarmanov beta-binomial, and the marginal beta-binomial model is more robust than the bivariate generalized linear mixed model under model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration. Copyright © 2015 John Wiley & Sons, Ltd.

Optimum runway orientation relative to crosswinds

NASA Technical Reports Server (NTRS)

Falls, L. W.; Brown, S. C.

1972-01-01

Specific magnitudes of crosswinds may exist that could be constraints to the success of an aircraft mission such as the landing of the proposed space shuttle. A method is required to determine the orientation or azimuth of the proposed runway which will minimize the probability of certain critical crosswinds. Two procedures for obtaining the optimum runway orientation relative to minimizing a specified crosswind speed are described and illustrated with examples. The empirical procedure requires only hand calculations on an ordinary wind rose. The theoretical method utilizes wind statistics computed after the bivariate normal elliptical distribution is applied to a data sample of component winds. This method requires only the assumption that the wind components are bivariate normally distributed. This assumption seems to be reasonable. Studies are currently in progress for testing wind components for bivariate normality for various stations. The close agreement between the theoretical and empirical results for the example chosen substantiates the bivariate normal assumption.

Bivariate extreme value distributions

NASA Technical Reports Server (NTRS)

Elshamy, M.

1992-01-01

In certain engineering applications, such as those occurring in the analyses of ascent structural loads for the Space Transportation System (STS), some of the load variables have a lower bound of zero. Thus, the need for practical models of bivariate extreme value probability distribution functions with lower limits was identified. We discuss the Gumbel models and present practical forms of bivariate extreme probability distributions of Weibull and Frechet types with two parameters. Bivariate extreme value probability distribution functions can be expressed in terms of the marginal extremel distributions and a 'dependence' function subject to certain analytical conditions. Properties of such bivariate extreme distributions, sums and differences of paired extremals, as well as the corresponding forms of conditional distributions, are discussed. Practical estimation techniques are also given.

Predictors of workplace violence among female sex workers in Tijuana, Mexico.

PubMed

Katsulis, Yasmina; Durfee, Alesha; Lopez, Vera; Robillard, Alyssa

2015-05-01

For sex workers, differences in rates of exposure to workplace violence are likely influenced by a variety of risk factors, including where one works and under what circumstances. Economic stressors, such as housing insecurity, may also increase the likelihood of exposure. Bivariate analyses demonstrate statistically significant associations between workplace violence and selected predictor variables, including age, drug use, exchanging sex for goods, soliciting clients outdoors, and experiencing housing insecurity. Multivariate regression analysis shows that after controlling for each of these variables in one model, only soliciting clients outdoors and housing insecurity emerge as statistically significant predictors for workplace violence. © The Author(s) 2014.

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

PubMed

Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg

2009-11-01

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

Explaining fruit and vegetable intake using a consumer marketing tool.

PubMed

Della, Lindsay J; Dejoy, David M; Lance, Charles E

2009-10-01

In response to calls to reinvent the 5 A Day fruit and vegetable campaign, this study assesses the utility of VALS, a consumer-based audience segmentation tool that divides the U.S. population into groups leading similar lifestyles. The study examines whether the impact of theory of planned behavior (TPB) constructs varies across VALS groups in a cross-sectional sample of 1,588 U.S. adults. In a multigroup structural equation model, the VALS audience group variable moderated latent TPB relationships. Attitudes, subjective norms, and perceived behavioral control explained 57% to 70% of the variation in intention to eat fruit and vegetables across 5 different VALS groups. Perceived behavioral control and intention also predicted self-reported consumption behavior (R2 = 20% to 71% across VALS groups). Bivariate z tests were calculated to determine statistical differences in parameter estimates across groups. Nine of the bivariate z tests were statistically significant (p < or = .04), with standardized coefficients ranging from .05 to .70. These findings confirm the efficacy of using the TPB to explain variation in fruit and vegetable consumption as well as the validity of using a consumer-based algorithm to segment audiences for fruit and vegetable consumption messaging.

Epidemiology of mixed martial arts and youth violence in an ethnically diverse sample.

PubMed

Hishinuma, Earl S; Umemoto, Karen N; Nguyen, Toan Gia; Chang, Janice Y; Bautista, Randy Paul M

2012-01-01

Mixed martial arts' (MMAs) growing international popularity has rekindled the discussion on the advantages (e.g., exercise) and disadvantages (e.g., possible injury) of contact sports. This study was the first of its kind to examine the psychosocial aspects of MMA and youth violence using an epidemiologic approach with an Asian American and Pacific Islander (AAPI) adolescent sample (N = 881). The results were consistent with the increased popularity of MMA with 52% (adolescent males = 73%, adolescent females = 39%) enjoying watching MMA and 24% (adolescent males = 39%, adolescent females = 13%) practicing professional fight moves with friends. Although statistically significant ethnic differences were found for the two MMA items on a bivariate level, these findings were not statistically significant when considering other variables in the model. The bivariate results revealed a cluster of risk-protective factors. Regarding the multiple regression findings, although enjoying watching MMA remained associated with positive attitudes toward violence and practicing fight moves remained associated with negative out-group orientation, the MMA items were not associated with unique variances of youth violence perpetration and victimization. Implications included the need for further research that includes other diverse samples, more comprehensive and objective MMA and violence measures, and observational and intervention longitudinal studies.

Statistical Modeling of Bivariate Data.

DTIC Science & Technology

1982-08-01

Technical S. PERFORMING ORG. REPORT NUMBER 7. AUTNOR(a) S. CONTRACT OR GRANT NUMBER(e) Terry Joe Woodfield DAAG29-80-C-0070 S. PERFORMING ORGANIZATION NAME...proaramming has caused it to be a widely practiced form of program construction. The idea behind this approach is to carefully organize a program so that it...flows smoothly from one computation to the next without haphazard placement of loops and branches. There are , J a variety of ways to organize a program

An Affine Invariant Bivariate Version of the Sign Test.

DTIC Science & Technology

1987-06-01

words: affine invariance, bivariate quantile, bivariate symmetry, model,. generalized median, influence function , permutation test, normal efficiency...calculate a bivariate version of the influence function , and the resulting form is bounded, as is the case for the univartate sign test, and shows the...terms of a blvariate analogue of IHmpel’s (1974) influence function . The latter, though usually defined as a von-Mises derivative of certain

Analysis of Blood Transfusion Data Using Bivariate Zero-Inflated Poisson Model: A Bayesian Approach.

PubMed

Mohammadi, Tayeb; Kheiri, Soleiman; Sedehi, Morteza

2016-01-01

Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables "number of blood donation" and "number of blood deferral": as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflated Poisson regression model was used for joint modeling of the number of blood donation and number of blood deferral. The data was analyzed using the Bayesian approach applying noninformative priors at the presence and absence of covariates. Estimating the parameters of the model, that is, correlation, zero-inflation parameter, and regression coefficients, was done through MCMC simulation. Eventually double-Poisson model, bivariate Poisson model, and bivariate zero-inflated Poisson model were fitted on the data and were compared using the deviance information criteria (DIC). The results showed that the bivariate zero-inflated Poisson regression model fitted the data better than the other models.

Analysis of Blood Transfusion Data Using Bivariate Zero-Inflated Poisson Model: A Bayesian Approach

PubMed Central

Mohammadi, Tayeb; Sedehi, Morteza

2016-01-01

Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables “number of blood donation” and “number of blood deferral”: as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflated Poisson regression model was used for joint modeling of the number of blood donation and number of blood deferral. The data was analyzed using the Bayesian approach applying noninformative priors at the presence and absence of covariates. Estimating the parameters of the model, that is, correlation, zero-inflation parameter, and regression coefficients, was done through MCMC simulation. Eventually double-Poisson model, bivariate Poisson model, and bivariate zero-inflated Poisson model were fitted on the data and were compared using the deviance information criteria (DIC). The results showed that the bivariate zero-inflated Poisson regression model fitted the data better than the other models. PMID:27703493

Systematic Review and Meta-Analysis of Studies Evaluating Diagnostic Test Accuracy: A Practical Review for Clinical Researchers-Part II. Statistical Methods of Meta-Analysis

PubMed Central

Lee, Juneyoung; Kim, Kyung Won; Choi, Sang Hyun; Huh, Jimi

2015-01-01

Meta-analysis of diagnostic test accuracy studies differs from the usual meta-analysis of therapeutic/interventional studies in that, it is required to simultaneously analyze a pair of two outcome measures such as sensitivity and specificity, instead of a single outcome. Since sensitivity and specificity are generally inversely correlated and could be affected by a threshold effect, more sophisticated statistical methods are required for the meta-analysis of diagnostic test accuracy. Hierarchical models including the bivariate model and the hierarchical summary receiver operating characteristic model are increasingly being accepted as standard methods for meta-analysis of diagnostic test accuracy studies. We provide a conceptual review of statistical methods currently used and recommended for meta-analysis of diagnostic test accuracy studies. This article could serve as a methodological reference for those who perform systematic review and meta-analysis of diagnostic test accuracy studies. PMID:26576107

Bivariate functional data clustering: grouping streams based on a varying coefficient model of the stream water and air temperature relationship

Treesearch

H. Li; X. Deng; Andy Dolloff; E. P. Smith

2015-01-01

A novel clustering method for bivariate functional data is proposed to group streams based on their water–air temperature relationship. A distance measure is developed for bivariate curves by using a time-varying coefficient model and a weighting scheme. This distance is also adjusted by spatial correlation of streams via the variogram. Therefore, the proposed...

A Vehicle for Bivariate Data Analysis

ERIC Educational Resources Information Center

Roscoe, Matt B.

2016-01-01

Instead of reserving the study of probability and statistics for special fourth-year high school courses, the Common Core State Standards for Mathematics (CCSSM) takes a "statistics for all" approach. The standards recommend that students in grades 6-8 learn to summarize and describe data distributions, understand probability, draw…

ASURV: Astronomical SURVival Statistics

NASA Astrophysics Data System (ADS)

Feigelson, E. D.; Nelson, P. I.; Isobe, T.; LaValley, M.

2014-06-01

ASURV (Astronomical SURVival Statistics) provides astronomy survival analysis for right- and left-censored data including the maximum-likelihood Kaplan-Meier estimator and several univariate two-sample tests, bivariate correlation measures, and linear regressions. ASURV is written in FORTRAN 77, and is stand-alone and does not call any specialized libraries.

A bivariate contaminated binormal model for robust fitting of proper ROC curves to a pair of correlated, possibly degenerate, ROC datasets.

PubMed

Zhai, Xuetong; Chakraborty, Dev P

2017-06-01

The objective was to design and implement a bivariate extension to the contaminated binormal model (CBM) to fit paired receiver operating characteristic (ROC) datasets-possibly degenerate-with proper ROC curves. Paired datasets yield two correlated ratings per case. Degenerate datasets have no interior operating points and proper ROC curves do not inappropriately cross the chance diagonal. The existing method, developed more than three decades ago utilizes a bivariate extension to the binormal model, implemented in CORROC2 software, which yields improper ROC curves and cannot fit degenerate datasets. CBM can fit proper ROC curves to unpaired (i.e., yielding one rating per case) and degenerate datasets, and there is a clear scientific need to extend it to handle paired datasets. In CBM, nondiseased cases are modeled by a probability density function (pdf) consisting of a unit variance peak centered at zero. Diseased cases are modeled with a mixture distribution whose pdf consists of two unit variance peaks, one centered at positive μ with integrated probability α, the mixing fraction parameter, corresponding to the fraction of diseased cases where the disease was visible to the radiologist, and one centered at zero, with integrated probability (1-α), corresponding to disease that was not visible. It is shown that: (a) for nondiseased cases the bivariate extension is a unit variances bivariate normal distribution centered at (0,0) with a specified correlation ρ 1 ; (b) for diseased cases the bivariate extension is a mixture distribution with four peaks, corresponding to disease not visible in either condition, disease visible in only one condition, contributing two peaks, and disease visible in both conditions. An expression for the likelihood function is derived. A maximum likelihood estimation (MLE) algorithm, CORCBM, was implemented in the R programming language that yields parameter estimates and the covariance matrix of the parameters, and other statistics. A limited simulation validation of the method was performed. CORCBM and CORROC2 were applied to two datasets containing nine readers each contributing paired interpretations. CORCBM successfully fitted the data for all readers, whereas CORROC2 failed to fit a degenerate dataset. All fits were visually reasonable. All CORCBM fits were proper, whereas all CORROC2 fits were improper. CORCBM and CORROC2 were in agreement (a) in declaring only one of the nine readers as having significantly different performances in the two modalities; (b) in estimating higher correlations for diseased cases than for nondiseased ones; and (c) in finding that the intermodality correlation estimates for nondiseased cases were consistent between the two methods. All CORCBM fits yielded higher area under curve (AUC) than the CORROC2 fits, consistent with the fact that a proper ROC model like CORCBM is based on a likelihood-ratio-equivalent decision variable, and consequently yields higher performance than the binormal model-based CORROC2. The method gave satisfactory fits to four simulated datasets. CORCBM is a robust method for fitting paired ROC datasets, always yielding proper ROC curves, and able to fit degenerate datasets. © 2017 American Association of Physicists in Medicine.

Bivariate at-site frequency analysis of simulated flood peak-volume data using copulas

NASA Astrophysics Data System (ADS)

Gaál, Ladislav; Viglione, Alberto; Szolgay, Ján.; Blöschl, Günter; Bacigál, Tomáå.¡

2010-05-01

In frequency analysis of joint hydro-climatological extremes (flood peaks and volumes, low flows and durations, etc.), usually, bivariate distribution functions are fitted to the observed data in order to estimate the probability of their occurrence. Bivariate models, however, have a number of limitations; therefore, in the recent past, dependence models based on copulas have gained increased attention to represent the joint probabilities of hydrological characteristics. Regardless of whether standard or copula based bivariate frequency analysis is carried out, one is generally interested in the extremes corresponding to low probabilities of the fitted joint cumulative distribution functions (CDFs). However, usually there is not enough flood data in the right tail of the empirical CDFs to derive reliable statistical inferences on the behaviour of the extremes. Therefore, different techniques are used to extend the amount of information for the statistical inference, i.e., temporal extension methods that allow for making use of historical data or spatial extension methods such as regional approaches. In this study, a different approach was adopted which uses simulated flood data by rainfall-runoff modelling, to increase the amount of data in the right tail of the CDFs. In order to generate artificial runoff data (i.e. to simulate flood records of lengths of approximately 106 years), a two-step procedure was used. (i) First, the stochastic rainfall generator proposed by Sivapalan et al. (2005) was modified for our purpose. This model is based on the assumption of discrete rainfall events whose arrival times, durations, mean rainfall intensity and the within-storm intensity patterns are all random, and can be described by specified distributions. The mean storm rainfall intensity is disaggregated further to hourly intensity patterns. (ii) Secondly, the simulated rainfall data entered a semi-distributed conceptual rainfall-runoff model that consisted of a snow routine, a soil moisture routine and a flow routing routine (Parajka et al., 2007). The applicability of the proposed method was demonstrated on selected sites in Slovakia and Austria. The pairs of simulated flood volumes and flood peaks were analysed in terms of their dependence structure and different families of copulas (Archimedean, extreme value, Gumbel-Hougaard, etc.) were fitted to the observed and simulated data. The question to what extent measured data can be used to find the right copula was discussed. The study is supported by the Austrian Academy of Sciences and the Austrian-Slovak Co-operation in Science and Education "Aktion". Parajka, J., Merz, R., Blöschl, G., 2007: Uncertainty and multiple objective calibration in regional water balance modeling - Case study in 320 Austrian catchments. Hydrological Processes, 21, 435-446. Sivapalan, M., Blöschl, G., Merz, R., Gutknecht, D., 2005: Linking flood frequency to long-term water balance: incorporating effects of seasonality. Water Resources Research, 41, W06012, doi:10.1029/2004WR003439.

Information retrieval from wide-band meteorological data - An example

NASA Technical Reports Server (NTRS)

Adelfang, S. I.; Smith, O. E.

1983-01-01

The methods proposed by Smith and Adelfang (1981) and Smith et al. (1982) are used to calculate probabilities over rectangles and sectors of the gust magnitude-gust length plane; probabilities over the same regions are also calculated from the observed distributions and a comparison is also presented to demonstrate the accuracy of the statistical model. These and other statistical results are calculated from samples of Jimsphere wind profiles at Cape Canaveral. The results are presented for a variety of wavelength bands, altitudes, and seasons. It is shown that wind perturbations observed in Jimsphere wind profiles in various wavelength bands can be analyzed by using digital filters. The relationship between gust magnitude and gust length is modeled with the bivariate gamma distribution. It is pointed out that application of the model to calculate probabilities over specific areas of the gust magnitude-gust length plane can be useful in aerospace design.

Surface to 90 km winds for Kennedy Space Center, Florida, and Vandenberg AFB, California

NASA Technical Reports Server (NTRS)

Johnson, D. L.; Brown, S. C.

1979-01-01

Bivariate normal wind statistics for a 90 degree flight azimuth, from 0 through 90 km altitude, for Kennedy Space Center, Florida, and Vandenberg AFB, California are presented. Wind probability distributions and statistics for any rotation of axes can be computed from the five given parameters.

A bivariate model for analyzing recurrent multi-type automobile failures

NASA Astrophysics Data System (ADS)

Sunethra, A. A.; Sooriyarachchi, M. R.

2017-09-01

The failure mechanism in an automobile can be defined as a system of multi-type recurrent failures where failures can occur due to various multi-type failure modes and these failures are repetitive such that more than one failure can occur from each failure mode. In analysing such automobile failures, both the time and type of the failure serve as response variables. However, these two response variables are highly correlated with each other since the timing of failures has an association with the mode of the failure. When there are more than one correlated response variables, the fitting of a multivariate model is more preferable than separate univariate models. Therefore, a bivariate model of time and type of failure becomes appealing for such automobile failure data. When there are multiple failure observations pertaining to a single automobile, such data cannot be treated as independent data because failure instances of a single automobile are correlated with each other while failures among different automobiles can be treated as independent. Therefore, this study proposes a bivariate model consisting time and type of failure as responses adjusted for correlated data. The proposed model was formulated following the approaches of shared parameter models and random effects models for joining the responses and for representing the correlated data respectively. The proposed model is applied to a sample of automobile failures with three types of failure modes and up to five failure recurrences. The parametric distributions that were suitable for the two responses of time to failure and type of failure were Weibull distribution and multinomial distribution respectively. The proposed bivariate model was programmed in SAS Procedure Proc NLMIXED by user programming appropriate likelihood functions. The performance of the bivariate model was compared with separate univariate models fitted for the two responses and it was identified that better performance is secured by the bivariate model. The proposed model can be used to determine the time and type of failure that would occur in the automobiles considered here.

[Use of bivariate survival curves for analyzing mortality of heart failure and sudden death in dilated cardiomiopathy].

PubMed

Gregori, Dario; Rosato, Rosalba; Zecchin, Massimo; Di Lenarda, Andrea

2005-01-01

This paper discusses the use of bivariate survival curves estimators within the competing risk framework. Competing risks models are used for the analysis of medical data with more than one cause of death. The case of dilated cardiomiopathy is explored. Bivariate survival curves plot the conjoint mortality processes. The different graphic representation of bivariate survival analysis is the major contribute of this methodology to the competing risks analysis.

Mental disorder and violence: is there a relationship beyond substance use?

PubMed

Van Dorn, Richard; Volavka, Jan; Johnson, Norman

2012-03-01

A general consensus exists that severe mental illness (SMI) increases violence risk. However, a recent report claimed that SMI "alone was not statistically related to future violence in bivariate or multivariate analyses." We reanalyze the data used to make this claim with a focus on causal relationships between SMI and violence, rather than the statistical prediction of violence. Data are from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), a two-wave study (N = 34,653: Wave 1: 2001-2003; Wave 2: 2004-2005). Indicators of mental disorder in the year prior to Wave 1 were used to examine violence between Waves 1 and 2. Those with SMI, irrespective of substance abuse status, were significantly more likely to be violent than those with no mental or substance use disorders. This finding held in both bivariate and multivariable models. Those with comorbid mental and substance use disorders had the highest risk of violence. Historical and current conditions were also associated with violence, including childhood abuse and neglect, household antisocial behavior, binge drinking and stressful life events. These results, in contrast to a recently published report, show that the NESARC data are consistent with the consensus view on mental disorder and violence: there is a statistically significant, yet modest relationship between SMI (within 12 months) and violence, and a stronger relationship between SMI with substance use disorder and violence. These results also highlight the importance of premorbid conditions, and other contemporaneous clinical factors, in violent behavior.

Optimal designs for copula models

PubMed Central

Perrone, E.; Müller, W.G.

2016-01-01

Copula modelling has in the past decade become a standard tool in many areas of applied statistics. However, a largely neglected aspect concerns the design of related experiments. Particularly the issue of whether the estimation of copula parameters can be enhanced by optimizing experimental conditions and how robust all the parameter estimates for the model are with respect to the type of copula employed. In this paper an equivalence theorem for (bivariate) copula models is provided that allows formulation of efficient design algorithms and quick checks of whether designs are optimal or at least efficient. Some examples illustrate that in practical situations considerable gains in design efficiency can be achieved. A natural comparison between different copula models with respect to design efficiency is provided as well. PMID:27453616

Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling

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

Li Yupeng, E-mail: yupeng@ualberta.ca; Deutsch, Clayton V.

2012-06-15

In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells.more » In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.« less

Bivariate Rainfall and Runoff Analysis Using Shannon Entropy Theory

NASA Astrophysics Data System (ADS)

Rahimi, A.; Zhang, L.

2012-12-01

Rainfall-Runoff analysis is the key component for many hydrological and hydraulic designs in which the dependence of rainfall and runoff needs to be studied. It is known that the convenient bivariate distribution are often unable to model the rainfall-runoff variables due to that they either have constraints on the range of the dependence or fixed form for the marginal distributions. Thus, this paper presents an approach to derive the entropy-based joint rainfall-runoff distribution using Shannon entropy theory. The distribution derived can model the full range of dependence and allow different specified marginals. The modeling and estimation can be proceeded as: (i) univariate analysis of marginal distributions which includes two steps, (a) using the nonparametric statistics approach to detect modes and underlying probability density, and (b) fitting the appropriate parametric probability density functions; (ii) define the constraints based on the univariate analysis and the dependence structure; (iii) derive and validate the entropy-based joint distribution. As to validate the method, the rainfall-runoff data are collected from the small agricultural experimental watersheds located in semi-arid region near Riesel (Waco), Texas, maintained by the USDA. The results of unviariate analysis show that the rainfall variables follow the gamma distribution, whereas the runoff variables have mixed structure and follow the mixed-gamma distribution. With this information, the entropy-based joint distribution is derived using the first moments, the first moments of logarithm transformed rainfall and runoff, and the covariance between rainfall and runoff. The results of entropy-based joint distribution indicate: (1) the joint distribution derived successfully preserves the dependence between rainfall and runoff, and (2) the K-S goodness of fit statistical tests confirm the marginal distributions re-derived reveal the underlying univariate probability densities which further assure that the entropy-based joint rainfall-runoff distribution are satisfactorily derived. Overall, the study shows the Shannon entropy theory can be satisfactorily applied to model the dependence between rainfall and runoff. The study also shows that the entropy-based joint distribution is an appropriate approach to capture the dependence structure that cannot be captured by the convenient bivariate joint distributions. Joint Rainfall-Runoff Entropy Based PDF, and Corresponding Marginal PDF and Histogram for W12 Watershed The K-S Test Result and RMSE on Univariate Distributions Derived from the Maximum Entropy Based Joint Probability Distribution;

Content and Method in the Teaching of Marketing Research Revisited

ERIC Educational Resources Information Center

Wilson, Holt; Neeley, Concha; Niedzwiecki, Kelly

2009-01-01

This paper presents the findings from a survey of marketing research faculty. The study finds SPSS is the most used statistical software, that cross tabulation, single, independent, and dependent t-tests, and ANOVA are among the most important statistical tools according to respondents. Bivariate and multiple regression are also considered…

Mixture models in diagnostic meta-analyses--clustering summary receiver operating characteristic curves accounted for heterogeneity and correlation.

PubMed

Schlattmann, Peter; Verba, Maryna; Dewey, Marc; Walther, Mario

2015-01-01

Bivariate linear and generalized linear random effects are frequently used to perform a diagnostic meta-analysis. The objective of this article was to apply a finite mixture model of bivariate normal distributions that can be used for the construction of componentwise summary receiver operating characteristic (sROC) curves. Bivariate linear random effects and a bivariate finite mixture model are used. The latter model is developed as an extension of a univariate finite mixture model. Two examples, computed tomography (CT) angiography for ruling out coronary artery disease and procalcitonin as a diagnostic marker for sepsis, are used to estimate mean sensitivity and mean specificity and to construct sROC curves. The suggested approach of a bivariate finite mixture model identifies two latent classes of diagnostic accuracy for the CT angiography example. Both classes show high sensitivity but mainly two different levels of specificity. For the procalcitonin example, this approach identifies three latent classes of diagnostic accuracy. Here, sensitivities and specificities are quite different as such that sensitivity increases with decreasing specificity. Additionally, the model is used to construct componentwise sROC curves and to classify individual studies. The proposed method offers an alternative approach to model between-study heterogeneity in a diagnostic meta-analysis. Furthermore, it is possible to construct sROC curves even if a positive correlation between sensitivity and specificity is present. Copyright © 2015 Elsevier Inc. All rights reserved.

BIVARIATE MODELLING OF CLUSTERED CONTINUOUS AND ORDERED CATEGORICAL OUTCOMES. (R824757)

EPA Science Inventory

Simultaneous observation of continuous and ordered categorical outcomes for each subject is common in biomedical research but multivariate analysis of the data is complicated by the multiple data types. Here we construct a model for the joint distribution of bivariate continuous ...

Is There a Critical Distance for Fickian Transport? - a Statistical Approach to Sub-Fickian Transport Modelling in Porous Media

NASA Astrophysics Data System (ADS)

Most, S.; Nowak, W.; Bijeljic, B.

2014-12-01

Transport processes in porous media are frequently simulated as particle movement. This process can be formulated as a stochastic process of particle position increments. At the pore scale, the geometry and micro-heterogeneities prohibit the commonly made assumption of independent and normally distributed increments to represent dispersion. Many recent particle methods seek to loosen this assumption. Recent experimental data suggest that we have not yet reached the end of the need to generalize, because particle increments show statistical dependency beyond linear correlation and over many time steps. The goal of this work is to better understand the validity regions of commonly made assumptions. We are investigating after what transport distances can we observe: A statistical dependence between increments, that can be modelled as an order-k Markov process, boils down to order 1. This would be the Markovian distance for the process, where the validity of yet-unexplored non-Gaussian-but-Markovian random walks would start. A bivariate statistical dependence that simplifies to a multi-Gaussian dependence based on simple linear correlation (validity of correlated PTRW). Complete absence of statistical dependence (validity of classical PTRW/CTRW). The approach is to derive a statistical model for pore-scale transport from a powerful experimental data set via copula analysis. The model is formulated as a non-Gaussian, mutually dependent Markov process of higher order, which allows us to investigate the validity ranges of simpler models.

[Calf circumference and its association with gait speed in elderly participants at Peruvian Naval Medical Center].

PubMed

Díaz Villegas, Gregory Mishell; Runzer Colmenares, Fernando

2015-01-01

To evaluate the association between calf circumference and gait speed in elderly patients 65 years or older at Geriatric day clinic at Peruvian Centro Médico Naval. Cross-sectional, retrospective study. We assessed 139 participants, 65 years or older at Peruvian Centro Médico Naval including calf circumference, gait speed and Short Physical Performance Battery. With bivariate analyses and logistic regression model we search for association between variables. The age mean was 79.37 years old (SD: 8.71). 59.71% were male, the 30.97% had a slow walking speed and the mean calf circumference was 33.42cm (SD: 5.61). After a bivariate analysis, we found a calf circumference mean of 30.35cm (SD: 3.74) in the slow speed group and, in normal gait group, a mean of 33.51cm (SD: 3.26) with significantly differences. We used logistic regression to analyze association with slow gait speed, founding statistically significant results adjusting model by disability and age. Low calf circumference is associated with slow speed walk in population over 65 years old. Copyright © 2014. Published by Elsevier Espana.

A comparison of bivariate, multivariate random-effects, and Poisson correlated gamma-frailty models to meta-analyze individual patient data of ordinal scale diagnostic tests.

PubMed

Simoneau, Gabrielle; Levis, Brooke; Cuijpers, Pim; Ioannidis, John P A; Patten, Scott B; Shrier, Ian; Bombardier, Charles H; de Lima Osório, Flavia; Fann, Jesse R; Gjerdingen, Dwenda; Lamers, Femke; Lotrakul, Manote; Löwe, Bernd; Shaaban, Juwita; Stafford, Lesley; van Weert, Henk C P M; Whooley, Mary A; Wittkampf, Karin A; Yeung, Albert S; Thombs, Brett D; Benedetti, Andrea

2017-11-01

Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Our objective was to compare the bivariate approach, which can be applied separately at every threshold, to two multivariate methods: the ordinal multivariate random-effects model and the Poisson correlated gamma-frailty model. Our comparison was empirical, using IPD from 13 studies that evaluated the diagnostic accuracy of the 9-item Patient Health Questionnaire depression screening tool, and included simulations. The empirical comparison showed that the implementation of the two multivariate methods is more laborious in terms of computational time and sensitivity to user-supplied values compared to the bivariate approach. Simulations showed that ignoring the within-study correlation of sensitivity and specificity across thresholds did not worsen inferences with the bivariate approach compared to the Poisson model. The ordinal approach was not suitable for simulations because the model was highly sensitive to user-supplied starting values. We tentatively recommend the bivariate approach rather than more complex multivariate methods for IPD diagnostic accuracy meta-analyses of ordinal scale tests, although the limited type of diagnostic data considered in the simulation study restricts the generalization of our findings. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Causal networks clarify productivity-richness interrelations, bivariate plots do not

USGS Publications Warehouse

Grace, James B.; Adler, Peter B.; Harpole, W. Stanley; Borer, Elizabeth T.; Seabloom, Eric W.

2014-01-01

We urge ecologists to consider productivity–richness relationships through the lens of causal networks to advance our understanding beyond bivariate analysis. Further, we emphasize that models based on a causal network conceptualization can also provide more meaningful guidance for conservation management than can a bivariate perspective. Measuring only two variables does not permit the evaluation of complex ideas nor resolve debates about underlying mechanisms.

Bivariate copula in fitting rainfall data

NASA Astrophysics Data System (ADS)

Yee, Kong Ching; Suhaila, Jamaludin; Yusof, Fadhilah; Mean, Foo Hui

2014-07-01

The usage of copula to determine the joint distribution between two variables is widely used in various areas. The joint distribution of rainfall characteristic obtained using the copula model is more ideal than the standard bivariate modelling where copula is belief to have overcome some limitation. Six copula models will be applied to obtain the most suitable bivariate distribution between two rain gauge stations. The copula models are Ali-Mikhail-Haq (AMH), Clayton, Frank, Galambos, Gumbel-Hoogaurd (GH) and Plackett. The rainfall data used in the study is selected from rain gauge stations which are located in the southern part of Peninsular Malaysia, during the period from 1980 to 2011. The goodness-of-fit test in this study is based on the Akaike information criterion (AIC).

Bivariate sub-Gaussian model for stock index returns

NASA Astrophysics Data System (ADS)

Jabłońska-Sabuka, Matylda; Teuerle, Marek; Wyłomańska, Agnieszka

2017-11-01

Financial time series are commonly modeled with methods assuming data normality. However, the real distribution can be nontrivial, also not having an explicitly formulated probability density function. In this work we introduce novel parameter estimation and high-powered distribution testing methods which do not rely on closed form densities, but use the characteristic functions for comparison. The approach applied to a pair of stock index returns demonstrates that such a bivariate vector can be a sample coming from a bivariate sub-Gaussian distribution. The methods presented here can be applied to any nontrivially distributed financial data, among others.

An empirical study of statistical properties of variance partition coefficients for multi-level logistic regression models

USGS Publications Warehouse

Li, Ji; Gray, B.R.; Bates, D.M.

2008-01-01

Partitioning the variance of a response by design levels is challenging for binomial and other discrete outcomes. Goldstein (2003) proposed four definitions for variance partitioning coefficients (VPC) under a two-level logistic regression model. In this study, we explicitly derived formulae for multi-level logistic regression model and subsequently studied the distributional properties of the calculated VPCs. Using simulations and a vegetation dataset, we demonstrated associations between different VPC definitions, the importance of methods for estimating VPCs (by comparing VPC obtained using Laplace and penalized quasilikehood methods), and bivariate dependence between VPCs calculated at different levels. Such an empirical study lends an immediate support to wider applications of VPC in scientific data analysis.

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

Özel, Gamze

Bivariate Kumaraswamy (BK) distribution whose marginals are Kumaraswamy distributions has been recently introduced. However, its statistical properties are not studied in detail. In this study, statistical properties of the BK distribution are investigated. We suggest that the BK could provide suitable description for the earthquakes characteristics of Turkey. We support this argument using earthquakesoccurred in Turkey between 1900 and 2009. We also find that the BK distribution simulates earthquakes well.

Genetic overlap between impulsivity and alcohol dependence: a large-scale national twin study.

PubMed

Khemiri, L; Kuja-Halkola, R; Larsson, H; Jayaram-Lindström, N

2016-04-01

Alcohol dependence is associated with increased levels of impulsivity, but the genetic and environmental underpinnings of this overlap remain unclear. The purpose of the current study was to investigate the degree to which genetic and environmental factors contribute to the overlap between alcohol dependence and impulsivity. Univariate and bivariate twin model fitting was conducted for alcohol dependence and impulsivity in a national sample of 16 819 twins born in Sweden from 1959 to 1985. The heritability estimate for alcohol dependence was 44% [95% confidence interval (CI) 31-57%] for males and 62% (95% CI 52-72%) for females. For impulsivity, the heritability was 33% (95% CI 30-36%) in males and females. The bivariate twin analysis indicated a statistically significant genetic correlation between alcohol dependence and impulsivity of 0.40 (95% CI 0.23-0.58) in males and 0.20 (95% CI 0.07-0.33) in females. The phenotypic correlation between alcohol dependence and impulsivity was 0.20 and 0.17 for males and females, respectively, and the bivariate heritability was 80% (95% CI 47-117%) for males and 53% (95% CI 19-86%) for females. The remaining variance in all models was accounted for by non-shared environmental factors. The association between alcohol dependence and impulsivity can be partially accounted for by shared genetic factors. The genetic correlation was greater in men compared with women, which may indicate different pathways to the development of alcohol dependence between sexes. The observed genetic overlap has clinical implications regarding treatment and prevention, and partially explains the substantial co-morbidity between alcohol dependence and psychiatric disorders characterized by impulsive behaviour.

Birth/birth-death processes and their computable transition probabilities with biological applications.

PubMed

Ho, Lam Si Tung; Xu, Jason; Crawford, Forrest W; Minin, Vladimir N; Suchard, Marc A

2018-03-01

Birth-death processes track the size of a univariate population, but many biological systems involve interaction between populations, necessitating models for two or more populations simultaneously. A lack of efficient methods for evaluating finite-time transition probabilities of bivariate processes, however, has restricted statistical inference in these models. Researchers rely on computationally expensive methods such as matrix exponentiation or Monte Carlo approximation, restricting likelihood-based inference to small systems, or indirect methods such as approximate Bayesian computation. In this paper, we introduce the birth/birth-death process, a tractable bivariate extension of the birth-death process, where rates are allowed to be nonlinear. We develop an efficient algorithm to calculate its transition probabilities using a continued fraction representation of their Laplace transforms. Next, we identify several exemplary models arising in molecular epidemiology, macro-parasite evolution, and infectious disease modeling that fall within this class, and demonstrate advantages of our proposed method over existing approaches to inference in these models. Notably, the ubiquitous stochastic susceptible-infectious-removed (SIR) model falls within this class, and we emphasize that computable transition probabilities newly enable direct inference of parameters in the SIR model. We also propose a very fast method for approximating the transition probabilities under the SIR model via a novel branching process simplification, and compare it to the continued fraction representation method with application to the 17th century plague in Eyam. Although the two methods produce similar maximum a posteriori estimates, the branching process approximation fails to capture the correlation structure in the joint posterior distribution.

Specification, testing, and interpretation of gene-by-measured-environment interaction models in the presence of gene-environment correlation

PubMed Central

Rathouz, Paul J.; Van Hulle, Carol A.; Lee Rodgers, Joseph; Waldman, Irwin D.; Lahey, Benjamin B.

2009-01-01

Purcell (2002) proposed a bivariate biometric model for testing and quantifying the interaction between latent genetic influences and measured environments in the presence of gene-environment correlation. Purcell’s model extends the Cholesky model to include gene-environment interaction. We examine a number of closely-related alternative models that do not involve gene-environment interaction but which may fit the data as well Purcell’s model. Because failure to consider these alternatives could lead to spurious detection of gene-environment interaction, we propose alternative models for testing gene-environment interaction in the presence of gene-environment correlation, including one based on the correlated factors model. In addition, we note mathematical errors in the calculation of effect size via variance components in Purcell’s model. We propose a statistical method for deriving and interpreting variance decompositions that are true to the fitted model. PMID:18293078

Ground winds and winds aloft for Edwards AFB, California (1978 revision)

NASA Technical Reports Server (NTRS)

Johnson, D. L.; Brown, S. C.

1978-01-01

Ground level runway wind statistics for the Edwards AFB, California area are presented. Crosswind, headwind, tailwind, and headwind reversal percentage frequencies are given with respect to month and hour for the two major Edwards AFB runways. Also presented are Edwards AFB bivariate normal wind statistics for a 90 degree flight azimuth for altitudes 0 through 27 km. Wind probability distributions and statistics for any rotation of axes can be computed from the five given parameters.

Constructing a bivariate distribution function with given marginals and correlation: application to the galaxy luminosity function

NASA Astrophysics Data System (ADS)

Takeuchi, Tsutomu T.

2010-08-01

We provide an analytic method to construct a bivariate distribution function (DF) with given marginal distributions and correlation coefficient. We introduce a convenient mathematical tool, called a copula, to connect two DFs with any prescribed dependence structure. If the correlation of two variables is weak (Pearson's correlation coefficient |ρ| < 1/3), the Farlie-Gumbel-Morgenstern (FGM) copula provides an intuitive and natural way to construct such a bivariate DF. When the linear correlation is stronger, the FGM copula cannot work anymore. In this case, we propose using a Gaussian copula, which connects two given marginals and is directly related to the linear correlation coefficient between two variables. Using the copulas, we construct the bivariate luminosity function (BLF) and discuss its statistical properties. We focus especially on the far-infrared-far-ulatraviolet (FUV-FIR) BLF, since these two wavelength regions are related to star-formation (SF) activity. Though both the FUV and FIR are related to SF activity, the univariate LFs have a very different functional form: the former is well described by the Schechter function whilst the latter has a much more extended power-law-like luminous end. We construct the FUV-FIR BLFs using the FGM and Gaussian copulas with different strengths of correlation, and examine their statistical properties. We then discuss some further possible applications of the BLF: the problem of a multiband flux-limited sample selection, the construction of the star-formation rate (SFR) function, and the construction of the stellar mass of galaxies (M*)-specific SFR (SFR/M*) relation. The copulas turn out to be a very useful tool to investigate all these issues, especially for including complicated selection effects.

A Bivariate return period for levee failure monitoring

NASA Astrophysics Data System (ADS)

Isola, M.; Caporali, E.

2017-12-01

Levee breaches are strongly linked with the interaction processes among water, soil and structure, thus many are the factors that affect the breach development. One of the main is the hydraulic load, characterized by intensity and duration, i.e. by the flood event hydrograph. On the magnitude of the hydraulic load is based the levee design, generally without considering the fatigue failure due to the load duration. Moreover, many are the cases in which the levee breach are characterized by flood of magnitude lower than the design one. In order to implement the strategies of flood risk management, we built here a procedure based on a multivariate statistical analysis of flood peak and volume together with the analysis of the past levee failure events. Particularly, in order to define the probability of occurrence of the hydraulic load on a levee, a bivariate copula model is used to obtain the bivariate joint distribution of flood peak and volume. Flood peak is the expression of the load magnitude, while the volume is the expression of the stress over time. We consider the annual flood peak and the relative volume. The volume is given by the hydrograph area between the beginning and the end of event. The beginning of the event is identified as an abrupt rise of the discharge by more than 20%. The end is identified as the point from which the receding limb is characterized by the baseflow, using a nonlinear reservoir algorithm as baseflow separation technique. By this, with the aim to define warning thresholds we consider the past levee failure events and the relative bivariate return period (BTr) compared with the estimation of a traditional univariate model. The discharge data of 30 hydrometric stations of Arno River in Tuscany, Italy, in the period 1995-2016 are analysed. The database of levee failure events, considering for each event the location as well as the failure mode, is also created. The events were registered in the period 2000-2014 by EEA-Europe Environment Agency, the Italian Civil Protection and ISPRA (the Italian National Institute for Environmental Protection and Research). Only two levee failures events occurred in the sub-basin of Era River have been detected and analysed. The estimated return period with the univariate model of flood peak is greater than 2 and 5 years while the BTr is greater of 25 and 30 years respectively.

A User’s Guide to BISAM (BIvariate SAMple): The Bivariate Data Modeling Program.

DTIC Science & Technology

1983-08-01

method for the null case specified and is then used to form the bivariate density-quantile function as described in section 4. If D(U) in stage...employed assigns average ranks for tied observations. Other methods for assigning ranks to tied observations are often employed but are not attempted...34 €.. . . . .. . .. . . . ,.. . ,•. . . ... *.., .. , - . . . . - - . . .. - -. .. observations will weaken the results obtained since underlying continuous distributions are assumed. One should avoid such situations if possible. Two methods

Bayesian models for cost-effectiveness analysis in the presence of structural zero costs

PubMed Central

Baio, Gianluca

2014-01-01

Bayesian modelling for cost-effectiveness data has received much attention in both the health economics and the statistical literature, in recent years. Cost-effectiveness data are characterised by a relatively complex structure of relationships linking a suitable measure of clinical benefit (e.g. quality-adjusted life years) and the associated costs. Simplifying assumptions, such as (bivariate) normality of the underlying distributions, are usually not granted, particularly for the cost variable, which is characterised by markedly skewed distributions. In addition, individual-level data sets are often characterised by the presence of structural zeros in the cost variable. Hurdle models can be used to account for the presence of excess zeros in a distribution and have been applied in the context of cost data. We extend their application to cost-effectiveness data, defining a full Bayesian specification, which consists of a model for the individual probability of null costs, a marginal model for the costs and a conditional model for the measure of effectiveness (given the observed costs). We presented the model using a working example to describe its main features. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:24343868

Bayesian models for cost-effectiveness analysis in the presence of structural zero costs.

PubMed

Baio, Gianluca

2014-05-20

Bayesian modelling for cost-effectiveness data has received much attention in both the health economics and the statistical literature, in recent years. Cost-effectiveness data are characterised by a relatively complex structure of relationships linking a suitable measure of clinical benefit (e.g. quality-adjusted life years) and the associated costs. Simplifying assumptions, such as (bivariate) normality of the underlying distributions, are usually not granted, particularly for the cost variable, which is characterised by markedly skewed distributions. In addition, individual-level data sets are often characterised by the presence of structural zeros in the cost variable. Hurdle models can be used to account for the presence of excess zeros in a distribution and have been applied in the context of cost data. We extend their application to cost-effectiveness data, defining a full Bayesian specification, which consists of a model for the individual probability of null costs, a marginal model for the costs and a conditional model for the measure of effectiveness (given the observed costs). We presented the model using a working example to describe its main features. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.

Meta-analysis of diagnostic tests accounting for disease prevalence: a new model using trivariate copulas.

PubMed

Hoyer, A; Kuss, O

2015-05-20

In real life and somewhat contrary to biostatistical textbook knowledge, sensitivity and specificity (and not only predictive values) of diagnostic tests can vary with the underlying prevalence of disease. In meta-analysis of diagnostic studies, accounting for this fact naturally leads to a trivariate expansion of the traditional bivariate logistic regression model with random study effects. In this paper, a new model is proposed using trivariate copulas and beta-binomial marginal distributions for sensitivity, specificity, and prevalence as an expansion of the bivariate model. Two different copulas are used, the trivariate Gaussian copula and a trivariate vine copula based on the bivariate Plackett copula. This model has a closed-form likelihood, so standard software (e.g., SAS PROC NLMIXED) can be used. The results of a simulation study have shown that the copula models perform at least as good but frequently better than the standard model. The methods are illustrated by two examples. Copyright © 2015 John Wiley & Sons, Ltd.

Statistical Development of Flood Frequency and Magnitude Equations for the Cosumnes and Mokelumne River Drainage Basins, Sierra Nevada, California

NASA Astrophysics Data System (ADS)

Burns, R. G.; Meyer, R. W.; Cornwell, K.

2003-12-01

In-basin statistical relations allow for development of regional flood frequency and magnitude equations in the Cosumnes River and Mokelumne River drainage basins. Current equations were derived from data collected through 1975, and do not reflect newer data with some significant flooding. Physical basin characteristics (area, mean basin elevation, slope of longest reach, and mean annual precipitation) were correlated against predicted flood discharges for each of the 5, 10, 25, 50, 100, 200, and 500-year recurrence intervals in a multivariate analysis. Predicted maximum instantaneous flood discharges were determined using the PEAKFQ program with default settings, for 24 stream gages within the study area presumed not affected by flow management practices. For numerical comparisons, GIS-based methods using Spatial Analyst and the Arc Hydro Tools extension were applied to derive physical basin characteristics as predictor variables from a 30m digital elevation model (DEM) and a mean annual precipitation raster (PRISM). In a bivariate analysis, examination of Pearson correlation coefficients, F-statistic, and t & p thresholds show good correlation between area and flood discharges. Similar analyses show poor correlation for mean basin elevation, slope and precipitation, with flood discharge. Bivariate analysis suggests slope may not be an appropriate predictor term for use in the multivariate analysis. Precipitation and elevation correlate very well, demonstrating possible orographic effects. From the multivariate analysis, less than 6% of the variability in the correlation is not explained for flood recurrences up to 25 years. Longer term predictions up to 500 years accrue greater uncertainty with as much as 15% of the variability in the correlation left unexplained.

Using SPSS to Analyze Book Collection Data.

ERIC Educational Resources Information Center

Townley, Charles T.

1981-01-01

Describes and illustrates Statistical Package for the Social Sciences (SPSS) procedures appropriate for book collection data analysis. Several different procedures for univariate, bivariate, and multivariate analysis are discussed, and applications of procedures for book collection studies are presented. Included are 24 tables illustrating output…

Dental Workforce Availability and Dental Services Utilization in Appalachia: A Geospatial Analysis

PubMed Central

Feng, Xue; Sambamoorthi, Usha; Wiener, R. Constance

2016-01-01

Objectives There is considerable variation in dental services utilization across Appalachian counties, and a plausible explanation is that individuals in some geographical areas do not utilize dental care due to dental workforce shortage. We conducted an ecological study on dental workforce availability and dental services utilization in Appalachia. Methods We derived county-level (n = 364) data on demographic, socio-economic characteristics and dental services utilization in Appalachia from the 2010 Behavioral Risk Factor Surveillance System (BRFSS) using person-level data. We obtained county-level dental workforce availability and physician-to-population ratio estimates from Area Health Resource File, and linked them to the county-level BRFSS data. The dependent variable was the proportion using dental services within the last year in each county (ranging from 16.6% to 91.0%). We described the association between dental workforce availability and dental services utilization using ordinary least squares regression and spatial regression techniques. Spatial analyses consisted of bivariate Local Indicators of Spatial Association (LISA) and geographically weighted regression (GWR). Results Bivariate LISA showed that counties in the central and southern Appalachian regions had significant (p < .05) low-low spatial clusters (low dental workforce availability, low percent dental services utilization). GWR revealed considerable local variations in the association between dental utilization and dental workforce availability. In the multivariate GWR models, 8.5% (t-statistics >1.96) and 13.45% (t-statistics >1.96) of counties showed positive and statistically significant relationships between the dental services utilization and workforce availability of dentists and dental hygienists, respectively. Conclusions Dental workforce availability was associated with dental services utilization in the Appalachian region; however, this association was not statistically significant in all counties. The findings suggest that program and policy efforts to improve dental services utilization need to focus on factors other than increasing the dental workforce availability for many counties in Appalachia. PMID:27957773

Nonparametric analysis of bivariate gap time with competing risks.

PubMed

Huang, Chiung-Yu; Wang, Chenguang; Wang, Mei-Cheng

2016-09-01

This article considers nonparametric methods for studying recurrent disease and death with competing risks. We first point out that comparisons based on the well-known cumulative incidence function can be confounded by different prevalence rates of the competing events, and that comparisons of the conditional distribution of the survival time given the failure event type are more relevant for investigating the prognosis of different patterns of recurrence disease. We then propose nonparametric estimators for the conditional cumulative incidence function as well as the conditional bivariate cumulative incidence function for the bivariate gap times, that is, the time to disease recurrence and the residual lifetime after recurrence. To quantify the association between the two gap times in the competing risks setting, a modified Kendall's tau statistic is proposed. The proposed estimators for the conditional bivariate cumulative incidence distribution and the association measure account for the induced dependent censoring for the second gap time. Uniform consistency and weak convergence of the proposed estimators are established. Hypothesis testing procedures for two-sample comparisons are discussed. Numerical simulation studies with practical sample sizes are conducted to evaluate the performance of the proposed nonparametric estimators and tests. An application to data from a pancreatic cancer study is presented to illustrate the methods developed in this article. © 2016, The International Biometric Society.

Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models

PubMed Central

Liu, Ziyue; Cappola, Anne R.; Crofford, Leslie J.; Guo, Wensheng

2013-01-01

The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this paper, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with both population-average and subject-specific components. The bivariate model is constructed by concatenating the univariate models based on the hypothesized relationship. Because of the flexible framework of state space form, the resultant models not only can handle complex individual profiles, but also can incorporate complex relationships between two hormones, including both concurrent and feedback relationship. Estimation and inference are based on marginal likelihood and posterior means and variances. Computationally efficient Kalman filtering and smoothing algorithms are used for implementation. Application of the proposed method to a study of chronic fatigue syndrome and fibromyalgia reveals that the relationships between adrenocorticotropic hormone and cortisol in the patient group are weaker than in healthy controls. PMID:24729646

Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models.

PubMed

Liu, Ziyue; Cappola, Anne R; Crofford, Leslie J; Guo, Wensheng

2014-01-01

The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this paper, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with both population-average and subject-specific components. The bivariate model is constructed by concatenating the univariate models based on the hypothesized relationship. Because of the flexible framework of state space form, the resultant models not only can handle complex individual profiles, but also can incorporate complex relationships between two hormones, including both concurrent and feedback relationship. Estimation and inference are based on marginal likelihood and posterior means and variances. Computationally efficient Kalman filtering and smoothing algorithms are used for implementation. Application of the proposed method to a study of chronic fatigue syndrome and fibromyalgia reveals that the relationships between adrenocorticotropic hormone and cortisol in the patient group are weaker than in healthy controls.

Compound estimation procedures in reliability

NASA Technical Reports Server (NTRS)

Barnes, Ron

1990-01-01

At NASA, components and subsystems of components in the Space Shuttle and Space Station generally go through a number of redesign stages. While data on failures for various design stages are sometimes available, the classical procedures for evaluating reliability only utilize the failure data on the present design stage of the component or subsystem. Often, few or no failures have been recorded on the present design stage. Previously, Bayesian estimators for the reliability of a single component, conditioned on the failure data for the present design, were developed. These new estimators permit NASA to evaluate the reliability, even when few or no failures have been recorded. Point estimates for the latter evaluation were not possible with the classical procedures. Since different design stages of a component (or subsystem) generally have a good deal in common, the development of new statistical procedures for evaluating the reliability, which consider the entire failure record for all design stages, has great intuitive appeal. A typical subsystem consists of a number of different components and each component has evolved through a number of redesign stages. The present investigations considered compound estimation procedures and related models. Such models permit the statistical consideration of all design stages of each component and thus incorporate all the available failure data to obtain estimates for the reliability of the present version of the component (or subsystem). A number of models were considered to estimate the reliability of a component conditioned on its total failure history from two design stages. It was determined that reliability estimators for the present design stage, conditioned on the complete failure history for two design stages have lower risk than the corresponding estimators conditioned only on the most recent design failure data. Several models were explored and preliminary models involving bivariate Poisson distribution and the Consael Process (a bivariate Poisson process) were developed. Possible short comings of the models are noted. An example is given to illustrate the procedures. These investigations are ongoing with the aim of developing estimators that extend to components (and subsystems) with three or more design stages.

First Generation College Students: Motivation, Integration, and Academic Achievement

ERIC Educational Resources Information Center

Prospero, Moises; Vohra-Gupta, Shetal

2007-01-01

The study reported in this article investigated motivation and integration dimensions that influence college academic achievement of first-generation students compared to nonfirst-generation students. Participants consisted of 277 ethnically diverse students who were attending a community college. Bivariate and multivariate statistical analyses…

Analysis of vector wind change with respect to time for Cape Kennedy, Florida: Wind aloft profile change vs. time, phase 1

NASA Technical Reports Server (NTRS)

Adelfang, S. I.

1977-01-01

Wind vector change with respect to time at Cape Kennedy, Florida, is examined according to the theory of multivariate normality. The joint distribution of the four variables represented by the components of the wind vector at an initial time and after a specified elapsed time is hypothesized to be quadravariate normal; the fourteen statistics of this distribution, calculated from fifteen years of twice daily Rawinsonde data are presented by monthly reference periods for each month from 0 to 27 km. The hypotheses that the wind component changes with respect to time is univariate normal, the joint distribution of wind component changes is bivariate normal, and the modulus of vector wind change is Rayleigh, has been tested by comparison with observed distributions. Statistics of the conditional bivariate normal distributions of vector wind at a future time given the vector wind at an initial time are derived. Wind changes over time periods from one to five hours, calculated from Jimsphere data, are presented.

Eat, sleep, work, play: associations of weight status and health-related behaviors among young adult college students.

PubMed

Quick, Virginia; Byrd-Bredbenner, Carol; White, Adrienne A; Brown, Onikia; Colby, Sarah; Shoff, Suzanne; Lohse, Barbara; Horacek, Tanya; Kidd, Tanda; Greene, Geoffrey

2014-01-01

To examine relationships of sleep, eating, and exercise behaviors; work time pressures; and sociodemographic characteristics by weight status (healthy weight [body mass index or BMI < 25] vs. overweight [BMI ≥ 25]) of young adults. Cross-sectional. Nine U.S. universities. Enrolled college students (N = 1252; 18-24 years; 80% white; 59% female). Survey included the Pittsburgh Sleep Quality Index (PSQI), Three-Factor Eating Questionnaire (TFEQ), Satter Eating Competence Inventory (ecSI), National Cancer Institute Fruit/Vegetable Screener, International Physical Activity Questionnaire, Work Time Pressure items, and sociodemographic characteristics. Chi-square and t-tests determined significant bivariate associations of sociodemographics, sleep behaviors, eating behaviors, physical activity behavior, and work time pressures with weight status (i.e., healthy vs. overweight/obese). Statistically significant bivariate associations with weight status were then entered into a multivariate logistic regression model that estimated associations with being overweight/obese. Sex (female), race (nonwhite), older age, higher Global PSQI score, lower ecSI total score, and higher TFEQ Emotional Eating Scale score were significantly (p < .05) associated with overweight/obesity in bivariate analyses. Multivariate logistic regression analysis showed that sex (female; odds ratio [OR] = 2.05, confidence interval [CI] = 1.54-2.74), older age (OR = 1.35, CI = 1.21-1.50), higher Global PSQI score (OR = 1.07, CI = 1.01-1.13), and lower ecSI score (OR = .96, CI = .94-.98), were significantly (p < .05) associated with overweight/obesity. Findings suggest that obesity prevention interventions for college students should include an education component to emphasize the importance of overall sleep quality and improving eating competence.

Vector wind profile gust model

NASA Technical Reports Server (NTRS)

Adelfang, S. I.

1981-01-01

To enable development of a vector wind gust model suitable for orbital flight test operations and trade studies, hypotheses concerning the distributions of gust component variables were verified. Methods for verification of hypotheses that observed gust variables, including gust component magnitude, gust length, u range, and L range, are gamma distributed and presented. Observed gust modulus has been drawn from a bivariate gamma distribution that can be approximated with a Weibull distribution. Zonal and meridional gust components are bivariate gamma distributed. An analytical method for testing for bivariate gamma distributed variables is presented. Two distributions for gust modulus are described and the results of extensive hypothesis testing of one of the distributions are presented. The validity of the gamma distribution for representation of gust component variables is established.

Univariate and Bivariate Loglinear Models for Discrete Test Score Distributions.

ERIC Educational Resources Information Center

Holland, Paul W.; Thayer, Dorothy T.

2000-01-01

Applied the theory of exponential families of distributions to the problem of fitting the univariate histograms and discrete bivariate frequency distributions that often arise in the analysis of test scores. Considers efficient computation of the maximum likelihood estimates of the parameters using Newton's Method and computationally efficient…

Combining cow and bull reference populations to increase accuracy of genomic prediction and genome-wide association studies.

PubMed

Calus, M P L; de Haas, Y; Veerkamp, R F

2013-10-01

Genomic selection holds the promise to be particularly beneficial for traits that are difficult or expensive to measure, such that access to phenotypes on large daughter groups of bulls is limited. Instead, cow reference populations can be generated, potentially supplemented with existing information from the same or (highly) correlated traits available on bull reference populations. The objective of this study, therefore, was to develop a model to perform genomic predictions and genome-wide association studies based on a combined cow and bull reference data set, with the accuracy of the phenotypes differing between the cow and bull genomic selection reference populations. The developed bivariate Bayesian stochastic search variable selection model allowed for an unbalanced design by imputing residuals in the residual updating scheme for all missing records. The performance of this model is demonstrated on a real data example, where the analyzed trait, being milk fat or protein yield, was either measured only on a cow or a bull reference population, or recorded on both. Our results were that the developed bivariate Bayesian stochastic search variable selection model was able to analyze 2 traits, even though animals had measurements on only 1 of 2 traits. The Bayesian stochastic search variable selection model yielded consistently higher accuracy for fat yield compared with a model without variable selection, both for the univariate and bivariate analyses, whereas the accuracy of both models was very similar for protein yield. The bivariate model identified several additional quantitative trait loci peaks compared with the single-trait models on either trait. In addition, the bivariate models showed a marginal increase in accuracy of genomic predictions for the cow traits (0.01-0.05), although a greater increase in accuracy is expected as the size of the bull population increases. Our results emphasize that the chosen value of priors in Bayesian genomic prediction models are especially important in small data sets. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Development and validation of a risk calculator predicting exercise-induced ventricular arrhythmia in patients with cardiovascular disease.

PubMed

Hermes, Ilarraza-Lomelí; Marianna, García-Saldivia; Jessica, Rojano-Castillo; Carlos, Barrera-Ramírez; Rafael, Chávez-Domínguez; María Dolores, Rius-Suárez; Pedro, Iturralde

2016-10-01

Mortality due to cardiovascular disease is often associated with ventricular arrhythmias. Nowadays, patients with cardiovascular disease are more encouraged to take part in physical training programs. Nevertheless, high-intensity exercise is associated to a higher risk for sudden death, even in apparently healthy people. During an exercise testing (ET), health care professionals provide patients, in a controlled scenario, an intense physiological stimulus that could precipitate cardiac arrhythmia in high risk individuals. There is still no clinical or statistical tool to predict this incidence. The aim of this study was to develop a statistical model to predict the incidence of exercise-induced potentially life-threatening ventricular arrhythmia (PLVA) during high intensity exercise. 6415 patients underwent a symptom-limited ET with a Balke ramp protocol. A multivariate logistic regression model where the primary outcome was PLVA was performed. Incidence of PLVA was 548 cases (8.5%). After a bivariate model, thirty one clinical or ergometric variables were statistically associated with PLVA and were included in the regression model. In the multivariate model, 13 of these variables were found to be statistically significant. A regression model (G) with a X(2) of 283.987 and a p<0.001, was constructed. Significant variables included: heart failure, antiarrhythmic drugs, myocardial lower-VD, age and use of digoxin, nitrates, among others. This study allows clinicians to identify patients at risk of ventricular tachycardia or couplets during exercise, and to take preventive measures or appropriate supervision. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models

NASA Astrophysics Data System (ADS)

Hong, Haoyuan; Pourghasemi, Hamid Reza; Pourtaghi, Zohre Sadat

2016-04-01

Landslides are an important natural hazard that causes a great amount of damage around the world every year, especially during the rainy season. The Lianhua area is located in the middle of China's southern mountainous area, west of Jiangxi Province, and is known to be an area prone to landslides. The aim of this study was to evaluate and compare landslide susceptibility maps produced using the random forest (RF) data mining technique with those produced by bivariate (evidential belief function and frequency ratio) and multivariate (logistic regression) statistical models for Lianhua County, China. First, a landslide inventory map was prepared using aerial photograph interpretation, satellite images, and extensive field surveys. In total, 163 landslide events were recognized in the study area, with 114 landslides (70%) used for training and 49 landslides (30%) used for validation. Next, the landslide conditioning factors-including the slope angle, altitude, slope aspect, topographic wetness index (TWI), slope-length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, annual precipitation, land use, normalized difference vegetation index (NDVI), and lithology-were derived from the spatial database. Finally, the landslide susceptibility maps of Lianhua County were generated in ArcGIS 10.1 based on the random forest (RF), evidential belief function (EBF), frequency ratio (FR), and logistic regression (LR) approaches and were validated using a receiver operating characteristic (ROC) curve. The ROC plot assessment results showed that for landslide susceptibility maps produced using the EBF, FR, LR, and RF models, the area under the curve (AUC) values were 0.8122, 0.8134, 0.7751, and 0.7172, respectively. Therefore, we can conclude that all four models have an AUC of more than 0.70 and can be used in landslide susceptibility mapping in the study area; meanwhile, the EBF and FR models had the best performance for Lianhua County, China. Thus, the resultant susceptibility maps will be useful for land use planning and hazard mitigation aims.

Spousal Caregiver Burden and Its Relation with Disability in Schizophrenia

PubMed Central

Arun, R.; Inbakamal, S.; Tharyan, Anna; Premkumar, Prasanna S.

2018-01-01

Background: Schizophrenia, a chronic psychiatric disorder, can affect one's productivity and psychosocial functioning. In Indian context, the responsibility of caring persons with schizophrenia is increasingly on their spouses. Spousal caregiver experience and its relation with disability in schizophrenia need to be studied. Materials and Methods: We conducted a cross-sectional study among 52 outpatients with schizophrenia and their spouses attending a tertiary psychiatric center. The objectives were: (a) to explore spousal caregiver burden in schizophrenia and (b) to assess the relation between disability and spousal caregiver burden. The study adopted recommended ethical principles. Scales such as Burden Assessment Schedule, Indian Disability Evaluation and Assessment Scale (IDEAS), and Positive and Negative Syndrome Scale were used to collect appropriate data. Descriptive analysis, bivariate analysis, and multivariate analysis were done in SPSS software version 16.0. Results: The mean spousal caregiver burden score was 73.5 (standard deviation: 14.0). In bivariate analysis, disability, duration of schizophrenia, severity of schizophrenia, place of residence, and socioeconomic status had statistically significant relation with spousal caregiver burden. Adjusted for spouses’ age, gender, and other significant factors in bivariate analysis, the IDEAS global disability score (2.6, [confidence interval 0.5–3.8, P = 0.013]) retained statistically significant association with spousal caregiver burden. Conclusion: Spouses of persons with schizophrenia experience significant caregiver burden. Disability was found to be the most powerful determinant of spousal caregiver burden in the sample. Focus on disability alleviation in the management of schizophrenia may help reduce spousal caregiver burden. PMID:29403125

[Bioimpedance vector analysis for body composition in Mexican population].

PubMed

Espinosa-Cuevas, Maria de los Angeles; Rivas-Rodríguez, Lucía; González-Medina, Enna Cristal; Atilano-Carsi, Ximena; Miranda-Alatriste, Paola; Correa-Rotter, Ricardo

2007-01-01

To construct bivariate tolerance ellipses from impedance values normalized for height, which can be used in Mexican population for the assessment of body composition and compare them with others made in different populations. Body composition was assessed by bioelectrical impedance analysis (BIA) in 439 subjects (204 men and 235 women), 18 to 82 years old, with a BMI between 18-31, using an impedanciometer Quadscan 4000. Resistance, reactance and phase angle were used to calculate bioelectrical impedance vectors and construct bivariate tolerance ellipses. Mean age in men was 47.1 +/- 16 years and 42.4 +/- 13 for women, mean weight (73.4 + 9 vs. 60.1 + 8 kg) and height (1.68 vs. 1.55 m) were significant greater in men than in women (p < 0.002). Women in comparison with men, had greater values of impedance (622.96 +/- 66.16 S2 vs. 523.59 +/- 56.56 D) and resistance (618.96 +/- 66.10 Q 61.97 vs. 521.73 +/- 61.97 2), as well as of resistance and reactance standardized by height (398.24 +/-46.30 S2/m vs. 308.66 +/- 38.44) (44.32 +/- 7.14 i/m vs. 39.75 +/-6.29) respectively, with a significant difference in all of them (p < 0.0001). Similarly, the reactance was greater in females, nevertheless this difference did not reach statistical significance (68.96 +/- 11.17 vs. 67.18 +/- 10.3; p = 0.0861). The phase angle was greater in men than in women, with a statistically significant difference (7.330 +/- 0.88 vs. 6.360 +/- 0.97; p < 0.0001). Bivariate tolerance ellipses (50%, 75% and 95%) derived from Mexican subjects showed a significant upward deviation (p < 0.05) from previously published references from Mexican American and Italian populations. New ellipses of tolerance were therefore constructed for the Mexican population. Bioimpedance vectors in Mexican subjects are significantly different from the existing ones, supporting the need of population specific bivariate tolerance ellipses for the evaluation of body composition.

Using Item Response Theory to Develop Measures of Acquisitive and Protective Self-Monitoring From the Original Self-Monitoring Scale.

PubMed

Wilmot, Michael P; Kostal, Jack W; Stillwell, David; Kosinski, Michal

2017-07-01

For the past 40 years, the conventional univariate model of self-monitoring has reigned as the dominant interpretative paradigm in the literature. However, recent findings associated with an alternative bivariate model challenge the conventional paradigm. In this study, item response theory is used to develop measures of the bivariate model of acquisitive and protective self-monitoring using original Self-Monitoring Scale (SMS) items, and data from two large, nonstudent samples ( Ns = 13,563 and 709). Results indicate that the new acquisitive (six-item) and protective (seven-item) self-monitoring scales are reliable, unbiased in terms of gender and age, and demonstrate theoretically consistent relations to measures of personality traits and cognitive ability. Additionally, by virtue of using original SMS items, previously collected responses can be reanalyzed in accordance with the alternative bivariate model. Recommendations for the reanalysis of archival SMS data, as well as directions for future research, are provided.

Non-Cognitive Factor Relationships to Hybrid Doctoral Course Satisfaction and Self-Efficacy

ERIC Educational Resources Information Center

Egbert, Jessica Dalby

2013-01-01

Through a quantitative, non-experimental design, the studied explored non-cognitive factor relationships to hybrid doctoral course satisfaction and self-efficacy, including the differences between the online and on-campus components of the student-selected hybrid courses. Descriptive, bivariate, and multivariate statistical analyses were used to…

Interpreting Bivariate Regression Coefficients: Going beyond the Average

ERIC Educational Resources Information Center

Halcoussis, Dennis; Phillips, G. Michael

2010-01-01

Statistics, econometrics, investment analysis, and data analysis classes often review the calculation of several types of averages, including the arithmetic mean, geometric mean, harmonic mean, and various weighted averages. This note shows how each of these can be computed using a basic regression framework. By recognizing when a regression model…

Working with Noise in Bivariate Data

ERIC Educational Resources Information Center

Groth, Randall E.; Jones, Matthew; Knaub, Mary

2017-01-01

The authors asked a group of students during a classroom research study to analyze data sets containing different amounts of noise. The authors use the word "noise" to refer to statistical variability. The four students in the group were finishing seventh grade and participating in summer mathematics instruction. The authors carefully…

Unadjusted Bivariate Two-Group Comparisons: When Simpler is Better.

PubMed

Vetter, Thomas R; Mascha, Edward J

2018-01-01

Hypothesis testing involves posing both a null hypothesis and an alternative hypothesis. This basic statistical tutorial discusses the appropriate use, including their so-called assumptions, of the common unadjusted bivariate tests for hypothesis testing and thus comparing study sample data for a difference or association. The appropriate choice of a statistical test is predicated on the type of data being analyzed and compared. The unpaired or independent samples t test is used to test the null hypothesis that the 2 population means are equal, thereby accepting the alternative hypothesis that the 2 population means are not equal. The unpaired t test is intended for comparing dependent continuous (interval or ratio) data from 2 study groups. A common mistake is to apply several unpaired t tests when comparing data from 3 or more study groups. In this situation, an analysis of variance with post hoc (posttest) intragroup comparisons should instead be applied. Another common mistake is to apply a series of unpaired t tests when comparing sequentially collected data from 2 study groups. In this situation, a repeated-measures analysis of variance, with tests for group-by-time interaction, and post hoc comparisons, as appropriate, should instead be applied in analyzing data from sequential collection points. The paired t test is used to assess the difference in the means of 2 study groups when the sample observations have been obtained in pairs, often before and after an intervention in each study subject. The Pearson chi-square test is widely used to test the null hypothesis that 2 unpaired categorical variables, each with 2 or more nominal levels (values), are independent of each other. When the null hypothesis is rejected, 1 concludes that there is a probable association between the 2 unpaired categorical variables. When comparing 2 groups on an ordinal or nonnormally distributed continuous outcome variable, the 2-sample t test is usually not appropriate. The Wilcoxon-Mann-Whitney test is instead preferred. When making paired comparisons on data that are ordinal, or continuous but nonnormally distributed, the Wilcoxon signed-rank test can be used. In analyzing their data, researchers should consider the continued merits of these simple yet equally valid unadjusted bivariate statistical tests. However, the appropriate use of an unadjusted bivariate test still requires a solid understanding of its utility, assumptions (requirements), and limitations. This understanding will mitigate the risk of misleading findings, interpretations, and conclusions.

How to compare cross-lagged associations in a multilevel autoregressive model.

PubMed

Schuurman, Noémi K; Ferrer, Emilio; de Boer-Sonnenschein, Mieke; Hamaker, Ellen L

2016-06-01

By modeling variables over time it is possible to investigate the Granger-causal cross-lagged associations between variables. By comparing the standardized cross-lagged coefficients, the relative strength of these associations can be evaluated in order to determine important driving forces in the dynamic system. The aim of this study was twofold: first, to illustrate the added value of a multilevel multivariate autoregressive modeling approach for investigating these associations over more traditional techniques; and second, to discuss how the coefficients of the multilevel autoregressive model should be standardized for comparing the strength of the cross-lagged associations. The hierarchical structure of multilevel multivariate autoregressive models complicates standardization, because subject-based statistics or group-based statistics can be used to standardize the coefficients, and each method may result in different conclusions. We argue that in order to make a meaningful comparison of the strength of the cross-lagged associations, the coefficients should be standardized within persons. We further illustrate the bivariate multilevel autoregressive model and the standardization of the coefficients, and we show that disregarding individual differences in dynamics can prove misleading, by means of an empirical example on experienced competence and exhaustion in persons diagnosed with burnout. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Assessment of Simulated and Aquarius-Observed Sea Surface Salinity Using Salinity Measurements in Dry Tortugas National Park, Florida, U.S.A.: Application for Pseudocoral δ18O

NASA Astrophysics Data System (ADS)

Chaichitehrani, N.; DeLong, K. L.

2016-02-01

Salinity plays a critical role in ocean physics thus is a target for paleoclimatologic and paleoceanographic reconstruction. Here we assess the quality of space-borne sea surface salinity (SSS) determinations and simulated SSS versus SSS measurements from an open ocean coral reef site, Dry Tortugas National Park (DTNP). The oxygen isotopic composition of seawater (δ18Osw) is related to SSS, thus SSS can be used to understand δ18Osw variability when measurements of δ18Osw are sparse. In marine carbonates such as corals, δ18Ocoral varies with temperature and δ18Osw creating a bivariate system, which is difficult to calibrate with two variables. Accurate determinations of SSS from satellites and simulations can be substituted for local SSS, converted to δ18Osw, in bivariate forward models to estimate δ18Ocoral or pseudocoral thus improving calibrations of δ18Ocoral for locations and time intervals without in situ observations. Monthly and daily Aquarius-retrieved SSS data Level-3 (Official Version 3.0) with spatial resolution are compared with local SSS in DTNP obtained from Water Quality Monitoring Project for the Florida Keys National Marine Sanctuary and southwest Florida shelf, which includes DTNP, for the concurrent interval from 2011-2014. Our statistical analysis shows a satisfactory agreement between daily Aquarius SSS and local SSS (r2=0.68; RMSE=0.24 psu). Additional SSS data are obtained from the National Data Buoy Center DRYF1 station in DTNP. Monthly-simulated SSS (Global Ocean Physics Reanalysis GLORYS2V3) obtained from the MyOcean WebPortal compares relatively well with DRYF1 monthly SSS data (r2=0.68; RMSE=0.35 psu) for the earlier interval from 1998-2002. Our analysis indicates that Aquarius-retrieved and simulated SSS can be utilized as a substitute for local SSS in bivariate forward models to calculate pseudocoral δ18Ocoral as well as forward models other marine carbonates for locations without SSS observations.

Modelling world gold prices and USD foreign exchange relationship using multivariate GARCH model

NASA Astrophysics Data System (ADS)

Ping, Pung Yean; Ahmad, Maizah Hura Binti

2014-12-01

World gold price is a popular investment commodity. The series have often been modeled using univariate models. The objective of this paper is to show that there is a co-movement between gold price and USD foreign exchange rate. Using the effect of the USD foreign exchange rate on the gold price, a model that can be used to forecast future gold prices is developed. For this purpose, the current paper proposes a multivariate GARCH (Bivariate GARCH) model. Using daily prices of both series from 01.01.2000 to 05.05.2014, a causal relation between the two series understudied are found and a bivariate GARCH model is produced.

Associations between individual and relationship characteristics and genital herpes disclosure.

PubMed

Myers, Jaime L; Buhi, Eric R; Marhefka, Stephanie; Daley, Ellen; Dedrick, Robert

2016-10-01

Disclosure is often a challenge for individuals living with genital herpes. This study explores determinants of genital herpes disclosure with one's most recent sexual partner using an online questionnaire (n = 93). The majority of participants reported (80.4%) disclosure. Among non-disclosers, fear of negative partner reactions was the primary reason for non-disclosure. Age, relationship commitment, time in relationship, and expectations of partner's reaction were statistically significant predictors at the bivariate level. Reaction expectations and relationship commitment remained significant in the multivariate logistic regression model. Findings indicate that future disclosure research should focus on relationship context and managing negative expectations to increase disclosure. © The Author(s) 2015.

Models and analysis for multivariate failure time data

NASA Astrophysics Data System (ADS)

Shih, Joanna Huang

The goal of this research is to develop and investigate models and analytic methods for multivariate failure time data. We compare models in terms of direct modeling of the margins, flexibility of dependency structure, local vs. global measures of association, and ease of implementation. In particular, we study copula models, and models produced by right neutral cumulative hazard functions and right neutral hazard functions. We examine the changes of association over time for families of bivariate distributions induced from these models by displaying their density contour plots, conditional density plots, correlation curves of Doksum et al, and local cross ratios of Oakes. We know that bivariate distributions with same margins might exhibit quite different dependency structures. In addition to modeling, we study estimation procedures. For copula models, we investigate three estimation procedures. the first procedure is full maximum likelihood. The second procedure is two-stage maximum likelihood. At stage 1, we estimate the parameters in the margins by maximizing the marginal likelihood. At stage 2, we estimate the dependency structure by fixing the margins at the estimated ones. The third procedure is two-stage partially parametric maximum likelihood. It is similar to the second procedure, but we estimate the margins by the Kaplan-Meier estimate. We derive asymptotic properties for these three estimation procedures and compare their efficiency by Monte-Carlo simulations and direct computations. For models produced by right neutral cumulative hazards and right neutral hazards, we derive the likelihood and investigate the properties of the maximum likelihood estimates. Finally, we develop goodness of fit tests for the dependency structure in the copula models. We derive a test statistic and its asymptotic properties based on the test of homogeneity of Zelterman and Chen (1988), and a graphical diagnostic procedure based on the empirical Bayes approach. We study the performance of these two methods using actual and computer generated data.

Job strain in public transport drivers: Data to assess the relationship between demand-control model indicators, traffic accidents and sanctions.

PubMed

Useche, Sergio; Montoro, Luis; Cendales, Boris; Gómez, Viviola

2018-08-01

This Data in Brief (DiB) article examines the association between the Job Demand-Control (JDC) model of stress and traffic safety outcomes (accidents and sanctions) in public transport drivers ( n = 780). The data was collected using a structured self-administrable questionnaire composed of measurements of work stress (Job Content Questionnaire), and demographics (professional driving experience, hours and days working/driving per week). The data contains 4 parts: descriptive statistics, bivariate correlations between the study variables, analysis of variance (ANOVA) and Post-Hoc comparisons between drivers classified different quadrants of the JDC model. For further information, it is convenient to read the full article entitled " Working conditions, job strain and traffic safety among three groups of public transport drivers ", published in Safety and Health at Work (SHAW) [1] (Useche et al., 2018).

Wind models for the NSTS ascent trajectory biasing for wind load alleviation

NASA Technical Reports Server (NTRS)

Smith, O. E.; Adelfang, S. I.; Batts, G. W.; Hill, C. K.

1989-01-01

New concepts are presented for aerospace vehicle ascent wind profile biasing. The purpose for wind biasing the ascent trajectory is to provide ascent wind loads relief and thus decrease the probability for launch delays due to wind loads exceeding critical limits. Wind biasing trajectories to the profile of monthly mean winds have been widely used for this purpose. The wind profile models presented give additional alternatives for wind biased trajectories. They are derived from the properties of the bivariate normal probability function using the available wind statistical parameters for the launch site. The analytical expressions are presented to permit generalizations. Specific examples are given to illustrate the procedures. The wind profile models can be used to establish the ascent trajectory steering commands to guide the vehicle through the first stage. For the National Space Transportation System (NSTS) program these steering commands are called I-loads.

Pearson's Correlation between Three Variables; Using Students' Basic Knowledge of Geometry for an Exercise in Mathematical Statistics

ERIC Educational Resources Information Center

Vos, Pauline

2009-01-01

When studying correlations, how do the three bivariate correlation coefficients between three variables relate? After transforming Pearson's correlation coefficient r into a Euclidean distance, undergraduate students can tackle this problem using their secondary school knowledge of geometry (Pythagoras' theorem and similarity of triangles).…

Survey of Working Conditions. Final Report on Univariate and Bivariate Tables.

ERIC Educational Resources Information Center

Michigan Univ., Ann Arbor. Survey Research Center.

A nationwide survey of employed persons was conducted to provide information on labor standards problems, assess the impact of working conditions on workers, develop job satisfaction measures, and establish statistics for similar data collections. The survey revealed that the majority of workers expressed satisfaction with their jobs but they also…

Critical Evaluation of Internet Resources for Teaching Trend and Variability in Bivariate Data

ERIC Educational Resources Information Center

Forster, Pat

2007-01-01

A search on the Internet for resources for teaching statistics yields multiple sites with data sets, projects, worksheets, applets, and software. Often these are made available without information on how they might benefit learning. This paper addresses potential benefits from resources that target trend and variability relationships in bivariate…

A Statistical Method for Synthesizing Mediation Analyses Using the Product of Coefficient Approach Across Multiple Trials

PubMed Central

Huang, Shi; MacKinnon, David P.; Perrino, Tatiana; Gallo, Carlos; Cruden, Gracelyn; Brown, C Hendricks

2016-01-01

Mediation analysis often requires larger sample sizes than main effect analysis to achieve the same statistical power. Combining results across similar trials may be the only practical option for increasing statistical power for mediation analysis in some situations. In this paper, we propose a method to estimate: 1) marginal means for mediation path a, the relation of the independent variable to the mediator; 2) marginal means for path b, the relation of the mediator to the outcome, across multiple trials; and 3) the between-trial level variance-covariance matrix based on a bivariate normal distribution. We present the statistical theory and an R computer program to combine regression coefficients from multiple trials to estimate a combined mediated effect and confidence interval under a random effects model. Values of coefficients a and b, along with their standard errors from each trial are the input for the method. This marginal likelihood based approach with Monte Carlo confidence intervals provides more accurate inference than the standard meta-analytic approach. We discuss computational issues, apply the method to two real-data examples and make recommendations for the use of the method in different settings. PMID:28239330

Predicting frequent emergency department visits among children with asthma using EHR data.

PubMed

Das, Lala T; Abramson, Erika L; Stone, Anne E; Kondrich, Janienne E; Kern, Lisa M; Grinspan, Zachary M

2017-07-01

For children with asthma, emergency department (ED) visits are common, expensive, and often avoidable. Though several factors are associated with ED use (demographics, comorbidities, insurance, medications), its predictability using electronic health record (EHR) data is understudied. We used a retrospective cohort study design and EHR data from one center to examine the relationship of patient factors in 1 year (2013) and the likelihood of frequent ED use (≥2 visits) in the following year (2014), using bivariate and multivariable statistics. We applied and compared several machine-learning algorithms to predict frequent ED use, then selected a model based on accuracy, parsimony, and interpretability. We identified 2691 children. In bivariate analyses, future frequent ED use was associated with demographics, co-morbidities, insurance status, medication history, and use of healthcare resources. Machine learning algorithms had very good AUC (area under the curve) values [0.66-0.87], though fair PPV (positive predictive value) [48-70%] and poor sensitivity [16-27%]. Our final multivariable logistic regression model contained two variables: insurance status and prior ED use. For publicly insured patients, the odds of frequent ED use were 3.1 [2.2-4.5] times that of privately insured patients. Publicly insured patients with 4+ ED visits and privately insured patients with 6+ ED visits in a year had ≥50% probability of frequent ED use the following year. The model had an AUC of 0.86, PPV of 56%, and sensitivity of 23%. Among children with asthma, prior frequent ED use and insurance status strongly predict future ED use. © 2017 Wiley Periodicals, Inc.

Statistical study of the correlation of hard X-ray and type 3 radio bursts in solar flares

NASA Technical Reports Server (NTRS)

Hamilton, Russell J.; Petrosian, Vahe

1989-01-01

A large number of hard X-ray events which were recorded by the Hard X-Ray Burst Spectrometer (HXRBS) on the Solar Maximum Mission (SMM) during the maximum of the 21st solar cycle (circa 1980) are analyzed in order to study their statistical correlation with type 3 bursts. The earlier finding by Kane (1981) are confirmed qualitatively that flares with stronger hard X-ray emission, especially those with harder spectra, are more likely to produce a type 3 burst. The observed distribution of hard X-ray and type 3 events and their correlations are shown to be satisfactorily described by a bivariate distribution consistent with the assumption of statistical linear dependence of X-ray and radio burst intensities. From this analysis it was determined that the distribution of the ratio of X-ray intensity (in counts/s) to type 3 intensity (in solar flux units) which has a wide range and a typical value for this ratio of about 10. The implications of the results for impulsive phase models are discussed.

Statistical analysis of the calibration procedure for personnel radiation measurement instruments

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

Bush, W.J.; Bengston, S.J.; Kalbeitzer, F.L.

1980-11-01

Thermoluminescent analyzer (TLA) calibration procedures were used to estimate personnel radiation exposure levels at the Idaho National Engineering Laboratory (INEL). A statistical analysis is presented herein based on data collected over a six month period in 1979 on four TLA's located in the Department of Energy (DOE) Radiological and Environmental Sciences Laboratory at the INEL. The data were collected according to the day-to-day procedure in effect at that time. Both gamma and beta radiation models are developed. Observed TLA readings of thermoluminescent dosimeters are correlated with known radiation levels. This correlation is then used to predict unknown radiation doses frommore » future analyzer readings of personnel thermoluminescent dosimeters. The statistical techniques applied in this analysis include weighted linear regression, estimation of systematic and random error variances, prediction interval estimation using Scheffe's theory of calibration, the estimation of the ratio of the means of two normal bivariate distributed random variables and their corresponding confidence limits according to Kendall and Stuart, tests of normality, experimental design, a comparison between instruments, and quality control.« less

Investigating NARCCAP Precipitation Extremes via Bivariate Extreme Value Theory (Invited)

NASA Astrophysics Data System (ADS)

Weller, G. B.; Cooley, D. S.; Sain, S. R.; Bukovsky, M. S.; Mearns, L. O.

2013-12-01

We introduce methodology from statistical extreme value theory to examine the ability of reanalysis-drive regional climate models to simulate past daily precipitation extremes. Going beyond a comparison of summary statistics such as 20-year return values, we study whether the most extreme precipitation events produced by climate model simulations exhibit correspondence to the most extreme events seen in observational records. The extent of this correspondence is formulated via the statistical concept of tail dependence. We examine several case studies of extreme precipitation events simulated by the six models of the North American Regional Climate Change Assessment Program (NARCCAP) driven by NCEP reanalysis. It is found that the NARCCAP models generally reproduce daily winter precipitation extremes along the Pacific coast quite well; in contrast, simulation of past daily summer precipitation extremes in a central US region is poor. Some differences in the strength of extremal correspondence are seen in the central region between models which employ spectral nudging and those which do not. We demonstrate how these techniques may be used to draw a link between extreme precipitation events and large-scale atmospheric drivers, as well as to downscale extreme precipitation simulated by a future run of a regional climate model. Specifically, we examine potential future changes in the nature of extreme precipitation along the Pacific coast produced by the pineapple express (PE) phenomenon. A link between extreme precipitation events and a "PE Index" derived from North Pacific sea-surface pressure fields is found. This link is used to study PE-influenced extreme precipitation produced by a future-scenario climate model run.

Pitfalls in statistical landslide susceptibility modelling

NASA Astrophysics Data System (ADS)

Schröder, Boris; Vorpahl, Peter; Märker, Michael; Elsenbeer, Helmut

2010-05-01

The use of statistical methods is a well-established approach to predict landslide occurrence probabilities and to assess landslide susceptibility. This is achieved by applying statistical methods relating historical landslide inventories to topographic indices as predictor variables. In our contribution, we compare several new and powerful methods developed in machine learning and well-established in landscape ecology and macroecology for predicting the distribution of shallow landslides in tropical mountain rainforests in southern Ecuador (among others: boosted regression trees, multivariate adaptive regression splines, maximum entropy). Although these methods are powerful, we think it is necessary to follow a basic set of guidelines to avoid some pitfalls regarding data sampling, predictor selection, and model quality assessment, especially if a comparison of different models is contemplated. We therefore suggest to apply a novel toolbox to evaluate approaches to the statistical modelling of landslide susceptibility. Additionally, we propose some methods to open the "black box" as an inherent part of machine learning methods in order to achieve further explanatory insights into preparatory factors that control landslides. Sampling of training data should be guided by hypotheses regarding processes that lead to slope failure taking into account their respective spatial scales. This approach leads to the selection of a set of candidate predictor variables considered on adequate spatial scales. This set should be checked for multicollinearity in order to facilitate model response curve interpretation. Model quality assesses how well a model is able to reproduce independent observations of its response variable. This includes criteria to evaluate different aspects of model performance, i.e. model discrimination, model calibration, and model refinement. In order to assess a possible violation of the assumption of independency in the training samples or a possible lack of explanatory information in the chosen set of predictor variables, the model residuals need to be checked for spatial auto¬correlation. Therefore, we calculate spline correlograms. In addition to this, we investigate partial dependency plots and bivariate interactions plots considering possible interactions between predictors to improve model interpretation. Aiming at presenting this toolbox for model quality assessment, we investigate the influence of strategies in the construction of training datasets for statistical models on model quality.

Epidemiological survey of oral lesions in children and adolescents in a Brazilian population.

PubMed

Pessôa, Camila Porto; Alves, Técia Daltro Borges; dos Santos, Nilton César Nogueira; dos Santos, Heloísa Laís Rosário; Azevedo, Alana de Cássia Silva; dos Santos, Jean Nunes; Oliveira, Márcio Campos

2015-11-01

To identify the most frequent oral lesions in children and adolescents in Reference Units of Oral Lesions of Public Universities of Bahia, Brazil, in the period between 1996 and 2010, and estimate the association between socio-demographic factors and type of oral lesions found. Cross-sectional study using secondary data obtained from medical records, records of requests and reports of biopsies from patients aged between 0 and 19 years treated in Reference Units of Oral Lesions of Public Universities in Bahia, Brazil, in the period between 1996 and 2010. For data analysis, we used descriptive analysis of the variables, bivariate analysis by calculating the prevalence ratios (PR) to assess the association between oral lesions and gender, age and skin color, and the analysis of potential modifying and confounding effects by logistic regression modeling. To calculate the p-value of associations, we used the chi-square test, and p<0.05 was considered statistically significant. There were 360 records of patients between 0 and 19 years (8.7% of total records). The results revealed 72 different types of lesions. The most prevalent lesions were mucoceles (14.2%), fibroma (5.6%) and pyogenic granuloma (5.3%). The variable "age" was the only socio-demographic characteristics among those analyzed that showed a statistically significant association with both neoplastic and non-neoplastic lesions, according to bivariate analysis, considering the rates adjusted for potential confounders. Neoplastic lesions appeared more often in the age group 0-9 years, while the non-neoplastic lesions were more prevalent in individuals 10-19 years. There was no effect modification noted in the predictive models analyzed. The study identified the existence of a broad range of oral lesions affecting children and adolescents. Most of the lesions found were of the non-neoplastic type. The age of individuals was associated with the type of oral lesion found. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Binge drinking and marijuana use among menthol and non-menthol adolescent smokers: findings from the youth smoking survey.

PubMed

Azagba, Sunday; Sharaf, Mesbah F

2014-03-01

Research has shown that smoking menthol cigarettes induces smoking initiation and hinders cessation efforts especially among youth. The objective of this paper is to examine the association between menthol cigarette smoking and substance use among adolescent students in Canada. A nationally representative cross-sectional sample of 4466 Canadian students in grades 7 to 12 from the 2010-2011 Youth Smoking Survey is analyzed. A bivariate probit model is used jointly to examine the association of menthol smoking status with binge drinking and marijuana use. 32% of the current smokers in grades 7 to 12 smoke mentholated cigarettes, 73% are binge drinkers and 79% use marijuana. Results of the bivariate probit regression analysis, controlling for other covariates, show statistically significant differences in the likelihood of binge drinking and marijuana use between menthol and non-menthol smokers. Menthol cigarette smokers are 6% (ME=0.06, 95% CI=0.03-0.09) more likely to binge drink and 7% (ME=0.07, 95% CI=0.05-0.10) more likely to use marijuana. Smoking menthol cigarettes is associated with a higher likelihood of binge drinking and marijuana use among Canadian adolescents. Banning menthol in cigarettes may be beneficial to public health. Copyright © 2013 Elsevier Ltd. All rights reserved.

What Factors Influence Community Oral and Maxillofacial Surgeons' Choice to Use Capnography in the Office-Based Ambulatory Anesthesia Setting?

PubMed

Matin, Mehdi B; Gonzalez, Martin L; Dodson, Thomas B

2015-08-01

The American Association of Oral and Maxillofacial Surgeons Board of Trustees mandated monitoring using capnography during moderate sedation (MS) and deep sedation or general anesthesia (DS/GA) delivered in the office setting effective January 1, 2014. The purpose of this study was to estimate the frequency of capnography use and to identify variables associated with a clinician's choice to use capnography before the mandate. To address the research purpose, the authors designed a prospective cohort study and enrolled 2 samples: 1) American private practicing oral and maxillofacial surgeons (OMSs) and 2) all eligible patients for whom these OMSs delivered MS or DS/GA. The predictor variables were categorized as surgeon or patient demographics, anesthesia risk factors, procedure-related variables, and anesthetic medications. The outcome variable was capnography use during MS or DS/GA. Descriptive, bivariate, and forward stepwise multiple logistic regression statistics were computed to evaluate the association between the predictor variables and capnography use, with statistical significance set at a P value less than or equal to .05. The surgeon sample was composed of 95 OMSs and 13.7% reported using capnography. The patient sample included 3,495 patients with a mean age of 30.6 years (standard deviation, 17.8 yr), 43.5% were men, and 5.6% were monitored using capnography. Based on bivariate analyses, 17 variables were associated with capnography use. Forward stepwise regression modeling identified 9 variables statistically associated with capnography use. These variables were patient's age, Mallampati airway score, alcohol consumption, board certification, sevoflurane use, number of monitoring methods, electrocardiogram use, precordial stethoscope use, and number of personnel in operating suite. Although this study might be of historical interest at this time, the results offer insight into OMSs' practice patterns before the mandatory requirement to use capnography. As more OMSs comply with the capnography mandate, their practice patterns involving variables found to statistically correlate with capnography use might become more similar to those of early adopters of this technology. Copyright © 2015 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Bayesian depth estimation from monocular natural images.

PubMed

Su, Che-Chun; Cormack, Lawrence K; Bovik, Alan C

2017-05-01

Estimating an accurate and naturalistic dense depth map from a single monocular photographic image is a difficult problem. Nevertheless, human observers have little difficulty understanding the depth structure implied by photographs. Two-dimensional (2D) images of the real-world environment contain significant statistical information regarding the three-dimensional (3D) structure of the world that the vision system likely exploits to compute perceived depth, monocularly as well as binocularly. Toward understanding how this might be accomplished, we propose a Bayesian model of monocular depth computation that recovers detailed 3D scene structures by extracting reliable, robust, depth-sensitive statistical features from single natural images. These features are derived using well-accepted univariate natural scene statistics (NSS) models and recent bivariate/correlation NSS models that describe the relationships between 2D photographic images and their associated depth maps. This is accomplished by building a dictionary of canonical local depth patterns from which NSS features are extracted as prior information. The dictionary is used to create a multivariate Gaussian mixture (MGM) likelihood model that associates local image features with depth patterns. A simple Bayesian predictor is then used to form spatial depth estimates. The depth results produced by the model, despite its simplicity, correlate well with ground-truth depths measured by a current-generation terrestrial light detection and ranging (LIDAR) scanner. Such a strong form of statistical depth information could be used by the visual system when creating overall estimated depth maps incorporating stereopsis, accommodation, and other conditions. Indeed, even in isolation, the Bayesian predictor delivers depth estimates that are competitive with state-of-the-art "computer vision" methods that utilize highly engineered image features and sophisticated machine learning algorithms.

Differential Neonatal and Postneonatal Infant Mortality Rates across US Counties: The Role of Socioeconomic Conditions and Rurality

ERIC Educational Resources Information Center

Sparks, P. Johnelle; McLaughlin, Diane K.; Stokes, C. Shannon

2009-01-01

Purpose: To examine differences in correlates of neonatal and postneonatal infant mortality rates, across counties, by degree of rurality. Methods: Neonatal and postneonatal mortality rates were calculated from the 1998 to 2002 Compressed Mortality Files from the National Center for Health Statistics. Bivariate analyses assessed the relationship…

A New Way to Teach (or Compute) Pearson's "r" without Reliance on Cross-Products

ERIC Educational Resources Information Center

Huck, Schuyler W.; Ren, Bixiang; Yang, Hongwei

2007-01-01

Many students have difficulty seeing the conceptual link between bivariate data displayed in a scatterplot and the statistical summary of the relationship, "r." This article shows how to teach (and compute) "r" such that each datum's direct and indirect influences are made apparent and used in a new formula for calculating Pearson's "r."

Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions.

PubMed

Najibi, Seyed Morteza; Maadooliat, Mehdi; Zhou, Lan; Huang, Jianhua Z; Gao, Xin

2017-01-01

Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.

An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ

NASA Astrophysics Data System (ADS)

Yilmaz, Işik; Marschalko, Marian; Bednarik, Martin

2013-04-01

The paper presented herein compares and discusses the use of bivariate, multivariate and soft computing techniques for collapse susceptibility modelling. Conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) models representing the bivariate, multivariate and soft computing techniques were used in GIS based collapse susceptibility mapping in an area from Sivas basin (Turkey). Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index (TWI), stream power index (SPI), Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from the models, and they were then compared by means of their validations. However, Area Under Curve (AUC) values obtained from all three models showed that the map obtained from soft computing (ANN) model looks like more accurate than the other models, accuracies of all three models can be evaluated relatively similar. The results also showed that the conditional probability is an essential method in preparation of collapse susceptibility map and highly compatible with GIS operating features.

Impact of wearing fixed orthodontic appliances on quality of life among adolescents: Case-control study.

PubMed

Costa, Andréa A; Serra-Negra, Júnia M; Bendo, Cristiane B; Pordeus, Isabela A; Paiva, Saul M

2016-01-01

To investigate the impact of wearing a fixed orthodontic appliance on oral health-related quality of life (OHRQoL) among adolescents. A case-control study (1 ∶ 2) was carried out with a population-based randomized sample of 327 adolescents aged 11 to 14 years enrolled at public and private schools in the City of Brumadinho, southeast of Brazil. The case group (n  =  109) was made up of adolescents with a high negative impact on OHRQoL, and the control group (n  =  218) was made up of adolescents with a low negative impact. The outcome variable was the impact on OHRQoL measured by the Brazilian version of the Child Perceptions Questionnaire (CPQ 11-14) - Impact Short Form (ISF:16). The main independent variable was wearing fixed orthodontic appliances. Malocclusion and the type of school were identified as possible confounding variables. Bivariate and multiple conditional logistic regressions were employed in the statistical analysis. A multiple conditional logistic regression model demonstrated that adolescents wearing fixed orthodontic appliances had a 4.88-fold greater chance of presenting high negative impact on OHRQoL (95% CI: 2.93-8.13; P < .001) than those who did not wear fixed orthodontic appliances. A bivariate conditional logistic regression demonstrated that malocclusion was significantly associated with OHRQoL (P  =  .017), whereas no statistically significant association was found between the type of school and OHRQoL (P  =  .108). Adolescents who wore fixed orthodontic appliances had a greater chance of reporting a negative impact on OHRQoL than those who did not wear such appliances.

Correlates of consistent condom use among men who have sex with men recruited through the Internet in Huzhou city: a cross-sectional survey.

PubMed

Jin, Meihua; Yang, Zhongrong; Dong, Zhengquan; Han, Jiankang

2013-12-01

There is growing evidence that men who have sex with men (MSM) are currently a group at high risk of HIV infection in China. Our study aims to know the factors affecting consistent condom use among MSM recruited through the internet in Huzhou city. An anonymous cross-sectional study was conducted by recruiting 410 MSM living in Huzhou city via the Internet. The socio-demographic profiles (age, education level, employment status, etc.) and sexual risk behaviors of the respondents were investigated. Bivariate logistic regression analyses were performed to compare the differences between consistent condom users and inconsistent condom users. Variables with significant bivariate between groups' differences were used as candidate variables in a stepwise multivariate logistic regression model. All statistical analyses were performed using SPSS for Windows 17.0, and a p value < 0.05 was considered to be statistically significant. According to their condom use, sixty-eight respondents were classified into two groups. One is consistent condom users, and the other is inconsistent condom users. Multivariate logistic regression showed that respondents who had a comprehensive knowledge of HIV (OR = 4.08, 95% CI: 1.85-8.99), who had sex with male sex workers (OR = 15.30, 95% CI: 5.89-39.75) and who had not drunk alcohol before sex (OR = 3.10, 95% CI: 1.38-6.95) were more likely to be consistent condom users. Consistent condom use among MSM was associated with comprehensive knowledge of HIV and a lack of alcohol use before sexual contact. As a result, reducing alcohol consumption and enhancing education regarding the risks of HIV among sexually active MSM would be effective in preventing of HIV transmission.

The bivariate regression model and its application

NASA Astrophysics Data System (ADS)

Pratikno, B.; Sulistia, L.; Saniyah

2018-03-01

The paper studied a bivariate regression model (BRM) and its application. The maximum power and minimum size are used to choose the eligible tests using non-sample prior information (NSPI). In the simulation study on real data, we used Wilk’s lamda to determine the best model of the BRM. The result showed that the power of the pre-test-test (PTT) on the NSPI is a significant choice of the tests among unrestricted test (UT) and restricted test (RT), and the best model of the BRM is Y (1) = ‑894 + 46X and Y (2) = 78 + 0.2X with significant Wilk’s lamda 0.88 < 0.90 (Wilk’s table).

Post-processing of multi-hydrologic model simulations for improved streamflow projections

NASA Astrophysics Data System (ADS)

khajehei, sepideh; Ahmadalipour, Ali; Moradkhani, Hamid

2016-04-01

Hydrologic model outputs are prone to bias and uncertainty due to knowledge deficiency in model and data. Uncertainty in hydroclimatic projections arises due to uncertainty in hydrologic model as well as the epistemic or aleatory uncertainties in GCM parameterization and development. This study is conducted to: 1) evaluate the recently developed multi-variate post-processing method for historical simulations and 2) assess the effect of post-processing on uncertainty and reliability of future streamflow projections in both high-flow and low-flow conditions. The first objective is performed for historical period of 1970-1999. Future streamflow projections are generated for 10 statistically downscaled GCMs from two widely used downscaling methods: Bias Corrected Statistically Downscaled (BCSD) and Multivariate Adaptive Constructed Analogs (MACA), over the period of 2010-2099 for two representative concentration pathways of RCP4.5 and RCP8.5. Three semi-distributed hydrologic models were employed and calibrated at 1/16 degree latitude-longitude resolution for over 100 points across the Columbia River Basin (CRB) in the pacific northwest USA. Streamflow outputs are post-processed through a Bayesian framework based on copula functions. The post-processing approach is relying on a transfer function developed based on bivariate joint distribution between the observation and simulation in historical period. Results show that application of post-processing technique leads to considerably higher accuracy in historical simulations and also reducing model uncertainty in future streamflow projections.

Bayesian hierarchical models for cost-effectiveness analyses that use data from cluster randomized trials.

PubMed

Grieve, Richard; Nixon, Richard; Thompson, Simon G

2010-01-01

Cost-effectiveness analyses (CEA) may be undertaken alongside cluster randomized trials (CRTs) where randomization is at the level of the cluster (for example, the hospital or primary care provider) rather than the individual. Costs (and outcomes) within clusters may be correlated so that the assumption made by standard bivariate regression models, that observations are independent, is incorrect. This study develops a flexible modeling framework to acknowledge the clustering in CEA that use CRTs. The authors extend previous Bayesian bivariate models for CEA of multicenter trials to recognize the specific form of clustering in CRTs. They develop new Bayesian hierarchical models (BHMs) that allow mean costs and outcomes, and also variances, to differ across clusters. They illustrate how each model can be applied using data from a large (1732 cases, 70 primary care providers) CRT evaluating alternative interventions for reducing postnatal depression. The analyses compare cost-effectiveness estimates from BHMs with standard bivariate regression models that ignore the data hierarchy. The BHMs show high levels of cost heterogeneity across clusters (intracluster correlation coefficient, 0.17). Compared with standard regression models, the BHMs yield substantially increased uncertainty surrounding the cost-effectiveness estimates, and altered point estimates. The authors conclude that ignoring clustering can lead to incorrect inferences. The BHMs that they present offer a flexible modeling framework that can be applied more generally to CEA that use CRTs.

Probabilistic modelling of drought events in China via 2-dimensional joint copula

NASA Astrophysics Data System (ADS)

Ayantobo, Olusola O.; Li, Yi; Song, Songbai; Javed, Tehseen; Yao, Ning

2018-04-01

Probabilistic modelling of drought events is a significant aspect of water resources management and planning. In this study, popularly applied and several relatively new bivariate Archimedean copulas were employed to derive regional and spatial based copula models to appraise drought risk in mainland China over 1961-2013. Drought duration (Dd), severity (Ds), and peak (Dp), as indicated by Standardized Precipitation Evapotranspiration Index (SPEI), were extracted according to the run theory and fitted with suitable marginal distributions. The maximum likelihood estimation (MLE) and curve fitting method (CFM) were used to estimate the copula parameters of nineteen bivariate Archimedean copulas. Drought probabilities and return periods were analysed based on appropriate bivariate copula in sub-region I-VII and entire mainland China. The goodness-of-fit tests as indicated by the CFM showed that copula NN19 in sub-regions III, IV, V, VI and mainland China, NN20 in sub-region I and NN13 in sub-region VII are the best for modeling drought variables. Bivariate drought probability across mainland China is relatively high, and the highest drought probabilities are found mainly in the Northwestern and Southwestern China. Besides, the result also showed that different sub-regions might suffer varying drought risks. The drought risks as observed in Sub-region III, VI and VII, are significantly greater than other sub-regions. Higher probability of droughts of longer durations in the sub-regions also corresponds to shorter return periods with greater drought severity. These results may imply tremendous challenges for the water resources management in different sub-regions, particularly the Northwestern and Southwestern China.

Evaluation of psychiatric and genetic risk factors among primary relatives of suicide completers in Delhi NCR region, India.

PubMed

Pasi, Shivani; Singh, Piyoosh Kumar; Pandey, Rajeev Kumar; Dikshit, P C; Jiloha, R C; Rao, V R

2015-10-30

Suicide as a public health problem is studied worldwide and association of psychiatric and genetic risk factors for suicidal behavior are the point of discussion in studies across different ethnic groups. The present study is aimed at evaluating psychiatric and genetic traits among primary relatives of suicide completer families in an urban Indian population. Bi-variate analysis shows significant increase in major depression (PHQ and Hamilton), stress, panic disorder, somatoform disorder and suicide attemptamong primary compared to other relatives. Sib pair correlations also reveal significant results for major depression (Hamilton), stress, suicide attempt, intensity of suicide ideation and other anxiety syndrome. 5-HTTLPR, 5-HTT (Stin2) and COMT risk alleles are higher among primary relatives, though statistically insignificant. Backward conditional logistic regression analysis show only independent variable, Depression (Hamilton) made a unique statistically significant contribution to the model in primary relatives. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Statistical Association Criteria in Forensic Psychiatry–A criminological evaluation of casuistry

PubMed Central

Gheorghiu, V; Buda, O; Popescu, I; Trandafir, MS

2011-01-01

Purpose. Identification of potential shared primary psychoprophylaxis and crime prevention is measured by analyzing the rate of commitments for patients–subjects to forensic examination. Material and method. The statistic trial is a retrospective, document–based study. The statistical lot consists of 770 initial examination reports performed and completed during the whole year 2007, primarily analyzed in order to summarize the data within the National Institute of Forensic Medicine, Bucharest, Romania (INML), with one of the group variables being ‘particularities of the psychiatric patient history’, containing the items ‘forensic onset’, ‘commitments within the last year prior to the examination’ and ‘absence of commitments within the last year prior to the examination’. The method used was the Kendall bivariate correlation. For this study, the authors separately analyze only the two items regarding commitments by other correlation alternatives and by modern, elaborate statistical analyses, i.e. recording of the standard case study variables, Kendall bivariate correlation, cross tabulation, factor analysis and hierarchical cluster analysis. Results. The results are varied, from theoretically presumed clinical nosography (such as schizophrenia or manic depression), to non–presumed (conduct disorders) or unexpected behavioral acts, and therefore difficult to interpret. Conclusions. One took into consideration the features of the batch as well as the results of the previous standard correlation of the whole statistical lot. The authors emphasize the role of medical security measures that are actually applied in the therapeutic management in general and in risk and second offence management in particular, as well as the role of forensic psychiatric examinations in the detection of certain aspects related to the monitoring of mental patients. PMID:21505571

GIS-based bivariate statistical techniques for groundwater potential analysis (an example of Iran)

NASA Astrophysics Data System (ADS)

Haghizadeh, Ali; Moghaddam, Davoud Davoudi; Pourghasemi, Hamid Reza

2017-12-01

Groundwater potential analysis prepares better comprehension of hydrological settings of different regions. This study shows the potency of two GIS-based data driven bivariate techniques namely statistical index (SI) and Dempster-Shafer theory (DST) to analyze groundwater potential in Broujerd region of Iran. The research was done using 11 groundwater conditioning factors and 496 spring positions. Based on the ground water potential maps (GPMs) of SI and DST methods, 24.22% and 23.74% of the study area is covered by poor zone of groundwater potential, and 43.93% and 36.3% of Broujerd region is covered by good and very good potential zones, respectively. The validation of outcomes displayed that area under the curve (AUC) of SI and DST techniques are 81.23% and 79.41%, respectively, which shows SI method has slightly a better performance than the DST technique. Therefore, SI and DST methods are advantageous to analyze groundwater capacity and scrutinize the complicated relation between groundwater occurrence and groundwater conditioning factors, which permits investigation of both systemic and stochastic uncertainty. Finally, it can be realized that these techniques are very beneficial for groundwater potential analyzing and can be practical for water-resource management experts.

Social Influence and Individual Risk Factors of HIV Unsafe Sex among Female Entertainment Workers in China

PubMed Central

Yang, Xiushi; Xia, Guomei; Li, Xiaoming; Latkin, Carl; Celentano, David

2010-01-01

Female entertainment workers in China are at increased sexual risk of HIV, but causes of their unprotected sex remain poorly understood. We develop a model that integrates information-motivation-behavioral skills (IMB) with social influences and test the model in a venue-based sample of 732 female entertainment workers in Shanghai. Most IMB and social influence measures are statistically significant in bivariate relationships to condom use; only HIV prevention motivation and behavioral self-efficacy remain significant in the multiple regressions. Self-efficacy in condom use is the most proximate correlate, mediating the relationship between information and motivation and condom use. Both peer and venue supports are important, but their influences over condom use are indirect and mediated through prevention motivation and/or self-efficacy. Behavioral intervention is urgently needed and should take a multi-level approach, emphasizing behavioral skills training and promoting a supportive social/working environment. PMID:20166789

Study designs, use of statistical tests, and statistical analysis software choice in 2015: Results from two Pakistani monthly Medline indexed journals.

PubMed

Shaikh, Masood Ali

2017-09-01

Assessment of research articles in terms of study designs used, statistical tests applied and the use of statistical analysis programmes help determine research activity profile and trends in the country. In this descriptive study, all original articles published by Journal of Pakistan Medical Association (JPMA) and Journal of the College of Physicians and Surgeons Pakistan (JCPSP), in the year 2015 were reviewed in terms of study designs used, application of statistical tests, and the use of statistical analysis programmes. JPMA and JCPSP published 192 and 128 original articles, respectively, in the year 2015. Results of this study indicate that cross-sectional study design, bivariate inferential statistical analysis entailing comparison between two variables/groups, and use of statistical software programme SPSS to be the most common study design, inferential statistical analysis, and statistical analysis software programmes, respectively. These results echo previously published assessment of these two journals for the year 2014.

Optimization of space system development resources

NASA Astrophysics Data System (ADS)

Kosmann, William J.; Sarkani, Shahram; Mazzuchi, Thomas

2013-06-01

NASA has had a decades-long problem with cost growth during the development of space science missions. Numerous agency-sponsored studies have produced average mission level cost growths ranging from 23% to 77%. A new study of 26 historical NASA Science instrument set developments using expert judgment to reallocate key development resources has an average cost growth of 73.77%. Twice in history, a barter-based mechanism has been used to reallocate key development resources during instrument development. The mean instrument set development cost growth was -1.55%. Performing a bivariate inference on the means of these two distributions, there is statistical evidence to support the claim that using a barter-based mechanism to reallocate key instrument development resources will result in a lower expected cost growth than using the expert judgment approach. Agent-based discrete event simulation is the natural way to model a trade environment. A NetLogo agent-based barter-based simulation of science instrument development was created. The agent-based model was validated against the Cassini historical example, as the starting and ending instrument development conditions are available. The resulting validated agent-based barter-based science instrument resource reallocation simulation was used to perform 300 instrument development simulations, using barter to reallocate development resources. The mean cost growth was -3.365%. A bivariate inference on the means was performed to determine that additional significant statistical evidence exists to support a claim that using barter-based resource reallocation will result in lower expected cost growth, with respect to the historical expert judgment approach. Barter-based key development resource reallocation should work on spacecraft development as well as it has worked on instrument development. A new study of 28 historical NASA science spacecraft developments has an average cost growth of 46.04%. As barter-based key development resource reallocation has never been tried in a spacecraft development, no historical results exist, and a simulation of using that approach must be developed. The instrument development simulation should be modified to account for spacecraft development market participant differences. The resulting agent-based barter-based spacecraft resource reallocation simulation would then be used to determine if significant statistical evidence exists to prove a claim that using barter-based resource reallocation will result in lower expected cost growth.

Bio-psychosocial factors are associated with pain intensity, physical functioning, and ability to work in female healthcare personnel with recurrent low back pain.

PubMed

Taulaniemi, Annika; Kuusinen, Lotta; Tokola, Kari; Kankaanpää, Markku; Suni, Jaana H

2017-08-31

To investigate associations of various bio-psychosocial factors with bodily pain, physical func-tioning, and ability to work in low back pain. Cross-sectional study. A total of 219 female healthcare workers with recurrent non-specific low back pain. Associations between several physical and psychosocial factors and: (i) bodily pain, (ii) physical functioning and (iii) ability to work were studied. Variables with statistically significant associations (p < 0.05) in bivariate analysis were set within a generalized linear model to analyse their relationship with each dependent variable. In generalized linear model analysis, perceived work-induced lumbar exertion (p < 0.001), multi-site pain (p <0.001) and work-related fear-avoidance beliefs (FAB-W) (p = 0.02) best explained bodily pain. Multi-site pain (p < 0.001), lumbar exertion (p = 0.005), FAB-W (p = 0.01) and physical performance in figure-of-eight running (p = 0.01) and modified push-ups (p = 0.05) best explained physical functioning; FAB-W (p <0.001), lumbar exertion (p = 0.003), depression (p = 0.01) and recovery after work (p = 0.03) best explained work ability. In bivariate analysis lumbar exertion was associated with poor physical performance. FAB-W and work-induced lumbar exertion were associated with levels of pain, physical functioning and ability to work. Poor physical performance capacity was associated with work-induced lumbar exertion. Interventions that aim to reduce fear-avoidance and increase fitness capacity might be beneficial.

PLASMA LIPIDOMIC PROFILE SIGNATURE OF HYPERTENSION IN MEXICAN AMERICAN FAMILIES: SPECIFIC ROLE OF DIACYLGLYCEROLS

PubMed Central

Kulkarni, Hemant; Meikle, Peter J.; Mamtani, Manju; Weir, Jacquelyn M.; Barlow, Christopher K.; Jowett, Jeremy B.; Bellis, Claire; Dyer, Thomas D.; Johnson, Matthew P.; Rainwater, David L.; Almasy, Laura; Mahaney, Michael C.; Commuzzie, Anthony G.; Blangero, John; Curran, Joanne E.

2013-01-01

Both as a component of metabolic syndrome and as an independent entity, hypertension poses a continued challenge with regard to its diagnosis, pathogenesis and treatment. Previous studies have documented connections between hypertension and indicators of lipid metabolism. Novel technologies like plasma lipidomic profiling promise a better understanding of disorders in which there is a derangement of the lipid metabolism. However, association of plasma lipidomic profiles with hypertension in a high-risk population, like Mexican Americans, has not been evaluated before. Using the rich data and sample resource from the ongoing San Antonio Family Heart Study, we conducted plasma lipidomic profiling by combining high performance liquid chromatography with tandem mass spectroscopy to characterize 319 lipid species in 1192 individuals from 42 large and extended Mexican American families. Robust statistical analyses employing polygenic regression models, liability threshold models and bivariate trait analyses implemented in the SOLAR software were conducted after accounting for obesity, insulin resistance and relative abundance of various lipoprotein fractions. Diacylglycerols in general and the DG 16:0/22:5 and DG 16:0/22:6 lipid species in particular were significantly associated with systolic, diastolic and mean arterial pressures as well as liability of incident hypertension measured during 7767.42 person-years of follow-up. Four lipid species, including the DG 16:0/22:5 and DG 16:0/22:6 species, showed significant genetic correlations with the liability of hypertension in bivariate trait analyses. Our results demonstrate the value of plasma lipidomic profiling in the context of hypertension and identify disturbance of diacyglycerol metabolism as an independent biomarker of hypertension. PMID:23798346

Statistical Validation of Surrogate Endpoints: Another Look at the Prentice Criterion and Other Criteria.

PubMed

Saraf, Sanatan; Mathew, Thomas; Roy, Anindya

2015-01-01

For the statistical validation of surrogate endpoints, an alternative formulation is proposed for testing Prentice's fourth criterion, under a bivariate normal model. In such a setup, the criterion involves inference concerning an appropriate regression parameter, and the criterion holds if the regression parameter is zero. Testing such a null hypothesis has been criticized in the literature since it can only be used to reject a poor surrogate, and not to validate a good surrogate. In order to circumvent this, an equivalence hypothesis is formulated for the regression parameter, namely the hypothesis that the parameter is equivalent to zero. Such an equivalence hypothesis is formulated as an alternative hypothesis, so that the surrogate endpoint is statistically validated when the null hypothesis is rejected. Confidence intervals for the regression parameter and tests for the equivalence hypothesis are proposed using bootstrap methods and small sample asymptotics, and their performances are numerically evaluated and recommendations are made. The choice of the equivalence margin is a regulatory issue that needs to be addressed. The proposed equivalence testing formulation is also adopted for other parameters that have been proposed in the literature on surrogate endpoint validation, namely, the relative effect and proportion explained.

Modeling continuous covariates with a "spike" at zero: Bivariate approaches.

PubMed

Jenkner, Carolin; Lorenz, Eva; Becher, Heiko; Sauerbrei, Willi

2016-07-01

In epidemiology and clinical research, predictors often take value zero for a large amount of observations while the distribution of the remaining observations is continuous. These predictors are called variables with a spike at zero. Examples include smoking or alcohol consumption. Recently, an extension of the fractional polynomial (FP) procedure, a technique for modeling nonlinear relationships, was proposed to deal with such situations. To indicate whether or not a value is zero, a binary variable is added to the model. In a two stage procedure, called FP-spike, the necessity of the binary variable and/or the continuous FP function for the positive part are assessed for a suitable fit. In univariate analyses, the FP-spike procedure usually leads to functional relationships that are easy to interpret. This paper introduces four approaches for dealing with two variables with a spike at zero (SAZ). The methods depend on the bivariate distribution of zero and nonzero values. Bi-Sep is the simplest of the four bivariate approaches. It uses the univariate FP-spike procedure separately for the two SAZ variables. In Bi-D3, Bi-D1, and Bi-Sub, proportions of zeros in both variables are considered simultaneously in the binary indicators. Therefore, these strategies can account for correlated variables. The methods can be used for arbitrary distributions of the covariates. For illustration and comparison of results, data from a case-control study on laryngeal cancer, with smoking and alcohol intake as two SAZ variables, is considered. In addition, a possible extension to three or more SAZ variables is outlined. A combination of log-linear models for the analysis of the correlation in combination with the bivariate approaches is proposed. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

US EPA 2012 Air Quality Fused Surface for the Conterminous U.S. Map Service

EPA Pesticide Factsheets

This web service contains a polygon layer that depicts fused air quality predictions for 2012 for census tracts in the conterminous United States. Fused air quality predictions (for ozone and PM2.5) are modeled using a Bayesian space-time downscaling fusion model approach described in a series of three published journal papers: 1) (Berrocal, V., Gelfand, A. E. and Holland, D. M. (2012). Space-time fusion under error in computer model output: an application to modeling air quality. Biometrics 68, 837-848; 2) Berrocal, V., Gelfand, A. E. and Holland, D. M. (2010). A bivariate space-time downscaler under space and time misalignment. The Annals of Applied Statistics 4, 1942-1975; and 3) Berrocal, V., Gelfand, A. E., and Holland, D. M. (2010). A spatio-temporal downscaler for output from numerical models. J. of Agricultural, Biological,and Environmental Statistics 15, 176-197) is used to provide daily, predictive PM2.5 (daily average) and O3 (daily 8-hr maximum) surfaces for 2012. Summer (O3) and annual (PM2.5) means calculated and published. The downscaling fusion model uses both air quality monitoring data from the National Air Monitoring Stations/State and Local Air Monitoring Stations (NAMS/SLAMS) and numerical output from the Models-3/Community Multiscale Air Quality (CMAQ). Currently, predictions at the US census tract centroid locations within the 12 km CMAQ domain are archived. Predictions at the CMAQ grid cell centroids, or any desired set of locations co

Lungworm Infections in German Dairy Cattle Herds — Seroprevalence and GIS-Supported Risk Factor Analysis

PubMed Central

Schunn, Anne-Marie; Conraths, Franz J.; Staubach, Christoph; Fröhlich, Andreas; Forbes, Andrew; Strube, Christina

2013-01-01

In November 2008, a total of 19,910 bulk tank milk (BTM) samples were obtained from dairy farms from all over Germany, corresponding to about 20% of all German dairy herds, and analysed for antibodies against the bovine lungworm Dictyocaulus viviparus by use of the recombinant MSP-ELISA. A total number of 3,397 (17.1%; n = 19,910) BTM samples tested seropositive. The prevalences in individual German federal states varied between 0.0% and 31.2% positive herds. A geospatial map was drawn to show the distribution of seropositive and seronegative herds per postal code area. ELISA results were further analysed for associations with land-use and climate data. Bivariate statistical analysis was used to identify potential spatial risk factors for dictyocaulosis. Statistically significant positive associations were found between lungworm seropositive herds and the proportion of water bodies and grassed area per postal code area. Variables that showed a statistically significant association with a positive BTM test were included in a logistic regression model, which was further refined by controlled stepwise selection of variables. The low Pseudo R2 values (0.08 for the full model and 0.06 for the final model) and further evaluation of the model by ROC analysis indicate that additional, unrecorded factors (e.g. management factors) or random effects may substantially contribute to lungworm infections in dairy cows. Veterinarians should include lungworms in the differential diagnosis of respiratory disease in dairy cattle, particularly those at pasture. Monitoring of herds through BTM screening for antibodies can help farmers and veterinarians plan and implement appropriate control measures. PMID:24040243

Drought assessment in the Duero basin (Central Spain) by means of multivariate extreme value statistics

NASA Astrophysics Data System (ADS)

Kallache, M.

2012-04-01

Droughts cause important losses. On the Iberian Peninsula, for example, non-irrigated agriculture and the tourism sector are affected in regular intervals. The goal of this study is the description of droughts and their dependence in the Duero basin in Central Spain. To do so, daily or monthly precipitation data is used. Here cumulative precipitation deficits below a threshold define meteorological droughts. This drought indicator is similar to the commonly used standard precipitation index. However, here the focus lies on the modeling of severe droughts, which is done by applying multivariate extreme value theory (MEVT) to model extreme drought events. Data from several stations are assessed jointly, thus the uncertainty of the results is reduced. Droughts are a complex phenomenon, their severity, spatial extension and duration has to be taken into account. Our approach captures severity and spatial extension. In general we find a high correlation between deficit volumes and drought duration, thus the duration is not explicitely modeled. We apply a MEVT model with asymmetric logistic dependence function, which is capable to model asymptotic dependence and independence (cf. Ramos and Ledford, 2009). To summarize the information on the dependence in the joint tail of the extreme drought events, we utilise the fragility index (Geluk et al., 2007). Results show that droughts also occur frequently in winter. Moreover, it is very common for one site to suffer dry conditions, whilst neighboring areas experience normal or even humid conditions. Interpolation is thus difficult. Bivariate extremal dependence is present in the data. However, most stations are at least asymptotically independent. The according fragility indices are important information for risk calculations. The emerging spatial patterns for bivariate dependence are mostly influenced by topography. When looking at the dependence between more than two stations, it shows that joint extremes can occur more often than randomly for up to 6 stations, this depends on the distance between the stations.

Technology-enhanced Interactive Teaching of Marginal, Joint and Conditional Probabilities: The Special Case of Bivariate Normal Distribution

PubMed Central

Dinov, Ivo D.; Kamino, Scott; Bhakhrani, Bilal; Christou, Nicolas

2014-01-01

Summary Data analysis requires subtle probability reasoning to answer questions like What is the chance of event A occurring, given that event B was observed? This generic question arises in discussions of many intriguing scientific questions such as What is the probability that an adolescent weighs between 120 and 140 pounds given that they are of average height? and What is the probability of (monetary) inflation exceeding 4% and housing price index below 110? To address such problems, learning some applied, theoretical or cross-disciplinary probability concepts is necessary. Teaching such courses can be improved by utilizing modern information technology resources. Students’ understanding of multivariate distributions, conditional probabilities, correlation and causation can be significantly strengthened by employing interactive web-based science educational resources. Independent of the type of a probability course (e.g. majors, minors or service probability course, rigorous measure-theoretic, applied or statistics course) student motivation, learning experiences and knowledge retention may be enhanced by blending modern technological tools within the classical conceptual pedagogical models. We have designed, implemented and disseminated a portable open-source web-application for teaching multivariate distributions, marginal, joint and conditional probabilities using the special case of bivariate Normal distribution. A real adolescent height and weight dataset is used to demonstrate the classroom utilization of the new web-application to address problems of parameter estimation, univariate and multivariate inference. PMID:25419016

Technology-enhanced Interactive Teaching of Marginal, Joint and Conditional Probabilities: The Special Case of Bivariate Normal Distribution.

PubMed

Dinov, Ivo D; Kamino, Scott; Bhakhrani, Bilal; Christou, Nicolas

2013-01-01

Data analysis requires subtle probability reasoning to answer questions like What is the chance of event A occurring, given that event B was observed? This generic question arises in discussions of many intriguing scientific questions such as What is the probability that an adolescent weighs between 120 and 140 pounds given that they are of average height? and What is the probability of (monetary) inflation exceeding 4% and housing price index below 110? To address such problems, learning some applied, theoretical or cross-disciplinary probability concepts is necessary. Teaching such courses can be improved by utilizing modern information technology resources. Students' understanding of multivariate distributions, conditional probabilities, correlation and causation can be significantly strengthened by employing interactive web-based science educational resources. Independent of the type of a probability course (e.g. majors, minors or service probability course, rigorous measure-theoretic, applied or statistics course) student motivation, learning experiences and knowledge retention may be enhanced by blending modern technological tools within the classical conceptual pedagogical models. We have designed, implemented and disseminated a portable open-source web-application for teaching multivariate distributions, marginal, joint and conditional probabilities using the special case of bivariate Normal distribution. A real adolescent height and weight dataset is used to demonstrate the classroom utilization of the new web-application to address problems of parameter estimation, univariate and multivariate inference.

Overweight and Obesity: Prevalence and Correlates in a Large Clinical Sample of Children with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Zuckerman, Katharine E.; Hill, Alison P.; Guion, Kimberly; Voltolina, Lisa; Fombonne, Eric

2014-01-01

Autism Spectrum Disorders (ASDs) and childhood obesity (OBY) are rising public health concerns. This study aimed to evaluate the prevalence of overweight (OWT) and OBY in a sample of 376 Oregon children with ASD, and to assess correlates of OWT and OBY in this sample. We used descriptive statistics, bivariate, and focused multivariate analyses to…

A method of using cluster analysis to study statistical dependence in multivariate data

NASA Technical Reports Server (NTRS)

Borucki, W. J.; Card, D. H.; Lyle, G. C.

1975-01-01

A technique is presented that uses both cluster analysis and a Monte Carlo significance test of clusters to discover associations between variables in multidimensional data. The method is applied to an example of a noisy function in three-dimensional space, to a sample from a mixture of three bivariate normal distributions, and to the well-known Fisher's Iris data.

An Exploratory Data Analysis System for Support in Medical Decision-Making

PubMed Central

Copeland, J. A.; Hamel, B.; Bourne, J. R.

1979-01-01

An experimental system was developed to allow retrieval and analysis of data collected during a study of neurobehavioral correlates of renal disease. After retrieving data organized in a relational data base, simple bivariate statistics of parametric and nonparametric nature could be conducted. An “exploratory” mode in which the system provided guidance in selection of appropriate statistical analyses was also available to the user. The system traversed a decision tree using the inherent qualities of the data (e.g., the identity and number of patients, tests, and time epochs) to search for the appropriate analyses to employ.

Mixed-Methods Research in the Discipline of Nursing.

PubMed

Beck, Cheryl Tatano; Harrison, Lisa

2016-01-01

In this review article, we examined the prevalence and characteristics of 294 mixed-methods studies in the discipline of nursing. Creswell and Plano Clark's typology was most frequently used along with concurrent timing. Bivariate statistics was most often the highest level of statistics reported in the results. As for qualitative data analysis, content analysis was most frequently used. The majority of nurse researchers did not specifically address the purpose, paradigm, typology, priority, timing, interaction, or integration of their mixed-methods studies. Strategies are suggested for improving the design, conduct, and reporting of mixed-methods studies in the discipline of nursing.

Spatial distribution of unspecified chronic kidney disease in El Salvador by crop area cultivated and ambient temperature.

PubMed

VanDervort, Darcy R; López, Dina L; Orantes, Carlos M; Rodríguez, David S

2014-04-01

Chronic kidney disease of unknown etiology is occurring in various geographic areas worldwide. Cases lack typical risk factors associated with chronic kidney disease, such as diabetes and hypertension. It is epidemic in El Salvador, Central America, where it is diagnosed with increasing frequency in young, otherwise-healthy male farmworkers. Suspected causes include agrochemical use (especially in sugarcane fields), physical heat stress, and heavy metal exposure. To evaluate the geographic relationship between unspecified chronic kidney disease (unCKD) and nondiabetic chronic renal failure (ndESRD) hospital admissions in El Salvador with the proximity to cultivated crops and ambient temperatures. Data on unCKD and ndESRD were compared with environmental variables, crop area cultivated (indicator of agrochemical use) and high ambient temperatures. Using geographically weighted regression analysis, two model sets were created using reported municipal hospital admission rates are per thousand population for unCKD 2006-2010 and rates of ndESRD 2005-2010 [corrected]. These were assessed against local percent of land cultivated by crop (sugarcane, coffee, corn, cotton, sorghum, and beans) and mean maximum ambient temperature, with Moran's indices determining data clustering. Two-dimensional geographic models illustrated parameter spatial distribution. Bivariate geographically weighted regressions showed statistically significant correlations between percent area of sugarcane, corn, cotton, coffee, and bean cultivation, as well as mean maximum ambient temperature with both unCKD and ndESRD hospital admission rates. Percent area of sugarcane cultivation had greatest statistical weight (p ≤ 0.001; Rp2 = 0.77 for unCKD). The most statistically significant multivariate geographically weighted regression model for unCKD included percent area of sugarcane, cotton and corn cultivation (p ≤ 0.001; Rp2 = 0.80), while, for ndESRD, it included the percent area of sugarcane, corn, cotton and coffee cultivation (Rp2 = 0.52). Univariate unCKD and ndESRD Moran's I (0.20 and 0.33, respectively) indicated some degree of clustering. Ambient temperature did not improve multivariate geographically-weighted regression models for unCKD or ndESRD. Local bivariate Moran's indices with relatively high positive values and statistical significance (0.3-1.0, p ≤0.05) indicated positive clustering between unCKD hospital admission rates and percent area of sugarcane as well as cotton cultivation. The greatest positive response for clustering values did not consistently plot near the highest temperatures; there were some positive clusters in regions of lower temperatures. Clusters of ndESRD were also observed, some in areas of relatively low chronic kidney disease incidence in western El Salvador. High temperatures do not appear to strongly influence occurrence of unCKDu proxies. CKDu in El Salvador may arise from proximity to agriculture to which agrochemicals are applied, especially in sugarcane cultivation. The findings of this preliminary ecological study suggest that more research is needed to assess and quantify presence of specific agrochemicals in high-CKDu areas.

Analysis of the Bivariate Parameter Wind Differences Between Jimsphere and Windsonde

NASA Technical Reports Server (NTRS)

Susko, Michael

1987-01-01

An analysis is presented for the bivariate parameter differences between the FPS-16 Radar/Jimsphere and the Meteorological Sounding System (MSS) Windsonde. The Jimsphere is used as the standard to measure the ascent wind during the Space Shuttle launches at Kennedy Space Center, Florida, and the Windsonde is the backup system. In addition, a discussion of the terrestrial environment (below 20 km) and a description of the Jimsphere and Windsonde wind sensors are given. Computation of the wind statistics from 64 paired Jimsphere and Windsonde balloon releases in support of 14 Space Shuttle launches shows a good agreement between the two wind sensors. From the analysis of buildup and back-off data for various scales of distance and the comparison of the cumulative percent frequency (CPF) versus wind speed change, it is shown that the wind speed change for various scales of distances for the Jimsphere and Windsonde compare favorably.

Revisiting the Marshmallow Test: A Conceptual Replication Investigating Links Between Early Delay of Gratification and Later Outcomes.

PubMed

Watts, Tyler W; Duncan, Greg J; Quan, Haonan

2018-05-01

We replicated and extended Shoda, Mischel, and Peake's (1990) famous marshmallow study, which showed strong bivariate correlations between a child's ability to delay gratification just before entering school and both adolescent achievement and socioemotional behaviors. Concentrating on children whose mothers had not completed college, we found that an additional minute waited at age 4 predicted a gain of approximately one tenth of a standard deviation in achievement at age 15. But this bivariate correlation was only half the size of those reported in the original studies and was reduced by two thirds in the presence of controls for family background, early cognitive ability, and the home environment. Most of the variation in adolescent achievement came from being able to wait at least 20 s. Associations between delay time and measures of behavioral outcomes at age 15 were much smaller and rarely statistically significant.

Modeling rainfall-runoff relationship using multivariate GARCH model

NASA Astrophysics Data System (ADS)

Modarres, R.; Ouarda, T. B. M. J.

2013-08-01

The traditional hydrologic time series approaches are used for modeling, simulating and forecasting conditional mean of hydrologic variables but neglect their time varying variance or the second order moment. This paper introduces the multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) modeling approach to show how the variance-covariance relationship between hydrologic variables varies in time. These approaches are also useful to estimate the dynamic conditional correlation between hydrologic variables. To illustrate the novelty and usefulness of MGARCH models in hydrology, two major types of MGARCH models, the bivariate diagonal VECH and constant conditional correlation (CCC) models are applied to show the variance-covariance structure and cdynamic correlation in a rainfall-runoff process. The bivariate diagonal VECH-GARCH(1,1) and CCC-GARCH(1,1) models indicated both short-run and long-run persistency in the conditional variance-covariance matrix of the rainfall-runoff process. The conditional variance of rainfall appears to have a stronger persistency, especially long-run persistency, than the conditional variance of streamflow which shows a short-lived drastic increasing pattern and a stronger short-run persistency. The conditional covariance and conditional correlation coefficients have different features for each bivariate rainfall-runoff process with different degrees of stationarity and dynamic nonlinearity. The spatial and temporal pattern of variance-covariance features may reflect the signature of different physical and hydrological variables such as drainage area, topography, soil moisture and ground water fluctuations on the strength, stationarity and nonlinearity of the conditional variance-covariance for a rainfall-runoff process.

Intercomparison of textural parameters of intertidal sediments generated by different statistical procedures, and implications for a unifying descriptive nomenclature

NASA Astrophysics Data System (ADS)

Fan, Daidu; Tu, Junbiao; Cai, Guofu; Shang, Shuai

2015-06-01

Grain-size analysis is a basic routine in sedimentology and related fields, but diverse methods of sample collection, processing and statistical analysis often make direct comparisons and interpretations difficult or even impossible. In this paper, 586 published grain-size datasets from the Qiantang Estuary (East China Sea) sampled and analyzed by the same procedures were merged and their textural parameters calculated by a percentile and two moment methods. The aim was to explore which of the statistical procedures performed best in the discrimination of three distinct sedimentary units on the tidal flats of the middle Qiantang Estuary. A Gaussian curve-fitting method served to simulate mixtures of two normal populations having different modal sizes, sorting values and size distributions, enabling a better understanding of the impact of finer tail components on textural parameters, as well as the proposal of a unifying descriptive nomenclature. The results show that percentile and moment procedures yield almost identical results for mean grain size, and that sorting values are also highly correlated. However, more complex relationships exist between percentile and moment skewness (kurtosis), changing from positive to negative correlations when the proportions of the finer populations decrease below 35% (10%). This change results from the overweighting of tail components in moment statistics, which stands in sharp contrast to the underweighting or complete amputation of small tail components by the percentile procedure. Intercomparisons of bivariate plots suggest an advantage of the Friedman & Johnson moment procedure over the McManus moment method in terms of the description of grain-size distributions, and over the percentile method by virtue of a greater sensitivity to small variations in tail components. The textural parameter scalings of Folk & Ward were translated into their Friedman & Johnson moment counterparts by application of mathematical functions derived by regression analysis of measured and modeled grain-size data, or by determining the abscissa values of intersections between auxiliary lines running parallel to the x-axis and vertical lines corresponding to the descriptive percentile limits along the ordinate of representative bivariate plots. Twofold limits were extrapolated for the moment statistics in relation to single descriptive terms in the cases of skewness and kurtosis by considering both positive and negative correlations between percentile and moment statistics. The extrapolated descriptive scalings were further validated by examining entire size-frequency distributions simulated by mixing two normal populations of designated modal size and sorting values, but varying in mixing ratios. These were found to match well in most of the proposed scalings, although platykurtic and very platykurtic categories were questionable when the proportion of the finer population was below 5%. Irrespective of the statistical procedure, descriptive nomenclatures should therefore be cautiously used when tail components contribute less than 5% to grain-size distributions.

[Compatible biomass models of natural spruce (Picea asperata)].

PubMed

Wang, Jin Chi; Deng, Hua Feng; Huang, Guo Sheng; Wang, Xue Jun; Zhang, Lu

2017-10-01

By using nonlinear measurement error method, the compatible tree volume and above ground biomass equations were established based on the volume and biomass data of 150 sampling trees of natural spruce (Picea asperata). Two approaches, controlling directly under total aboveground biomass and controlling jointly from level to level, were used to design the compatible system for the total aboveground biomass and the biomass of four components (stem, bark, branch and foliage), and the total ground biomass could be estimated independently or estimated simultaneously in the system. The results showed that the R 2 of the one variable and bivariate compatible tree volume and aboveground biomass equations were all above 0.85, and the maximum value reached 0.99. The prediction effect of the volume equations could be improved significantly when tree height was included as predictor, while it was not significant in biomass estimation. For the compatible biomass systems, the one variable model based on controlling jointly from level to level was better than the model using controlling directly under total above ground biomass, but the bivariate models of the two methods were similar. Comparing the imitative effects of the one variable and bivariate compatible biomass models, the results showed that the increase of explainable variables could significantly improve the fitness of branch and foliage biomass, but had little effect on other components. Besides, there was almost no difference between the two methods of estimation based on the comparison.

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.

Explanatory models and distress in primary caregivers of patients with acute psychotic presentations: A study from South India.

PubMed

Joy, Deepa S; Manoranjitham, S D; Samuel, P; Jacob, K S

2017-11-01

Emotional distress among caregivers of people with mental illness is common, changes overtime and requires appropriate coping strategies to prevent long-term disability. Explanatory models, which underpin understanding of disease and illness, are crucial to coping. To study the association of explanatory models and distress among caregivers of people with acute psychotic illness. A total of 60 consecutive patients and their primary caregivers who presented to the Department of Psychiatry, Christian Medical College, Vellore, were recruited for the study. Positive and Negative Syndrome Scale (PANSS), Short Explanatory Model Interview (SEMI) and the General Health Questionnaire-12 (GHQ-12) were used to assess severity of psychosis, explanatory models of illness and emotional distress. Standard bivariate and multivariable statistics were employed. Majority of the caregivers simultaneously held multiple models of illness, which included medical and non-medical perspectives. The GHQ-12 score were significantly lower in people who held multiple explanatory models of illness when compared to the caregivers who believed single explanations. Explanatory models affect coping in caregivers of patients with acute psychotic presentations. There is a need to have a broad-based approach to recovery and care.

Population Estimates, Health Care Characteristics, and Material Hardship Experiences of U.S. Children with Parent-Reported Speech-Language Difficulties: Evidence from Three Nationally Representative Surveys

ERIC Educational Resources Information Center

Sonik, Rajan A.; Parish, Susan L.; Akorbirshoev, Ilhom; Son, Esther; Rosenthal, Eliana

2014-01-01

Purpose: To provide estimates for the prevalence of parent-reported speech-language difficulties in U.S. children, and to describe the levels of health care access and material hardship in this population. Method: We tabulated descriptive and bivariate statistics using cross-sectional data from the 2007 and 2011/2012 iterations of the National…

Determination of statistics for any rotation of axes of a bivariate normal elliptical distribution. [of wind vector components

NASA Technical Reports Server (NTRS)

Falls, L. W.; Crutcher, H. L.

1976-01-01

Transformation of statistics from a dimensional set to another dimensional set involves linear functions of the original set of statistics. Similarly, linear functions will transform statistics within a dimensional set such that the new statistics are relevant to a new set of coordinate axes. A restricted case of the latter is the rotation of axes in a coordinate system involving any two correlated random variables. A special case is the transformation for horizontal wind distributions. Wind statistics are usually provided in terms of wind speed and direction (measured clockwise from north) or in east-west and north-south components. A direct application of this technique allows the determination of appropriate wind statistics parallel and normal to any preselected flight path of a space vehicle. Among the constraints for launching space vehicles are critical values selected from the distribution of the expected winds parallel to and normal to the flight path. These procedures are applied to space vehicle launches at Cape Kennedy, Florida.

Statistical shape modelling to aid surgical planning: associations between surgical parameters and head shapes following spring-assisted cranioplasty.

PubMed

Rodriguez-Florez, Naiara; Bruse, Jan L; Borghi, Alessandro; Vercruysse, Herman; Ong, Juling; James, Greg; Pennec, Xavier; Dunaway, David J; Jeelani, N U Owase; Schievano, Silvia

2017-10-01

Spring-assisted cranioplasty is performed to correct the long and narrow head shape of children with sagittal synostosis. Such corrective surgery involves osteotomies and the placement of spring-like distractors, which gradually expand to widen the skull until removal about 4 months later. Due to its dynamic nature, associations between surgical parameters and post-operative 3D head shape features are difficult to comprehend. The current study aimed at applying population-based statistical shape modelling to gain insight into how the choice of surgical parameters such as craniotomy size and spring positioning affects post-surgical head shape. Twenty consecutive patients with sagittal synostosis who underwent spring-assisted cranioplasty at Great Ormond Street Hospital for Children (London, UK) were prospectively recruited. Using a nonparametric statistical modelling technique based on mathematical currents, a 3D head shape template was computed from surface head scans of sagittal patients after spring removal. Partial least squares (PLS) regression was employed to quantify and visualise trends of localised head shape changes associated with the surgical parameters recorded during spring insertion: anterior-posterior and lateral craniotomy dimensions, anterior spring position and distance between anterior and posterior springs. Bivariate correlations between surgical parameters and corresponding PLS shape vectors demonstrated that anterior-posterior (Pearson's [Formula: see text]) and lateral craniotomy dimensions (Spearman's [Formula: see text]), as well as the position of the anterior spring ([Formula: see text]) and the distance between both springs ([Formula: see text]) on average had significant effects on head shapes at the time of spring removal. Such effects were visualised on 3D models. Population-based analysis of 3D post-operative medical images via computational statistical modelling tools allowed for detection of novel associations between surgical parameters and head shape features achieved following spring-assisted cranioplasty. The techniques described here could be extended to other cranio-maxillofacial procedures in order to assess post-operative outcomes and ultimately facilitate surgical decision making.

Datamining approaches for modeling tumor control probability.

PubMed

Naqa, Issam El; Deasy, Joseph O; Mu, Yi; Huang, Ellen; Hope, Andrew J; Lindsay, Patricia E; Apte, Aditya; Alaly, James; Bradley, Jeffrey D

2010-11-01

Tumor control probability (TCP) to radiotherapy is determined by complex interactions between tumor biology, tumor microenvironment, radiation dosimetry, and patient-related variables. The complexity of these heterogeneous variable interactions constitutes a challenge for building predictive models for routine clinical practice. We describe a datamining framework that can unravel the higher order relationships among dosimetric dose-volume prognostic variables, interrogate various radiobiological processes, and generalize to unseen data before when applied prospectively. Several datamining approaches are discussed that include dose-volume metrics, equivalent uniform dose, mechanistic Poisson model, and model building methods using statistical regression and machine learning techniques. Institutional datasets of non-small cell lung cancer (NSCLC) patients are used to demonstrate these methods. The performance of the different methods was evaluated using bivariate Spearman rank correlations (rs). Over-fitting was controlled via resampling methods. Using a dataset of 56 patients with primary NCSLC tumors and 23 candidate variables, we estimated GTV volume and V75 to be the best model parameters for predicting TCP using statistical resampling and a logistic model. Using these variables, the support vector machine (SVM) kernel method provided superior performance for TCP prediction with an rs=0.68 on leave-one-out testing compared to logistic regression (rs=0.4), Poisson-based TCP (rs=0.33), and cell kill equivalent uniform dose model (rs=0.17). The prediction of treatment response can be improved by utilizing datamining approaches, which are able to unravel important non-linear complex interactions among model variables and have the capacity to predict on unseen data for prospective clinical applications.

The discriminant (and convergent) validity of the Personality Inventory for DSM-5.

PubMed

Crego, Cristina; Gore, Whitney L; Rojas, Stephanie L; Widiger, Thomas A

2015-10-01

A considerable body of research has rapidly accumulated with respect to the validity of the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) dimensional trait model as it is assessed by the Personality Inventory for Diagnostic and Statistical Manual of Mental Disorders (PID-5; Krueger et al., 2012). This research though has not focused specifically on discriminant validity, although allusions to potentially problematic discriminant validity have been raised. The current study addressed discriminant validity, reporting for the first time the correlations among the PID-5 domain scales. Also reported are the bivariate correlations of the 25 PID-5 maladaptive trait scales with the personality domain scales of the NEO Personality Inventory-Revised (Costa & McCrae, 1992), the International Personality Item Pool-NEO (Goldberg et al., 2006), the Inventory of Personal Characteristics (Almagor et al., 1995), the 5-Dimensional Personality Test (van Kampen, 2012), and the HEXACO Personality Inventory-Revised (Lee & Ashton, 2004). The results are discussed with respect to the implications of and alternative explanations for potentially problematic discriminant validity. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

A tutorial on Bayesian bivariate meta-analysis of mixed binary-continuous outcomes with missing treatment effects.

PubMed

Gajic-Veljanoski, Olga; Cheung, Angela M; Bayoumi, Ahmed M; Tomlinson, George

2016-05-30

Bivariate random-effects meta-analysis (BVMA) is a method of data synthesis that accounts for treatment effects measured on two outcomes. BVMA gives more precise estimates of the population mean and predicted values than two univariate random-effects meta-analyses (UVMAs). BVMA also addresses bias from incomplete reporting of outcomes. A few tutorials have covered technical details of BVMA of categorical or continuous outcomes. Limited guidance is available on how to analyze datasets that include trials with mixed continuous-binary outcomes where treatment effects on one outcome or the other are not reported. Given the advantages of Bayesian BVMA for handling missing outcomes, we present a tutorial for Bayesian BVMA of incompletely reported treatment effects on mixed bivariate outcomes. This step-by-step approach can serve as a model for our intended audience, the methodologist familiar with Bayesian meta-analysis, looking for practical advice on fitting bivariate models. To facilitate application of the proposed methods, we include our WinBUGS code. As an example, we use aggregate-level data from published trials to demonstrate the estimation of the effects of vitamin K and bisphosphonates on two correlated bone outcomes, fracture, and bone mineral density. We present datasets where reporting of the pairs of treatment effects on both outcomes was 'partially' complete (i.e., pairs completely reported in some trials), and we outline steps for modeling the incompletely reported data. To assess what is gained from the additional work required by BVMA, we compare the resulting estimates to those from separate UVMAs. We discuss methodological findings and make four recommendations. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

The role of drop velocity in statistical spray description

NASA Technical Reports Server (NTRS)

Groeneweg, J. F.; El-Wakil, M. M.; Myers, P. S.; Uyehara, O. A.

1978-01-01

The justification for describing a spray by treating drop velocity as a random variable on an equal statistical basis with drop size was studied experimentally. A double exposure technique using fluorescent drop photography was used to make size and velocity measurements at selected locations in a steady ethanol spray formed by a swirl atomizer. The size velocity data were categorized to construct bivariate spray density functions to describe the spray immediately after formation and during downstream propagation. Bimodal density functions were formed by environmental interaction during downstream propagation. Large differences were also found between spatial mass density and mass flux size distribution at the same location.

Subjective cognitive complaints and neuropsychological performance in former smokers with and without chronic obstructive pulmonary disease.

PubMed

Brunette, Amanda M; Holm, Kristen E; Wamboldt, Frederick S; Kozora, Elizabeth; Moser, David J; Make, Barry J; Crapo, James D; Meschede, Kimberly; Weinberger, Howard D; Moreau, Kerrie L; Bowler, Russell P; Hoth, Karin F

2018-05-01

This study examined the association of perceived cognitive difficulties with objective cognitive performance in former smokers. We hypothesized that greater perceived cognitive difficulties would be associated with poorer performance on objective executive and memory tasks. Participants were 95 former smokers recruited from the COPDGene study. They completed questionnaires (including the Cognitive Difficulties Scale [CDS] and the Hospital Anxiety and Depression Scale [HADS]), neuropsychological assessment, and pulmonary function testing. Pearson correlations and t-tests were conducted to examine the bivariate association of the CDS (total score and subscales for attention/concentration, praxis, delayed recall, orientation for persons, temporal orientation, and prospective memory) with each domain of objective cognitive functioning (memory recall, executive functioning/processing speed, visuospatial processing, and language). Simultaneous multiple linear regression was used to further examine all statistically significant bivariate associations. The following covariates were included in all regression models: age, sex, pack-years, premorbid functioning (WRAT-IV Reading), HADS total score, and chronic obstructive pulmonary disease (COPD) status (yes/no based on GOLD criteria). In regression models, greater perceived cognitive difficulties overall (using CDS total score) were associated with poorer performance on executive functioning/processing speed tasks (b = -0.07, SE = 0.03, p = .037). Greater perceived cognitive difficulties on the CDS praxis subscale were associated with poorer performance on executive functioning/processing speed tasks (b = -3.65, SE = 1.25, p = .005), memory recall tasks (b = -4.60, SE = 1.75, p = .010), and language tasks (b = -3.89, SE = 1.39, p = .006). Clinicians should be aware that cognitive complaints may be indicative of problems with the executive functioning/processing speed and memory of former smokers with and without COPD.

Social capital, women's autonomy and smoking among married women in low-income urban neighborhoods of Beirut, Lebanon.

PubMed

Afifi, Rema A; Nakkash, Rima T; Khawaja, Marwan

2010-01-01

We sought to examine the associations between social capital, women's empowerment, and smoking behavior among married women in three low-income neighborhoods in Beirut, Lebanon. Data from currently married women aged 15 to 59 years in the 2003 Urban Health Study were used. The dependent variable was cigarette smoking. The main independent variables were five social capital items and three women's empowerment indices. Other socioeconomic variables as well as mental distress, happiness, and community of residence were included as covariates. Bivariate associations were conducted on all variables using chi-square tests. Adjusted odds ratios from binary logistic regression models were then modeled on smoking behavior separately for younger and older women. More than one third (35.9%) of married women reported smoking cigarettes. At the bivariate level, a variety of socioeconomic and demographic variables predicted smoking. With respect to social capital, women who lacked trust and were dissatisfied with the number friends or relatives living nearby were more likely to smoke. As for women's autonomy, high decision making and high mobility were associated with smoking. When analyzed multivariately, social capital items were statistically significant for younger women but not for older women. And the mobility variables were significant for older women but not younger women. Our results support the conclusion that determinants of women's tobacco use are multilayered, and include social capital and women's autonomy. Our results also suggest that younger and older married women may be influenced by differential determinants. Reasons for these differences are explored. Interventions may need to be tailored to each age group separately. Copyright 2010 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

A Bivariate Genetic Analysis of Drug Abuse ascertained through medical and criminal registries in Swedish Twins, Siblings and Half-Siblings

PubMed Central

Maes, Hermine H.; Neale, Michael C.; Ohlsson, Henrik; Zahery, Mahsa; Lichtenstein, Paul; Sundquist, Kristina; Sundquist, Jan; Kendler, Kenneth S.

2016-01-01

Objective Using Swedish nationwide registry data, the authors investigated the correlation of genetic and environmental risk factors in the etiology of drug abuse as ascertained from medical and criminal registries by modeling twin and sibling data. Methods Medical drug abuse was defined using public inpatient and outpatient records, while criminal drug abuse was ascertained through legal records. Twin, full and half sibling pairs were obtained from the national twin and genealogical registers. Information about sibling pair residence within the same household was obtained from Statistics Sweden. Standard bivariate genetic structural equation modeling was applied to the population-based data on drug abuse ascertained through medical and crime registries, using OpenMx. Results Analyses of all possible pairs of twins (MZ: N=4,482; DZ: N=9,838 pairs), full- (N=1,278,086) and half-siblings (paternal: N=7,767; maternal N=70,553) who grew up together suggested that factors explaining familial resemblance for drug abuse as defined through medical or criminal registries were mostly the same. Results showed substantial heritability and moderate contributions of shared environmental factors to drug abuse; both were higher in males versus females, and higher for drug abuse ascertained through criminal than medical records. Because of the low prevalence of both assessments of drug abuse, having access to population data was crucial to obtain stable estimates. Conclusions Using objective registry data, the authors found that drug abuse - whether ascertained through medical versus criminal records - was highly heritable. Furthermore, shared environmental factors contributed significantly to the liability of drug abuse. Genetic and shared environmental risk factors for these two forms of drug abuse were highly correlated. PMID:27480873

Determinants of job satisfaction for novice nurse managers employed in hospitals.

PubMed

Djukic, Maja; Jun, Jin; Kovner, Christine; Brewer, Carol; Fletcher, Jason

Numbering close to 300,000 nurse managers represent the largest segment of the health care management workforce. Their effectiveness is, in part, influenced by their job satisfaction. We examined factors associated with job satisfaction of novice frontline nurse managers. We used a cross-sectional, correlational survey design. The sample consisted of responders to the fifth wave of a multiyear study of new nurses in 2013 (N = 1,392; response rate of 69%) who reported working as managers (n = 209). The parent study sample consisted of registered nurses who were licensed for the first time by exam 6-18 months prior in 1 of 51 selected metropolitan statistical areas and 9 rural areas across 34 U.S. states and the District of Columbia. We examined bivariate correlations between job satisfaction and 31 personal and structural variables. All variables significantly related to job satisfaction in bivariate analysis were included in a multivariate linear regression model. In addition, we tested the interaction effects of procedural justice and negative affectivity, autonomy, and organizational constraints on job satisfaction. The Cronbach's alphas for all multi-item scales ranged from .74 to .96. In the multivariate analysis, negative affectivity (β = -.169; p = .006) and procedural justice (β = .210; p = .016) were significantly correlated with job satisfaction. The combination of predictors in the model accounted for half of the variability in job satisfaction ratings (R = .51, adjusted R = .47; p <. 001). Health care executives who want to cultivate an effective novice frontline nurse manager workforce can best ensure their satisfaction by creating an organization with strong procedural justice. This could be achieved by involving managers in decision-making processes and ensuring transparency about how decisions that affect nursing are made.

HIV and syphilis infection among men attending a [corrected] sexually transmitted infection clinic in Puerto Rico.

PubMed

Colón-López, Vivian; Ortiz, Ana P; Banerjee, Geetanjoli; Gertz, Alida M; García, Hermes

2013-03-01

This study aimed to assess the demographic, behavioral, and clinical factors associated with HIV and syphilis infection among a sample of men attending a sexually transmitted infection clinic during 2009 to 2010 in San Juan, Puerto Rico (PR). A sample of 350 clinical records from men visiting the clinic for the first time during 2009 to 2010 was reviewed. Descriptive statistics were used to describe the study sample, and bivariate analyses were performed separately for HIV and syphilis to identify factors associated with these infectious diseases. Variables that were significantly associated (p < 0.05) with HIV and syphilis in the bivariate analysis were considered for inclusion in the logistic regression models. Overall, 11.2% and 14.1% of the men were infected with HIV and syphilis, respectively, and 5.1% were coinfected with HIV and syphilis. In multivariate logistic regression models, ever injecting drugs (POR = 8.1; 95% CI 3.0, 21.8) and being a man who has sex with men (MSM) (POR = 5.3; 95% CI 2.3, 11.9) were positively associated with HIV infection. Being a man older than 45 years (POR = 4.0; 95% CI: 1.9, 8.9) and being an MSM (POR = 2.5; 95% CI: 1.3, 4.9) were both significantly associated with syphilis infection. These findings reinforce the need for greater education and prevention efforts for HIV and other STIs among men in PR, particularly those who are MSM. However, there is a need to make an a priori assessment of the level of health literacy in the members of this group so that a culturally sensitive intervention can be provided to the men who attend this STI clinic.

HIV and Syphilis Infection among Men attending a Sexually Transmitted Infection Clinic in Puerto Rico

PubMed Central

Colón-López, Vivian; Ortiz, Ana P.; Banerjee, Geetanjoli; Gertz, Alida M.; García, Hermes

2013-01-01

Objective This study aimed to assess the demographic, behavioral, and clinical factors associated with HIV and syphilis infection among a sample of men attending a sexually transmitted infection clinic during 2009 to 2010 in San Juan, Puerto Rico (PR). Methods A sample of 350 clinical records from men visiting the clinic for the first time during 2009 to 2010 was reviewed. Descriptive statistics were used to describe the study sample, and bivariate analyses were performed separately for HIV and syphilis to identify factors associated with these infectious diseases. Variables that were significantly associated (p<0.05) with HIV and syphilis in the bivariate analysis were considered for inclusion in the logistic regression models. Results Overall, 11.2% and 14.1% of the men were infected with HIV and syphilis, respectively, and 5.1% were coinfected with HIV and syphilis. In multivariate logistic regression models, ever injecting drugs (POR = 8.1; 95%Cl 3.0, 21.8) and being a man who has sex with men (MSM) (POR = 5.3; 95%CI 2.3, 11.9) were positively associated with HIV infection. Being a man older than 45 years (POR = 4.0; 95%CI: 1.9, 8.9) and being an MSM (POR = 2.5; 95%CI: 1.3, 4.9) were both significantly associated with syphilis infection. Conclusion These findings reinforce the need for greater education and prevention efforts for HIV and other STIs among men in PR, particularly those who are MSM. However, there is a need to make an a priori assessment of the level of health literacy in the members of this group so that a culturally sensitive intervention can be provided to the men who attend this STI clinic. PMID:23556260

A Bivariate Genetic Analysis of Drug Abuse Ascertained Through Medical and Criminal Registries in Swedish Twins, Siblings and Half-Siblings.

PubMed

Maes, Hermine H; Neale, Michael C; Ohlsson, Henrik; Zahery, Mahsa; Lichtenstein, Paul; Sundquist, Kristina; Sundquist, Jan; Kendler, Kenneth S

2016-11-01

Using Swedish nationwide registry data, the authors investigated the correlation of genetic and environmental risk factors in the etiology of drug abuse as ascertained from medical and criminal registries by modeling twin and sibling data. Medical drug abuse was defined using public inpatient and outpatient records, while criminal drug abuse was ascertained through legal records. Twin, full and half sibling pairs were obtained from the national twin and genealogical registers. Information about sibling pair residence within the same household was obtained from Statistics Sweden. Standard bivariate genetic structural equation modeling was applied to the population-based data on drug abuse ascertained through medical and crime registries, using OpenMx. Analyses of all possible pairs of twins (MZ: N = 4482; DZ: N = 9838 pairs), full- (N = 1,278,086) and half-siblings (paternal: N = 7767; maternal N = 70,553) who grew up together suggested that factors explaining familial resemblance for drug abuse as defined through medical or criminal registries were mostly the same. Results showed substantial heritability and moderate contributions of shared environmental factors to drug abuse; both were higher in males versus females, and higher for drug abuse ascertained through criminal than medical records. Because of the low prevalence of both assessments of drug abuse, having access to population data was crucial to obtain stable estimates. Using objective registry data, the authors found that drug abuse-whether ascertained through medical versus criminal records-was highly heritable. Furthermore, shared environmental factors contributed significantly to the liability of drug abuse. Genetic and shared environmental risk factors for these two forms of drug abuse were highly correlated.

A power study of bivariate LOD score analysis of a complex trait and fear/discomfort with strangers

PubMed Central

Ji, Fei; Lee, Dayoung; Mendell, Nancy Role

2005-01-01

Complex diseases are often reported along with disease-related traits (DRT). Sometimes investigators consider both disease and DRT phenotypes separately and sometimes they consider individuals as affected if they have either the disease or the DRT, or both. We propose instead to consider the joint distribution of the disease and the DRT and do a linkage analysis assuming a pleiotropic model. We evaluated our results through analysis of the simulated datasets provided by Genetic Analysis Workshop 14. We first conducted univariate linkage analysis of the simulated disease, Kofendrerd Personality Disorder and one of its simulated associated traits, phenotype b (fear/discomfort with strangers). Subsequently, we considered the bivariate phenotype, which combined the information on Kofendrerd Personality Disorder and fear/discomfort with strangers. We developed a program to perform bivariate linkage analysis using an extension to the Elston-Stewart peeling method of likelihood calculation. Using this program we considered the microsatellites within 30 cM of the gene pleiotropic for this simulated disease and DRT. Based on 100 simulations of 300 families we observed excellent power to detect linkage within 10 cM of the disease locus using the DRT and the bivariate trait. PMID:16451570

A power study of bivariate LOD score analysis of a complex trait and fear/discomfort with strangers.

PubMed

Ji, Fei; Lee, Dayoung; Mendell, Nancy Role

2005-12-30

Complex diseases are often reported along with disease-related traits (DRT). Sometimes investigators consider both disease and DRT phenotypes separately and sometimes they consider individuals as affected if they have either the disease or the DRT, or both. We propose instead to consider the joint distribution of the disease and the DRT and do a linkage analysis assuming a pleiotropic model. We evaluated our results through analysis of the simulated datasets provided by Genetic Analysis Workshop 14. We first conducted univariate linkage analysis of the simulated disease, Kofendrerd Personality Disorder and one of its simulated associated traits, phenotype b (fear/discomfort with strangers). Subsequently, we considered the bivariate phenotype, which combined the information on Kofendrerd Personality Disorder and fear/discomfort with strangers. We developed a program to perform bivariate linkage analysis using an extension to the Elston-Stewart peeling method of likelihood calculation. Using this program we considered the microsatellites within 30 cM of the gene pleiotropic for this simulated disease and DRT. Based on 100 simulations of 300 families we observed excellent power to detect linkage within 10 cM of the disease locus using the DRT and the bivariate trait.

eHealth literacy in chronic disease patients: An item response theory analysis of the eHealth literacy scale (eHEALS).

PubMed

Paige, Samantha R; Krieger, Janice L; Stellefson, Michael; Alber, Julia M

2017-02-01

Chronic disease patients are affected by low computer and health literacy, which negatively affects their ability to benefit from access to online health information. To estimate reliability and confirm model specifications for eHealth Literacy Scale (eHEALS) scores among chronic disease patients using Classical Test (CTT) and Item Response Theory techniques. A stratified sample of Black/African American (N=341) and Caucasian (N=343) adults with chronic disease completed an online survey including the eHEALS. Item discrimination was explored using bi-variate correlations and Cronbach's alpha for internal consistency. A categorical confirmatory factor analysis tested a one-factor structure of eHEALS scores. Item characteristic curves, in-fit/outfit statistics, omega coefficient, and item reliability and separation estimates were computed. A 1-factor structure of eHEALS was confirmed by statistically significant standardized item loadings, acceptable model fit indices (CFI/TLI>0.90), and 70% variance explained by the model. Item response categories increased with higher theta levels, and there was evidence of acceptable reliability (ω=0.94; item reliability=89; item separation=8.54). eHEALS scores are a valid and reliable measure of self-reported eHealth literacy among Internet-using chronic disease patients. Providers can use eHEALS to help identify patients' eHealth literacy skills. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Comparison of connectivity analyses for resting state EEG data

NASA Astrophysics Data System (ADS)

Olejarczyk, Elzbieta; Marzetti, Laura; Pizzella, Vittorio; Zappasodi, Filippo

2017-06-01

Objective. In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated. Approach. The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory. Main results. The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization. Significance. Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

Modeling heterogeneous (co)variances from adjacent-SNP groups improves genomic prediction for milk protein composition traits.

PubMed

Gebreyesus, Grum; Lund, Mogens S; Buitenhuis, Bart; Bovenhuis, Henk; Poulsen, Nina A; Janss, Luc G

2017-12-05

Accurate genomic prediction requires a large reference population, which is problematic for traits that are expensive to measure. Traits related to milk protein composition are not routinely recorded due to costly procedures and are considered to be controlled by a few quantitative trait loci of large effect. The amount of variation explained may vary between regions leading to heterogeneous (co)variance patterns across the genome. Genomic prediction models that can efficiently take such heterogeneity of (co)variances into account can result in improved prediction reliability. In this study, we developed and implemented novel univariate and bivariate Bayesian prediction models, based on estimates of heterogeneous (co)variances for genome segments (BayesAS). Available data consisted of milk protein composition traits measured on cows and de-regressed proofs of total protein yield derived for bulls. Single-nucleotide polymorphisms (SNPs), from 50K SNP arrays, were grouped into non-overlapping genome segments. A segment was defined as one SNP, or a group of 50, 100, or 200 adjacent SNPs, or one chromosome, or the whole genome. Traditional univariate and bivariate genomic best linear unbiased prediction (GBLUP) models were also run for comparison. Reliabilities were calculated through a resampling strategy and using deterministic formula. BayesAS models improved prediction reliability for most of the traits compared to GBLUP models and this gain depended on segment size and genetic architecture of the traits. The gain in prediction reliability was especially marked for the protein composition traits β-CN, κ-CN and β-LG, for which prediction reliabilities were improved by 49 percentage points on average using the MT-BayesAS model with a 100-SNP segment size compared to the bivariate GBLUP. Prediction reliabilities were highest with the BayesAS model that uses a 100-SNP segment size. The bivariate versions of our BayesAS models resulted in extra gains of up to 6% in prediction reliability compared to the univariate versions. Substantial improvement in prediction reliability was possible for most of the traits related to milk protein composition using our novel BayesAS models. Grouping adjacent SNPs into segments provided enhanced information to estimate parameters and allowing the segments to have different (co)variances helped disentangle heterogeneous (co)variances across the genome.

Meta-analysis for the comparison of two diagnostic tests to a common gold standard: A generalized linear mixed model approach.

PubMed

Hoyer, Annika; Kuss, Oliver

2018-05-01

Meta-analysis of diagnostic studies is still a rapidly developing area of biostatistical research. Especially, there is an increasing interest in methods to compare different diagnostic tests to a common gold standard. Restricting to the case of two diagnostic tests, in these meta-analyses the parameters of interest are the differences of sensitivities and specificities (with their corresponding confidence intervals) between the two diagnostic tests while accounting for the various associations across single studies and between the two tests. We propose statistical models with a quadrivariate response (where sensitivity of test 1, specificity of test 1, sensitivity of test 2, and specificity of test 2 are the four responses) as a sensible approach to this task. Using a quadrivariate generalized linear mixed model naturally generalizes the common standard bivariate model of meta-analysis for a single diagnostic test. If information on several thresholds of the tests is available, the quadrivariate model can be further generalized to yield a comparison of full receiver operating characteristic (ROC) curves. We illustrate our model by an example where two screening methods for the diagnosis of type 2 diabetes are compared.

Some New Approaches to Multivariate Probability Distributions.

DTIC Science & Technology

1986-12-01

Krishnaiah (1977). The following example may serve as an illustration of this point. EXAMPLE 2. (Fre^*chet’s bivariate continuous distribution...the error in the theorem of "" Prakasa Rao (1974) and to Dr. P.R. Krishnaiah for his valuable comments on the initial draft, his monumental patience and...M. and Proschan, F. (1984). Nonparametric Concepts and Methods in Reliability, Handbook of Statistics, 4, 613-655, (eds. P.R. Krishnaiah and P.K

Global assessment of predictability of water availability: A bivariate probabilistic Budyko analysis

NASA Astrophysics Data System (ADS)

Wang, Weiguang; Fu, Jianyu

2018-02-01

Estimating continental water availability is of great importance for water resources management, in terms of maintaining ecosystem integrity and sustaining society development. To more accurately quantify the predictability of water availability, on the basis of univariate probabilistic Budyko framework, a bivariate probabilistic Budyko approach was developed using copula-based joint distribution model for considering the dependence between parameter ω of Wang-Tang's equation and the Normalized Difference Vegetation Index (NDVI), and was applied globally. The results indicate the predictive performance in global water availability is conditional on the climatic condition. In comparison with simple univariate distribution, the bivariate one produces the lower interquartile range under the same global dataset, especially in the regions with higher NDVI values, highlighting the importance of developing the joint distribution by taking into account the dependence structure of parameter ω and NDVI, which can provide more accurate probabilistic evaluation of water availability.

Urban poverty and infant mortality rate disparities.

PubMed

Sims, Mario; Sims, Tammy L; Bruce, Marino A

2007-04-01

This study examined whether the relationship between high poverty and infant mortality rates (IMRs) varied across race- and ethnic-specific populations in large urban areas. Data were drawn from 1990 Census and 1992-1994 Vital Statistics for selected U.S. metropolitan areas. High-poverty areas were defined as neighborhoods in which > or = 40% of the families had incomes below the federal poverty threshold. Bivariate models showed that high poverty was a significant predictor of IMR for each group; however, multivariate analyses demonstrate that maternal health and regional factors explained most of the variance in the group-specific models of IMR. Additional analysis revealed that high poverty was significantly associated with minority-white IMR disparities, and country of origin is an important consideration for ethnic birth outcomes. Findings from this study provide a glimpse into the complexity associated with infant mortality in metropolitan areas because they suggest that the factors associated with infant mortality in urban areas vary by race and ethnicity.

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting.

PubMed

Young, Robin L; Weinberg, Janice; Vieira, Verónica; Ozonoff, Al; Webster, Thomas F

2010-07-19

A common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics. This research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases. The GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic.

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting

PubMed Central

2010-01-01

Background A common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics. Results This research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases. Conclusions The GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic. PMID:20642827

A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence.

PubMed

Nikoloulopoulos, Aristidis K

2017-10-01

A bivariate copula mixed model has been recently proposed to synthesize diagnostic test accuracy studies and it has been shown that it is superior to the standard generalized linear mixed model in this context. Here, we call trivariate vine copulas to extend the bivariate meta-analysis of diagnostic test accuracy studies by accounting for disease prevalence. Our vine copula mixed model includes the trivariate generalized linear mixed model as a special case and can also operate on the original scale of sensitivity, specificity, and disease prevalence. Our general methodology is illustrated by re-analyzing the data of two published meta-analyses. Our study suggests that there can be an improvement on trivariate generalized linear mixed model in fit to data and makes the argument for moving to vine copula random effects models especially because of their richness, including reflection asymmetric tail dependence, and computational feasibility despite their three dimensionality.

Effectiveness of Tongue-lip Adhesion for Obstructive Sleep Apnea in Infants With Robin Sequence Measured by Polysomnography.

PubMed

Resnick, Cory M; Dentino, Kelley; Katz, Eliot; Mulliken, John B; Padwa, Bonnie L

2016-09-01

Tongue-lip adhesion (TLA) is commonly used to relieve obstructive sleep apnea (OSA) in infants with Robin sequence (RS), but few studies have evaluated its efficacy with objective measures. The purpose of this study was to measure TLA outcomes using polysomnography. Our hypothesis was that TLA relieves OSA in most infants. This is a retrospective study of infants with RS who underwent TLA from 2011 to 2014 and had at least a postoperative polysomnogram. Predictor variables included demographic and birth characteristics, surgeon, syndromic diagnosis, GILLS score, preoperative OSA severity, and clinical course. A successful outcome was defined as minimal OSA (apnea-hypopnea index score < 5) on postoperative polysomnogram and no need for additional airway intervention. Descriptive, bivariate, and regression statistics were computed, and statistical significance was set at P < .05. Eighteen subjects who had TLA at a mean age of 28 ± 4.7 days were included. Thirteen (72.2%) had a confirmed or suspected syndrome, and the mean GILLS score was 3 ± 0.3. All parameters trended toward improvement from the preoperative to postoperative polysomnograms, and improvement in OSA severity, oxygen saturation nadir, and arousals per hour was statistically significant (P < .02). This effect was significant across categories of surgeon, syndrome, and GILLS score. Nine subjects (50%) met the criteria for a successful outcome. Bivariate and regression analyses did not demonstrate a significant relationship between success and any predictor variable. TLA improved airway obstruction in all infants with RS but resolved OSA in only nine patients, and success was unpredictable.

Contributory fault and level of personal injury to drivers involved in head-on collisions: Application of copula-based bivariate ordinal models.

PubMed

Wali, Behram; Khattak, Asad J; Xu, Jingjing

2018-01-01

The main objective of this study is to simultaneously investigate the degree of injury severity sustained by drivers involved in head-on collisions with respect to fault status designation. This is complicated to answer due to many issues, one of which is the potential presence of correlation between injury outcomes of drivers involved in the same head-on collision. To address this concern, we present seemingly unrelated bivariate ordered response models by analyzing the joint injury severity probability distribution of at-fault and not-at-fault drivers. Moreover, the assumption of bivariate normality of residuals and the linear form of stochastic dependence implied by such models may be unduly restrictive. To test this, Archimedean copula structures and normal mixture marginals are integrated into the joint estimation framework, which can characterize complex forms of stochastic dependencies and non-normality in residual terms. The models are estimated using 2013 Virginia police reported two-vehicle head-on collision data, where exactly one driver is at-fault. The results suggest that both at-fault and not-at-fault drivers sustained serious/fatal injuries in 8% of crashes, whereas, in 4% of the cases, the not-at-fault driver sustained a serious/fatal injury with no injury to the at-fault driver at all. Furthermore, if the at-fault driver is fatigued, apparently asleep, or has been drinking the not-at-fault driver is more likely to sustain a severe/fatal injury, controlling for other factors and potential correlations between the injury outcomes. While not-at-fault vehicle speed affects injury severity of at-fault driver, the effect is smaller than the effect of at-fault vehicle speed on at-fault injury outcome. Contrarily, and importantly, the effect of at-fault vehicle speed on injury severity of not-at-fault driver is almost equal to the effect of not-at-fault vehicle speed on injury outcome of not-at-fault driver. Compared to traditional ordered probability models, the study provides evidence that copula based bivariate models can provide more reliable estimates and richer insights. Practical implications of the results are discussed. Published by Elsevier Ltd.

On System Engineering a Barter-Based Re-allocation of Space System Key Development Resources

NASA Astrophysics Data System (ADS)

Kosmann, William J.

NASA has had a decades-long problem with cost growth during the development of space science missions. Numerous agency-sponsored studies have produced average mission level development cost growths ranging from 23 to 77%. A new study of 26 historical NASA science instrument set developments using expert judgment to re-allocate key development resources has an average cost growth of 73.77%. Twice in history, during the Cassini and EOS-Terra science instrument developments, a barter-based mechanism has been used to re-allocate key development resources. The mean instrument set development cost growth was -1.55%. Performing a bivariate inference on the means of these two distributions, there is statistical evidence to support the claim that using a barter-based mechanism to re-allocate key instrument development resources will result in a lower expected cost growth than using the expert judgment approach. Agent-based discrete event simulation is the natural way to model a trade environment. A NetLogo agent-based barter-based simulation of science instrument development was created. The agent-based model was validated against the Cassini historical example, as the starting and ending instrument development conditions are available. The resulting validated agent-based barter-based science instrument resource re-allocation simulation was used to perform 300 instrument development simulations, using barter to re-allocate development resources. The mean cost growth was -3.365%. A bivariate inference on the means was performed to determine that additional significant statistical evidence exists to support a claim that using barter-based resource re-allocation will result in lower expected cost growth, with respect to the historical expert judgment approach. Barter-based key development resource re-allocation should work on science spacecraft development as well as it has worked on science instrument development. A new study of 28 historical NASA science spacecraft developments has an average cost growth of 46.04%. As barter-based key development resource re-allocation has never been tried in a spacecraft development, no historical results exist, and an inference on the means test is not possible. A simulation of using barter-based resource re-allocation should be developed. The NetLogo instrument development simulation should be modified to account for spacecraft development market participant differences. The resulting agent-based barter-based spacecraft resource re-allocation simulation would then be used to determine if significant statistical evidence exists to prove a claim that using barter-based resource re-allocation will result in lower expected cost growth.

A Local Agreement Pattern Measure Based on Hazard Functions for Survival Outcomes

PubMed Central

Dai, Tian; Guo, Ying; Peng, Limin; Manatunga, Amita K.

2017-01-01

Summary Assessing agreement is often of interest in biomedical and clinical research when measurements are obtained on the same subjects by different raters or methods. Most classical agreement methods have been focused on global summary statistics, which cannot be used to describe various local agreement patterns. The objective of this work is to study the local agreement pattern between two continuous measurements subject to censoring. In this paper, we propose a new agreement measure based on bivariate hazard functions to characterize the local agreement pattern between two correlated survival outcomes. The proposed measure naturally accommodates censored observations, fully captures the dependence structure between bivariate survival times and provides detailed information on how the strength of agreement evolves over time. We develop a nonparametric estimation method for the proposed local agreement pattern measure and study theoretical properties including strong consistency and asymptotical normality. We then evaluate the performance of the estimator through simulation studies and illustrate the method using a prostate cancer data example. PMID:28724196

A local agreement pattern measure based on hazard functions for survival outcomes.

PubMed

Dai, Tian; Guo, Ying; Peng, Limin; Manatunga, Amita K

2018-03-01

Assessing agreement is often of interest in biomedical and clinical research when measurements are obtained on the same subjects by different raters or methods. Most classical agreement methods have been focused on global summary statistics, which cannot be used to describe various local agreement patterns. The objective of this work is to study the local agreement pattern between two continuous measurements subject to censoring. In this article, we propose a new agreement measure based on bivariate hazard functions to characterize the local agreement pattern between two correlated survival outcomes. The proposed measure naturally accommodates censored observations, fully captures the dependence structure between bivariate survival times and provides detailed information on how the strength of agreement evolves over time. We develop a nonparametric estimation method for the proposed local agreement pattern measure and study theoretical properties including strong consistency and asymptotical normality. We then evaluate the performance of the estimator through simulation studies and illustrate the method using a prostate cancer data example. © 2017, The International Biometric Society.

Role of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in local dengue epidemics in Taiwan.

PubMed

Tsai, Pui-Jen; Teng, Hwa-Jen

2016-11-09

Aedes mosquitoes in Taiwan mainly comprise Aedes albopictus and Ae. aegypti. However, the species contributing to autochthonous dengue spread and the extent at which it occurs remain unclear. Thus, in this study, we spatially analyzed real data to determine spatial features related to local dengue incidence and mosquito density, particularly that of Ae. albopictus and Ae. aegypti. We used bivariate Moran's I statistic and geographically weighted regression (GWR) spatial methods to analyze the globally spatial dependence and locally regressed relationship between (1) imported dengue incidences and Breteau indices (BIs) of Ae. albopictus, (2) imported dengue incidences and BI of Ae. aegypti, (3) autochthonous dengue incidences and BI of Ae. albopictus, (4) autochthonous dengue incidences and BI of Ae. aegypti, (5) all dengue incidences and BI of Ae. albopictus, (6) all dengue incidences and BI of Ae. aegypti, (7) BI of Ae. albopictus and human population density, and (8) BI of Ae. aegypti and human population density in 348 townships in Taiwan. In the GWR models, regression coefficients of spatially regressed relationships between the incidence of autochthonous dengue and vector density of Ae. aegypti were significant and positive in most townships in Taiwan. However, Ae. albopictus had significant but negative regression coefficients in clusters of dengue epidemics. In the global bivariate Moran's index, spatial dependence between the incidence of autochthonous dengue and vector density of Ae. aegypti was significant and exhibited positive correlation in Taiwan (bivariate Moran's index = 0.51). However, Ae. albopictus exhibited positively significant but low correlation (bivariate Moran's index = 0.06). Similar results were observed in the two spatial methods between all dengue incidences and Aedes mosquitoes (Ae. aegypti and Ae. albopictus). The regression coefficients of spatially regressed relationships between imported dengue cases and Aedes mosquitoes (Ae. aegypti and Ae. albopictus) were significant in 348 townships in Taiwan. The results indicated that local Aedes mosquitoes do not contribute to the dengue incidence of imported cases. The density of Ae. aegypti positively correlated with the density of human population. By contrast, the density of Ae. albopictus negatively correlated with the density of human population in the areas of southern Taiwan. The results indicated that Ae. aegypti has more opportunities for human-mosquito contact in dengue endemic areas in southern Taiwan. Ae. aegypti, but not Ae. albopictus, and human population density in southern Taiwan are closely associated with an increased risk of autochthonous dengue incidence.

A Bivariate Generalized Linear Item Response Theory Modeling Framework to the Analysis of Responses and Response Times.

PubMed

Molenaar, Dylan; Tuerlinckx, Francis; van der Maas, Han L J

2015-01-01

A generalized linear modeling framework to the analysis of responses and response times is outlined. In this framework, referred to as bivariate generalized linear item response theory (B-GLIRT), separate generalized linear measurement models are specified for the responses and the response times that are subsequently linked by cross-relations. The cross-relations can take various forms. Here, we focus on cross-relations with a linear or interaction term for ability tests, and cross-relations with a curvilinear term for personality tests. In addition, we discuss how popular existing models from the psychometric literature are special cases in the B-GLIRT framework depending on restrictions in the cross-relation. This allows us to compare existing models conceptually and empirically. We discuss various extensions of the traditional models motivated by practical problems. We also illustrate the applicability of our approach using various real data examples, including data on personality and cognitive ability.

A Semiparametric Approach to Simultaneous Covariance Estimation for Bivariate Sparse Longitudinal Data

PubMed Central

Das, Kiranmoy; Daniels, Michael J.

2014-01-01

Summary Estimation of the covariance structure for irregular sparse longitudinal data has been studied by many authors in recent years but typically using fully parametric specifications. In addition, when data are collected from several groups over time, it is known that assuming the same or completely different covariance matrices over groups can lead to loss of efficiency and/or bias. Nonparametric approaches have been proposed for estimating the covariance matrix for regular univariate longitudinal data by sharing information across the groups under study. For the irregular case, with longitudinal measurements that are bivariate or multivariate, modeling becomes more difficult. In this article, to model bivariate sparse longitudinal data from several groups, we propose a flexible covariance structure via a novel matrix stick-breaking process for the residual covariance structure and a Dirichlet process mixture of normals for the random effects. Simulation studies are performed to investigate the effectiveness of the proposed approach over more traditional approaches. We also analyze a subset of Framingham Heart Study data to examine how the blood pressure trajectories and covariance structures differ for the patients from different BMI groups (high, medium and low) at baseline. PMID:24400941

Cost-offsets of prescription drug expenditures: data analysis via a copula-based bivariate dynamic hurdle model.

PubMed

Deb, Partha; Trivedi, Pravin K; Zimmer, David M

2014-10-01

In this paper, we estimate a copula-based bivariate dynamic hurdle model of prescription drug and nondrug expenditures to test the cost-offset hypothesis, which posits that increased expenditures on prescription drugs are offset by reductions in other nondrug expenditures. We apply the proposed methodology to data from the Medical Expenditure Panel Survey, which have the following features: (i) the observed bivariate outcomes are a mixture of zeros and continuously measured positives; (ii) both the zero and positive outcomes show state dependence and inter-temporal interdependence; and (iii) the zeros and the positives display contemporaneous association. The point mass at zero is accommodated using a hurdle or a two-part approach. The copula-based approach to generating joint distributions is appealing because the contemporaneous association involves asymmetric dependence. The paper studies samples categorized by four health conditions: arthritis, diabetes, heart disease, and mental illness. There is evidence of greater than dollar-for-dollar cost-offsets of expenditures on prescribed drugs for relatively low levels of spending on drugs and less than dollar-for-dollar cost-offsets at higher levels of drug expenditures. Copyright © 2013 John Wiley & Sons, Ltd.

Examining the association of abortion history and current mental health: A reanalysis of the National Comorbidity Survey using a common-risk-factors model.

PubMed

Steinberg, Julia R; Finer, Lawrence B

2011-01-01

Using the US National Comorbidity Survey (NCS), Coleman, Coyle, Shuping, and Rue (2009) published an analysis indicating that compared to women who had never had an abortion, women who had reported an abortion were at an increased risk of several anxiety, mood, and substance use disorders. Here, we show that those results are not replicable. That is, using the same data, sample, and codes as indicated by those authors, it is not possible to replicate the simple bivariate statistics testing the relationship of ever having had an abortion to each mental health disorder when no factors were controlled for in analyses (Table 2 in Coleman et al., 2009). Furthermore, among women with prior pregnancies in the NCS, we investigated whether having zero, one, or multiple abortions (abortion history) was associated with having a mood, anxiety, or substance use disorder at the time of the interview. In doing this, we tested two competing frameworks: the abortion-as-trauma versus the common-risk-factors approach. Our results support the latter framework. In the bivariate context when no other factors were included in models, abortion history was not related to having a mood disorder, but it was related to having an anxiety or substance use disorder. When prior mental health and violence experience were controlled in our models, no significant relation was found between abortion history and anxiety disorders. When these same risk factors and other background factors were controlled, women who had multiple abortions remained at an increased risk of having a substance use disorder compared to women who had no abortions, likely because we were unable to control for other risk factors associated with having an abortion and substance use. Policy, practice, and research should focus on assisting women at greatest risk of having unintended pregnancies and having poor mental health-those with violence in their lives and prior mental health problems. Copyright © 2010 Elsevier Ltd. All rights reserved.

Segmentation and intensity estimation of microarray images using a gamma-t mixture model.

PubMed

Baek, Jangsun; Son, Young Sook; McLachlan, Geoffrey J

2007-02-15

We present a new approach to the analysis of images for complementary DNA microarray experiments. The image segmentation and intensity estimation are performed simultaneously by adopting a two-component mixture model. One component of this mixture corresponds to the distribution of the background intensity, while the other corresponds to the distribution of the foreground intensity. The intensity measurement is a bivariate vector consisting of red and green intensities. The background intensity component is modeled by the bivariate gamma distribution, whose marginal densities for the red and green intensities are independent three-parameter gamma distributions with different parameters. The foreground intensity component is taken to be the bivariate t distribution, with the constraint that the mean of the foreground is greater than that of the background for each of the two colors. The degrees of freedom of this t distribution are inferred from the data but they could be specified in advance to reduce the computation time. Also, the covariance matrix is not restricted to being diagonal and so it allows for nonzero correlation between R and G foreground intensities. This gamma-t mixture model is fitted by maximum likelihood via the EM algorithm. A final step is executed whereby nonparametric (kernel) smoothing is undertaken of the posterior probabilities of component membership. The main advantages of this approach are: (1) it enjoys the well-known strengths of a mixture model, namely flexibility and adaptability to the data; (2) it considers the segmentation and intensity simultaneously and not separately as in commonly used existing software, and it also works with the red and green intensities in a bivariate framework as opposed to their separate estimation via univariate methods; (3) the use of the three-parameter gamma distribution for the background red and green intensities provides a much better fit than the normal (log normal) or t distributions; (4) the use of the bivariate t distribution for the foreground intensity provides a model that is less sensitive to extreme observations; (5) as a consequence of the aforementioned properties, it allows segmentation to be undertaken for a wide range of spot shapes, including doughnut, sickle shape and artifacts. We apply our method for gridding, segmentation and estimation to cDNA microarray real images and artificial data. Our method provides better segmentation results in spot shapes as well as intensity estimation than Spot and spotSegmentation R language softwares. It detected blank spots as well as bright artifact for the real data, and estimated spot intensities with high-accuracy for the synthetic data. The algorithms were implemented in Matlab. The Matlab codes implementing both the gridding and segmentation/estimation are available upon request. Supplementary material is available at Bioinformatics online.

Dental erosion and its association with diet in Libyan schoolchildren.

PubMed

Huew, R; Waterhouse, P J; Moynihan, P J; Kometa, S; Maguire, A

2011-10-01

To investigate any association between dental erosion and its potential dietary risk factors in a group of schoolchildren in Benghazi, Libya. A cross-sectional observational study. A random sample of 12-year-old schoolchildren in 36 randomly selected schools completed a questionnaire to provide dietary data and underwent dental examination. Dental erosion was assessed using UK National Diet and Nutrition Survey (2000) criteria. Associations between erosion and dietary variables under study were investigated through processes of bivariate and multivariate analyses. Of 791 schoolchildren dentally examined, 40.8% had dental erosion; erosion into enamel affecting 32.5%, into dentine affecting 8% and into pulp affecting 0.3% of subjects. Bivariate analysis showed frequency of fruit-based sugary drink intake was statistically significantly and positively associated with erosion (p=0.006, Odds Ratio; 1.498, 95% CI; 1.124, 1.996) as was the length of time taken to consume acidic drinks (p≠0.005, Odds Ratio; 1.593, 95%CI; 1.161, 2.186). Additionally, multivariate analysis showed frequency of consumption of fruit other than bananas, sugared tea with milk and flavoured milk to also be positively associated with erosion (p=<0.05). In this group of Libyan 12-year-olds, frequency of consumption of fruit-based sugary drinks and length of time taken to consume acidic drinks were the primary statistically significant positive risk factors for dental erosion.

A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring.

PubMed

Takahashi, Kunihiko; Kulldorff, Martin; Tango, Toshiro; Yih, Katherine

2008-04-11

Early detection of disease outbreaks enables public health officials to implement disease control and prevention measures at the earliest possible time. A time periodic geographical disease surveillance system based on a cylindrical space-time scan statistic has been used extensively for disease surveillance along with the SaTScan software. In the purely spatial setting, many different methods have been proposed to detect spatial disease clusters. In particular, some spatial scan statistics are aimed at detecting irregularly shaped clusters which may not be detected by the circular spatial scan statistic. Based on the flexible purely spatial scan statistic, we propose a flexibly shaped space-time scan statistic for early detection of disease outbreaks. The performance of the proposed space-time scan statistic is compared with that of the cylindrical scan statistic using benchmark data. In order to compare their performances, we have developed a space-time power distribution by extending the purely spatial bivariate power distribution. Daily syndromic surveillance data in Massachusetts, USA, are used to illustrate the proposed test statistic. The flexible space-time scan statistic is well suited for detecting and monitoring disease outbreaks in irregularly shaped areas.

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance.

PubMed

Gordon, Derek; Londono, Douglas; Patel, Payal; Kim, Wonkuk; Finch, Stephen J; Heiman, Gary A

2016-01-01

Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes. © 2017 S. Karger AG, Basel.

Hydrologic risk analysis in the Yangtze River basin through coupling Gaussian mixtures into copulas

NASA Astrophysics Data System (ADS)

Fan, Y. R.; Huang, W. W.; Huang, G. H.; Li, Y. P.; Huang, K.; Li, Z.

2016-02-01

In this study, a bivariate hydrologic risk framework is proposed through coupling Gaussian mixtures into copulas, leading to a coupled GMM-copula method. In the coupled GMM-Copula method, the marginal distributions of flood peak, volume and duration are quantified through Gaussian mixture models and the joint probability distributions of flood peak-volume, peak-duration and volume-duration are established through copulas. The bivariate hydrologic risk is then derived based on the joint return period of flood variable pairs. The proposed method is applied to the risk analysis for the Yichang station on the main stream of the Yangtze River, China. The results indicate that (i) the bivariate risk for flood peak-volume would keep constant for the flood volume less than 1.0 × 105 m3/s day, but present a significant decreasing trend for the flood volume larger than 1.7 × 105 m3/s day; and (ii) the bivariate risk for flood peak-duration would not change significantly for the flood duration less than 8 days, and then decrease significantly as duration value become larger. The probability density functions (pdfs) of the flood volume and duration conditional on flood peak can also be generated through the fitted copulas. The results indicate that the conditional pdfs of flood volume and duration follow bimodal distributions, with the occurrence frequency of the first vertex decreasing and the latter one increasing as the increase of flood peak. The obtained conclusions from the bivariate hydrologic analysis can provide decision support for flood control and mitigation.

A survey of variable selection methods in two Chinese epidemiology journals

PubMed Central

2010-01-01

Background Although much has been written on developing better procedures for variable selection, there is little research on how it is practiced in actual studies. This review surveys the variable selection methods reported in two high-ranking Chinese epidemiology journals. Methods Articles published in 2004, 2006, and 2008 in the Chinese Journal of Epidemiology and the Chinese Journal of Preventive Medicine were reviewed. Five categories of methods were identified whereby variables were selected using: A - bivariate analyses; B - multivariable analysis; e.g. stepwise or individual significance testing of model coefficients; C - first bivariate analyses, followed by multivariable analysis; D - bivariate analyses or multivariable analysis; and E - other criteria like prior knowledge or personal judgment. Results Among the 287 articles that reported using variable selection methods, 6%, 26%, 30%, 21%, and 17% were in categories A through E, respectively. One hundred sixty-three studies selected variables using bivariate analyses, 80% (130/163) via multiple significance testing at the 5% alpha-level. Of the 219 multivariable analyses, 97 (44%) used stepwise procedures, 89 (41%) tested individual regression coefficients, but 33 (15%) did not mention how variables were selected. Sixty percent (58/97) of the stepwise routines also did not specify the algorithm and/or significance levels. Conclusions The variable selection methods reported in the two journals were limited in variety, and details were often missing. Many studies still relied on problematic techniques like stepwise procedures and/or multiple testing of bivariate associations at the 0.05 alpha-level. These deficiencies should be rectified to safeguard the scientific validity of articles published in Chinese epidemiology journals. PMID:20920252

Bivariate genome-wide association analyses identified genetic pleiotropic effects for bone mineral density and alcohol drinking in Caucasians

PubMed Central

Lu, Shan; Zhao, Lan-Juan; Chen, Xiang-Ding; Papasian, Christopher J.; Wu, Ke-Hao; Tan, Li-Jun; Wang, Zhuo-Er; Pei, Yu-Fang; Tian, Qing

2018-01-01

Several studies indicated bone mineral density (BMD) and alcohol intake might share common genetic factors. The study aimed to explore potential SNPs/genes related to both phenotypes in US Caucasians at the genome-wide level. A bivariate genome-wide association study (GWAS) was performed in 2069 unrelated participants. Regular drinking was graded as 1, 2, 3, 4, 5, or 6, representing drinking alcohol never, less than once, once or twice, three to six times, seven to ten times, or more than ten times per week respectively. Hip, spine, and whole body BMDs were measured. The bivariate GWAS was conducted on the basis of a bivariate linear regression model. Sex-stratified association analyses were performed in the male and female subgroups. In males, the most significant association signal was detected in SNP rs685395 in DYNC2H1 with bivariate spine BMD and alcohol drinking (P = 1.94 × 10−8). SNP rs685395 and five other SNPs, rs657752, rs614902, rs682851, rs626330, and rs689295, located in the same haplotype block in DYNC2H1 were the top ten most significant SNPs in the bivariate GWAS in males. Additionally, two SNPs in GRIK4 in males and three SNPs in OPRM1 in females were suggestively associated with BMDs (of the hip, spine, and whole body) and alcohol drinking. Nine SNPs in IL1RN were only suggestively associated with female whole body BMD and alcohol drinking. Our study indicated that DYNC2H1 may contribute to the genetic mechanisms of both spine BMD and alcohol drinking in male Caucasians. Moreover, our study suggested potential pleiotropic roles of OPRM1 and IL1RN in females and GRIK4 in males underlying variation of both BMD and alcohol drinking. PMID:28012008

Statistical methods for astronomical data with upper limits. II - Correlation and regression

NASA Technical Reports Server (NTRS)

Isobe, T.; Feigelson, E. D.; Nelson, P. I.

1986-01-01

Statistical methods for calculating correlations and regressions in bivariate censored data where the dependent variable can have upper or lower limits are presented. Cox's regression and the generalization of Kendall's rank correlation coefficient provide significant levels of correlations, and the EM algorithm, under the assumption of normally distributed errors, and its nonparametric analog using the Kaplan-Meier estimator, give estimates for the slope of a regression line. Monte Carlo simulations demonstrate that survival analysis is reliable in determining correlations between luminosities at different bands. Survival analysis is applied to CO emission in infrared galaxies, X-ray emission in radio galaxies, H-alpha emission in cooling cluster cores, and radio emission in Seyfert galaxies.

Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)

NASA Astrophysics Data System (ADS)

Trigila, Alessandro; Iadanza, Carla; Esposito, Carlo; Scarascia-Mugnozza, Gabriele

2015-11-01

The aim of this work is to define reliable susceptibility models for shallow landslides using Logistic Regression and Random Forests multivariate statistical techniques. The study area, located in North-East Sicily, was hit on October 1st 2009 by a severe rainstorm (225 mm of cumulative rainfall in 7 h) which caused flash floods and more than 1000 landslides. Several small villages, such as Giampilieri, were hit with 31 fatalities, 6 missing persons and damage to buildings and transportation infrastructures. Landslides, mainly types such as earth and debris translational slides evolving into debris flows, were triggered on steep slopes and involved colluvium and regolith materials which cover the underlying metamorphic bedrock. The work has been carried out with the following steps: i) realization of a detailed event landslide inventory map through field surveys coupled with observation of high resolution aerial colour orthophoto; ii) identification of landslide source areas; iii) data preparation of landslide controlling factors and descriptive statistics based on a bivariate method (Frequency Ratio) to get an initial overview on existing relationships between causative factors and shallow landslide source areas; iv) choice of criteria for the selection and sizing of the mapping unit; v) implementation of 5 multivariate statistical susceptibility models based on Logistic Regression and Random Forests techniques and focused on landslide source areas; vi) evaluation of the influence of sample size and type of sampling on results and performance of the models; vii) evaluation of the predictive capabilities of the models using ROC curve, AUC and contingency tables; viii) comparison of model results and obtained susceptibility maps; and ix) analysis of temporal variation of landslide susceptibility related to input parameter changes. Models based on Logistic Regression and Random Forests have demonstrated excellent predictive capabilities. Land use and wildfire variables were found to have a strong control on the occurrence of very rapid shallow landslides.

General models for the distributions of electric field gradients in disordered solids

NASA Astrophysics Data System (ADS)

LeCaër, G.; Brand, R. A.

1998-11-01

Hyperfine studies of disordered materials often yield the distribution of the electric field gradient (EFG) or related quadrupole splitting (QS). The question of the structural information that may be extracted from such distributions has been considered for more than fifteen years. Experimentally most studies have been performed using Mössbauer spectroscopy, especially on 0953-8984/10/47/020/img5. However, NMR, NQR, EPR and PAC methods have also received some attention. The EFG distribution for a random distribution of electric charges was for instance first investigated by Czjzek et al [1] and a general functional form was derived for the joint (bivariate) distribution of the principal EFG tensor component 0953-8984/10/47/020/img6 and the asymmetry parameter 0953-8984/10/47/020/img7. The importance of the Gauss distribution for such rotationally invariant structural models was thus evidenced. Extensions of that model which are based on degenerate multivariate Gauss distributions for the elements of the EFG tensor were proposed by Czjzek. The latter extensions have been used since that time, more particularly in Mössbauer spectroscopy, under the name `shell models'. The mathematical foundations of all the previous models are presented and critically discussed as they are evidenced by simple calculations in the case of the EFG tensor. The present article only focuses on those aspects of the EFG distribution in disordered solids which can be discussed without explicitly looking at particular physical mechanisms. We present studies of three different model systems. A reference model directly related to the first model of Czjzek, called the Gaussian isotropic model (GIM), is shown to be the limiting case for many different models with a large number of independent contributions to the EFG tensor and not restricted to a point-charge model. The extended validity of the marginal distribution of 0953-8984/10/47/020/img7 in the GIM model is discussed. It is also shown that the second model based on degenerate multivariate normal distributions for the EFG components yields questionable results and has been exaggeratedly used in experimental studies. The latter models are further discussed in the light of new results. The problems raised by these extensions are due to the fact that the consequences of the statistical invariance by rotation of the EFG tensor have not been sufficiently taken into account. Further difficulties arise because the structural degrees of freedom of the disordered solid under consideration have been confused with the degrees of freedom of QS distributions. The relations which are derived and discussed are further illustrated by the case of the EFG tensor distribution created at the centre of a sphere by m charges randomly distributed on its surface. The third model, a simple extension of the GIM, considers the case of an EFG tensor which is the sum of a fixed part and of a random part with variable weights. The bivariate distribution 0953-8984/10/47/020/img9 is calculated exactly in the most symmetric case and the effect of the random part is investigated as a function of its weight. The various models are more particularly discussed in connection with short-range order in disordered solids. An ambiguity problem which arises in the evaluation of bivariate distributions of centre lineshift (isomer shift) and quadrupole splitting from 0953-8984/10/47/020/img10 Mössbauer spectra is finally quantitatively considered.

In vitro burn model illustrating heat conduction patterns using compressed thermal papers.

PubMed

Lee, Jun Yong; Jung, Sung-No; Kwon, Ho

2015-01-01

To date, heat conduction from heat sources to tissue has been estimated by complex mathematical modeling. In the present study, we developed an intuitive in vitro skin burn model that illustrates heat conduction patterns inside the skin. This was composed of tightly compressed thermal papers with compression frames. Heat flow through the model left a trace by changing the color of thermal papers. These were digitized and three-dimensionally reconstituted to reproduce the heat conduction patterns in the skin. For standardization, we validated K91HG-CE thermal paper using a printout test and bivariate correlation analysis. We measured the papers' physical properties and calculated the estimated depth of heat conduction using Fourier's equation. Through contact burns of 5, 10, 15, 20, and 30 seconds on porcine skin and our burn model using a heated brass comb, and comparing the burn wound and heat conduction trace, we validated our model. The heat conduction pattern correlation analysis (intraclass correlation coefficient: 0.846, p < 0.001) and the heat conduction depth correlation analysis (intraclass correlation coefficient: 0.93, p < 0.001) showed statistically significant high correlations between the porcine burn wound and our model. Our model showed good correlation with porcine skin burn injury and replicated its heat conduction patterns. © 2014 by the Wound Healing Society.

Galaxy And Mass Assembly (GAMA): bivariate functions of Hα star-forming galaxies

NASA Astrophysics Data System (ADS)

Gunawardhana, M. L. P.; Hopkins, A. M.; Taylor, E. N.; Bland-Hawthorn, J.; Norberg, P.; Baldry, I. K.; Loveday, J.; Owers, M. S.; Wilkins, S. M.; Colless, M.; Brown, M. J. I.; Driver, S. P.; Alpaslan, M.; Brough, S.; Cluver, M.; Croom, S.; Kelvin, L.; Lara-López, M. A.; Liske, J.; López-Sánchez, A. R.; Robotham, A. S. G.

2015-02-01

We present bivariate luminosity and stellar mass functions of Hα star-forming galaxies drawn from the Galaxy And Mass Assembly (GAMA) survey. While optically deep spectroscopic observations of GAMA over a wide sky area enable the detection of a large number of 0.001 < SFRHα (M⊙ yr-1) < 100 galaxies, the requirement for an Hα detection in targets selected from an r-band magnitude-limited survey leads to an incompleteness due to missing optically faint star-forming galaxies. Using z < 0.1 bivariate distributions as a reference we model the higher-z distributions, thereby approximating a correction for the missing optically faint star-forming galaxies to the local star formation rate (SFR) and M densities. Furthermore, we obtain the r-band luminosity functions (LFs) and stellar mass functions of Hα star-forming galaxies from the bivariate LFs. As our sample is selected on the basis of detected Hα emission, a direct tracer of ongoing star formation, this sample represents a true star-forming galaxy sample, and is drawn from both photometrically classified blue and red subpopulations, though mostly from the blue population. On average 20-30 per cent of red galaxies at all stellar masses are star forming, implying that these galaxies may be dusty star-forming systems.

Risk prediction and impaired tactile sensory perception among cancer patients during chemotherapy.

PubMed

Cardoso, Ana Carolina Lima Ramos; Araújo, Diego Dias de; Chianca, Tânia Couto Machado

2018-01-08

to estimate the prevalence of impaired tactile sensory perception, identify risk factors, and establish a risk prediction model among adult patients receiving antineoplastic chemotherapy. historical cohort study based on information obtained from the medical files of 127 patients cared for in the cancer unit of a private hospital in a city in Minas Gerais, Brazil. Data were analyzed using descriptive and bivariate statistics, with survival and multivariate analysis by Cox regression. 57% of the 127 patients included in the study developed impaired tactile sensory perception. The independent variables that caused significant impact, together with time elapsed from the beginning of treatment up to the onset of the condition, were: bone, hepatic and regional lymph node metastases; alcoholism; palliative chemotherapy; and discomfort in lower limbs. impaired tactile sensory perception was common among adult patients during chemotherapy, indicating the need to implement interventions designed for early identification and treatment of this condition.

Bioelectrical impedance vector distribution in the first year of life.

PubMed

Savino, Francesco; Grasso, Giulia; Cresi, Francesco; Oggero, Roberto; Silvestro, Leandra

2003-06-01

We assessed the bioelectrical impedance vector distribution in a sample of healthy infants in the first year of life, which is not available in literature. The study was conducted as a cross-sectional study in 153 healthy Caucasian infants (90 male and 63 female) younger than 1 y, born at full term, adequate for gestational age, free from chronic diseases or growth problems, and not feverish. Z scores for weight, length, cranial circumference, and body mass index for the study population were within the range of +/-1.5 standard deviations according to the Euro-Growth Study references. Concurrent anthropometrics (weight, length, and cranial circumference), body mass index, and bioelectrical impedance (resistance and reactance) measurements were made by the same operator. Whole-body (hand to foot) tetrapolar measurements were performed with a single-frequency (50 kHz), phase-sensitive impedance analyzer. The study population was subdivided into three classes of age for statistical analysis: 0 to 3.99 mo, 4 to 7.99 mo, and 8 to 11.99 mo. Using the bivariate normal distribution of resistance and reactance components standardized by the infant's length, the bivariate 95% confidence limits for the mean impedance vector separated by sex and age groups were calculated and plotted. Further, the bivariate 95%, 75%, and 50% tolerance intervals for individual vector measurements in the first year of life were plotted. Resistance and reactance values often fluctuated during the first year of life, particularly as raw measurements (without normalization by subject's length). However, 95% confidence ellipses of mean vectors from the three age groups overlapped each other, as did confidence ellipses by sex for each age class, indicating no significant vector migration during the first year of life. We obtained an estimate of mean impedance vector in a sample of healthy infants in the first year of life and calculated the bivariate values for an individual vector (95%, 75%, and 50% tolerance ellipses).

A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses

ERIC Educational Resources Information Center

Vasdekis, Vassilis G. S.; Cagnone, Silvia; Moustaki, Irini

2012-01-01

The paper proposes a composite likelihood estimation approach that uses bivariate instead of multivariate marginal probabilities for ordinal longitudinal responses using a latent variable model. The model considers time-dependent latent variables and item-specific random effects to be accountable for the interdependencies of the multivariate…

Bivariate discrete beta Kernel graduation of mortality data.

PubMed

Mazza, Angelo; Punzo, Antonio

2015-07-01

Various parametric/nonparametric techniques have been proposed in literature to graduate mortality data as a function of age. Nonparametric approaches, as for example kernel smoothing regression, are often preferred because they do not assume any particular mortality law. Among the existing kernel smoothing approaches, the recently proposed (univariate) discrete beta kernel smoother has been shown to provide some benefits. Bivariate graduation, over age and calendar years or durations, is common practice in demography and actuarial sciences. In this paper, we generalize the discrete beta kernel smoother to the bivariate case, and we introduce an adaptive bandwidth variant that may provide additional benefits when data on exposures to the risk of death are available; furthermore, we outline a cross-validation procedure for bandwidths selection. Using simulations studies, we compare the bivariate approach proposed here with its corresponding univariate formulation and with two popular nonparametric bivariate graduation techniques, based on Epanechnikov kernels and on P-splines. To make simulations realistic, a bivariate dataset, based on probabilities of dying recorded for the US males, is used. Simulations have confirmed the gain in performance of the new bivariate approach with respect to both the univariate and the bivariate competitors.

Spanking, corporal punishment and negative long-term outcomes: a meta-analytic review of longitudinal studies.

PubMed

Ferguson, Christopher J

2013-02-01

Social scientists continue to debate the impact of spanking and corporal punishment (CP) on negative child outcomes including externalizing and internalizing behavior problems and cognitive performance. Previous meta-analytic reviews have mixed long- and short-term studies and relied on bivariate r, which may inflate effect sizes. The current meta-analysis focused on longitudinal studies, and compared effects using bivariate r and better controlled partial r coefficients controlling for time-1 outcome variables. Consistent with previous findings, results based on bivariate r found small but non-trivial long-term relationships between spanking/CP use and negative outcomes. Spanking and CP correlated .14 and .18 respectively with externalizing problems, .12 and .21 with internalizing problems and -.09 and -.18 with cognitive performance. However, when better controlled partial r coefficients (pr) were examined, results were statistically significant but trivial (at or below pr = .10) for externalizing (.07 for spanking, .08 for CP) and internalizing behaviors (.10 for spanking, insufficient studies for CP) and near the threshold of trivial for cognitive performance (-.11 for CP, insufficient studies for spanking). It is concluded that the impact of spanking and CP on the negative outcomes evaluated here (externalizing, internalizing behaviors and low cognitive performance) are minimal. It is advised that psychologists take a more nuanced approach in discussing the effects of spanking/CP with the general public, consistent with the size as well as the significance of their longitudinal associations with adverse outcomes.

Geovisualization of land use and land cover using bivariate maps and Sankey flow diagrams

NASA Astrophysics Data System (ADS)

Strode, Georgianna; Mesev, Victor; Thornton, Benjamin; Jerez, Marjorie; Tricarico, Thomas; McAlear, Tyler

2018-05-01

The terms `land use' and `land cover' typically describe categories that convey information about the landscape. Despite the major difference of land use implying some degree of anthropogenic disturbance, the two terms are commonly used interchangeably, especially when anthropogenic disturbance is ambiguous, say managed forestland or abandoned agricultural fields. Cartographically, land use and land cover are also sometimes represented interchangeably within common legends, giving with the impression that the landscape is a seamless continuum of land use parcels spatially adjacent to land cover tracts. We believe this is misleading, and feel we need to reiterate the well-established symbiosis of land uses as amalgams of land covers; in other words land covers are subsets of land use. Our paper addresses this spatially complex, and frequently ambiguous relationship, and posits that bivariate cartographic techniques are an ideal vehicle for representing both land use and land cover simultaneously. In more specific terms, we explore the use of nested symbology as ways to represent graphically land use and land cover, where land cover are circles nested with land use squares. We also investigate bivariate legends for representing statistical covariance as a means for visualizing the combinations of land use and cover. Lastly, we apply Sankey flow diagrams to further illustrate the complex, multifaceted relationships between land use and land cover. Our work is demonstrated on data representing land use and cover data for the US state of Florida.

The intention to use HIV-pre-exposure prophylaxis (PrEP) among men who have sex with men in Switzerland: testing an extended explanatory model drawing on the unified theory of acceptance and use of technology (UTAUT).

PubMed

Nideröst, Sibylle; Gredig, Daniel; Hassler, Benedikt; Uggowitzer, Franziska; Weber, Patrick

2018-01-01

The aim of this study was to determine the intention to use pre-exposure prophylaxis (PrEP) when available and to identify predictors of the intention to use PrEP among men who have sex with men (MSM) living in Switzerland. The theoretical model drew on the Unified Theory of Acceptance and Use of Technology and considered additional variables related specifically to PrEP, HIV protection and the resources of MSM. For data collection, we used an anonymous, standardized self-administered online questionnaire. In 2015, we gathered a convenience sample of 556 HIV-negative MSM living in Switzerland. We analyzed the data using descriptive and bivariate statistics and used structural equation modeling to test the hypothesized model. Predictors of respondents' moderate intention to use PrEP were performance expectancy, effort expectancy, perceived social influence, concerns about using PrEP, attitudes toward condom use, negative experiences of condom use and age. These variables were predicted by HIV protection-related aspects and resources. The findings provide insights into the complex dynamic underlying the intention to use PrEP.

Evolution of association between renal and liver functions while awaiting heart transplant: An application using a bivariate multiphase nonlinear mixed effects model.

PubMed

Rajeswaran, Jeevanantham; Blackstone, Eugene H; Barnard, John

2018-07-01

In many longitudinal follow-up studies, we observe more than one longitudinal outcome. Impaired renal and liver functions are indicators of poor clinical outcomes for patients who are on mechanical circulatory support and awaiting heart transplant. Hence, monitoring organ functions while waiting for heart transplant is an integral part of patient management. Longitudinal measurements of bilirubin can be used as a marker for liver function and glomerular filtration rate for renal function. We derive an approximation to evolution of association between these two organ functions using a bivariate nonlinear mixed effects model for continuous longitudinal measurements, where the two submodels are linked by a common distribution of time-dependent latent variables and a common distribution of measurement errors.

Uncertainty Quantification Techniques for Population Density Estimates Derived from Sparse Open Source Data

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

Stewart, Robert N; White, Devin A; Urban, Marie L

2013-01-01

The Population Density Tables (PDT) project at the Oak Ridge National Laboratory (www.ornl.gov) is developing population density estimates for specific human activities under normal patterns of life based largely on information available in open source. Currently, activity based density estimates are based on simple summary data statistics such as range and mean. Researchers are interested in improving activity estimation and uncertainty quantification by adopting a Bayesian framework that considers both data and sociocultural knowledge. Under a Bayesian approach knowledge about population density may be encoded through the process of expert elicitation. Due to the scale of the PDT effort whichmore » considers over 250 countries, spans 40 human activity categories, and includes numerous contributors, an elicitation tool is required that can be operationalized within an enterprise data collection and reporting system. Such a method would ideally require that the contributor have minimal statistical knowledge, require minimal input by a statistician or facilitator, consider human difficulties in expressing qualitative knowledge in a quantitative setting, and provide methods by which the contributor can appraise whether their understanding and associated uncertainty was well captured. This paper introduces an algorithm that transforms answers to simple, non-statistical questions into a bivariate Gaussian distribution as the prior for the Beta distribution. Based on geometric properties of the Beta distribution parameter feasibility space and the bivariate Gaussian distribution, an automated method for encoding is developed that responds to these challenging enterprise requirements. Though created within the context of population density, this approach may be applicable to a wide array of problem domains requiring informative priors for the Beta distribution.« less

Meta-Analyses of Diagnostic Accuracy in Imaging Journals: Analysis of Pooling Techniques and Their Effect on Summary Estimates of Diagnostic Accuracy.

PubMed

McGrath, Trevor A; McInnes, Matthew D F; Korevaar, Daniël A; Bossuyt, Patrick M M

2016-10-01

Purpose To determine whether authors of systematic reviews of diagnostic accuracy studies published in imaging journals used recommended methods for meta-analysis, and to evaluate the effect of traditional methods on summary estimates of sensitivity and specificity. Materials and Methods Medline was searched for published systematic reviews that included meta-analysis of test accuracy data limited to imaging journals published from January 2005 to May 2015. Two reviewers independently extracted study data and classified methods for meta-analysis as traditional (univariate fixed- or random-effects pooling or summary receiver operating characteristic curve) or recommended (bivariate model or hierarchic summary receiver operating characteristic curve). Use of methods was analyzed for variation with time, geographical location, subspecialty, and journal. Results from reviews in which study authors used traditional univariate pooling methods were recalculated with a bivariate model. Results Three hundred reviews met the inclusion criteria, and in 118 (39%) of those, authors used recommended meta-analysis methods. No change in the method used was observed with time (r = 0.54, P = .09); however, there was geographic (χ(2) = 15.7, P = .001), subspecialty (χ(2) = 46.7, P < .001), and journal (χ(2) = 27.6, P < .001) heterogeneity. Fifty-one univariate random-effects meta-analyses were reanalyzed with the bivariate model; the average change in the summary estimate was -1.4% (P < .001) for sensitivity and -2.5% (P < .001) for specificity. The average change in width of the confidence interval was 7.7% (P < .001) for sensitivity and 9.9% (P ≤ .001) for specificity. Conclusion Recommended methods for meta-analysis of diagnostic accuracy in imaging journals are used in a minority of reviews; this has not changed significantly with time. Traditional (univariate) methods allow overestimation of diagnostic accuracy and provide narrower confidence intervals than do recommended (bivariate) methods. (©) RSNA, 2016 Online supplemental material is available for this article.

On Deriving and Solving the Generalized Bivariate, Linear Location Problems.

DTIC Science & Technology

1982-09-01

average (Eisenhart, 1978). Francis Galton indirectly coined the term "regression" in his 1885 publication, Natural Inheritance, when he studied sweet...David, F. N. Francis Galton . In W. H. Kruskal & J. Tanur (Eds.), International encyclopedia of statistics (Vol. 1). New York: Free Press, 1978. Dean, W...mhhhEmhnhhEEEI I fllfllfllfllfllfllfl EEEMMhMhMhhhMhI 1111 . I 28 12.5 1.:, 1 2 . 1.21111 1 4 11111I. IIIII~ JIII1L MICROCOPY RESOLUTION TEST CHART NATIONAL

Combine bivariate statistics analysis and multivariate statistics analysis to assess landslide susceptibility in Chen-Yu-Lan watershed, Nantou, Taiwan.

NASA Astrophysics Data System (ADS)

Ngan Nguyen, Thi To; Liu, Cheng-Chien

2013-04-01

How landslides occurred and which factors triggered and sped up landslide occurrences were usually asked by researchers in the past decades. Many investigations carried out in many places in the world to finding out methods that predict and prevent damages from landslides phenomena. Chen-Yu-Lan River watershed is reputed as a 'hot pot' of landslide researches in Taiwan by its complicated geological structures with the significant tectonic fault systems and steeply mountainous terrain. Beside annual high precipitation concentration and the abrupt slopes, some natural disaster, as typhoons (Sinlaku-2008, Kalmaegi-2008, and Marakot-2009) and earthquake (Chi-Chi earthquake-1999) are also the triggered factors cause landslides with serious damages in this place. This research expresses the quantitative approaches to generate landslide susceptible map for Chen-Yu-Lan watershed, a mountainous area in the central Taiwan. Landslide inventories data, which were detected from the Formosat-2 imageries for eight years from 2004 to 2011, were applied to carry out landslide susceptibility mapping. Bivariate statistics analysis and multivariate statistics analysis would be applied to calculate susceptible index of landslides. The weights of parameters were computed based on landslide data for eight years from 2004 to 2011. To validate effective levels of factors to landslide occurrences, this method built some multivariate algorithms and compared these results with real landslide occurrences. Besides this method, the historical data of landslides were also used to assess and classify landslide susceptibility levels. From long-term landslide data, relation between landslide susceptibility levels and landslide repetition was assigned. The results demonstrated differently effective levels of potential factors, such as, slope gradient, drainage density, lithology and land use to landslide phenomena. The results also showed logical relationship between weights and characteristics of factors' classes. Depending on these results be able to help planning managers localize the high risk areas of landslide or safely areas by building and human activities.

Temperature and Humidity Effects on Hospital Morbidity in Darwin, Australia.

PubMed

Goldie, James; Sherwood, Steven C; Green, Donna; Alexander, Lisa

2015-01-01

Many studies have explored the relationship between temperature and health in the context of a changing climate, but few have considered the effects of humidity, particularly in tropical locations, on human health and well-being. To investigate this potential relationship, this study assessed the main and interacting effects of daily temperature and humidity on hospital admission rates for selected heat-relevant diagnoses in Darwin, Australia. Univariate and bivariate Poisson generalized linear models were used to find statistically significant predictors and the admission rates within bins of predictors were compared to explore nonlinear effects. The analysis indicated that nighttime humidity was the most statistically significant predictor (P < 0.001), followed by daytime temperature and average daily humidity (P < 0.05). There was no evidence of a significant interaction between them or other predictors. The nighttime humidity effect appeared to be strongly nonlinear: Hot days appeared to have higher admission rates when they were preceded by high nighttime humidity. From this analysis, we suggest that heat-health policies in tropical regions similar to Darwin need to accommodate the effects of temperature and humidity at different times of day. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Cross-cultural variation of memory colors of familiar objects.

PubMed

Smet, Kevin A G; Lin, Yandan; Nagy, Balázs V; Németh, Zoltan; Duque-Chica, Gloria L; Quintero, Jesús M; Chen, Hung-Shing; Luo, Ronnier M; Safi, Mahdi; Hanselaer, Peter

2014-12-29

The effect of cross-regional or cross-cultural differences on color appearance ratings and memory colors of familiar objects was investigated in seven different countries/regions - Belgium, Hungary, Brazil, Colombia, Taiwan, China and Iran. In each region the familiar objects were presented on a calibrated monitor in over 100 different colors to a test panel of observers that were asked to rate the similarity of the presented object color with respect to what they thought the object looks like in reality (memory color). For each object and region the mean observer ratings were modeled by a bivariate Gaussian function. A statistical analysis showed significant (p < 0.001) differences between the region average observers and the global average observer obtained by pooling the data from all regions. However, the effect size of geographical region or culture was found to be small. In fact, the differences between the region average observers and the global average observer were found to of the same magnitude or smaller than the typical within region inter-observer variability. Thus, although statistical differences in color appearance ratings and memory between regions were found, regional impact is not likely to be of practical importance.

TEMPORAL CORRELATION OF CLASSIFICATIONS IN REMOTE SENSING

EPA Science Inventory

A bivariate binary model is developed for estimating the change in land cover from satellite images obtained at two different times. The binary classifications of a pixel at the two times are modeled as potentially correlated random variables, conditional on the true states of th...

Colorectal cancer screening and adverse childhood experiences: Which adversities matter?

PubMed

Alcalá, Héctor E; Keim-Malpass, Jessica; Mitchell, Emma

2017-07-01

Adverse Childhood Experiences (ACEs) have been associated with an increased risk of a variety of diseases, including cancer. However, research has not paid enough attention to the association between ACEs and cancer screening. As such, the present study examined the association between ACEs and ever using colorectal cancer (CRC) screening, among adults age 50 and over. Analyses used the 2011 Behavioral Risk Factor Surveillance System (n=24,938) to model odds of ever engaging in CRC screening from nine different adversities. Bivariate and multivariate models were fit. In bivariate models, physical abuse, having parents that were divorced or separated, and living in a household where adults treated each other violently were associated with lower odds of engaging in CRC. In multivariate models that accounted for potential confounders, emotional and sexual abuse were each associated with higher odds of engaging in CRC. Results suggest potential pathways by which early childhood experiences can impact future health behaviors. Future research should examine this association longitudinally. Copyright © 2017 Elsevier Ltd. All rights reserved.

The maternal and neonatal outcomes for an urban Indigenous population compared with their non-Indigenous counterparts and a trend analysis over four triennia.

PubMed

Kildea, Sue; Stapleton, Helen; Murphy, Rebecca; Kosiak, Machellee; Gibbons, Kristen

2013-08-30

Indigenous Australians experience significantly disproportionate poorer health outcomes compared to their non-Indigenous counterparts. Despite the recognised importance of maternal infant health (MIH), there is surprisingly little empirical research to guide service redesign that successfully addresses the disparities. This paper reports on a service evaluation that also compared key MIH indicators for Indigenous and non-Indigenous mothers and babies over a 12-year period 1998-2009. Trend analysis with logistic regression, using the independent variables of ethnicity and triennia, explored changes over time (1998-2009) between two cohorts: 1,523 births to Indigenous mothers and 43,693 births to non-Indigenous mothers. We included bivariate and multivariate analysis on key indicators (e.g. teenage births, preterm birth, low birth weight, smoking) and report odds ratios (ORs), 95% CIs and logistic regression adjusting for important confounders. We excluded transfers in from other areas which are identified within the database. Bivariate analysis revealed Indigenous women were statistically more likely to have spontaneous onset of labour and a non-instrumental vaginal birth. They were less likely to take epidurals for pain relief in labour, have assisted births, caesarean sections or perineal trauma. Despite better labour outcomes, Indigenous babies were more likely to be born preterm (< 37 weeks) and be low birth weight (< 2500 g); these differences remained significant in multivariate analysis. The trend analysis revealed relatively stable rates for teenage pregnancy, small for gestational age, low birth weight babies, and perinatal mortality for both cohorts, with the gap between cohorts consistent over time. A statistical widening of the gap in preterm birth and smoking rates was found with preterm birth demonstrating a relative increase of 51% over this period. The comprehensive database from a large urban hospital allowed a thorough examination of outcomes and contributing factors. The gap between both cohorts remains static in several areas but in some cases worsened. Alternative models for delivering care to Indigenous women and their babies have shown improved outcomes, including preterm birth, though not all have been sustained over time and none are available Australia-wide. New models of care, which recognise the heterogeneity of Indigenous communities, incorporate a multiagency approach, and are set within a research framework, are urgently needed.

Improving nutrition in home child care: are food costs a barrier?

PubMed Central

Monsivais, Pablo; Johnson, Donna B

2015-01-01

Objective Child-care providers have a key role to play in promoting child nutrition, but the higher cost of nutritious foods may pose a barrier. The present study tested the hypothesis that higher nutritional quality of foods served was associated with higher food expenditures in child care homes participating in the Child and Adult Care Food Program (CACFP). Design In this cross-sectional study, nutritional quality of foods served to children and food expenditures were analysed based on 5 d menus and food shopping receipts. Nutritional quality was based on servings of whole grains, fresh whole fruits and vegetables, energy density (kJ/g) and mean nutrient adequacy (mean percentage of dietary reference intake) for seven nutrients of concern for child health. Food expenditures were calculated by linking receipt and menu data. Associations between food expenditures and menu quality were examined using bivariate statistics and multiple linear regression models. Setting USA in 2008–2009. Subjects Sixty child-care providers participating in CACFP in King County, Washington State. Results In bivariate analyses, higher daily food expenditures were associated with higher total food energy and higher nutritional quality of menus. Controlling for energy and other covariates, higher food expenditures were strongly and positively associated with number of portions of whole grains and fresh produce served (P = 0·001 and 0·005, respectively), with lower energy density and with higher mean nutrient adequacy of menus overall (P = 0·003 and 0·032, respectively). Conclusions The results indicate that improving the nutritional quality of foods in child care may require higher food spending. PMID:22014448

The return period analysis of natural disasters with statistical modeling of bivariate joint probability distribution.

PubMed

Li, Ning; Liu, Xueqin; Xie, Wei; Wu, Jidong; Zhang, Peng

2013-01-01

New features of natural disasters have been observed over the last several years. The factors that influence the disasters' formation mechanisms, regularity of occurrence and main characteristics have been revealed to be more complicated and diverse in nature than previously thought. As the uncertainty involved increases, the variables need to be examined further. This article discusses the importance and the shortage of multivariate analysis of natural disasters and presents a method to estimate the joint probability of the return periods and perform a risk analysis. Severe dust storms from 1990 to 2008 in Inner Mongolia were used as a case study to test this new methodology, as they are normal and recurring climatic phenomena on Earth. Based on the 79 investigated events and according to the dust storm definition with bivariate, the joint probability distribution of severe dust storms was established using the observed data of maximum wind speed and duration. The joint return periods of severe dust storms were calculated, and the relevant risk was analyzed according to the joint probability. The copula function is able to simulate severe dust storm disasters accurately. The joint return periods generated are closer to those observed in reality than the univariate return periods and thus have more value in severe dust storm disaster mitigation, strategy making, program design, and improvement of risk management. This research may prove useful in risk-based decision making. The exploration of multivariate analysis methods can also lay the foundation for further applications in natural disaster risk analysis. © 2012 Society for Risk Analysis.

Intrinsic motivation, neurocognition and psychosocial functioning in schizophrenia: testing mediator and moderator effects.

PubMed

Nakagami, Eri; Xie, Bin; Hoe, Maanse; Brekke, John S

2008-10-01

This study examined the nature of the relationships among neurocognition, intrinsic motivation, and psychosocial functioning for persons with schizophrenia. Hypotheses concerning both mediator and moderator mechanisms were tested. 120 individuals diagnosed with schizophrenia were recruited as they entered outpatient psychosocial rehabilitation programs. Measures of psychosocial functioning and intrinsic motivation were administered at baseline. Measures of neurocognition were administered at baseline by testers blind to scores on other study variables. Data were analyzed using latent construct modeling to test for mediator and moderator effects. There were strong bivariate relationships between neurocognition, intrinsic motivation, and psychosocial functioning. The results demonstrated that intrinsic motivation strongly mediated the relationship between neurocognition and psychosocial functioning. This mediation was evidenced by: (i) the direct path from neurocognition to functional outcome no longer being statistically significant after the introduction of motivation into the model, (ii) the statistical significance of the indirect path from neurocognition through motivation to functional outcome. There was no support for the two moderation hypotheses: the level of neurocognition did not influence the relationship between intrinsic motivation and psychosocial functioning, nor did the level of intrinsic motivation influence the relationship between neurocognition and psychosocial functioning. Neurocognition influences psychosocial functioning through its relationship with intrinsic motivation. Intrinsic motivation is a critical mechanism for explaining the relationship between neurocognition and psychosocial functioning. Implications for the theoretical understanding and psychosocial treatment of intrinsic motivation in schizophrenia are discussed.

Academic performance, educational aspiration and birth outcomes among adolescent mothers: a national longitudinal study

PubMed Central

2014-01-01

Background Maternal educational attainment has been associated with birth outcomes among adult mothers. However, limited research explores whether academic performance and educational aspiration influence birth outcomes among adolescent mothers. Methods Data from Waves I and IV of the National Longitudinal Study of Adolescent Health (Add Health) were used. Adolescent girls whose first pregnancy occurred after Wave I, during their adolescence, and ended with a singleton live birth were included. Adolescents’ grade point average (GPA), experience of ever skipping a grade and ever repeating a grade, and their aspiration to attend college were examined as predictors of birth outcomes (birthweight and gestational age; n = 763). Univariate statistics, bivariate analyses and multivariable models were run stratified on race using survey procedures. Results Among Black adolescents, those who ever skipped a grade had higher offspring’s birthweight. Among non-Black adolescents, ever skipping a grade and higher educational aspiration were associated with higher offspring’s birthweight; ever skipping a grade was also associated with higher gestational age. GPA was not statistically significantly associated with either birth outcome. The addition of smoking during pregnancy and prenatal care visit into the multivariable models did not change these associations. Conclusions Some indicators of higher academic performance and aspiration are associated with better birth outcomes among adolescents. Investing in improving educational opportunities may improve birth outcomes among teenage mothers. PMID:24422664

A Bivariate Mixed Distribution with a Heavy-tailed Component and its Application to Single-site Daily Rainfall Simulation

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

Li, Chao ..; Singh, Vijay P.; Mishra, Ashok K.

2013-02-06

This paper presents an improved brivariate mixed distribution, which is capable of modeling the dependence of daily rainfall from two distinct sources (e.g., rainfall from two stations, two consecutive days, or two instruments such as satellite and rain gauge). The distribution couples an existing framework for building a bivariate mixed distribution, the theory of copulae and a hybrid marginal distribution. Contributions of the improved distribution are twofold. One is the appropriate selection of the bivariate dependence structure from a wider admissible choice (10 candidate copula families). The other is the introduction of a marginal distribution capable of better representing lowmore » to moderate values as well as extremes of daily rainfall. Among several applications of the improved distribution, particularly presented here is its utility for single-site daily rainfall simulation. Rather than simulating rainfall occurrences and amounts separately, the developed generator unifies the two processes by generalizing daily rainfall as a Markov process with autocorrelation described by the improved bivariate mixed distribution. The generator is first tested on a sample station in Texas. Results reveal that the simulated and observed sequences are in good agreement with respect to essential characteristics. Then, extensive simulation experiments are carried out to compare the developed generator with three other alternative models: the conventional two-state Markov chain generator, the transition probability matrix model and the semi-parametric Markov chain model with kernel density estimation for rainfall amounts. Analyses establish that overall the developed generator is capable of reproducing characteristics of historical extreme rainfall events and is apt at extrapolating rare values beyond the upper range of available observed data. Moreover, it automatically captures the persistence of rainfall amounts on consecutive wet days in a relatively natural and easy way. Another interesting observation is that the recognized ‘overdispersion’ problem in daily rainfall simulation ascribes more to the loss of rainfall extremes than the under-representation of first-order persistence. The developed generator appears to be a sound option for daily rainfall simulation, especially in particular hydrologic planning situations when rare rainfall events are of great importance.« less

Dissecting the Complexities of the Relationship Between Police Officer-Civilian Race/Ethnicity Dyads and Less-Than-Lethal Use of Force.

PubMed

Jetelina, Katelyn K; Jennings, Wesley G; Bishopp, Stephen A; Piquero, Alex R; Reingle Gonzalez, Jennifer M

2017-07-01

To examine how sublethal use-of-force patterns vary across officer-civilian race/ethnicity while accounting for officer-, civilian-, and situational-level factors. We extracted cross-sectional data from 5630 use-of-force reports from the Dallas Police Department in 2014 and 2015. We categorized each officer-civilian interaction into race/ethnicity dyads. We used multilevel, mixed logistic regression models to evaluate the relationship between race/ethnicity dyads and the types of use of force. Forty-eight percent of use-of-force interactions occurred between a White officer and a non-White civilian (White-non-White). In bivariate models, the odds of hard-empty hand control and intermediate weapon use were significantly higher among White-Black dyads compared with White-White dyads. The bivariate odds of intermediate weapon use were also significantly higher among Black-Black, Hispanic-White, Black-Hispanic, and Hispanic-Black dyads compared with White-White dyads. However, after we controlled for individual and situational factors, the relationship between race/ethnicity dyad and hard-empty hand control was no longer significant. Although we observed significant bivariate relationships between race/ethnicity dyads and use of force, these relationships largely dissipated after we controlled for other factors.

Multivariate statistical assessment of predictors of firefighters' muscular and aerobic work capacity.

PubMed

Lindberg, Ann-Sofie; Oksa, Juha; Antti, Henrik; Malm, Christer

2015-01-01

Physical capacity has previously been deemed important for firefighters physical work capacity, and aerobic fitness, muscular strength, and muscular endurance are the most frequently investigated parameters of importance. Traditionally, bivariate and multivariate linear regression statistics have been used to study relationships between physical capacities and work capacities among firefighters. An alternative way to handle datasets consisting of numerous correlated variables is to use multivariate projection analyses, such as Orthogonal Projection to Latent Structures. The first aim of the present study was to evaluate the prediction and predictive power of field and laboratory tests, respectively, on firefighters' physical work capacity on selected work tasks. Also, to study if valid predictions could be achieved without anthropometric data. The second aim was to externally validate selected models. The third aim was to validate selected models on firefighters' and on civilians'. A total of 38 (26 men and 12 women) + 90 (38 men and 52 women) subjects were included in the models and the external validation, respectively. The best prediction (R2) and predictive power (Q2) of Stairs, Pulling, Demolition, Terrain, and Rescue work capacities included field tests (R2 = 0.73 to 0.84, Q2 = 0.68 to 0.82). The best external validation was for Stairs work capacity (R2 = 0.80) and worst for Demolition work capacity (R2 = 0.40). In conclusion, field and laboratory tests could equally well predict physical work capacities for firefighting work tasks, and models excluding anthropometric data were valid. The predictive power was satisfactory for all included work tasks except Demolition.

Joint probabilities of extreme precipitation and wind gusts in Germany

NASA Astrophysics Data System (ADS)

von Waldow, H.; Martius, O.

2012-04-01

Extreme meteorological events such as storms, heavy rain, floods, droughts and heat waves can have devastating consequences for human health, infrastructure and ecosystems. Concomitantly occurring extreme events might interact synergistically to produce a particularly hazardous impact. The joint occurrence of droughts and heat waves, for example, can have a very different impact on human health and ecosystems both in quantity and quality, than just one of the two extreme events. The co-occurrence of certain types of extreme events is plausible from physical and dynamical considerations, for example heavy precipitation and high wind speeds in the pathway of strong extratropical cyclones. The winter storm Kyrill not only caused wind gust speeds well in excess of 30 m/s across Europe, but also brought 24 h precipitation sums greater than the mean January accumulations in some regions. However, the existence of such compound risks is currently not accounted for by insurance companies, who assume independence of extreme weather events to calculate their premiums. While there are established statistical methods to model the extremes of univariate meteorological variables, the modelling of multidimensional extremes calls for an approach that is tailored to the specific problem at hand. A first step involves defining extreme bivariate wind/precipitation events. Because precipitation and wind gusts caused by the same cyclone or convective cell do not occur at exactly the same location and at the same time, it is necessary to find a sound definition of "extreme compound event" for this case. We present a data driven method to choose appropriate time and space intervals that define "concomitance" for wind and precipitation extremes. Based on station data of wind speed and gridded precipitation data, we arrive at time and space intervals that compare well with the typical time and space scales of extratropical cyclones, i.e. a maximum time lag of 1 day and a maximum distance of about 300 km between associated wind and rain events. After modelling extreme precipitation and wind separately, we explore the practicability of characterising their joint distribution using a bivariate threshold excess model. In particular, we present different dependence measures and report about the computational feasibility and available computer codes.

An efficient algorithm for generating random number pairs drawn from a bivariate normal distribution

NASA Technical Reports Server (NTRS)

Campbell, C. W.

1983-01-01

An efficient algorithm for generating random number pairs from a bivariate normal distribution was developed. Any desired value of the two means, two standard deviations, and correlation coefficient can be selected. Theoretically the technique is exact and in practice its accuracy is limited only by the quality of the uniform distribution random number generator, inaccuracies in computer function evaluation, and arithmetic. A FORTRAN routine was written to check the algorithm and good accuracy was obtained. Some small errors in the correlation coefficient were observed to vary in a surprisingly regular manner. A simple model was developed which explained the qualities aspects of the errors.

Bivariate spline solution of time dependent nonlinear PDE for a population density over irregular domains.

PubMed

Gutierrez, Juan B; Lai, Ming-Jun; Slavov, George

2015-12-01

We study a time dependent partial differential equation (PDE) which arises from classic models in ecology involving logistic growth with Allee effect by introducing a discrete weak solution. Existence, uniqueness and stability of the discrete weak solutions are discussed. We use bivariate splines to approximate the discrete weak solution of the nonlinear PDE. A computational algorithm is designed to solve this PDE. A convergence analysis of the algorithm is presented. We present some simulations of population development over some irregular domains. Finally, we discuss applications in epidemiology and other ecological problems. Copyright © 2015 Elsevier Inc. All rights reserved.

Computational approach to Thornley's problem by bivariate operational calculus

NASA Astrophysics Data System (ADS)

Bazhlekova, E.; Dimovski, I.

2012-10-01

Thornley's problem is an initial-boundary value problem with a nonlocal boundary condition for linear onedimensional reaction-diffusion equation, used as a mathematical model of spiral phyllotaxis in botany. Applying a bivariate operational calculus we find explicit representation of the solution, containing two convolution products of special solutions and the arbitrary initial and boundary functions. We use a non-classical convolution with respect to the space variable, extending in this way the classical Duhamel principle. The special solutions involved are represented in the form of fast convergent series. Numerical examples are considered to show the application of the present technique and to analyze the character of the solution.

Childhood separation anxiety disorder and adult onset panic attacks share a common genetic diathesis.

PubMed

Roberson-Nay, Roxann; Eaves, Lindon J; Hettema, John M; Kendler, Kenneth S; Silberg, Judy L

2012-04-01

Childhood separation anxiety disorder (SAD) is hypothesized to share etiologic roots with panic disorder. The aim of this study was to estimate the genetic and environmental sources of covariance between childhood SAD and adult onset panic attacks (AOPA), with the primary goal to determine whether these two phenotypes share a common genetic diathesis. Participants included parents and their monozygotic or dizygotic twins (n = 1,437 twin pairs) participating in the Virginia Twin Study of Adolescent Behavioral Development and those twins who later completed the Young Adult Follow-Up (YAFU). The Child and Adolescent Psychiatric Assessment was completed at three waves during childhood/adolescence followed by the Structured Clinical Interview for DSM-III-R at the YAFU. Two separate, bivariate Cholesky models were fit to childhood diagnoses of SAD and overanxious disorder (OAD), respectively, and their relation with AOPA; a trivariate Cholesky model also examined the collective influence of childhood SAD and OAD on AOPA. In the best-fitting bivariate model, the covariation between SAD and AOPA was accounted for by genetic and unique environmental factors only, with the genetic factor associated with childhood SAD explaining significant variance in AOPA. Environmental risk factors were not significantly shared between SAD and AOPA. By contrast, the genetic factor associated with childhood OAD did not contribute significantly to AOPA. Results of the trivariate Cholesky reaffirmed outcomes of bivariate models. These data indicate that childhood SAD and AOPA share a common genetic diathesis that is not observed for childhood OAD, strongly supporting the hypothesis of a specific genetic etiologic link between the two phenotypes. © 2012 Wiley Periodicals, Inc.

Bivariate analysis of floods in climate impact assessments.

PubMed

Brunner, Manuela Irene; Sikorska, Anna E; Seibert, Jan

2018-03-01

Climate impact studies regarding floods usually focus on peak discharges and a bivariate assessment of peak discharges and hydrograph volumes is not commonly included. A joint consideration of peak discharges and hydrograph volumes, however, is crucial when assessing flood risks for current and future climate conditions. Here, we present a methodology to develop synthetic design hydrographs for future climate conditions that jointly consider peak discharges and hydrograph volumes. First, change factors are derived based on a regional climate model and are applied to observed precipitation and temperature time series. Second, the modified time series are fed into a calibrated hydrological model to simulate runoff time series for future conditions. Third, these time series are used to construct synthetic design hydrographs. The bivariate flood frequency analysis used in the construction of synthetic design hydrographs takes into account the dependence between peak discharges and hydrograph volumes, and represents the shape of the hydrograph. The latter is modeled using a probability density function while the dependence between the design variables peak discharge and hydrograph volume is modeled using a copula. We applied this approach to a set of eight mountainous catchments in Switzerland to construct catchment-specific and season-specific design hydrographs for a control and three scenario climates. Our work demonstrates that projected climate changes have an impact not only on peak discharges but also on hydrograph volumes and on hydrograph shapes both at an annual and at a seasonal scale. These changes are not necessarily proportional which implies that climate impact assessments on future floods should consider more flood characteristics than just flood peaks. Copyright © 2017. Published by Elsevier B.V.

Modeling animal movements using stochastic differential equations

Treesearch

Haiganoush K. Preisler; Alan A. Ager; Bruce K. Johnson; John G. Kie

2004-01-01

We describe the use of bivariate stochastic differential equations (SDE) for modeling movements of 216 radiocollared female Rocky Mountain elk at the Starkey Experimental Forest and Range in northeastern Oregon. Spatially and temporally explicit vector fields were estimated using approximating difference equations and nonparametric regression techniques. Estimated...

An analytical study of physical models with inherited temporal and spatial memory

NASA Astrophysics Data System (ADS)

Jaradat, Imad; Alquran, Marwan; Al-Khaled, Kamel

2018-04-01

Du et al. (Sci. Reb. 3, 3431 (2013)) demonstrated that the fractional derivative order can be physically interpreted as a memory index by fitting the test data of memory phenomena. The aim of this work is to study analytically the joint effect of the memory index on time and space coordinates simultaneously. For this purpose, we introduce a novel bivariate fractional power series expansion that is accompanied by twofold fractional derivatives ordering α, β\\in(0,1]. Further, some convergence criteria concerning our expansion are presented and an analog of the well-known bivariate Taylor's formula in the sense of mixed fractional derivatives is obtained. Finally, in order to show the functionality and efficiency of this expansion, we employ the corresponding Taylor's series method to obtain closed-form solutions of various physical models with inherited time and space memory.

Genetic correlations between body condition scores and fertility in dairy cattle using bivariate random regression models.

PubMed

De Haas, Y; Janss, L L G; Kadarmideen, H N

2007-10-01

Genetic correlations between body condition score (BCS) and fertility traits in dairy cattle were estimated using bivariate random regression models. BCS was recorded by the Swiss Holstein Association on 22,075 lactating heifers (primiparous cows) from 856 sires. Fertility data during first lactation were extracted for 40,736 cows. The fertility traits were days to first service (DFS), days between first and last insemination (DFLI), calving interval (CI), number of services per conception (NSPC) and conception rate to first insemination (CRFI). A bivariate model was used to estimate genetic correlations between BCS as a longitudinal trait by random regression components, and daughter's fertility at the sire level as a single lactation measurement. Heritability of BCS was 0.17, and heritabilities for fertility traits were low (0.01-0.08). Genetic correlations between BCS and fertility over the lactation varied from: -0.45 to -0.14 for DFS; -0.75 to 0.03 for DFLI; from -0.59 to -0.02 for CI; from -0.47 to 0.33 for NSPC and from 0.08 to 0.82 for CRFI. These results show (genetic) interactions between fat reserves and reproduction along the lactation trajectory of modern dairy cows, which can be useful in genetic selection as well as in management. Maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in mid lactation when the genetic variance for BCS is largest, and the genetic correlations between BCS and fertility is strongest.

Toward the improvement in fetal monitoring during labor with the inclusion of maternal heart rate analysis.

PubMed

Gonçalves, Hernâni; Pinto, Paula; Silva, Manuela; Ayres-de-Campos, Diogo; Bernardes, João

2016-04-01

Fetal heart rate (FHR) monitoring is used routinely in labor, but conventional methods have a limited capacity to detect fetal hypoxia/acidosis. An exploratory study was performed on the simultaneous assessment of maternal heart rate (MHR) and FHR variability, to evaluate their evolution during labor and their capacity to detect newborn acidemia. MHR and FHR were simultaneously recorded in 51 singleton term pregnancies during the last two hours of labor and compared with newborn umbilical artery blood (UAB) pH. Linear/nonlinear indices were computed separately for MHR and FHR. Interaction between MHR and FHR was quantified through the same indices on FHR-MHR and through their correlation and cross-entropy. Univariate and bivariate statistical analysis included nonparametric confidence intervals and statistical tests, receiver operating characteristic curves and linear discriminant analysis. Progression of labor was associated with a significant increase in most MHR and FHR linear indices, whereas entropy indices decreased. FHR alone and in combination with MHR as FHR-MHR evidenced the highest auROC values for prediction of fetal acidemia, with 0.76 and 0.88 for the UAB pH thresholds 7.20 and 7.15, respectively. The inclusion of MHR on bivariate analysis achieved sensitivity and specificity values of nearly 100 and 89.1%, respectively. These results suggest that simultaneous analysis of MHR and FHR may improve the identification of fetal acidemia compared with FHR alone, namely during the last hour of labor.

Exposure to the dental environment and prevalence of respiratory illness in dental student populations.

PubMed

Scannapieco, Frank A; Ho, Alex W; DiTolla, Maris; Chen, Casey; Dentino, Andrew R

2004-03-01

To determine if the prevalence of respiratory disease among dental students and dental residents varies with their exposure to the clinical dental environment. A detailed questionnaire was administered to 817 students at 3 dental schools. The questionnaire sought information concerning demographic characteristics, school year, exposure to the dental environment and dental procedures, and history of respiratory disease. The data obtained were subjected to bivariate and multiple logistic regression analysis. Respondents reported experiencing the following respiratory conditions during the previous year: asthma (26 cases), bronchitis (11 cases), chronic lung disease (6 cases), pneumonia (5 cases) and streptococcal pharyngitis (50 cases). Bivariate statistical analyses indicated no significant associations between the prevalence of any of the respiratory conditions and year in dental school, except for asthma, for which there was a significantly higher prevalence at 1 school compared to the other 2 schools. When all cases of respiratory disease were combined as a composite variable and subjected to multivariate logistic regression analysis controlling for age, sex, race, dental school, smoking history and alcohol consumption, no statistically significant association was observed between respiratory condition and year in dental school or exposure to the dental environment as a dental patient. No association was found between the prevalence of respiratory disease and a student's year in dental school or previous exposure to the dental environment as a patient. These results suggest that exposure to the dental environment does not increase the risk for respiratory infection in healthy dental health care workers.

[Burnout syndrome in teachers from two universities in Popayán, Colombia].

PubMed

Correa-Correa, Zamanda; Muñoz-Zambrano, Isabel; Chaparro, Andrés F

2010-08-01

Evaluating professional exhaustion or burnout syndrome: background, syndrome and consequences amongst half-time and full-time staff working in two private universities in the city of Popayán during 2008. The study population included 44 male and female participants aged 20 to 40 who were evaluated by using a brief burnout questionnaire (BBQ). This questionnaire had been validated for Latin-American and for teachers. It was not exclusively focused on the structure of the syndrome itself but rather included background elements and consequences. The study was quantitative and cross-sectional, having a deductive hypothetical methodological focus. Descriptive statistics and the Chi-square test were used for data analysis, accepting p<0.05 statistical significance. The analysis was univariate and bivariate. The results indicated low burnout syndrome frequency in the study population. However, 9.1 % high depersonalization frequency was found (i.e. teachers had developed negative attitudes and were insensitive to those receiving their services) and 15.9 % and 9.1 % frequencies for high physical and social consequences, respectively. Bivariate analysis revealed significant association of several factors. The results indicated low burnout syndrome frequency in this population. However, factors which were highly associated with physical and social consequences were: being male, aged 20 to 40, having a marital relationship with a habitual partner, working full-time, working at home and spending more than 75 % of the working day interacting with the beneficiaries of the services being provided.

Grain size statistics and depositional pattern of the Ecca Group sandstones, Karoo Supergroup in the Eastern Cape Province, South Africa

NASA Astrophysics Data System (ADS)

Baiyegunhi, Christopher; Liu, Kuiwu; Gwavava, Oswald

2017-11-01

Grain size analysis is a vital sedimentological tool used to unravel the hydrodynamic conditions, mode of transportation and deposition of detrital sediments. In this study, detailed grain-size analysis was carried out on thirty-five sandstone samples from the Ecca Group in the Eastern Cape Province of South Africa. Grain-size statistical parameters, bivariate analysis, linear discriminate functions, Passega diagrams and log-probability curves were used to reveal the depositional processes, sedimentation mechanisms, hydrodynamic energy conditions and to discriminate different depositional environments. The grain-size parameters show that most of the sandstones are very fine to fine grained, moderately well sorted, mostly near-symmetrical and mesokurtic in nature. The abundance of very fine to fine grained sandstones indicate the dominance of low energy environment. The bivariate plots show that the samples are mostly grouped, except for the Prince Albert samples that show scattered trend, which is due to the either mixture of two modes in equal proportion in bimodal sediments or good sorting in unimodal sediments. The linear discriminant function analysis is dominantly indicative of turbidity current deposits under shallow marine environments for samples from the Prince Albert, Collingham and Ripon Formations, while those samples from the Fort Brown Formation are lacustrine or deltaic deposits. The C-M plots indicated that the sediments were deposited mainly by suspension and saltation, and graded suspension. Visher diagrams show that saltation is the major process of transportation, followed by suspension.

Analysis of vector wind change with respect to time for Cape Kennedy, Florida

NASA Technical Reports Server (NTRS)

Adelfang, S. I.

1978-01-01

Multivariate analysis was used to determine the joint distribution of the four variables represented by the components of the wind vector at an initial time and after a specified elapsed time is hypothesized to be quadravariate normal; the fourteen statistics of this distribution, calculated from 15 years of twice-daily rawinsonde data are presented by monthly reference periods for each month from 0 to 27 km. The hypotheses that the wind component changes with respect to time is univariate normal, that the joint distribution of wind component change with respect to time is univariate normal, that the joint distribution of wind component changes is bivariate normal, and that the modulus of vector wind change is Rayleigh are tested by comparison with observed distributions. Statistics of the conditional bivariate normal distributions of vector wind at a future time given the vector wind at an initial time are derived. Wind changes over time periods from 1 to 5 hours, calculated from Jimsphere data, are presented. Extension of the theoretical prediction (based on rawinsonde data) of wind component change standard deviation to time periods of 1 to 5 hours falls (with a few exceptions) within the 95 percentile confidence band of the population estimate obtained from the Jimsphere sample data. The joint distributions of wind change components, conditional wind components, and 1 km vector wind shear change components are illustrated by probability ellipses at the 95 percentile level.

A Large-Scale Initiative Inviting Patients to Share Personal Fitness Tracker Data with Their Providers: Initial Results

PubMed Central

Pevnick, Joshua M.; Fuller, Garth; Duncan, Ray; Spiegel, Brennan M. R.

2016-01-01

Background Personal fitness trackers (PFT) have substantial potential to improve healthcare. Objective To quantify and characterize early adopters who shared their PFT data with providers. Methods We used bivariate statistics and logistic regression to compare patients who shared any PFT data vs. patients who did not. Results A patient portal was used to invite 79,953 registered portal users to share their data. Of 66,105 users included in our analysis, 499 (0.8%) uploaded data during an initial 37-day study period. Bivariate and regression analysis showed that early adopters were more likely than non-adopters to be younger, male, white, health system employees, and to have higher BMIs. Neither comorbidities nor utilization predicted adoption. Conclusion Our results demonstrate that patients had little intrinsic desire to share PFT data with their providers, and suggest that patients most at risk for poor health outcomes are least likely to share PFT data. Marketing, incentives, and/or cultural change may be needed to induce such data-sharing. PMID:27846287

CWD prevalence, perceived human health risks, and state influences on deer hunting participation.

PubMed

Vaske, Jerry J; Lyon, Katie M

2011-03-01

This study examined factors predicted by previous research to influence hunters' decisions to stop hunting deer in a state. Data were obtained from mail surveys of resident and nonresident deer hunters in Arizona, North Dakota, South Dakota, and Wisconsin (n = 3,518). Hunters were presented with six scenarios depicting hypothetical CWD prevalence levels and human health risks from the disease (e.g., death), and asked if they would continue or stop hunting deer in the state. Bivariate analyses examined the influence of five predictor variables: (a) CWD prevalence, (b) hypothetical human death from CWD, (c) perceived human health risks from CWD, (d) state, and (e) residency. In the bivariate analyses, prevalence was the strongest predictor of quitting hunting in the state followed by hypothetical human death and perceived risk. The presence of CWD in a state and residency were weak, but statistically significant, predictors. Interactions among these predictors increased the potential for stopping hunting in the state. Multivariate analyses suggested that 64% of our respondents would quit hunting in the worst-case scenario. © 2010 Society for Risk Analysis.

Bivariate empirical mode decomposition for ECG-based biometric identification with emotional data.

PubMed

Ferdinando, Hany; Seppanen, Tapio; Alasaarela, Esko

2017-07-01

Emotions modulate ECG signals such that they might affect ECG-based biometric identification in real life application. It motivated in finding good feature extraction methods where the emotional state of the subjects has minimum impacts. This paper evaluates feature extraction based on bivariate empirical mode decomposition (BEMD) for biometric identification when emotion is considered. Using the ECG signal from the Mahnob-HCI database for affect recognition, the features were statistical distributions of dominant frequency after applying BEMD analysis to ECG signals. The achieved accuracy was 99.5% with high consistency using kNN classifier in 10-fold cross validation to identify 26 subjects when the emotional states of the subjects were ignored. When the emotional states of the subject were considered, the proposed method also delivered high accuracy, around 99.4%. We concluded that the proposed method offers emotion-independent features for ECG-based biometric identification. The proposed method needs more evaluation related to testing with other classifier and variation in ECG signals, e.g. normal ECG vs. ECG with arrhythmias, ECG from various ages, and ECG from other affective databases.

Daily commuting to work is not associated with variables of health.

PubMed

Mauss, Daniel; Jarczok, Marc N; Fischer, Joachim E

2016-01-01

Commuting to work is thought to have a negative impact on employee health. We tested the association of work commute and different variables of health in German industrial employees. Self-rated variables of an industrial cohort (n = 3805; 78.9 % male) including absenteeism, presenteeism and indices reflecting stress and well-being were assessed by a questionnaire. Fasting blood samples, heart-rate variability and anthropometric data were collected. Commuting was grouped into one of four categories: 0-19.9, 20-44.9, 45-59.9, ≥60 min travelling one way to work. Bivariate associations between commuting and all variables under study were calculated. Linear regression models tested this association further, controlling for potential confounders. Commuting was positively correlated with waist circumference and inversely with triglycerides. These associations did not remain statistically significant in linear regression models controlling for age, gender, marital status, and shiftwork. No other association with variables of physical, psychological, or mental health and well-being could be found. The results indicate that commuting to work has no significant impact on well-being and health of German industrial employees.

Exploring HIV-testing intentions in young Asian/Pacific Islander (API) women as it relates to acculturation, theory of gender and power (TGP), and the AIDS risk reduction model (ARRM).

PubMed

Salud, Margaret C; Marshak, Helen Hopp; Natto, Zuhair S; Montgomery, Susanne

2014-01-01

While HIV rates are low for Asian/Pacific Islanders (APIs), they have been increasing, especially for API women in the USA. We conducted a cross-sectional study with 299 young API women (18-24 years old) in the Inland Empire region of Southern California to better understand their intention for HIV testing and their perceptions about HIV/AIDS. Data analyses included descriptive statistics, bivariate exploration for model building and multivariate analyses to determine variables associated with HIV-testing intentions. Results suggest that more lifetime sexual partners, greater perceived gender susceptibility, higher HIV/AIDS knowledge, sexually active, more positive attitudes about HIV testing and higher self-perceptions/experiences related to risk contribute to stronger intentions for HIV testing in young API women. Findings from this study will contribute to the limited literature on HIV/AIDS in API women and provide information that can be used for developing and implementing culturally appropriate programs that encourage HIV prevention and testing in this population.

Exploring HIV-testing intentions in young Asian/Pacific Islander (API) women as it relates to acculturation, theory of gender and power (TGP), and the AIDS risk reduction model (ARRM)

PubMed Central

Salud, Margaret C.; Marshak, Helen Hopp; Natto, Zuhair S.; Montgomery, Susanne

2015-01-01

While HIV rates are low for Asian/Pacific Islanders (APIs), they have been increasing, especially for API women in the USA. We conducted a cross-sectional study with 299 young API women (18–24 years old) in the Inland Empire region of Southern California to better understand their intention for HIV testing and their perceptions about HIV/AIDS. Data analyses included descriptive statistics, bivariate exploration for model building and multivariate analyses to determine variables associated with HIV-testing intentions. Results suggest that more lifetime sexual partners, greater perceived gender susceptibility, higher HIV/AIDS knowledge, sexually active, more positive attitudes about HIV testing and higher self-perceptions/experiences related to risk contribute to stronger intentions for HIV testing in young API women. Findings from this study will contribute to the limited literature on HIV/AIDS in API women and provide information that can be used for developing and implementing culturally appropriate programs that encourage HIV prevention and testing in this population. PMID:24111859

Evaluating Evidence for Conceptually Related Constructs Using Bivariate Correlations

ERIC Educational Resources Information Center

Swank, Jacqueline M.; Mullen, Patrick R.

2017-01-01

The article serves as a guide for researchers in developing evidence of validity using bivariate correlations, specifically construct validity. The authors outline the steps for calculating and interpreting bivariate correlations. Additionally, they provide an illustrative example and discuss the implications.

Semiparametric bivariate zero-inflated Poisson models with application to studies of abundance for multiple species

USGS Publications Warehouse

Arab, Ali; Holan, Scott H.; Wikle, Christopher K.; Wildhaber, Mark L.

2012-01-01

Ecological studies involving counts of abundance, presence–absence or occupancy rates often produce data having a substantial proportion of zeros. Furthermore, these types of processes are typically multivariate and only adequately described by complex nonlinear relationships involving externally measured covariates. Ignoring these aspects of the data and implementing standard approaches can lead to models that fail to provide adequate scientific understanding of the underlying ecological processes, possibly resulting in a loss of inferential power. One method of dealing with data having excess zeros is to consider the class of univariate zero-inflated generalized linear models. However, this class of models fails to address the multivariate and nonlinear aspects associated with the data usually encountered in practice. Therefore, we propose a semiparametric bivariate zero-inflated Poisson model that takes into account both of these data attributes. The general modeling framework is hierarchical Bayes and is suitable for a broad range of applications. We demonstrate the effectiveness of our model through a motivating example on modeling catch per unit area for multiple species using data from the Missouri River Benthic Fishes Study, implemented by the United States Geological Survey.

A latent process model for forecasting multiple time series in environmental public health surveillance.

PubMed

Morrison, Kathryn T; Shaddick, Gavin; Henderson, Sarah B; Buckeridge, David L

2016-08-15

This paper outlines a latent process model for forecasting multiple health outcomes arising from a common environmental exposure. Traditionally, surveillance models in environmental health do not link health outcome measures, such as morbidity or mortality counts, to measures of exposure, such as air pollution. Moreover, different measures of health outcomes are treated as independent, while it is known that they are correlated with one another over time as they arise in part from a common underlying exposure. We propose modelling an environmental exposure as a latent process, and we describe the implementation of such a model within a hierarchical Bayesian framework and its efficient computation using integrated nested Laplace approximations. Through a simulation study, we compare distinct univariate models for each health outcome with a bivariate approach. The bivariate model outperforms the univariate models in bias and coverage of parameter estimation, in forecast accuracy and in computational efficiency. The methods are illustrated with a case study using healthcare utilization and air pollution data from British Columbia, Canada, 2003-2011, where seasonal wildfires produce high levels of air pollution, significantly impacting population health. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Temperature modifies the health effects of particulate matter in Brisbane, Australia

NASA Astrophysics Data System (ADS)

Ren, Cizao; Tong, Shilu

2006-11-01

A few epidemiological studies have examined whether there was an interactive effect between temperature and ambient particulate matter on cardiorespiratory morbidity and mortality, but the results were inconsistent. The present study used three time-series approaches to explore whether maximum temperature modified the impact of ambient particulate matter less than 10 μm in diameter (PM10) on daily respiratory hospital admissions, cardiovascular hospital admissions, respiratory emergency visits, cardiovascular emergency visits, non-external cause mortality and cardiovascular mortality in Brisbane between 1996 and 2001. The analytical approaches included a bivariate response surface model, a non-stratification parametric model and a stratification parametric model. Results show that there existed a statistically significant interaction between PM10 and temperature on most health outcomes at various lags. PM10 exhibited more adverse health effects on warm days than cold days. The choice of the degree of freedom for smoothers to adjust for confounders and the selection of arbitrary cut-offs for temperature affected the interaction estimates to a certain extent, but did not change the overall conclusion. The results imply that it is important to control and reduce the emission of air particles in Brisbane, particularly when temperature increases.

Descriptive Epidemiology of Factors Associated with HIV Infections Among Men and Transgender Women Who Have Sex with Men in South India.

PubMed

Shaw, Souradet Y; Lorway, Robert; Bhattacharjee, Parinita; Reza-Paul, Sushena; du Plessis, Elsabé; McKinnon, Lyle; Thompson, Laura H; Isac, Shajy; Ramesh, Banadakoppa M; Washington, Reynold; Moses, Stephen; Blanchard, James F

2016-08-01

Men and transgender women who have sex with men (MTWSM) continue to be an at-risk population for human immunodeficiency virus (HIV) infection in India. Identification of risk factors and determinants of HIV infection is urgently needed to inform prevention and intervention programming. Data were collected from cross-sectional biological and behavioral surveys from four districts in Karnataka, India. Multivariable logistic regression models were constructed to examine factors related to HIV infection. Sociodemographic, sexual history, sex work history, condom practices, and substance use covariates were included in regression models. A total of 456 participants were included; HIV prevalence was 12.4%, with the highest prevalence (26%) among MTWSM from Bellary District. In bivariate analyses, district (P = 0.002), lack of a current regular female partner (P = 0.022), and reported consumption of an alcoholic drink in the last month (P = 0.004) were associated with HIV infection. In multivariable models, only alcohol use remained statistically significant (adjusted odds ratios: 2.6, 95% confidence intervals: 1.2-5.8; P = 0.02). The prevalence of HIV continues to be high among MTWSM, with the highest prevalence found in Bellary district.

Estimation of synthetic flood design hydrographs using a distributed rainfall-runoff model coupled with a copula-based single storm rainfall generator

NASA Astrophysics Data System (ADS)

Candela, A.; Brigandì, G.; Aronica, G. T.

2014-07-01

In this paper a procedure to derive synthetic flood design hydrographs (SFDH) using a bivariate representation of rainfall forcing (rainfall duration and intensity) via copulas, which describes and models the correlation between two variables independently of the marginal laws involved, coupled with a distributed rainfall-runoff model, is presented. Rainfall-runoff modelling (R-R modelling) for estimating the hydrological response at the outlet of a catchment was performed by using a conceptual fully distributed procedure based on the Soil Conservation Service - Curve Number method as an excess rainfall model and on a distributed unit hydrograph with climatic dependencies for the flow routing. Travel time computation, based on the distributed unit hydrograph definition, was performed by implementing a procedure based on flow paths, determined from a digital elevation model (DEM) and roughness parameters obtained from distributed geographical information. In order to estimate the primary return period of the SFDH, which provides the probability of occurrence of a hydrograph flood, peaks and flow volumes obtained through R-R modelling were treated statistically using copulas. Finally, the shapes of hydrographs have been generated on the basis of historically significant flood events, via cluster analysis. An application of the procedure described above has been carried out and results presented for the case study of the Imera catchment in Sicily, Italy.

Impact of urinary incontinence on healthcare resource utilization, health-related quality of life and productivity in patients with overactive bladder.

PubMed

Tang, Derek H; Colayco, Danielle C; Khalaf, Kristin M; Piercy, James; Patel, Vaishali; Globe, Denise; Ginsberg, David

2014-03-01

To evaluate the impact of urinary incontinence (UI) on healthcare resource utilization (HRU), health-related quality of life (HRQoL) and productivity measures in patients with overactive bladder (OAB). This retrospective, cross-sectional study used data from the Adelphi OAB/UI Disease Specific Programme, a multinational survey of patient- and physician-reported data, fielded between November 2010 and February 2011. The primary patient groups of interest were those with OAB, both with and without UI. Health-related quality of life and productivity measures were derived from the EuroQoL-5D, the Incontinence Quality of Life questionnaire, the Overactive Bladder Questionnaire, and the Work Productivity and Activity Impairment Questionnaire. Measures of HRU included OAB-related surgeries, OAB-related hospitalizations, incontinence pads, anticholinergic use and physician visits. Multivariate linear regression models and literature-based minimal clinically important differences were used to assess statistically significant and clinically meaningful differences in HRQoL and productivity measures between patients with OAB with UI and those without UI. A total of 1 730 patients were identified, with a mean age of 60.7 years, and 77.0% of them were women, 84.2% were non-Hispanic whites, and 71% were incontinent. Bivariate analyses showed that HRU was significantly higher among patients with OAB with UI than among those without UI in all categories except for the number of OAB-related physician visits. In both bivariate and multivariate analyses, incontinent patients presented with clinically and statistically significantly lower HRQoL and productivity measures with respect to all study endpoints, except for percentage of work time missed due to their OAB/UI. Urinary incontinence was associated with significantly higher HRU and lower HRQoL and productivity in this population of patients with OAB from five different countries. In addition to clinical considerations, the economic and humanistic impact of UI should be taken into account when evaluating treatment options for patients with OAB. © 2013 The Authors. BJU International © 2013 BJU International.

Prognostic Effect of Changes in Physical Function Over Prior Year on Subsequent Mortality and Long-Term Nursing Home Admission.

PubMed

Gill, Thomas M; Han, Ling; Gahbauer, Evelyne A; Leo-Summers, Linda; Allore, Heather G

2018-05-02

To evaluate the prognostic effect of changes in physical function at different intervals over the prior year on subsequent outcomes after accounting for present function. Prospective longitudinal study. Greater New Haven, Connecticut, from March 1998 to January 2006. Community-living persons aged 71 and older who completed an 18-month comprehensive assessment (N=658). Disability in 13 activities of daily living, instrumental activities of daily living, and mobility activities was assessed at the 18-month comprehensive assessment and at 12, 6, and 3 months before 18 months. Time to death and long-term nursing home admission, defined as 3 months and longer, were ascertained for up to 5 years after 18 months. In the bivariate models, disability at 18 months and change in disability between 18 months and each of the 3 prior time-points (12, 6, 3 months) were significantly associated with time to death. The risk of death, for example, increased by 24% for each 1-point increase in 18-month disability score (on a scale from 0 to 13) and by 22% for each 1-point change in disability score between 18 months and prior 12 months (on a scale from -13 to 13). In a set of multivariable models with and without covariates, the associations were maintained for 18-month disability but not for change in disability between 18 months and each of the 3 prior time-points. The results were comparable for time to long-term nursing home admission except that 2 of the associations were not statistically significant. When evaluating risk of adverse outcomes, such as death and long-term nursing home admission, an assessment of change in physical function at different intervals over the prior year, although a strong bivariate predictor, did not provide useful prognostic information beyond that available from current level of function. © 2018, Copyright the Authors Journal compilation © 2018, The American Geriatrics Society.

A Basic Bivariate Structure of Personality Attributes Evident Across Nine Languages.

PubMed

Saucier, Gerard; Thalmayer, Amber Gayle; Payne, Doris L; Carlson, Robert; Sanogo, Lamine; Ole-Kotikash, Leonard; Church, A Timothy; Katigbak, Marcia S; Somer, Oya; Szarota, Piotr; Szirmák, Zsofia; Zhou, Xinyue

2014-02-01

Here, two studies seek to characterize a parsimonious common-denominator personality structure with optimal cross-cultural replicability. Personality differences are observed in all human populations and cultures, but lexicons for personality attributes contain so many distinctions that parsimony is lacking. Models stipulating the most important attributes have been formulated by experts or by empirical studies drawing on experience in a very limited range of cultures. Factor analyses of personality lexicons of nine languages of diverse provenance (Chinese, Korean, Filipino, Turkish, Greek, Polish, Hungarian, Maasai, and Senoufo) were examined, and their common structure was compared to that of several prominent models in psychology. A parsimonious bivariate model showed evidence of substantial convergence and ubiquity across cultures. Analyses involving key markers of these dimensions in English indicate that they are broad dimensions involving the overlapping content of the interpersonal circumplex, models of communion and agency, and morality/warmth and competence. These "Big Two" dimensions-Social Self-Regulation and Dynamism-provide a common-denominator model involving the two most crucial axes of personality variation, ubiquitous across cultures. The Big Two might serve as an umbrella model serving to link diverse theoretical models and associated research literatures. © 2013 Wiley Periodicals, Inc.

Applying Emax model and bivariate thin plate splines to assess drug interactions

PubMed Central

Kong, Maiying; Lee, J. Jack

2014-01-01

We review the semiparametric approach previously proposed by Kong and Lee and extend it to a case in which the dose-effect curves follow the Emax model instead of the median effect equation. When the maximum effects for the investigated drugs are different, we provide a procedure to obtain the additive effect based on the Loewe additivity model. Then, we apply a bivariate thin plate spline approach to estimate the effect beyond additivity along with its 95% point-wise confidence interval as well as its 95% simultaneous confidence interval for any combination dose. Thus, synergy, additivity, and antagonism can be identified. The advantages of the method are that it provides an overall assessment of the combination effect on the entire two-dimensional dose space spanned by the experimental doses, and it enables us to identify complex patterns of drug interaction in combination studies. In addition, this approach is robust to outliers. To illustrate this procedure, we analyzed data from two case studies. PMID:20036878

Applying Emax model and bivariate thin plate splines to assess drug interactions.

PubMed

Kong, Maiying; Lee, J Jack

2010-01-01

We review the semiparametric approach previously proposed by Kong and Lee and extend it to a case in which the dose-effect curves follow the Emax model instead of the median effect equation. When the maximum effects for the investigated drugs are different, we provide a procedure to obtain the additive effect based on the Loewe additivity model. Then, we apply a bivariate thin plate spline approach to estimate the effect beyond additivity along with its 95 per cent point-wise confidence interval as well as its 95 per cent simultaneous confidence interval for any combination dose. Thus, synergy, additivity, and antagonism can be identified. The advantages of the method are that it provides an overall assessment of the combination effect on the entire two-dimensional dose space spanned by the experimental doses, and it enables us to identify complex patterns of drug interaction in combination studies. In addition, this approach is robust to outliers. To illustrate this procedure, we analyzed data from two case studies.

Intensity-Curvature Measurement Approaches for the Diagnosis of Magnetic Resonance Imaging Brain Tumors.

PubMed

Ciulla, Carlo; Veljanovski, Dimitar; Rechkoska Shikoska, Ustijana; Risteski, Filip A

2015-11-01

This research presents signal-image post-processing techniques called Intensity-Curvature Measurement Approaches with application to the diagnosis of human brain tumors detected through Magnetic Resonance Imaging (MRI). Post-processing of the MRI of the human brain encompasses the following model functions: (i) bivariate cubic polynomial, (ii) bivariate cubic Lagrange polynomial, (iii) monovariate sinc, and (iv) bivariate linear. The following Intensity-Curvature Measurement Approaches were used: (i) classic-curvature, (ii) signal resilient to interpolation, (iii) intensity-curvature measure and (iv) intensity-curvature functional. The results revealed that the classic-curvature, the signal resilient to interpolation and the intensity-curvature functional are able to add additional information useful to the diagnosis carried out with MRI. The contribution to the MRI diagnosis of our study are: (i) the enhanced gray level scale of the tumor mass and the well-behaved representation of the tumor provided through the signal resilient to interpolation, and (ii) the visually perceptible third dimension perpendicular to the image plane provided through the classic-curvature and the intensity-curvature functional.

Intensity-Curvature Measurement Approaches for the Diagnosis of Magnetic Resonance Imaging Brain Tumors

PubMed Central

Ciulla, Carlo; Veljanovski, Dimitar; Rechkoska Shikoska, Ustijana; Risteski, Filip A.

2015-01-01

This research presents signal-image post-processing techniques called Intensity-Curvature Measurement Approaches with application to the diagnosis of human brain tumors detected through Magnetic Resonance Imaging (MRI). Post-processing of the MRI of the human brain encompasses the following model functions: (i) bivariate cubic polynomial, (ii) bivariate cubic Lagrange polynomial, (iii) monovariate sinc, and (iv) bivariate linear. The following Intensity-Curvature Measurement Approaches were used: (i) classic-curvature, (ii) signal resilient to interpolation, (iii) intensity-curvature measure and (iv) intensity-curvature functional. The results revealed that the classic-curvature, the signal resilient to interpolation and the intensity-curvature functional are able to add additional information useful to the diagnosis carried out with MRI. The contribution to the MRI diagnosis of our study are: (i) the enhanced gray level scale of the tumor mass and the well-behaved representation of the tumor provided through the signal resilient to interpolation, and (ii) the visually perceptible third dimension perpendicular to the image plane provided through the classic-curvature and the intensity-curvature functional. PMID:26644943

An Examination of New Paradigms for Spline Approximations.

PubMed

Witzgall, Christoph; Gilsinn, David E; McClain, Marjorie A

2006-01-01

Lavery splines are examined in the univariate and bivariate cases. In both instances relaxation based algorithms for approximate calculation of Lavery splines are proposed. Following previous work Gilsinn, et al. [7] addressing the bivariate case, a rotationally invariant functional is assumed. The version of bivariate splines proposed in this paper also aims at irregularly spaced data and uses Hseih-Clough-Tocher elements based on the triangulated irregular network (TIN) concept. In this paper, the univariate case, however, is investigated in greater detail so as to further the understanding of the bivariate case.

Predictors of workplace sexual health policy at sex work establishments in the Philippines.

PubMed

Withers, M; Dornig, K; Morisky, D E

2007-09-01

Based on the literature, we identified manager and establishment characteristics that we hypothesized are related to workplace policies that support HIV protective behavior. We developed a sexual health policy index consisting of 11 items as our outcome variable. We utilized both bivariate and multivariate analysis of variance. The significant variables in our bivariate analyses (establishment type, number of employees, manager age, and membership in manager association) were entered into a multivariate regression model. The model was significant (p<.01), and predicted 42) of the variability in the development and management of a workplace sexual health policy supportive of condom use. The significant predictors were number of employees and establishment type. In addition to individually-focused CSW interventions, HIV prevention programs should target managers and establishment policies. Future HIV prevention programs may need to focus on helping smaller establishments, in particular those with less employees, to build capacity and develop sexual health policy guidelines.

Local linear estimation of concordance probability with application to covariate effects models on association for bivariate failure-time data.

PubMed

Ding, Aidong Adam; Hsieh, Jin-Jian; Wang, Weijing

2015-01-01

Bivariate survival analysis has wide applications. In the presence of covariates, most literature focuses on studying their effects on the marginal distributions. However covariates can also affect the association between the two variables. In this article we consider the latter issue by proposing a nonstandard local linear estimator for the concordance probability as a function of covariates. Under the Clayton copula, the conditional concordance probability has a simple one-to-one correspondence with the copula parameter for different data structures including those subject to independent or dependent censoring and dependent truncation. The proposed method can be used to study how covariates affect the Clayton association parameter without specifying marginal regression models. Asymptotic properties of the proposed estimators are derived and their finite-sample performances are examined via simulations. Finally, for illustration, we apply the proposed method to analyze a bone marrow transplant data set.

The relation between working conditions, aberrant driving behaviour and crash propensity among taxi drivers in China.

PubMed

Wang, Yonggang; Li, Linchao; Prato, Carlo G

2018-04-03

Although the taxi industry is playing an important role in Chinese everyday life, little attention has been posed towards occupational health issues concerning the taxi drivers' working conditions, driving behaviour and road safety. A cross-sectional survey was administered to 1021 taxi drivers from 21 companies in four Chinese cities and collected information about (i) sociodemographic characteristics, (ii) working conditions, (iii) frequency of daily aberrant driving behaviour, and (iv) involvement in property-damage-only (PDO) and personal injury (PI) crashes over the past two years. A hybrid bivariate model of crash involvement was specified: (i) the hybrid part concerned a latent variable model capturing unobserved traits of the taxi drivers; (ii) the bivariate part modelled jointly both types of crashes while capturing unobserved correlation between error terms. The survey answers paint a gloomy picture in terms of workload, as taxi drivers reported averages of 9.4 working hours per day and 6.7 working days per week that amount on average to about 63.0 working hours per week. Moreover, the estimates of the hybrid bivariate model reveal that increasing levels of fatigue, reckless behaviour and aggressive behaviour are positively related to a higher propensity of crash involvement. Lastly, the heavy workload is also positively correlated with the higher propensity of crashing, not only directly as a predictor of crash involvement, but also indirectly as a covariate of fatigue and aberrant driving behaviour. The findings from this study provide insights into potential strategies for preventive education and taxi industry management to improve the working conditions and hence reduce fatigue and road risk for the taxi drivers. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

Modeling both of the number of pausibacillary and multibacillary leprosy patients by using bivariate poisson regression

NASA Astrophysics Data System (ADS)

Winahju, W. S.; Mukarromah, A.; Putri, S.

2015-03-01

Leprosy is a chronic infectious disease caused by bacteria of leprosy (Mycobacterium leprae). Leprosy has become an important thing in Indonesia because its morbidity is quite high. Based on WHO data in 2014, in 2012 Indonesia has the highest number of new leprosy patients after India and Brazil with a contribution of 18.994 people (8.7% of the world). This number makes Indonesia automatically placed as the country with the highest number of leprosy morbidity of ASEAN countries. The province that most contributes to the number of leprosy patients in Indonesia is East Java. There are two kind of leprosy. They consist of pausibacillary and multibacillary. The morbidity of multibacillary leprosy is higher than pausibacillary leprosy. This paper will discuss modeling both of the number of multibacillary and pausibacillary leprosy patients as responses variables. These responses are count variables, so modeling will be conducted by using bivariate poisson regression method. Unit experiment used is in East Java, and predictors involved are: environment, demography, and poverty. The model uses data in 2012, and the result indicates that all predictors influence significantly.

Historical and future drought in Bangladesh using copula-based bivariate regional frequency analysis

NASA Astrophysics Data System (ADS)

Mortuza, Md Rubayet; Moges, Edom; Demissie, Yonas; Li, Hong-Yi

2018-02-01

The study aims at regional and probabilistic evaluation of bivariate drought characteristics to assess both the past and future drought duration and severity in Bangladesh. The procedures involve applying (1) standardized precipitation index to identify drought duration and severity, (2) regional frequency analysis to determine the appropriate marginal distributions for both duration and severity, (3) copula model to estimate the joint probability distribution of drought duration and severity, and (4) precipitation projections from multiple climate models to assess future drought trends. Since drought duration and severity in Bangladesh are often strongly correlated and do not follow same marginal distributions, the joint and conditional return periods of droughts are characterized using the copula-based joint distribution. The country is divided into three homogeneous regions using Fuzzy clustering and multivariate discordancy and homogeneity measures. For given severity and duration values, the joint return periods for a drought to exceed both values are on average 45% larger, while to exceed either value are 40% less than the return periods from the univariate frequency analysis, which treats drought duration and severity independently. These suggest that compared to the bivariate drought frequency analysis, the standard univariate frequency analysis under/overestimate the frequency and severity of droughts depending on how their duration and severity are related. Overall, more frequent and severe droughts are observed in the west side of the country. Future drought trend based on four climate models and two scenarios showed the possibility of less frequent drought in the future (2020-2100) than in the past (1961-2010).

Employment program for patients with severe mental illness in Malaysia: a 3-month outcome.

PubMed

Wan Kasim, Syarifah Hafizah; Midin, Marhani; Abu Bakar, Abdul Kadir; Sidi, Hatta; Nik Jaafar, Nik Ruzyanei; Das, Srijit

2014-01-01

This study aimed to examine the rate and predictive factors of successful employment at 3 months upon enrolment into an employment program among patients with severe mental illness (SMI). A cross-sectional study using universal sampling technique was conducted on patients with SMI who completed a 3-month period of being employed at Hospital Permai, Malaysia. A total of 147 patients were approached and 126 were finally included in the statistical analyses. Successful employment was defined as the ability to work 40 or more hours per month. Factors significantly associated with successful employment from bivariate analyses were entered into a multiple logistic regression analysis to identify predictors of successful employment. The rate of successful employment at 3 months was 68.3% (n=81). Significant factors associated with successful employment from bivariate analyses were having past history of working, good family support, less number of psychiatric admissions, good compliance to medicine, good interest in work, living in hostel, being motivated to work, satisfied with the job or salary, getting a preferred job, being in competitive or supported employment and having higher than median scores of PANNS on the positive, negative and general psychopathology. Significant predictors of employment, from a logistic regression model were having good past history of working (p<0.021; OR 6.12; [95% CI 2.1-11.9]) and getting a preferred job (p<0.032; [OR 4.021; 95% CI 1.83-12.1]). Results showed a high employment rate among patients with SMI. Good past history of working and getting a preferred job were significant predictors of successful employment. Copyright © 2014 Elsevier Inc. All rights reserved.

Effect of catchment properties and flood generation regime on copula selection for bivariate flood frequency analysis

NASA Astrophysics Data System (ADS)

Filipova, Valeriya; Lawrence, Deborah; Klempe, Harald

2018-02-01

Applying copula-based bivariate flood frequency analysis is advantageous because the results provide information on both the flood peak and volume. More data are, however, required for such an analysis, and it is often the case that only data series with a limited record length are available. To overcome this issue of limited record length, data regarding climatic and geomorphological properties can be used to complement statistical methods. In this paper, we present a study of 27 catchments located throughout Norway, in which we assess whether catchment properties, flood generation processes and flood regime have an effect on the correlation between flood peak and volume and, in turn, on the selection of copulas. To achieve this, the annual maximum flood events were first classified into events generated primarily by rainfall, snowmelt or a combination of these. The catchments were then classified into flood regime, depending on the predominant flood generation process producing the annual maximum flood events. A contingency table and Fisher's exact test were used to determine the factors that affect the selection of copulas in the study area. The results show that the two-parameter copulas BB1 and BB7 are more commonly selected in catchments with high steepness, high mean annual runoff and rainfall flood regime. These findings suggest that in these types of catchments, the dependence structure between flood peak and volume is more complex and cannot be modeled effectively using a one-parameter copula. The results illustrate that by relating copula types to flood regime and catchment properties, additional information can be supplied for selecting copulas in catchments with limited data.

Probabilistic models for capturing more physicochemical properties on protein-protein interface.

PubMed

Guo, Fei; Li, Shuai Cheng; Du, Pufeng; Wang, Lusheng

2014-06-23

Protein-protein interactions play a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. It is of great interest to understand how proteins interact with each other. The general approach is to explore all possible poses and identify near-native ones with the energy function. The key issue here is to design an effective energy function, based on various physicochemical properties. In this paper, we first identify two new features, the coupled dihedral angles on the interfaces and the geometrical information on π-π interactions. We study these two features through statistical methods: a mixture of bivariate von Mises distributions is used to model the correlation of the coupled dihedral angles, while a mixture of bivariate normal distributions is used to model the orientation of the aromatic rings on π-π interactions. Using 6438 complexes, we parametrize the joint distribution of each new feature. Then, we propose a novel method to construct the energy function for protein-protein interface prediction, which includes the new features as well as the existing energy items such as dDFIRE energy, side-chain energy, atom contact energy, and amino acid energy. Experiments show that our method outperforms the state-of-the-art methods, ZRANK and ClusPro. We use the CAPRI evaluation criteria, Irmsd value, and Fnat value. On Benchmark v4.0, our method has an average Irmsd value of 3.39 Å and Fnat value of 62%, which improves upon the average Irmsd value of 3.89 Å and Fnat value of 49% for ZRANK, and the average Irmsd value of 3.99 Å and Fnat value of 46% for ClusPro. On the CAPRI targets, our method has an average Irmsd value of 3.56 Å and Fnat value of 42%, which improves upon the average Irmsd value of 4.27 Å and Fnat value of 39% for ZRANK, the average Irmsd value of 5.15 Å and Fnat value of 30% for ClusPro.

Pediatric patient safety events during hospitalization: approaches to accounting for institution-level effects.

PubMed

Slonim, Anthony D; Marcin, James P; Turenne, Wendy; Hall, Matt; Joseph, Jill G

2007-12-01

To determine the rates, patient, and institutional characteristics associated with the occurrence of patient safety indicators (PSIs) in hospitalized children and the degree of statistical difference derived from using three approaches of controlling for institution level effects. Pediatric Health Information System Dataset consisting of all pediatric discharges (<21 years of age) from 34 academic, freestanding children's hospitals for calendar year 2003. The rates of PSIs were computed for all discharges. The patient and institutional characteristics associated with these PSIs were calculated. The analyses sequentially applied three increasingly conservative methods to control for the institution-level effects robust standard error estimation, a fixed effects model, and a random effects model. The degree of difference from a "base state," which excluded institution-level variables, and between the models was calculated. The effects of these analyses on the interpretation of the PSIs are presented. PSIs are relatively infrequent events in hospitalized children ranging from 0 per 10,000 (postoperative hip fracture) to 87 per 10,000 (postoperative respiratory failure). Significant variables associated PSIs included age (neonates), race (Caucasians), payor status (public insurance), severity of illness (extreme), and hospital size (>300 beds), which all had higher rates of PSIs than their reference groups in the bivariable logistic regression results. The three different approaches of adjusting for institution-level effects demonstrated that there were similarities in both the clinical and statistical significance across each of the models. Institution-level effects can be appropriately controlled for by using a variety of methods in the analyses of administrative data. Whenever possible, resource-conservative methods should be used in the analyses especially if clinical implications are minimal.

Predicting radiotherapy outcomes using statistical learning techniques

NASA Astrophysics Data System (ADS)

El Naqa, Issam; Bradley, Jeffrey D.; Lindsay, Patricia E.; Hope, Andrew J.; Deasy, Joseph O.

2009-09-01

Radiotherapy outcomes are determined by complex interactions between treatment, anatomical and patient-related variables. A common obstacle to building maximally predictive outcome models for clinical practice is the failure to capture potential complexity of heterogeneous variable interactions and applicability beyond institutional data. We describe a statistical learning methodology that can automatically screen for nonlinear relations among prognostic variables and generalize to unseen data before. In this work, several types of linear and nonlinear kernels to generate interaction terms and approximate the treatment-response function are evaluated. Examples of institutional datasets of esophagitis, pneumonitis and xerostomia endpoints were used. Furthermore, an independent RTOG dataset was used for 'generalizabilty' validation. We formulated the discrimination between risk groups as a supervised learning problem. The distribution of patient groups was initially analyzed using principle components analysis (PCA) to uncover potential nonlinear behavior. The performance of the different methods was evaluated using bivariate correlations and actuarial analysis. Over-fitting was controlled via cross-validation resampling. Our results suggest that a modified support vector machine (SVM) kernel method provided superior performance on leave-one-out testing compared to logistic regression and neural networks in cases where the data exhibited nonlinear behavior on PCA. For instance, in prediction of esophagitis and pneumonitis endpoints, which exhibited nonlinear behavior on PCA, the method provided 21% and 60% improvements, respectively. Furthermore, evaluation on the independent pneumonitis RTOG dataset demonstrated good generalizabilty beyond institutional data in contrast with other models. This indicates that the prediction of treatment response can be improved by utilizing nonlinear kernel methods for discovering important nonlinear interactions among model variables. These models have the capacity to predict on unseen data. Part of this work was first presented at the Seventh International Conference on Machine Learning and Applications, San Diego, CA, USA, 11-13 December 2008.

Multivariate methods for indoor PM10 and PM2.5 modelling in naturally ventilated schools buildings

NASA Astrophysics Data System (ADS)

Elbayoumi, Maher; Ramli, Nor Azam; Md Yusof, Noor Faizah Fitri; Yahaya, Ahmad Shukri Bin; Al Madhoun, Wesam; Ul-Saufie, Ahmed Zia

2014-09-01

In this study the concentrations of PM10, PM2.5, CO and CO2 concentrations and meteorological variables (wind speed, air temperature, and relative humidity) were employed to predict the annual and seasonal indoor concentration of PM10 and PM2.5 using multivariate statistical methods. The data have been collected in twelve naturally ventilated schools in Gaza Strip (Palestine) from October 2011 to May 2012 (academic year). The bivariate correlation analysis showed that the indoor PM10 and PM2.5 were highly positive correlated with outdoor concentration of PM10 and PM2.5. Further, Multiple linear regression (MLR) was used for modelling and R2 values for indoor PM10 were determined as 0.62 and 0.84 for PM10 and PM2.5 respectively. The Performance indicators of MLR models indicated that the prediction for PM10 and PM2.5 annual models were better than seasonal models. In order to reduce the number of input variables, principal component analysis (PCA) and principal component regression (PCR) were applied by using annual data. The predicted R2 were 0.40 and 0.73 for PM10 and PM2.5, respectively. PM10 models (MLR and PCR) show the tendency to underestimate indoor PM10 concentrations as it does not take into account the occupant's activities which highly affect the indoor concentrations during the class hours.

Novel Atmospheric and Sea State Modeling in Ocean Energy Applications

NASA Astrophysics Data System (ADS)

Kallos, George; Galanis, George; Kalogeri, Christina; Larsen, Xiaoli Guo

2013-04-01

The rapidly increasing use of renewable energy sources poses new challenges for the research and technological community today. The integration of the, usually, highly variable wind and wave energy amounts into the general grid, the optimization of energy transition and the forecast of extreme values that could lead to instabilities and failures of the system can be listed among them. In the present work, novel methodologies based on state of the art numerical wind/wave simulation systems and advanced statistical techniques addressing such type of problems are discussed. In particular, extremely high resolution modeling systems simulating the atmospheric and sea state conditions with spatial resolution of 100 meters or less and temporal discretization of a few seconds are utilized in order to simulate in the most detailed way the combined wind-wave energy potential at offshore sites. In addition, a statistical analysis based on a variety of mean and variation measures as well as univariate and bivariate probability distributions is used for the estimation of the variability of the power potential revealing the advantages of the use of combined forms of energy by offshore platforms able to produce wind and wave power simultaneously. The estimation and prediction of extreme wind/wave conditions - a critical issue both for site assessment and infrastructure maintenance - is also studied by means of the 50-year return period over areas with increased power potential. This work has been carried out within the framework of the FP7 project MARINA Platform (http://www.marina-platform.info/index.aspx).

[Chronic low back pain and associated risk factors, in patients with social security medical attention: A case-control study].

PubMed

Durán-Nah, Jaime Jesús; Benítez-Rodríguez, Carlos René; Miam-Viana, Emilio Jesús

2016-01-01

Chronic low back pain (CLBP) is frequently seen in the orthopedic outpatient consultation. The aim of this paper is to identify risk factors associated with CLBP in patients cared for during the year 2012, at a General Hospital belonging to Instituto Mexicano del Seguro Social, in Yucatán, Mexico. Data of 95 patients with CLBP (cases) was compared with data of 190 patients without CLBP (controls) using a binary logistic model (BLM), from which odd ratios (OR) and 95 % confidence intervals (95 % CI) were obtained. School level, body mass index (BMI) as a continuous variable, story of heavy weight lifting, some types of comorbidities and dyslipidemia, were identified as statistically significant in the bivariate analysis (p ≤ 0.05 each). In a second step, secondary school level (OR 0.25, 95 % CI: 0.08-0.81), dyslipidemia (OR 0.26, 95 % CI: 0.12-0.56), heavy weights lifting (OR 0.22, 95 % CI: 0.12-0.42), and BMI (OR 1.22, 95 % CI: 1.12-1.32) were all identified by the BLM as statistically significant. In this sample, secondary school level, dislipidemia and heavy weights lifting reduced the risk of CLBP, while the BMI increased the risk.

Caregivers' health literacy and their young children's oral-health-related expenditures.

PubMed

Vann, W F; Divaris, K; Gizlice, Z; Baker, A D; Lee, J Y

2013-07-01

Caregivers' health literacy has emerged as an important determinant of young children's health care and outcomes. We examined the hypothesis that caregivers' health literacy influences children's oral-health-care-related expenditures. This was a prospective cohort study of 1,132 child/caregiver dyads (children's mean age = 19 months), participating in the Carolina Oral Health Literacy Project. Health literacy was measured by the REALD-30 (word recognition based) and NVS (comprehension based) instruments. Follow-up data included child Medicaid claims for CY2008-10. We quantified expenditures using annualized 2010 fee-adjusted Medicaid-paid dollars for oral-health-related visits involving preventive, restorative, and emergency care. We used descriptive, bivariate, and multivariate statistical methods based on generalized gamma models. Mean oral-health-related annual expenditures totaled $203: preventive--$81, restorative--$99, and emergency care--$22. Among children who received services, mean expenditures were: emergency hospital-based--$1282, preventive--$106, and restorative care--$343. Caregivers' low literacy in the oral health context was associated with a statistically non-significant increase in total expenditures (average annual difference = $40; 95% confidence interval, -32, 111). Nevertheless, with both instruments, emergency dental care expenditures were consistently elevated among children of low-literacy caregivers. These findings provide initial support for health literacy as an important determinant of the meaningful use and cost of oral health care.

Religious Coping and Quality of LifefAmong Individuals Living With Schizophrenia

PubMed Central

Nolan, Jennifer A.; McEvoy, Joseph P.; Koenig, Harold G.; Hooten, Elizabeth G.; Whetten, Kathryn; Pieper, Carl F.

2013-01-01

Objective This study investigated the relationship between positive and negative religious coping and quality of life among outpatients with schizophrenia. Methods Interviews were conducted with 63 adults in the southeastern United States. Religious coping was measured by the 14-item RCOPE and quality of life by the World Health Organization Quality of Life–BREF. Data were examined via descriptive bivariate statistics and controlled analyses. Results Most participants reported participation in private religious or spiritual activities (91%) and participation in public religious services or activities (68%). Positive religious coping was related to the quality-of-life facet of psychological health (r=.28, p=.03). Negative religious coping and quality of life were inversely related (r=−.30, p=.02). Positive religious coping was associated with psychological health in the reduced univariate general linear model (B=.72, p=.03, adjusted R2=.08). Conclusions Greater awareness of the importance of religion in this population may improve cultural competence in treatment and community support. PMID:23032680

Perceived discrimination among three groups of refugees resettled in the USA: associations with language, time in the USA, and continent of origin.

PubMed

Hadley, Craig; Patil, Crystal

2009-12-01

The objectives of this study were to assess the prevalence and predictors of discrimination among a community-based sample of refugees resettled in the USA. We sought to test whether language, gender, time in the USA and country of origin were associated with the experience of discrimination among individuals resettled in the USA as part of the refugee resettlement program. Perceived discrimination was assessed among individuals from East Africa (n = 92), West Africa (n = 74), and from Eastern Europe (n = 112) using a multi-item measure of discrimination. Bivariate associations revealed statistically significant associations between experiences of discrimination and time in the USA, language ability, and sending country. A logistic regression model revealed that refugees from African sending countries were more likely than Eastern European individuals to experience discrimination, even after controlling for potentially confounding factors. We interpret this finding as evidence of racism and discuss the implications for population health and resettlement practice.

A Randomized Study of Incentivizing HIV Testing for Parolees in Community Aftercare.

PubMed

Saxena, Preeta; Hall, Elizabeth A; Prendergast, Michael

2016-04-01

HIV risk-behaviors are high in criminal justice populations and more efforts are necessary to address them among criminal justice-involved substance abusers. This study examines the role of incentives in promoting HIV testing among parolees. Participants were randomly assigned to either an incentive (n = 104) or education group (control; n = 98), where the incentive group received a voucher for testing for HIV. Bivariate comparisons showed that a larger proportion of those in the incentive group received HIV testing (59% versus 47%), but this was not statistically significant (p = .09). However, in a multivariate logistic regression model controlling for covariates likely to influence HIV-testing behavior, those in the incentive group had increased odds of HIV testing in comparison to those in the education group (OR = 1.99, p < .05, CI [1.05, 3.78]). As a first of its kind, this study provides a foundation for further research on the utility of incentives in promoting HIV testing and other healthy behaviors in criminal justice populations.

Implementation of the presence of companions during hospital admission for childbirth: data from the Birth in Brazil national survey.

PubMed

Diniz, Carmen Simone Grilo; d'Orsi, Eleonora; Domingues, Rosa Maria Soares Madeira; Torres, Jacqueline Alves; Dias, Marcos Augusto Bastos; Schneck, Camilla A; Lansky, Sônia; Teixeira, Neuma Zamariano Fanaia; Rance, Susanna; Sandall, Jane

2014-08-01

Robust evidence of the benefits of continuous support during childbirth led to the recommendation that it should be offered for all women. In Brazil, it has been guaranteed by law since 2005, but scarce data on implementation is available. We aimed to estimate the frequency and associated socio-demographic, obstetric and institutional predictors of women having companionship during childbirth in the Birth in Brazil survey. Descriptive statistical analysis was done for the characterization of companions (at different moments of hospital stay), maternal and institutional factors; associations were investigated in bivariate and multivariate models. We found that 24.5% of women had no companion at all, 18.8% had continuous companionship and 56.7% had partial companionship. Independent predictors of having no or partial companionship at birth were: lower income and education, brown color of skin, using the public sector, multiparity, and vaginal delivery. Implementation of companionship was associated with having an appropriate environment, and clear institution al rules about women's rights to companionship.

Investigation of the relation between the return periods of major drought characteristics using copula functions

NASA Astrophysics Data System (ADS)

Hüsami Afşar, Mehdi; Unal Şorman, Ali; Tugrul Yilmaz, Mustafa

2016-04-01

Different drought characteristics (e.g. duration, average severity, and average areal extent) often have monotonic relation that increased magnitude of one often follows a similar increase in the magnitude of the other drought characteristic. Hence it is viable to establish a relationship between different drought characteristics with the goal of predicting one using other ones. Copula functions that relate different variables using their joint and conditional cumulative probability distributions are often used to statistically model the drought characteristics. In this study bivariate and trivariate joint probabilities of these characteristics are obtained over Ankara (Turkey) between 1960 and 2013. Copula-based return period estimation of drought characteristics of duration, average severity, and average areal extent show joint probabilities of these characteristics can be satisfactorily achieved. Among different copula families investigated in this study, elliptical family (i.e. including normal and t-student copula functions) resulted in the lowest root mean square error. "This study was supported by TUBITAK fund #114Y676)."

Recognition Memory zROC Slopes for Items with Correct versus Incorrect Source Decisions Discriminate the Dual Process and Unequal Variance Signal Detection Models

ERIC Educational Resources Information Center

Starns, Jeffrey J.; Rotello, Caren M.; Hautus, Michael J.

2014-01-01

We tested the dual process and unequal variance signal detection models by jointly modeling recognition and source confidence ratings. The 2 approaches make unique predictions for the slope of the recognition memory zROC function for items with correct versus incorrect source decisions. The standard bivariate Gaussian version of the unequal…

A Comparison of Limited-Information and Full-Information Methods in M"plus" for Estimating Item Response Theory Parameters for Nonnormal Populations

ERIC Educational Resources Information Center

DeMars, Christine E.

2012-01-01

In structural equation modeling software, either limited-information (bivariate proportions) or full-information item parameter estimation routines could be used for the 2-parameter item response theory (IRT) model. Limited-information methods assume the continuous variable underlying an item response is normally distributed. For skewed and…

A Genetic Study of Attention Deficit Hyperactivity Disorder, Conduct Disorder, Oppositional Defiant Disorder and Reading Disability: Aetiological Overlaps and Implications

ERIC Educational Resources Information Center

Martin, Neilson C.; Levy, Florence; Pieka, Jan; Hay, David A.

2006-01-01

Attention Deficit Hyperactivity Disorder (ADHD) commonly co-occurs with Oppositional Defiant Disorder, Conduct Disorder and Reading Disability. Twin studies are an important approach to understanding and modelling potential causes of such comorbidity. Univariate and bivariate genetic models were fitted to maternal report data from 2040 families of…

Conceptual and statistical problems associated with the use of diversity indices in ecology.

PubMed

Barrantes, Gilbert; Sandoval, Luis

2009-09-01

Diversity indices, particularly the Shannon-Wiener index, have extensively been used in analyzing patterns of diversity at different geographic and ecological scales. These indices have serious conceptual and statistical problems which make comparisons of species richness or species abundances across communities nearly impossible. There is often no a single statistical method that retains all information needed to answer even a simple question. However, multivariate analyses could be used instead of diversity indices, such as cluster analyses or multiple regressions. More complex multivariate analyses, such as Canonical Correspondence Analysis, provide very valuable information on environmental variables associated to the presence and abundance of the species in a community. In addition, particular hypotheses associated to changes in species richness across localities, or change in abundance of one, or a group of species can be tested using univariate, bivariate, and/or rarefaction statistical tests. The rarefaction method has proved to be robust to standardize all samples to a common size. Even the simplest method as reporting the number of species per taxonomic category possibly provides more information than a diversity index value.

Modeling Tropical Cyclone Storm Surge and Wind Induced Risk Along the Bay of Bengal Coastline Using a Statistical Copula

NASA Astrophysics Data System (ADS)

Bushra, N.; Trepanier, J. C.; Rohli, R. V.

2017-12-01

High winds, torrential rain, and storm surges from tropical cyclones (TCs) cause massive destruction to property and cost the lives of many people. The coastline of the Bay of Bengal (BoB) ranks as one of the most susceptible to TC storm surges in the world due to low-lying elevation and a high frequency of occurrence. Bangladesh suffers the most due to its geographical setting and population density. Various models have been developed to predict storm surge in this region but none of them quantify statistical risk with empirical data. This study describes the relationship and dependency between empirical TC storm surge and peak reported wind speed at the BoB using a bivariate statistical copula and data from 1885-2011. An Archimedean, Gumbel copula with margins defined by the empirical distributions is specified as the most appropriate choice for the BoB. The model provides return periods for pairs of TC storm surge and peak wind along the BoB coastline. The BoB can expect a TC with peak reported winds of at least 24 m s-1 and surge heights of at least 4.0 m, on average, once every 3.2 years, with a quartile pointwise confidence interval of 2.7-3.8 years. In addition, the BoB can expect peak reported winds of 62 m s-1 and surge heights of at least 8.0 m, on average, once every 115.4 years, with a quartile pointwise confidence interval of 55.8-381.1 years. The purpose of the analysis is to increase the understanding of these dangerous TC characteristics to reduce fatalities and monetary losses into the future. Application of the copula will mitigate future threats of storm surge impacts on coastal communities of the BoB.

Sequential deconvolution from wave-front sensing using bivariate simplex splines

NASA Astrophysics Data System (ADS)

Guo, Shiping; Zhang, Rongzhi; Li, Jisheng; Zou, Jianhua; Xu, Rong; Liu, Changhai

2015-05-01

Deconvolution from wave-front sensing (DWFS) is an imaging compensation technique for turbulence degraded images based on simultaneous recording of short exposure images and wave-front sensor data. This paper employs the multivariate splines method for the sequential DWFS: a bivariate simplex splines based average slopes measurement model is built firstly for Shack-Hartmann wave-front sensor; next, a well-conditioned least squares estimator for the spline coefficients is constructed using multiple Shack-Hartmann measurements; then, the distorted wave-front is uniquely determined by the estimated spline coefficients; the object image is finally obtained by non-blind deconvolution processing. Simulated experiments in different turbulence strength show that our method performs superior image restoration results and noise rejection capability especially when extracting the multidirectional phase derivatives.

Nonlinear bivariate dependency of price-volume relationships in agricultural commodity futures markets: A perspective from Multifractal Detrended Cross-Correlation Analysis

NASA Astrophysics Data System (ADS)

He, Ling-Yun; Chen, Shu-Peng

2011-01-01

Nonlinear dependency between characteristic financial and commodity market quantities (variables) is crucially important, especially between trading volume and market price. Studies on nonlinear dependency between price and volume can provide practical insights into market trading characteristics, as well as the theoretical understanding of market dynamics. Actually, nonlinear dependency and its underlying dynamical mechanisms between price and volume can help researchers and technical analysts in understanding the market dynamics by integrating the market variables, instead of investigating them in the current literature. Therefore, for investigating nonlinear dependency of price-volume relationships in agricultural commodity futures markets in China and the US, we perform a new statistical test to detect cross-correlations and apply a new methodology called Multifractal Detrended Cross-Correlation Analysis (MF-DCCA), which is an efficient algorithm to analyze two spatially or temporally correlated time series. We discuss theoretically the relationship between the bivariate cross-correlation exponent and the generalized Hurst exponents for time series of respective variables. We also perform an empirical study and find that there exists a power-law cross-correlation between them, and that multifractal features are significant in all the analyzed agricultural commodity futures markets.

Meta-analysis of diagnostic accuracy studies accounting for disease prevalence: alternative parameterizations and model selection.

PubMed

Chu, Haitao; Nie, Lei; Cole, Stephen R; Poole, Charles

2009-08-15

In a meta-analysis of diagnostic accuracy studies, the sensitivities and specificities of a diagnostic test may depend on the disease prevalence since the severity and definition of disease may differ from study to study due to the design and the population considered. In this paper, we extend the bivariate nonlinear random effects model on sensitivities and specificities to jointly model the disease prevalence, sensitivities and specificities using trivariate nonlinear random-effects models. Furthermore, as an alternative parameterization, we also propose jointly modeling the test prevalence and the predictive values, which reflect the clinical utility of a diagnostic test. These models allow investigators to study the complex relationship among the disease prevalence, sensitivities and specificities; or among test prevalence and the predictive values, which can reveal hidden information about test performance. We illustrate the proposed two approaches by reanalyzing the data from a meta-analysis of radiological evaluation of lymph node metastases in patients with cervical cancer and a simulation study. The latter illustrates the importance of carefully choosing an appropriate normality assumption for the disease prevalence, sensitivities and specificities, or the test prevalence and the predictive values. In practice, it is recommended to use model selection techniques to identify a best-fitting model for making statistical inference. In summary, the proposed trivariate random effects models are novel and can be very useful in practice for meta-analysis of diagnostic accuracy studies. Copyright 2009 John Wiley & Sons, Ltd.

Profile Of 'Original Articles' Published In 2016 By The Journal Of Ayub Medical College, Pakistan.

PubMed

Shaikh, Masood Ali

2018-01-01

Journal of Ayub Medical College (JAMC) is the only Medline indexed biomedical journal of Pakistan that is edited and published by a medical college. Assessing the trends of study designs employed, statistical methods used, and statistical analysis software used in the articles of medical journals help understand the sophistication of research published. The objectives of this descriptive study were to assess all original articles published by JAMC in the year 2016. JAMC published 147 original articles in the year 2016. The most commonly used study design was crosssectional studies, with 64 (43.5%) articles reporting its use. Statistical tests involving bivariate analysis were most common and reported by 73 (49.6%) articles. Use of SPSS software was reported by 109 (74.1%) of articles. Most 138 (93.9%) of the original articles published were based on studies conducted in Pakistan. The number and sophistication of analysis reported in JAMC increased from year 2014 to 2016.

Association between dental caries experience and sense of coherence among adolescents and mothers.

PubMed

Lage, Carolina Freitas; Fulgencio, Livia Bonfim; Corrêa-Faria, Patricia; Serra-Negra, Junia Maria; Paiva, Saul Martins; Pordeus, Isabela Almeida

2017-09-01

Sense of coherence (SOC) is associated with oral health. Investigate associations between dental caries experience and SOC among mothers and adolescents. A cross-sectional study was conducted with 1195 adolescents and their mothers. Data were collected through a questionnaire, the short version of the SOC and oral clinical examinations. The data were statistically analyzed using bivariate analysis, Poisson regression models with robust variance, and Spearman's correlation coefficient. The prevalence of dental caries experience was 41.8%. A moderate correlation was found between the SOC of mothers and adolescents (r = 0.563; P < 0.001). A higher mother's SOC (PR: 0.44; 95% CI: 0.36-0.53) and adolescent's SOC (PR: 0.46; 95% CI: 0.39-0.55) were protective factors against dental caries experience in the adolescents. The prevalence of dental caries experience was higher among adolescents with visible plaque (Model 1-PR: 1.77; 95% CI: 1.53-2.04; Model 2-PR: 1.59; 95% CI: 1.37-1.84) and those whose families were in a lower economic class (Model 1-PR: 1.56; 95% CI: 1.35-1.80; Model 2-PR: 1.57; 95% CI: 1.36-1.81). Dental caries in adolescents was associated with social determinants evaluated through the sense of coherence. © 2016 BSPD, IAPD and John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Flood risk analysis for flood control and sediment transportation: a case study in the catchments of the Loess Plateau, China

NASA Astrophysics Data System (ADS)

Wang, Y.; Chang, J.; Guo, A.

2017-12-01

Traditional flood risk analysis focuses on the probability of flood events exceeding the design flood of downstream hydraulic structures while neglecting the influence of sedimentation in river channels on flood control systems. Given this focus, a univariate and copula-based bivariate hydrological risk framework focusing on flood control and sediment transport is proposed in the current work. Additionally, the conditional probabilities of occurrence of different flood events under various extreme precipitation scenarios are estimated by exploiting the copula model. Moreover, a Monte Carlo-based algorithm is used to evaluate the uncertainties of univariate and bivariate hydrological risk. Two catchments located on the Loess plateau are selected as study regions: the upper catchments of the Xianyang and Huaxian stations (denoted as UCX and UCH, respectively). The results indicate that (1) 2-day and 3-day consecutive rainfall are highly correlated with the annual maximum flood discharge (AMF) in UCX and UCH, respectively; and (2) univariate and bivariate return periods, risk and reliability for the purposes of flood control and sediment transport are successfully estimated. Sedimentation triggers higher risks of damaging the safety of local flood control systems compared with the AMF, exceeding the design flood of downstream hydraulic structures in the UCX and UCH. Most importantly, there was considerable sampling uncertainty in the univariate and bivariate hydrologic risk analysis, which would greatly challenge measures of future flood mitigation. The proposed hydrological risk framework offers a promising technical reference for flood risk analysis in sandy regions worldwide.

Bayesian bivariate meta-analysis of diagnostic test studies with interpretable priors.

PubMed

Guo, Jingyi; Riebler, Andrea; Rue, Håvard

2017-08-30

In a bivariate meta-analysis, the number of diagnostic studies involved is often very low so that frequentist methods may result in problems. Using Bayesian inference is particularly attractive as informative priors that add a small amount of information can stabilise the analysis without overwhelming the data. However, Bayesian analysis is often computationally demanding and the selection of the prior for the covariance matrix of the bivariate structure is crucial with little data. The integrated nested Laplace approximations method provides an efficient solution to the computational issues by avoiding any sampling, but the important question of priors remain. We explore the penalised complexity (PC) prior framework for specifying informative priors for the variance parameters and the correlation parameter. PC priors facilitate model interpretation and hyperparameter specification as expert knowledge can be incorporated intuitively. We conduct a simulation study to compare the properties and behaviour of differently defined PC priors to currently used priors in the field. The simulation study shows that the PC prior seems beneficial for the variance parameters. The use of PC priors for the correlation parameter results in more precise estimates when specified in a sensible neighbourhood around the truth. To investigate the usage of PC priors in practice, we reanalyse a meta-analysis using the telomerase marker for the diagnosis of bladder cancer and compare the results with those obtained by other commonly used modelling approaches. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

The role of loss of control eating in purging disorder.

PubMed

Forney, K Jean; Haedt-Matt, Alissa A; Keel, Pamela K

2014-04-01

Purging Disorder (PD), an Other Specified Feeding or Eating Disorder (APA, 2013), is characterized by recurrent purging in the absence of binge eating. Though objectively large binge episodes are not present, individuals with PD may experience a loss of control (LOC) while eating a normal or small amounts of food. The present study sought to examine the role of LOC eating in PD using archival data from 101 women with PD. Participants completed diagnostic interviews and self-report questionnaires. Analyses examined the relationship between LOC eating and eating disorder features, psychopathology, personality traits, and impairment in bivariate models and then in multivariate models controlling for purging frequency, age, and body mass index. Across bivariate and multivariate models, LOC eating frequency was associated with greater disinhibition around food, hunger, depressive symptoms, negative urgency, distress, and impairment. LOC eating is a clinically significant feature of PD and should be considered in future definitions of PD. Future research should examine whether LOC eating better represents a dimension of severity in PD or a specifier that may impact treatment response or course. Copyright © 2013 Wiley Periodicals, Inc.

Principal component regression analysis with SPSS.

PubMed

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Hospitalized Patients' Responses to Offers of Prayer.

PubMed

McMillan, Kathy; Taylor, Elizabeth Johnston

2018-02-01

Most Americans pray; many pray about their health. When they are hospitalized, however, do patients want an offer of prayer from a healthcare provider? This project allowed for the measurement of hospitalized patient's responses to massage therapists' offers of a colloquial prayer after a massage. After the intervention, 78 patients completed questionnaires that elicited quantitative data that were analyzed using uni- and bivariate statistical analyses. In this sample, 88% accepted the offer of prayer, 85% found it helpful, and 51% wanted prayer daily. Patients may welcome prayer, as long as the clinician shows "genuine kindness and respect."

Prevalence and related factors for choosing self-medication among pharmacies visitors based on health belief model in Hamadan Province, west of Iran.

PubMed

Jalilian, Farzad; Hazavehei, Seyed Mohammad Mehdi; Vahidinia, Ali Asghar; Jalilian, Mohsen; Moghimbeigi, Abbas

2013-05-29

Self-medication has increased in the last decade in Iran; can be followed several complications. The aim of this study was to determine the prevalence and factors influencing self-medication based on health belief model. A cross-sectional study was conducted among 1400 Hamadan Province pharmacies visitors, during spring and summer 2012 which was randomly selected with the proportional to size among different pharmacy at Hamadan for participation in this study. A structured questionnaire was applied for collecting data, which were analyzed by SPSS version 16 using bivariate correlations and logistic regression statistical tests. 35.4% of the participants had self-medication. Pain medication (10.6%), antibiotics (7.3%) and anti-cough and cold medications (4.5%) had the largest consumption. The main reasons of self-medication among participants were previous use of medication, symptoms improve and similar prescribed. The best predictor for self-medication was perceived severity with odds ratio estimate of 0.790 [95% CI: 0.694, 0.900]. It seems that designing and implementation of educational programs to increase seriousness about side effect of self-medication may be usefulness of the results in order to prevent of self-medication.

The Role of Psychosocial and Belief Factors in Self-Reported Cigarette Smoking Among University Students in Malaysia

PubMed Central

Al-Dubai, Sami; Ganasegeran, Kurubaran; Alshagga, Mustafa; Hawash, Aamenah; Wajih, Wahid; Kassim, Saba

2014-01-01

This study aimed to explore factors associated, specifically belief factors, with self-reported tobacco smoking status. A sample of 300 students was recruited from a private university in Malaysia. Data was collected using a pre-tested self-administrated questionnaire that investigated various factors including socio-demographics, socio-economic status, smoking behavior and beliefs on tobacco smoking. The main tobacco use in this study sample was cigarettes and the estimated prevalence of self-reported cigarette smoking was 10.3%. In bivariate analysis, self-reported cigarette smoking was significantly associated with socio-demographic, behavioral factors and faculty of study (P<0.05). In multivariate modeling, being male and a non-medical student, did not exercise, having a smoker father and brother or sister, suffering from financial difficulties and having the belief that smokers had more friends, all had statistically significant associations (P<0.05) with self-reported cigarette smoking. Social and interpersonal factors were associated with self-reported cigarette smoking status. A comprehensive health model focusing on changing the social norms of parent and sibling tobacco smoking and students’ beliefs, alongside nurturing skills of dealing with stressful situations, warrant implementation. PMID:26973928

Hospital utilization outcome of an assertive outreach model for schizophrenic patients - results of a quasi-experimental study.

PubMed

Büchtemann, Dorothea; Kästner, Denise; Warnke, Ingeborg; Radisch, Jeanett; Baumgardt, Johanna; Giersberg, Steffi; Kleine-Budde, Katja; Moock, Jörn; Kawohl, Wolfram; Rössler, Wulf

2016-07-30

We assessed whether an Assertive Outreach (AO) program for patients with schizophrenia implemented in German routine care in rural areas reduces psychiatric hospital admissions and/or psychiatric hospital days. We conducted a quasi-experimental controlled study with 5 assessments in 12 months. Data collection included health care utilization (Client Sociodemographic and Service Receipt Inventory), and clinical parameters. The assessments took place in the practices of the psychiatrists. Admission incidence rates were calculated. For bivariate group comparison, we used U-tests, T-tests and Chi(2)-Tests, multivariate analysis was conducted using zero-inflated regression models. For hospital outcomes, data of 295 patients was analysed. No statistically significant differences between AO and TAU patients in terms of hospital admissions or hospital days were found. Overall hospital utilization was low (8%). Advantages of AO over TAU referring to hospital utilization were not found. However, a spill-over effect might have reduced hospital utilization in both groups. Further research should differentiate patient subgroups. These two appear to be key factors to explain effects or absence of effects and to draw conclusions for the mental health care delivery. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Maternal Attitudes, Normative Beliefs, and Subjective Norms of Mothers of 2- and 3-Year-Old Children.

PubMed

Northrup, Angela A; Smaldone, Arlene

This exploratory study examined maternal attitudes, normative beliefs, subjective norms, and meal selection behaviors of mothers of 2- and 3-year-old children. Guided by the Theory of Reasoned Action, we had mothers complete three surveys, two interviews, and a feeding simulation exercise. Data were analyzed using descriptive and bivariate statistics and multivariate linear regression. A total of 31 mothers (50% Latino, 34% Black, 46.9% ≤ high school education, 31.3% poor health literacy) of 32 children (37.5% overweight/obese) participated in this study. Maternal normative beliefs (knowledge of U.S. Department of Agriculture recommendations) did not reflect actual U.S. Department of Agriculture recommendations. Collectively, regression models explained 13% (dairy) to 51% (vegetables) of the variance in behavioral intent, with normative belief an independent predictor in all models except grain and dairy. Meal selection behaviors, on average, were predicted by poor knowledge of U.S. Department of Agriculture recommendations. Dietary guidance appropriate to health literacy level should be incorporated into well-child visits. Copyright © 2016 National Association of Pediatric Nurse Practitioners. Published by Elsevier Inc. All rights reserved.

Solid/liquid extraction equilibria of phenolic compounds with trioctylphosphine oxide impregnated in polymeric membranes.

PubMed

Praveen, Prashant; Loh, Kai-Chee

2016-06-01

Trioctylphosphine oxide based extractant impregnated membranes (EIM) were used for extraction of phenol and its methyl, hydroxyl and chloride substituted derivatives. The distribution coefficients of the phenols varied from 2 to 234, in the order of 1-napthol > p-chlorophenol > m-cresol > p-cresol > o-cresol > phenol > catechol > pyrogallol > hydroquinone, when initial phenols loadings was varied in 100-2000 mg/L. An extraction model, based on the law of mass action, was formulated to predict the equilibrium distribution of the phenols. The model was in excellent agreement (R(2) > 0.97) with the experimental results at low phenols concentrations (<800 mg/L). At higher phenols loadings though, Langmuir isotherm was better suited for equilibrium prediction (R(2) > 0.95), which signified high mass transfer resistance in the EIMs. Examination of the effects of ring substitution on equilibrium, and bivariate statistical analysis between the amounts of phenols extracted into the EIMs and factors affecting phenols interaction with TOPO, indicated the dominant role of hydrophobicity in equilibrium determination. These results improve understanding of the solid/liquid equilibrium process between phenols and the EIMs, and these will be useful in designing phenol recovery process from wastewater. Copyright © 2016 Elsevier Ltd. All rights reserved.

The etiology of associations between negative emotionality and childhood externalizing disorders.

PubMed

Singh, Amber L; Waldman, Irwin D

2010-05-01

Despite consistent documentation of associations between childhood negative emotionality and externalizing psychopathology, few genetically informative studies have investigated the etiology of that association. The goal of the current study was to delineate the etiology of the covariation of negative emotionality and childhood externalizing problems (e.g., oppositional defiant disorder, conduct disorder, inattention, and hyperactivity/impulsivity). Twin families were recruited from Georgia state birth records and completed parental report questionnaires of negative emotionality and common Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; American Psychiatric Association, 2000) child psychiatric disorders. Results suggest both genetic and environmental influences underlying negative emotionality and each externalizing symptom dimension, with additional evidence for sibling competition/rater contrast effects for inattention and hyperactivity/impulsivity. Bivariate model-fitting analyses indicated that a portion of the additive (43%-75%) and nonadditive (26%-100%) genetic influences underlying each symptom dimension was accounted for by the genetic influences underlying negative emotionality. Finally, an independent pathways model examining the etiology of the association between negative emotionality and the externalizing dimensions indicated that a substantial portion of the additive genetic, nonadditive genetic, and nonshared environmental influences underlying externalizing behavior is shared with negative emotionality.

Diffusion Processes Satisfying a Conservation Law Constraint

DOE PAGES

Bakosi, J.; Ristorcelli, J. R.

2014-03-04

We investigate coupled stochastic differential equations governing N non-negative continuous random variables that satisfy a conservation principle. In various fields a conservation law requires that a set of fluctuating variables be non-negative and (if appropriately normalized) sum to one. As a result, any stochastic differential equation model to be realizable must not produce events outside of the allowed sample space. We develop a set of constraints on the drift and diffusion terms of such stochastic models to ensure that both the non-negativity and the unit-sum conservation law constraint are satisfied as the variables evolve in time. We investigate the consequencesmore » of the developed constraints on the Fokker-Planck equation, the associated system of stochastic differential equations, and the evolution equations of the first four moments of the probability density function. We show that random variables, satisfying a conservation law constraint, represented by stochastic diffusion processes, must have diffusion terms that are coupled and nonlinear. The set of constraints developed enables the development of statistical representations of fluctuating variables satisfying a conservation law. We exemplify the results with the bivariate beta process and the multivariate Wright-Fisher, Dirichlet, and Lochner’s generalized Dirichlet processes.« less

Diffusion Processes Satisfying a Conservation Law Constraint

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

Bakosi, J.; Ristorcelli, J. R.

We investigate coupled stochastic differential equations governing N non-negative continuous random variables that satisfy a conservation principle. In various fields a conservation law requires that a set of fluctuating variables be non-negative and (if appropriately normalized) sum to one. As a result, any stochastic differential equation model to be realizable must not produce events outside of the allowed sample space. We develop a set of constraints on the drift and diffusion terms of such stochastic models to ensure that both the non-negativity and the unit-sum conservation law constraint are satisfied as the variables evolve in time. We investigate the consequencesmore » of the developed constraints on the Fokker-Planck equation, the associated system of stochastic differential equations, and the evolution equations of the first four moments of the probability density function. We show that random variables, satisfying a conservation law constraint, represented by stochastic diffusion processes, must have diffusion terms that are coupled and nonlinear. The set of constraints developed enables the development of statistical representations of fluctuating variables satisfying a conservation law. We exemplify the results with the bivariate beta process and the multivariate Wright-Fisher, Dirichlet, and Lochner’s generalized Dirichlet processes.« less

Full-range public health leadership, part 1: quantitative analysis.

PubMed

Carlton, Erik L; Holsinger, James W; Riddell, Martha; Bush, Heather

2015-01-01

Workforce and leadership development are central to the future of public health. However, public health has been slow to translate and apply leadership models from other professions and to incorporate local perspectives in understanding public health leadership. This study utilized the full-range leadership model in order to examine public health leadership. Specifically, it sought to measure leadership styles among local health department directors and to understand the context of leadership in local health departments. Leadership styles among local health department directors (n = 13) were examined using survey methodology. Quantitative analysis methods included descriptive statistics, boxplots, and Pearson bivariate correlations using SPSS v18.0. Self-reported leadership styles were highly correlated to leadership outcomes at the organizational level. However, they were not related to county health rankings. Results suggest the preeminence of leader behaviors and providing individual consideration to staff as compared to idealized attributes of leaders, intellectual stimulation, or inspirational motivation. Holistic leadership assessment instruments such as the multifactor leadership questionnaire can be useful in assessing public health leaders' approaches and outcomes. Comprehensive, 360-degree reviews may be especially helpful. Further research is needed to examine the effectiveness of public health leadership development models, as well as the extent that public health leadership impacts public health outcomes.

The rate of country-level improvements of the infant mortality rate is mainly determined by previous history.

PubMed

Bremberg, Sven G

2016-08-01

Studies of country-level determinants of health have produced conflicting results even when the analyses have been restricted to high-income counties. Yet, most of these studies have not taken historical, country-specific developments into account. Thus, it is appropriate to separate the influence of current exposures from historical aspects. Determinants of the infant mortality rate (IMR) were studied in 28 OECD countries over the period 1990-2012. Twelve determinants were selected. They refer to the level of general resources, resources that specifically address child health and characteristics that affect knowledge dissemination, including level of trust, and a health related behaviour: the rate of female smoking. Bivariate analyses with the IMR in year 2000 as outcome and the 12 determinants produced six statistically significant models. In multivariate analyses, the rate of decrease in the IMR was investigated as outcome and a history variable (IMR in 1990) was included in the models. The history variable alone explained 95% of the variation. None of the multivariate models, with the 12 determinants included, explained significantly more variation. Taking into account the historical development of the IMR will critically affect correlations between country-level determinants and the IMR. © The Author 2016. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

Factors associated with interest in novel interfaces for upper limb prosthesis control

PubMed Central

Engdahl, Susannah M.; Chestek, Cynthia A.; Kelly, Brian; Davis, Alicia

2017-01-01

Background Surgically invasive interfaces for upper limb prosthesis control may allow users to operate advanced, multi-articulated devices. Given the potential medical risks of these invasive interfaces, it is important to understand what factors influence an individual’s decision to try one. Methods We conducted an anonymous online survey of individuals with upper limb loss. A total of 232 participants provided personal information (such as age, amputation level, etc.) and rated how likely they would be to try noninvasive (myoelectric) and invasive (targeted muscle reinnervation, peripheral nerve interfaces, cortical interfaces) interfaces for prosthesis control. Bivariate relationships between interest in each interface and 16 personal descriptors were examined. Significant variables from the bivariate analyses were then entered into multiple logistic regression models to predict interest in each interface. Results While many of the bivariate relationships were significant, only a few variables remained significant in the regression models. The regression models showed that participants were more likely to be interested in all interfaces if they had unilateral limb loss (p ≤ 0.001, odds ratio ≥ 2.799). Participants were more likely to be interested in the three invasive interfaces if they were younger (p < 0.001, odds ratio ≤ 0.959) and had acquired limb loss (p ≤ 0.012, odds ratio ≥ 3.287). Participants who used a myoelectric device were more likely to be interested in myoelectric control than those who did not (p = 0.003, odds ratio = 24.958). Conclusions Novel prosthesis control interfaces may be accepted most readily by individuals who are young, have unilateral limb loss, and/or have acquired limb loss However, this analysis did not include all possible factors that may have influenced participant’s opinions on the interfaces, so additional exploration is warranted. PMID:28767716

Factors associated with interest in novel interfaces for upper limb prosthesis control.

PubMed

Engdahl, Susannah M; Chestek, Cynthia A; Kelly, Brian; Davis, Alicia; Gates, Deanna H

2017-01-01

Surgically invasive interfaces for upper limb prosthesis control may allow users to operate advanced, multi-articulated devices. Given the potential medical risks of these invasive interfaces, it is important to understand what factors influence an individual's decision to try one. We conducted an anonymous online survey of individuals with upper limb loss. A total of 232 participants provided personal information (such as age, amputation level, etc.) and rated how likely they would be to try noninvasive (myoelectric) and invasive (targeted muscle reinnervation, peripheral nerve interfaces, cortical interfaces) interfaces for prosthesis control. Bivariate relationships between interest in each interface and 16 personal descriptors were examined. Significant variables from the bivariate analyses were then entered into multiple logistic regression models to predict interest in each interface. While many of the bivariate relationships were significant, only a few variables remained significant in the regression models. The regression models showed that participants were more likely to be interested in all interfaces if they had unilateral limb loss (p ≤ 0.001, odds ratio ≥ 2.799). Participants were more likely to be interested in the three invasive interfaces if they were younger (p < 0.001, odds ratio ≤ 0.959) and had acquired limb loss (p ≤ 0.012, odds ratio ≥ 3.287). Participants who used a myoelectric device were more likely to be interested in myoelectric control than those who did not (p = 0.003, odds ratio = 24.958). Novel prosthesis control interfaces may be accepted most readily by individuals who are young, have unilateral limb loss, and/or have acquired limb loss However, this analysis did not include all possible factors that may have influenced participant's opinions on the interfaces, so additional exploration is warranted.

Correlation of hard X-ray and type 3 bursts in solar flares

NASA Technical Reports Server (NTRS)

Petrosian, V.; Leach, J.

1982-01-01

Correlations between X-ray and type 3 radio emission of solar bursts are described through a bivariate distribution function. Procedures for determining the form of this distribution are described. A model is constructed to explain the correlation between the X-ray spectral index and the ratio of X-ray to radio intensities. Implications of the model are discussed.

Modeling hardwood crown radii using circular data analysis

Treesearch

Paul F. Doruska; Hal O. Liechty; Douglas J. Marshall

2003-01-01

Cylindrical data are bivariate data composed of a linear and an angular component. One can use uniform, first-order (one maximum and one minimum) or second-order (two maxima and two minima) models to relate the linear component to the angular component. Crown radii can be treated as cylindrical data when the azimuths at which the radii are measured are also recorded....

Effect of Changes in Living Conditions on Well-Being: A Prospective Top-Down Bottom-Up Model

ERIC Educational Resources Information Center

Nakazato, Naoki; Schimmack, Ulrich; Oishi, Shigehiro

2011-01-01

Using the German Socio-Economic Panel, we examined life-satisfaction and housing satisfaction before and after moving (N = 3,658 participants from 2,162 households) with univariate and bivariate two-intercept two-slope latent growth models. The main findings were (a) a strong and persistent increase in average levels of housing satisfaction, (b)…

A Simulation Study Comparison of Bayesian Estimation with Conventional Methods for Estimating Unknown Change Points

ERIC Educational Resources Information Center

Wang, Lijuan; McArdle, John J.

2008-01-01

The main purpose of this research is to evaluate the performance of a Bayesian approach for estimating unknown change points using Monte Carlo simulations. The univariate and bivariate unknown change point mixed models were presented and the basic idea of the Bayesian approach for estimating the models was discussed. The performance of Bayesian…

Dynamic Relationship between Gross Domestic Product and Domestic Investment in Rwanda

ERIC Educational Resources Information Center

Ocaya, Bruno; Ruranga, Charles; Kaberuka, William

2012-01-01

This study uses a VAR model to analyse the dynamic relationship between gross domestic product (GDP) and domestic investment (DI) in Rwanda for the period 1970 to 2011. Several selection lag criteria chose a maximum lag of one, and a bivariate VAR(1) model specification in levels was adopted. Unit root tests show that both GDP and DI series are…

Using the Bivariate Dale Model to jointly estimate predictors of frequency and quantity of alcohol use.

PubMed

McMillan, Garnett P; Hanson, Tim; Bedrick, Edward J; Lapham, Sandra C

2005-09-01

This study demonstrates the usefulness of the Bivariate Dale Model (BDM) as a method for estimating the relationship between risk factors and the quantity and frequency of alcohol use, as well as the degree of association between these highly correlated drinking measures. The BDM is used to evaluate childhood sexual abuse, along with age and gender, as risk factors for the quantity and frequency of beer consumption in a sample of driving-while-intoxicated (DWI) offenders (N = 1,964; 1,612 men). The BDM allows one to estimate the relative odds of drinking up to each level of ordinal-scaled quantity and frequency of alcohol use, as well as model the degree of association between quantity and frequency of alcohol consumption as a function of covariates. Individuals who experienced childhood sexual abuse have increased risks of higher quantity and frequency of beer consumption. History of childhood sexual abuse has a greater effect on women, causing them to drink higher quantities of beer per drinking occasion. The BDM is a useful method for evaluating predictors of the quantity-frequency of alcohol consumption. SAS macrocode for fitting the BDM model is provided.

Copula-based regression modeling of bivariate severity of temporary disability and permanent motor injuries.

PubMed

Ayuso, Mercedes; Bermúdez, Lluís; Santolino, Miguel

2016-04-01

The analysis of factors influencing the severity of the personal injuries suffered by victims of motor accidents is an issue of major interest. Yet, most of the extant literature has tended to address this question by focusing on either the severity of temporary disability or the severity of permanent injury. In this paper, a bivariate copula-based regression model for temporary disability and permanent injury severities is introduced for the joint analysis of the relationship with the set of factors that might influence both categories of injury. Using a motor insurance database with 21,361 observations, the copula-based regression model is shown to give a better performance than that of a model based on the assumption of independence. The inclusion of the dependence structure in the analysis has a higher impact on the variance estimates of the injury severities than it does on the point estimates. By taking into account the dependence between temporary and permanent severities a more extensive factor analysis can be conducted. We illustrate that the conditional distribution functions of injury severities may be estimated, thus, providing decision makers with valuable information. Copyright © 2016 Elsevier Ltd. All rights reserved.

Exploring the calibration of a wind forecast ensemble for energy applications

NASA Astrophysics Data System (ADS)

Heppelmann, Tobias; Ben Bouallegue, Zied; Theis, Susanne

2015-04-01

In the German research project EWeLiNE, Deutscher Wetterdienst (DWD) and Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) are collaborating with three German Transmission System Operators (TSO) in order to provide the TSOs with improved probabilistic power forecasts. Probabilistic power forecasts are derived from probabilistic weather forecasts, themselves derived from ensemble prediction systems (EPS). Since the considered raw ensemble wind forecasts suffer from underdispersiveness and bias, calibration methods are developed for the correction of the model bias and the ensemble spread bias. The overall aim is to improve the ensemble forecasts such that the uncertainty of the possible weather deployment is depicted by the ensemble spread from the first forecast hours. Additionally, the ensemble members after calibration should remain physically consistent scenarios. We focus on probabilistic hourly wind forecasts with horizon of 21 h delivered by the convection permitting high-resolution ensemble system COSMO-DE-EPS which has become operational in 2012 at DWD. The ensemble consists of 20 ensemble members driven by four different global models. The model area includes whole Germany and parts of Central Europe with a horizontal resolution of 2.8 km and a vertical resolution of 50 model levels. For verification we use wind mast measurements around 100 m height that corresponds to the hub height of wind energy plants that belong to wind farms within the model area. Calibration of the ensemble forecasts can be performed by different statistical methods applied to the raw ensemble output. Here, we explore local bivariate Ensemble Model Output Statistics at individual sites and quantile regression with different predictors. Applying different methods, we already show an improvement of ensemble wind forecasts from COSMO-DE-EPS for energy applications. In addition, an ensemble copula coupling approach transfers the time-dependencies of the raw ensemble to the calibrated ensemble. The calibrated wind forecasts are evaluated first with univariate probabilistic scores and additionally with diagnostics of wind ramps in order to assess the time-consistency of the calibrated ensemble members.

A Bivariate Space-time Downscaler Under Space and Time Misalignment

EPA Science Inventory

Ozone and particulate matter PM_2:5 are co-pollutants that have long been associated with increased public health risks. Information on concentration levels for both pollutants come from two sources: monitoring sites and output from complex numerical models that produce...

Assessing characteristics related to the use of seatbelts and cell phones by drivers: application of a bivariate probit model.

PubMed

Russo, Brendan J; Kay, Jonathan J; Savolainen, Peter T; Gates, Timothy J

2014-06-01

The effects of cell phone use and safety belt use have been an important focus of research related to driver safety. Cell phone use has been shown to be a significant source of driver distraction contributing to substantial degradations in driver performance, while safety belts have been demonstrated to play a vital role in mitigating injuries to crash-involved occupants. This study examines the prevalence of cell phone use and safety belt non-use among the driving population through direct observation surveys. A bivariate probit model is developed to simultaneously examine the factors that affect cell phone and safety belt use among motor vehicle drivers. The results show that several factors may influence drivers' decision to use cell phones and safety belts, and that these decisions are correlated. Understanding the factors that affect both cell phone use and safety belt non-use is essential to targeting policy and programs that reduce such behavior. Copyright © 2014 Elsevier Ltd. All rights reserved.

Prognostic models for predicting posttraumatic seizures during acute hospitalization, and at 1 and 2 years following traumatic brain injury.

PubMed

Ritter, Anne C; Wagner, Amy K; Szaflarski, Jerzy P; Brooks, Maria M; Zafonte, Ross D; Pugh, Mary Jo V; Fabio, Anthony; Hammond, Flora M; Dreer, Laura E; Bushnik, Tamara; Walker, William C; Brown, Allen W; Johnson-Greene, Doug; Shea, Timothy; Krellman, Jason W; Rosenthal, Joseph A

2016-09-01

Posttraumatic seizures (PTS) are well-recognized acute and chronic complications of traumatic brain injury (TBI). Risk factors have been identified, but considerable variability in who develops PTS remains. Existing PTS prognostic models are not widely adopted for clinical use and do not reflect current trends in injury, diagnosis, or care. We aimed to develop and internally validate preliminary prognostic regression models to predict PTS during acute care hospitalization, and at year 1 and year 2 postinjury. Prognostic models predicting PTS during acute care hospitalization and year 1 and year 2 post-injury were developed using a recent (2011-2014) cohort from the TBI Model Systems National Database. Potential PTS predictors were selected based on previous literature and biologic plausibility. Bivariable logistic regression identified variables with a p-value < 0.20 that were used to fit initial prognostic models. Multivariable logistic regression modeling with backward-stepwise elimination was used to determine reduced prognostic models and to internally validate using 1,000 bootstrap samples. Fit statistics were calculated, correcting for overfitting (optimism). The prognostic models identified sex, craniotomy, contusion load, and pre-injury limitation in learning/remembering/concentrating as significant PTS predictors during acute hospitalization. Significant predictors of PTS at year 1 were subdural hematoma (SDH), contusion load, craniotomy, craniectomy, seizure during acute hospitalization, duration of posttraumatic amnesia, preinjury mental health treatment/psychiatric hospitalization, and preinjury incarceration. Year 2 significant predictors were similar to those of year 1: SDH, intraparenchymal fragment, craniotomy, craniectomy, seizure during acute hospitalization, and preinjury incarceration. Corrected concordance (C) statistics were 0.599, 0.747, and 0.716 for acute hospitalization, year 1, and year 2 models, respectively. The prognostic model for PTS during acute hospitalization did not discriminate well. Year 1 and year 2 models showed fair to good predictive validity for PTS. Cranial surgery, although medically necessary, requires ongoing research regarding potential benefits of increased monitoring for signs of epileptogenesis, PTS prophylaxis, and/or rehabilitation/social support. Future studies should externally validate models and determine clinical utility. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

The Relation between the Fear-Avoidance Model and Constructs from the Social Cognitive Theory in Acute WAD.

PubMed

Sandborgh, Maria; Johansson, Ann-Christin; Söderlund, Anne

2016-01-01

In the fear-avoidance (FA) model social cognitive constructs could add to explaining the disabling process in whiplash associated disorder (WAD). The aim was to exemplify the possible input from Social Cognitive Theory on the FA model. Specifically the role of functional self-efficacy and perceived responses from a spouse/intimate partner was studied. A cross-sectional and correlational design was used. Data from 64 patients with acute WAD were used. Measures were pain intensity measured with a numerical rating scale, the Pain Disability Index, support, punishing responses, solicitous responses, and distracting responses subscales from the Multidimensional Pain Inventory, the Catastrophizing subscale from the Coping Strategies Questionnaire, the Tampa Scale of Kinesiophobia, and the Self-Efficacy Scale. Bivariate correlational, simple linear regression, and multiple regression analyses were used. In the statistical prediction models high pain intensity indicated high punishing responses, which indicated high catastrophizing. High catastrophizing indicated high fear of movement, which indicated low self-efficacy. Low self-efficacy indicated high disability, which indicated high pain intensity. All independent variables together explained 66.4% of the variance in pain disability, p < 0.001. Results suggest a possible link between one aspect of the social environment, perceived punishing responses from a spouse/intimate partner, pain intensity, and catastrophizing. Further, results support a mediating role of self-efficacy between fear of movement and disability in WAD.

Testing novel patient financial incentives to increase breast cancer screening.

PubMed

Merrick, Elizabeth Levy; Hodgkin, Dominic; Horgan, Constance M; Lorenz, Laura S; Panas, Lee; Ritter, Grant A; Kasuba, Paul; Poskanzer, Debra; Nefussy, Renee Altman

2015-11-01

To examine the effects of 3 types of low-cost financial incentives for patients, including a novel "person-centered" approach on breast cancer screening (mammogram) rates. Randomized controlled trial with 4 arms: 3 types of financial incentives ($15 gift card, entry into lottery for $250 gift card, and a person-centered incentive with choice of $15 gift card or lottery) and a control group. Sample included privately insured Tufts Health Plan members in Massachusetts who were women aged 42 to 69 years with no mammogram claim in ≥ 2.6 years. A sample of 4700 eligible members were randomized to 4 study arms. The control group received a standard reminder letter and the incentive groups received a reminder letter plus an incentive offer for obtaining a mammogram within the next 4 months. Bivariate tests and multivariate logistic regression were used to assess the incentives' impact on mammogram receipt. Data were analyzed for 4427 members (after exclusions such as undeliverable mail). The percent of members receiving a mammogram during the study was 11.7% (gift card), 12.1% (lottery), 13.4% (person-centered/choice), and 11.9% (controls). Differences were not statistically significant in bivariate or multivariate full-sample analyses. In exploratory subgroup analyses of members with a mammogram during the most recent year prior to the study-defined gap, person-centered incentives were associated with a higher likelihood of mammogram receipt. None of the low-cost incentives tested had a statistically significant effect on mammogram rates in the full sample. Exploratory findings for members who were more recently screened suggest that they may be more responsive to person-centered incentives.

Effects of reservoir heterogeneity on scaling of effective mass transfer coefficient for solute transport

NASA Astrophysics Data System (ADS)

Leung, Juliana Y.; Srinivasan, Sanjay

2016-09-01

Modeling transport process at large scale requires proper scale-up of subsurface heterogeneity and an understanding of its interaction with the underlying transport mechanisms. A technique based on volume averaging is applied to quantitatively assess the scaling characteristics of effective mass transfer coefficient in heterogeneous reservoir models. The effective mass transfer coefficient represents the combined contribution from diffusion and dispersion to the transport of non-reactive solute particles within a fluid phase. Although treatment of transport problems with the volume averaging technique has been published in the past, application to geological systems exhibiting realistic spatial variability remains a challenge. Previously, the authors developed a new procedure where results from a fine-scale numerical flow simulation reflecting the full physics of the transport process albeit over a sub-volume of the reservoir are integrated with the volume averaging technique to provide effective description of transport properties. The procedure is extended such that spatial averaging is performed at the local-heterogeneity scale. In this paper, the transport of a passive (non-reactive) solute is simulated on multiple reservoir models exhibiting different patterns of heterogeneities, and the scaling behavior of effective mass transfer coefficient (Keff) is examined and compared. One such set of models exhibit power-law (fractal) characteristics, and the variability of dispersion and Keff with scale is in good agreement with analytical expressions described in the literature. This work offers an insight into the impacts of heterogeneity on the scaling of effective transport parameters. A key finding is that spatial heterogeneity models with similar univariate and bivariate statistics may exhibit different scaling characteristics because of the influence of higher order statistics. More mixing is observed in the channelized models with higher-order continuity. It reinforces the notion that the flow response is influenced by the higher-order statistical description of heterogeneity. An important implication is that when scaling-up transport response from lab-scale results to the field scale, it is necessary to account for the scale-up of heterogeneity. Since the characteristics of higher-order multivariate distributions and large-scale heterogeneity are typically not captured in small-scale experiments, a reservoir modeling framework that captures the uncertainty in heterogeneity description should be adopted.

Flood risk analysis for flood control and sediment transportation in sandy regions: A case study in the Loess Plateau, China

NASA Astrophysics Data System (ADS)

Guo, Aijun; Chang, Jianxia; Wang, Yimin; Huang, Qiang; Zhou, Shuai

2018-05-01

Traditional flood risk analysis focuses on the probability of flood events exceeding the design flood of downstream hydraulic structures while neglecting the influence of sedimentation in river channels on regional flood control systems. This work advances traditional flood risk analysis by proposing a univariate and copula-based bivariate hydrological risk framework which incorporates both flood control and sediment transport. In developing the framework, the conditional probabilities of different flood events under various extreme precipitation scenarios are estimated by exploiting the copula-based model. Moreover, a Monte Carlo-based algorithm is designed to quantify the sampling uncertainty associated with univariate and bivariate hydrological risk analyses. Two catchments located on the Loess plateau are selected as study regions: the upper catchments of the Xianyang and Huaxian stations (denoted as UCX and UCH, respectively). The univariate and bivariate return periods, risk and reliability in the context of uncertainty for the purposes of flood control and sediment transport are assessed for the study regions. The results indicate that sedimentation triggers higher risks of damaging the safety of local flood control systems compared with the event that AMF exceeds the design flood of downstream hydraulic structures in the UCX and UCH. Moreover, there is considerable sampling uncertainty affecting the univariate and bivariate hydrologic risk evaluation, which greatly challenges measures of future flood mitigation. In addition, results also confirm that the developed framework can estimate conditional probabilities associated with different flood events under various extreme precipitation scenarios aiming for flood control and sediment transport. The proposed hydrological risk framework offers a promising technical reference for flood risk analysis in sandy regions worldwide.

Continental Spatio-Temporal Data Analysis with Linear Spectral Mixture Model Using FOSS

NASA Technical Reports Server (NTRS)

Kumar, Uttam; Nemani, Ramakrishna; Ganguly, Sangram; Milesi, Cristina; Raja, Kumar; Wang, Weile; Votava, Petr; Michaelis, Andrew

2015-01-01

This work demonstrates the development and implementation of a Fully Constrained Least Squares (FCLS) unmixing model developed in C++ programming language with OpenCV package and boost C++ libraries in the NASA Earth Exchange (NEX). Visualization of the results is supported by GRASS GIS and statistical analysis is carried in R in a Linux system environment. FCLS was first tested on computer simulated data with Gaussian noise of various signal-to-noise ratio, and Landsat data of an agricultural scenario and an urban environment using a set of global end members of substrate (soils, sediments, rocks, and non-photosynthetic vegetation), vegetation that includes green photosynthetic plants and dark objects which encompasses absorptive substrate materials, clear water, deep shadows, etc. For the agricultural scenario, a spectrally diverse collection of 11 scenes of Level 1 terrain corrected, cloud free Landsat-5 TM data of Fresno, California, USA were unmixed and the results were validated with the corresponding ground data. To study an urbanized landscape, a clear sky Landsat-5 TM data were unmixed and validated with coincident World View-2 abundance maps (of 2 m spatial resolution) for an area of San Francisco, California, USA. The results were evaluated using descriptive statistics, correlation coefficient, RMSE, probability of success, boxplot and bivariate distribution function. Finally, FCLS was used for sub-pixel land cover analysis of the monthly WELD (Wen-enabled Landsat data) repository from 2008 to 2011 of North America. The abundance maps in conjunction with DMSP-OLS nighttime lights data were used to extract the urban land cover features and analyze their spatial-temporal growth.

Determinants of helmet use behaviour among employed motorcycle riders in Yazd, Iran based on theory of planned behaviour.

PubMed

Ali, Mehri; Saeed, Mazloomy Mahmoodabad Seyed; Ali, Morowatisharifabad Mohammad; Haidar, Nadrian

2011-09-01

This paper reports on predictors of helmet use behaviour, using variables based on the theory of planned behaviour model among the employed motorcycle riders in Yazd-Iran, in an attempt to identify influential factors that may be addressed through intervention efforts. In 2007, a cluster random sample of 130 employed motorcycle riders in the city of Yazd in central Iran, participated in the study. Appropriate instruments were designed to measure the variables of interest (attitude, subjective norms, perceived behaviour control, intention along with helmet use behaviour). Reliability and validity of the instruments were examined and approved. The statistical analysis of the data included descriptive statistics, bivariate correlations, and multiple regression. Based on the results, 56 out of all the respondents (43.1%) had history of accident by motorcycle. Of these motorcycle riders only 10.7% were wearing their helmet at the time of their accident. Intention and perceived behavioural control showed a significant relationship with helmet use behaviour and perceived behaviour control was the strongest predictor of helmet use intention, followed by subjective norms, and attitude. It was found that that helmet use rate among motorcycle riders was very low. The findings of present study provide a preliminary support for the TPB model as an effective framework for examining helmet use in motorcycle riders. Understanding motorcycle rider's thoughts, feelings and beliefs about helmet use behaviour can assist intervention specialists to develop and implement effective programs in order to promote helmet use among motorcycle riders. Copyright © 2010 Elsevier Ltd. All rights reserved.

Continental Spatio-temporal Data Analysis with Linear Spectral Mixture Model using FOSS

NASA Astrophysics Data System (ADS)

Kumar, U.; Nemani, R. R.; Ganguly, S.; Milesi, C.; Raja, K. S.; Wang, W.; Votava, P.; Michaelis, A.

2015-12-01

This work demonstrates the development and implementation of a Fully Constrained Least Squares (FCLS) unmixing model developed in C++ programming language with OpenCV package and boost C++ libraries in the NASA Earth Exchange (NEX). Visualization of the results is supported by GRASS GIS and statistical analysis is carried in R in a Linux system environment. FCLS was first tested on computer simulated data with Gaussian noise of various signal-to-noise ratio, and Landsat data of an agricultural scenario and an urban environment using a set of global endmembers of substrate (soils, sediments, rocks, and non-photosynthetic vegetation), vegetation that includes green photosynthetic plants and dark objects which encompasses absorptive substrate materials, clear water, deep shadows, etc. For the agricultural scenario, a spectrally diverse collection of 11 scenes of Level 1 terrain corrected, cloud free Landsat-5 TM data of Fresno, California, USA were unmixed and the results were validated with the corresponding ground data. To study an urbanized landscape, a clear sky Landsat-5 TM data were unmixed and validated with coincident World View-2 abundance maps (of 2 m spatial resolution) for an area of San Francisco, California, USA. The results were evaluated using descriptive statistics, correlation coefficient, RMSE, probability of success, boxplot and bivariate distribution function. Finally, FCLS was used for sub-pixel land cover analysis of the monthly WELD (Wen-enabled Landsat data) repository from 2008 to 2011 of North America. The abundance maps in conjunction with DMSP-OLS nighttime lights data were used to extract the urban land cover features and analyze their spatial-temporal growth.

Affective dysregulation predicts incident nonmedical prescription analgesic use among college students.

PubMed

Morioka, Christine K; Howard, Donna E; Caldeira, Kimberly M; Wang, Min Qi; Arria, Amelia M

2018-01-01

This study investigated the relationship between four suspected risk factors-affective dysregulation, conduct problems, depressive symptoms, and psychological distress-and incident nonmedical prescription analgesic (NPA) use among college students. The sample was derived from 929 college students from a large, mid-Atlantic university who completed the third annual College Life Study assessment (Y 3 ) and were NPA use naïve at baseline (Y 1 ). A series of logistic regression analyses were conducted to evaluate the predictors of incident NPA use by Y 3 . Separate models were developed to evaluate the association between the suspected risk factors and (a) NPA use relative to non-use of other drugs, including nonmedical use of other drug classes, (b) NPA use relative to other drug use, and (c) other drug use relative to non-use. All models included gender, parental education level, and race/ethnicity. Affective dysregulation was significantly associated with becoming an incident NPA user relative to both drug users without NPA use as well as non-users, after statistically controlling for demographic characteristics and other factors. Conduct problems in early childhood were positively related to both incident NPA use and other drug use without NPA use relative to non-users, after statistically controlling for demographic characteristics and other factors. Depressive symptoms were associated with NPA incidence at the bivariate level only. These findings extend previous research suggesting that NPA use might be related to deficits in regulating negative emotional states, and highlight possible markers for screening and intervention to prevent NPA use. Copyright © 2017 Elsevier Ltd. All rights reserved.

Health and human rights: a statistical measurement framework using household survey data in Uganda.

PubMed

Wesonga, Ronald; Owino, Abraham; Ssekiboobo, Agnes; Atuhaire, Leonard; Jehopio, Peter

2015-05-03

Health is intertwined with human rights as is clearly reflected in the right to life. Promotion of health practices in the context of human rights can be accomplished if there is a better understanding of the level of human rights observance. In this paper, we evaluate and present an appraisal for a possibility of applying household survey to study the determinants of health and human rights and also derive the probability that human rights are observed; an important ingredient into the national planning framework. Data from the Uganda National Governance Baseline Survey were used. A conceptual framework for predictors of a hybrid dependent variable was developed and both bivariate and multivariate statistical techniques employed. Multivariate post estimation computations were derived after evaluations of the significance of coefficients of health and human rights predictors. Findings, show that household characteristics of respondents considered in this study were statistically significant (p < 0.05) to provide a reliable assessment of human rights observance. For example, a unit increase of respondents' schooling levels results in an increase of about 34% level of positively assessing human rights observance. Additionally, the study establishes, through the three models presented, that household assessment of health and human rights observance was 20% which also represents how much of the entire continuum of human rights is demanded. Findings propose important evidence for monitoring and evaluation of health in the context human rights using household survey data. They provide a benchmark for health and human rights assessments with a focus on international and national development plans to achieve socio-economic transformation and health in society.

Is metabolic dysregulation associated with antidepressant response in depressed women in climacteric treated with individualized homeopathic medicines or fluoxetine? The HOMDEP-MENOP Study.

PubMed

Macías-Cortés, Emma Del Carmen; Llanes-González, Lidia; Aguilar-Faisal, Leopoldo; Asbun-Bojalil, Juan

2017-02-01

Climacteric is associated with both depression and metabolic dysregulation. Scarce evidence suggests that metabolic dysregulation may predict poor response to conventional antidepressants. Response to depression treatment has not been studied in homeopathic medicine. The aim of this study was to investigate the prevalence of metabolic disorders in depressed climacteric women treated with homeopathic medicines, fluoxetine or placebo, and if these alterations have any association with response to depression treatment. One hundred and thirty-three Mexican women (40-65 years) with depression, enrolled in the HOMDEP-MENOP study, a randomized, placebo-controlled, double-blind, double-dummy, three-arm trial with a 6 week follow-up, underwent a complete medical history and clinical examination. Metabolic parameters were assessed at baseline. Association between baseline metabolic parameters and response to depression treatment was analyzed with bivariate analysis in the three groups. Odds ratios (OR) with their 95% confidence interval (95% CI) were calculated. Metabolic parameters were considered for inclusion in the logistic regression model if they had a statistically significant relationship with response rate on bivariate analysis at p<0.05 or if they were clinically relevant. Overall combined prevalence (obesity and overweight) was 86.5%; 52.3% had hypertriglyceridemia; 44.7% hypercholesterolemia; 46.7% insulin resistance; and 16% subclinical hypothyroidism. There was no statistically significant association between dyslipidemia, overweight, or insulin resistance and non-response in the homeopathy group [OR (95% CI) 1.57 (0.46-5.32), p=0.467; 0.37 (0.003-1.11), p=0.059; 0.67 (0.16-2.7), p=0.579, respectively]. Metabolic dysregulation was not significantly associated with response to depression treatment in depressed climacteric women treated with individualized homeopathic treatment (IHT), fluoxetine or placebo. Due to the high prevalence of metabolic disorders and its relationship with depression in the climacteric, further investigation should be focused on whether individualized prescriptions based on classical homeopathy for depressed climacteric women have an effect on metabolic parameters, and/or if treating the metabolic disorders at the same time could lead to higher response rates. ClinicalTrials.gov Identifier: NCT01635218 URL: http://clinicaltrials.gov/ct2/show/NCT01635218?term=depression+homeopathy&rank=1. Copyright © 2016 The Faculty of Homeopathy. Published by Elsevier Ltd. All rights reserved.

Racial and Gender Disparities in the Physician Assistant Profession.

PubMed

Smith, Darron T; Jacobson, Cardell K

2016-06-01

To examine whether racial, gender, and ethnic salary disparities exist in the physician assistant (PA) profession and what factors, if any, are associated with the differentials. We use a nationally representative survey of 15,105 PAs from the American Academy of Physician Assistants (AAPA). We use bivariate and multivariate statistics to analyze pay differentials from the 2009 AAPA survey. Women represent nearly two-thirds of the profession but receive approximately $18,000 less in primary compensation. The differential reduces to just over $9,500 when the analysis includes a variety of other variables. According to AAPA survey, minority PAs tend to make slightly higher salaries than White PAs nationally, although the differences are not statistically significant once the control variables are included in the analysis. Despite the rough parity in primary salary, PAs of color are vastly underrepresented in the profession. The salaries of women lag in comparison to their male counterparts. © Health Research and Educational Trust.

Chronic effects of workplace noise on blood pressure and heart rate.

PubMed

Lusk, Sally L; Hagerty, Bonnie M; Gillespie, Brenda; Caruso, Claire C

2002-01-01

Environmental noise levels in the United States are increasing, yet there are few studies in which the nonauditory effects of workplace noise are assessed. In the current study, the authors examined chronic effects of noise on blood pressure and heart rate in 374 workers at an automobile plant. Data were collected from subjects prior to the start of their workshift. Participants completed questionnaires about diet, alcohol use, lifestyle, noise annoyance, use of hearing protection, noise exposure outside of the work environment, personal and family health histories, and demographic information. Resting blood pressure, heart rate, and body mass index were obtained. Noise exposure levels were extracted retrospectively from company records for each participant for the past 5 yr. Summary statistics were generated for each variable, and the authors performed bivariate correlations to identify any unadjusted associations. The authors then completed statistical modeling to investigate the effects of noise on blood pressure and heart rate, after they controlled for other variables (e.g., gender, race, age). The authors controlled for confounding variables, after which use of hearing protection in high-noise areas was a significant predictor of a decrease in both systolic and diastolic blood pressures. The results suggested that the reduction of noise exposure by means of engineering controls or by consistent use of hearing protection by workers may positively affect health outcomes.

Bivariate versus multivariate smart spectrophotometric calibration methods for the simultaneous determination of a quaternary mixture of mosapride, pantoprazole and their degradation products.

PubMed

Hegazy, M A; Yehia, A M; Moustafa, A A

2013-05-01

The ability of bivariate and multivariate spectrophotometric methods was demonstrated in the resolution of a quaternary mixture of mosapride, pantoprazole and their degradation products. The bivariate calibrations include bivariate spectrophotometric method (BSM) and H-point standard addition method (HPSAM), which were able to determine the two drugs, simultaneously, but not in the presence of their degradation products, the results showed that simultaneous determinations could be performed in the concentration ranges of 5.0-50.0 microg/ml for mosapride and 10.0-40.0 microg/ml for pantoprazole by bivariate spectrophotometric method and in the concentration ranges of 5.0-45.0 microg/ml for both drugs by H-point standard addition method. Moreover, the applied multivariate calibration methods were able for the determination of mosapride, pantoprazole and their degradation products using concentration residuals augmented classical least squares (CRACLS) and partial least squares (PLS). The proposed multivariate methods were applied to 17 synthetic samples in the concentration ranges of 3.0-12.0 microg/ml mosapride, 8.0-32.0 microg/ml pantoprazole, 1.5-6.0 microg/ml mosapride degradation products and 2.0-8.0 microg/ml pantoprazole degradation products. The proposed bivariate and multivariate calibration methods were successfully applied to the determination of mosapride and pantoprazole in their pharmaceutical preparations.

Wraparound Retrospective: Factors Predicting Positive Outcomes

ERIC Educational Resources Information Center

Cox, Kathy; Baker, Dawniel; Wong, Mary Ann

2010-01-01

While research regarding the effectiveness of the wraparound process is steadily mounting, little is known about how this service delivery model works and for whom. Using data gathered on 176 youth who participated in the wraparound process, the authors examine client and service factors associated with outcomes. Bivariate logistic regression…

Care Seeking Patterns of STIs-Associated Symptoms in Iran: Findings of a Population-Based Survey.

PubMed

Nasirian, Maryam; Karamouzian, Mohammad; Kamali, Kianoush; Nabipour, Amir Reza; Maghsoodi, Ahmad; Nikaeen, Roja; Razzaghi, Ali Reza; Mirzazadeh, Ali; Baneshi, Mohammad Reza; Haghdoost, Ali Akbar

2015-08-09

Understanding the prevalence of symptoms associated with sexually transmitted infections (STIs) and how care is sought for those symptoms are important components of STIs control and prevention. People's preference between public and private service providers is another important part of developing a well-functioning STIs surveillance system. This cross-sectional survey was carried out in spring 2011, using a nonrandom quota sample of 1190 participants (52% female) in 4 densely-populated cities of Tehran, Kerman, Shiraz, and Babol. Two predictive logistic regression models were constructed to assess the association between the socio-demographic determinants (independent variables) and the dependent variables of history of STIs-associated symptom and seeking care. Around 57% (677 out of 1190; men: 29.70% and women: 81.80%) had experienced at least one STIs-associated symptom during the previous year. History of experiencing STIs-associated symptoms among men, was negatively significantly associated with older age (adjusted odds ratio [AOR] = 0.34, CI 95%: 0.17-0.67). Women who were married, in older ages, and had higher educations were more likely to report a recent (past year) STIs symptom, however all were statistically insignificant in both bivariate and multivariable models. Among those who have had STIs-associated symptoms in the last year, 31.15% did nothing to improve their symptoms, 8.03% attempted self-treatment by over-the-counter (OTC) medications or traditional remedies, and 60.93% sought care in health facilities. In both bivariate and multivariable analyses, care seeking among men was insignificantly associated with any of the collected demographic variables. Care seeking among women was positively significantly associated with being married (AOR = 2.48, 95% CI: 1.60-3.84). The reported prevalence of STIs-associated symptoms among our participants is concerning. A considerable number of participants had delayed seeking care and treatment or self-medicated. People should be informed about their sexual health and the consequences of delaying or avoiding seeking care for STIs. Participants preferred seeking care at private sectors which calls for engaging both public and private health sectors for reporting and following up STIs cases. © 2016 by Kerman University of Medical Sciences.

Validation of the conceptual research utilization scale: an application of the standards for educational and psychological testing in healthcare.

PubMed

Squires, Janet E; Estabrooks, Carole A; Newburn-Cook, Christine V; Gierl, Mark

2011-05-19

There is a lack of acceptable, reliable, and valid survey instruments to measure conceptual research utilization (CRU). In this study, we investigated the psychometric properties of a newly developed scale (the CRU Scale). We used the Standards for Educational and Psychological Testing as a validation framework to assess four sources of validity evidence: content, response processes, internal structure, and relations to other variables. A panel of nine international research utilization experts performed a formal content validity assessment. To determine response process validity, we conducted a series of one-on-one scale administration sessions with 10 healthcare aides. Internal structure and relations to other variables validity was examined using CRU Scale response data from a sample of 707 healthcare aides working in 30 urban Canadian nursing homes. Principal components analysis and confirmatory factor analyses were conducted to determine internal structure. Relations to other variables were examined using: (1) bivariate correlations; (2) change in mean values of CRU with increasing levels of other kinds of research utilization; and (3) multivariate linear regression. Content validity index scores for the five items ranged from 0.55 to 1.00. The principal components analysis predicted a 5-item 1-factor model. This was inconsistent with the findings from the confirmatory factor analysis, which showed best fit for a 4-item 1-factor model. Bivariate associations between CRU and other kinds of research utilization were statistically significant (p < 0.01) for the latent CRU scale score and all five CRU items. The CRU scale score was also shown to be significant predictor of overall research utilization in multivariate linear regression. The CRU scale showed acceptable initial psychometric properties with respect to responses from healthcare aides in nursing homes. Based on our validity, reliability, and acceptability analyses, we recommend using a reduced (four-item) version of the CRU scale to yield sound assessments of CRU by healthcare aides. Refinement to the wording of one item is also needed. Planned future research will include: latent scale scoring, identification of variables that predict and are outcomes to conceptual research use, and longitudinal work to determine CRU Scale sensitivity to change.

Regression analysis for bivariate gap time with missing first gap time data.

PubMed

Huang, Chia-Hui; Chen, Yi-Hau

2017-01-01

We consider ordered bivariate gap time while data on the first gap time are unobservable. This study is motivated by the HIV infection and AIDS study, where the initial HIV contracting time is unavailable, but the diagnosis times for HIV and AIDS are available. We are interested in studying the risk factors for the gap time between initial HIV contraction and HIV diagnosis, and gap time between HIV and AIDS diagnoses. Besides, the association between the two gap times is also of interest. Accordingly, in the data analysis we are faced with two-fold complexity, namely data on the first gap time is completely missing, and the second gap time is subject to induced informative censoring due to dependence between the two gap times. We propose a modeling framework for regression analysis of bivariate gap time under the complexity of the data. The estimating equations for the covariate effects on, as well as the association between, the two gap times are derived through maximum likelihood and suitable counting processes. Large sample properties of the resulting estimators are developed by martingale theory. Simulations are performed to examine the performance of the proposed analysis procedure. An application of data from the HIV and AIDS study mentioned above is reported for illustration.

Quantitative methods used in Australian health promotion research: a review of publications from 1992-2002.

PubMed

Smith, Ben J; Zehle, Katharina; Bauman, Adrian E; Chau, Josephine; Hawkshaw, Barbara; Frost, Steven; Thomas, Margaret

2006-04-01

This study examined the use of quantitative methods in Australian health promotion research in order to identify methodological trends and priorities for strengthening the evidence base for health promotion. Australian health promotion articles were identified by hand searching publications from 1992-2002 in six journals: Health Promotion Journal of Australia, Australian and New Zealand journal of Public Health, Health Promotion International, Health Education Research, Health Education and Behavior and the American Journal of Health Promotion. The study designs and statistical methods used in articles presenting quantitative research were recorded. 591 (57.7%) of the 1,025 articles used quantitative methods. Cross-sectional designs were used in the majority (54.3%) of studies with pre- and post-test (14.6%) and post-test only (9.5%) the next most common designs. Bivariate statistical methods were used in 45.9% of papers, multivariate methods in 27.1% and simple numbers and proportions in 25.4%. Few studies used higher-level statistical techniques. While most studies used quantitative methods, the majority were descriptive in nature. The study designs and statistical methods used provided limited scope for demonstrating intervention effects or understanding the determinants of change.

Pelvic-floor strength in women with incontinence as assessed by the brink scale.

PubMed

FitzGerald, Mary P; Burgio, Kathryn L; Borello-France, Diane F; Menefee, Shawn A; Schaffer, Joseph; Kraus, Stephen; Mallett, Veronica T; Xu, Yan

2007-10-01

The purpose of this study was to describe how clinical pelvic-floor muscle (PFM) strength (force-generating capacity) is related to patient characteristics, lower urinary tract symptoms, and fecal incontinence symptoms. Data were obtained from 643 women who were participating in a randomized surgical trial for treatment of stress urinary incontinence. Patient demographic variables, baseline urinary and fecal incontinence symptom questionnaires, urodynamic data and urinary diary data, pad test results, and standardized assessment of pelvic organ support were compared with PFM strength as described by the Brink scoring system. Bivariate analysis of factors associated with the Brink scale score was done using analysis of variance and linear regression. Multivariate analysis included patient variables that were significant on bivariate analysis. The mean Brink scale score was 9 (SD=2) and did not vary widely in this large, but highly select, patient sample. We found a weak, but statistically strong, relationship between age and Brink score. Brink scores were not related to diary and pad test measures of incontinence severity. Overall, PFM strength was good in this sample of women with stress incontinence. Scores tended to be similar, and it is possible that the Brink scale does not reflect real clinical differences in PFM strength.

Male and female child murderers: an empirical analysis of U.S. arrest data.

PubMed

Sellers, Brian G; Heide, Kathleen M

2012-08-01

Recent U.S. cases of murders by children below age 11 have captured national headlines. A review of the literature reveals that little is known about this population of juvenile homicide offenders (JHOs). Most studies on juvenile murderers have used small clinical samples, focused on adolescents, and concentrated on male offenders. Studies that have used Supplementary Homicide Report (SHR) data have found significant gender differences among juveniles below 18 years arrested for murder. This study investigated gender differences among 226 juvenile murderers ages 6 through 10 involved in single-victim incidents using bivariate and multivariate statistical techniques. Consistent with previous research, bivariate analyses revealed gender differences with respect to the type of weapon used, age of the victim, relationship to the victim, and circumstances of the crime. Logistic regression analysis identified female JHOs as more likely to use a knife, kill a family member, and kill a victim below age 5, when compared with male JHOs. From these findings, profiles of young male and female JHOs can be drawn. The article concludes with a discussion of the study's implications for prevention and treatment. The authors recommend that future research in gender differences among young children focus on examining psychological, neurological, and sociological variables not included in the SHR data set.

Role of Surgical Services in Profitability of Hospitals in California: An Analysis of Office of Statewide Health Planning and Development Annual Financial Data.

PubMed

Moazzez, Ashkan; de Virgilio, Christian

2016-10-01

With constant changes in health-care laws and payment methods, profitability, and financial sustainability of hospitals are of utmost importance. The purpose of this study is to determine the relationship between surgical services and hospital profitability. The Office of Statewide Health Planning and Development annual financial databases for the years 2009 to 2011 were used for this study. The hospitals' characteristics and income statement elements were extracted for statistical analysis using bivariate and multivariate linear regression. A total of 989 financial records of 339 hospitals were included. On bivariate analysis, the number of inpatient and ambulatory operating rooms (ORs), the number of cases done both as inpatient and outpatient in each OR, and the average minutes used in inpatient ORs were significantly related with the net income of the hospital. On multivariate regression analysis, when controlling for hospitals' payer mix and the study year, only the number of inpatient cases done in the inpatient ORs (β = 832, P = 0.037), and the number of ambulatory ORs (β = 1,485, 466, P = 0.001) were significantly related with the net income of the hospital. These findings suggest that hospitals can maximize their profitability by diverting and allocating outpatient surgeries to ambulatory ORs, to allow for more inpatient surgeries.

Predictors of anemia among pregnant women in Westmoreland, Jamaica

PubMed Central

Charles, Alyson M.; Campbell-Stennett, Dianne; Yatich, Nelly; Jolly, Pauline E.

2010-01-01

Anemia in pregnancy is a worldwide problem, but it is most prevalent in the developing world. This research project was conducted to determine the predictors of anemia in pregnant women in Westmoreland, Jamaica. A cross-sectional study design was conducted and descriptive, bivariate, and multiple logistic regression analyses were used. Body mass index, Mid-upper arm circumference, and the number of antenatal care visits showed a statistically significant association with anemia. Based on the results, we believe that maintaining a healthy body weight, and frequently visiting an antenatal clinic, will help to lower the prevalence of anemia among pregnant women in Westmoreland. PMID:20526925

Bivariate Gaussian bridges: directional factorization of diffusion in Brownian bridge models.

PubMed

Kranstauber, Bart; Safi, Kamran; Bartumeus, Frederic

2014-01-01

In recent years high resolution animal tracking data has become the standard in movement ecology. The Brownian Bridge Movement Model (BBMM) is a widely adopted approach to describe animal space use from such high resolution tracks. One of the underlying assumptions of the BBMM is isotropic diffusive motion between consecutive locations, i.e. invariant with respect to the direction. Here we propose to relax this often unrealistic assumption by separating the Brownian motion variance into two directional components, one parallel and one orthogonal to the direction of the motion. Our new model, the Bivariate Gaussian bridge (BGB), tracks movement heterogeneity across time. Using the BGB and identifying directed and non-directed movement within a trajectory resulted in more accurate utilisation distributions compared to dynamic Brownian bridges, especially for trajectories with a non-isotropic diffusion, such as directed movement or Lévy like movements. We evaluated our model with simulated trajectories and observed tracks, demonstrating that the improvement of our model scales with the directional correlation of a correlated random walk. We find that many of the animal trajectories do not adhere to the assumptions of the BBMM. The proposed model improves accuracy when describing the space use both in simulated correlated random walks as well as observed animal tracks. Our novel approach is implemented and available within the "move" package for R.

Bivariate tensor product [Formula: see text]-analogue of Kantorovich-type Bernstein-Stancu-Schurer operators.

PubMed

Cai, Qing-Bo; Xu, Xiao-Wei; Zhou, Guorong

2017-01-01

In this paper, we construct a bivariate tensor product generalization of Kantorovich-type Bernstein-Stancu-Schurer operators based on the concept of [Formula: see text]-integers. We obtain moments and central moments of these operators, give the rate of convergence by using the complete modulus of continuity for the bivariate case and estimate a convergence theorem for the Lipschitz continuous functions. We also give some graphs and numerical examples to illustrate the convergence properties of these operators to certain functions.

Accounting for respiration is necessary to reliably infer Granger causality from cardiovascular variability series.

PubMed

Porta, Alberto; Bassani, Tito; Bari, Vlasta; Pinna, Gian D; Maestri, Roberto; Guzzetti, Stefano

2012-03-01

This study was designed to demonstrate the need of accounting for respiration (R) when causality between heart period (HP) and systolic arterial pressure (SAP) is under scrutiny. Simulations generated according to a bivariate autoregressive closed-loop model were utilized to assess how causality changes as a function of the model parameters. An exogenous (X) signal was added to the bivariate autoregressive closed-loop model to evaluate the bias on causality induced when the X source was disregarded. Causality was assessed in the time domain according to a predictability improvement approach (i.e., Granger causality). HP and SAP variability series were recorded with R in 19 healthy subjects during spontaneous and controlled breathing at 10, 15, and 20 breaths/min. Simulations proved the importance of accounting for X signals. During spontaneous breathing, assessing causality without taking into consideration R leads to a significantly larger percentage of closed-loop interactions and a smaller fraction of unidirectional causality from HP to SAP. This finding was confirmed during paced breathing and it was independent of the breathing rate. These results suggest that the role of baroreflex cannot be correctly assessed without accounting for R.

Estimating Contraceptive Prevalence Using Logistics Data for Short-Acting Methods: Analysis Across 30 Countries.

PubMed

Cunningham, Marc; Bock, Ariella; Brown, Niquelle; Sacher, Suzy; Hatch, Benjamin; Inglis, Andrew; Aronovich, Dana

2015-09-01

Contraceptive prevalence rate (CPR) is a vital indicator used by country governments, international donors, and other stakeholders for measuring progress in family planning programs against country targets and global initiatives as well as for estimating health outcomes. Because of the need for more frequent CPR estimates than population-based surveys currently provide, alternative approaches for estimating CPRs are being explored, including using contraceptive logistics data. Using data from the Demographic and Health Surveys (DHS) in 30 countries, population data from the United States Census Bureau International Database, and logistics data from the Procurement Planning and Monitoring Report (PPMR) and the Pipeline Monitoring and Procurement Planning System (PipeLine), we developed and evaluated 3 models to generate country-level, public-sector contraceptive prevalence estimates for injectable contraceptives, oral contraceptives, and male condoms. Models included: direct estimation through existing couple-years of protection (CYP) conversion factors, bivariate linear regression, and multivariate linear regression. Model evaluation consisted of comparing the referent DHS prevalence rates for each short-acting method with the model-generated prevalence rate using multiple metrics, including mean absolute error and proportion of countries where the modeled prevalence rate for each method was within 1, 2, or 5 percentage points of the DHS referent value. For the methods studied, family planning use estimates from public-sector logistics data were correlated with those from the DHS, validating the quality and accuracy of current public-sector logistics data. Logistics data for oral and injectable contraceptives were significantly associated (P<.05) with the referent DHS values for both bivariate and multivariate models. For condoms, however, that association was only significant for the bivariate model. With the exception of the CYP-based model for condoms, models were able to estimate public-sector prevalence rates for each short-acting method to within 2 percentage points in at least 85% of countries. Public-sector contraceptive logistics data are strongly correlated with public-sector prevalence rates for short-acting methods, demonstrating the quality of current logistics data and their ability to provide relatively accurate prevalence estimates. The models provide a starting point for generating interim estimates of contraceptive use when timely survey data are unavailable. All models except the condoms CYP model performed well; the regression models were most accurate but the CYP model offers the simplest calculation method. Future work extending the research to other modern methods, relating subnational logistics data with prevalence rates, and tracking that relationship over time is needed. © Cunningham et al.

Estimating Contraceptive Prevalence Using Logistics Data for Short-Acting Methods: Analysis Across 30 Countries

PubMed Central

Cunningham, Marc; Brown, Niquelle; Sacher, Suzy; Hatch, Benjamin; Inglis, Andrew; Aronovich, Dana

2015-01-01

Background: Contraceptive prevalence rate (CPR) is a vital indicator used by country governments, international donors, and other stakeholders for measuring progress in family planning programs against country targets and global initiatives as well as for estimating health outcomes. Because of the need for more frequent CPR estimates than population-based surveys currently provide, alternative approaches for estimating CPRs are being explored, including using contraceptive logistics data. Methods: Using data from the Demographic and Health Surveys (DHS) in 30 countries, population data from the United States Census Bureau International Database, and logistics data from the Procurement Planning and Monitoring Report (PPMR) and the Pipeline Monitoring and Procurement Planning System (PipeLine), we developed and evaluated 3 models to generate country-level, public-sector contraceptive prevalence estimates for injectable contraceptives, oral contraceptives, and male condoms. Models included: direct estimation through existing couple-years of protection (CYP) conversion factors, bivariate linear regression, and multivariate linear regression. Model evaluation consisted of comparing the referent DHS prevalence rates for each short-acting method with the model-generated prevalence rate using multiple metrics, including mean absolute error and proportion of countries where the modeled prevalence rate for each method was within 1, 2, or 5 percentage points of the DHS referent value. Results: For the methods studied, family planning use estimates from public-sector logistics data were correlated with those from the DHS, validating the quality and accuracy of current public-sector logistics data. Logistics data for oral and injectable contraceptives were significantly associated (P<.05) with the referent DHS values for both bivariate and multivariate models. For condoms, however, that association was only significant for the bivariate model. With the exception of the CYP-based model for condoms, models were able to estimate public-sector prevalence rates for each short-acting method to within 2 percentage points in at least 85% of countries. Conclusions: Public-sector contraceptive logistics data are strongly correlated with public-sector prevalence rates for short-acting methods, demonstrating the quality of current logistics data and their ability to provide relatively accurate prevalence estimates. The models provide a starting point for generating interim estimates of contraceptive use when timely survey data are unavailable. All models except the condoms CYP model performed well; the regression models were most accurate but the CYP model offers the simplest calculation method. Future work extending the research to other modern methods, relating subnational logistics data with prevalence rates, and tracking that relationship over time is needed. PMID:26374805

A Method for Approximating the Bivariate Normal Correlation Coefficient.

ERIC Educational Resources Information Center

Kirk, David B.

Improvements of the Gaussian quadrature in conjunction with the Newton-Raphson iteration technique (TM 000 789) are discussed as effective methods of calculating the bivariate normal correlation coefficient. (CK)

Multiple mediation path model of pain's cascading influence on physical disability in individuals with SCI from Colombia, South America.

PubMed

Perrin, Paul B; Paredes, Alejandra Morlett; Olivera, Silvia Leonor; Lozano, Juan Esteban; Leal, Wendy Tatiana; Ahmad, Usman F; Arango-Lasprilla, Juan Carlos

2017-01-01

Research has begun to document the bivariate connections between pain in individuals with spinal cord injury (SCI) and various aspects of health related quality of life (HRQOL), such as fatigue, social functioning, mental health, and physical functioning. The purpose of this study was to construct and test a theoretical path model illuminating the stage-wise and sequential (cascading) HRQOL pathways through which pain increases physical disability in individuals with SCI in a sample from Colombia, South America. It was hypothesized that increased pain would lead to decreased energy, which would lead to decreased mental health and social functioning, which both would lead to emotional role limitations, which finally would lead to physical role limitations. A cross-sectional study assessed individuals with SCI (n = 40) in Neiva, Colombia. Participants completed a measure indexing various aspects of HRQOL. The path model overall showed excellent fit indices, and each individual path within the model was statistically significant. Pain exerted significant indirect effects through all possible mediators in the model, ultimately suggesting that energy, mental health, social functioning, and role limitations-emotional were likely pathways through which pain exerted its effects on physical disability in individuals with SCI. These findings uncover several potential nodes for clinical intervention which if targeted in the context of rehabilitation or outpatient services, could result in salubrious direct and indirect effects reverberating down the theoretical causal chain and ultimately reducing physical disability in individuals with SCI.

Lead Emissions and Population Vulnerability in the Detroit (Michigan, USA) Metropolitan Area, 2006-2013: A Spatial and Temporal Analysis.

PubMed

Moody, Heather; Grady, Sue C

2017-11-23

Objective : The purpose of this research is to geographically model airborne lead emission concentrations and total lead deposition in the Detroit Metropolitan Area (DMA) from 2006 to 2013. Further, this study characterizes the racial and socioeconomic composition of recipient neighborhoods and estimates the potential for IQ (Intelligence Quotient) loss of children residing there. Methods : Lead emissions were modeled from emitting facilities in the DMA using AERMOD (American Meteorological Society/Environmental Protection Agency Regulatory Model). Multilevel modeling was used to estimate local racial residential segregation, controlling for poverty. Global Moran's I bivariate spatial autocorrelation statistics were used to assess modeled emissions with increasing segregation. Results : Lead emitting facilities were primarily located in, and moving to, highly black segregated neighborhoods regardless of poverty levels-a phenomenon known as environmental injustice. The findings from this research showed three years of elevated airborne emission concentrations in these neighborhoods to equate to a predicted 1.0 to 3.0 reduction in IQ points for children living there. Across the DMA there are many areas where annual lead deposition was substantially higher than recommended for aquatic (rivers, lakes, etc.) and terrestrial (forests, dunes, etc.) ecosystems. These lead levels result in decreased reproductive and growth rates in plants and animals, and neurological deficits in vertebrates. Conclusions : This lead-hazard and neighborhood context assessment will inform future childhood lead exposure studies and potential health consequences in the DMA.

Detecting event-related changes of multivariate phase coupling in dynamic brain networks.

PubMed

Canolty, Ryan T; Cadieu, Charles F; Koepsell, Kilian; Ganguly, Karunesh; Knight, Robert T; Carmena, Jose M

2012-04-01

Oscillatory phase coupling within large-scale brain networks is a topic of increasing interest within systems, cognitive, and theoretical neuroscience. Evidence shows that brain rhythms play a role in controlling neuronal excitability and response modulation (Haider B, McCormick D. Neuron 62: 171-189, 2009) and regulate the efficacy of communication between cortical regions (Fries P. Trends Cogn Sci 9: 474-480, 2005) and distinct spatiotemporal scales (Canolty RT, Knight RT. Trends Cogn Sci 14: 506-515, 2010). In this view, anatomically connected brain areas form the scaffolding upon which neuronal oscillations rapidly create and dissolve transient functional networks (Lakatos P, Karmos G, Mehta A, Ulbert I, Schroeder C. Science 320: 110-113, 2008). Importantly, testing these hypotheses requires methods designed to accurately reflect dynamic changes in multivariate phase coupling within brain networks. Unfortunately, phase coupling between neurophysiological signals is commonly investigated using suboptimal techniques. Here we describe how a recently developed probabilistic model, phase coupling estimation (PCE; Cadieu C, Koepsell K Neural Comput 44: 3107-3126, 2010), can be used to investigate changes in multivariate phase coupling, and we detail the advantages of this model over the commonly employed phase-locking value (PLV; Lachaux JP, Rodriguez E, Martinerie J, Varela F. Human Brain Map 8: 194-208, 1999). We show that the N-dimensional PCE is a natural generalization of the inherently bivariate PLV. Using simulations, we show that PCE accurately captures both direct and indirect (network mediated) coupling between network elements in situations where PLV produces erroneous results. We present empirical results on recordings from humans and nonhuman primates and show that the PCE-estimated coupling values are different from those using the bivariate PLV. Critically on these empirical recordings, PCE output tends to be sparser than the PLVs, indicating fewer significant interactions and perhaps a more parsimonious description of the data. Finally, the physical interpretation of PCE parameters is straightforward: the PCE parameters correspond to interaction terms in a network of coupled oscillators. Forward modeling of a network of coupled oscillators with parameters estimated by PCE generates synthetic data with statistical characteristics identical to empirical signals. Given these advantages over the PLV, PCE is a useful tool for investigating multivariate phase coupling in distributed brain networks.

Indicators of satisfaction in clickers-aided EFL class.

PubMed

Yu, Zhonggen

2015-01-01

How to identify whether students are satisfied with clickers-aided EFL class might be largely a mystery for most researchers since satisfaction is deeply hidden in human psychology which is subtle and intangible. This study, by using bivariate correlation analysis and structural equation modeling, survey scales claimed both valid and internally consistent, and data collected from randomly selected 227 participants, explored the indicators of satisfaction in clickers-aided EFL class, together with gender differences in the indicators. It was concluded that satisfaction was positively correlated with interaction, self-efficacy and self-regulation in clickers-aided EFL class without statistically significant gender differences. Furthermore, interaction, self-efficacy and self-regulation were mutually and significantly correlated. Although indicators of satisfaction might not be limited to these three factors, the findings should be helpful to future researchers who desire to determine whether users are satisfied with the polling technology. Then teachers could decide what teaching style and contents should be adopted. In order to satisfy users of clickers, future lecturing might be designed to promote peer interaction, self-efficacy and self-regulation.

Suicide Risk Factors Among Older Adults: Exploring Thwarted Belongingness and Perceived Burdensomeness in Relation to Personality and Self-Esteem.

PubMed

Eades, Allison; Segal, Daniel L; Coolidge, Frederick L

2018-01-01

The objective of this study was to explore the role of personality and self-esteem in later life within two established risk factors for suicidal ideation (SI)-Thwarted Belongingness (TB) and Perceived Burdensomeness (PB). The data about personality (i.e., Five Factor Model [FFM] and Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition Personality Disorders [PD]), self-esteem, TB, PB, and SI were collected from 102 community-dwelling older adults and analyzed using bivariate and multivariate techniques. All FFM domains and most PD traits were significantly correlated with SI, TB, and PB. Furthermore, FFM and PD traits explained a significant and meaningful amount of variance of SI, TB, and PB. Self-esteem demonstrated strong negative relationships with SI, TB, and PB. Personality features and self-esteem are important associated features for SI, TB, and PB. Clinicians should consider this information when assessing and evaluating for suicidal risk among older adults. The findings also highlight the need to consider personality traits in developing prevention strategies.

Indicators of satisfaction in clickers-aided EFL class

PubMed Central

Yu, Zhonggen

2015-01-01

How to identify whether students are satisfied with clickers-aided EFL class might be largely a mystery for most researchers since satisfaction is deeply hidden in human psychology which is subtle and intangible. This study, by using bivariate correlation analysis and structural equation modeling, survey scales claimed both valid and internally consistent, and data collected from randomly selected 227 participants, explored the indicators of satisfaction in clickers-aided EFL class, together with gender differences in the indicators. It was concluded that satisfaction was positively correlated with interaction, self-efficacy and self-regulation in clickers-aided EFL class without statistically significant gender differences. Furthermore, interaction, self-efficacy and self-regulation were mutually and significantly correlated. Although indicators of satisfaction might not be limited to these three factors, the findings should be helpful to future researchers who desire to determine whether users are satisfied with the polling technology. Then teachers could decide what teaching style and contents should be adopted. In order to satisfy users of clickers, future lecturing might be designed to promote peer interaction, self-efficacy and self-regulation. PMID:25999898

Sensation seeking and impulsive traits as personality endophenotypes for antisocial behavior: Evidence from two independent samples

PubMed Central

Mann, Frank D.; Engelhardt, Laura; Briley, Daniel A.; Grotzinger, Andrew D.; Patterson, Megan W.; Tackett, Jennifer L.; Strathan, Dixie B.; Heath, Andrew; Lynskey, Michael; Slutske, Wendy; Martin, Nicholas G.; Tucker-Drob, Elliot M.; Harden, K. Paige

2017-01-01

Sensation seeking and impulsivity are personality traits that are correlated with risk for antisocial behavior (ASB). This paper uses two independent samples of twins to (a) test the extent to which sensation seeking and impulsivity statistically mediate genetic influence on ASB, and (b) compare this to genetic influences accounted for by other personality traits. In Sample 1, delinquent behavior, as well as impulsivity, sensation seeking and Big Five personality traits, were measured in adolescent twins from the Texas Twin Project. In Sample 2, adult twins from the Australian Twin Registry responded to questionnaires that assessed individual differences in Eysenck's and Cloninger's personality dimensions, and a structured telephone interview that asked participants to retrospectively report DSM-defined symptoms of conduct disorder. Bivariate quantitative genetic models were used to identify genetic overlap between personality traits and ASB. Across both samples, novelty/sensation seeking and impulsive traits accounted for larger portions of genetic variance in ASB than other personality traits. We discuss whether sensation seeking and impulsive personality are causal endophenotypes for ASB, or merely index genetic liability for ASB. PMID:28824215

Using social cognitive theory to explain discretionary, "leisure-time" physical exercise among high school students.

PubMed

Winters, Eric R; Petosa, Rick L; Charlton, Thomas E

2003-06-01

To examine whether knowledge of high school students' actions of self-regulation, and perceptions of self-efficacy to overcome exercise barriers, social situation, and outcome expectation will predict non-school related moderate and vigorous physical exercise. High school students enrolled in introductory Physical Education courses completed questionnaires that targeted selected Social Cognitive Theory variables. They also self-reported their typical "leisure-time" exercise participation using a standardized questionnaire. Bivariate correlation statistic and hierarchical regression were conducted on reports of moderate and vigorous exercise frequency. Each predictor variable was significantly associated with measures of moderate and vigorous exercise frequency. All predictor variables were significant in the final regression model used to explain vigorous exercise. After controlling for the effects of gender, the psychosocial variables explained 29% of variance in vigorous exercise frequency. Three of four predictor variables were significant in the final regression equation used to explain moderate exercise. The final regression equation accounted for 11% of variance in moderate exercise frequency. Professionals who attempt to increase the prevalence of physical exercise through educational methods should focus on the psychosocial variables utilized in this study.

Impact of some types of mass gatherings on current suicide risk in an urban population: statistical and negative binominal regression analysis of time series.

PubMed

Usenko, Vasiliy S; Svirin, Sergey N; Shchekaturov, Yan N; Ponarin, Eduard D

2014-04-04

Many studies have investigated the impact of a wide range of social events on suicide-related behaviour. However, these studies have predominantly examined national events. The aim of this study is to provide a statistical evaluation of the relationship between mass gatherings in some relatively small urban sub-populations and the general suicide rates of a major city. The data were gathered in the Ukrainian city of Dnipropetrovsk, with a population of 1 million people, in 2005-2010. Suicide attempts, suicides, and the total amount of suicide-related behaviours were registered daily for each sex. Bivariate and multivariate statistical analysis, including negative binomial regression, were applied to assess the risk of suicide-related behaviour in the city's general population for 7 days before and after 427 mass gatherings, such as concerts, football games, and non-regular mass events organized by the Orthodox Church and new religious movements. The bivariate and multivariate statistical analyses found significant changes in some suicide-related behaviour rates in the city's population after certain kinds of mass gatherings. In particular, we observed an increased relative risk (RR) of male suicide-related behaviour after a home defeat of the local football team (RR = 1.32, p = 0.047; regression coefficient beta = 0.371, p = 0.002), and an increased risk of male suicides (RR = 1.29, p = 0.006; beta =0.255, p = 0.002), male suicide-related behaviour (RR = 1.25, p = 0.019; beta =0.251, p < 0.001), and total suicide-related behaviour (RR = 1.23 p < 0.001; beta =0.187, p < 0.001) after events organized by the new religious movements. Although football games and mass events organized by new religious movements involved a relatively small part of an urban population (1.6 and 0.3%, respectively), we observed a significant increase of the some suicide-related behaviour rates in the whole population. It is likely that the observed effect on suicide-related behaviour is related to one's personal presence at the event rather than to its broadcast. Our findings can be explained largely in terms of Gabennesch's theory of the 'broken-promises effect' with regard to intra- and interpersonal conflict and, in terms of crowd behaviour effects.

Knowledge, practice and associated factors of essential newborn care at home among mothers in Gulomekada District, Eastern Tigray, Ethiopia, 2014.

PubMed

Misgna, Haftom Gebrehiwot; Gebru, Haftu Berhe; Birhanu, Mulugeta Molla

2016-06-21

Around the world, more than three million newborns die in their first months of life every year. In Ethiopia during the last five years period; neonatal mortality is 37 deaths per 1000 live births. Even though there is an improvement compared to the past five years, there is still high home delivery 90 %, and high neonatal mortality about the Millennium Development Goal, which aims to be less than 32/1000 live births in Ethiopia. The purpose of this study is to assess maternal knowledge, practice and associated factors of essential newborn care at home in Gulomekada District Eastern Tigray, Ethiopia. A community-based cross-sectional study is conducted in 296 mothers from Gulomekada District by using simple random sampling technique. Data entry and analysis is carried out by using Statistical Package for Social Sciences-20. The magnitude of the association between different variables about the outcome variable is measured by odds ratio with 95 % confidence interval. A binary logistic regression analysis is made to obtain odds ratio and the confidence interval of statistical associations. The goodness of fit had tested by Hosmer-Lemeshow statistic and all variables with P-value greater than 0.05 are fitted to the multivariate model. Variables with P < 0.2 in the bivariate analysis are included in the final model, and statistical significance is declared at P < 0.05. Eighty percent (80.4 %) study participants had good knowledge on essential new born care and 92.9 % had the good practice of essential new born care. About 60 % of mothers applied butter or oil on the cord stump for their last baby. Marital status and education are significantly associated with knowledge, whereas urban residence mothers with good knowledge on essential newborn care and employed mothers are significantly associated with mothers' practice of essential newborn care. Almost all mothers know and practice essential newborn care correctly except oil or butter application to the cord stump is highly practiced which should be avoided. Only marital status and educational status are significantly associated with mothers' knowledge.

Intimate Partner Violence in Colombia: Who Is at Risk?

ERIC Educational Resources Information Center

Friedemann-Sanchez, Greta; Lovaton, Rodrigo

2012-01-01

The role that domestic violence plays in perpetuating poverty is often overlooked as a development issue. Using data from the 2005 Demographic Health Survey, this paper examines the prevalence of intimate partner violence in Colombia. Employing an intrahousehold bargaining framework and a bivariate probit model, it assesses the prevalence of and…

The Bivariate (Complex) Fibonacci and Lucas Polynomials: An Historical Investigation with the Maple's Help

ERIC Educational Resources Information Center

Alves, Francisco Regis Vieira; Catarino, Paula Maria Machado Cruz

2016-01-01

The current research around the Fibonacci's and Lucas' sequence evidences the scientific vigor of both mathematical models that continue to inspire and provide numerous specializations and generalizations, especially from the sixties. One of the current of research and investigations around the Generalized Sequence of Lucas, involves it's…

Discriminating low frequency components from long range persistent fluctuations in daily atmospheric temperature variability

NASA Astrophysics Data System (ADS)

Lanfredi, M.; Simoniello, T.; Cuomo, V.; Macchiato, M.

2009-02-01

This study originated from recent results reported in literature, which support the existence of long-range (power-law) persistence in atmospheric temperature fluctuations on monthly and inter-annual scales. We investigated the results of Detrended Fluctuation Analysis (DFA) carried out on twenty-two historical daily time series recorded in Europe in order to evaluate the reliability of such findings in depth. More detailed inspections emphasized systematic deviations from power-law and high statistical confidence for functional form misspecification. Rigorous analyses did not support scale-free correlation as an operative concept for Climate modelling, as instead suggested in literature. In order to understand the physical implications of our results better, we designed a bivariate Markov process, parameterised on the basis of the atmospheric observational data by introducing a slow dummy variable. The time series generated by this model, analysed both in time and frequency domains, tallied with the real ones very well. They accounted for both the deceptive scaling found in literature and the correlation details enhanced by our analysis. Our results seem to evidence the presence of slow fluctuations from another climatic sub-system such as ocean, which inflates temperature variance up to several months. They advise more precise re-analyses of temperature time series before suggesting dynamical paradigms useful for Climate modelling and for the assessment of Climate Change.

Discriminating low frequency components from long range persistent fluctuations in daily atmospheric temperature variability

NASA Astrophysics Data System (ADS)

Lanfredi, M.; Simoniello, T.; Cuomo, V.; Macchiato, M.

2009-07-01

This study originated from recent results reported in literature, which support the existence of long-range (power-law) persistence in atmospheric temperature fluctuations on monthly and inter-annual scales. We investigated the results of Detrended Fluctuation Analysis (DFA) carried out on twenty-two historical daily time series recorded in Europe in order to evaluate the reliability of such findings in depth. More detailed inspections emphasized systematic deviations from power-law and high statistical confidence for functional form misspecification. Rigorous analyses did not support scale-free correlation as an operative concept for Climate modelling, as instead suggested in literature. In order to understand the physical implications of our results better, we designed a bivariate Markov process, parameterised on the basis of the atmospheric observational data by introducing a slow dummy variable. The time series generated by this model, analysed both in time and frequency domains, tallied with the real ones very well. They accounted for both the deceptive scaling found in literature and the correlation details enhanced by our analysis. Our results seem to evidence the presence of slow fluctuations from another climatic sub-system such as ocean, which inflates temperature variance up to several months. They advise more precise re-analyses of temperature time series before suggesting dynamical paradigms useful for Climate modelling and for the assessment of Climate Change.

The relationship of area-level sociodemographic characteristics, household composition and individual-level socioeconomic status on walking behavior among adults.

PubMed

Hearst, Mary O; Sirard, John R; Forsyth, Ann; Parker, Emily D; Klein, Elizabeth G; Green, Christine G; Lytle, Leslie A

2013-04-01

Understanding the contextual factors associated with why adults walk is important for those interested in increasing walking as a mode of transportation and leisure. This paper investigates the relationships between neighborhood-level sociodemographic context, individual level sociodemographic characteristics and walking for leisure and transport. Data from two community-based studies of adults (n=550) were used to determine the association between the area-sociodemographic environment (ASDE), calculated from U.S. Census variables, and individual-level SES as potential correlates of walking behavior. Descriptive statistics, mean comparisons and Pearson's correlations coefficients were used to assess bivariate relationships. Generalized estimating equations were used to model the relationship between ASDE, as quartiles, and walking behavior. Adjusted models suggest adults engage in more minutes of walking for transportation and less walking for leisure in the most disadvantaged compared to the least disadvantaged neighborhoods but adding individual level demographics and SES eliminated the significant results. However, when models were stratified for free or reduced cost lunch, of those with children who qualified for free or reduced lunch, those who lived in the wealthiest neighborhoods engaged in 10.7 minutes less of total walking per day compared to those living in the most challenged neighborhoods (p<0.001). Strategies to increase walking for transportation or leisure need to take account of individual level socioeconomic factors in addition to area-level measures.

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

PubMed Central

Song, Yong-Ze; Yang, Hong-Lei; Peng, Jun-Huan; Song, Yi-Rong; Sun, Qian; Li, Yuan

2015-01-01

Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi’an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5. PMID:26540446

Impact of malocclusion on oral health related quality of life in young people

PubMed Central

2013-01-01

Background The objectives for this study were to assess Oral Health Related Quality of Life (OHRQoL) in young people aged 15–25 who sought orthodontic treatment, and to measure the association between orthodontic treatment need (using the IOTN), sex, age and education level, and oral health related quality of life (OHRQoL). Methods Survey of a consecutive series of 323 young adults aged 15 to 25 years, attending orthodontic clinics at the Faculty of Dentistry, Universiti Teknologi MARA. Participants completed the Oral Health Impact Profile-14 (OHIP-14) and had a clinical examination including the Index of Orthodontic Treatment Need- Dental Health Component (IOTN-DHC). Data analyses included descriptive statistics, One-way ANOVA and bivariate and multivariate regression models. Results The mean overall score (± SD) for OHIP-14 in young people aged 15–25 was 22.6 ± 12.5. The psychological discomfort domain was the domain where highest impact was recorded with a mean (± SD) of 4.0 ± 1.9. The regression analyses showed a significant association of IOTN-DHC with overall OHIP-14 score (p < 0.05). Although females reported a slightly higher impact than males, this was not significant in both bivariate and multivariate analyses. Age group had a significant negative association with overall OHIP-14 score (p < 0.05). The 15–18 year old group showed the highest impact on their quality of life due to malocclusion. Participants with a university education report a significantly higher impact on OHRQoL as compared to participants with only secondary education. Conclusion Malocclusion has a significant negative impact on OHRQoL and its domains. This is greatest for the psychological discomfort domain. Younger people and those with a university education report higher levels of impact. There was no reported difference in impact between male and females. PMID:23443041

Modelling the vicious circle between obesity and physical activity in children and adolescents using a bivariate probit model with endogenous regressors.

PubMed

Yeh, C-Y; Chen, L-J; Ku, P-W; Chen, C-M

2015-01-01

The increasing prevalence of obesity in children and adolescents has become one of the most important public health issues around the world. Lack of physical activity is a risk factor for obesity, while being obese could reduce the likelihood of participating in physical activity. Failing to account for the endogeneity between obesity and physical activity would result in biased estimation. This study investigates the relationship between overweight and physical activity by taking endogeneity into consideration. It develops an endogenous bivariate probit model estimated by the maximum likelihood method. The data included 4008 boys and 4197 girls in the 5th-9th grades in Taiwan in 2007-2008. The relationship between overweight and physical activity is significantly negative in the endogenous model, but insignificant in the comparative exogenous model. This endogenous relationship presents a vicious circle in which lower levels of physical activity lead to overweight, while those who are already overweight engage in less physical activity. The results not only reveal the importance of endogenous treatment, but also demonstrate the robust negative relationship between these two factors. An emphasis should be put on overweight and obese children and adolescents in order to break the vicious circle. Promotion of physical activity by appropriate counselling programmes and peer support could be effective in reducing the prevalence of obesity in children and adolescents.

Metocean design parameter estimation for fixed platform based on copula functions

NASA Astrophysics Data System (ADS)

Zhai, Jinjin; Yin, Qilin; Dong, Sheng

2017-08-01

Considering the dependent relationship among wave height, wind speed, and current velocity, we construct novel trivariate joint probability distributions via Archimedean copula functions. Total 30-year data of wave height, wind speed, and current velocity in the Bohai Sea are hindcast and sampled for case study. Four kinds of distributions, namely, Gumbel distribution, lognormal distribution, Weibull distribution, and Pearson Type III distribution, are candidate models for marginal distributions of wave height, wind speed, and current velocity. The Pearson Type III distribution is selected as the optimal model. Bivariate and trivariate probability distributions of these environmental conditions are established based on four bivariate and trivariate Archimedean copulas, namely, Clayton, Frank, Gumbel-Hougaard, and Ali-Mikhail-Haq copulas. These joint probability models can maximize marginal information and the dependence among the three variables. The design return values of these three variables can be obtained by three methods: univariate probability, conditional probability, and joint probability. The joint return periods of different load combinations are estimated by the proposed models. Platform responses (including base shear, overturning moment, and deck displacement) are further calculated. For the same return period, the design values of wave height, wind speed, and current velocity obtained by the conditional and joint probability models are much smaller than those by univariate probability. Considering the dependence among variables, the multivariate probability distributions provide close design parameters to actual sea state for ocean platform design.

PLASMA DIHYDROCERAMIDE SPECIES ASSOCIATE WITH WAIST CIRCUMFERENCE IN MEXICAN AMERICAN FAMILIES

PubMed Central

Mamtani, Manju; Meikle, Peter J.; Kulkarni, Hemant; Weir, Jacquelyn M.; Barlow, Christopher K.; Jowett, Jeremy B.; Bellis, Claire; Dyer, Thomas D.; Almasy, Laura; Mahaney, Michael C.; Duggirala, Ravindranath; Comuzzie, Anthony G.; Blangero, John; Curran, Joanne E.

2013-01-01

Objective Waist circumference (WC), the clinical marker of central obesity, is gaining popularity as a screening tool for type 2 diabetes (T2D). While there is epidemiologic evidence favoring the WC-T2D association, its biological substantiation is generally weak. Our objective was to determine the independent association of plasma lipid repertoire with WC. Design and methods We used samples and data from the San Antonio Family Heart Study of 1208 Mexican Americans from 42 extended families. We determined association of plasma lipidomic profiles with the cross-sectionally assessed WC. Plasma lipidomic profiling entailed liquid chromatography with mass spectrometry. Statistical analyses included multivariable polygenic regression models and bivariate trait analyses using the SOLAR software. Results After adjusting for age and sex interactions, body mass index, homeostasis model of assessment – insulin resistance, total cholesterol, triglycerides, high density lipoproteins and use of lipid lowering drugs, dihydroceramides as a class were associated with WC. Dihydroceramide species 18:0, 20:0, 22:0 and 24:1 were significantly associated and genetically correlated with WC. Two sphingomyelin species (31:1 and 41:1) were also associated with WC. Conclusions Plasma dihydroceramide levels independently associate with WC. Thus, high resolution plasma lipidomic studies can provide further credence to the biological underpinnings of the association of WC with T2D. PMID:23929697

Household food insecurity and symptoms of neurologic disorder in Ethiopia: an observational analysis.

PubMed

El-Sayed, Abdulrahman M; Hadley, Craig; Tessema, Fasil; Tegegn, Ayelew; Cowan, John A; Galea, Sandro

2010-12-31

Food insecurity (FI) has been shown to be associated with poor health both in developing and developed countries. Little is known about the relation between FI and neurological disorder. We assessed the relation between FI and risk for neurologic symptoms in southwest Ethiopia. Data about food security, gender, age, household assets, and self-reported neurologic symptoms were collected from a representative, community-based sample of adults (N = 900) in Jimma Zone, Ethiopia. We calculated univariate statistics and used bivariate chi-square tests and multivariate logistic regression models to assess the relation between FI and risk of neurologic symptoms including seizures, extremity weakness, extremity numbness, tremors/ataxia, aphasia, carpal tunnel syndrome, vision dysfunction, and spinal pain. In separate multivariate models by outcome and gender, adjusting for age and household socioeconomic status, severe FI was associated with higher odds of seizures, movement abnormalities, carpal tunnel, vision dysfunction, spinal pain, and comorbid disorders among women. Severe FI was associated with higher odds of seizures, extremity numbness, movement abnormalities, difficulty speaking, carpal tunnel, vision dysfunction, and comorbid disorders among men. We found that FI was associated with symptoms of neurologic disorder. Given the cross-sectional nature of our study, the directionality of these associations is unclear. Future research should assess causal mechanisms relating FI to neurologic symptoms in sub-Saharan Africa.

Identifying and Validating Selection Tools for Predicting Officer Performance and Retention

DTIC Science & Technology

2017-05-01

Performance composite. Findings: Simple bivariate correlations indicated that the RBI Fitness Motivation scale was the strongest predictor of...Scored Job Knowledge Tests (JKTs) ............................................................ 14 Self-Report: Career History Survey (CHS...36 Bivariate Correlations

Small Sample Properties of Asymptotically Efficient Estimators of the Parameters of a Bivariate Gaussian–Weibull Distribution

Treesearch

Steve P. Verrill; James W. Evans; David E. Kretschmann; Cherilyn A. Hatfield

2012-01-01

Two important wood properties are stiffness (modulus of elasticity or MOE) and bending strength (modulus of rupture or MOR). In the past, MOE has often been modeled as a Gaussian and MOR as a lognormal or a two or three parameter Weibull. It is well known that MOE and MOR are positively correlated. To model the simultaneous behavior of MOE and MOR for the purposes of...

Maximum likelihood estimation of the parameters of a bivariate Gaussian-Weibull distribution from machine stress-rated data

Treesearch

Steve P. Verrill; David E. Kretschmann; James W. Evans

2016-01-01

Two important wood properties are stiffness (modulus of elasticity, MOE) and bending strength (modulus of rupture, MOR). In the past, MOE has often been modeled as a Gaussian and MOR as a lognormal or a two- or threeparameter Weibull. It is well known that MOE and MOR are positively correlated. To model the simultaneous behavior of MOE and MOR for the purposes of wood...

Trend Detection and Bivariate Frequency Analysis for Nonstrationary Rainfall Data

NASA Astrophysics Data System (ADS)

Joo, K.; Kim, H.; Shin, J. Y.; Heo, J. H.

2017-12-01

Multivariate frequency analysis has been developing for hydro-meteorological data such as rainfall, flood, and drought. Particularly, the copula has been used as a useful tool for multivariate probability model which has no limitation on deciding marginal distributions. The time-series rainfall data can be characterized to rainfall event by inter-event time definition (IETD) and each rainfall event has a rainfall depth and rainfall duration. In addition, nonstationarity in rainfall event has been studied recently due to climate change and trend detection of rainfall event is important to determine the data has nonstationarity or not. With the rainfall depth and duration of a rainfall event, trend detection and nonstationary bivariate frequency analysis has performed in this study. 62 stations from Korea Meteorological Association (KMA) over 30 years of hourly recorded data used in this study and the suitability of nonstationary copula for rainfall event has examined by the goodness-of-fit test.

A non-stationary cost-benefit based bivariate extreme flood estimation approach

NASA Astrophysics Data System (ADS)

Qi, Wei; Liu, Junguo

2018-02-01

Cost-benefit analysis and flood frequency analysis have been integrated into a comprehensive framework to estimate cost effective design values. However, previous cost-benefit based extreme flood estimation is based on stationary assumptions and analyze dependent flood variables separately. A Non-Stationary Cost-Benefit based bivariate design flood estimation (NSCOBE) approach is developed in this study to investigate influence of non-stationarities in both the dependence of flood variables and the marginal distributions on extreme flood estimation. The dependence is modeled utilizing copula functions. Previous design flood selection criteria are not suitable for NSCOBE since they ignore time changing dependence of flood variables. Therefore, a risk calculation approach is proposed based on non-stationarities in both marginal probability distributions and copula functions. A case study with 54-year observed data is utilized to illustrate the application of NSCOBE. Results show NSCOBE can effectively integrate non-stationarities in both copula functions and marginal distributions into cost-benefit based design flood estimation. It is also found that there is a trade-off between maximum probability of exceedance calculated from copula functions and marginal distributions. This study for the first time provides a new approach towards a better understanding of influence of non-stationarities in both copula functions and marginal distributions on extreme flood estimation, and could be beneficial to cost-benefit based non-stationary bivariate design flood estimation across the world.

Family Medicine or Primary Care Residency Selection: Effects of Family Medicine Interest Groups, MD/MPH Dual Degrees, and Rural Medical Education.

PubMed

Wei McIntosh, Elizabeth; Morley, Christopher P

2016-05-01

If medical schools are to produce primary care physicians (family medicine, pediatrics, or general internal medicine), they must provide educational experiences that enable medical students to maintain existing or form new interests in such careers. This study examined three mechanisms for doing so, at one medical school: participation as an officer in a family medicine interest group (FMIG), completion of a dual medical/public health (MD/MPH) degree program, and participation in a rural medical education (RMED) clinical track. Specialty Match data for students who graduated from the study institution between 2006 and 2015 were included as dependent variables in bivariate analysis (c2) and logistic regression models, examining FMIG, MD/MPH, and RMED participation as independent predictors of specialty choice (family medicine yes/no, or any primary care (PC) yes/no), controlling for student demographic data. In bivariate c2 analyses, FMIG officership did not significantly predict matching with family medicine or any PC; RMED and MD/MPH education were significant predictors of both family medicine and PC. Binary logistic regression analyses replicated the bivariate findings, controlling for student demographics. Dual MD/MPH and rural medical education had stronger effects in producing primary care physicians than participation in a FMIG as an officer, at one institution. Further study at multiple institutions is warranted.

Family Medicine Department Chairs' Opinions Regarding Scope of Practice.

PubMed

Peterson, Lars E; Blackburn, Brenna; Phillips, Robert L; Mainous, Arch G

2015-12-01

Family physicians are trained broadly to provide the majority of health care across multiple settings; however, their scope of practice has narrowed. Department chairs' role modeling of a broad scope of practice may set the tone for faculty and trainees. In 2013, the authors surveyed family medicine department chairs about their scope of practice, personal and department characteristics, and attitudes and beliefs about scope of practice and role modeling. They used descriptive statistics and bivariate analyses to test for associations between scope of practice, personal and department characteristics, and attitudes and beliefs. They created a Scope of Practice Index by summing the number of services each respondent provided to compare scope of practice across chairs. Of 146 chairs, 88 responded (60.3% response rate); 85 were included in the final analysis. Sixty-five (77.4%) respondents were male; 73 (86.9%) were 51 years or older. Respondents spent a mean of 19.7% of their time in direct patient care and had a mean Scope of Practice Index of 11.9. Fifty-three (62.4%) disagreed that the scope of practice of family medicine was too broad for practicing physicians to keep up in all areas, and 56 (65.9%) believed that faculty should role model the full scope of practice to learners. Responses generally did not vary by respondents' personal scope of practice. Family medicine department chairs believe that role modeling a broad scope of practice increases students' interest in family medicine and encourages residency graduates to provide a wide range of services.

Pattern of Utilisation of Dental Health Care Among HIV-positive Adult Nigerians.

PubMed

Adedigba, Michael A; Adekanmbi, Victor T; Asa, Sola; Fakande, Ibiyemi

2016-01-01

To determine the pattern of dental care utilisation of people living with HIV (PLHIV). A cross-sectional questionnaire survey of 239 PLHIV patients in three care centres was done. Information on sociodemographics, dental visit, risk groups, living arrangement, medical insurance and need of dental care was recorded. The EC Clearinghouse and WHO clinical staging was used to determine the stage of HIV/AIDS infection following routine oral examinations under natural daylight. Multivariate logistic regression models were created after adjusting for all the covariates that were statistically significant at univariate/bivariate levels. The majority of subjects were younger than 50 years, about 93% had not seen a dentist before being diagnosed HIV positive and 92% reported no dental visit after contracting HIV. Among nonusers of dental care, 14.3% reported that they wanted care but were afraid to seek it. Other reasons included poor awareness, lack of money and stigmatisation. Multivariate analysis showed that lack of dental care was associated with employment status, living arrangements, educational status, income per annum and presenting with oral symptoms. The area under the receiver operating curve was 84% for multivariate logistic regression model 1, 70% for model 2, 67% for model 3 and 71% for model 4, which means that the predictive power of the models were good. Contrary to our expectations, dental utilisation among PLHIV was generally poor among this group of patients. There is serious and immediate need to improve the awareness of PLHIVs in African settings and barriers to dental care utilisation should also be removed or reduced.

Accuracy of Presurgical Functional MR Imaging for Language Mapping of Brain Tumors: A Systematic Review and Meta-Analysis.

PubMed

Weng, Hsu-Huei; Noll, Kyle R; Johnson, Jason M; Prabhu, Sujit S; Tsai, Yuan-Hsiung; Chang, Sheng-Wei; Huang, Yen-Chu; Lee, Jiann-Der; Yang, Jen-Tsung; Yang, Cheng-Ta; Tsai, Ying-Huang; Yang, Chun-Yuh; Hazle, John D; Schomer, Donald F; Liu, Ho-Ling

2018-02-01

Purpose To compare functional magnetic resonance (MR) imaging for language mapping (hereafter, language functional MR imaging) with direct cortical stimulation (DCS) in patients with brain tumors and to assess factors associated with its accuracy. Materials and Methods PubMed/MEDLINE and related databases were searched for research articles published between January 2000 and September 2016. Findings were pooled by using bivariate random-effects and hierarchic summary receiver operating characteristic curve models. Meta-regression and subgroup analyses were performed to evaluate whether publication year, functional MR imaging paradigm, magnetic field strength, statistical threshold, and analysis software affected classification accuracy. Results Ten articles with a total of 214 patients were included in the analysis. On a per-patient basis, the pooled sensitivity and specificity of functional MR imaging was 44% (95% confidence interval [CI]: 14%, 78%) and 80% (95% CI: 54%, 93%), respectively. On a per-tag basis (ie, each DCS stimulation site or "tag" was considered a separate data point across all patients), the pooled sensitivity and specificity were 67% (95% CI: 51%, 80%) and 55% (95% CI: 25%, 82%), respectively. The per-tag analysis showed significantly higher sensitivity for studies with shorter functional MR imaging session times (P = .03) and relaxed statistical threshold (P = .05). Significantly higher specificity was found when expressive language task (P = .02), longer functional MR imaging session times (P < .01), visual presentation of stimuli (P = .04), and stringent statistical threshold (P = .01) were used. Conclusion Results of this study showed moderate accuracy of language functional MR imaging when compared with intraoperative DCS, and the included studies displayed significant methodologic heterogeneity. © RSNA, 2017 Online supplemental material is available for this article.

Findings regarding the relationships between sociodemographic, psychological, comorbidity factors, and functional status, in geriatric inpatients.

PubMed

Capisizu, Ana; Aurelian, Sorina; Zamfirescu, Andreea; Omer, Ioana; Haras, Monica; Ciobotaru, Camelia; Onose, Liliana; Spircu, Tiberiu; Onose, Gelu

2015-01-01

To assess the impact of socio-demographic and comorbidity factors, and quantified depressive symptoms on disability in inpatients. Observational cross-sectional study, including a number of 80 elderly (16 men, 64 women; mean age 72.48 years; standard deviation 9.95 years) admitted in the Geriatrics Clinic of "St. Luca" Hospital, Bucharest, between May-July, 2012. We used the Functional Independence Measure, Geriatric Depression Scale and an array of socio-demographic and poly-pathology parameters. Statistical analysis included Wilcoxon and Kruskal-Wallis tests for ordinal variables, linear bivariate correlations, general linear model analysis, ANOVA. FIM scores were negatively correlated with age (R=-0.301; 95%CI=-0.439 -0.163; p=0.007); GDS scores had a statistically significant negative correlation (R=-0.322; 95% CI=-0.324 -0.052; p=0.004) with FIM scores. A general linear model, including other variables (gender, age, provenance, matrimonial state, living conditions, education, respectively number of chronic illnesses) as factors, found living conditions (p=0.027) and the combination of matrimonial state and gender (p=0.004) to significantly influence FIM scores. ANOVA showed significant differences in FIM scores stratified by the number of chronic diseases (p=0.035). Our study objectified the negative impact of depression on functional status; interestingly, education had no influence on FIM scores; living conditions and a combination of matrimonial state and gender had an important impact: patients with living spouses showed better functional scores than divorced/widowers; the number of chronic diseases also affected the FIM scores: lower in patients with significant polypathology. These findings should be considered when designing geriatric rehabilitation programs, especially for home--including skilled--cares.

Breed effects and genetic parameter estimates for calving difficulty and birth weight in a multi-breed population

USDA-ARS?s Scientific Manuscript database

Birth weight (BWT) and calving difficulty (CD) were recorded on 4,579 first parity females from the Germplasm Evaluation (GPE) program at the U.S. Meat Animal Research Center (USMARC). Both traits were analyzed using a bivariate animal model with direct and maternal effects. Calving difficulty was...

Tuition Assistance Usage and First-Term Military Retention.

ERIC Educational Resources Information Center

Buddin, Richard; Kapur, Kanika

Tuition Assistance (TA) is a military-sponsored program that reimburses military members for 75% of the tuition costs of college classes while on active duty in the hope of making military service more attractive to young people and encouraging them to remain in the military. TA's effectiveness was examined by using two models--a bivariate probit…

A Preliminary Comparison of the Effectiveness of Cluster Analysis Weighting Procedures for Within-Group Covariance Structure.

ERIC Educational Resources Information Center

Donoghue, John R.

A Monte Carlo study compared the usefulness of six variable weighting methods for cluster analysis. Data were 100 bivariate observations from 2 subgroups, generated according to a finite normal mixture model. Subgroup size, within-group correlation, within-group variance, and distance between subgroup centroids were manipulated. Of the clustering…

General Education Development (GED) Recipients' Life Course Experiences: Humanizing the Findings

ERIC Educational Resources Information Center

Hartigan, Lacey A.

2017-01-01

This study examines a range of GED recipients' life course contexts and experiences and their relationship with long-term outcomes. Using descriptive comparisons, bivariate tests, and propensity-score matched regression models to analyze data from rounds 1-15 of the National Longitudinal Survey of Youth, 1997, analyses aim to examine: (1)…

Perceived Autonomy-Support, Expectancy, Value, Metacognitive Strategies and Performance in Chemistry: A Structural Equation Model in Undergraduates

ERIC Educational Resources Information Center

González, Antonio; Paoloni, Paola-Verónica

2015-01-01

Research in chemistry education has highlighted a number of variables that predict learning and performance, such as teacher-student interactions, academic motivation and metacognition. Most of this chemistry research has examined these variables by identifying dyadic relationships through bivariate correlations. The main purpose of this study was…

Informing Federal Policy on Firearm Restrictions for Veterans with Fiduciaries: Risk Indicators in the Post-Deployment Mental Health Study.

PubMed

Swanson, Jeffrey; Easter, Michele; Brancu, Mira; Fairbank, John A

2018-05-24

This article examines the public safety rationale for a federal policy of prohibiting gun sales to veterans with psychiatric disabilities who are assigned a fiduciary to manage their benefits from the Department of Veterans Affairs. The policy was evaluated using data on 3200 post-deployment veterans from the Iraq and Afghanistan war era. Three proxy measures of fiduciary need-based on intellectual disability, drug abuse, or acute psychopathology-were associated in bivariate analysis with interpersonal violence and suicidality. In multivariate analysis, statistical significance remained only for the measure based on acute psychopathology. Implications for reforms to the fiduciary firearm restriction policy are discussed.

Correlation between quantitative traits and correlation between corresponding LOD scores: detection of pleiotropic effects.

PubMed

Ulgen, Ayse; Han, Zhihua; Li, Wentian

2003-12-31

We address the question of whether statistical correlations among quantitative traits lead to correlation of linkage results of these traits. Five measured quantitative traits (total cholesterol, fasting glucose, HDL cholesterol, blood pressure, and triglycerides), and one derived quantitative trait (total cholesterol divided by the HDL cholesterol) are used for phenotype correlation studies. Four of them are used for linkage analysis. We show that although correlation among phenotypes partially reflects the correlation among linkage analysis results, the LOD-score correlations are on average low. The most significant peaks found by using different traits do not often overlap. Studying covariances at specific locations in LOD scores may provide clues for further bivariate linkage analyses.

A discrimination method for the detection of pneumonia using chest radiograph.

PubMed

Noor, Norliza Mohd; Rijal, Omar Mohd; Yunus, Ashari; Abu-Bakar, S A R

2010-03-01

This paper presents a statistical method for the detection of lobar pneumonia when using digitized chest X-ray films. Each region of interest was represented by a vector of wavelet texture measures which is then multiplied by the orthogonal matrix Q(2). The first two elements of the transformed vectors were shown to have a bivariate normal distribution. Misclassification probabilities were estimated using probability ellipsoids and discriminant functions. The result of this study recommends the detection of pneumonia by constructing probability ellipsoids or discriminant function using maximum energy and maximum column sum energy texture measures where misclassification probabilities were less than 0.15. 2009 Elsevier Ltd. All rights reserved.

Why Do Some First Nations Communities Have Safe Water and Others Not? Socioeconomic Determinants of Drinking Water Risk

PubMed Central

Brown, Brandon; Wachowiak-Smolíková, Renata; Spence, Nicholas D.; Wachowiak, Mark P.; Walters, Dan F.

2016-01-01

Securing safe and adequate drinking water is an ongoing issue for many Canadian First Nations communities despite nearly 15 years of reports, studies, policy changes, financial commitments, and regulations. The federal drinking water evaluation scheme is narrowly scoped, ignoring community level social factors, which may play a role in access to safe water in First Nations. This research used the 2006 Aboriginal Affairs and Northern Development Canada First Nations Drinking Water System Risk Survey data and the Community Well-Being Index, including labour force, education, housing, and income, from the 2006 Census. Bivariate analysis was conducted using the Spearman’s correlation, Kendall’s tau correlation, and Pearson’s correlation. Multivariable analysis was conducted using an ordinal (proportional or cumulative odds) regression model. Results showed that the regression model was significant. Community socioeconomic indicators had no relationship with drinking water risk characterization in both the bivariate and multivariable models, with the sole exception of labour force, which had a significantly positive effect on drinking water risk rankings. Socioeconomic factors were not important in explaining access to safe drinking water in First Nations communities. Improvements in the quality of safe water data as well as an examination of other community processes are required to address this pressing policy issue. PMID:27157172

Why Do Some First Nations Communities Have Safe Water and Others Not? Socioeconomic Determinants of Drinking Water Risk.

PubMed

Brown, Brandon; Wachowiak-Smolíková, Renata; Spence, Nicholas D; Wachowiak, Mark P; Walters, Dan F

2016-09-01

Securing safe and adequate drinking water is an ongoing issue for many Canadian First Nations communities despite nearly 15 years of reports, studies, policy changes, financial commitments, and regulations. The federal drinking water evaluation scheme is narrowly scoped, ignoring community level social factors, which may play a role in access to safe water in First Nations. This research used the 2006 Aboriginal Affairs and Northern Development Canada First Nations Drinking Water System Risk Survey data and the Community Well-Being Index, including labour force, education, housing, and income, from the 2006 Census. Bivariate analysis was conducted using the Spearman's correlation, Kendall's tau correlation, and Pearson's correlation. Multivariable analysis was conducted using an ordinal (proportional or cumulative odds) regression model. Results showed that the regression model was significant. Community socioeconomic indicators had no relationship with drinking water risk characterization in both the bivariate and multivariable models, with the sole exception of labour force, which had a significantly positive effect on drinking water risk rankings. Socioeconomic factors were not important in explaining access to safe drinking water in First Nations communities. Improvements in the quality of safe water data as well as an examination of other community processes are required to address this pressing policy issue.

A preliminary study on drought events in Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Zin, Wan Zawiah Wan; Nahrawi, Siti Aishah; Jemain, Abdul Aziz; Zahari, Marina

2014-06-01

In this research, the Standard Precipitation Index (SPI) is used to represent the dry condition in Peninsular Malaysia. To do this, data of monthly rainfall from 75 stations in Peninsular Malaysia is used to obtain the SPI values at scale one. From the SPI values, two drought characteristics that are commonly used to represent the dry condition in an area that is the duration and severity of a drought period are identified and their respective values calculated for every station. Spatial mappings are then used to identify areas which are more likely to be affected by longer and more severe drought condition from the results. As the two drought characteristics may be correlated with each other, the joint distribution of severity and duration of dry condition is considered. Bivariate copula model is used and five copula models were tested, namely, the Gumbel-Hougard, Clayton, Frank, Joe and Galambos copulas. The copula model, which best represents the relationship between severity and duration, is determined using Akaike information criterion. The results showed that the Joe and Clayton copulas are well-fitted by close to 60% of the stations under study. Based on the results on the most appropriate copula-based joint distribution for each station, some bivariate probabilistic properties of droughts can then be calculated, which will be continued in future research.

Evaluating Dynamic Bivariate Correlations in Resting-state fMRI: A comparison study and a new approach

PubMed Central

Lindquist, Martin A.; Xu, Yuting; Nebel, Mary Beth; Caffo, Brain S.

2014-01-01

To date, most functional Magnetic Resonance Imaging (fMRI) studies have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant across time. However, recently, there has been increased interest in quantifying possible dynamic changes in FC during fMRI experiments, as it is thought this may provide insight into the fundamental workings of brain networks. In this work we focus on the specific problem of estimating the dynamic behavior of pair-wise correlations between time courses extracted from two different regions of the brain. We critique the commonly used sliding-windows technique, and discuss some alternative methods used to model volatility in the finance literature that could also prove useful in the neuroimaging setting. In particular, we focus on the Dynamic Conditional Correlation (DCC) model, which provides a model-based approach towards estimating dynamic correlations. We investigate the properties of several techniques in a series of simulation studies and find that DCC achieves the best overall balance between sensitivity and specificity in detecting dynamic changes in correlations. We also investigate its scalability beyond the bivariate case to demonstrate its utility for studying dynamic correlations between more than two brain regions. Finally, we illustrate its performance in an application to test-retest resting state fMRI data. PMID:24993894

Disordered gambling as defined by the Diagnostic and Statistical Manual of Mental Disorders and the South Oaks Gambling Screen: evidence for a common etiologic structure.

PubMed

Slutske, Wendy S; Zhu, Gu; Meier, Madeline H; Martin, Nicholas G

2011-08-01

In a previous article, we demonstrated in a large twin study that disordered gambling (DG), as defined by the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV), ran in families, that about half of the variation in liability for DG was due to familial factors, and that all of this was explained by shared genetic rather than shared environmental influences (Slutske, Zhu, Meier, & Martin, 2010). The purpose of the present study is to extend this work to include an alternative conceptualization of DG that is provided by the South Oaks Gambling Screen (SOGS) item set in order to (a) compare the magnitude of the familial resemblance obtained when using the two definitions of DG (based on the DSM-IV and the SOGS), (b) examine the extent to which the 2 definitions tap the same underlying sources of genetic and environmental variation, and (c) examine whether the same results will be obtained among men and women. The results of bivariate twin model-fitting analyses suggested that DG, as defined by the DSM-IV and the SOGS, substantially overlapped at the etiologic level among both men and women, which supports the construct validity of both the DSM and the SOGS conceptualizations of DG. This study highlights the utility of twin studies for appraising the validity of the diagnostic nomenclature. © 2011 American Psychological Association

A Graphic Anthropometric Aid for Seating and Workplace Design.

DTIC Science & Technology

1984-04-01

required proportion of the pdf . Suppose that some attribute is distributed according to a bivariate Normal pdf of zero mean value and equal variances a...2󈧓 Note that circular contours. dran at the normaliwed radii presented above, will enclose the respective proportions of the bi artate Normal pdf ...INTRODUCTION 1 2. A TWO-DIMENSIONAL MODEL BASE 2 3. CONCEPT OF USE 4 4. VALIDATION OF THE TWO-DIMENSIONAL MODEL 8 4.1 Conventional Anthropometry 9 4.2

Nurses' attitudes and knowledge regarding organ and tissue donation and transplantation in a provincial hospital: A descriptive and multivariate analysis.

PubMed

Lomero, Maria Del Mar; Jiménez-Herrera, María F; Rasero, Maria José; Sandiumenge, Alberto

2017-09-01

The attitudes and knowledge of nursing personnel regarding organ and tissue donation can influence the decision to donate. This study aimed to determine these two factors among nurses at a district hospital in Barcelona, Spain. A survey was carried out using a 35 item questionnaire. Results were subjected to descriptive and comparative statistical analyses using bivariate and multivariate analyses to examine the relation between demographic data and attitudes toward donation. The completion rate was 68.2%, with 98.6% of those responding stating that they were in favor of organ donation. The respondents were unsure as to whether the criteria for inclusion in transplant waiting lists were appropriate (57.5%), whereas 72.2% agreed that brain death is equivalent to death. The bivariate analysis revealed a significant association between a positive attitude toward donation and working on permanent night shift no religious beliefs. Attitudes toward donation among nurses were generally positive; a negative attitude, although attitudes towards donation among the nurses participating in the study were generally positive, it should be pointed out that when a negative attitude does exist this affects significant aspects such as belief in the diagnosis of brain death or the criteria for inclusion on the waiting list, amongst others, which reflects that specific training in donation focused on nurses continues to be needed. © 2017 John Wiley & Sons Australia, Ltd.

Human Papillomavirus (HPV) Vaccination and Adolescent Girls' Knowledge and Sexuality in Western Uganda: A Comparative Cross-Sectional Study.

PubMed

Turiho, Andrew Kampikaho; Muhwezi, Wilson Winston; Okello, Elialilia Sarikiaeli; Tumwesigye, Nazarius Mbona; Banura, Cecil; Katahoire, Anne Ruhweza

2015-01-01

The purpose of the study was to investigate the influence of human papillomavirus (HPV) vaccination on adolescent girls' knowledge of HPV and HPV vaccine, perception of sexual risk and intentions for sexual debut. This cross-sectional comparative study was conducted in Ibanda and Mbarara districts. Data was collected using a standardized self-administered questionnaire and analyzed using the Statistical Package for the Social Sciences computer software. Univariate, bivariate, and logistic regression analyses were conducted with significance level set at p < .05. Results showed that HPV vaccination was associated with being knowledgeable (Crude OR: 5.26, CI: 2.32-11.93; p = 0.000). Vaccination against HPV did not predict perception of sexual risk. Knowledge was low (only 87/385 or 22.6% of vaccinated girls were knowledgeable), but predicted perception of a high sexual risk (Adjusted OR: 3.12, CI: 1.37-3.63; p = 0.008). HPV vaccination, knowledge and perceived sexual risk did not predict sexual behaviour intentions. High parental communication was associated with adolescent attitudes that support postponement of sexual debut in both bivariate and multiple regression analyses. In conclusion, findings of this study suggest that HPV vaccination is not likely to encourage adolescent sexual activity. Influence of knowledge on sexual behaviour intentions was not definitively explained. Prospective cohort studies were proposed to address the emerging questions.

Parameters Selection for Bivariate Multiscale Entropy Analysis of Postural Fluctuations in Fallers and Non-Fallers Older Adults.

PubMed

Ramdani, Sofiane; Bonnet, Vincent; Tallon, Guillaume; Lagarde, Julien; Bernard, Pierre Louis; Blain, Hubert

2016-08-01

Entropy measures are often used to quantify the regularity of postural sway time series. Recent methodological developments provided both multivariate and multiscale approaches allowing the extraction of complexity features from physiological signals; see "Dynamical complexity of human responses: A multivariate data-adaptive framework," in Bulletin of Polish Academy of Science and Technology, vol. 60, p. 433, 2012. The resulting entropy measures are good candidates for the analysis of bivariate postural sway signals exhibiting nonstationarity and multiscale properties. These methods are dependant on several input parameters such as embedding parameters. Using two data sets collected from institutionalized frail older adults, we numerically investigate the behavior of a recent multivariate and multiscale entropy estimator; see "Multivariate multiscale entropy: A tool for complexity analysis of multichannel data," Physics Review E, vol. 84, p. 061918, 2011. We propose criteria for the selection of the input parameters. Using these optimal parameters, we statistically compare the multivariate and multiscale entropy values of postural sway data of non-faller subjects to those of fallers. These two groups are discriminated by the resulting measures over multiple time scales. We also demonstrate that the typical parameter settings proposed in the literature lead to entropy measures that do not distinguish the two groups. This last result confirms the importance of the selection of appropriate input parameters.

Negative mood states and related factors in a sample of adolescent secondary-school students in Barcelona (Spain).

PubMed

Ahonen, Emily Q; Nebot, Manel; Giménez, Emmanuel

2007-01-01

Poor mental health is a common problem in adolescence. Little information is available, however, about the factors influencing negative mood states in otherwise healthy adolescents. We aimed to describe the mood states and related factors in a sample of adolescents in the city of Barcelona (Spain). We administered a health survey to a sample of 2,727 students from public, subsidized, and private schools in Barcelona, aged approximately 14, 16, and 18 years old. To analyze the associations among moods and related factors, we used bivariate logistic regression, and fitted multivariate logistic regressions using the statistically significant variables from the bivariate analysis. To examine the possible group effects of the school on individual students, we employed multilevel analysis. The frequencies of negative mood states increased with age, with girls consistently reporting more frequent negative mood states than boys. The factors associated with negative mood states were problematic alcohol use, perceived mistreatment or abuse, antisocial behavior, intention to use or current use of illegal drugs (not including cannabis), lower perceived academic performance, and feeling isolated. Mood states are influenced by lifestyle and social factors, about which there is little local information. To plan and implement appropriate public health interventions, more complete information about the possible areas of influence is required. To complement the information obtained from studies such as the present study, longitudinal and qualitative studies would be desirable.

Childhood adversity in association with personality disorder dimensions: new findings in an old debate.

PubMed

Hengartner, M P; Ajdacic-Gross, V; Rodgers, S; Müller, M; Rössler, W

2013-10-01

Various studies have reported a positive relationship between child maltreatment and personality disorders (PDs). However, few studies included all DSM-IV PDs and even fewer adjusted for other forms of childhood adversity, e.g. bullying or family problems. We analyzed questionnaires completed by 512 participants of the ZInEP epidemiology survey, a comprehensive psychiatric survey of the general population in Zurich, Switzerland. Associations between childhood adversity and PDs were analyzed bivariately via simple regression analyses and multivariately via multiple path analysis. The bivariate analyses revealed that all PD dimensions were significantly related to various forms of family and school problems as well as child abuse. In contrast, according to the multivariate analysis only school problems and emotional abuse were associated with various PDs. Poverty was uniquely associated with schizotypal PD, conflicts with parents with obsessive-compulsive PD, physical abuse with antisocial PD, and physical neglect with narcissistic PD. Sexual abuse was statistically significantly associated with schizotypal and borderline PD, but corresponding effect sizes were small. Childhood adversity has a serious impact on PDs. Bullying and violence in schools and emotional abuse appear to be more salient markers of general personality pathology than other forms of childhood adversity. Associations with sexual abuse were negligible when adjusted for other forms of adversity. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Gene selection and cancer type classification of diffuse large-B-cell lymphoma using a bivariate mixture model for two-species data.

PubMed

Su, Yuhua; Nielsen, Dahlia; Zhu, Lei; Richards, Kristy; Suter, Steven; Breen, Matthew; Motsinger-Reif, Alison; Osborne, Jason

2013-01-05

: A bivariate mixture model utilizing information across two species was proposed to solve the fundamental problem of identifying differentially expressed genes in microarray experiments. The model utility was illustrated using a dog and human lymphoma data set prepared by a group of scientists in the College of Veterinary Medicine at North Carolina State University. A small number of genes were identified as being differentially expressed in both species and the human genes in this cluster serve as a good predictor for classifying diffuse large-B-cell lymphoma (DLBCL) patients into two subgroups, the germinal center B-cell-like diffuse large B-cell lymphoma and the activated B-cell-like diffuse large B-cell lymphoma. The number of human genes that were observed to be significantly differentially expressed (21) from the two-species analysis was very small compared to the number of human genes (190) identified with only one-species analysis (human data). The genes may be clinically relevant/important, as this small set achieved low misclassification rates of DLBCL subtypes. Additionally, the two subgroups defined by this cluster of human genes had significantly different survival functions, indicating that the stratification based on gene-expression profiling using the proposed mixture model provided improved insight into the clinical differences between the two cancer subtypes.

Assessing landslide susceptibility by statistical data analysis and GIS: the case of Daunia (Apulian Apennines, Italy)

NASA Astrophysics Data System (ADS)

Ceppi, C.; Mancini, F.; Ritrovato, G.

2009-04-01

This study aim at the landslide susceptibility mapping within an area of the Daunia (Apulian Apennines, Italy) by a multivariate statistical method and data manipulation in a Geographical Information System (GIS) environment. Among the variety of existing statistical data analysis techniques, the logistic regression was chosen to produce a susceptibility map all over an area where small settlements are historically threatened by landslide phenomena. By logistic regression a best fitting between the presence or absence of landslide (dependent variable) and the set of independent variables is performed on the basis of a maximum likelihood criterion, bringing to the estimation of regression coefficients. The reliability of such analysis is therefore due to the ability to quantify the proneness to landslide occurrences by the probability level produced by the analysis. The inventory of dependent and independent variables were managed in a GIS, where geometric properties and attributes have been translated into raster cells in order to proceed with the logistic regression by means of SPSS (Statistical Package for the Social Sciences) package. A landslide inventory was used to produce the bivariate dependent variable whereas the independent set of variable concerned with slope, aspect, elevation, curvature, drained area, lithology and land use after their reductions to dummy variables. The effect of independent parameters on landslide occurrence was assessed by the corresponding coefficient in the logistic regression function, highlighting a major role played by the land use variable in determining occurrence and distribution of phenomena. Once the outcomes of the logistic regression are determined, data are re-introduced in the GIS to produce a map reporting the proneness to landslide as predicted level of probability. As validation of results and regression model a cell-by-cell comparison between the susceptibility map and the initial inventory of landslide events was performed and an agreement at 75% level achieved.

Mind the Gap! A Multilevel Analysis of Factors Related to Variation in Published Cost-Effectiveness Estimates within and between Countries.

PubMed

Boehler, Christian E H; Lord, Joanne

2016-01-01

Published cost-effectiveness estimates can vary considerably, both within and between countries. Despite extensive discussion, little is known empirically about factors relating to these variations. To use multilevel statistical modeling to integrate cost-effectiveness estimates from published economic evaluations to investigate potential causes of variation. Cost-effectiveness studies of statins for cardiovascular disease prevention were identified by systematic review. Estimates of incremental costs and effects were extracted from reported base case, sensitivity, and subgroup analyses, with estimates grouped in studies and in countries. Three bivariate models were developed: a cross-classified model to accommodate data from multinational studies, a hierarchical model with multinational data allocated to a single category at country level, and a hierarchical model excluding multinational data. Covariates at different levels were drawn from a long list of factors suggested in the literature. We found 67 studies reporting 2094 cost-effectiveness estimates relating to 23 countries (6 studies reporting for more than 1 country). Data and study-level covariates included patient characteristics, intervention and comparator cost, and some study methods (e.g., discount rates and time horizon). After adjusting for these factors, the proportion of variation attributable to countries was negligible in the cross-classified model but moderate in the hierarchical models (14%-19% of total variance). Country-level variables that improved the fit of the hierarchical models included measures of income and health care finance, health care resources, and population risks. Our analysis suggested that variability in published cost-effectiveness estimates is related more to differences in study methods than to differences in national context. Multinational studies were associated with much lower country-level variation than single-country studies. These findings are for a single clinical question and may be atypical. © The Author(s) 2015.

[Personalized nursing care in hospital and its effects on the patient-nurse trust relationship].

PubMed

García-Juárez, María del Rosario; López-Alonso, Sergio R; Moreno-Verdugo, Ana; Guerra-González, Sara; Fernández-Corchero, Juana; Márquez-Borrego, M José; Orozco-Cózar, M José; Ramos-Bosquet, Gádor

2013-01-01

To determine the level of implementation of an inpatient personalized nursing care model in four hospitals of the Andalusian Health Service, and to determine if there is an association between this model and the perception of trust in the nurse by the patient. An observational cross-sectional study included the patients discharged during a period of 12 months from hospital wards that used the Inpatient Personalized Nursing Care Model of the Andalusian Health Service (based on Primary Nursing Model). The level of implemention was evaluated using the Nursing Care Personalized Index (IPC), made by «patient report» methodology, and the nurse-patient trust relationship was evaluated at the same time as the IPC. Statistical analysis included descriptive data analysis, Chi-squared test, and bivariate and multivariate logistic regression, with and without stratifying by hospitals wards. A total of 817 patient were included. The implementation of the inpatient personalized nursing care model varied between 61 and 79%. The IPC values showed a strong association with the nurse-patient trust relationship, and that for each point increase in the IPC score, the probability of a nurse-patient trust relationship increased between 50 and 130% (0.120.58). The implementation of a personalized nursing care model in the wards studied was higher in the surgicals wards and at regular level in medical wards. Furthermore, the influence of the inpatient personalized nursing care model on the nurse-patient trust relationship has been demonstrated using the IPC model. This trust is the main component for the establishment of a therapeutic relationship. Copyright © 2013 Elsevier España, S.L. All rights reserved.

CI2 for creating and comparing confidence-intervals for time-series bivariate plots.

PubMed

Mullineaux, David R

2017-02-01

Currently no method exists for calculating and comparing the confidence-intervals (CI) for the time-series of a bivariate plot. The study's aim was to develop 'CI2' as a method to calculate the CI on time-series bivariate plots, and to identify if the CI between two bivariate time-series overlap. The test data were the knee and ankle angles from 10 healthy participants running on a motorised standard-treadmill and non-motorised curved-treadmill. For a recommended 10+ trials, CI2 involved calculating 95% confidence-ellipses at each time-point, then taking as the CI the points on the ellipses that were perpendicular to the direction vector between the means of two adjacent time-points. Consecutive pairs of CI created convex quadrilaterals, and any overlap of these quadrilaterals at the same time or ±1 frame as a time-lag calculated using cross-correlations, indicated where the two time-series differed. CI2 showed no group differences between left and right legs on both treadmills, but the same legs between treadmills for all participants showed differences of less knee extension on the curved-treadmill before heel-strike. To improve and standardise the use of CI2 it is recommended to remove outlier time-series, use 95% confidence-ellipses, and scale the ellipse by the fixed Chi-square value as opposed to the sample-size dependent F-value. For practical use, and to aid in standardisation or future development of CI2, Matlab code is provided. CI2 provides an effective method to quantify the CI of bivariate plots, and to explore the differences in CI between two bivariate time-series. Copyright © 2016 Elsevier B.V. All rights reserved.

Grain size analysis and depositional environment of shallow marine to basin floor, Kelantan River Delta

NASA Astrophysics Data System (ADS)

Afifah, M. R. Nurul; Aziz, A. Che; Roslan, M. Kamal

2015-09-01

Sediment samples were collected from the shallow marine from Kuala Besar, Kelantan outwards to the basin floor of South China Sea which consisted of quaternary bottom sediments. Sixty five samples were analysed for their grain size distribution and statistical relationships. Basic statistical analysis like mean, standard deviation, skewness and kurtosis were calculated and used to differentiate the depositional environment of the sediments and to derive the uniformity of depositional environment either from the beach or river environment. The sediments of all areas were varied in their sorting ranging from very well sorted to poorly sorted, strongly negative skewed to strongly positive skewed, and extremely leptokurtic to very platykurtic in nature. Bivariate plots between the grain-size parameters were then interpreted and the Coarsest-Median (CM) pattern showed the trend suggesting relationships between sediments influenced by three ongoing hydrodynamic factors namely turbidity current, littoral drift and waves dynamic, which functioned to control the sediments distribution pattern in various ways.

Examining Impulse-Variability Theory and the Speed-Accuracy Trade-Off in Children's Overarm Throwing Performance.

PubMed

Molina, Sergio L; Stodden, David F

2018-04-01

This study examined variability in throwing speed and spatial error to test the prediction of an inverted-U function (i.e., impulse-variability [IV] theory) and the speed-accuracy trade-off. Forty-five 9- to 11-year-old children were instructed to throw at a specified percentage of maximum speed (45%, 65%, 85%, and 100%) and hit the wall target. Results indicated no statistically significant differences in variable error across the target conditions (p = .72), failing to support the inverted-U hypothesis. Spatial accuracy results indicated no statistically significant differences with mean radial error (p = .18), centroid radial error (p = .13), and bivariate variable error (p = .08) also failing to support the speed-accuracy trade-off in overarm throwing. As neither throwing performance variability nor accuracy changed across percentages of maximum speed in this sample of children as well as in a previous adult sample, current policy and practices of practitioners may need to be reevaluated.

To Identify the Important Soil Properties Affecting Dinoseb Adsorption with Statistical Analysis

PubMed Central

Guan, Yiqing; Wei, Jianhui; Zhang, Danrong; Zu, Mingjuan; Zhang, Liru

2013-01-01

Investigating the influences of soil characteristic factors on dinoseb adsorption parameter with different statistical methods would be valuable to explicitly figure out the extent of these influences. The correlation coefficients and the direct, indirect effects of soil characteristic factors on dinoseb adsorption parameter were analyzed through bivariate correlation analysis, and path analysis. With stepwise regression analysis the factors which had little influence on the adsorption parameter were excluded. Results indicate that pH and CEC had moderate relationship and lower direct effect on dinoseb adsorption parameter due to the multicollinearity with other soil factors, and organic carbon and clay contents were found to be the most significant soil factors which affect the dinoseb adsorption process. A regression is thereby set up to explore the relationship between the dinoseb adsorption parameter and the two soil factors: the soil organic carbon and clay contents. A 92% of the variation of dinoseb sorption coefficient could be attributed to the variation of the soil organic carbon and clay contents. PMID:23737715

Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3)

NASA Astrophysics Data System (ADS)

Zhang, Yonggen; Schaap, Marcel G.

2017-04-01

Pedotransfer functions (PTFs) have been widely used to predict soil hydraulic parameters in favor of expensive laboratory or field measurements. Rosetta (Schaap et al., 2001, denoted as Rosetta1) is one of many PTFs and is based on artificial neural network (ANN) analysis coupled with the bootstrap re-sampling method which allows the estimation of van Genuchten water retention parameters (van Genuchten, 1980, abbreviated here as VG), saturated hydraulic conductivity (Ks), and their uncertainties. In this study, we present an improved set of hierarchical pedotransfer functions (Rosetta3) that unify the water retention and Ks submodels into one. Parameter uncertainty of the fit of the VG curve to the original retention data is used in the ANN calibration procedure to reduce bias of parameters predicted by the new PTF. One thousand bootstrap replicas were used to calibrate the new models compared to 60 or 100 in Rosetta1, thus allowing the uni-variate and bi-variate probability distributions of predicted parameters to be quantified in greater detail. We determined the optimal weights for VG parameters and Ks, the optimal number of hidden nodes in ANN, and the number of bootstrap replicas required for statistically stable estimates. Results show that matric potential-dependent bias was reduced significantly while root mean square error (RMSE) for water content were reduced modestly; RMSE for Ks was increased by 0.9% (H3w) to 3.3% (H5w) in the new models on log scale of Ks compared with the Rosetta1 model. It was found that estimated distributions of parameters were mildly non-Gaussian and could instead be described rather well with heavy-tailed α-stable distributions. On the other hand, arithmetic means had only a small estimation bias for most textures when compared with the mean-like "shift" parameter of the α-stable distributions. Arithmetic means and (co-)variances are therefore still recommended as summary statistics of the estimated distributions. However, it may be necessary to parameterize the distributions in different ways if the new estimates are used in stochastic analyses of vadose zone flow and transport. Rosetta1 and Posetta3 were implemented in the python programming language, and the source code as well as additional documentation is available at: http://www.cals.arizona.edu/research/rosettav3.html.

Modeling of chemical inhibition from amyloid protein aggregation kinetics.

PubMed

Vázquez, José Antonio

2014-02-27

The process of amyloid proteins aggregation causes several human neuropathologies. In some cases, e.g. fibrillar deposits of insulin, the problems are generated in the processes of production and purification of protein and in the pump devices or injectable preparations for diabetics. Experimental kinetics and adequate modelling of chemical inhibition from amyloid aggregation are of practical importance in order to study the viable processing, formulation and storage as well as to predict and optimize the best conditions to reduce the effect of protein nucleation. In this manuscript, experimental data of insulin, Aβ42 amyloid protein and apomyoglobin fibrillation from recent bibliography were selected to evaluate the capability of a bivariate sigmoid equation to model them. The mathematical functions (logistic combined with Weibull equation) were used in reparameterized form and the effect of inhibitor concentrations on kinetic parameters from logistic equation were perfectly defined and explained. The surfaces of data were accurately described by proposed model and the presented analysis characterized the inhibitory influence on the protein aggregation by several chemicals. Discrimination between true and apparent inhibitors was also confirmed by the bivariate equation. EGCG for insulin (working at pH = 7.4/T = 37°C) and taiwaniaflavone for Aβ42 were the compounds studied that shown the greatest inhibition capacity. An accurate, simple and effective model to investigate the inhibition of chemicals on amyloid protein aggregation has been developed. The equation could be useful for the clear quantification of inhibitor potential of chemicals and rigorous comparison among them.

Assessing protein conformational sampling methods based on bivariate lag-distributions of backbone angles

PubMed Central

Maadooliat, Mehdi; Huang, Jianhua Z.

2013-01-01

Despite considerable progress in the past decades, protein structure prediction remains one of the major unsolved problems in computational biology. Angular-sampling-based methods have been extensively studied recently due to their ability to capture the continuous conformational space of protein structures. The literature has focused on using a variety of parametric models of the sequential dependencies between angle pairs along the protein chains. In this article, we present a thorough review of angular-sampling-based methods by assessing three main questions: What is the best distribution type to model the protein angles? What is a reasonable number of components in a mixture model that should be considered to accurately parameterize the joint distribution of the angles? and What is the order of the local sequence–structure dependency that should be considered by a prediction method? We assess the model fits for different methods using bivariate lag-distributions of the dihedral/planar angles. Moreover, the main information across the lags can be extracted using a technique called Lag singular value decomposition (LagSVD), which considers the joint distribution of the dihedral/planar angles over different lags using a nonparametric approach and monitors the behavior of the lag-distribution of the angles using singular value decomposition. As a result, we developed graphical tools and numerical measurements to compare and evaluate the performance of different model fits. Furthermore, we developed a web-tool (http://www.stat.tamu.edu/∼madoliat/LagSVD) that can be used to produce informative animations. PMID:22926831

Whole-coal versus ash basis in coal geochemistry: a mathematical approach to consistent interpretations

USGS Publications Warehouse

Geboy, Nicholas J.; Engle, Mark A.; Hower, James C.

2013-01-01

Several standard methods require coal to be ashed prior to geochemical analysis. Researchers, however, are commonly interested in the compositional nature of the whole-coal, not its ash. Coal geochemical data for any given sample can, therefore, be reported in the ash basis on which it is analyzed or the whole-coal basis to which the ash basis data are back calculated. Basic univariate (mean, variance, distribution, etc.) and bivariate (correlation coefficients, etc.) measures of the same suite of samples can be very different depending which reporting basis the researcher uses. These differences are not real, but an artifact resulting from the compositional nature of most geochemical data. The technical term for this artifact is subcompositional incoherence. Since compositional data are forced to a constant sum, such as 100% or 1,000,000 ppm, they possess curvilinear properties which make the Euclidean principles on which most statistical tests rely inappropriate, leading to erroneous results. Applying the isometric logratio (ilr) transformation to compositional data allows them to be represented in Euclidean space and evaluated using traditional tests without fear of producing mathematically inconsistent results. When applied to coal geochemical data, the issues related to differences between the two reporting bases are resolved as demonstrated in this paper using major oxide and trace metal data from the Pennsylvanian-age Pond Creek coal of eastern Kentucky, USA. Following ilr transformation, univariate statistics, such as mean and variance, still differ between the ash basis and whole-coal basis, but in predictable and calculated manners. Further, the stability between two different components, a bivariate measure, is identical, regardless of the reporting basis. The application of ilr transformations addresses both the erroneous results of Euclidean-based measurements on compositional data as well as the inconsistencies observed on coal geochemical data reported on different bases.

Clustering of fast-food restaurants around schools: a novel application of spatial statistics to the study of food environments.

PubMed

Austin, S Bryn; Melly, Steven J; Sanchez, Brisa N; Patel, Aarti; Buka, Stephen; Gortmaker, Steven L

2005-09-01

We examined the concentration of fast food restaurants in areas proximal to schools to characterize school neighborhood food environments. We used geocoded databases of restaurant and school addresses to examine locational patterns of fast-food restaurants and kindergartens and primary and secondary schools in Chicago. We used the bivariate K function statistical method to quantify the degree of clustering (spatial dependence) of fast-food restaurants around school locations. The median distance from any school in Chicago to the nearest fast-food restaurant was 0.52 km, a distance that an adult can walk in little more than 5 minutes, and 78% of schools had at least 1 fast-food restaurant within 800 m. Fast-food restaurants were statistically significantly clustered in areas within a short walking distance from schools, with an estimated 3 to 4 times as many fast-food restaurants within 1.5 km from schools than would be expected if the restaurants were distributed throughout the city in a way unrelated to school locations. Fast-food restaurants are concentrated within a short walking distance from schools, exposing children to poor-quality food environments in their school neighborhoods.

Oral cancer associated with chronic mechanical irritation of the oral mucosa.

PubMed

Piemonte, E; Lazos, J; Belardinelli, P; Secchi, D; Brunotto, M; Lanfranchi-Tizeira, H

2018-03-01

Most of the studies dealing with Chronic Mechanical Irritation (CMI) and Oral Cancer (OC) only considered prosthetic and dental variables separately, and CMI functional factors are not registered. Thus, the aim of this study was to assess OC risk in individuals with dental, prosthetic and functional CMI. Also, we examined CMI presence in relation to tumor size. A case-control study was carried out from 2009 to 2013. Study group were squamous cell carcinoma cases; control group was patients seeking dental treatment in the same institution. 153 patients were studied (Study group n=53, Control group n=100). CMI reproducibility displayed a correlation coefficient of 1 (p<0.0001). Bivariate analysis showed statistically significant associations for all variables (age, gender, tobacco and alcohol consumption and CMI). Multivariate analysis exhibited statistical significance for age, alcohol, and CMI, but not for gender or tobacco. Relationship of CMI with tumor size showed no statistically significant differences. CMI could be regarded as a risk factor for oral cancer. In individuals with other OC risk factors, proper treatment of the mechanical injuring factors (dental, prosthetic and functional) could be an important measure to reduce the risk of oral cancer.

Clustering of Fast-Food Restaurants Around Schools: A Novel Application of Spatial Statistics to the Study of Food Environments

PubMed Central

Austin, S. Bryn; Melly, Steven J.; Sanchez, Brisa N.; Patel, Aarti; Buka, Stephen; Gortmaker, Steven L.

2005-01-01

Objectives. We examined the concentration of fast food restaurants in areas proximal to schools to characterize school neighborhood food environments. Methods. We used geocoded databases of restaurant and school addresses to examine locational patterns of fast-food restaurants and kindergartens and primary and secondary schools in Chicago. We used the bivariate K function statistical method to quantify the degree of clustering (spatial dependence) of fast-food restaurants around school locations. Results. The median distance from any school in Chicago to the nearest fast-food restaurant was 0.52 km, a distance that an adult can walk in little more than 5 minutes, and 78% of schools had at least 1 fast-food restaurant within 800 m. Fast-food restaurants were statistically significantly clustered in areas within a short walking distance from schools, with an estimated 3 to 4 times as many fast-food restaurants within 1.5 km from schools than would be expected if the restaurants were distributed throughout the city in a way unrelated to school locations. Conclusions. Fast-food restaurants are concentrated within a short walking distance from schools, exposing children to poor-quality food environments in their school neighborhoods. PMID:16118369

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods.

PubMed

Vizcaíno, Iván P; Carrera, Enrique V; Muñoz-Romero, Sergio; Cumbal, Luis H; Rojo-Álvarez, José Luis

2017-10-16

Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, we propose an enhanced statistical interpolation method to provide water quality managers with statistically interpolated representations of spatial-temporal dynamics. Specifically, our proposal makes efficient use of the a priori available information of the quality parameter measurements through Support Vector Regression (SVR) based on Mercer's kernels. The methods are benchmarked against previously proposed methods in three segments of the Machángara River and one segment of the San Pedro River in Ecuador, and their different dynamics are shown by statistically interpolated spatial-temporal maps. The best interpolation performance in terms of mean absolute error was the SVR with Mercer's kernel given by either the Mahalanobis spatial-temporal covariance matrix or by the bivariate estimated autocorrelation function. In particular, the autocorrelation kernel provides with significant improvement of the estimation quality, consistently for all the six water quality variables, which points out the relevance of including a priori knowledge of the problem.

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods

PubMed Central

Vizcaíno, Iván P.; Muñoz-Romero, Sergio; Cumbal, Luis H.

2017-01-01

Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, we propose an enhanced statistical interpolation method to provide water quality managers with statistically interpolated representations of spatial-temporal dynamics. Specifically, our proposal makes efficient use of the a priori available information of the quality parameter measurements through Support Vector Regression (SVR) based on Mercer’s kernels. The methods are benchmarked against previously proposed methods in three segments of the Machángara River and one segment of the San Pedro River in Ecuador, and their different dynamics are shown by statistically interpolated spatial-temporal maps. The best interpolation performance in terms of mean absolute error was the SVR with Mercer’s kernel given by either the Mahalanobis spatial-temporal covariance matrix or by the bivariate estimated autocorrelation function. In particular, the autocorrelation kernel provides with significant improvement of the estimation quality, consistently for all the six water quality variables, which points out the relevance of including a priori knowledge of the problem. PMID:29035333

A comprehensive model to determine the effects of temperature and species fluctuations on reactions in turbulent reacting flows

NASA Technical Reports Server (NTRS)

Antaki, P. J.

1981-01-01

The joint probability distribution function (pdf), which is a modification of the bivariate Gaussian pdf, is discussed and results are presented for a global reaction model using the joint pdf. An alternative joint pdf is discussed. A criterion which permits the selection of temperature pdf's in different regions of turbulent, reacting flow fields is developed. Two principal approaches to the determination of reaction rates in computer programs containing detailed chemical kinetics are outlined. These models represent a practical solution to the modeling of species reaction rates in turbulent, reacting flows.

Exploring the Co-Development of Reading Fluency and Reading Comprehension: A Twin Study

ERIC Educational Resources Information Center

Little, Callie W.; Hart, Sara A.; Quinn, Jamie M.; Tucker-Drob, Elliot M.; Taylor, Jeanette; Schatschneider, Christopher

2017-01-01

This study explores the co-development of two related but separate reading skills, reading fluency and reading comprehension, across Grades 1-4. A bivariate biometric dual change score model was applied to longitudinal data collected from 1,784 twin pairs between the ages of 6 and 10 years. Grade 1 skills were influenced by highly overlapping…

Paths to Success in Young Adulthood from Mental Health and Life Transitions in Emerging Adulthood

ERIC Educational Resources Information Center

Howard, Andrea L.; Galambos, Nancy L.; Krahn, Harvey J.

2010-01-01

This study followed a school-based sample (N = 920) to explore how trajectories of depressive symptoms and expressed anger from age 18 to 25, along with important life transitions, predicted life and career satisfaction at age 32. A two-group (women and men) bivariate growth model revealed that higher depressive symptoms at age 18 predicted lower…

Certification of family forests: What influences owners’ awareness and participation?

Treesearch

Selmin F. Creamer; Keith A. Blatner; Brett J. Butler

2012-01-01

In the United States, 35% of the forestland is owned by family forest owners with approximately 0.2% of this land reported to be enrolled in a forest certification system. The current study was conducted to provide insights into factors influencing family forest owners’ decisions to certify their lands. The bivariate probit model with sample selection results suggests...

Bayesian Analysis of Item Response Curves. Research Report 84-1. Mathematical Sciences Technical Report No. 132.

ERIC Educational Resources Information Center

Tsutakawa, Robert K.; Lin, Hsin Ying

Item response curves for a set of binary responses are studied from a Bayesian viewpoint of estimating the item parameters. For the two-parameter logistic model with normally distributed ability, restricted bivariate beta priors are used to illustrate the computation of the posterior mode via the EM algorithm. The procedure is illustrated by data…

Testing for the Endogenous Nature between Women's Empowerment and Antenatal Health Care Utilization: Evidence from a Cross-Sectional Study in Egypt

PubMed Central

Hussein, Mohamed Ali

2014-01-01

Women's relative lack of decision-making power and their unequal access to employment, finances, education, basic health care, and other resources are considered to be the root causes of their ill-health and that of their children. The main purpose of this paper is to examine the interactive relation between women's empowerment and the use of maternal health care. Two model specifications are tested. One assumes no correlation between empowerment and antenatal care while the second specification allows for correlation. Both the univariate and the recursive bivariate probit models are tested. The data used in this study is EDHS 2008. Factor Analysis Technique is also used to construct some of the explanatory variables such as the availability and quality of health services indicators. The findings show that women's empowerment and receiving regular antenatal care are simultaneously determined and the recursive bivariate probit is a better approximation to the relationship between them. Women's empowerment has significant and positive impact on receiving regular antenatal care. The availability and quality of health services do significantly increase the likelihood of receiving regular antenatal care. PMID:25140310

Bivariate spatiotemporal disease mapping of cancer of the breast and cervix uteri among Iranian women.

PubMed

Raei, Mehdi; Schmid, Volker Johann; Mahaki, Behzad

2018-05-08

Cervical cancer in women is one of the most common cancers and breast cancer has grown dramatically in recent years. The purpose of this study was to map the incidence of breast and cervix uteri cancer among Iranian women over a 6-year period (2004-2009) searching for trend changes and risk factors. Cancer incidence data were extracted from the annual reports of the National Cancer Registry in Iran. Hierarchical Bayesian models, including random spatial and temporal effects was utilized together with bivariate, spatio-temporal shared component modelling. The provinces Tehran, Isfahan, Mazandaran and Gilan were found to have the highest relative risk (RR) of breast cancer, while the highest RR of cervix uteri cancer was observed in Tehran, Golestan, Khuzestan and Khorasan Razavi. Shared risk factors (smoking component) between the two cancers were seen to have the highest influence in Tehran, Khorasan Razavi, Yazd, Isfahan, Golestan, Khuzestan, Fars and Mazandaran, while the least were observed in Kohgiluyeh Boyerahmad. Apparent differences and distinctions between high-risk and low-risk provinces reveal a pattern of obvious dispersion for these cancers in Iran that should be considered when allocating healthcare resources and services in different areas.

Accuracy of mucocutaneous leishmaniasis diagnosis using polymerase chain reaction: systematic literature review and meta-analysis

PubMed Central

Gomes, Ciro Martins; Mazin, Suleimy Cristina; dos Santos, Elisa Raphael; Cesetti, Mariana Vicente; Bächtold, Guilherme Albergaria Brízida; Cordeiro, João Henrique de Freitas; Theodoro, Fabrício Claudino Estrela Terra; Damasco, Fabiana dos Santos; Carranza, Sebastián Andrés Vernal; Santos, Adriana de Oliveira; Roselino, Ana Maria; Sampaio, Raimunda Nonata Ribeiro

2015-01-01

The diagnosis of mucocutaneous leishmaniasis (MCL) is hampered by the absence of a gold standard. An accurate diagnosis is essential because of the high toxicity of the medications for the disease. This study aimed to assess the ability of polymerase chain reaction (PCR) to identify MCL and to compare these results with clinical research recently published by the authors. A systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA Statement was performed using comprehensive search criteria and communication with the authors. A meta-analysis considering the estimates of the univariate and bivariate models was performed. Specificity near 100% was common among the papers. The primary reason for accuracy differences was sensitivity. The meta-analysis, which was only possible for PCR samples of lesion fragments, revealed a sensitivity of 71% [95% confidence interval (CI) = 0.59; 0.81] and a specificity of 93% (95% CI = 0.83; 0.98) in the bivariate model. The search for measures that could increase the sensitivity of PCR should be encouraged. The quality of the collected material and the optimisation of the amplification of genetic material should be prioritised. PMID:25946238

surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials.

PubMed

Rotolo, Federico; Paoletti, Xavier; Michiels, Stefan

2018-03-01

Surrogate endpoints are attractive for use in clinical trials instead of well-established endpoints because of practical convenience. To validate a surrogate endpoint, two important measures can be estimated in a meta-analytic context when individual patient data are available: the R indiv 2 or the Kendall's τ at the individual level, and the R trial 2 at the trial level. We aimed at providing an R implementation of classical and well-established as well as more recent statistical methods for surrogacy assessment with failure time endpoints. We also intended incorporating utilities for model checking and visualization and data generating methods described in the literature to date. In the case of failure time endpoints, the classical approach is based on two steps. First, a Kendall's τ is estimated as measure of individual level surrogacy using a copula model. Then, the R trial 2 is computed via a linear regression of the estimated treatment effects; at this second step, the estimation uncertainty can be accounted for via measurement-error model or via weights. In addition to the classical approach, we recently developed an approach based on bivariate auxiliary Poisson models with individual random effects to measure the Kendall's τ and treatment-by-trial interactions to measure the R trial 2 . The most common data simulation models described in the literature are based on: copula models, mixed proportional hazard models, and mixture of half-normal and exponential random variables. The R package surrosurv implements the classical two-step method with Clayton, Plackett, and Hougaard copulas. It also allows to optionally adjusting the second-step linear regression for measurement-error. The mixed Poisson approach is implemented with different reduced models in addition to the full model. We present the package functions for estimating the surrogacy models, for checking their convergence, for performing leave-one-trial-out cross-validation, and for plotting the results. We illustrate their use in practice on individual patient data from a meta-analysis of 4069 patients with advanced gastric cancer from 20 trials of chemotherapy. The surrosurv package provides an R implementation of classical and recent statistical methods for surrogacy assessment of failure time endpoints. Flexible simulation functions are available to generate data according to the methods described in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

Bayesian meta-analytical methods to incorporate multiple surrogate endpoints in drug development process.

PubMed

Bujkiewicz, Sylwia; Thompson, John R; Riley, Richard D; Abrams, Keith R

2016-03-30

A number of meta-analytical methods have been proposed that aim to evaluate surrogate endpoints. Bivariate meta-analytical methods can be used to predict the treatment effect for the final outcome from the treatment effect estimate measured on the surrogate endpoint while taking into account the uncertainty around the effect estimate for the surrogate endpoint. In this paper, extensions to multivariate models are developed aiming to include multiple surrogate endpoints with the potential benefit of reducing the uncertainty when making predictions. In this Bayesian multivariate meta-analytic framework, the between-study variability is modelled in a formulation of a product of normal univariate distributions. This formulation is particularly convenient for including multiple surrogate endpoints and flexible for modelling the outcomes which can be surrogate endpoints to the final outcome and potentially to one another. Two models are proposed, first, using an unstructured between-study covariance matrix by assuming the treatment effects on all outcomes are correlated and second, using a structured between-study covariance matrix by assuming treatment effects on some of the outcomes are conditionally independent. While the two models are developed for the summary data on a study level, the individual-level association is taken into account by the use of the Prentice's criteria (obtained from individual patient data) to inform the within study correlations in the models. The modelling techniques are investigated using an example in relapsing remitting multiple sclerosis where the disability worsening is the final outcome, while relapse rate and MRI lesions are potential surrogates to the disability progression. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Impact of some types of mass gatherings on current suicide risk in an urban population: statistical and negative binominal regression analysis of time series

PubMed Central

2014-01-01

Background Many studies have investigated the impact of a wide range of social events on suicide-related behaviour. However, these studies have predominantly examined national events. The aim of this study is to provide a statistical evaluation of the relationship between mass gatherings in some relatively small urban sub-populations and the general suicide rates of a major city. Methods The data were gathered in the Ukrainian city of Dnipropetrovsk, with a population of 1 million people, in 2005–2010. Suicide attempts, suicides, and the total amount of suicide-related behaviours were registered daily for each sex. Bivariate and multivariate statistical analysis, including negative binomial regression, were applied to assess the risk of suicide-related behaviour in the city’s general population for 7 days before and after 427 mass gatherings, such as concerts, football games, and non-regular mass events organized by the Orthodox Church and new religious movements. Results The bivariate and multivariate statistical analyses found significant changes in some suicide-related behaviour rates in the city’s population after certain kinds of mass gatherings. In particular, we observed an increased relative risk (RR) of male suicide-related behaviour after a home defeat of the local football team (RR = 1.32, p = 0.047; regression coefficient beta = 0.371, p = 0.002), and an increased risk of male suicides (RR = 1.29, p = 0.006; beta =0.255, p = 0.002), male suicide-related behaviour (RR = 1.25, p = 0.019; beta =0.251, p < 0.001), and total suicide-related behaviour (RR = 1.23 p < 0.001; beta =0.187, p < 0.001) after events organized by the new religious movements. Conclusions Although football games and mass events organized by new religious movements involved a relatively small part of an urban population (1.6 and 0.3%, respectively), we observed a significant increase of the some suicide-related behaviour rates in the whole population. It is likely that the observed effect on suicide-related behaviour is related to one’s personal presence at the event rather than to its broadcast. Our findings can be explained largely in terms of Gabennesch’s theory of the ‘broken-promises effect’ with regard to intra- and interpersonal conflict and, in terms of crowd behaviour effects. PMID:24708574

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.

Genetic correlation of longevity with growth, post-mortem, docility and some morphological traits in the Pirenaica beef cattle breed.

PubMed

Varona, L; Moreno, C; Altarriba, J

2012-06-01

Survival or longevity is an economically relevant trait in cattle. However, it is not currently included in cattle selection criteria because of the delayed recording of phenotypic data and the high computational demand of survival techniques under proportional hazard models. The identification of longevity-correlated traits that can be early registered in lifetime would therefore be very useful for beef cattle selection processes. The aim of this study was to estimate the genetic correlation of survival (SURV) with: growth - birth weight (BW), weight at 120 days (W120), weight at 210 days (W210); carcass - cold carcass weight (CCW), conformation (CON), fatness (FAT) and meat colour (COL); teat morphology - teat thickness (TT), teat length (TL) and udder depth (UD); leg morphology - forward (FL) and backward legs (BL); milk production (MILK) and docility (DOC). In the statistical analysis, SURV was measured in discrete-time intervals and modelled via a sequential threshold model. A series of independent bivariate Bayesian analyses between cow survival and each recorded trait were carried out. The posterior mean estimates (and posterior standard deviation) for the heritability of SURV was 0.05 (0.01); and for the relevant genetic correlations with SURV were 0.07 (0.04), 0.12 (0.05), 0.10 (0.05), 0.15 (0.05), -0.18 (0.06), 0.33 (0.06) and 0.27 (0.15) for BW, W120, W210, CCW, CON, FAT and COL, respectively.

Validation of the conceptual research utilization scale: an application of the standards for educational and psychological testing in healthcare

PubMed Central

2011-01-01

Background There is a lack of acceptable, reliable, and valid survey instruments to measure conceptual research utilization (CRU). In this study, we investigated the psychometric properties of a newly developed scale (the CRU Scale). Methods We used the Standards for Educational and Psychological Testing as a validation framework to assess four sources of validity evidence: content, response processes, internal structure, and relations to other variables. A panel of nine international research utilization experts performed a formal content validity assessment. To determine response process validity, we conducted a series of one-on-one scale administration sessions with 10 healthcare aides. Internal structure and relations to other variables validity was examined using CRU Scale response data from a sample of 707 healthcare aides working in 30 urban Canadian nursing homes. Principal components analysis and confirmatory factor analyses were conducted to determine internal structure. Relations to other variables were examined using: (1) bivariate correlations; (2) change in mean values of CRU with increasing levels of other kinds of research utilization; and (3) multivariate linear regression. Results Content validity index scores for the five items ranged from 0.55 to 1.00. The principal components analysis predicted a 5-item 1-factor model. This was inconsistent with the findings from the confirmatory factor analysis, which showed best fit for a 4-item 1-factor model. Bivariate associations between CRU and other kinds of research utilization were statistically significant (p < 0.01) for the latent CRU scale score and all five CRU items. The CRU scale score was also shown to be significant predictor of overall research utilization in multivariate linear regression. Conclusions The CRU scale showed acceptable initial psychometric properties with respect to responses from healthcare aides in nursing homes. Based on our validity, reliability, and acceptability analyses, we recommend using a reduced (four-item) version of the CRU scale to yield sound assessments of CRU by healthcare aides. Refinement to the wording of one item is also needed. Planned future research will include: latent scale scoring, identification of variables that predict and are outcomes to conceptual research use, and longitudinal work to determine CRU Scale sensitivity to change. PMID:21595888

Tumor and liver determinants of prognosis in unresectable hepatocellular carcinoma: a large case cohort study.

PubMed

Carr, Brian I; Pancoska, Petr; Branch, Robert A

2009-12-24

967 patients with unresectable and untransplantable, biopsy-proven hepatocellular carcinoma (HCC) were prospectively evaluated at baseline and followed up till death. Survival was the end point. We found that male gender, ascites, cirrhosis, portal vein thrombosis (PVT), elevated AFP or bilirubin, or alkaline phosphatase, were each statistically significant adverse prognostic factors. Patients with normal AFP survived longer than those with elevated AFP, even in the presence of PVT, large or bilobar tumors or cirrhosis. We used a bivariate analysis to separate patient sub groups based on liver function and tumor characteristics and found clear discrimination in survival between subsets; in addition both cirrhosis and presence of PVT were significant factors. We also used a purely mathematical approach to derive subgroups and a prognostic model for individual patients. Interestingly, the two approaches gave similar predictive information, which opens the possibility of a more detailed mathematical analysis in the future. The results of this large dataset show that amongst non-surgical HCC patients, there are clear subsets with longer survival. The data supports the concept of heterogeneity of HCC. The three factors, bilirubin, AFP, and PVT predominate in prognosis.

Psychosocial Predictors of Weight Loss among American Indian and Alaska Native Participants in a Diabetes Prevention Translational Project

PubMed Central

Dill, Edward J.; Manson, Spero M.; Jiang, Luohua; Pratte, Katherine A.; Gutilla, Margaret J.; Knepper, Stephanie L.; Beals, Janette; Roubideaux, Yvette; Special Diabetes Program for Indians Diabetes Prevention Demonstration Project

2016-01-01

The association of psychosocial factors (psychological distress, coping skills, family support, trauma exposure, and spirituality) with initial weight and weight loss among American Indians and Alaska Natives (AI/ANs) in a diabetes prevention translational project was investigated. Participants (n = 3,135) were confirmed as prediabetic and subsequently enrolled in the Special Diabetes Program for Indians Diabetes Prevention (SDPI-DP) demonstration project implemented at 36 Indian health care programs. Measures were obtained at baseline and after completing a 16-session educational curriculum focusing on weight loss through behavioral changes. At baseline, psychological distress and negative family support were linked to greater weight, whereas cultural spirituality was correlated with lower weight. Furthermore, psychological distress and negative family support predicted less weight loss, and positive family support predicted greater weight loss, over the course of the intervention. These bivariate relationships between psychosocial factors and weight remained statistically significant within a multivariate model, after controlling for sociodemographic characteristics. Conversely, coping skills and trauma exposure were not significantly associated with baseline weight or change in weight. These findings demonstrate the influence of psychosocial factors on weight loss in AI/AN communities and have substantial implications for incorporating adjunctive intervention components. PMID:26649314

Social support is a primary influence on home fruit, 100% juice, and vegetable availability.

PubMed

Baranowski, Tom; Watson, Kathy; Missaghian, Mariam; Broadfoot, Alison; Cullen, Karen; Nicklas, Theresa; Fisher, Jennifer; Baranowski, Janice; O'Donnell, Sharon

2008-07-01

Children tend to eat more fruit and vegetables when more are available in the home. We proposed and tested a model that predicts the availability at home (hereinafter termed "home availability") of fruit, 100% juice, and vegetables, using new measures of frequency of food shopping, purchase, and comparative purchase outcome expectancies (ie, the perceived benefits and costs of purchasing fruit and vegetables), home food pantry management practices, family social support for purchasing fruit and vegetables, food shopping practices, and body mass index (BMI). Participants (N=98) were recruited in 2004 in front of grocery stores and completed two telephone interviews. Cross-sectional hierarchical regression was employed with backward deletion of nonsignificant variables. Despite many statistically significant bivariate correlations between the new variables and home fruit, 100% juice, and vegetable availability, social support was the primary predictor of home fruit availability in multivariate regression. BMI and home 100% juice pantry management were the primary predictors of home 100% juice availability. Social support, BMI, and shopping practices were the primary predictors of home vegetable availability. Social support for purchasing fruit, 100% juice, and vegetables was an important, consistent predictor of home availability. These findings need to be replicated in larger samples.

Impact of e-Discipline on Children's Screen Time.

PubMed

Hawi, Nazir S; Rupert, Maya Samaha

2015-06-01

With rapid technological advancement, the prevalence and undesirable effects of excess screen time on children have become a mounting issue worldwide. There are many studies investigating the phenomenon's impact on society (e.g., behavioral, academic, health), but studies that uncover the causes and factors that increase the odds of children's excess screen time are limited. To this end, this study introduces the term "e-discipline" to refer to systematic practices that use screen devices as discipline tools. As such, the aim of this study is to investigate the association between e-discipline and children's screen time by gender. Analysis was performed on 3,141 children aged 7-11 years old. Bivariate logistic regression models were used to calculate the odds of exceeding the American Academy of Pediatrics guidelines of 2 hours of screen time per day by boys and girls whose parents practice e-discipline. The results showed that children whose parents used screen devices as discipline tools had significantly more screen time compared to children whose parents did not. Furthermore, no statistically significant gender differences were found in the odds of exceeding the recommended screen time under e-discipline. Recommendations stemming from all the results are discussed.

An empirical comparison of methods for analyzing correlated data from a discrete choice survey to elicit patient preference for colorectal cancer screening

PubMed Central

2012-01-01

Background A discrete choice experiment (DCE) is a preference survey which asks participants to make a choice among product portfolios comparing the key product characteristics by performing several choice tasks. Analyzing DCE data needs to account for within-participant correlation because choices from the same participant are likely to be similar. In this study, we empirically compared some commonly-used statistical methods for analyzing DCE data while accounting for within-participant correlation based on a survey of patient preference for colorectal cancer (CRC) screening tests conducted in Hamilton, Ontario, Canada in 2002. Methods A two-stage DCE design was used to investigate the impact of six attributes on participants' preferences for CRC screening test and willingness to undertake the test. We compared six models for clustered binary outcomes (logistic and probit regressions using cluster-robust standard error (SE), random-effects and generalized estimating equation approaches) and three models for clustered nominal outcomes (multinomial logistic and probit regressions with cluster-robust SE and random-effects multinomial logistic model). We also fitted a bivariate probit model with cluster-robust SE treating the choices from two stages as two correlated binary outcomes. The rank of relative importance between attributes and the estimates of β coefficient within attributes were used to assess the model robustness. Results In total 468 participants with each completing 10 choices were analyzed. Similar results were reported for the rank of relative importance and β coefficients across models for stage-one data on evaluating participants' preferences for the test. The six attributes ranked from high to low as follows: cost, specificity, process, sensitivity, preparation and pain. However, the results differed across models for stage-two data on evaluating participants' willingness to undertake the tests. Little within-patient correlation (ICC ≈ 0) was found in stage-one data, but substantial within-patient correlation existed (ICC = 0.659) in stage-two data. Conclusions When small clustering effect presented in DCE data, results remained robust across statistical models. However, results varied when larger clustering effect presented. Therefore, it is important to assess the robustness of the estimates via sensitivity analysis using different models for analyzing clustered data from DCE studies. PMID:22348526

Area and volume ratios for prediction of visual outcome in idiopathic macular hole.

PubMed

Geng, Xing-Yun; Wu, Hui-Qun; Jiang, Jie-Hui; Jiang, Kui; Zhu, Jun; Xu, Yi; Dong, Jian-Cheng; Yan, Zhuang-Zhi

2017-01-01

To predict the visual outcome in patients undergoing macular hole surgery by two novel three-dimensional morphological parameters on optical coherence tomography (OCT): area ratio factor (ARF) and volume ratio factor (VRF). A clinical case series was conducted, including 54 eyes of 54 patients with an idiopathic macular hole (IMH). Each patient had an OCT examination before and after surgery. Morphological parameters of the macular hole, such as minimum diameter, base diameter, and height were measured. Then, the macular hole index (MHI), tractional hole index (THI), and hole form factor (HFF) were calculated. Meanwhile, novel postoperative macular hole (MH) factors, ARF and VRF were calculated by three-dimensional morphology. Bivariate correlations were performed to acquire asymptotic significance values between the steady best corrected visual acuity (BCVA) after surgery and 2D/3D arguments of MH by the Pearson method with two-tailed test. All significant factors were analyzed by the receiver operating characteristic (ROC) curve analysis of SPSS software which were responsible for vision recovery. ROC curves analyses were performed to further discuss the different parameters on the prediction of visual outcome. The mean and standard deviation values of patients' age, symptoms duration, and follow-up time were 64.8±8.9y (range: 28-81), 18.6±11.5d (range: 2-60), and 11.4±0.4mo (range: 6-24), respectively. Steady-post-BCVA analyzed with bivariate correlations was found to be significantly correlated with base diameter ( r =0.521, P <0.001), minimum diameter ( r =0.514, P <0.001), MHI ( r =-0.531, P <0.001), THI ( r =-0.386, P =0.004), HFF ( r =-0.508, P <0.001), and ARF ( r =-0.532, P <0.001). Other characteristic parameters such as age, duration of surgery, height, diameter hole index, and VRF were not statistically significant with steady-post-BCVA. According to area under the curve (AUC) values, values of ARF, MHI, HFF, minimum diameter, THI, and base diameter are 0.806, 0.772, 0.750, 0.705, 0.690, and 0.686, respectively. However, Steady-post-BCVA analysis with bivariate correlations for VRF was no statistical significance. Results of ROC curve analysis indicated that the MHI value, HFF, and ARF was greater than 0.427, 1.027 and 1.558 respectively which could correlate with better visual acuity. Compared with MHI and HFF, ARF could effectively express three-dimensional characteristics of macular hole and achieve better sensitivity and specificity. Thus, ARF could be the most effective parameter to predict the visual outcome in macular hole surgery.

Unsupervised Outlier Profile Analysis

PubMed Central

Ghosh, Debashis; Li, Song

2014-01-01

In much of the analysis of high-throughput genomic data, “interesting” genes have been selected based on assessment of differential expression between two groups or generalizations thereof. Most of the literature focuses on changes in mean expression or the entire distribution. In this article, we explore the use of C(α) tests, which have been applied in other genomic data settings. Their use for the outlier expression problem, in particular with continuous data, is problematic but nevertheless motivates new statistics that give an unsupervised analog to previously developed outlier profile analysis approaches. Some simulation studies are used to evaluate the proposal. A bivariate extension is described that can accommodate data from two platforms on matched samples. The proposed methods are applied to data from a prostate cancer study. PMID:25452686

Factors that affect willingness to donate blood for the purpose of biospecimen research in the Korean American community.

PubMed

Yen, Glorian P; Davey, Adam; Ma, Grace X

2015-04-01

Biorepositories have been key resources in examining genetically-linked diseases, particularly cancer. Asian Americans contribute to biorepositories at lower rates than other racial groups, but the reasons for this are unclear. We hypothesized that attitudes toward biospecimen research mediate the relationship between demographic and healthcare access factors, and willingness to donate blood for research purposes among individuals of Korean heritage. Descriptive statistics and bivariate analyses were utilized to characterize the sample with respect to demographic, psychosocial, and behavioral variables. Structural equation modeling with 5000 re-sample bootstrapping was used to assess each component of the proposed simple mediation models. Attitudes towards biospecimen research fully mediate associations between age, income, number of years lived in the United States, and having a regular physician and willingness to donate blood for the purpose of research. Participants were willing to donate blood for the purpose of research despite having neutral feelings towards biospecimen research as a whole. Participants reported higher willingness to donate blood for research purposes when they were older, had lived in the United States longer, had higher income, and had a regular doctor that they visited. Many of the significant relationships between demographic and health care access factors, attitudes towards biospecimen research, and willingness to donate blood for the purpose of research may be explained by the extent of acculturation of the participants in the United States.

Relationships between Religion and Two Forms of Homonegativity in Europe--A Multilevel Analysis of Effects of Believing, Belonging and Religious Practice.

PubMed

Doebler, Stefanie

2015-01-01

This paper examines relationships between religion and two forms of homonegativity across 43 European countries using a bivariate response binary logistic multilevel model. The model analyzes effects of religious believing, belonging and practice on two response variables: a) a moral rejection of homosexuality as a practice and b) intolerance toward homosexuals as a group. The findings indicate that both forms of homonegativity are prevalent in Europe. Traditional doctrinal religious believing (belief in a personal God) is positively related to a moral rejection of homosexuality but to a much lesser extent associated with intolerance toward homosexuals as a group. Members of religious denominations are more likely than non-members to reject homosexuality as morally wrong and to reject homosexuals as neighbors. The analysis found significant differences between denominations that are likely context-dependent. Attendance at religious services is positively related to homonegativity in a majority of countries. The findings vary considerably across countries: Religion is more strongly related to homonegativity in Western than in Eastern Europe. In the post-soviet countries homonegativity appears to be largely a secular phenomenon. National contexts of high religiosity, high perceived government corruption, high income inequality and shortcomings in the implementation of gay rights in the countries' legislations are statistically related to higher levels of both moralistic homonegativity and intolerance toward homosexuals as a group.

Care-Seeking Patterns and Direct Economic Burden of Injuries in Bangladesh.

PubMed

Alfonso, Natalia Y; Alonge, Olakunle; Hoque, Dewan Md Emdadul; Baset, Kamran Ul; Hyder, Adnan A; Bishai, David

2017-04-29

This study provides a comprehensive review of the care-seeking patterns and direct economic burden of injuries from the victims' perspective in rural Bangladesh using a 2013 household survey covering 1.17 million people. Descriptive statistics and bivariate analyses were used to derive rates and test the association between variables. An analytic model was used to estimate total injury out-of-pocket (OOP) payments and a multivariate probit regression model assessed the relationship between financial distress and injury type. Results show non-fatal injuries occur to 1 in 5 people in our sample per year. With average household size of 4.5 in Bangladesh--every household has an injury every year. Most non-fatally injured patients sought healthcare from drug sellers. Less than half of fatal injuries sought healthcare and half of those with care were hospitalized. Average OOP payments varied significantly (range: $8-$830) by injury type and outcome (fatal vs. non-fatal). Total injury OOP expenditure was $$355,795 and $5000 for non-fatal and fatal injuries, respectively, per 100,000 people. The majority of household heads with injuries reported financial distress. This study can inform injury prevention advocates on disparities in healthcare usage, OOP costs and financial distress. Reallocation of resources to the most at risk populations can accelerate reduction of preventable injuries and prevent injury related catastrophic payments and impoverishment.

Treatment of the residual cavity during hepatic hydatidosis surgery: a cohort study of capitonnage vs. omentoplasty.

PubMed

Manterola, Carlos; Roa, Juan Carlos; Urrutia, Sebastián

2013-12-01

To determine the efficacy of omentoplasty (OP) and capitonnage (CA) in residual cavity management during the hepatic hydatidosis (HH) surgery in terms of the postoperative morbidity. Prospective cohort study. Patients with non-complicated HH treated with subtotal pericystectomy in the Department of Surgery of the Temuco Regional Hospital between 2001 and 2008 were studied. We compared those managed with CA with those managed with OP. A sample size of 40 patients in each group was estimated to be needed to adequately compare the outcomes of the approaches. The primary endpoint was postoperative morbidity. Descriptive statistics, bivariate analyses and logistic regression models were applied. The absolute risk (AR) and relative risk (RR) were calculated. The cohorts comprised 88 patients (CA 40 and OP 48), with a median age of 40 years (15-84), and 62.5 % were females. A general postoperative morbidity rate of 11.4 % was noted after a median follow-up of 60 months (12-84 months). Significant differences in postoperative morbidity were found (p = 0.044). Logistic regression models verified that there were no confounding variables. The AR of the postoperative morbidity for the CA and PO cohorts was 0.025 and 0.1875, respectively, and the RR was 0.13 [0.03, 0.70] 95 % CI. Residual cavity management with CA is associated with a lower postoperative morbidity risk than OP.

Risk perception and sexual behavior in HPV-vaccinated and unvaccinated young Colombian women.

PubMed

Ruiz-Sternberg, Angela M; Pinzón-Rondón, Ángela M

2014-09-01

To compare sexual behaviors and risk perception between young women vaccinated for HPV and unvaccinated Colombian women. In a cross-sectional design study, 1436 women (231 adolescents, <18 years; 1205 young women, 18-26 years) completed a self-administered questionnaire between May 2011 and March 2012 in Bogotá, Colombia. Data from vaccinated and unvaccinated women were compared by descriptive statistics and multivariate models. Sexual risk behaviors were not associated with vaccination after adjustment for risk perception, age, educational level, and HPV knowledge. By contrast, vaccination was associated with higher routine Pap smear screening (odds ratio [OR], 2.35; 95% confidence interval [CI], 1.69-3.28), use of modern contraceptives (OR, 2.02; 95% CI, 1.26-3.22), and consistent use of condoms (OR, 1.49; 95% CI, 1.11-2.01). Vaccinated young women were more likely to have had sex (OR, 2.08; 95% CI, 1.56-2.78), but sexual debut among adolescents was not associated with vaccination. In bivariate and multivariate analyses, vaccination status was negatively associated with perceived risk of HPV infection, warts, and cervical cancer. There was no association between vaccination and perceived risk of sexually transmitted infections in any model. No association was found between changes in risk perception after HPV vaccination and sexual risk behaviors. Copyright © 2014 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Care-Seeking Patterns and Direct Economic Burden of Injuries in Bangladesh

PubMed Central

Alfonso, Yira Natalia; Alonge, Olakunle; Hoque, Dewan Md Emdadul; Ul Baset, Md Kamran; Hyder, Adnan A.; Bishai, David

2017-01-01

This study provides a comprehensive review of the care-seeking patterns and direct economic burden of injuries from the victims’ perspective in rural Bangladesh using a 2013 household survey covering 1.17 million people. Descriptive statistics and bivariate analyses were used to derive rates and test the association between variables. An analytic model was used to estimate total injury out-of-pocket (OOP) payments and a multivariate probit regression model assessed the relationship between financial distress and injury type. Results show non-fatal injuries occur to 1 in 5 people in our sample per year. With average household size of 4.5 in Bangladesh--every household has an injury every year. Most non-fatally injured patients sought healthcare from drug sellers. Less than half of fatal injuries sought healthcare and half of those with care were hospitalized. Average OOP payments varied significantly (range: $8–$830) by injury type and outcome (fatal vs. non-fatal). Total injury OOP expenditure was $355,795 and $5000 for non-fatal and fatal injuries, respectively, per 100,000 people. The majority of household heads with injuries reported financial distress. This study can inform injury prevention advocates on disparities in healthcare usage, OOP costs and financial distress. Reallocation of resources to the most at risk populations can accelerate reduction of preventable injuries and prevent injury related catastrophic payments and impoverishment. PMID:28468240

Relationships between Religion and Two Forms of Homonegativity in Europe—A Multilevel Analysis of Effects of Believing, Belonging and Religious Practice

PubMed Central

Doebler, Stefanie

2015-01-01

This paper examines relationships between religion and two forms of homonegativity across 43 European countries using a bivariate response binary logistic multilevel model. The model analyzes effects of religious believing, belonging and practice on two response variables: a) a moral rejection of homosexuality as a practice and b) intolerance toward homosexuals as a group. The findings indicate that both forms of homonegativity are prevalent in Europe. Traditional doctrinal religious believing (belief in a personal God) is positively related to a moral rejection of homosexuality but to a much lesser extent associated with intolerance toward homosexuals as a group. Members of religious denominations are more likely than non-members to reject homosexuality as morally wrong and to reject homosexuals as neighbors. The analysis found significant differences between denominations that are likely context-dependent. Attendance at religious services is positively related to homonegativity in a majority of countries. The findings vary considerably across countries: Religion is more strongly related to homonegativity in Western than in Eastern Europe. In the post-soviet countries homonegativity appears to be largely a secular phenomenon. National contexts of high religiosity, high perceived government corruption, high income inequality and shortcomings in the implementation of gay rights in the countries’ legislations are statistically related to higher levels of both moralistic homonegativity and intolerance toward homosexuals as a group. PMID:26247352

A New Multivariate Approach in Generating Ensemble Meteorological Forcings for Hydrological Forecasting

NASA Astrophysics Data System (ADS)

Khajehei, Sepideh; Moradkhani, Hamid

2015-04-01

Producing reliable and accurate hydrologic ensemble forecasts are subject to various sources of uncertainty, including meteorological forcing, initial conditions, model structure, and model parameters. Producing reliable and skillful precipitation ensemble forecasts is one approach to reduce the total uncertainty in hydrological applications. Currently, National Weather Prediction (NWP) models are developing ensemble forecasts for various temporal ranges. It is proven that raw products from NWP models are biased in mean and spread. Given the above state, there is a need for methods that are able to generate reliable ensemble forecasts for hydrological applications. One of the common techniques is to apply statistical procedures in order to generate ensemble forecast from NWP-generated single-value forecasts. The procedure is based on the bivariate probability distribution between the observation and single-value precipitation forecast. However, one of the assumptions of the current method is fitting Gaussian distribution to the marginal distributions of observed and modeled climate variable. Here, we have described and evaluated a Bayesian approach based on Copula functions to develop an ensemble precipitation forecast from the conditional distribution of single-value precipitation forecasts. Copula functions are known as the multivariate joint distribution of univariate marginal distributions, which are presented as an alternative procedure in capturing the uncertainties related to meteorological forcing. Copulas are capable of modeling the joint distribution of two variables with any level of correlation and dependency. This study is conducted over a sub-basin in the Columbia River Basin in USA using the monthly precipitation forecasts from Climate Forecast System (CFS) with 0.5x0.5 Deg. spatial resolution to reproduce the observations. The verification is conducted on a different period and the superiority of the procedure is compared with Ensemble Pre-Processor approach currently used by National Weather Service River Forecast Centers in USA.

Bivariate drought frequency analysis using the copula method

NASA Astrophysics Data System (ADS)

Mirabbasi, Rasoul; Fakheri-Fard, Ahmad; Dinpashoh, Yagob

2012-04-01

Droughts are major natural hazards with significant environmental and economic impacts. In this study, two-dimensional copulas were applied to the analysis of the meteorological drought characteristics of the Sharafkhaneh gauge station, located in the northwest of Iran. Two major drought characteristics, duration and severity, as defined by the standardized precipitation index, were abstracted from observed drought events. Since drought duration and severity exhibited a significant correlation and since they were modeled using different distributions, copulas were used to construct the joint distribution function of the drought characteristics. The parameter of copulas was estimated using the method of the Inference Function for Margins. Several copulas were tested in order to determine the best data fit. According to the error analysis and the tail dependence coefficient, the Galambos copula provided the best fit for the observed drought data. Some bivariate probabilistic properties of droughts, based on the derived copula-based joint distribution, were also investigated. These probabilistic properties can provide useful information for water resource planning and management.

On the joint spectral density of bivariate random sequences. Thesis Technical Report No. 21

NASA Technical Reports Server (NTRS)

Aalfs, David D.

1995-01-01

For univariate random sequences, the power spectral density acts like a probability density function of the frequencies present in the sequence. This dissertation extends that concept to bivariate random sequences. For this purpose, a function called the joint spectral density is defined that represents a joint probability weighing of the frequency content of pairs of random sequences. Given a pair of random sequences, the joint spectral density is not uniquely determined in the absence of any constraints. Two approaches to constraining the sequences are suggested: (1) assume the sequences are the margins of some stationary random field, (2) assume the sequences conform to a particular model that is linked to the joint spectral density. For both approaches, the properties of the resulting sequences are investigated in some detail, and simulation is used to corroborate theoretical results. It is concluded that under either of these two constraints, the joint spectral density can be computed from the non-stationary cross-correlation.

Intra- and inter-basin mercury comparisons: Importance of basin scale and time-weighted methylmercury estimates

USGS Publications Warehouse

Bradley, Paul M.; Journey, Celeste A.; Bringham, Mark E.; Burns, Douglas A.; Button, Daniel T.; Riva-Murray, Karen

2013-01-01

To assess inter-comparability of fluvial mercury (Hg) observations at substantially different scales, Hg concentrations, yields, and bivariate-relations were evaluated at nested-basin locations in the Edisto River, South Carolina and Hudson River, New York. Differences between scales were observed for filtered methylmercury (FMeHg) in the Edisto (attributed to wetland coverage differences) but not in the Hudson. Total mercury (THg) concentrations and bivariate-relationships did not vary substantially with scale in either basin. Combining results of this and a previously published multi-basin study, fish Hg correlated strongly with sampled water FMeHg concentration (p = 0.78; p = 0.003) and annual FMeHg basin yield (p = 0.66; p = 0.026). Improved correlation (p = 0.88; p < 0.0001) was achieved with time-weighted mean annual FMeHg concentrations estimated from basin-specific LOADEST models and daily streamflow. Results suggest reasonable scalability and inter-comparability for different basin sizes if wetland area or related MeHg-source-area metrics are considered.

Tools for quantifying isotopic niche space and dietary variation at the individual and population level.

USGS Publications Warehouse

Newsome, Seth D.; Yeakel, Justin D.; Wheatley, Patrick V.; Tinker, M. Tim

2012-01-01

Ecologists are increasingly using stable isotope analysis to inform questions about variation in resource and habitat use from the individual to community level. In this study we investigate data sets from 2 California sea otter (Enhydra lutris nereis) populations to illustrate the advantages and potential pitfalls of applying various statistical and quantitative approaches to isotopic data. We have subdivided these tools, or metrics, into 3 categories: IsoSpace metrics, stable isotope mixing models, and DietSpace metrics. IsoSpace metrics are used to quantify the spatial attributes of isotopic data that are typically presented in bivariate (e.g., δ13C versus δ15N) 2-dimensional space. We review IsoSpace metrics currently in use and present a technique by which uncertainty can be included to calculate the convex hull area of consumers or prey, or both. We then apply a Bayesian-based mixing model to quantify the proportion of potential dietary sources to the diet of each sea otter population and compare this to observational foraging data. Finally, we assess individual dietary specialization by comparing a previously published technique, variance components analysis, to 2 novel DietSpace metrics that are based on mixing model output. As the use of stable isotope analysis in ecology continues to grow, the field will need a set of quantitative tools for assessing isotopic variance at the individual to community level. Along with recent advances in Bayesian-based mixing models, we hope that the IsoSpace and DietSpace metrics described here will provide another set of interpretive tools for ecologists.

Groundwater source contamination mechanisms: Physicochemical profile clustering, risk factor analysis and multivariate modelling

NASA Astrophysics Data System (ADS)

Hynds, Paul; Misstear, Bruce D.; Gill, Laurence W.; Murphy, Heather M.

2014-04-01

An integrated domestic well sampling and "susceptibility assessment" programme was undertaken in the Republic of Ireland from April 2008 to November 2010. Overall, 211 domestic wells were sampled, assessed and collated with local climate data. Based upon groundwater physicochemical profile, three clusters have been identified and characterised by source type (borehole or hand-dug well) and local geological setting. Statistical analysis indicates that cluster membership is significantly associated with the prevalence of bacteria (p = 0.001), with mean Escherichia coli presence within clusters ranging from 15.4% (Cluster-1) to 47.6% (Cluster-3). Bivariate risk factor analysis shows that on-site septic tank presence was the only risk factor significantly associated (p < 0.05) with bacterial presence within all clusters. Point agriculture adjacency was significantly associated with both borehole-related clusters. Well design criteria were associated with hand-dug wells and boreholes in areas characterised by high permeability subsoils, while local geological setting was significant for hand-dug wells and boreholes in areas dominated by low/moderate permeability subsoils. Multivariate susceptibility models were developed for all clusters, with predictive accuracies of 84% (Cluster-1) to 91% (Cluster-2) achieved. Septic tank setback was a common variable within all multivariate models, while agricultural sources were also significant, albeit to a lesser degree. Furthermore, well liner clearance was a significant factor in all models, indicating that direct surface ingress is a significant well contamination mechanism. Identification and elucidation of cluster-specific contamination mechanisms may be used to develop improved overall risk management and wellhead protection strategies, while also informing future remediation and maintenance efforts.

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

NASA Astrophysics Data System (ADS)

Tehrany, Mahyat Shafapour; Pradhan, Biswajeet; Jebur, Mustafa Neamah

2014-05-01

Flood is one of the most devastating natural disasters that occur frequently in Terengganu, Malaysia. Recently, ensemble based techniques are getting extremely popular in flood modeling. In this paper, weights-of-evidence (WoE) model was utilized first, to assess the impact of classes of each conditioning factor on flooding through bivariate statistical analysis (BSA). Then, these factors were reclassified using the acquired weights and entered into the support vector machine (SVM) model to evaluate the correlation between flood occurrence and each conditioning factor. Through this integration, the weak point of WoE can be solved and the performance of the SVM will be enhanced. The spatial database included flood inventory, slope, stream power index (SPI), topographic wetness index (TWI), altitude, curvature, distance from the river, geology, rainfall, land use/cover (LULC), and soil type. Four kernel types of SVM (linear kernel (LN), polynomial kernel (PL), radial basis function kernel (RBF), and sigmoid kernel (SIG)) were used to investigate the performance of each kernel type. The efficiency of the new ensemble WoE and SVM method was tested using area under curve (AUC) which measured the prediction and success rates. The validation results proved the strength and efficiency of the ensemble method over the individual methods. The best results were obtained from RBF kernel when compared with the other kernel types. Success rate and prediction rate for ensemble WoE and RBF-SVM method were 96.48% and 95.67% respectively. The proposed ensemble flood susceptibility mapping method could assist researchers and local governments in flood mitigation strategies.

Bivariate normal, conditional and rectangular probabilities: A computer program with applications

NASA Technical Reports Server (NTRS)

Swaroop, R.; Brownlow, J. D.; Ashwworth, G. R.; Winter, W. R.

1980-01-01

Some results for the bivariate normal distribution analysis are presented. Computer programs for conditional normal probabilities, marginal probabilities, as well as joint probabilities for rectangular regions are given: routines for computing fractile points and distribution functions are also presented. Some examples from a closed circuit television experiment are included.

Contributions to the Underlying Bivariate Normal Method for Factor Analyzing Ordinal Data

ERIC Educational Resources Information Center

Xi, Nuo; Browne, Michael W.

2014-01-01

A promising "underlying bivariate normal" approach was proposed by Jöreskog and Moustaki for use in the factor analysis of ordinal data. This was a limited information approach that involved the maximization of a composite likelihood function. Its advantage over full-information maximum likelihood was that very much less computation was…

Simultaneous determination of Nifuroxazide and Drotaverine hydrochloride in pharmaceutical preparations by bivariate and multivariate spectral analysis

NASA Astrophysics Data System (ADS)

Metwally, Fadia H.

2008-02-01

The quantitative predictive abilities of the new and simple bivariate spectrophotometric method are compared with the results obtained by the use of multivariate calibration methods [the classical least squares (CLS), principle component regression (PCR) and partial least squares (PLS)], using the information contained in the absorption spectra of the appropriate solutions. Mixtures of the two drugs Nifuroxazide (NIF) and Drotaverine hydrochloride (DRO) were resolved by application of the bivariate method. The different chemometric approaches were applied also with previous optimization of the calibration matrix, as they are useful in simultaneous inclusion of many spectral wavelengths. The results found by application of the bivariate, CLS, PCR and PLS methods for the simultaneous determinations of mixtures of both components containing 2-12 μg ml -1 of NIF and 2-8 μg ml -1 of DRO are reported. Both approaches were satisfactorily applied to the simultaneous determination of NIF and DRO in pure form and in pharmaceutical formulation. The results were in accordance with those given by the EVA Pharma reference spectrophotometric method.

Assessing Outgroup Prejudice among 13-15-Year-Old Students Attending Catholic and Protestant Secondary Schools in Northern Ireland: An Empirical Enquiry

ERIC Educational Resources Information Center

Francis, Leslie J.; Village, Andrew

2015-01-01

Northern Ireland has been and remains a religiously divided community. This study sets out to examine outgroup prejudice among a sample of 1799 13-15-year-old students attending Catholic or Protestant schools and employs both bivariate analyses and hierarchical modelling to chart the associations between outgroup prejudice and personal factors…

A Compendium of Wind Statistics and Models for the NASA Space Shuttle and Other Aerospace Vehicle Programs

NASA Technical Reports Server (NTRS)

Smith, O. E.; Adelfang, S. I.

1998-01-01

The wind profile with all of its variations with respect to altitude has been, is now, and will continue to be important for aerospace vehicle design and operations. Wind profile databases and models are used for the vehicle ascent flight design for structural wind loading, flight control systems, performance analysis, and launch operations. This report presents the evolution of wind statistics and wind models from the empirical scalar wind profile model established for the Saturn Program through the development of the vector wind profile model used for the Space Shuttle design to the variations of this wind modeling concept for the X-33 program. Because wind is a vector quantity, the vector wind models use the rigorous mathematical probability properties of the multivariate normal probability distribution. When the vehicle ascent steering commands (ascent guidance) are wind biased to the wind profile measured on the day-of-launch, ascent structural wind loads are reduced and launch probability is increased. This wind load alleviation technique is recommended in the initial phase of vehicle development. The vehicle must fly through the largest load allowable versus altitude to achieve its mission. The Gumbel extreme value probability distribution is used to obtain the probability of exceeding (or not exceeding) the load allowable. The time conditional probability function is derived from the Gumbel bivariate extreme value distribution. This time conditional function is used for calculation of wind loads persistence increments using 3.5-hour Jimsphere wind pairs. These increments are used to protect the commit-to-launch decision. Other topics presented include the Shuttle Shuttle load-response to smoothed wind profiles, a new gust model, and advancements in wind profile measuring systems. From the lessons learned and knowledge gained from past vehicle programs, the development of future launch vehicles can be accelerated. However, new vehicle programs by their very nature will require specialized support for new databases and analyses for wind, atmospheric parameters (pressure, temperature, and density versus altitude), and weather. It is for this reason that project managers are encouraged to collaborate with natural environment specialists early in the conceptual design phase. Such action will give the lead time necessary to meet the natural environment design and operational requirements, and thus, reduce development costs.

["Who profits?" - patient characteristics as outcome predictors in psychosomatic rehabilitation].

PubMed

Oster, J; Müller, G; Wietersheim, J von

2009-04-01

The study was to examine how far treatment success in psychosomatic rehabilitation can be predicted from patients' characteristics. The aim of this study included the development of outcome criteria, the analysis of bivariate correlations, as well as development and examination of multivariate models. The motivation for dealing with job-related problems was evaluated separately. Data were available from admission, discharge and three-months follow-up. The data of 463 patients were included. Generated were success criteria concerning sociomedical development, health as well as the ability to work. All success criteria were dichotomized. In the criteria defined, successful outcomes were found in 40 to 60% of the patients. In the bivariate analyses, it was shown that many sick days before rehabilitation, applications for pension, severe disability, high impairment, and suggestion for rehabilitation by the insurance agency, have basically negative effects on success. Correlations with the variables concerning motivation for dealing with job-related problems were rather weak. In multivariate model development, models of different quality were found. For prediction of working ability at discharge, there was an explained variance of nearly 60%. In the other success criteria as well, explained variance amounted to over 20%. The models consist of different constellations of variables, the number of sick days before rehabilitation, variables of application for pension and severity of the impairment frequently included. In case of a current sick leave, rehabilitation should be started early, sociomedical problems have to be dealt with explicitly, and rehabilitation should be accompanied by preparatory and aftercare measures.

Different responses of influenza epidemic to weather factors among Shanghai, Hong Kong, and British Columbia.

PubMed

Wang, Xi-Ling; Yang, Lin; He, Dai-Hai; Chiu, Alice Py; Chan, Kwok-Hung; Chan, King-Pan; Zhou, Maigeng; Wong, Chit-Ming; Guo, Qing; Hu, Wenbiao

2017-06-01

Weather factors have long been considered as key sources for regional heterogeneity of influenza seasonal patterns. As influenza peaks coincide with both high and low temperature in subtropical cities, weather factors may nonlinearly or interactively affect influenza activity. This study aims to assess the nonlinear and interactive effects of weather factors with influenza activity and compare the responses of influenza epidemic to weather factors in two subtropical regions of southern China (Shanghai and Hong Kong) and one temperate province of Canada (British Columbia). Weekly data on influenza activity and weather factors (i.e., mean temperature and relative humidity (RH)) were obtained from pertinent government departments for the three regions. Absolute humidity (AH) was measured by vapor pressure (VP), which could be converted from temperature and RH. Generalized additive models were used to assess the exposure-response relationship between weather factors and influenza virus activity. Interactions of weather factors were further assessed by bivariate response models and stratification analyses. The exposure-response curves of temperature and VP, but not RH, were consistent among three regions/cities. Bivariate response model revealed a significant interactive effect between temperature (or VP) and RH (P < 0.05). Influenza peaked at low temperature or high temperature with high RH. Temperature and VP are important weather factors in developing a universal model to explain seasonal outbreaks of influenza. However, further research is needed to assess the association between weather factors and influenza activity in a wider context of social and environmental conditions.

Application of selection and estimation regular vine copula on go public company share

NASA Astrophysics Data System (ADS)

Hasna Afifah, R.; Noviyanti, Lienda; Bachrudin, Achmad

2018-03-01

The accuracy of financial risk management involving a large number of assets is needed, but information about dependencies among assets cannot be adequately analyzed. To analyze dependencies on a number of assets, several tools have been added to standard multivariate copula. However, these tools have not been adequately used in apps with higher dimensions. The bivariate parametric copula families can be used to solve it. The multivariate copula can be built from the bivariate parametric copula which is connected by a graphical representation to become Pair Copula Constructions (PCCs) or vine copula. The application of C-vine and D-vine copula have been used in some researches, but the use of C-vine and D-vine copula is more limited than R-vine copula. Therefore, this study used R-vine copula to provide flexibility for modeling complex dependencies on a high dimension. Since copula is a static model, while stock values change over time, then copula should be combined with the ARMA- GARCH model for modeling the movement of shares (volatility). The objective of this paper is to select and estimate R-vine copula which is used to analyze PT Jasa Marga (Persero) Tbk (JSMR), PT Waskita Karya (Persero) Tbk (WSKT), and PT Bank Mandiri (Persero) Tbk (BMRI) from august 31, 2014 to august 31, 2017. From the method it is obtained that the selected copulas for 2 edges at the first tree are survival Gumbel and the copula for edge at the second tree is Gaussian.

The Role of Negative Affect in the Assessment of Quality of Life among Women with Type 1 Diabetes Mellitus

PubMed Central

Gawlik, Nicola R.

2018-01-01

Background The purpose of this study is to determine the impact of negative affect (defined in terms of lack of optimism, depressogenic attributional style, and hopelessness depression) on the quality of life of women with type 1 diabetes mellitus. Methods Participants (n=177) completed either an online or paper questionnaire made available to members of Australian diabetes support groups. Measures of optimism, attributional style, hopelessness depression, disease-specific data, and diabetes-related quality of life were sought. Bivariate correlations informed the construction of a structural equation model. Results Participants were 36.3±11.3 years old, with a disease duration of 18.4±11.2 years. Age and recent glycosylated hemoglobin readings were significant contextual variables in the model. All bivariate associations involving the components of negative affect were as hypothesized. That is, poorer quality of life was associated with a greater depressogenic attributional style, higher hopelessness depression, and lower optimism. The structural equation model demonstrated significant direct effects of depressogenic attributional style and hopelessness depression on quality of life, while (lack of) optimism contributed to quality of life indirectly by way of these variables. Conclusion The recognition of negative affect presentations among patients, and an understanding of its relevance to diabetes-related quality of life, is a valuable tool for the practitioner. PMID:29199406

Age and size at maturity: a quantitative review of diet-induced reaction norms in insects.

PubMed

Teder, Tiit; Vellau, Helen; Tammaru, Toomas

2014-11-01

Optimality models predict that diet-induced bivariate reaction norms for age and size at maturity can have diverse shapes, with the slope varying from negative to positive. To evaluate these predictions, we perform a quantitative review of relevant data, using a literature-derived database of body sizes and development times for over 200 insect species. We show that bivariate reaction norms with a negative slope prevail in nearly all taxonomic and ecological categories of insects as well as in some other ectotherm taxa with comparable life histories (arachnids and amphibians). In insects, positive slopes are largely limited to species, which feed on discrete resource items, parasitoids in particular. By contrast, with virtually no meaningful exceptions, herbivorous and predatory insects display reaction norms with a negative slope. This is consistent with the idea that predictable resource depletion, a scenario selecting for positively sloped reaction norms, is not frequent for these insects. Another source of such selection-a positive correlation between resource levels and juvenile mortality rates-should similarly be rare among insects. Positive slopes can also be predicted by models which integrate life-history evolution and population dynamics. As bottom-up regulation is not common in most insect groups, such models may not be most appropriate for insects. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.

Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2D2) bias correction

NASA Astrophysics Data System (ADS)

Vrac, Mathieu

2018-06-01

Climate simulations often suffer from statistical biases with respect to observations or reanalyses. It is therefore common to correct (or adjust) those simulations before using them as inputs into impact models. However, most bias correction (BC) methods are univariate and so do not account for the statistical dependences linking the different locations and/or physical variables of interest. In addition, they are often deterministic, and stochasticity is frequently needed to investigate climate uncertainty and to add constrained randomness to climate simulations that do not possess a realistic variability. This study presents a multivariate method of rank resampling for distributions and dependences (R2D2) bias correction allowing one to adjust not only the univariate distributions but also their inter-variable and inter-site dependence structures. Moreover, the proposed R2D2 method provides some stochasticity since it can generate as many multivariate corrected outputs as the number of statistical dimensions (i.e., number of grid cell × number of climate variables) of the simulations to be corrected. It is based on an assumption of stability in time of the dependence structure - making it possible to deal with a high number of statistical dimensions - that lets the climate model drive the temporal properties and their changes in time. R2D2 is applied on temperature and precipitation reanalysis time series with respect to high-resolution reference data over the southeast of France (1506 grid cell). Bivariate, 1506-dimensional and 3012-dimensional versions of R2D2 are tested over a historical period and compared to a univariate BC. How the different BC methods behave in a climate change context is also illustrated with an application to regional climate simulations over the 2071-2100 period. The results indicate that the 1d-BC basically reproduces the climate model multivariate properties, 2d-R2D2 is only satisfying in the inter-variable context, 1506d-R2D2 strongly improves inter-site properties and 3012d-R2D2 is able to account for both. Applications of the proposed R2D2 method to various climate datasets are relevant for many impact studies. The perspectives of improvements are numerous, such as introducing stochasticity in the dependence itself, questioning its stability assumption, and accounting for temporal properties adjustment while including more physics in the adjustment procedures.

An operator calculus for surface and volume modeling

NASA Technical Reports Server (NTRS)

Gordon, W. J.

1984-01-01

The mathematical techniques which form the foundation for most of the surface and volume modeling techniques used in practice are briefly described. An outline of what may be termed an operator calculus for the approximation and interpolation of functions of more than one independent variable is presented. By considering the linear operators associated with bivariate and multivariate interpolation/approximation schemes, it is shown how they can be compounded by operator multiplication and Boolean addition to obtain a distributive lattice of approximation operators. It is then demonstrated via specific examples how this operator calculus leads to practical techniques for sculptured surface and volume modeling.

Probabilistic Verification of Multi-Robot Missions in Uncertain Environments

DTIC Science & Technology

2015-11-01

has been used to measure the environment, including any dynamic obstacles. However, no matter how the model originates, this approach is based on...modeled as bivariate Gaussian distributions and estimated by calibration measurements . The Robot process model is described in prior work [13...sn〉 (pR,pE)(obR) = In〈pR〉〈p〉 ; In〈pE〉〈e〉 ; ( Gtr〈 d(p,e), sr〉〈p1〉 ; Out〈obR,p1〉 | Lte 〈 d(p,e), sr〉〈p2〉 ; Out〈obR, sn+p2 〉 ) ; Sensors

Effectiveness of intensive healthcare waste management training model among health professionals at teaching hospitals of Pakistan: a quasi-experimental study.

PubMed

Kumar, Ramesh; Somrongthong, Ratana; Shaikh, Babar Tasneem

2015-02-28

Infectious waste management has always remained a neglected public health problem in the developing countries, resulting in high burden of environmental pollution affecting general masses. Health workers are the key personnel who are responsible for the management of infectious waste at any hospital, however, their proper training and education is must for an optimal performance. This interventional study was conducted to assess the effectiveness of Intensive healthcare waste management (IHWM) training model at two tertiary care hospitals of Rawalpindi city, Pakistan. This study was quasi-experimental pre and post design with control and intervention groups. Out of 275 health care workers enrolled for the study, 138 workers were assigned for intervention group for 3 months trainings, hands-on practicum and reminders on infectious waste management; whereas 137 workers were assigned to the control hospital where routine activities on infectious health care waste management were performed. Pre and post intervention assessment was done for knowledge, attitude and practices (KAP); and was statistically analyzed. Bivariate and multivariate analysis, independent, paired and unpaired t-test, chi-square with p values, and mean of the responses were calculated. Overall the response rate was 92% at the end of intervention. During the baseline survey, 275 healthcare workers (HCW) included doctors, nurses, paramedics and sanitary workers, and after 3 months of intervention, 255 were reached out to complete the questionnaire. With regard to KAP at baseline, there were no significant differences between two groups at baseline, except for gender and department. However, in the post intervention survey, statistically significance difference (<0.05) between intervention and control group's knowledge, attitude and practices was found. Moreover, within the control group no statistically significant difference was reported (>0.05) after 3 months. Study results suggest that IHWM training could be an effective intervention for improving knowledge, attitudes and practices among health workers regarding infectious waste management. Such training should become a regular feature of all hospitals for reducing the hazards attached with infectious wastes.

Bivariate and multivariate analyses of the influence of blood variables of patients submitted to Roux-en-Y gastric bypass on the stability of erythrocyte membrane against the chaotropic action of ethanol.

PubMed

de Arvelos, Leticia Ramos; Rocha, Vanessa Custódio Afonso; Felix, Gabriela Pereira; da Cunha, Cleine Chagas; Bernardino Neto, Morun; da Silva Garrote Filho, Mario; de Fátima Pinheiro, Conceição; Resende, Elmiro Santos; Penha-Silva, Nilson

2013-03-01

The stability of the erythrocyte membrane, which is essential for the maintenance of cell functions, occurs in a critical region of fluidity, which depends largely on its composition and the composition and characteristics of the medium. As the composition of the erythrocyte membrane is influenced by several blood variables, the stability of the erythrocyte membrane must have relations with them. The present study aimed to evaluate, by bivariate and multivariate statistical analyses, the correlations and causal relationships between hematologic and biochemical variables and the stability of the erythrocyte membrane against the chaotropic action of ethanol. The validity of this type of analysis depends on the homogeneity of the population and on the variability of the studied parameters, conditions that can be filled by patients who undergo bariatric surgery by the technique of Roux-en-Y gastric bypass since they will suffer feeding restrictions that have great impact on their blood composition. Pathway analysis revealed that an increase in hemoglobin leads to decreased stability of the cell, probably through a process mediated by an increase in mean corpuscular volume. Furthermore, an increase in the mean corpuscular hemoglobin (MCH) leads to an increase in erythrocyte membrane stability, probably because higher values of MCH are associated with smaller quantities of red blood cells and a larger contact area between the cell membrane and ethanol present in the medium.

Determinants of child maltreatment in Nepal: Results from the 2014 Nepal multiple indicator cluster survey (the 2014 NMICS).

PubMed

Atteraya, Madhu Sudhan; Ebrahim, Nasser B; Gnawali, Shreejana

2018-02-01

We examined the prevalence of child maltreatment as measured by the level of physical (moderate to severe) and emotional abuse and child labor, and the associated household level determinants of child maltreatment in Nepal. We used a nationally representative data set from the fifth round of the Nepal Multiple Indicator Cluster Survey (the 2014 NMICS). The main independent variables were household level characteristics. Dependent variables included child experience of moderate to severe physical abuse, emotional abuse, and child labor (domestic work and economic activities). Bivariate analyses and logistic regressions were used to examine the associations between independent and dependent variables. The results showed that nearly half of the children (49.8%) had experienced moderate physical abuse, 21.5% experienced severe physical abuse, and 77.3% experienced emotional abuse. About 27% of the children had engaged in domestic work and 46.7% in various economic activities. At bivariate level, educational level of household's head and household wealth status had shown significant statistical association with child maltreatment (p<0.001). Results from multivariate logistic regressions showed that higher education levels and higher household wealth status protected children from moderate to severe physical abuse, emotional abuse and child labor. In general, child maltreatment is a neglected social issue in Nepal and the high rates of child maltreatment calls for mass awareness programs focusing on parents, and involving all stakeholders including governments, local, and international organizations. Copyright © 2017 Elsevier Ltd. All rights reserved.

The use of copulas to practical estimation of multivariate stochastic differential equation mixed effects models

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

Rupšys, P.

A system of stochastic differential equations (SDE) with mixed-effects parameters and multivariate normal copula density function were used to develop tree height model for Scots pine trees in Lithuania. A two-step maximum likelihood parameter estimation method is used and computational guidelines are given. After fitting the conditional probability density functions to outside bark diameter at breast height, and total tree height, a bivariate normal copula distribution model was constructed. Predictions from the mixed-effects parameters SDE tree height model calculated during this research were compared to the regression tree height equations. The results are implemented in the symbolic computational language MAPLE.

Structural Zeros and Their Implications with Log-Linear Bivariate Presmoothing under the Internal-Anchor Design

ERIC Educational Resources Information Center

Kim, Hyung Jin; Brennan, Robert L.; Lee, Won-Chan

2017-01-01

In equating, when common items are internal and scoring is conducted in terms of the number of correct items, some pairs of total scores ("X") and common-item scores ("V") can never be observed in a bivariate distribution of "X" and "V"; these pairs are called "structural zeros." This simulation…

Multi scale habitat relationships of Martes americana in northern Idaho, U.S.A.

Treesearch

Tzeidle N. Wasserman; Samuel A. Cushman; David O. Wallin; Jim Hayden

2012-01-01

We used bivariate scaling and logistic regression to investigate multiple-scale habitat selection by American marten (Martes americana). Bivariate scaling reveals dramatic differences in the apparent nature and strength of relationships between marten occupancy and a number of habitat variables across a range of spatial scales. These differences include reversals in...

Assessing Community Coalition Capacity and its Association with Underage Drinking Prevention Effectiveness in the Context of the SPF SIG.

PubMed

Flewelling, Robert L; Hanley, Sean M

2016-10-01

Community coalitions are a prominent organizational structure through which community-based substance abuse prevention efforts are implemented. There is little empirical evidence, however, regarding the association between coalition attributes and success in achieving community-level reductions in substance abuse behaviors. In this study, we assessed the relationship between coalition capacity, based on coalition coordinator responses to 16 survey items, and reductions in underage drinking prevalence rates. The coalitions were funded through the federally sponsored Strategic Prevention Framework State Incentive Grant (SPF SIG). We first examined whether coalition capacity increased over the life of the projects. Mean capacity scores increased for all 16 capacity items examined (N = 318 coalitions), the majority of which were statistically significant. Analysis of the associations between capacity and reductions in underage drinking was limited to coalitions that targeted underage drinking and provided usable outcome measures based on student survey data for either past 30-day alcohol use (N = 129) or binge drinking (N = 100). Bivariate associations between the capacity items and prevalence reductions for each outcome were consistently positive, although many were not statistically significant. Composite measures of correlated items were then created to represent six different capacity constructs, and included in multivariate models to predict reductions in the targeted outcomes. Constructs that significantly predicted reductions in one or both outcome measures included internal organization and structure, community connections and outreach, and funding from multiple sources. The findings provide support for the expectation that high functioning community coalitions can be effective agents for producing desirable community-level changes in targeted substance abuse behaviors.

Common mental disorder and its association with academic performance among Debre Berhan University students, Ethiopia.

PubMed

Haile, Yohannes Gebreegziabhere; Alemu, Sisay Mulugeta; Habtewold, Tesfa Dejenie

2017-01-01

Common mental disorder (CMD) is prevalent in industrialized and non-industrialized countries. The prevalence of CMD among university students was 28.8-44.7% and attributed to several risk factors, such as schooling. The aim of this study was to assess the prevalence and risk factors of CMD. In addition, the association between CMD and academic performance was tested. Institution based cross-sectional study was conducted with 422 students at Debre Berhan university from March to April 2015. CMD was the primary outcome variable whereas academic performance was the secondary outcome variable. Kessler psychological distress (K10) scale was used to assess CMD. Bivariate and multiple logistic regression analysis were performed for modeling the primary outcome variable; independent samples T test and linear regression analysis were carried out for modeling the secondary outcome variable. The strength of association was interpreted using odds ratio and regression coefficient (β) and decision on statistical significance was made at a p value of 0.05. Data were entered using EPI-data version 3.1 software and analyzed using the Statistical Package for the Social Sciences (SPSS) version 20.01 software. The prevalence of CMD was 63.1%. Field of study (p = 0.008, OR = 0.2, 95% CI 0.04-0.61), worshiping (p = 0.04, OR = 1.8, 95% CI 1.02-3.35), insomnia (p < 0.001, OR = 3.8, 95% CI 2.21-6.57), alcohol drinking (p = 0.006, OR = 2.7, 95% CI 1.33-5.66), and headache (p = 0.02, OR = 2.1, 95% CI 1.10-3.86) were identified risk factors for CMD. The mean cumulative grade point average of students with CMD was lower by 0.02 compared to those without CMD, but not statistically significant (p = 0.70, β = -0.02, 95% CI -0.15 to 0.10). CMD explained only 0.8% (r 2  = 0.008) of the difference in academic performance between students. At least three out of five students fulfilled CMD diagnostic criteria. The statistically significant risk factors were field of study, worshiping, insomnia, alcohol drinking, and headache. Moreover, there was no statistically significant association between CMD and academic performance. Undertaking integrated evidence-based intervention focusing on students with poor sleep quality, poor physical health, and who drink alcohol is essential if the present finding confirmed by a longitudinal study.

Characterization of small-to-medium head-and-face dimensions for developing respirator fit test panels and evaluating fit of filtering facepiece respirators with different faceseal design

PubMed Central

Lin, Yi-Chun

2017-01-01

A respirator fit test panel (RFTP) with facial size distribution representative of intended users is essential to the evaluation of respirator fit for new models of respirators. In this study an anthropometric survey was conducted among youths representing respirator users in mid-Taiwan to characterize head-and-face dimensions key to RFTPs for application to small-to-medium facial features. The participants were fit-tested for three N95 masks of different facepiece design and the results compared to facial size distribution specified in the RFTPs of bivariate and principal component analysis design developed in this study to realize the influence of facial characteristics to respirator fit in relation to facepiece design. Nineteen dimensions were measured for 206 participants. In fit testing the qualitative fit test (QLFT) procedures prescribed by the U.S. Occupational Safety and Health Administration were adopted. As the results show, the bizygomatic breadth of the male and female participants were 90.1 and 90.8% of their counterparts reported for the U.S. youths (P < 0.001), respectively. Compared to the bivariate distribution, the PCA design better accommodated variation in facial contours among different respirator user groups or populations, with the RFTPs reported in this study and from literature consistently covering over 92% of the participants. Overall, the facial fit of filtering facepieces increased with increasing facial dimensions. The total percentages of the tests wherein the final maneuver being completed was “Moving head up-and-down”, “Talking” or “Bending over” in bivariate and PCA RFTPs were 13.3–61.9% and 22.9–52.8%, respectively. The respirators with a three-panel flat fold structured in the facepiece provided greater fit, particularly when the users moved heads. When the facial size distribution in a bivariate RFTP did not sufficiently represent petite facial size, the fit testing was inclined to overestimate the general fit, thus for small-to-medium facial dimensions a distinct RFTP should be considered. PMID:29176833

Characterization of small-to-medium head-and-face dimensions for developing respirator fit test panels and evaluating fit of filtering facepiece respirators with different faceseal design.

PubMed

Lin, Yi-Chun; Chen, Chen-Peng

2017-01-01

A respirator fit test panel (RFTP) with facial size distribution representative of intended users is essential to the evaluation of respirator fit for new models of respirators. In this study an anthropometric survey was conducted among youths representing respirator users in mid-Taiwan to characterize head-and-face dimensions key to RFTPs for application to small-to-medium facial features. The participants were fit-tested for three N95 masks of different facepiece design and the results compared to facial size distribution specified in the RFTPs of bivariate and principal component analysis design developed in this study to realize the influence of facial characteristics to respirator fit in relation to facepiece design. Nineteen dimensions were measured for 206 participants. In fit testing the qualitative fit test (QLFT) procedures prescribed by the U.S. Occupational Safety and Health Administration were adopted. As the results show, the bizygomatic breadth of the male and female participants were 90.1 and 90.8% of their counterparts reported for the U.S. youths (P < 0.001), respectively. Compared to the bivariate distribution, the PCA design better accommodated variation in facial contours among different respirator user groups or populations, with the RFTPs reported in this study and from literature consistently covering over 92% of the participants. Overall, the facial fit of filtering facepieces increased with increasing facial dimensions. The total percentages of the tests wherein the final maneuver being completed was "Moving head up-and-down", "Talking" or "Bending over" in bivariate and PCA RFTPs were 13.3-61.9% and 22.9-52.8%, respectively. The respirators with a three-panel flat fold structured in the facepiece provided greater fit, particularly when the users moved heads. When the facial size distribution in a bivariate RFTP did not sufficiently represent petite facial size, the fit testing was inclined to overestimate the general fit, thus for small-to-medium facial dimensions a distinct RFTP should be considered.

Comparing adult users of public and private dental services in the state of Minas Gerais, Brazil.

PubMed

Pinto, Rafaela da Silveira; de Abreu, Mauro Henrique Nogueira Guimarães; Vargas, Andrea Maria Duarte

2014-08-06

Studying the factors associated with the use of dental services can provide the necessary knowledge to understand the reasons why individuals seek out public healthcare services and the formulation of more appropriate public policies for the present-day reality. This work was a cross-sectional epidemiological study consisting of a sample of adults found in a research databank concerning the conditions of the oral health of the population of the state of Minas Gerais, Brazil. This study examined both main oral health disorders and relevant socioeconomic aspects. The dependent variable was defined as the type of service used, categorized under public and private use. The independent variables were selected and grouped to be inserted in the analysis model according to an adaptation of the behavioral model described by Andersen and Davidson. A hierarchical model was used to analyze the data. The description of variables and bivariate analyses were performed in an attempt to verify possible associations. For each group of variables at each hierarchical level, the gross and adjusted odds ratios (OR) and the respective 95% confidence intervals (CI) were estimated by means of logistic regression. The Complex Samples model from the SPSS statistics program, version 19.0, was used to analyze the sample framework. In the final model, the factors associated with the use of public healthcare services by adults were directly related to the socioeconomic and demographic conditions of the individuals, including: being of a dark-skinned black race/color, belonging to families with more than four household residents and with a lower income level, residing in small towns, having more teeth that need treatment. According to the findings from this study, socioeconomic and demographic factors, as well as normative treatment needs, are associated with the use of public dental services.

A bivariate gamma probability distribution with application to gust modeling. [for the ascent flight of the space shuttle

NASA Technical Reports Server (NTRS)

Smith, O. E.; Adelfang, S. I.; Tubbs, J. D.

1982-01-01

A five-parameter gamma distribution (BGD) having two shape parameters, two location parameters, and a correlation parameter is investigated. This general BGD is expressed as a double series and as a single series of the modified Bessel function. It reduces to the known special case for equal shape parameters. Practical functions for computer evaluations for the general BGD and for special cases are presented. Applications to wind gust modeling for the ascent flight of the space shuttle are illustrated.

BROJA-2PID: A Robust Estimator for Bivariate Partial Information Decomposition

NASA Astrophysics Data System (ADS)

Makkeh, Abdullah; Theis, Dirk; Vicente, Raul

2018-04-01

Makkeh, Theis, and Vicente found in [8] that Cone Programming model is the most robust to compute the Bertschinger et al. partial information decompostion (BROJA PID) measure [1]. We developed a production-quality robust software that computes the BROJA PID measure based on the Cone Programming model. In this paper, we prove the important property of strong duality for the Cone Program and prove an equivalence between the Cone Program and the original Convex problem. Then describe in detail our software and how to use it.\

Application of continuous normal-lognormal bivariate density functions in a sensitivity analysis of municipal solid waste landfill.

PubMed

Petrovic, Igor; Hip, Ivan; Fredlund, Murray D

2016-09-01

The variability of untreated municipal solid waste (MSW) shear strength parameters, namely cohesion and shear friction angle, with respect to waste stability problems, is of primary concern due to the strong heterogeneity of MSW. A large number of municipal solid waste (MSW) shear strength parameters (friction angle and cohesion) were collected from published literature and analyzed. The basic statistical analysis has shown that the central tendency of both shear strength parameters fits reasonably well within the ranges of recommended values proposed by different authors. In addition, it was established that the correlation between shear friction angle and cohesion is not strong but it still remained significant. Through use of a distribution fitting method it was found that the shear friction angle could be adjusted to a normal probability density function while cohesion follows the log-normal density function. The continuous normal-lognormal bivariate density function was therefore selected as an adequate model to ascertain rational boundary values ("confidence interval") for MSW shear strength parameters. It was concluded that a curve with a 70% confidence level generates a "confidence interval" within the reasonable limits. With respect to the decomposition stage of the waste material, three different ranges of appropriate shear strength parameters were indicated. Defined parameters were then used as input parameters for an Alternative Point Estimated Method (APEM) stability analysis on a real case scenario of the Jakusevec landfill. The Jakusevec landfill is the disposal site of the capital of Croatia - Zagreb. The analysis shows that in the case of a dry landfill the most significant factor influencing the safety factor was the shear friction angle of old, decomposed waste material, while in the case of a landfill with significant leachate level the most significant factor influencing the safety factor was the cohesion of old, decomposed waste material. The analysis also showed that a satisfactory level of performance with a small probability of failure was produced for the standard practice design of waste landfills as well as an analysis scenario immediately after the landfill closure. Copyright © 2015 Elsevier Ltd. All rights reserved.

Determinants of timely initiation of breastfeeding among mothers in Goba Woreda, South East Ethiopia: a cross sectional study.

PubMed

Setegn, Tesfaye; Gerbaba, Mulusew; Belachew, Tefera

2011-04-08

Although breastfeeding is universal in Ethiopia, ranges of regional differences in timely initiation of breastfeeding have been documented. Initiation of breastfeeding is highly bound to cultural factors that may either enhance or inhibit the optimal practices. The government of Ethiopia developed National Infant and Young Child Feeding Guideline in 2004 and behavior change communications on breast feeding have been going on since then. However, there is a little information on the practice of timely initiation of breast feeding and factors that predict these practices after the implementation of the national guideline. The objective of this study is to determine the prevalence and determinant factors of timely initiation of breastfeeding among mothers in Bale Goba District, South East Ethiopia. A community based cross sectional study was carried out from February to March 2010 using both quantitative and qualitative methods of data collection. A total of 608 mother infant pairs were selected using simple random sampling method and key informants for the in-depth interview were selected conveniently. Descriptive statistics, bivariate analysis and multivariable logistic regression analyses were employed to identify factors associated with timely initiation of breast feeding. The prevalence of timely initiation of breastfeeding was 52.4%. Bivariate analysis showed that attendance of formal education, being urban resident, institutional delivery and postnatal counseling on breast feeding were significantly associated with timely initiation of breastfeeding (P < 0.05). After adjust sting for other factors on the multivariable logistic model, being in the urban area [AOR: 4.1 (95%C.I: 2.31-7.30)] and getting postnatal counseling [AOR: 2.7(1.86-3.94)] were independent predictors of timely initiation of breastfeeding. The practice of timely initiation of breast feeding is low as nearly half the mothers did not start breastfeeding with one hour after delivery. The results suggest that breast feeding behavior change communication especially during the post natal period is critical in promoting optimal practice in the initiation of breast feeding. Rural mothers need special attention as they are distant from various information sources. © 2011 Gerbaba et al; licensee BioMed Central Ltd.

Determinants of timely initiation of breastfeeding among mothers in Goba Woreda, South East Ethiopia: A cross sectional study

PubMed Central

2011-01-01

Background Although breastfeeding is universal in Ethiopia, ranges of regional differences in timely initiation of breastfeeding have been documented. Initiation of breastfeeding is highly bound to cultural factors that may either enhance or inhibit the optimal practices. The government of Ethiopia developed National Infant and Young Child Feeding Guideline in 2004 and behavior change communications on breast feeding have been going on since then. However, there is a little information on the practice of timely initiation of breast feeding and factors that predict these practices after the implementation of the national guideline. The objective of this study is to determine the prevalence and determinant factors of timely initiation of breastfeeding among mothers in Bale Goba District, South East Ethiopia. Methods A community based cross sectional study was carried out from February to March 2010 using both quantitative and qualitative methods of data collection. A total of 608 mother infant pairs were selected using simple random sampling method and key informants for the in-depth interview were selected conveniently. Descriptive statistics, bivariate analysis and multivariable logistic regression analyses were employed to identify factors associated with timely initiation of breast feeding. Results The prevalence of timely initiation of breastfeeding was 52.4%. Bivariate analysis showed that attendance of formal education, being urban resident, institutional delivery and postnatal counseling on breast feeding were significantly associated with timely initiation of breastfeeding (P < 0.05). After adjust sting for other factors on the multivariable logistic model, being in the urban area [AOR: 4.1 (95%C.I: 2.31-7.30)] and getting postnatal counseling [AOR: 2.7(1.86-3.94)] were independent predictors of timely initiation of breastfeeding. Conclusions The practice of timely initiation of breast feeding is low as nearly half the mothers did not start breastfeeding with one hour after delivery. The results suggest that breast feeding behavior change communication especially during the post natal period is critical in promoting optimal practice in the initiation of breast feeding. Rural mothers need special attention as they are distant from various information sources. PMID:21473791

Correlates of Suicidality: Investigation of a Representative Sample of Manitoba First Nations Adolescents

PubMed Central

Mota, Natalie; Elias, Brenda; Tefft, Bruce; Medved, Maria; Munro, Garry

2012-01-01

Objectives. We examined individual, friend or family, and community or tribe correlates of suicidality in a representative on-reserve sample of First Nations adolescents. Methods. Data came from the 2002–2003 Manitoba First Nations Regional Longitudinal Health Survey of Youth. Interviews were conducted with adolescents aged 12 to 17 years (n = 1125) from 23 First Nations communities in Manitoba. We used bivariate logistic regression analyses to examine the relationships between a range of factors and lifetime suicidality. We conducted sex-by-correlate interactions for each significant correlate at the bivariate level. A multivariate logistic regression analysis identified those correlates most strongly related to suicidality. Results. We found several variables to be associated with an increased likelihood of suicidality in the multivariate model, including being female, depressed mood, abuse or fear of abuse, a hospital stay, and substance use (adjusted odds ratio range = 2.43–11.73). Perceived community caring was protective against suicidality (adjusted odds ratio = 0.93; 95% confidence interval = 0.88, 0.97) in the same model. Conclusions. Results of this study may be important in informing First Nations and government policy related to the implementation of suicide prevention strategies in First Nations communities. PMID:22676500