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).

Multilevel Preconditioners for Discontinuous Galerkin Approximations of Elliptic Problems with Jump Coefficients

DTIC Science & Technology

2010-12-01

discontinuous coefficients on geometrically nonconforming substructures. Technical Report Serie A 634, Instituto de Matematica Pura e Aplicada, Brazil, 2009...Instituto de Matematica Pura e Aplicada, Brazil, 2010. submitted. [41] M. Dryja, M. V. Sarkis, and O. B. Widlund. Multilevel Schwarz methods for

Multilevel modeling and panel data analysis in educational research (Case study: National examination data senior high school in West Java)

NASA Astrophysics Data System (ADS)

Zulvia, Pepi; Kurnia, Anang; Soleh, Agus M.

2017-03-01

Individual and environment are a hierarchical structure consist of units grouped at different levels. Hierarchical data structures are analyzed based on several levels, with the lowest level nested in the highest level. This modeling is commonly call multilevel modeling. Multilevel modeling is widely used in education research, for example, the average score of National Examination (UN). While in Indonesia UN for high school student is divided into natural science and social science. The purpose of this research is to develop multilevel and panel data modeling using linear mixed model on educational data. The first step is data exploration and identification relationships between independent and dependent variable by checking correlation coefficient and variance inflation factor (VIF). Furthermore, we use a simple model approach with highest level of the hierarchy (level-2) is regency/city while school is the lowest of hierarchy (level-1). The best model was determined by comparing goodness-of-fit and checking assumption from residual plots and predictions for each model. Our finding that for natural science and social science, the regression with random effects of regency/city and fixed effects of the time i.e multilevel model has better performance than the linear mixed model in explaining the variability of the dependent variable, which is the average scores of UN.

Do high-commitment work systems affect creativity? A multilevel combinational approach to employee creativity.

PubMed

Chang, Song; Jia, Liangding; Takeuchi, Riki; Cai, Yahua

2014-07-01

In this article, some information about the data used in the article and a citation were not included. The details of the corrections are provided.] This study uses 3-level, 2-wave time-lagged data from a random sample of 55 high-technology firms, 238 teams, and 1,059 individuals in China to investigate a multilevel combinational model of employee creativity. First, we hypothesize that firm (macrolevel) high-commitment work systems are conducive to individual (microlevel) creativity. Furthermore, we hypothesize that this positive crosslevel main impact may be combined with middle-level (mesolevel) factors, including team cohesion and team task complexity, such that the positive impact of firm high-commitment work systems on individual creativity is stronger when team cohesion is high and the team task more complex. The findings from random coefficient modeling analyses provide support for our hypotheses. These sets of results offer novel insight into how firms can use macrolevel and mesolevel contextual variables in a systematic manner to promote employee creativity in the workplace, despite its complex nature.

Multilevel geometry optimization

NASA Astrophysics Data System (ADS)

Rodgers, Jocelyn M.; Fast, Patton L.; Truhlar, Donald G.

2000-02-01

Geometry optimization has been carried out for three test molecules using six multilevel electronic structure methods, in particular Gaussian-2, Gaussian-3, multicoefficient G2, multicoefficient G3, and two multicoefficient correlation methods based on correlation-consistent basis sets. In the Gaussian-2 and Gaussian-3 methods, various levels are added and subtracted with unit coefficients, whereas the multicoefficient Gaussian-x methods involve noninteger parameters as coefficients. The multilevel optimizations drop the average error in the geometry (averaged over the 18 cases) by a factor of about two when compared to the single most expensive component of a given multilevel calculation, and in all 18 cases the accuracy of the atomization energy for the three test molecules improves; with an average improvement of 16.7 kcal/mol.

Handling Correlations between Covariates and Random Slopes in Multilevel Models

ERIC Educational Resources Information Center

Bates, Michael David; Castellano, Katherine E.; Rabe-Hesketh, Sophia; Skrondal, Anders

2014-01-01

This article discusses estimation of multilevel/hierarchical linear models that include cluster-level random intercepts and random slopes. Viewing the models as structural, the random intercepts and slopes represent the effects of omitted cluster-level covariates that may be correlated with included covariates. The resulting correlations between…

A Multilevel AR(1) Model: Allowing for Inter-Individual Differences in Trait-Scores, Inertia, and Innovation Variance.

PubMed

Jongerling, Joran; Laurenceau, Jean-Philippe; Hamaker, Ellen L

2015-01-01

In this article we consider a multilevel first-order autoregressive [AR(1)] model with random intercepts, random autoregression, and random innovation variance (i.e., the level 1 residual variance). Including random innovation variance is an important extension of the multilevel AR(1) model for two reasons. First, between-person differences in innovation variance are important from a substantive point of view, in that they capture differences in sensitivity and/or exposure to unmeasured internal and external factors that influence the process. Second, using simulation methods we show that modeling the innovation variance as fixed across individuals, when it should be modeled as a random effect, leads to biased parameter estimates. Additionally, we use simulation methods to compare maximum likelihood estimation to Bayesian estimation of the multilevel AR(1) model and investigate the trade-off between the number of individuals and the number of time points. We provide an empirical illustration by applying the extended multilevel AR(1) model to daily positive affect ratings from 89 married women over the course of 42 consecutive days.

A multilevel nonvolatile magnetoelectric memory

NASA Astrophysics Data System (ADS)

Shen, Jianxin; Cong, Junzhuang; Shang, Dashan; Chai, Yisheng; Shen, Shipeng; Zhai, Kun; Sun, Young

2016-09-01

The coexistence and coupling between magnetization and electric polarization in multiferroic materials provide extra degrees of freedom for creating next-generation memory devices. A variety of concepts of multiferroic or magnetoelectric memories have been proposed and explored in the past decade. Here we propose a new principle to realize a multilevel nonvolatile memory based on the multiple states of the magnetoelectric coefficient (α) of multiferroics. Because the states of α depends on the relative orientation between magnetization and polarization, one can reach different levels of α by controlling the ratio of up and down ferroelectric domains with external electric fields. Our experiments in a device made of the PMN-PT/Terfenol-D multiferroic heterostructure confirm that the states of α can be well controlled between positive and negative by applying selective electric fields. Consequently, two-level, four-level, and eight-level nonvolatile memory devices are demonstrated at room temperature. This kind of multilevel magnetoelectric memory retains all the advantages of ferroelectric random access memory but overcomes the drawback of destructive reading of polarization. In contrast, the reading of α is nondestructive and highly efficient in a parallel way, with an independent reading coil shared by all the memory cells.

Multilevel Analysis Methods for Partially Nested Cluster Randomized Trials

ERIC Educational Resources Information Center

Sanders, Elizabeth A.

2011-01-01

This paper explores multilevel modeling approaches for 2-group randomized experiments in which a treatment condition involving clusters of individuals is compared to a control condition involving only ungrouped individuals, otherwise known as partially nested cluster randomized designs (PNCRTs). Strategies for comparing groups from a PNCRT in the…

Sample size requirements for the design of reliability studies: precision consideration.

PubMed

Shieh, Gwowen

2014-09-01

In multilevel modeling, the intraclass correlation coefficient based on the one-way random-effects model is routinely employed to measure the reliability or degree of resemblance among group members. To facilitate the advocated practice of reporting confidence intervals in future reliability studies, this article presents exact sample size procedures for precise interval estimation of the intraclass correlation coefficient under various allocation and cost structures. Although the suggested approaches do not admit explicit sample size formulas and require special algorithms for carrying out iterative computations, they are more accurate than the closed-form formulas constructed from large-sample approximations with respect to the expected width and assurance probability criteria. This investigation notes the deficiency of existing methods and expands the sample size methodology for the design of reliability studies that have not previously been discussed in the literature.

Assessment of Daily and Weekly Fatigue among African American Cancer Survivors

PubMed Central

Sobel, Rina M.; McSorley, Anna-Michelle M.; Roesch, Scott C.; Malcarne, Vanessa L.; Hawes, Starlyn M.; Sadler, Georgia Robins

2013-01-01

This investigation evaluates two common measures of cancer-related fatigue, one multidimensional/retrospective and one unidimensional/same-day. Fifty-two African American survivors of diverse cancers completed fatigue visual analogue scales once daily, and the Multidimensional Fatigue Symptom Inventory (MFSI) once weekly, for four weeks. Zero-order correlations showed retrospectivefatigue was significantly related to average, peak, and most recent same-dayfatigue. Multilevel random coefficient modeling showed unidimensional fatigue shared the most variance with the MFSI’s General subscale for three weeks, and with the Vigor subscale for one week. Researchers and clinicians may wish to prioritize multidimensional measures when assessing cancer-related fatigue, if appropriate. PMID:23844922

Optimal sample sizes for the design of reliability studies: power consideration.

PubMed

Shieh, Gwowen

2014-09-01

Intraclass correlation coefficients are used extensively to measure the reliability or degree of resemblance among group members in multilevel research. This study concerns the problem of the necessary sample size to ensure adequate statistical power for hypothesis tests concerning the intraclass correlation coefficient in the one-way random-effects model. In view of the incomplete and problematic numerical results in the literature, the approximate sample size formula constructed from Fisher's transformation is reevaluated and compared with an exact approach across a wide range of model configurations. These comprehensive examinations showed that the Fisher transformation method is appropriate only under limited circumstances, and therefore it is not recommended as a general method in practice. For advance design planning of reliability studies, the exact sample size procedures are fully described and illustrated for various allocation and cost schemes. Corresponding computer programs are also developed to implement the suggested algorithms.

A comparison of two indices for the intraclass correlation coefficient.

PubMed

Shieh, Gwowen

2012-12-01

In the present study, we examined the behavior of two indices for measuring the intraclass correlation in the one-way random effects model: the prevailing ICC(1) (Fisher, 1938) and the corrected eta-squared (Bliese & Halverson, 1998). These two procedures differ both in their methods of estimating the variance components that define the intraclass correlation coefficient and in their performance of bias and mean squared error in the estimation of the intraclass correlation coefficient. In contrast with the natural unbiased principle used to construct ICC(1), in the present study it was analytically shown that the corrected eta-squared estimator is identical to the maximum likelihood estimator and the pairwise estimator under equal group sizes. Moreover, the empirical results obtained from the present Monte Carlo simulation study across various group structures revealed the mutual dominance relationship between their truncated versions for negative values. The corrected eta-squared estimator performs better than the ICC(1) estimator when the underlying population intraclass correlation coefficient is small. Conversely, ICC(1) has a clear advantage over the corrected eta-squared for medium and large magnitudes of population intraclass correlation coefficient. The conceptual description and numerical investigation provide guidelines to help researchers choose between the two indices for more accurate reliability analysis in multilevel research.

Racial discrimination and the stress process.

PubMed

Ong, Anthony D; Fuller-Rowell, Thomas; Burrow, Anthony L

2009-06-01

The unique and combined effects of chronic and daily racial discrimination on psychological distress were examined in a sample of 174 African American doctoral students and graduates. Using a daily process design, 5 models of the stress process were tested. Multilevel random coefficient modeling analyses revealed that chronic exposure to racial discrimination predicted greater daily discrimination and psychological distress. Further, results show that differences in daily discrimination and negative events accounted for meaningful variation in daily distress responses. Finally, findings indicate that daily discrimination and negative events mediated the relationship between chronic discrimination and psychological distress. The study provides support for the need to measure chronic strains as distinctive from daily stressors in the lives of African Americans.

Predicting Performance on State Achievement Tests Using Curriculum-Based Measurement in Reading: A Multilevel Meta-Analysis

ERIC Educational Resources Information Center

Yeo, Seungsoo

2010-01-01

The purpose of this synthesis was to examine the relationship between Curriculum-Based Measurement (CBM) and statewide achievement tests in reading. A multilevel meta-analysis was used to calculate the correlation coefficient of the population for 27 studies that met the inclusion criteria. Results showed an overall large correlation coefficient…

The Impact of Sample Size and Other Factors When Estimating Multilevel Logistic Models

ERIC Educational Resources Information Center

Schoeneberger, Jason A.

2016-01-01

The design of research studies utilizing binary multilevel models must necessarily incorporate knowledge of multiple factors, including estimation method, variance component size, or number of predictors, in addition to sample sizes. This Monte Carlo study examined the performance of random effect binary outcome multilevel models under varying…

Modeling of Academic Achievement of Primary School Students in Ethiopia Using Bayesian Multilevel Approach

ERIC Educational Resources Information Center

Sebro, Negusse Yohannes; Goshu, Ayele Taye

2017-01-01

This study aims to explore Bayesian multilevel modeling to investigate variations of average academic achievement of grade eight school students. A sample of 636 students is randomly selected from 26 private and government schools by a two-stage stratified sampling design. Bayesian method is used to estimate the fixed and random effects. Input and…

Assessing variance components in multilevel linear models using approximate Bayes factors: A case study of ethnic disparities in birthweight

PubMed Central

Saville, Benjamin R.; Herring, Amy H.; Kaufman, Jay S.

2013-01-01

Racial/ethnic disparities in birthweight are a large source of differential morbidity and mortality worldwide and have remained largely unexplained in epidemiologic models. We assess the impact of maternal ancestry and census tract residence on infant birth weights in New York City and the modifying effects of race and nativity by incorporating random effects in a multilevel linear model. Evaluating the significance of these predictors involves the test of whether the variances of the random effects are equal to zero. This is problematic because the null hypothesis lies on the boundary of the parameter space. We generalize an approach for assessing random effects in the two-level linear model to a broader class of multilevel linear models by scaling the random effects to the residual variance and introducing parameters that control the relative contribution of the random effects. After integrating over the random effects and variance components, the resulting integrals needed to calculate the Bayes factor can be efficiently approximated with Laplace’s method. PMID:24082430

Effectiveness Trial of Community-Based I Choose Life-Africa Human Immunodeficiency Virus Prevention Program in Kenya

PubMed Central

Adam, Mary B.

2014-01-01

We measured the effectiveness of a human immunodeficiency virus (HIV) prevention program developed in Kenya and carried out among university students. A total of 182 student volunteers were randomized into an intervention group who received a 32-hour training course as HIV prevention peer educators and a control group who received no training. Repeated measures assessed HIV-related attitudes, intentions, knowledge, and behaviors four times over six months. Data were analyzed by using linear mixed models to compare the rate of change on 13 dependent variables that examined sexual risk behavior. Based on multi-level models, the slope coefficients for four variables showed reliable change in the hoped for direction: abstinence from oral, vaginal, or anal sex in the last two months, condom attitudes, HIV testing, and refusal skill. The intervention demonstrated evidence of non-zero slope coefficients in the hoped for direction on 12 of 13 dependent variables. The intervention reduced sexual risk behavior. PMID:24957544

Effectiveness trial of community-based I Choose Life-Africa human immunodeficiency virus prevention program in Kenya.

PubMed

Adam, Mary B

2014-09-01

We measured the effectiveness of a human immunodeficiency virus (HIV) prevention program developed in Kenya and carried out among university students. A total of 182 student volunteers were randomized into an intervention group who received a 32-hour training course as HIV prevention peer educators and a control group who received no training. Repeated measures assessed HIV-related attitudes, intentions, knowledge, and behaviors four times over six months. Data were analyzed by using linear mixed models to compare the rate of change on 13 dependent variables that examined sexual risk behavior. Based on multi-level models, the slope coefficients for four variables showed reliable change in the hoped for direction: abstinence from oral, vaginal, or anal sex in the last two months, condom attitudes, HIV testing, and refusal skill. The intervention demonstrated evidence of non-zero slope coefficients in the hoped for direction on 12 of 13 dependent variables. The intervention reduced sexual risk behavior. © The American Society of Tropical Medicine and Hygiene.

The Effects of Including Observed Means or Latent Means as Covariates in Multilevel Models for Cluster Randomized Trials

ERIC Educational Resources Information Center

Aydin, Burak; Leite, Walter L.; Algina, James

2016-01-01

We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte…

Detecting Intervention Effects in a Cluster-Randomized Design Using Multilevel Structural Equation Modeling for Binary Responses

ERIC Educational Resources Information Center

Cho, Sun-Joo; Preacher, Kristopher J.; Bottge, Brian A.

2015-01-01

Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test--post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test…

Conceptualizing and Testing Random Indirect Effects and Moderated Mediation in Multilevel Models: New Procedures and Recommendations

ERIC Educational Resources Information Center

Bauer, Daniel J.; Preacher, Kristopher J.; Gil, Karen M.

2006-01-01

The authors propose new procedures for evaluating direct, indirect, and total effects in multilevel models when all relevant variables are measured at Level 1 and all effects are random. Formulas are provided for the mean and variance of the indirect and total effects and for the sampling variances of the average indirect and total effects.…

The role of the clinical departments for understanding patient heterogeneity in one-year mortality after a diagnosis of heart failure: A multilevel analysis of individual heterogeneity for profiling provider outcomes

PubMed Central

Frølich, Anne; Merlo, Juan

2017-01-01

Purpose To evaluate the general contextual effect (GCE) of the hospital department on one-year mortality in Swedish and Danish patients with heart failure (HF) by applying a multilevel analysis of individual heterogeneity. Methods Using the Swedish patient register, we obtained data on 36,943 patients who were 45–80 years old and admitted for HF to the hospital between 2007 and 2009. From the Danish Heart Failure Database (DHFD), we obtained data on 12,001 patients with incident HF who were 18 years or older and treated at hospitals between June 2010 and June2013. For each year, we applied two-step single and multilevel logistic regression models. We evaluated the general effects of the department by quantifying the intra-class correlation coefficient (ICC) and the increment in the area under the receiver operating characteristic curve (AUC) obtained by adding the random effects of the department in a multilevel logistic regression analysis. Results One-year mortality for Danish incident HF patients was low in the three audit years (around 11.1% -13.1%) and departments performed homogeneously (ICC ≈1.5% - 3.5%). The discriminatory accuracy of a model including age and gender was rather high (AUC≈ 0.71–0.73) but the increment in AUC after adding the department random effects into these models was only about 0.011–0.022 units in the three years. One-year mortality in Swedish patients with first hospitalization for heart failure, was relatively higher for 2007–2009 (≈21.3% - 22%) and departments performed homogeneously (ICC ≈ 1.5% - 3%). The discriminatory accuracy of a model including age, gender and patient risk score was rather high (AUC≈ 0.726–0.728) but the increment in AUC after adding the department random effects was only about 0.010–0.017 units in the three years. Conclusion Using the DHFD standard benchmark for one-year mortality, Danish departments had a good, homogeneous performance. In reference to literature, Swedish departments had a homogeneous performance and the mortality rates for patients with first hospitalization for heart failure were similar to those reported since 2000. Considering this, if health authorities decide to further reduce mortality rates, a comprehensive quality strategy should focus on all Swedish hospitals. Yet, a complementary assessment for the period after the study period is required to confirm whether department performance is still homogeneous or not to determine the most appropriate action. PMID:29211785

Lending a Helping Hand at Work: A Multilevel Investigation of Prosocial Motivation, Inclusive Climate and Inclusive Behavior.

PubMed

Nelissen, Philippe T J H; Hülsheger, Ute R; van Ruitenbeek, Gemma M C; Zijlstra, Fred R H

2017-09-01

Purpose People with disabilities often encounter difficulties at the workplace such as exclusion or unfair treatment. Researchers have therefore pointed to the need to focus on behavior that fosters inclusion as well as variables that are antecedents of such 'inclusive behavior'. Therefore the purpose of this study was to research the relationship between prosocial motivation, team inclusive climate and employee inclusive behavior. Method A survey was conducted among a sample of 282 paired employees and colleagues, which were nested in 84 teams. Employees self-rated prosocial motivation and team inclusive climate, their inclusive behavior was assessed by colleagues. Hypotheses were tested using multilevel random coefficient modeling. Results Employees who are prosocially motivated will display more inclusive behavior towards people with disabilities, and this relationship is moderated by team inclusive climate in such a way that the relationship is stronger when the inclusive climate is high. Conclusion This study shows that inclusive organizations, which value a diverse workforce, need to be aware of not only individual employee characteristics, but also team level climate to ensure the smooth integrations of people with disabilities into regular work teams.

Elder abuse and socioeconomic inequalities: a multilevel study in 7 European countries.

PubMed

Fraga, Sílvia; Lindert, Jutta; Barros, Henrique; Torres-González, Francisco; Ioannidi-Kapolou, Elisabeth; Melchiorre, Maria Gabriella; Stankunas, Mindaugas; Soares, Joaquim F

2014-04-01

To compare the prevalence of elder abuse using a multilevel approach that takes into account the characteristics of participants as well as socioeconomic indicators at city and country level. In 2009, the project on abuse of elderly in Europe (ABUEL) was conducted in seven cities (Stuttgart, Germany; Ancona, Italy; Kaunas, Lithuania, Stockholm, Sweden; Porto, Portugal; Granada, Spain; Athens, Greece) comprising 4467 individuals aged 60-84 years. We used a 3-level hierarchical structure of data: 1) characteristics of participants; 2) mean of tertiary education of each city; and 3) country inequality indicator (Gini coefficient). Multilevel logistic regression was used and proportional changes in Intraclass Correlation Coefficient (ICC) were inspected to assert explained variance between models. The prevalence of elder abuse showed large variations across sites. Adding tertiary education to the regression model reduced the country level variance for psychological abuse (ICC=3.4%), with no significant decrease in the explained variance for the other types of abuse. When the Gini coefficient was considered, the highest drop in ICC was observed for financial abuse (from 9.5% to 4.3%). There is a societal and community level dimension that adds information to individual variability in explaining country differences in elder abuse, highlighting underlying socioeconomic inequalities leading to such behavior. Copyright © 2014 Elsevier Inc. All rights reserved.

The Importance of Isomorphism for Conclusions about Homology: A Bayesian Multilevel Structural Equation Modeling Approach with Ordinal Indicators.

PubMed

Guenole, Nigel

2016-01-01

We describe a Monte Carlo study examining the impact of assuming item isomorphism (i.e., equivalent construct meaning across levels of analysis) on conclusions about homology (i.e., equivalent structural relations across levels of analysis) under varying degrees of non-isomorphism in the context of ordinal indicator multilevel structural equation models (MSEMs). We focus on the condition where one or more loadings are higher on the between level than on the within level to show that while much past research on homology has ignored the issue of psychometric isomorphism, psychometric isomorphism is in fact critical to valid conclusions about homology. More specifically, when a measurement model with non-isomorphic items occupies an exogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the within level exogenous latent variance is under-estimated leading to over-estimation of the within level structural coefficient, while the between level exogenous latent variance is overestimated leading to underestimation of the between structural coefficient. When a measurement model with non-isomorphic items occupies an endogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the endogenous within level latent variance is under-estimated leading to under-estimation of the within level structural coefficient while the endogenous between level latent variance is over-estimated leading to over-estimation of the between level structural coefficient. The innovative aspect of this article is demonstrating that even minor violations of psychometric isomorphism render claims of homology untenable. We also show that posterior predictive p-values for ordinal indicator Bayesian MSEMs are insensitive to violations of isomorphism even when they lead to severely biased within and between level structural parameters. We highlight conditions where poor estimation of even correctly specified models rules out empirical examination of isomorphism and homology without taking precautions, for instance, larger Level-2 sample sizes, or using informative priors.

The Importance of Isomorphism for Conclusions about Homology: A Bayesian Multilevel Structural Equation Modeling Approach with Ordinal Indicators

PubMed Central

Guenole, Nigel

2016-01-01

We describe a Monte Carlo study examining the impact of assuming item isomorphism (i.e., equivalent construct meaning across levels of analysis) on conclusions about homology (i.e., equivalent structural relations across levels of analysis) under varying degrees of non-isomorphism in the context of ordinal indicator multilevel structural equation models (MSEMs). We focus on the condition where one or more loadings are higher on the between level than on the within level to show that while much past research on homology has ignored the issue of psychometric isomorphism, psychometric isomorphism is in fact critical to valid conclusions about homology. More specifically, when a measurement model with non-isomorphic items occupies an exogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the within level exogenous latent variance is under-estimated leading to over-estimation of the within level structural coefficient, while the between level exogenous latent variance is overestimated leading to underestimation of the between structural coefficient. When a measurement model with non-isomorphic items occupies an endogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the endogenous within level latent variance is under-estimated leading to under-estimation of the within level structural coefficient while the endogenous between level latent variance is over-estimated leading to over-estimation of the between level structural coefficient. The innovative aspect of this article is demonstrating that even minor violations of psychometric isomorphism render claims of homology untenable. We also show that posterior predictive p-values for ordinal indicator Bayesian MSEMs are insensitive to violations of isomorphism even when they lead to severely biased within and between level structural parameters. We highlight conditions where poor estimation of even correctly specified models rules out empirical examination of isomorphism and homology without taking precautions, for instance, larger Level-2 sample sizes, or using informative priors. PMID:26973580

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.

Multilevel Modeling with Correlated Effects

ERIC Educational Resources Information Center

Kim, Jee-Seon; Frees, Edward W.

2007-01-01

When there exist omitted effects, measurement error, and/or simultaneity in multilevel models, explanatory variables may be correlated with random components, and standard estimation methods do not provide consistent estimates of model parameters. This paper introduces estimators that are consistent under such conditions. By employing generalized…

Multi-level obstruction in obstructive sleep apnoea: prevalence, severity and predictive factors.

PubMed

Phua, C Q; Yeo, W X; Su, C; Mok, P K H

2017-11-01

To characterise multi-level obstruction in terms of prevalence, obstructive sleep apnoea severity and predictive factors, and to collect epidemiological data on upper airway morphology in obstructive sleep apnoea patients. Retrospective review of 250 obstructive sleep apnoea patients. On clinical examination, 171 patients (68.4 per cent) had multi-level obstruction, 49 (19.6 per cent) had single-level obstruction and 30 (12 per cent) showed no obstruction. Within each category of obstructive sleep apnoea severity, multi-level obstruction was more prevalent. Multi-level obstruction was associated with severe obstructive sleep apnoea (more than 30 events per hour) (p = 0.001). Obstructive sleep apnoea severity increased with the number of obstruction sites (correlation coefficient = 0.303, p < 0.001). Multi-level obstruction was more likely in younger (p = 0.042), male (p = 0.045) patients, with high body mass index (more than 30 kg/m2) (p < 0.001). Palatal (p = 0.004), tongue (p = 0.026) and lateral pharyngeal wall obstructions (p = 0.006) were associated with severe obstructive sleep apnoea. Multi-level obstruction is more prevalent in obstructive sleep apnoea and is associated with increased severity. Obstruction at certain anatomical levels contributes more towards obstructive sleep apnoea severity.

Hemostatic techniques following multilevel posterior lumbar spine surgery: a randomized control trial.

PubMed

Wu, Jian; Jin, Yongming; Zhang, Jun; Shao, Haiyu; Yang, Di; Chen, Jinping

2014-12-01

This was a prospective, randomized controlled clinical study. To determine the efficacy of absorbable gelatin sponge in reducing blood loss, as well as shortening the length of hospital stay in patients undergoing multilevel posterior lumbar spinal surgery. Absorbable gelatin sponge is reported to decrease postoperative drain output and the length of hospital stay after multilevel posterior cervical spine surgery. However, there is a dearth of literature on prospective study of the efficacy of absorbable gelatin sponge in reducing postoperative blood loss, as well as shortening the length of hospital stay in patients undergoing multilevel posterior lumbar spinal surgery. A total of 82 consecutive patients who underwent multilevel posterior lumbar fusion or posterior lumbar interbody fusion between June 2011 and June 2012 were prospectively randomized into one of the 2 groups according to whether absorbable gelatin sponge for postoperative blood management was used or not. Demographic distribution, total drain output, blood transfusion rate, the length of stay, the number of readmissions, and postoperative complications were analyzed. Total drain output averaged 173 mL in the study group and 392 mL in the control group (P=0.000). Perioperative allogeneic blood transfusion rate were lower in the Gelfoam group (34.1% vs. 58.5%, P=0.046); moreover, length of stay in patients with the use of absorbable gelatin sponge (12.58 d) was significantly shorter (P=0.009) than the patients in the control group (14.46 d). No patient developed adverse reactions attributable to the absorbable gelatin sponge. Application of absorbable gelatin sponge at the end of multilevel posterior lumbar fusion can significantly decrease postoperative drain output and length of hospital stay.

A multilevel analysis of gatekeeper characteristics and consistent condom use among establishment-based female sex workers in Guangxi, China.

PubMed

Li, Qing; Li, Xiaoming; Stanton, Bonita; Fang, Xiaoyi; Zhao, Ran

2010-11-01

Multilevel analytical techniques are being applied in condom use research to ensure the validity of investigation on environmental/structural influences and clustered data from venue-based sampling. The literature contains reports of consistent associations between perceived gatekeeper support and condom use among entertainment establishment-based female sex workers (FSWs) in Guangxi, China. However, the clustering inherent in the data (FSWs being clustered within establishment) has not been accounted in most of the analyses. We used multilevel analyses to examine perceived features of gatekeepers and individual correlates of consistent condom use among FSWs and to validate the findings in the existing literature. We analyzed cross-sectional data from 318 FSWs from 29 entertainment establishments in Guangxi, China in 2004, with a minimum of 5 FSWs per establishment. The Hierarchical Linear Models program with Laplace estimation was used to estimate the parameters in models containing random effects and binary outcomes. About 11.6% of women reported consistent condom use with clients. The intraclass correlation coefficient indicated 18.5% of the variance in condom use could be attributed to their similarity between FSWs within the same establishments. Women's perceived gatekeeper support and education remained positively associated with condom use (P < 0.05), after controlling for other individual characteristics and clustering. After adjusting for data clustering, perceived gatekeeper support remains associated with consistent condom use with clients among FSWs in China. The results imply that combined interventions to intervene both gatekeepers and individual FSW may effectively promote consistent condom use.

A Multilevel Analysis of Gatekeeper Characteristics and Consistent Condom Use Among Establishment-Based Female Sex Workers in Guangxi, China

PubMed Central

Li, Qing; Li, Xiaoming; Stanton, Bonita; Fang, Xiaoyi; Zhao, Ran

2010-01-01

Background Multilevel analytical techniques are being applied in condom use research to ensure the validity of investigation on environmental/structural influences and clustered data from venue-based sampling. The literature contains reports of consistent associations between perceived gatekeeper support and condom use among entertainments establishment-based female sex workers (FSWs) in Guangxi, China. However, the clustering inherent in the data (FSWs being clustered within establishment) has not been accounted in most of the analyses. We used multilevel analyses to examine perceived features of gatekeepers and individual correlates of consistent condom use among FSWs and to validate the findings in the existing literature. Methods We analyzed cross-sectional data from 318 FSWs from 29 entertainment establishments in Guangxi, China in 2004, with a minimum of 5 FSWs per establishment. The Hierarchical Linear Models program with Laplace estimation was used to estimate the parameters in models containing random effects and binary outcomes. Results About 11.6% of women reported consistent condom use with clients. The intraclass correlation coefficient indicated 18.5% of the variance in condom use could be attributed to their similarity between FSWs within the same establishments. Women’s perceived gatekeeper support and education remained positively associated with condom use (P < 0.05), after controlling for other individual characteristics and clustering. Conclusions After adjusting for data clustering, perceived gatekeeper support remains associated with consistent condom use with clients among FSWs in China. The results imply that combined interventions to intervene both gatekeepers and individual FSW may effectively promote consistent condom use. PMID:20539262

A Multilevel, Hierarchical Sampling Technique for Spatially Correlated Random Fields

DOE PAGES

Osborn, Sarah; Vassilevski, Panayot S.; Villa, Umberto

2017-10-26

In this paper, we propose an alternative method to generate samples of a spatially correlated random field with applications to large-scale problems for forward propagation of uncertainty. A classical approach for generating these samples is the Karhunen--Loève (KL) decomposition. However, the KL expansion requires solving a dense eigenvalue problem and is therefore computationally infeasible for large-scale problems. Sampling methods based on stochastic partial differential equations provide a highly scalable way to sample Gaussian fields, but the resulting parametrization is mesh dependent. We propose a multilevel decomposition of the stochastic field to allow for scalable, hierarchical sampling based on solving amore » mixed finite element formulation of a stochastic reaction-diffusion equation with a random, white noise source function. Lastly, numerical experiments are presented to demonstrate the scalability of the sampling method as well as numerical results of multilevel Monte Carlo simulations for a subsurface porous media flow application using the proposed sampling method.« less

A Multilevel, Hierarchical Sampling Technique for Spatially Correlated Random Fields

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

Osborn, Sarah; Vassilevski, Panayot S.; Villa, Umberto

In this paper, we propose an alternative method to generate samples of a spatially correlated random field with applications to large-scale problems for forward propagation of uncertainty. A classical approach for generating these samples is the Karhunen--Loève (KL) decomposition. However, the KL expansion requires solving a dense eigenvalue problem and is therefore computationally infeasible for large-scale problems. Sampling methods based on stochastic partial differential equations provide a highly scalable way to sample Gaussian fields, but the resulting parametrization is mesh dependent. We propose a multilevel decomposition of the stochastic field to allow for scalable, hierarchical sampling based on solving amore » mixed finite element formulation of a stochastic reaction-diffusion equation with a random, white noise source function. Lastly, numerical experiments are presented to demonstrate the scalability of the sampling method as well as numerical results of multilevel Monte Carlo simulations for a subsurface porous media flow application using the proposed sampling method.« less

Standardized Mean Differences in Two-Level Cross-Classified Random Effects Models

ERIC Educational Resources Information Center

Lai, Mark H. C.; Kwok, Oi-Man

2014-01-01

Multilevel modeling techniques are becoming more popular in handling data with multilevel structure in educational and behavioral research. Recently, researchers have paid more attention to cross-classified data structure that naturally arises in educational settings. However, unlike traditional single-level research, methodological studies about…

Neighborhood deprivation is strongly associated with participation in a population-based health check.

PubMed

Bender, Anne Mette; Kawachi, Ichiro; Jørgensen, Torben; Pisinger, Charlotta

2015-01-01

We sought to examine whether neighborhood deprivation is associated with participation in a large population-based health check. Such analyses will help answer the question whether health checks, which are designed to meet the needs of residents in deprived neighborhoods, may increase participation and prove to be more effective in preventing disease. In Europe, no study has previously looked at the association between neighborhood deprivation and participation in a population-based health check. The study population comprised 12,768 persons invited for a health check including screening for ischemic heart disease and lifestyle counseling. The study population was randomly drawn from a population of 179,097 persons living in 73 neighborhoods in Denmark. Data on neighborhood deprivation (percentage with basic education, with low income and not in work) and individual socioeconomic position were retrieved from national administrative registers. Multilevel regression analyses with log links and binary distributions were conducted to obtain relative risks, intraclass correlation coefficients and proportional change in variance. Large differences between neighborhoods existed in both deprivation levels and neighborhood health check participation rate (mean 53%; range 35-84%). In multilevel analyses adjusted for age and sex, higher levels of all three indicators of neighborhood deprivation and a deprivation score were associated with lower participation in a dose-response fashion. Persons living in the most deprived neighborhoods had up to 37% decreased probability of participating compared to those living in the least deprived neighborhoods. Inclusion of individual socioeconomic position in the model attenuated the neighborhood deprivation coefficients, but all except for income deprivation remained statistically significant. Neighborhood deprivation was associated with participation in a population-based health check in a dose-response manner, in which increasing neighborhood deprivation was associated with decreasing participation. This suggests the need to develop preventive health checks tailored to deprived neighborhoods.

Within-Cluster and Across-Cluster Matching with Observational Multilevel Data

ERIC Educational Resources Information Center

Kim, Jee-Seon; Steiner, Peter M.; Hall, Courtney; Thoemmes, Felix

2013-01-01

When randomized experiments cannot be conducted in practice, propensity score (PS) techniques for matching treated and control units are frequently used for estimating causal treatment effects from observational data. Despite the popularity of PS techniques, they are not yet well studied for matching multilevel data where selection into treatment…

Testing Group Mean Differences of Latent Variables in Multilevel Data Using Multiple-Group Multilevel CFA and Multilevel MIMIC Modeling.

PubMed

Kim, Eun Sook; Cao, Chunhua

2015-01-01

Considering that group comparisons are common in social science, we examined two latent group mean testing methods when groups of interest were either at the between or within level of multilevel data: multiple-group multilevel confirmatory factor analysis (MG ML CFA) and multilevel multiple-indicators multiple-causes modeling (ML MIMIC). The performance of these methods were investigated through three Monte Carlo studies. In Studies 1 and 2, either factor variances or residual variances were manipulated to be heterogeneous between groups. In Study 3, which focused on within-level multiple-group analysis, six different model specifications were considered depending on how to model the intra-class group correlation (i.e., correlation between random effect factors for groups within cluster). The results of simulations generally supported the adequacy of MG ML CFA and ML MIMIC for multiple-group analysis with multilevel data. The two methods did not show any notable difference in the latent group mean testing across three studies. Finally, a demonstration with real data and guidelines in selecting an appropriate approach to multilevel multiple-group analysis are provided.

Cross-Classified Random Effects Models in Institutional Research

ERIC Educational Resources Information Center

Meyers, Laura E.

2012-01-01

Multilevel modeling offers researchers a rich array of tools that can be used for a variety of purposes, such as analyzing specific institutional issues, looking for macro-level trends, and helping to shape and inform educational policy. One of the more complex multilevel modeling tools available to institutional researchers is cross-classified…

"Using Power Tables to Compute Statistical Power in Multilevel Experimental Designs"

ERIC Educational Resources Information Center

Konstantopoulos, Spyros

2009-01-01

Power computations for one-level experimental designs that assume simple random samples are greatly facilitated by power tables such as those presented in Cohen's book about statistical power analysis. However, in education and the social sciences experimental designs have naturally nested structures and multilevel models are needed to compute the…

Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway.

PubMed

Shi, Qi; Abdel-Aty, Mohamed; Yu, Rongjie

2016-03-01

In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature. Copyright © 2015 Elsevier Ltd. All rights reserved.

Data Mining of Web-Based Documents on Social Networking Sites That Included Suicide-Related Words Among Korean Adolescents.

PubMed

Song, Juyoung; Song, Tae Min; Seo, Dong-Chul; Jin, Jae Hyun

2016-12-01

To investigate online search activity of suicide-related words in South Korean adolescents through data mining of social media Web sites as the suicide rate in South Korea is one of the highest in the world. Out of more than 2.35 billion posts for 2 years from January 1, 2011 to December 31, 2012 on 163 social media Web sites in South Korea, 99,693 suicide-related documents were retrieved by Crawler and analyzed using text mining and opinion mining. These data were further combined with monthly employment rate, monthly rental prices index, monthly youth suicide rate, and monthly number of reported bully victims to fit multilevel models as well as structural equation models. The link from grade pressure to suicide risk showed the largest standardized path coefficient (beta = .357, p < .001) in structural models and a significant random effect (p < .01) in multilevel models. Depression was a partial mediator between suicide risk and grade pressure, low body image, victims of bullying, and concerns about disease. The largest total effect was observed in the grade pressure to depression to suicide risk. The multilevel models indicate about 27% of the variance in the daily suicide-related word search activity is explained by month-to-month variations. A lower employment rate, a higher rental prices index, and more bullying were associated with an increased suicide-related word search activity. Academic pressure appears to be the biggest contributor to Korean adolescents' suicide risk. Real-time suicide-related word search activity monitoring and response system needs to be developed. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Interviewer effects on non-response propensity in longitudinal surveys: a multilevel modelling approach

PubMed Central

Vassallo, Rebecca; Durrant, Gabriele B; Smith, Peter W F; Goldstein, Harvey

2015-01-01

The paper investigates two different multilevel approaches, the multilevel cross-classified and the multiple-membership models, for the analysis of interviewer effects on wave non-response in longitudinal surveys. The models proposed incorporate both interviewer and area effects to account for the non-hierarchical structure, the influence of potentially more than one interviewer across waves and possible confounding of area and interviewer effects arising from the non-random allocation of interviewers across areas. The methods are compared by using a data set: the UK Family and Children Survey. PMID:25598587

Resistance controllability and variability improvement in a TaO{sub x}-based resistive memory for multilevel storage application

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

Prakash, A., E-mail: amitknp@postech.ac.kr, E-mail: amit.knp02@gmail.com, E-mail: hwanghs@postech.ac.kr; Song, J.; Hwang, H., E-mail: amitknp@postech.ac.kr, E-mail: amit.knp02@gmail.com, E-mail: hwanghs@postech.ac.kr

In order to obtain reliable multilevel cell (MLC) characteristics, resistance controllability between the different resistance levels is required especially in resistive random access memory (RRAM), which is prone to resistance variability mainly due to its intrinsic random nature of defect generation and filament formation. In this study, we have thoroughly investigated the multilevel resistance variability in a TaO{sub x}-based nanoscale (<30 nm) RRAM operated in MLC mode. It is found that the resistance variability not only depends on the conductive filament size but also is a strong function of oxygen vacancy concentration in it. Based on the gained insights through experimentalmore » observations and simulation, it is suggested that forming thinner but denser conductive filament may greatly improve the temporal resistance variability even at low operation current despite the inherent stochastic nature of resistance switching process.« less

At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study.

PubMed

Hamaker, E L; Asparouhov, T; Brose, A; Schmiedek, F; Muthén, B

2018-04-06

With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent-but mostly unresolved-issues in the area of dynamic multilevel modeling.

Multilevel Multidimensional Item Response Model with a Multilevel Latent Covariate

ERIC Educational Resources Information Center

Cho, Sun-Joo; Bottge, Brian A.

2015-01-01

In a pretest-posttest cluster-randomized trial, one of the methods commonly used to detect an intervention effect involves controlling pre-test scores and other related covariates while estimating an intervention effect at post-test. In many applications in education, the total post-test and pre-test scores that ignores measurement error in the…

Using Design-Based Latent Growth Curve Modeling with Cluster-Level Predictor to Address Dependency

ERIC Educational Resources Information Center

Wu, Jiun-Yu; Kwok, Oi-Man; Willson, Victor L.

2014-01-01

The authors compared the effects of using the true Multilevel Latent Growth Curve Model (MLGCM) with single-level regular and design-based Latent Growth Curve Models (LGCM) with or without the higher-level predictor on various criterion variables for multilevel longitudinal data. They found that random effect estimates were biased when the…

Three-Level Models for Indirect Effects in School- and Class-Randomized Experiments in Education

ERIC Educational Resources Information Center

Pituch, Keenan A.; Murphy, Daniel L.; Tate, Richard L.

2009-01-01

Due to the clustered nature of field data, multi-level modeling has become commonly used to analyze data arising from educational field experiments. While recent methodological literature has focused on multi-level mediation analysis, relatively little attention has been devoted to mediation analysis when three levels (e.g., student, class,…

Scalable hierarchical PDE sampler for generating spatially correlated random fields using nonmatching meshes: Scalable hierarchical PDE sampler using nonmatching meshes

DOE PAGES

Osborn, Sarah; Zulian, Patrick; Benson, Thomas; ...

2018-01-30

This work describes a domain embedding technique between two nonmatching meshes used for generating realizations of spatially correlated random fields with applications to large-scale sampling-based uncertainty quantification. The goal is to apply the multilevel Monte Carlo (MLMC) method for the quantification of output uncertainties of PDEs with random input coefficients on general and unstructured computational domains. We propose a highly scalable, hierarchical sampling method to generate realizations of a Gaussian random field on a given unstructured mesh by solving a reaction–diffusion PDE with a stochastic right-hand side. The stochastic PDE is discretized using the mixed finite element method on anmore » embedded domain with a structured mesh, and then, the solution is projected onto the unstructured mesh. This work describes implementation details on how to efficiently transfer data from the structured and unstructured meshes at coarse levels, assuming that this can be done efficiently on the finest level. We investigate the efficiency and parallel scalability of the technique for the scalable generation of Gaussian random fields in three dimensions. An application of the MLMC method is presented for quantifying uncertainties of subsurface flow problems. Here, we demonstrate the scalability of the sampling method with nonmatching mesh embedding, coupled with a parallel forward model problem solver, for large-scale 3D MLMC simulations with up to 1.9·109 unknowns.« less

Scalable hierarchical PDE sampler for generating spatially correlated random fields using nonmatching meshes: Scalable hierarchical PDE sampler using nonmatching meshes

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

Osborn, Sarah; Zulian, Patrick; Benson, Thomas

This work describes a domain embedding technique between two nonmatching meshes used for generating realizations of spatially correlated random fields with applications to large-scale sampling-based uncertainty quantification. The goal is to apply the multilevel Monte Carlo (MLMC) method for the quantification of output uncertainties of PDEs with random input coefficients on general and unstructured computational domains. We propose a highly scalable, hierarchical sampling method to generate realizations of a Gaussian random field on a given unstructured mesh by solving a reaction–diffusion PDE with a stochastic right-hand side. The stochastic PDE is discretized using the mixed finite element method on anmore » embedded domain with a structured mesh, and then, the solution is projected onto the unstructured mesh. This work describes implementation details on how to efficiently transfer data from the structured and unstructured meshes at coarse levels, assuming that this can be done efficiently on the finest level. We investigate the efficiency and parallel scalability of the technique for the scalable generation of Gaussian random fields in three dimensions. An application of the MLMC method is presented for quantifying uncertainties of subsurface flow problems. Here, we demonstrate the scalability of the sampling method with nonmatching mesh embedding, coupled with a parallel forward model problem solver, for large-scale 3D MLMC simulations with up to 1.9·109 unknowns.« less

Complex versus Simple Modeling for DIF Detection: When the Intraclass Correlation Coefficient (?) of the Studied Item Is Less Than the ? of the Total Score

ERIC Educational Resources Information Center

Jin, Ying; Myers, Nicholas D.; Ahn, Soyeon

2014-01-01

Previous research has demonstrated that differential item functioning (DIF) methods that do not account for multilevel data structure could result in too frequent rejection of the null hypothesis (i.e., no DIF) when the intraclass correlation coefficient (?) of the studied item was the same as the ? of the total score. The current study extended…

Inventory of forest resources (including water) by multi-level sampling. [nine northern Virginia coastal plain counties

NASA Technical Reports Server (NTRS)

Aldrich, R. C.; Dana, R. W.; Roberts, E. H. (Principal Investigator)

1977-01-01

The author has identified the following significant results. A stratified random sample using LANDSAT band 5 and 7 panchromatic prints resulted in estimates of water in counties with sampling errors less than + or - 9% (67% probability level). A forest inventory using a four band LANDSAT color composite resulted in estimates of forest area by counties that were within + or - 6.7% and + or - 3.7% respectively (67% probability level). Estimates of forest area for counties by computer assisted techniques were within + or - 21% of operational forest survey figures and for all counties the difference was only one percent. Correlations of airborne terrain reflectance measurements with LANDSAT radiance verified a linear atmospheric model with an additive (path radiance) term and multiplicative (transmittance) term. Coefficients of determination for 28 of the 32 modeling attempts, not adverseley affected by rain shower occurring between the times of LANDSAT passage and aircraft overflights, exceeded 0.83.

Can we get some cooperation around here? The mediating role of group norms on the relationship between team personality and individual helping behaviors.

PubMed

Gonzalez-Mulé, Erik; DeGeest, David S; McCormick, Brian W; Seong, Jee Young; Brown, Kenneth G

2014-09-01

Drawing on the group-norms theory of organizational citizenship behaviors and person-environment fit theory, we introduce and test a multilevel model of the effects of additive and dispersion composition models of team members' personality characteristics on group norms and individual helping behaviors. Our model was tested using regression and random coefficients modeling on 102 research and development teams. Results indicated that high mean levels of extraversion are positively related to individual helping behaviors through the mediating effect of cooperative group norms. Further, low variance on agreeableness (supplementary fit) and high variance on extraversion (complementary fit) promote the enactment of individual helping behaviors, but only the effects of extraversion were mediated by cooperative group norms. Implications of these findings for theories of helping behaviors in teams are discussed. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Differential Reactivity and the Within-person Job Stressor-Satisfaction Relationship.

PubMed

Rudolph, Cort W; Clark, Malissa A; Jundt, Dustin K; Baltes, Boris B

2016-12-01

An experience sampling methodology was used to study the direct and conditional within-person relationship between job stressors and job satisfaction. One hundred and one full-time administrative staff completed momentary measures of job stressors and job satisfaction three times a day on six different workdays over a 3-week period (N = 1818 observations). Multilevel random coefficients models were specified, and the results suggest that within-person stressors are negatively related to within-person job satisfaction. These results stand when controlling for the effects of time, demographics, work characteristics, baseline levels of job stressors and satisfaction, and between-person effects of job stressors. Furthermore, consistent with the differential reactivity model, the results suggest that the observed within-person stressors-satisfaction relationship is conditional upon locus of control and positive affect. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

How the differential treatment of siblings is linked with parent-child relationship quality.

PubMed

Kowal, Amanda K; Krull, Jennifer L; Kramer, Laurie

2004-12-01

Little is currently known about the significance of parents' unequal treatment of siblings and their relationships with their children; for example, are high levels of differential treatment consistently indicative of poorer parent-child relationships? Associations among differential parenting practices, perceptions of the fairness of these practices, and parent-child relationship quality were assessed from the perspectives of adolescent siblings and their parents in 74 maritally intact families. Multilevel random coefficient modeling revealed that the magnitude of differential treatment was associated with more negative parent-child relationships only when adolescents perceived differential treatment to be unfair. Differential treatment judged to be fair is not linked with negative parent-child relationships. Results highlight the importance of examining all family members' viewpoints about the legitimacy of differential treatment and of encouraging family members to discuss their understanding of these events. ((c) 2004 APA, all rights reserved).

An accelerated lambda iteration method for multilevel radiative transfer. I - Non-overlapping lines with background continuum

NASA Technical Reports Server (NTRS)

Rybicki, G. B.; Hummer, D. G.

1991-01-01

A method is presented for solving multilevel transfer problems when nonoverlapping lines and background continuum are present and active continuum transfer is absent. An approximate lambda operator is employed to derive linear, 'preconditioned', statistical-equilibrium equations. A method is described for finding the diagonal elements of the 'true' numerical lambda operator, and therefore for obtaining the coefficients of the equations. Iterations of the preconditioned equations, in conjunction with the transfer equation's formal solution, are used to solve linear equations. Some multilevel problems are considered, including an eleven-level neutral helium atom. Diagonal and tridiagonal approximate lambda operators are utilized in the problems to examine the convergence properties of the method, and it is found to be effective for the line transfer problems.

Multilevel Modeling in Psychosomatic Medicine Research

PubMed Central

Myers, Nicholas D.; Brincks, Ahnalee M.; Ames, Allison J.; Prado, Guillermo J.; Penedo, Frank J.; Benedict, Catherine

2012-01-01

The primary purpose of this manuscript is to provide an overview of multilevel modeling for Psychosomatic Medicine readers and contributors. The manuscript begins with a general introduction to multilevel modeling. Multilevel regression modeling at two-levels is emphasized because of its prevalence in psychosomatic medicine research. Simulated datasets based on some core ideas from the Familias Unidas effectiveness study are used to illustrate key concepts including: communication of model specification, parameter interpretation, sample size and power, and missing data. Input and key output files from Mplus and SAS are provided. A cluster randomized trial with repeated measures (i.e., three-level regression model) is then briefly presented with simulated data based on some core ideas from a cognitive behavioral stress management intervention in prostate cancer. PMID:23107843

Tests of Hypotheses Arising In the Correlated Random Coefficient Model*

PubMed Central

Heckman, James J.; Schmierer, Daniel

2010-01-01

This paper examines the correlated random coefficient model. It extends the analysis of Swamy (1971), who pioneered the uncorrelated random coefficient model in economics. We develop the properties of the correlated random coefficient model and derive a new representation of the variance of the instrumental variable estimator for that model. We develop tests of the validity of the correlated random coefficient model against the null hypothesis of the uncorrelated random coefficient model. PMID:21170148

The Role of School Culture and Basic Psychological Needs on Iranian Adolescents' Academic Alienation: A Multi-Level Examination

ERIC Educational Resources Information Center

Mahmoudi, Hojjat; Brown, Monica R.; Amani Saribagloo, Javad; Dadashzadeh, Shiva

2018-01-01

This aim of this current research was a multi-level analysis of the relationship between school culture, basic psychological needs, and adolescents' academic alienation. One thousand twenty-nine (N = 1,029) high school students from Qom City were randomly selected through a multi-phase cluster sampling method and answered questions regarding…

Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression.

PubMed

Zhang, Xinyan; Li, Bingzong; Han, Huiying; Song, Sha; Xu, Hongxia; Hong, Yating; Yi, Nengjun; Zhuang, Wenzhuo

2018-05-10

Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.

The Number of Feedbacks Needed for Reliable Evaluation. A Multilevel Analysis of the Reliability, Stability and Generalisability of Students' Evaluation of Teaching

ERIC Educational Resources Information Center

Rantanen, Pekka

2013-01-01

A multilevel analysis approach was used to analyse students' evaluation of teaching (SET). The low value of inter-rater reliability stresses that any solid conclusions on teaching cannot be made on the basis of single feedbacks. To assess a teacher's general teaching effectiveness, one needs to evaluate four randomly chosen course implementations.…

Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

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

2016-01-01

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

Multivariate Longitudinal Analysis with Bivariate Correlation Test.

PubMed

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

2016-01-01

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

Socio-economic inequality in multiple health complaints among adolescents: international comparative study in 37 countries.

PubMed

Holstein, Bjørn E; Currie, Candace; Boyce, Will; Damsgaard, Mogens T; Gobina, Inese; Kökönyei, Gyöngyi; Hetland, Jørn; de Looze, Margaretha; Richter, Matthias; Due, Pernille

2009-09-01

To use comparable data from many countries to examine 1) socio-economic inequality in multiple health complaints among adolescents, 2) whether the countries' absolute wealth and economic inequality was associated with symptom load among adolescents, and 3) whether the countries' absolute wealth and economic inequality explained part of the individual level socio-economic variation in health complaints. The Health Behaviour in School-aged Children (HBSC) international study from 2005/06 provided data on 204,534 11-, 13- and 15-year old students from nationally random samples of schools in 37 countries in Europe and North America. The outcome measure was prevalence of at least two daily health complaints, measured by the HBSC Symptom Check List. We included three independent variables at the individual level (sex, age group, family affluence measured by the Family Affluence Scale FAS) and two macro level measures on the country's economic situation: wealth measured by Gross National Product (GNP) and distribution of income measured by the Gini coefficient. There was a significant socio-economic variation in health complaints in 31 of the 37 countries. The overall OR (95 % CI) for 2+ daily health complaints for all countries was 1.31 (1.27-1.36) in the medium versus high FAS group and 2.07 (2.00-2.14) in the low versus high FAS group. This socio-economic gradient in health complaints attenuated somewhat in the multilevel models which included macro level data. There was no association between GNP and health complaints. The OR for high symptom load was 1.35 (1.08-1.69) per 10 % increase in Gini coefficient. The socio-economic gradient in health complaints at the individual level was somewhat attenuated in the multilevel models which included macro level data. There was a significant association between low FAS and high level of health complaints in 30 of 37 countries. Health complaints increased significantly by increasing income inequality in the country.

Intermediate and advanced topics in multilevel logistic regression analysis

PubMed Central

Merlo, Juan

2017-01-01

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher‐level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within‐cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population‐average effect of covariates measured at the subject and cluster level, in contrast to the within‐cluster or cluster‐specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster‐level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28543517

Intermediate and advanced topics in multilevel logistic regression analysis.

PubMed

Austin, Peter C; Merlo, Juan

2017-09-10

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Multilevel resistive information storage and retrieval

DOEpatents

Lohn, Andrew; Mickel, Patrick R.

2016-08-09

The present invention relates to resistive random-access memory (RRAM or ReRAM) systems, as well as methods of employing multiple state variables to form degenerate states in such memory systems. The methods herein allow for precise write and read steps to form multiple state variables, and these steps can be performed electrically. Such an approach allows for multilevel, high density memory systems with enhanced information storage capacity and simplified information retrieval.

Estimation of Indirect Effects in the Presence of Unmeasured Confounding for the Mediator-Outcome Relationship in a Multilevel 2-1-1 Mediation Model

ERIC Educational Resources Information Center

Talloen, Wouter; Moerkerke, Beatrijs; Loeys, Tom; De Naeghel, Jessie; Van Keer, Hilde; Vansteelandt, Stijn

2016-01-01

To assess the direct and indirect effect of an intervention, multilevel 2-1-1 studies with intervention randomized at the upper (class) level and mediator and outcome measured at the lower (student) level are frequently used in educational research. In such studies, the mediation process may flow through the student-level mediator (the within…

Disentangling the Relative Influence of Schools and Neighborhoods on Adolescents’ Risk for Depressive Symptoms

PubMed Central

Milliren, Carly E.; Evans, Clare R.; Subramanian, S. V.; Richmond, Tracy K.

2015-01-01

Objectives. Although schools and neighborhoods influence health, little is known about their relative importance, or the influence of one context after the influence of the other has been taken into account. We simultaneously examined the influence of each setting on depression among adolescents. Methods. Analyzing data from wave 1 (1994–1995) of the National Longitudinal Study of Adolescent Health, we used cross-classified multilevel modeling to examine between-level variation and individual-, school-, and neighborhood-level predictors of adolescent depressive symptoms. Also, we compared the results of our cross-classified multilevel models (CCMMs) with those of a multilevel model wherein either school or neighborhood was excluded. Results. In CCMMs, the school-level random effect was significant and more than 3 times the neighborhood-level random effect, even after individual-level characteristics had been taken into account. Individual-level indicators (e.g., race/ethnicity, socioeconomic status) were associated with depressive symptoms, but there was no association with either school- or neighborhood-level fixed effects. The between-level variance in depressive symptoms was driven largely by schools as opposed to neighborhoods. Conclusions. Schools appear to be more salient than neighborhoods in explaining variation in depressive symptoms. Future work incorporating cross-classified multilevel modeling is needed to understand the relative effects of schools and neighborhoods. PMID:25713969

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

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

2014-01-01

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

Using multilevel models to quantify heterogeneity in resource selection

USGS Publications Warehouse

Wagner, Tyler; Diefenbach, Duane R.; Christensen, Sonja; Norton, Andrew S.

2011-01-01

Models of resource selection are being used increasingly to predict or model the effects of management actions rather than simply quantifying habitat selection. Multilevel, or hierarchical, models are an increasingly popular method to analyze animal resource selection because they impose a relatively weak stochastic constraint to model heterogeneity in habitat use and also account for unequal sample sizes among individuals. However, few studies have used multilevel models to model coefficients as a function of predictors that may influence habitat use at different scales or quantify differences in resource selection among groups. We used an example with white-tailed deer (Odocoileus virginianus) to illustrate how to model resource use as a function of distance to road that varies among deer by road density at the home range scale. We found that deer avoidance of roads decreased as road density increased. Also, we used multilevel models with sika deer (Cervus nippon) and white-tailed deer to examine whether resource selection differed between species. We failed to detect differences in resource use between these two species and showed how information-theoretic and graphical measures can be used to assess how resource use may have differed. Multilevel models can improve our understanding of how resource selection varies among individuals and provides an objective, quantifiable approach to assess differences or changes in resource selection.

A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications

PubMed Central

Austin, Peter C.

2017-01-01

Summary Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster-specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster-specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow-up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log–log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within-cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata). PMID:29307954

A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications.

PubMed

Austin, Peter C

2017-08-01

Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster-specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster-specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow-up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log-log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within-cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata).

Estimating the concentration of gold nanoparticles incorporated on natural rubber membranes using multi-level starlet optimal segmentation

NASA Astrophysics Data System (ADS)

de Siqueira, A. F.; Cabrera, F. C.; Pagamisse, A.; Job, A. E.

2014-12-01

This study consolidates multi-level starlet segmentation (MLSS) and multi-level starlet optimal segmentation (MLSOS) techniques for photomicrograph segmentation, based on starlet wavelet detail levels to separate areas of interest in an input image. Several segmentation levels can be obtained using MLSS; after that, Matthews correlation coefficient is used to choose an optimal segmentation level, giving rise to MLSOS. In this paper, MLSOS is employed to estimate the concentration of gold nanoparticles with diameter around 47 nm, reduced on natural rubber membranes. These samples were used for the construction of SERS/SERRS substrates and in the study of the influence of natural rubber membranes with incorporated gold nanoparticles on the physiology of Leishmania braziliensis. Precision, recall, and accuracy are used to evaluate the segmentation performance, and MLSOS presents an accuracy greater than 88 % for this application.

Understanding Statistical Power in Cluster Randomized Trials: Challenges Posed by Differences in Notation and Terminology

ERIC Educational Resources Information Center

Spybrook, Jessaca; Hedges, Larry; Borenstein, Michael

2014-01-01

Research designs in which clusters are the unit of randomization are quite common in the social sciences. Given the multilevel nature of these studies, the power analyses for these studies are more complex than in a simple individually randomized trial. Tools are now available to help researchers conduct power analyses for cluster randomized…

Delamination detection by Multi-Level Wavelet Processing of Continuous Scanning Laser Doppler Vibrometry data

NASA Astrophysics Data System (ADS)

Chiariotti, P.; Martarelli, M.; Revel, G. M.

2017-12-01

A novel non-destructive testing procedure for delamination detection based on the exploitation of the simultaneous time and spatial sampling provided by Continuous Scanning Laser Doppler Vibrometry (CSLDV) and the feature extraction capability of Multi-Level wavelet-based processing is presented in this paper. The processing procedure consists in a multi-step approach. Once the optimal mother-wavelet is selected as the one maximizing the Energy to Shannon Entropy Ratio criterion among the mother-wavelet space, a pruning operation aiming at identifying the best combination of nodes inside the full-binary tree given by Wavelet Packet Decomposition (WPD) is performed. The pruning algorithm exploits, in double step way, a measure of the randomness of the point pattern distribution on the damage map space with an analysis of the energy concentration of the wavelet coefficients on those nodes provided by the first pruning operation. A combination of the point pattern distributions provided by each node of the ensemble node set from the pruning algorithm allows for setting a Damage Reliability Index associated to the final damage map. The effectiveness of the whole approach is proven on both simulated and real test cases. A sensitivity analysis related to the influence of noise on the CSLDV signal provided to the algorithm is also discussed, showing that the processing developed is robust enough to measurement noise. The method is promising: damages are well identified on different materials and for different damage-structure varieties.

Individual and group-level job resources and their relationships with individual work engagement.

PubMed

Füllemann, Désirée; Brauchli, Rebecca; Jenny, Gregor J; Bauer, Georg F

2016-06-16

This study adds a multilevel perspective to the well-researched individual-level relationship between job resources and work engagement. In addition, we explored whether individual job resources cluster within work groups because of a shared psychosocial environment and investigated whether a resource-rich psychosocial work group environment is beneficial for employee engagement over and above the beneficial effect of individual job resources and independent of their variability within groups. Data of 1,219 employees nested in 103 work groups were obtained from a baseline employee survey of a large stress management intervention project implemented in six medium and large-sized organizations in diverse sectors. A variety of important job resources were assessed and grouped to an overall job resource factor with three subfactors (manager behavior, peer behavior, and task-related resources). Data were analyzed using multilevel random coefficient modeling. The results indicated that job resources cluster within work groups and can be aggregated to a group-level job resources construct. However, a resource-rich environment, indicated by high group-level job resources, did not additionally benefit employee work engagement but on the contrary, was negatively related to it. On the basis of this unexpected result, replication studies are encouraged and suggestions for future studies on possible underlying within-group processes are discussed. The study supports the presumed value of integrating work group as a relevant psychosocial environment into the motivational process and indicates a need to further investigate emergent processes involved in aggregation procedures across levels.

Multilevel Mechanisms of Implementation Strategies in Mental Health: Integrating Theory, Research, and Practice

PubMed Central

2015-01-01

A step toward the development of optimally effective, efficient, and feasible implementation strategies that increase evidence-based treatment integration in mental health services involves identification of the multilevel mechanisms through which these strategies influence implementation outcomes. This article (a) provides an orientation to, and rationale for, consideration of multilevel mediating mechanisms in implementation trials, and (b) systematically reviews randomized controlled trials that examined mediators of implementation strategies in mental health. Nine trials were located. Mediation-related methodological deficiencies were prevalent and no trials supported a hypothesized mediator. The most common reason was failure to engage the mediation target. Discussion focuses on directions to accelerate implementation strategy development in mental health. PMID:26474761

Longitudinal analysis of the strengths and difficulties questionnaire scores of the Millennium Cohort Study children in England using M-quantile random-effects regression.

PubMed

Tzavidis, Nikos; Salvati, Nicola; Schmid, Timo; Flouri, Eirini; Midouhas, Emily

2016-02-01

Multilevel modelling is a popular approach for longitudinal data analysis. Statistical models conventionally target a parameter at the centre of a distribution. However, when the distribution of the data is asymmetric, modelling other location parameters, e.g. percentiles, may be more informative. We present a new approach, M -quantile random-effects regression, for modelling multilevel data. The proposed method is used for modelling location parameters of the distribution of the strengths and difficulties questionnaire scores of children in England who participate in the Millennium Cohort Study. Quantile mixed models are also considered. The analyses offer insights to child psychologists about the differential effects of risk factors on children's outcomes.

Impact of daily mood, work hours, and iso-strain variables on self-reported health behaviors.

PubMed

Jones, Fiona; O'Connor, Daryl B; Conner, Mark; McMillan, Brian; Ferguson, Eamonn

2007-11-01

Four hundred and twenty-two employees completed daily diaries measuring positive affect, negative affect, work hours, and health behaviors (snacking, smoking, exercise, alcohol, caffeine consumption) on work days over a 4-week period. In addition, measures of job demands, job control, and social support (iso-strain variables) were completed on 1 occasion. Multilevel random coefficient modeling was used to examine relationships between the job characteristics, daily work variables, and self-reported health behaviors. Results indicated a more important role for within-person daily fluctuations than for between-persons variations in predicting health behaviors. Whereas negative affect was negatively related to health behavior for both men and women, work hours had negative impacts for women only. Iso-strain variables showed few main effects and a modest number of interactions with daily variables (mainly for men). Findings point to the limited impact of stable features of work design compared to the effects of daily work stressors on health behaviors. (c) 2007 APA

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

PubMed

Li, Baoyue; Bruyneel, Luk; Lesaffre, Emmanuel

2014-05-20

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

Model Selection with the Linear Mixed Model for Longitudinal Data

ERIC Educational Resources Information Center

Ryoo, Ji Hoon

2011-01-01

Model building or model selection with linear mixed models (LMMs) is complicated by the presence of both fixed effects and random effects. The fixed effects structure and random effects structure are codependent, so selection of one influences the other. Most presentations of LMM in psychology and education are based on a multilevel or…

Intraclass Correlations and Covariate Outcome Correlations for Planning Two-and Three-Level Cluster-Randomized Experiments in Education

ERIC Educational Resources Information Center

Hedges, Larry V.; Hedberg, E. C.

2013-01-01

Background: Cluster-randomized experiments that assign intact groups such as schools or school districts to treatment conditions are increasingly common in educational research. Such experiments are inherently multilevel designs whose sensitivity (statistical power and precision of estimates) depends on the variance decomposition across levels.…

Intraclass Correlations and Covariate Outcome Correlations for Planning 2 and 3 Level Cluster Randomized Experiments in Education

ERIC Educational Resources Information Center

Hedges, Larry V.; Hedberg, Eric C.

2013-01-01

Background: Cluster randomized experiments that assign intact groups such as schools or school districts to treatment conditions are increasingly common in educational research. Such experiments are inherently multilevel designs whose sensitivity (statistical power and precision of estimates) depends on the variance decomposition across levels.…

Multilevel covariance regression with correlated random effects in the mean and variance structure.

PubMed

Quintero, Adrian; Lesaffre, Emmanuel

2017-09-01

Multivariate regression methods generally assume a constant covariance matrix for the observations. In case a heteroscedastic model is needed, the parametric and nonparametric covariance regression approaches can be restrictive in the literature. We propose a multilevel regression model for the mean and covariance structure, including random intercepts in both components and allowing for correlation between them. The implied conditional covariance function can be different across clusters as a result of the random effect in the variance structure. In addition, allowing for correlation between the random intercepts in the mean and covariance makes the model convenient for skewedly distributed responses. Furthermore, it permits us to analyse directly the relation between the mean response level and the variability in each cluster. Parameter estimation is carried out via Gibbs sampling. We compare the performance of our model to other covariance modelling approaches in a simulation study. Finally, the proposed model is applied to the RN4CAST dataset to identify the variables that impact burnout of nurses in Belgium. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Improving Urban African Americans’ Blood Pressure Control through Multi-level Interventions in the Achieving Blood Pressure Control Together (ACT) Study: A Randomized Clinical Trial

PubMed Central

Ephraim, Patti L.; Hill-Briggs, Felicia; Roter, Debra; Bone, Lee; Wolff, Jennifer; Lewis-Boyer, LaPricia; Levine, David; Aboumatar, Hanan; Cooper, Lisa A; Fitzpatrick, Stephanie; Gudzune, Kimberly; Albert, Michael; Monroe, Dwyan; Simmons, Michelle; Hickman, Debra; Purnell, Leon; Fisher, Annette; Matens, Richard; Noronha, Gary; Fagan, Peter; Ramamurthi, Hema; Ameling, Jessica; Charlston, Jeanne; Sam, Tanyka; Carson, Kathryn A.; Wang, Nae-Yuh; Crews, Deidra; Greer, Raquel; Sneed, Valerie; Flynn, Sarah J.; DePasquale, Nicole; Boulware, L. Ebony

2014-01-01

Background Given their high rates of uncontrolled blood pressure, urban African Americans comprise a particularly vulnerable subgroup of persons with hypertension. Substantial evidence has demonstrated the important role of family and community support in improving patients’ management of a variety of chronic illnesses. However, studies of multilevel interventions designed specifically to improve urban African American patients’ blood pressure self-management by simultaneously leveraging patient, family, and community strengths are lacking. Methods/Design We report the protocol of the Achieving Blood Pressure Control Together (ACT) study, a randomized controlled trial designed to study the effectiveness of interventions that engage patient, family, and community-level resources to facilitate urban African American hypertensive patients’ improved hypertension self-management and subsequent hypertension control. African American patients with uncontrolled hypertension receiving health care in an urban primary care clinic will be randomly assigned to receive 1) an educational intervention led by a community health worker alone, 2) the community health worker intervention plus a patient and family communication activation intervention, or 3) the community health worker intervention plus a problem-solving intervention. All participants enrolled in the study will receive and be trained to use a digital home blood pressure machine. The primary outcome of the randomized controlled trial will be patients’ blood pressure control at 12 months. Discussion Results from the ACT study will provide needed evidence on the effectiveness of comprehensive multi-level interventions to improve urban African American patients’ hypertension control. PMID:24956323

Improving urban African Americans' blood pressure control through multi-level interventions in the Achieving Blood Pressure Control Together (ACT) study: a randomized clinical trial.

PubMed

Ephraim, Patti L; Hill-Briggs, Felicia; Roter, Debra L; Bone, Lee R; Wolff, Jennifer L; Lewis-Boyer, LaPricia; Levine, David M; Aboumatar, Hanan J; Cooper, Lisa A; Fitzpatrick, Stephanie J; Gudzune, Kimberly A; Albert, Michael C; Monroe, Dwyan; Simmons, Michelle; Hickman, Debra; Purnell, Leon; Fisher, Annette; Matens, Richard; Noronha, Gary J; Fagan, Peter J; Ramamurthi, Hema C; Ameling, Jessica M; Charlston, Jeanne; Sam, Tanyka S; Carson, Kathryn A; Wang, Nae-Yuh; Crews, Deidra C; Greer, Raquel C; Sneed, Valerie; Flynn, Sarah J; DePasquale, Nicole; Boulware, L Ebony

2014-07-01

Given their high rates of uncontrolled blood pressure, urban African Americans comprise a particularly vulnerable subgroup of persons with hypertension. Substantial evidence has demonstrated the important role of family and community support in improving patients' management of a variety of chronic illnesses. However, studies of multi-level interventions designed specifically to improve urban African American patients' blood pressure self-management by simultaneously leveraging patient, family, and community strengths are lacking. We report the protocol of the Achieving Blood Pressure Control Together (ACT) study, a randomized controlled trial designed to study the effectiveness of interventions that engage patient, family, and community-level resources to facilitate urban African American hypertensive patients' improved hypertension self-management and subsequent hypertension control. African American patients with uncontrolled hypertension receiving health care in an urban primary care clinic will be randomly assigned to receive 1) an educational intervention led by a community health worker alone, 2) the community health worker intervention plus a patient and family communication activation intervention, or 3) the community health worker intervention plus a problem-solving intervention. All participants enrolled in the study will receive and be trained to use a digital home blood pressure machine. The primary outcome of the randomized controlled trial will be patients' blood pressure control at 12months. Results from the ACT study will provide needed evidence on the effectiveness of comprehensive multi-level interventions to improve urban African American patients' hypertension control. Copyright © 2014 Elsevier Inc. All rights reserved.

Neighbourhood socioeconomic deprivation and health-related quality of life: A multilevel analysis

PubMed Central

Ribeiro, Ana Isabel; Severo, Milton; Barros, Henrique; Fraga, Sílvia

2017-01-01

Objective To assess the relationship between socioeconomic deprivation and health-related quality of life in urban neighbourhoods, using a multilevel approach. Methods Of the population-based cohort EPIPorto, 1154 georeferenced participants completed the 36-Item Short-Form Health Survey. Neighbourhood socioeconomic deprivation classes were estimated using latent-class analysis. Multilevel models measured clustering and contextual effects of neighbourhood deprivation on physical and mental HRQoL. Results Residents from the least deprived neighbourhoods had higher physical HRQoL. Neighbourhood socioeconomic deprivation together with individual-level variables (age, gender and education) and health-related factors (smoking, alcohol consumption, sedentariness and chronic diseases) explained 98% of the total between-neighbourhood variance. Neighbourhood socioeconomic deprivation was significantly associated with physical health when comparing least and most deprived neighbourhoods (class 2—beta coefficient: -0.60; 95% confidence interval:-1.76;-0.56; class 3 –beta coefficient: -2.28; 95% confidence interval:-3.96;-0.60), and as neighbourhood deprivation increases, a decrease in all values of physical health dimensions (physical functioning, role physical, bodily pain and general health) was also observed. Regarding the mental health dimension, no neighbourhood clustering or contextual effects were found. However, as neighbourhood deprivation increases, the values of vitality and role emotional dimensions significantly decreased. Conclusion Neighbourhood socioeconomic deprivation is associated with HRQoL, affecting particularly physical health. This study suggests that to improve HRQoL, people and places should be targeted simultaneously. PMID:29236719

Work-Related CBT versus Vocational Services As Usual for Unemployed Persons with Social Anxiety Disorder: A Randomized Controlled Pilot Trial

PubMed Central

Himle, Joseph A.; Bybee, Deborah; Steinberger, Edward; Laviolette, Wayne T.; Weaver, Addie; Vlnka, Sarah; Golenberg, Zipora; Levine, Debra Siegel; Heimberg, Richard G.; O’Donnell, Lisa A.

2014-01-01

We designed and pilot-tested a group-based, work-related cognitive-behavioral therapy (WCBT) for unemployed individuals with social anxiety disorder (SAD). WCBT, delivered in a vocational service setting by vocational service professionals, aims to reduce social anxiety and enable individuals to seek, obtain, and retain employment. We compared WCBT to a vocational services as usual control condition (VSAU). Participants were unemployed, homeless, largely African American, vocational service-seeking adults with SAD (N=58), randomized to receive either eight sessions of WCBT plus VSAU or VSAU alone and followed three months post-treatment. Multilevel modeling revealed significantly greater reductions in social anxiety, general anxiety, depression, and functional impairment for WCBT compared to VSAU. Coefficients for job search activity and self-efficacy indicated greater increases for WCBT. Hours worked per week in the follow-up period did not differ between the groups, but small sample size and challenges associated with measuring work hours may have contributed to this finding. Overall, the results of this study suggest that unemployed persons with SAD can be effectively treated with specialized work-related CBT administered by vocational service professionals. Future testing of WCBT with a larger sample, a longer follow-up period, and adequate power to assess employment outcomes is warranted. PMID:25461793

Intra-class correlation estimates for assessment of vitamin A intake in children.

PubMed

Agarwal, Girdhar G; Awasthi, Shally; Walter, Stephen D

2005-03-01

In many community-based surveys, multi-level sampling is inherent in the design. In the design of these studies, especially to calculate the appropriate sample size, investigators need good estimates of intra-class correlation coefficient (ICC), along with the cluster size, to adjust for variation inflation due to clustering at each level. The present study used data on the assessment of clinical vitamin A deficiency and intake of vitamin A-rich food in children in a district in India. For the survey, 16 households were sampled from 200 villages nested within eight randomly-selected blocks of the district. ICCs and components of variances were estimated from a three-level hierarchical random effects analysis of variance model. Estimates of ICCs and variance components were obtained at village and block levels. Between-cluster variation was evident at each level of clustering. In these estimates, ICCs were inversely related to cluster size, but the design effect could be substantial for large clusters. At the block level, most ICC estimates were below 0.07. At the village level, many ICC estimates ranged from 0.014 to 0.45. These estimates may provide useful information for the design of epidemiological studies in which the sampled (or allocated) units range in size from households to large administrative zones.

Variance approach for multi-objective linear programming with fuzzy random of objective function coefficients

NASA Astrophysics Data System (ADS)

Indarsih, Indrati, Ch. Rini

2016-02-01

In this paper, we define variance of the fuzzy random variables through alpha level. We have a theorem that can be used to know that the variance of fuzzy random variables is a fuzzy number. We have a multi-objective linear programming (MOLP) with fuzzy random of objective function coefficients. We will solve the problem by variance approach. The approach transform the MOLP with fuzzy random of objective function coefficients into MOLP with fuzzy of objective function coefficients. By weighted methods, we have linear programming with fuzzy coefficients and we solve by simplex method for fuzzy linear programming.

Improving multilevel Monte Carlo for stochastic differential equations with application to the Langevin equation

PubMed Central

Müller, Eike H.; Scheichl, Rob; Shardlow, Tony

2015-01-01

This paper applies several well-known tricks from the numerical treatment of deterministic differential equations to improve the efficiency of the multilevel Monte Carlo (MLMC) method for stochastic differential equations (SDEs) and especially the Langevin equation. We use modified equations analysis as an alternative to strong-approximation theory for the integrator, and we apply this to introduce MLMC for Langevin-type equations with integrators based on operator splitting. We combine this with extrapolation and investigate the use of discrete random variables in place of the Gaussian increments, which is a well-known technique for the weak approximation of SDEs. We show that, for small-noise problems, discrete random variables can lead to an increase in efficiency of almost two orders of magnitude for practical levels of accuracy. PMID:27547075

Improving multilevel Monte Carlo for stochastic differential equations with application to the Langevin equation.

PubMed

Müller, Eike H; Scheichl, Rob; Shardlow, Tony

2015-04-08

This paper applies several well-known tricks from the numerical treatment of deterministic differential equations to improve the efficiency of the multilevel Monte Carlo (MLMC) method for stochastic differential equations (SDEs) and especially the Langevin equation. We use modified equations analysis as an alternative to strong-approximation theory for the integrator, and we apply this to introduce MLMC for Langevin-type equations with integrators based on operator splitting. We combine this with extrapolation and investigate the use of discrete random variables in place of the Gaussian increments, which is a well-known technique for the weak approximation of SDEs. We show that, for small-noise problems, discrete random variables can lead to an increase in efficiency of almost two orders of magnitude for practical levels of accuracy.

Measurement Error Correction Formula for Cluster-Level Group Differences in Cluster Randomized and Observational Studies

ERIC Educational Resources Information Center

Cho, Sun-Joo; Preacher, Kristopher J.

2016-01-01

Multilevel modeling (MLM) is frequently used to detect cluster-level group differences in cluster randomized trial and observational studies. Group differences on the outcomes (posttest scores) are detected by controlling for the covariate (pretest scores) as a proxy variable for unobserved factors that predict future attributes. The pretest and…

Multilevel poisson regression modelling for determining factors of dengue fever cases in bandung

NASA Astrophysics Data System (ADS)

Arundina, Davila Rubianti; Tantular, Bertho; Pontoh, Resa Septiani

2017-03-01

Scralatina or Dengue Fever is a kind of fever caused by serotype virus which Flavivirus genus and be known as Dengue Virus. Dengue Fever caused by Aedes Aegipty Mosquito bites who infected by a dengue virus. The study was conducted in 151 villages in Bandung. Health Analysts believes that there are two factors that affect the dengue cases, Internal factor (individual) and external factor (environment). The data who used in this research is hierarchical data. The method is used for hierarchical data modelling is multilevel method. Which is, the level 1 is village and level 2 is sub-district. According exploration data analysis, the suitable Multilevel Method is Random Intercept Model. Penalized Quasi Likelihood (PQL) approach on multilevel Poisson is a proper analysis to determine factors that affecting dengue cases in the city of Bandung. Clean and Healthy Behavior factor from the village level have an effect on the number of cases of dengue fever in the city of Bandung. Factor from the sub-district level has no effect.

Individual and group-level job resources and their relationships with individual work engagement

PubMed Central

Füllemann, Désirée; Brauchli, Rebecca; Jenny, Gregor J.; Bauer, Georg F.

2016-01-01

Objectives: This study adds a multilevel perspective to the well-researched individual-level relationship between job resources and work engagement. In addition, we explored whether individual job resources cluster within work groups because of a shared psychosocial environment and investigated whether a resource-rich psychosocial work group environment is beneficial for employee engagement over and above the beneficial effect of individual job resources and independent of their variability within groups. Methods: Data of 1,219 employees nested in 103 work groups were obtained from a baseline employee survey of a large stress management intervention project implemented in six medium and large-sized organizations in diverse sectors. A variety of important job resources were assessed and grouped to an overall job resource factor with three subfactors (manager behavior, peer behavior, and task-related resources). Data were analyzed using multilevel random coefficient modeling. Results: The results indicated that job resources cluster within work groups and can be aggregated to a group-level job resources construct. However, a resource-rich environment, indicated by high group-level job resources, did not additionally benefit employee work engagement but on the contrary, was negatively related to it. Conclusions: On the basis of this unexpected result, replication studies are encouraged and suggestions for future studies on possible underlying within-group processes are discussed. The study supports the presumed value of integrating work group as a relevant psychosocial environment into the motivational process and indicates a need to further investigate emergent processes involved in aggregation procedures across levels. PMID:27108639

Understanding screen-related sedentary behavior and its contributing factors among school-aged children: a social-ecologic exploration.

PubMed

He, Meizi; Harris, Stewart; Piché, Leonard; Beynon, Charlene

2009-01-01

To explore the factors that contribute to children's screen-related sedentary (S-RS) behaviors. Elementary schools. A random sample of children in grades five and six and their parents. The outcome measure was children's S-RS activity level measured by a self-administered questionnaire. A full spectrum of potential contributing factors for children's S-RS behaviors was obtained through surveys. Multilevel linear regression methods were used to determine the associations between these factors and children's screen time (hours per day) and results were expressed as regression coefficients (g). Of 955 child-parent pairs in 14 participating schools, 508 pairs (53%) completed the surveys. At an intrapersonal level, protective factors included being a girl (g = -.71); belonging to a sports team inside (g = -.56) or outside (g = -.49) of school; having a negative attitude toward S-RS activities (g = -.13); and having a positive attitude toward physical activity (g = .48). At the interpersonal and social levels, parental leisure S-RS behaviors (g = .32) were positively associated, whereas strict parental rules on computer use (g = -.27) and family income (g = -.32) were inversely correlated with S-RS behavior. At the environmental level, the presence of TVs in children's bedrooms (g = .44) and owning videogame devices (g = .58) increased the risk of S-RS behaviors, whereas after school programs (g = - .86) and schools' participation in the Turn Off the Screen Week campaign (g = -.91) decreased the risk. Public health interventions should target multilevel factors, including increasing children's awareness, promoting parental involvement in healthy lifestyle pursuits, and creating less screenogenic environments.

Stability of Boolean multilevel networks.

PubMed

Cozzo, Emanuele; Arenas, Alex; Moreno, Yamir

2012-09-01

The study of the interplay between the structure and dynamics of complex multilevel systems is a pressing challenge nowadays. In this paper, we use a semiannealed approximation to study the stability properties of random Boolean networks in multiplex (multilayered) graphs. Our main finding is that the multilevel structure provides a mechanism for the stabilization of the dynamics of the whole system even when individual layers work on the chaotic regime, therefore identifying new ways of feedback between the structure and the dynamics of these systems. Our results point out the need for a conceptual transition from the physics of single-layered networks to the physics of multiplex networks. Finally, the fact that the coupling modifies the phase diagram and the critical conditions of the isolated layers suggests that interdependency can be used as a control mechanism.

Spatial Random Effects Survival Models to Assess Geographical Inequalities in Dengue Fever Using Bayesian Approach: a Case Study

NASA Astrophysics Data System (ADS)

Astuti Thamrin, Sri; Taufik, Irfan

2018-03-01

Dengue haemorrhagic fever (DHF) is an infectious disease caused by dengue virus. The increasing number of people with DHF disease correlates with the neighbourhood, for example sub-districts, and the characteristics of the sub-districts are formed from individuals who are domiciled in the sub-districts. Data containing individuals and sub-districts is a hierarchical data structure, called multilevel analysis. Frequently encountered response variable of the data is the time until an event occurs. Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in DHF survival. Using a case study approach, we report on the implications of using multilevel with spatial survival models to study geographical inequalities in all cause survival.

Family, Community and Clinic Collaboration to Treat Overweight and Obese Children: Stanford GOALS -- a Randomized Controlled Trial of a Three-Year, Multi-Component, Multi-Level, Multi-Setting Intervention

PubMed Central

Robinson, Thomas N.; Matheson, Donna; Desai, Manisha; Wilson, Darrell M.; Weintraub, Dana L.; Haskell, William L.; McClain, Arianna; McClure, Samuel; Banda, Jorge; Sanders, Lee M.; Haydel, K. Farish; Killen, Joel D.

2013-01-01

Objective To test the effects of a three-year, community-based, multi-component, multi-level, multi-setting (MMM) approach for treating overweight and obese children. Design Two-arm, parallel group, randomized controlled trial with measures at baseline, 12, 24, and 36 months after randomization. Participants Seven through eleven year old, overweight and obese children (BMI ≥ 85th percentile) and their parents/caregivers recruited from community locations in low-income, primarily Latino neighborhoods in Northern California. Interventions Families are randomized to the MMM intervention versus a community health education active-placebo comparison intervention. Interventions last for three years for each participant. The MMM intervention includes a community-based after school team sports program designed specifically for overweight and obese children, a home-based family intervention to reduce screen time, alter the home food/eating environment, and promote self-regulatory skills for eating and activity behavior change, and a primary care behavioral counseling intervention linked to the community and home interventions. The active-placebo comparison intervention includes semi-annual health education home visits, monthly health education newsletters for children and for parents/guardians, and a series of community-based health education events for families. Main Outcome Measure Body mass index trajectory over the three-year study. Secondary outcome measures include waist circumference, triceps skinfold thickness, accelerometer-measured physical activity, 24-hour dietary recalls, screen time and other sedentary behaviors, blood pressure, fasting lipids, glucose, insulin, hemoglobin A1c, C-reactive protein, alanine aminotransferase, and psychosocial measures. Conclusions The Stanford GOALS trial is testing the efficacy of a novel community-based multi-component, multi-level, multi-setting treatment for childhood overweight and obesity in low-income, Latino families. PMID:24028942

Family, community and clinic collaboration to treat overweight and obese children: Stanford GOALS-A randomized controlled trial of a three-year, multi-component, multi-level, multi-setting intervention.

PubMed

Robinson, Thomas N; Matheson, Donna; Desai, Manisha; Wilson, Darrell M; Weintraub, Dana L; Haskell, William L; McClain, Arianna; McClure, Samuel; Banda, Jorge A; Sanders, Lee M; Haydel, K Farish; Killen, Joel D

2013-11-01

To test the effects of a three-year, community-based, multi-component, multi-level, multi-setting (MMM) approach for treating overweight and obese children. Two-arm, parallel group, randomized controlled trial with measures at baseline, 12, 24, and 36 months after randomization. Seven through eleven year old, overweight and obese children (BMI ≥ 85th percentile) and their parents/caregivers recruited from community locations in low-income, primarily Latino neighborhoods in Northern California. Families are randomized to the MMM intervention versus a community health education active-placebo comparison intervention. Interventions last for three years for each participant. The MMM intervention includes a community-based after school team sports program designed specifically for overweight and obese children, a home-based family intervention to reduce screen time, alter the home food/eating environment, and promote self-regulatory skills for eating and activity behavior change, and a primary care behavioral counseling intervention linked to the community and home interventions. The active-placebo comparison intervention includes semi-annual health education home visits, monthly health education newsletters for children and for parents/guardians, and a series of community-based health education events for families. Body mass index trajectory over the three-year study. Secondary outcome measures include waist circumference, triceps skinfold thickness, accelerometer-measured physical activity, 24-hour dietary recalls, screen time and other sedentary behaviors, blood pressure, fasting lipids, glucose, insulin, hemoglobin A1c, C-reactive protein, alanine aminotransferase, and psychosocial measures. The Stanford GOALS trial is testing the efficacy of a novel community-based multi-component, multi-level, multi-setting treatment for childhood overweight and obesity in low-income, Latino families. © 2013 Elsevier Inc. All rights reserved.

Reducing Playground Bullying and Supporting Beliefs: An Experimental Trial of the Steps to Respect Program

ERIC Educational Resources Information Center

Frey, Karin S.; Hirschstein, Miriam K.; Snell, Jennie L.; Edstrom, Leihua Van Schoiack; MacKenzie, Elizabeth P.; Broderick, Carole J.

2005-01-01

Six schools were randomly assigned to a multilevel bullying intervention or a control condition. Children in Grades 3-6 (N=1,023) completed pre- and posttest surveys of behaviors and beliefs and were rated by teachers. Observers coded playground behavior of a random subsample (n=544). Hierarchical analyses of changes in playground behavior…

Evaluation of Model Specification, Variable Selection, and Adjustment Methods in Relation to Propensity Scores and Prognostic Scores in Multilevel Data

ERIC Educational Resources Information Center

Yu, Bing; Hong, Guanglei

2012-01-01

This study uses simulation examples representing three types of treatment assignment mechanisms in data generation (the random intercept and slopes setting, the random intercept setting, and a third setting with a cluster-level treatment and an individual-level outcome) in order to determine optimal procedures for reducing bias and improving…

Mediation and Spillover Effects in Group-Randomized Trials with Application to the 4Rs Evaluation

ERIC Educational Resources Information Center

VanderWeele, Tyler J.; Hong, Guanglei; Jones, Stephanie M.; Brown, Joshua L.

2011-01-01

In this paper the authors extend recent work on mediation in a multilevel setting and on causal inference under interference among units to develop a template for the mediation analysis of group randomized educational interventions. The present work will contribute to the literature on interference, in particular on interference in the context of…

A Community-Based Randomized Trial of Hepatitis B Screening Among High-Risk Vietnamese Americans.

PubMed

Ma, Grace X; Fang, Carolyn Y; Seals, Brenda; Feng, Ziding; Tan, Yin; Siu, Philip; Yeh, Ming Chin; Golub, Sarit A; Nguyen, Minhhuyen T; Tran, Tam; Wang, Minqi

2017-03-01

To evaluate the effectiveness of a community-based liver cancer prevention program on hepatitis B virus (HBV) screening among low-income, underserved Vietnamese Americans at high risk. We conducted a cluster randomized trial involving 36 Vietnamese community-based organizations and 2337 participants in Pennsylvania, New Jersey, and New York City between 2009 and 2014. We randomly assigned 18 community-based organizations to a community-based multilevel HBV screening intervention (n = 1131). We randomly assigned the remaining 18 community-based organizations to a general cancer education program (n = 1206), which included information about HBV-related liver cancer prevention. We assessed HBV screening rates at 6-month follow-up. Intervention participants were significantly more likely to have undergone HBV screening (88.1%) than were control group participants (4.6%). In a Cochran-Mantel-Haenszel analysis, the intervention effect on screening outcomes remained statistically significant after adjustment for demographic and health care access variables, including income, having health insurance, having a regular health provider, and English proficiency. A community-based, culturally appropriate, multilevel HBV screening intervention effectively increases screening rates in a high-risk, hard-to-reach Vietnamese American population.

Method of Grassland Information Extraction Based on Multi-Level Segmentation and Cart Model

NASA Astrophysics Data System (ADS)

Qiao, Y.; Chen, T.; He, J.; Wen, Q.; Liu, F.; Wang, Z.

2018-04-01

It is difficult to extract grassland accurately by traditional classification methods, such as supervised method based on pixels or objects. This paper proposed a new method combing the multi-level segmentation with CART (classification and regression tree) model. The multi-level segmentation which combined the multi-resolution segmentation and the spectral difference segmentation could avoid the over and insufficient segmentation seen in the single segmentation mode. The CART model was established based on the spectral characteristics and texture feature which were excavated from training sample data. Xilinhaote City in Inner Mongolia Autonomous Region was chosen as the typical study area and the proposed method was verified by using visual interpretation results as approximate truth value. Meanwhile, the comparison with the nearest neighbor supervised classification method was obtained. The experimental results showed that the total precision of classification and the Kappa coefficient of the proposed method was 95 % and 0.9, respectively. However, the total precision of classification and the Kappa coefficient of the nearest neighbor supervised classification method was 80 % and 0.56, respectively. The result suggested that the accuracy of classification proposed in this paper was higher than the nearest neighbor supervised classification method. The experiment certificated that the proposed method was an effective extraction method of grassland information, which could enhance the boundary of grassland classification and avoid the restriction of grassland distribution scale. This method was also applicable to the extraction of grassland information in other regions with complicated spatial features, which could avoid the interference of woodland, arable land and water body effectively.

Patient satisfaction with ambulatory care in Germany: effects of patient- and medical practice-related factors.

PubMed

Auras, Silke; Ostermann, Thomas; de Cruppé, Werner; Bitzer, Eva-Maria; Diel, Franziska; Geraedts, Max

2016-12-01

The study aimed to illustrate the effect of the patients' sex, age, self-rated health and medical practice specialization on patient satisfaction. Secondary analysis of patient survey data using multilevel analysis (generalized linear mixed model, medical practice as random effect) using a sequential modelling strategy. We examined the effects of the patients' sex, age, self-rated health and medical practice specialization on four patient satisfaction dimensions: medical practice organization, information, interaction, professional competence. The study was performed in 92 German medical practices providing ambulatory care in general medicine, internal medicine or gynaecology. In total, 9888 adult patients participated in a patient survey using the validated 'questionnaire on satisfaction with ambulatory care-quality from the patient perspective [ZAP]'. We calculated four models for each satisfaction dimension, revealing regression coefficients with 95% confidence intervals (CIs) for all independent variables, and using Wald Chi-Square statistic for each modelling step (model validity) and LR-Tests to compare the models of each step with the previous model. The patients' sex and age had a weak effect (maximum regression coefficient 1.09, CI 0.39; 1.80), and the patients' self-rated health had the strongest positive effect (maximum regression coefficient 7.66, CI 6.69; 8.63) on satisfaction ratings. The effect of medical practice specialization was heterogeneous. All factors studied, specifically the patients' self-rated health, affected patient satisfaction. Adjustment should always be considered because it improves the comparability of patient satisfaction in medical practices with atypically varying patient populations and increases the acceptance of comparisons. © The Author 2016. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Random effects coefficient of determination for mixed and meta-analysis models

PubMed Central

Demidenko, Eugene; Sargent, James; Onega, Tracy

2011-01-01

The key feature of a mixed model is the presence of random effects. We have developed a coefficient, called the random effects coefficient of determination, Rr2, that estimates the proportion of the conditional variance of the dependent variable explained by random effects. This coefficient takes values from 0 to 1 and indicates how strong the random effects are. The difference from the earlier suggested fixed effects coefficient of determination is emphasized. If Rr2 is close to 0, there is weak support for random effects in the model because the reduction of the variance of the dependent variable due to random effects is small; consequently, random effects may be ignored and the model simplifies to standard linear regression. The value of Rr2 apart from 0 indicates the evidence of the variance reduction in support of the mixed model. If random effects coefficient of determination is close to 1 the variance of random effects is very large and random effects turn into free fixed effects—the model can be estimated using the dummy variable approach. We derive explicit formulas for Rr2 in three special cases: the random intercept model, the growth curve model, and meta-analysis model. Theoretical results are illustrated with three mixed model examples: (1) travel time to the nearest cancer center for women with breast cancer in the U.S., (2) cumulative time watching alcohol related scenes in movies among young U.S. teens, as a risk factor for early drinking onset, and (3) the classic example of the meta-analysis model for combination of 13 studies on tuberculosis vaccine. PMID:23750070

Multilevel Monte Carlo for two phase flow and Buckley–Leverett transport in random heterogeneous porous media

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

Müller, Florian, E-mail: florian.mueller@sam.math.ethz.ch; Jenny, Patrick, E-mail: jenny@ifd.mavt.ethz.ch; Meyer, Daniel W., E-mail: meyerda@ethz.ch

2013-10-01

Monte Carlo (MC) is a well known method for quantifying uncertainty arising for example in subsurface flow problems. Although robust and easy to implement, MC suffers from slow convergence. Extending MC by means of multigrid techniques yields the multilevel Monte Carlo (MLMC) method. MLMC has proven to greatly accelerate MC for several applications including stochastic ordinary differential equations in finance, elliptic stochastic partial differential equations and also hyperbolic problems. In this study, MLMC is combined with a streamline-based solver to assess uncertain two phase flow and Buckley–Leverett transport in random heterogeneous porous media. The performance of MLMC is compared tomore » MC for a two dimensional reservoir with a multi-point Gaussian logarithmic permeability field. The influence of the variance and the correlation length of the logarithmic permeability on the MLMC performance is studied.« less

The Effectiveness of a Group Triple P with Chinese Parents Who Have a Child with Developmental Disabilities: A Randomized Controlled Trial

ERIC Educational Resources Information Center

Leung, Cynthia; Fan, Angel; Sanders, Matthew R.

2013-01-01

The study examined the effectiveness of Group Triple P, a Level 4 variant of the Triple P multilevel system of parenting support, with Chinese parents who had a preschool aged child with a developmental disability, using randomized controlled trial design. Participants (Intervention group: 42; Waitlist Control group: 39) completed measures on…

Performance of time-varying predictors in multilevel models under an assumption of fixed or random effects.

PubMed

Baird, Rachel; Maxwell, Scott E

2016-06-01

Time-varying predictors in multilevel models are a useful tool for longitudinal research, whether they are the research variable of interest or they are controlling for variance to allow greater power for other variables. However, standard recommendations to fix the effect of time-varying predictors may make an assumption that is unlikely to hold in reality and may influence results. A simulation study illustrates that treating the time-varying predictor as fixed may allow analyses to converge, but the analyses have poor coverage of the true fixed effect when the time-varying predictor has a random effect in reality. A second simulation study shows that treating the time-varying predictor as random may have poor convergence, except when allowing negative variance estimates. Although negative variance estimates are uninterpretable, results of the simulation show that estimates of the fixed effect of the time-varying predictor are as accurate for these cases as for cases with positive variance estimates, and that treating the time-varying predictor as random and allowing negative variance estimates performs well whether the time-varying predictor is fixed or random in reality. Because of the difficulty of interpreting negative variance estimates, 2 procedures are suggested for selection between fixed-effect and random-effect models: comparing between fixed-effect and constrained random-effect models with a likelihood ratio test or fitting a fixed-effect model when an unconstrained random-effect model produces negative variance estimates. The performance of these 2 procedures is compared. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

The interactive effects of housing and neighbourhood quality on psychological well-being.

PubMed

Jones-Rounds, McKenzie L; Evans, Gary W; Braubach, Matthias

2014-02-01

Many individuals are subject to the physically and mentally detrimental effects of living in substandard housing and inadequate neighbourhoods. We propose that better physical neighbourhood quality can partially offset some of the negative effects of poor housing quality on psychological well-being. Interviews and questionnaires were used to collect data in a cross-sectional study of housing quality, the state of the surrounding environment, and individual health and well-being for 5605 European adults from the Large Analysis and Review of European housing and health Status conducted by WHO in eight European cities. Multilevel random coefficient modelling was used to statistically analyse the main and interactive effects of housing quality and neighbourhood quality on psychological well-being. Socioeconomic status, employment status, gender and marital status were included as statistical controls. Substandard housing quality and poor neighbourhood quality each contribute to lower psychological well-being. Furthermore better neighbourhood quality buffers against the negative effects of poor housing quality on psychological well-being. These results fill a gap in research concerning the ability of neighbourhood quality to amplify or attenuate housing quality impacts on well-being.

Evidence for universality in phenomenological emotion response system coherence.

PubMed

Matsumoto, David; Nezlek, John B; Koopmann, Birgit

2007-02-01

The authors reanalyzed data from Scherer and Wallbott's (Scherer, 1997b; Scherer & Wallbott, 1994) International Study of Emotion Antecedents and Reactions to examine how phenomenological reports of emotional experience, expression, and physiological sensations were related to each other within cultures and to determine if these relationships were moderated by cultural differences, which were operationally defined using Hofstede's (2001) typology. Multilevel random coefficient modeling analyses produced several findings of note. First, the vast majority of the variance in ratings was within countries (i.e., at the individual level); a much smaller proportion of the total variance was between countries. Second, there were negative relationships between country-level means and long- versus short-term orientation for numerous measures. Greater long-term orientation was associated with lowered emotional expressivity and fewer physiological sensations. Third, at the individual (within-culture) level, across the 7 emotions, there were consistent and reliable positive relationships among the response systems, indicating coherence among them. Fourth, such relationships were not moderated by cultural differences, as measured by the Hofstede dimensions. (c) 2007 APA, all rights reserved.

Perceived collective burnout: a multilevel explanation of burnout.

PubMed

González-Morales, M Gloria; Peiró, José M; Rodríguez, Isabel; Bliese, Paul D

2012-01-01

Building up on the socially induced model of burnout and the job demands-resources model, we examine how burnout can transfer without direct contagion or close contact among employees. Based on the social information processing approach and the conservation of resources theory, we propose that perceived collective burnout emerges as an organizational-level construct (employees' shared perceptions about how burned out are their colleagues) and that it predicts individual burnout over and above indicators of demands and resources. Data were gathered during the first term and again during the last term of the academic year among 555 teachers from 100 schools. The core dimensions of burnout, exhaustion, and cynicism were measured at the individual and collective level. Random coefficient models were computed in a lagged effects design. Results showed that perceived collective burnout at Time 1 was a significant predictor of burnout at Time 2 after considering previous levels of burnout, demands (workload, teacher-student ratio, and absenteeism rates), and resources (quality of school facilities). These findings suggest that perceived collective burnout is an important characteristic of the work environment that can be a significant factor in the development of burnout.

On "feeling right" in cultural contexts: how person-culture match affects self-esteem and subjective well-being.

PubMed

Fulmer, C Ashley; Gelfand, Michele J; Kruglanski, Arie W; Kim-Prieto, Chu; Diener, Ed; Pierro, Antonio; Higgins, E Tory

2010-11-01

Whether one is in one's native culture or abroad, one's personality can differ markedly from the personalities of the majority, thus failing to match the "cultural norm." Our studies examined how the interaction of individual- and cultural-level personality affects people's self-esteem and well-being. We propose a person-culture match hypothesis that predicts that when a person's personality matches the prevalent personalities of other people in a culture, culture functions as an important amplifier of the positive effect of personality on self-esteem and subjective well-being at the individual level. Across two studies, using data from more than 7,000 individuals from 28 societies, multilevel random-coefficient analyses showed that when a relation between a given personality trait and well-being or self-esteem exists at the individual level, the relation is stronger in cultures characterized by high levels of that personality dimension. Results were replicated across extraversion, promotion focus, and locomotive regulatory mode. Our research has practical implications for the well-being of both cultural natives and migrants.

Neighborhood influences on the association between maternal age and birthweight: a multilevel investigation of age-related disparities in health.

PubMed

Cerdá, Magdalena; Buka, Stephen L; Rich-Edwards, Janet W

2008-05-01

It was hypothesized that the relationship between maternal age and infant birthweight varies significantly across neighborhoods and that such variation can be predicted by neighborhood characteristics. We analyzed 229,613 singleton births of mothers aged 20-45 years from Chicago, USA in 1997-2002. Random coefficient models were used to estimate the between-neighborhood variation in age-birthweight slopes, and both intercepts- and-slopes-as-outcomes models were used to evaluate area-level predictors of such variation. The crude maternal age-birthweight slopes for neighborhoods ranged from a decrease of 17 g to an increase of 10 g per year of maternal age. Adjustment for individual-level covariates reduced but did not eliminate this between-neighborhood variation. Concentrated poverty was a significant neighborhood-level predictor of the age-birthweight slope, explaining 44.4% of the between-neighborhood variation in slopes. Neighborhoods of higher economic disadvantage showed a more negative age-birthweight slope. The findings support the hypothesis that the relationship between maternal age and birthweight varies between neighborhoods. Indicators of neighborhood disadvantage help to explain such differences.

Two-bit multi-level phase change random access memory with a triple phase change material stack structure

NASA Astrophysics Data System (ADS)

Gyanathan, Ashvini; Yeo, Yee-Chia

2012-11-01

This work demonstrates a novel two-bit multi-level device structure comprising three phase change material (PCM) layers, separated by SiN thermal barrier layers. This triple PCM stack consisted of (from bottom to top), Ge2Sb2Te5 (GST), an ultrathin SiN barrier, nitrogen-doped GST, another ultrathin SiN barrier, and Ag0.5In0.5Sb3Te6. The PCM layers can selectively amorphize to form 4 different resistance levels ("00," "01," "10," and "11") using respective voltage pulses. Electrical characterization was extensively performed on these devices. Thermal analysis was also done to understand the physics behind the phase changing characteristics of the two-bit memory devices. The melting and crystallization temperatures of the PCMs play important roles in the power consumption of the multi-level devices. The electrical resistivities and thermal conductivities of the PCMs and the SiN thermal barrier are also crucial factors contributing to the phase changing behaviour of the PCMs in the two-bit multi-level PCRAM device.

Assessing intervention fidelity in a multi-level, multi-component, multi-site program: the Children's Healthy Living (CHL) program.

PubMed

Butel, Jean; Braun, Kathryn L; Novotny, Rachel; Acosta, Mark; Castro, Rose; Fleming, Travis; Powers, Julianne; Nigg, Claudio R

2015-12-01

Addressing complex chronic disease prevention, like childhood obesity, requires a multi-level, multi-component culturally relevant approach with broad reach. Models are lacking to guide fidelity monitoring across multiple levels, components, and sites engaged in such interventions. The aim of this study is to describe the fidelity-monitoring approach of The Children's Healthy Living (CHL) Program, a multi-level multi-component intervention in five Pacific jurisdictions. A fidelity-monitoring rubric was developed. About halfway during the intervention, community partners were randomly selected and interviewed independently by local CHL staff and by Coordinating Center representatives to assess treatment fidelity. Ratings were compared and discussed by local and Coordinating Center staff. There was good agreement between the teams (Kappa = 0.50, p < 0.001), and intervention improvement opportunities were identified through data review and group discussion. Fidelity for the multi-level, multi-component, multi-site CHL intervention was successfully assessed, identifying adaptations as well as ways to improve intervention delivery prior to the end of the intervention.

Covariate Selection for Multilevel Models with Missing Data

PubMed Central

Marino, Miguel; Buxton, Orfeu M.; Li, Yi

2017-01-01

Missing covariate data hampers variable selection in multilevel regression settings. Current variable selection techniques for multiply-imputed data commonly address missingness in the predictors through list-wise deletion and stepwise-selection methods which are problematic. Moreover, most variable selection methods are developed for independent linear regression models and do not accommodate multilevel mixed effects regression models with incomplete covariate data. We develop a novel methodology that is able to perform covariate selection across multiply-imputed data for multilevel random effects models when missing data is present. Specifically, we propose to stack the multiply-imputed data sets from a multiple imputation procedure and to apply a group variable selection procedure through group lasso regularization to assess the overall impact of each predictor on the outcome across the imputed data sets. Simulations confirm the advantageous performance of the proposed method compared with the competing methods. We applied the method to reanalyze the Healthy Directions-Small Business cancer prevention study, which evaluated a behavioral intervention program targeting multiple risk-related behaviors in a working-class, multi-ethnic population. PMID:28239457

Detecting Intervention Effects in a Cluster-Randomized Design Using Multilevel Structural Equation Modeling for Binary Responses

PubMed Central

Cho, Sun-Joo; Preacher, Kristopher J.; Bottge, Brian A.

2015-01-01

Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test–post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test scores that are most often used in MLM are summed item responses (or total scores). In prior research, there have been concerns regarding measurement error in the use of total scores in using MLM. To correct for measurement error in the covariate and outcome, a theoretical justification for the use of multilevel structural equation modeling (MSEM) has been established. However, MSEM for binary responses has not been widely applied to detect intervention effects (group differences) in intervention studies. In this article, the use of MSEM for intervention studies is demonstrated and the performance of MSEM is evaluated via a simulation study. Furthermore, the consequences of using MLM instead of MSEM are shown in detecting group differences. Results of the simulation study showed that MSEM performed adequately as the number of clusters, cluster size, and intraclass correlation increased and outperformed MLM for the detection of group differences. PMID:29881032

Weight gain prevention in the school worksite setting: Results of a multi-level cluster randomized trial

PubMed Central

Lemon, Stephenie C.; Wang, Monica L.; Wedick, Nicole M.; Estabrook, Barbara; Druker, Susan; Schneider, Kristin L.; Li, Wenjun; Pbert, Lori

2014-01-01

Objective To describe the effectiveness, reach and implementation of a weight gain prevention intervention among public school employees. Method A multi-level intervention was tested in a cluster randomized trial among 782 employees in 12 central Massachusetts public high schools from 2009 to 2012. The intervention targeted the nutrition and physical activity environment and policies, the social environment and individual knowledge, attitudes and skills. The intervention was compared to a materials only condition. The primary outcome measures were change in weight and body mass index (BMI) at 24-month follow-up. Implementation of physical environment, policy and social environment strategies at the school and interpersonal levels, and intervention participation at the individual level were assessed. Results At 24-month follow-up, there was a net change (difference of the difference) of −3.03 pounds (p=.04) and of −.48 BMI units (p=.05) between intervention and comparison conditions. The majority of intervention strategies were successfully implemented by all intervention schools, although establishing formal policies was challenging. Employee participation in programs targeting the physical and social environment was maintained over time. Conclusion This study supports that a multi-level intervention integrated within the organizational culture can be successfully implemented and prevent weight gain in public high school employees. PMID:24345602

Detecting Intervention Effects in a Cluster-Randomized Design Using Multilevel Structural Equation Modeling for Binary Responses.

PubMed

Cho, Sun-Joo; Preacher, Kristopher J; Bottge, Brian A

2015-11-01

Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test-post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test scores that are most often used in MLM are summed item responses (or total scores). In prior research, there have been concerns regarding measurement error in the use of total scores in using MLM. To correct for measurement error in the covariate and outcome, a theoretical justification for the use of multilevel structural equation modeling (MSEM) has been established. However, MSEM for binary responses has not been widely applied to detect intervention effects (group differences) in intervention studies. In this article, the use of MSEM for intervention studies is demonstrated and the performance of MSEM is evaluated via a simulation study. Furthermore, the consequences of using MLM instead of MSEM are shown in detecting group differences. Results of the simulation study showed that MSEM performed adequately as the number of clusters, cluster size, and intraclass correlation increased and outperformed MLM for the detection of group differences.

Multilevel Models for Intensive Longitudinal Data with Heterogeneous Autoregressive Errors: The Effect of Misspecification and Correction with Cholesky Transformation

PubMed Central

Jahng, Seungmin; Wood, Phillip K.

2017-01-01

Intensive longitudinal studies, such as ecological momentary assessment studies using electronic diaries, are gaining popularity across many areas of psychology. Multilevel models (MLMs) are most widely used analytical tools for intensive longitudinal data (ILD). Although ILD often have individually distinct patterns of serial correlation of measures over time, inferences of the fixed effects, and random components in MLMs are made under the assumption that all variance and autocovariance components are homogenous across individuals. In the present study, we introduced a multilevel model with Cholesky transformation to model ILD with individually heterogeneous covariance structure. In addition, the performance of the transformation method and the effects of misspecification of heterogeneous covariance structure were investigated through a Monte Carlo simulation. We found that, if individually heterogeneous covariances are incorrectly assumed as homogenous independent or homogenous autoregressive, MLMs produce highly biased estimates of the variance of random intercepts and the standard errors of the fixed intercept and the fixed effect of a level 2 covariate when the average autocorrelation is high. For intensive longitudinal data with individual specific residual covariance, the suggested transformation method showed lower bias in those estimates than the misspecified models when the number of repeated observations within individuals is 50 or more. PMID:28286490

Work-related CBT versus vocational services as usual for unemployed persons with social anxiety disorder: A randomized controlled pilot trial.

PubMed

Himle, Joseph A; Bybee, Deborah; Steinberger, Edward; Laviolette, Wayne T; Weaver, Addie; Vlnka, Sarah; Golenberg, Zipora; Levine, Debra Siegel; Heimberg, Richard G; O'Donnell, Lisa A

2014-12-01

We designed and pilot-tested a group-based, work-related cognitive-behavioral therapy (WCBT) for unemployed individuals with social anxiety disorder (SAD). WCBT, delivered in a vocational service setting by vocational service professionals, aims to reduce social anxiety and enable individuals to seek, obtain, and retain employment. We compared WCBT to a vocational services as usual control condition (VSAU). Participants were unemployed, homeless, largely African American, vocational service-seeking adults with SAD (N = 58), randomized to receive either eight sessions of WCBT plus VSAU or VSAU alone and followed three months post-treatment. Multilevel modeling revealed significantly greater reductions in social anxiety, general anxiety, depression, and functional impairment for WCBT compared to VSAU. Coefficients for job search activity and self-efficacy indicated greater increases for WCBT. Hours worked per week in the follow-up period did not differ between the groups, but small sample size and challenges associated with measuring work hours may have contributed to this finding. Overall, the results of this study suggest that unemployed persons with SAD can be effectively treated with specialized work-related CBT administered by vocational service professionals. Future testing of WCBT with a larger sample, a longer follow-up period, and adequate power to assess employment outcomes is warranted. Copyright © 2014 Elsevier Ltd. All rights reserved.

Assessing dose–response effects of national essential medicine policy in China: comparison of two methods for handling data with a stepped wedge-like design and hierarchical structure

PubMed Central

Ren, Yan; Yang, Min; Li, Qian; Pan, Jay; Chen, Fei; Li, Xiaosong; Meng, Qun

2017-01-01

Objectives To introduce multilevel repeated measures (RM) models and compare them with multilevel difference-in-differences (DID) models in assessing the linear relationship between the length of the policy intervention period and healthcare outcomes (dose–response effect) for data from a stepped-wedge design with a hierarchical structure. Design The implementation of national essential medicine policy (NEMP) in China was a stepped-wedge-like design of five time points with a hierarchical structure. Using one key healthcare outcome from the national NEMP surveillance data as an example, we illustrate how a series of multilevel DID models and one multilevel RM model can be fitted to answer some research questions on policy effects. Setting Routinely and annually collected national data on China from 2008 to 2012. Participants 34 506 primary healthcare facilities in 2675 counties of 31 provinces. Outcome measures Agreement and differences in estimates of dose–response effect and variation in such effect between the two methods on the logarithm-transformed total number of outpatient visits per facility per year (LG-OPV). Results The estimated dose–response effect was approximately 0.015 according to four multilevel DID models and precisely 0.012 from one multilevel RM model. Both types of model estimated an increase in LG-OPV by 2.55 times from 2009 to 2012, but 2–4.3 times larger SEs of those estimates were found by the multilevel DID models. Similar estimates of mean effects of covariates and random effects of the average LG-OPV among all levels in the example dataset were obtained by both types of model. Significant variances in the dose–response among provinces, counties and facilities were estimated, and the ‘lowest’ or ‘highest’ units by their dose–response effects were pinpointed only by the multilevel RM model. Conclusions For examining dose–response effect based on data from multiple time points with hierarchical structure and the stepped wedge-like designs, multilevel RM models are more efficient, convenient and informative than the multilevel DID models. PMID:28399510

Kids Safe and Smokefree (KiSS): a randomized controlled trial of a multilevel intervention to reduce secondhand tobacco smoke exposure in children.

PubMed

Lepore, Stephen J; Winickoff, Jonathan P; Moughan, Beth; Bryant-Stephens, Tyra C; Taylor, Daniel R; Fleece, David; Davey, Adam; Nair, Uma S; Godfrey, Melissa; Collins, Bradley N

2013-08-30

Secondhand smoke exposure (SHSe) harms children's health, yet effective interventions to reduce child SHSe in the home and car have proven difficult to operationalize in pediatric practice. A multilevel intervention combining pediatric healthcare providers' advice with behavioral counseling and navigation to pharmacological cessation aids may improve SHSe control in pediatric populations. This trial uses a randomized, two-group design with three measurement periods: pre-intervention, end of treatment and 12-month follow-up. Smoking parents of children < 11-years-old are recruited from pediatric clinics. The clinic-level intervention includes integrating tobacco intervention guideline prompts into electronic health record screens. The prompts guide providers to ask all parents about child SHSe, advise about SHSe harms, and refer smokers to cessation resources. After receiving clinic intervention, eligible parents are randomized to receive: (a) a 3-month telephone-based behavioral counseling intervention designed to promote reduction in child SHSe, parent smoking cessation, and navigation to access nicotine replacement therapy or cessation medication or (b) an attention control nutrition education intervention. Healthcare providers and assessors are blind to group assignment. Cotinine is used to bioverify child SHSe (primary outcome) and parent quit status. This study tests an innovative multilevel approach to reducing child SHSe. The approach is sustainable, because clinics can easily integrate the tobacco intervention prompts related to "ask, advise, and refer" guidelines into electronic health records and refer smokers to free evidence-based behavioral counseling interventions, such as state quitlines. NCT01745393 (clinicaltrials.gov).

Kids Safe and Smokefree (KiSS): a randomized controlled trial of a multilevel intervention to reduce secondhand tobacco smoke exposure in children

PubMed Central

2013-01-01

Background Secondhand smoke exposure (SHSe) harms children’s health, yet effective interventions to reduce child SHSe in the home and car have proven difficult to operationalize in pediatric practice. A multilevel intervention combining pediatric healthcare providers’ advice with behavioral counseling and navigation to pharmacological cessation aids may improve SHSe control in pediatric populations. Methods/design This trial uses a randomized, two-group design with three measurement periods: pre-intervention, end of treatment and 12-month follow-up. Smoking parents of children < 11-years-old are recruited from pediatric clinics. The clinic-level intervention includes integrating tobacco intervention guideline prompts into electronic health record screens. The prompts guide providers to ask all parents about child SHSe, advise about SHSe harms, and refer smokers to cessation resources. After receiving clinic intervention, eligible parents are randomized to receive: (a) a 3-month telephone-based behavioral counseling intervention designed to promote reduction in child SHSe, parent smoking cessation, and navigation to access nicotine replacement therapy or cessation medication or (b) an attention control nutrition education intervention. Healthcare providers and assessors are blind to group assignment. Cotinine is used to bioverify child SHSe (primary outcome) and parent quit status. Discussion This study tests an innovative multilevel approach to reducing child SHSe. The approach is sustainable, because clinics can easily integrate the tobacco intervention prompts related to “ask, advise, and refer” guidelines into electronic health records and refer smokers to free evidence-based behavioral counseling interventions, such as state quitlines. Trial registration NCT01745393 (clinicaltrials.gov). PMID:23987302

Effect of a smoking cessation intervention for women in subsidized neighborhoods: A randomized controlled trial.

PubMed

Andrews, Jeannette O; Mueller, Martina; Dooley, Mary; Newman, Susan D; Magwood, Gayenell S; Tingen, Martha S

2016-09-01

To evaluate the effectiveness of a community based participatory research (CBPR) developed, multi-level smoking cessation intervention among women in subsidized housing neighborhoods in the Southeastern US. A total of n=409 women in 14 subsidized housing neighborhoods in Georgia and South Carolina participated in this group randomized controlled trial conducted from 2009 to 2013. Intervention neighborhoods received a 24-week intervention with 1:1 community health worker contact, behavioral peer group sessions, and nicotine replacement. Control neighborhoods received written cessation materials at weeks 1, 6, 12, 18. Random coefficient models were used to compare smoking abstinence outcomes at 6 and 12months. Significance was set a p<0.05. The majority of participants (91.2%) were retained during the 12-month intervention period. Smoking abstinence rates at 12months for intervention vs. control were 9% vs. 4.3%, p=0.05. Additional analyses accounting for passive smoke exposure in these multi-unit housing settings demonstrated 12month abstinence rates of 12% vs. 5.3%, p=0.016. However, in the multivariate regression analyses, there was no significant effect of the intervention on the odds of being a non-smoker (OR=0.44, 95% CI: 0.18-1.07). Intervention participants who kept coach visits, attended group sessions, and used patches were more likely to remain abstinent. This CBPR developed intervention showed potential to engage smokers and reduce smoking among women in these high-poverty neighborhoods. Effectiveness in promoting cessation in communities burdened with fiscal, environmental and social inequities remains a public health priority. Copyright © 2016 Elsevier Inc. All rights reserved.

Implementation of Hybrid V-Cycle Multilevel Methods for Mixed Finite Element Systems with Penalty

NASA Technical Reports Server (NTRS)

Lai, Chen-Yao G.

1996-01-01

The goal of this paper is the implementation of hybrid V-cycle hierarchical multilevel methods for the indefinite discrete systems which arise when a mixed finite element approximation is used to solve elliptic boundary value problems. By introducing a penalty parameter, the perturbed indefinite system can be reduced to a symmetric positive definite system containing the small penalty parameter for the velocity unknown alone. We stabilize the hierarchical spatial decomposition approach proposed by Cai, Goldstein, and Pasciak for the reduced system. We demonstrate that the relative condition number of the preconditioner is bounded uniformly with respect to the penalty parameter, the number of levels and possible jumps of the coefficients as long as they occur only across the edges of the coarsest elements.

Multilevel Sequential Monte Carlo Samplers for Normalizing Constants

DOE PAGES

Moral, Pierre Del; Jasra, Ajay; Law, Kody J. H.; ...

2017-08-24

This article considers the sequential Monte Carlo (SMC) approximation of ratios of normalizing constants associated to posterior distributions which in principle rely on continuum models. Therefore, the Monte Carlo estimation error and the discrete approximation error must be balanced. A multilevel strategy is utilized to substantially reduce the cost to obtain a given error level in the approximation as compared to standard estimators. Two estimators are considered and relative variance bounds are given. The theoretical results are numerically illustrated for two Bayesian inverse problems arising from elliptic partial differential equations (PDEs). The examples involve the inversion of observations of themore » solution of (i) a 1-dimensional Poisson equation to infer the diffusion coefficient, and (ii) a 2-dimensional Poisson equation to infer the external forcing.« less

Multilevel Methods for Elliptic Problems with Highly Varying Coefficients on Nonaligned Coarse Grids

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

Scheichl, Robert; Vassilevski, Panayot S.; Zikatanov, Ludmil T.

2012-06-21

We generalize the analysis of classical multigrid and two-level overlapping Schwarz methods for 2nd order elliptic boundary value problems to problems with large discontinuities in the coefficients that are not resolved by the coarse grids or the subdomain partition. The theoretical results provide a recipe for designing hierarchies of standard piecewise linear coarse spaces such that the multigrid convergence rate and the condition number of the Schwarz preconditioned system do not depend on the coefficient variation or on any mesh parameters. One assumption we have to make is that the coarse grids are sufficiently fine in the vicinity of crossmore » points or where regions with large diffusion coefficients are separated by a narrow region where the coefficient is small. We do not need to align them with possible discontinuities in the coefficients. The proofs make use of novel stable splittings based on weighted quasi-interpolants and weighted Poincaré-type inequalities. Finally, numerical experiments are included that illustrate the sharpness of the theoretical bounds and the necessity of the technical assumptions.« less

An instrumental variable random-coefficients model for binary outcomes

PubMed Central

Chesher, Andrew; Rosen, Adam M

2014-01-01

In this paper, we study a random-coefficients model for a binary outcome. We allow for the possibility that some or even all of the explanatory variables are arbitrarily correlated with the random coefficients, thus permitting endogeneity. We assume the existence of observed instrumental variables Z that are jointly independent with the random coefficients, although we place no structure on the joint determination of the endogenous variable X and instruments Z, as would be required for a control function approach. The model fits within the spectrum of generalized instrumental variable models, and we thus apply identification results from our previous studies of such models to the present context, demonstrating their use. Specifically, we characterize the identified set for the distribution of random coefficients in the binary response model with endogeneity via a collection of conditional moment inequalities, and we investigate the structure of these sets by way of numerical illustration. PMID:25798048

Random effects coefficient of determination for mixed and meta-analysis models.

PubMed

Demidenko, Eugene; Sargent, James; Onega, Tracy

2012-01-01

The key feature of a mixed model is the presence of random effects. We have developed a coefficient, called the random effects coefficient of determination, [Formula: see text], that estimates the proportion of the conditional variance of the dependent variable explained by random effects. This coefficient takes values from 0 to 1 and indicates how strong the random effects are. The difference from the earlier suggested fixed effects coefficient of determination is emphasized. If [Formula: see text] is close to 0, there is weak support for random effects in the model because the reduction of the variance of the dependent variable due to random effects is small; consequently, random effects may be ignored and the model simplifies to standard linear regression. The value of [Formula: see text] apart from 0 indicates the evidence of the variance reduction in support of the mixed model. If random effects coefficient of determination is close to 1 the variance of random effects is very large and random effects turn into free fixed effects-the model can be estimated using the dummy variable approach. We derive explicit formulas for [Formula: see text] in three special cases: the random intercept model, the growth curve model, and meta-analysis model. Theoretical results are illustrated with three mixed model examples: (1) travel time to the nearest cancer center for women with breast cancer in the U.S., (2) cumulative time watching alcohol related scenes in movies among young U.S. teens, as a risk factor for early drinking onset, and (3) the classic example of the meta-analysis model for combination of 13 studies on tuberculosis vaccine.

Islanding detection technique using wavelet energy in grid-connected PV system

NASA Astrophysics Data System (ADS)

Kim, Il Song

2016-08-01

This paper proposes a new islanding detection method using wavelet energy in a grid-connected photovoltaic system. The method detects spectral changes in the higher-frequency components of the point of common coupling voltage and obtains wavelet coefficients by multilevel wavelet analysis. The autocorrelation of the wavelet coefficients can clearly identify islanding detection, even in the variations of the grid voltage harmonics during normal operating conditions. The advantage of the proposed method is that it can detect islanding condition the conventional under voltage/over voltage/under frequency/over frequency methods fail to detect. The theoretical method to obtain wavelet energies is evolved and verified by the experimental result.

Improvement of the Work Environment and Work-Related Stress: A Cross-Sectional Multilevel Study of a Nationally Representative Sample of Japanese Workers.

PubMed

Watanabe, Kazuhiro; Tabuchi, Takahiro; Kawakami, Norito

2017-03-01

This cross-sectional multilevel study aimed to investigate the relationship between improvement of the work environment and work-related stress in a nationally representative sample in Japan. The study was based on a national survey that randomly sampled 1745 worksites and 17,500 nested employees. The survey asked the worksites whether improvements of the work environment were conducted; and it asked the employees to report the number of work-related stresses they experienced. Multilevel multinominal logistic and linear regression analyses were conducted. Improvement of the work environment was not significantly associated with any level of work-related stress. Among men, it was significantly and negatively associated with the severe level of work-related stress. The association was not significant among women. Improvements to work environments may be associated with reduced work-related stress among men nationwide in Japan.

Affective Balance, Team Prosocial Efficacy and Team Trust: A Multilevel Analysis of Prosocial Behavior in Small Groups.

PubMed

Cuadrado, Esther; Tabernero, Carmen

2015-01-01

Little research has focused on how individual- and team-level characteristics jointly influence, via interaction, how prosocially individuals behave in teams and few studies have considered the potential influence of team context on prosocial behavior. Using a multilevel perspective, we examined the relationships between individual (affective balance) and group (team prosocial efficacy and team trust) level variables and prosocial behavior towards team members. The participants were 123 students nested in 45 small teams. A series of multilevel random models was estimated using hierarchical linear and nonlinear modeling. Individuals were more likely to behave prosocially towards in-group members when they were feeling good. Furthermore, the relationship between positive affective balance and prosocial behavior was stronger in teams with higher team prosocial efficacy levels as well as in teams with higher team trust levels. Finally, the relevance of team trust had a stronger influence on behavior than team prosocial efficacy.

Role Stress and Emotional Exhaustion Among Health Care Workers: The Buffering Effect of Supportive Coworker Climate in a Multilevel Perspective.

PubMed

Portoghese, Igor; Galletta, Maura; Burdorf, Alex; Cocco, Pierluigi; D'Aloja, Ernesto; Campagna, Marcello

2017-10-01

The aim of the study was to examine the relationship between role stress, emotional exhaustion, and a supportive coworker climate among health care workers, by adopting a multilevel perspective. Aggregated data of 738 health care workers nested within 67 teams of three Italian hospitals were collected. Multilevel regression analysis with a random intercept model was used. Hierarchical linear modeling showed that a lack of role clarity was significantly linked to emotional exhaustion at the individual level. At the unit level, the cross-level interaction revealed that a supportive coworker climate moderated the relationship between lack of role clarity and emotional exhaustion. This study supports previous results of single-level burnout studies, extending the existing literature with evidence on the multidimensional and cross-level interaction associations of a supportive coworker climate as a key aspect of job resources on burnout.

Community Audit of Social, Civil, and Activity Domains in Diverse Environments (CASCADDE)

PubMed Central

Knapp, Emily A.; Nau, Claudia; Brandau, Sy; DeWalle, Joseph; Hirsch, Annemarie G.; Bailey-Davis, Lisa; Schwartz, Brian S.; Glass, Thomas A.

2017-01-01

There are currently no direct observation environmental audit tools that measure diverse aspects of the obesity-related environment efficiently and reliably in a variety of geographical settings. The goal was to develop a new instrument to reliably characterize the overall properties and features of rural, suburban, and urban settings along multiple dimensions. The Community Audit of Social, Civil, and Activity Domains in Diverse Environments (CASCADDE) is an iPad-based instrument that incorporates global positioning system coordinates and photography and is comprised of 214 items yielding 7 summary indices. A comprehensive spatial sampling strategy, training manual, and supporting data analysis code were also developed. Random geospatial sampling using GIS was used to captured features of the community as a whole. A single auditor collected 510 observation points in 30 communities (2013–2015). This analysis was done in 2015–2016. Correlation coefficients were used to compare items and indices to each other and to standard measures. Multilevel unconditional means models were used to calculate intraclass correlation coefficients (ICC) to determine if there was significant variation between communities. Results suggest that CASCADDE measures aspects of communities not previously captured by secondary data sources. Additionally, 7 summary indices capture meaningful differences between communities based on 15 observations per community. Community audit tools such as CASCADDE complement secondary data sources and have the potential to offer new insights about the mechanisms through which communities affect obesity and other health outcomes. PMID:28209283

Community Audit of Social, Civil, and Activity Domains in Diverse Environments (CASCADDE).

PubMed

Knapp, Emily A; Nau, Claudia; Brandau, Sy; DeWalle, Joseph; Hirsch, Annemarie G; Bailey-Davis, Lisa; Schwartz, Brian S; Glass, Thomas A

2017-04-01

There are currently no direct observation environmental audit tools that measure diverse aspects of the obesity-related environment efficiently and reliably in a variety of geographic settings. The goal was to develop a new instrument to reliably characterize the overall properties and features of rural, suburban, and urban settings along multiple dimensions. The Community Audit of Social, Civil, and Activity Domains in Diverse Environments (CASCADDE) is an iPad-based instrument that incorporates GPS coordinates and photography and comprises 214 items yielding seven summary indices. A comprehensive spatial sampling strategy, training manual, and supporting data analysis code were also developed. Random geospatial sampling using GIS was used to capture features of the community as a whole. A single auditor collected 510 observation points in 30 communities (2013-2015). This analysis was done in 2015-2016. Correlation coefficients were used to compare items and indices to each other and to standard measures. Multilevel unconditional means models were used to calculate intraclass correlation coefficients to determine if there was significant variation between communities. Results suggest that CASCADDE measures aspects of communities not previously captured by secondary data sources. Additionally, seven summary indices capture meaningful differences between communities based on 15 observations per community. Community audit tools such as CASCADDE complement secondary data sources and have the potential to offer new insights about the mechanisms through which communities affect obesity and other health outcomes. Copyright © 2017 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

A Randomized Controlled Trial of Low-Dose Tranexamic Acid versus Placebo to Reduce Red Blood Cell Transfusion During Complex Multilevel Spine Fusion Surgery.

PubMed

Carabini, Louanne M; Moreland, Natalie C; Vealey, Ryan J; Bebawy, John F; Koski, Tyler R; Koht, Antoun; Gupta, Dhanesh K; Avram, Michael J

2018-02-01

Multilevel spine fusion surgery for adult deformity correction is associated with significant blood loss and coagulopathy. Tranexamic acid reduces blood loss in high-risk surgery, but the efficacy of a low-dose regimen is unknown. Sixty-one patients undergoing multilevel complex spinal fusion with and without osteotomies were randomly assigned to receive low-dose tranexamic acid (10 mg/kg loading dose, then 1 mg·kg -1 ·hr -1 throughout surgery) or placebo. The primary outcome was the total volume of red blood cells transfused intraoperatively. Thirty-one patients received tranexamic acid, and 30 patients received placebo. Patient demographics, risk of major transfusion, preoperative hemoglobin, and surgical risk of the 2 groups were similar. There was a significant decrease in total volume of red blood cells transfused (placebo group median 1460 mL vs. tranexamic acid group 1140 mL; median difference 463 mL, 95% confidence interval 15 to 914 mL, P = 0.034), with a decrease in cell saver transfusion (placebo group median 490 mL vs. tranexamic acid group 256 mL; median difference 166 mL, 95% confidence interval 0 to 368 mL, P = 0.042). The decrease in packed red blood cell transfusion did not reach statistical significance (placebo group median 1050 mL vs. tranexamic acid group 600 mL; median difference 300 mL, 95% confidence interval 0 to 600 mL, P = 0.097). Our results support the use of low-dose tranexamic acid during complex multilevel spine fusion surgery to decrease total red blood cell transfusion. Copyright © 2017 Elsevier Inc. All rights reserved.

The impact of a disease management programme for type 2 diabetes on health-related quality of life: multilevel analysis of a cluster-randomised controlled trial.

PubMed

Panisch, Sigrid; Johansson, Tim; Flamm, Maria; Winkler, Henrike; Weitgasser, Raimund; Sönnichsen, Andreas C

2018-01-01

Type 2 diabetes is a chronic disease associated with poorer health outcomes and decreased health related quality of life (HRQoL). The aim of this analysis was to explore the impact of a disease management programme (DMP) in type 2 diabetes on HRQoL. A multilevel model was used to explain the variation in EQ-VAS. A cluster-randomized controlled trial-analysis of the secondary endpoint HRQoL. Our study population were general practitioners and patients in the province of Salzburg. The DMP "Therapie-Aktiv" was implemented in the intervention group, and controls received usual care. Outcome measure was a change in EQ-VAS after 12 months. For comparison of rates, we used Fisher's Exact test; for continuous variables the independent T test or Welch test were used. In the multilevel modeling, we examined various models, continuously adding variables to explain the variation in the dependent variable, starting with an empty model, including only the random intercept. We analysed random effects parameters in order to disentangle variation of the final EQ-VAS. The EQ-VAS significantly increased within the intervention group (mean difference 2.19, p = 0.005). There was no significant difference in EQ-VAS between groups (mean difference 1.00, p = 0.339). In the intervention group the improvement was more distinct in women (2.46, p = 0.036) compared to men (1.92, p = 0.063). In multilevel modeling, sex, age, family and work circumstances, any macrovascular diabetic complication, duration of diabetes, baseline body mass index and baseline EQ-VAS significantly influence final EQ-VAS, while DMP does not. The final model explains 28.9% (EQ-VAS) of the total variance. Most of the unexplained variance was found on patient-level (95%) and less on GP-level (5%). DMP "Therapie-Aktiv" has no significant impact on final EQ-VAS. The impact of DMPs in type 2 diabetes on HRQoL is still unclear and future programmes should focus on patient specific needs and predictors in order to improve HRQoL. Trial registration Current Controlled trials Ltd., ISRCTN27414162.

The Multigroup Multilevel Categorical Latent Growth Curve Models

ERIC Educational Resources Information Center

Hung, Lai-Fa

2010-01-01

Longitudinal data describe developmental patterns and enable predictions of individual changes beyond sampled time points. Major methodological issues in longitudinal data include modeling random effects, subject effects, growth curve parameters, and autoregressive residuals. This study embedded the longitudinal model within a multigroup…

Multilevel multi-informant structure of the authoritative school climate survey.

PubMed

Konold, Timothy; Cornell, Dewey; Huang, Francis; Meyer, Patrick; Lacey, Anna; Nekvasil, Erin; Heilbrun, Anna; Shukla, Kathan

2014-09-01

The Authoritative School Climate Survey was designed to provide schools with a brief assessment of 2 key characteristics of school climate--disciplinary structure and student support--that are hypothesized to influence 2 important school climate outcomes--student engagement and prevalence of teasing and bullying in school. The factor structure of these 4 constructs was examined with exploratory and confirmatory factor analyses in a statewide sample of 39,364 students (Grades 7 and 8) attending 423 schools. Notably, the analyses used a multilevel structural approach to model the nesting of students in schools for purposes of evaluating factor structure, demonstrating convergent and concurrent validity and gauging the structural invariance of concurrent validity coefficients across gender. These findings provide schools with a core group of school climate measures guided by authoritative discipline theory. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Contemporary New Zealand coefficients for the Trauma Injury Severity Score: TRISS(NZ).

PubMed

Schluter, Philip J; Cameron, Cate M; Davey, Tamzyn M; Civil, Ian; Orchard, Jodie; Dansey, Rangi; Hamill, James; Naylor, Helen; James, Carolyn; Dorrian, Jenny; Christey, Grant; Pollard, Cliff; McClure, Rod J

2009-09-11

To develop local contemporary coefficients for the Trauma Injury Severity Score in New Zealand, TRISS(NZ), and to evaluate their performance at predicting survival against the original TRISS coefficients. Retrospective cohort study of adults who sustained a serious traumatic injury, and who survived until presentation at Auckland City, Middlemore, Waikato, or North Shore Hospitals between 2002 and 2006. Coefficients were estimated using ordinary and multilevel mixed-effects logistic regression models. 1735 eligible patients were identified, 1672 (96%) injured from a blunt mechanism and 63 (4%) from a penetrating mechanism. For blunt mechanism trauma, 1250 (75%) were male and average age was 38 years (range: 15-94 years). TRISS information was available for 1565 patients of whom 204 (13%) died. Area under the Receiver Operating Characteristic (ROC) curves was 0.901 (95%CI: 0.879-0.923) for the TRISS(NZ) model and 0.890 (95% CI: 0.866-0.913) for TRISS (P<0.001). Insufficient data were available to determine coefficients for penetrating mechanism TRISS(NZ) models. Both TRISS models accurately predicted survival for blunt mechanism trauma. However, TRISS(NZ) coefficients were statistically superior to TRISS coefficients. A strong case exists for replacing TRISS coefficients in the New Zealand benchmarking software with these updated TRISS(NZ) estimates.

Estimation of the Nonlinear Random Coefficient Model when Some Random Effects Are Separable

ERIC Educational Resources Information Center

du Toit, Stephen H. C.; Cudeck, Robert

2009-01-01

A method is presented for marginal maximum likelihood estimation of the nonlinear random coefficient model when the response function has some linear parameters. This is done by writing the marginal distribution of the repeated measures as a conditional distribution of the response given the nonlinear random effects. The resulting distribution…

Individual- and Neighbourhood-Level Indicators of Subjective Well-Being in a Small and Poor Eastern Cape Township: The Effect of Health, Social Capital, Marital Status, and Income

ERIC Educational Resources Information Center

Cramm, J. M.; Moller, V.; Nieboer, A. P.

2012-01-01

Our study used multilevel regression analysis to identify individual- and neighbourhood-level factors that determine individual-level subjective well-being in Rhini, a deprived suburb of Grahamstown in the Eastern Cape province of South Africa. The Townsend index and Gini coefficient were used to investigate whether contextual neighbourhood-level…

Assessing dose-response effects of national essential medicine policy in China: comparison of two methods for handling data with a stepped wedge-like design and hierarchical structure.

PubMed

Ren, Yan; Yang, Min; Li, Qian; Pan, Jay; Chen, Fei; Li, Xiaosong; Meng, Qun

2017-02-22

To introduce multilevel repeated measures (RM) models and compare them with multilevel difference-in-differences (DID) models in assessing the linear relationship between the length of the policy intervention period and healthcare outcomes (dose-response effect) for data from a stepped-wedge design with a hierarchical structure. The implementation of national essential medicine policy (NEMP) in China was a stepped-wedge-like design of five time points with a hierarchical structure. Using one key healthcare outcome from the national NEMP surveillance data as an example, we illustrate how a series of multilevel DID models and one multilevel RM model can be fitted to answer some research questions on policy effects. Routinely and annually collected national data on China from 2008 to 2012. 34 506 primary healthcare facilities in 2675 counties of 31 provinces. Agreement and differences in estimates of dose-response effect and variation in such effect between the two methods on the logarithm-transformed total number of outpatient visits per facility per year (LG-OPV). The estimated dose-response effect was approximately 0.015 according to four multilevel DID models and precisely 0.012 from one multilevel RM model. Both types of model estimated an increase in LG-OPV by 2.55 times from 2009 to 2012, but 2-4.3 times larger SEs of those estimates were found by the multilevel DID models. Similar estimates of mean effects of covariates and random effects of the average LG-OPV among all levels in the example dataset were obtained by both types of model. Significant variances in the dose-response among provinces, counties and facilities were estimated, and the 'lowest' or 'highest' units by their dose-response effects were pinpointed only by the multilevel RM model. For examining dose-response effect based on data from multiple time points with hierarchical structure and the stepped wedge-like designs, multilevel RM models are more efficient, convenient and informative than the multilevel DID models. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Adaptive threshold shearlet transform for surface microseismic data denoising

NASA Astrophysics Data System (ADS)

Tang, Na; Zhao, Xian; Li, Yue; Zhu, Dan

2018-06-01

Random noise suppression plays an important role in microseismic data processing. The microseismic data is often corrupted by strong random noise, which would directly influence identification and location of microseismic events. Shearlet transform is a new multiscale transform, which can effectively process the low magnitude of microseismic data. In shearlet domain, due to different distributions of valid signals and random noise, shearlet coefficients can be shrunk by threshold. Therefore, threshold is vital in suppressing random noise. The conventional threshold denoising algorithms usually use the same threshold to process all coefficients, which causes noise suppression inefficiency or valid signals loss. In order to solve above problems, we propose the adaptive threshold shearlet transform (ATST) for surface microseismic data denoising. In the new algorithm, we calculate the fundamental threshold for each direction subband firstly. In each direction subband, the adjustment factor is obtained according to each subband coefficient and its neighboring coefficients, in order to adaptively regulate the fundamental threshold for different shearlet coefficients. Finally we apply the adaptive threshold to deal with different shearlet coefficients. The experimental denoising results of synthetic records and field data illustrate that the proposed method exhibits better performance in suppressing random noise and preserving valid signal than the conventional shearlet denoising method.

Use of multilevel logistic regression to identify the causes of differential item functioning.

PubMed

Balluerka, Nekane; Gorostiaga, Arantxa; Gómez-Benito, Juana; Hidalgo, María Dolores

2010-11-01

Given that a key function of tests is to serve as evaluation instruments and for decision making in the fields of psychology and education, the possibility that some of their items may show differential behaviour is a major concern for psychometricians. In recent decades, important progress has been made as regards the efficacy of techniques designed to detect this differential item functioning (DIF). However, the findings are scant when it comes to explaining its causes. The present study addresses this problem from the perspective of multilevel analysis. Starting from a case study in the area of transcultural comparisons, multilevel logistic regression is used: 1) to identify the item characteristics associated with the presence of DIF; 2) to estimate the proportion of variation in the DIF coefficients that is explained by these characteristics; and 3) to evaluate alternative explanations of the DIF by comparing the explanatory power or fit of different sequential models. The comparison of these models confirmed one of the two alternatives (familiarity with the stimulus) and rejected the other (the topic area) as being a cause of differential functioning with respect to the compared groups.

Testing self-regulation interventions to increase walking using factorial randomized N-of-1 trials.

PubMed

Sniehotta, Falko F; Presseau, Justin; Hobbs, Nicola; Araújo-Soares, Vera

2012-11-01

To investigate the suitability of N-of-1 randomized controlled trials (RCTs) as a means of testing the effectiveness of behavior change techniques based on self-regulation theory (goal setting and self-monitoring) for promoting walking in healthy adult volunteers. A series of N-of-1 RCTs in 10 normal and overweight adults ages 19-67 (M = 36.9 years). We randomly allocated 60 days within each individual to text message-prompted daily goal-setting and/or self-monitoring interventions in accordance with a 2 (step-count goal prompt vs. alternative goal prompt) × 2 (self-monitoring: open vs. blinded Omron-HJ-113-E pedometer) factorial design. Aggregated data were analyzed using random intercept multilevel models. Single cases were analyzed individually. The primary outcome was daily pedometer step counts over 60 days. Single-case analyses showed that 4 participants significantly increased walking: 2 on self-monitoring days and 2 on goal-setting days, compared with control days. Six participants did not benefit from the interventions. In aggregated analyses, mean step counts were higher on goal-setting days (8,499.9 vs. 7,956.3) and on self-monitoring days (8,630.3 vs. 7,825.9). Multilevel analyses showed a significant effect of the self-monitoring condition (p = .01), the goal-setting condition approached significance (p = .08), and there was a small linear increase in walking over time (p = .03). N-of-1 randomized trials are a suitable means to test behavioral interventions in individual participants.

Investigating the origins of high multilevel resistive switching in forming free Ti/TiO2-x-based memory devices through experiments and simulations

NASA Astrophysics Data System (ADS)

Bousoulas, P.; Giannopoulos, I.; Asenov, P.; Karageorgiou, I.; Tsoukalas, D.

2017-03-01

Although multilevel capability is probably the most important property of resistive random access memory (RRAM) technology, it is vulnerable to reliability issues due to the stochastic nature of conducting filament (CF) creation. As a result, the various resistance states cannot be clearly distinguished, which leads to memory capacity failure. In this work, due to the gradual resistance switching pattern of TiO2-x-based RRAM devices, we demonstrate at least six resistance states with distinct memory margin and promising temporal variability. It is shown that the formation of small CFs with high density of oxygen vacancies enhances the uniformity of the switching characteristics in spite of the random nature of the switching effect. Insight into the origin of the gradual resistance modulation mechanisms is gained by the application of a trap-assisted-tunneling model together with numerical simulations of the filament formation physical processes.

Dendritic growth model of multilevel marketing

NASA Astrophysics Data System (ADS)

Pang, James Christopher S.; Monterola, Christopher P.

2017-02-01

Biologically inspired dendritic network growth is utilized to model the evolving connections of a multilevel marketing (MLM) enterprise. Starting from agents at random spatial locations, a network is formed by minimizing a distance cost function controlled by a parameter, termed the balancing factor bf, that weighs the wiring and the path length costs of connection. The paradigm is compared to an actual MLM membership data and is shown to be successful in statistically capturing the membership distribution, better than the previously reported agent based preferential attachment or analytic branching process models. Moreover, it recovers the known empirical statistics of previously studied MLM, specifically: (i) a membership distribution characterized by the existence of peak levels indicating limited growth, and (ii) an income distribution obeying the 80 - 20 Pareto principle. Extensive types of income distributions from uniform to Pareto to a "winner-take-all" kind are also modeled by varying bf. Finally, the robustness of our dendritic growth paradigm to random agent removals is explored and its implications to MLM income distributions are discussed.

Variability in the performance of preventive services and in the degree of control of identified health problems: A primary care study protocol

PubMed Central

Bolíbar, Bonaventura; Pareja, Clara; Astier-Peña, M Pilar; Morán, Julio; Rodríguez-Blanco, Teresa; Rosell-Murphy, Magdalena; Iglesias, Manuel; Juncosa, Sebastián; Mascort, Juanjo; Violan, Concepció; Magallón, Rosa; Apezteguia, Javier

2008-01-01

Background Preventive activities carried out in primary care have important variability that makes necessary to know which factors have an impact in order to establish future strategies for improvement. The present study has three objectives: 1) To describe the variability in the implementation of 7 preventive services (screening for smoking status, alcohol abuse, hypertension, hypercholesterolemia, obesity, influenza and tetanus immunization) and to determine their related factors; 2) To describe the degree of control of 5 identified health problems (smoking, alcohol abuse, hypertension, hypercholesterolemia and obesity); 3) To calculate intraclass correlation coefficients. Design Multi-centered cross-sectional study of a randomised sample of primary health care teams from 3 regions of Spain designed to analyse variability and related factors of 7 selected preventive services in years 2006 and 2007. At the end of 2008, we will perform a cross-sectional study of a cohort of patients attended in 2006 or 2007 to asses the degree of control of 5 identified health problems. All subjects older than16 years assigned to a randomised sample of 22 computerized primary health care teams and attended during the study period are included in each region providing a sample with more than 850.000 subjects. The main outcome measures will be implementation of 7 preventive services and control of 5 identified health problems. Furthermore, there will be 3 levels of data collection: 1) Patient level (age, gender, morbidity, preventive services, attendance); 2) Health-care professional level (professional characteristics, years working at the team, workload); 3) Team level (characteristics, electronic clinical record system). Data will be transferred from electronic clinical records to a central database with prior encryption and dissociation of subject, professional and team identity. Global and regional analysis will be performed including standard analysis for primary health care teams and health-care professional level. Linear and logistic regression multilevel analysis adjusted for individual and cluster variables will also be performed. Variability in the number of preventive services implemented will be calculated with Poisson multilevel models. Team and health-care professional will be considered random effects. Intraclass correlation coefficients, standard error and variance components for the different outcome measures will be calculated. PMID:18691407

Development of an efficient multigrid method for the NEM form of the multigroup neutron diffusion equation

NASA Astrophysics Data System (ADS)

Al-Chalabi, Rifat M. Khalil

1997-09-01

Development of an improvement to the computational efficiency of the existing nested iterative solution strategy of the Nodal Exapansion Method (NEM) nodal based neutron diffusion code NESTLE is presented. The improvement in the solution strategy is the result of developing a multilevel acceleration scheme that does not suffer from the numerical stalling associated with a number of iterative solution methods. The acceleration scheme is based on the multigrid method, which is specifically adapted for incorporation into the NEM nonlinear iterative strategy. This scheme optimizes the computational interplay between the spatial discretization and the NEM nonlinear iterative solution process through the use of the multigrid method. The combination of the NEM nodal method, calculation of the homogenized, neutron nodal balance coefficients (i.e. restriction operator), efficient underlying smoothing algorithm (power method of NESTLE), and the finer mesh reconstruction algorithm (i.e. prolongation operator), all operating on a sequence of coarser spatial nodes, constitutes the multilevel acceleration scheme employed in this research. Two implementations of the multigrid method into the NESTLE code were examined; the Imbedded NEM Strategy and the Imbedded CMFD Strategy. The main difference in implementation between the two methods is that in the Imbedded NEM Strategy, the NEM solution is required at every MG level. Numerical tests have shown that the Imbedded NEM Strategy suffers from divergence at coarse- grid levels, hence all the results for the different benchmarks presented here were obtained using the Imbedded CMFD Strategy. The novelties in the developed MG method are as follows: the formulation of the restriction and prolongation operators, and the selection of the relaxation method. The restriction operator utilizes a variation of the reactor physics, consistent homogenization technique. The prolongation operator is based upon a variant of the pin power reconstruction methodology. The relaxation method, which is the power method, utilizes a constant coefficient matrix within the NEM non-linear iterative strategy. The choice of the MG nesting within the nested iterative strategy enables the incorporation of other non-linear effects with no additional coding effort. In addition, if an eigenvalue problem is being solved, it remains an eigenvalue problem at all grid levels, simplifying coding implementation. The merit of the developed MG method was tested by incorporating it into the NESTLE iterative solver, and employing it to solve four different benchmark problems. In addition to the base cases, three different sensitivity studies are performed, examining the effects of number of MG levels, homogenized coupling coefficients correction (i.e. restriction operator), and fine-mesh reconstruction algorithm (i.e. prolongation operator). The multilevel acceleration scheme developed in this research provides the foundation for developing adaptive multilevel acceleration methods for steady-state and transient NEM nodal neutron diffusion equations. (Abstract shortened by UMI.)

Modeling spatial effects of PM{sub 2.5} on term low birth weight in Los Angeles County

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

Coker, Eric, E-mail: cokerer@onid.orst.edu; Ghosh, Jokay; Jerrett, Michael

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM{sub 2.5}) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure–response of PM{sub 2.5} on TLBW to be the same throughout a large geographical area. Health effects related to PM{sub 2.5} exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure–response relationship between individual-level exposure to PM{sub 2.5} and TLBW. Here, we examine the overall and spatially varying exposure–response relationship between PM{sub 2.5} and TLBW throughout urbanmore » Los Angeles (LA) County, California. We estimated PM{sub 2.5} from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM{sub 2.5} level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure–response for PM{sub 2.5} and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure–response estimates for PM{sub 2.5} on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective. - Highlights: • We model the spatial dependency of PM{sub 2.5} effects on term low birth weight (TLBW). • PM{sub 2.5} effects on TLBW are shown to vary spatially across urban LA County. • Modeling spatial dependency of PM{sub 2.5} health effects may identify effect 'hotspots'. • Birth outcomes studies should consider the spatial dependency of PM{sub 2.5} effects.« less

The effect of a school-based intervention on sunbed use in Danish pupils at continuation schools: a cluster-randomized controlled trial.

PubMed

Aarestrup, Cecilie; Bonnesen, Camilla T; Thygesen, Lau C; Krarup, Anne F; Waagstein, Anne B; Jensen, Poul D; Bentzen, Joan

2014-02-01

To examine the effect of an educational intervention on sunbed use and intentions and attitudes toward sunbed use in 14- to 18-year-olds at continuation schools. We randomized 33 continuation schools either to receive the educational intervention (n = 16) or to be controls (n = 17). Intervention schools received an e-magazine addressing the health risks of sunbed use. Information on behavior and intentions and attitudes toward sunbed use was gathered through self-administrated questionnaires before the intervention and at 6 months as a follow-up. The effect of the intervention was examined by multilevel linear regression and logistic regression. Sunbed use was significantly lower at follow-up among pupils at intervention schools versus pupils at control schools (girls: odds ratio .60, 95% confidence interval .42-.86; Boys: odds ratio .58, 95% confidence interval .35-.96). The intervention had no effect on intention to use sunbeds or attitudes toward sunbed use. The analyses revealed a significant impact of school on attitudes toward sunbed; the intraclass correlation coefficient was estimated to be 6.0% and 7.8% for girls and boys, respectively. The findings from the present study provide new evidence of a positive effect of an educational intervention on sunbed use among pupils aged 14-18 years at continuation schools. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

The Variance of Intraclass Correlations in Three- and Four-Level Models

ERIC Educational Resources Information Center

Hedges, Larry V.; Hedberg, E. C.; Kuyper, Arend M.

2012-01-01

Intraclass correlations are used to summarize the variance decomposition in populations with multilevel hierarchical structure. There has recently been considerable interest in estimating intraclass correlations from surveys or designed experiments to provide design parameters for planning future large-scale randomized experiments. The large…

Impact of Contextual Factors on Prostate Cancer Risk and Outcomes

DTIC Science & Technology

2013-07-01

framework with random effects (“frailty models”) while the case-control analyses (Aim 4) will use multilevel unconditional logistic regression models...contextual-level SES on prostate cancer risk within racial/ethnic groups. The survival analyses (Aims 1-3) will utilize a proportional hazards regression

The Variance of Intraclass Correlations in Three and Four Level

ERIC Educational Resources Information Center

Hedges, Larry V.; Hedberg, Eric C.; Kuyper, Arend M.

2012-01-01

Intraclass correlations are used to summarize the variance decomposition in popula- tions with multilevel hierarchical structure. There has recently been considerable interest in estimating intraclass correlations from surveys or designed experiments to provide design parameters for planning future large-scale randomized experiments. The large…

Practical Effects of Classwide Mathematics Intervention

ERIC Educational Resources Information Center

VanDerHeyden, Amanda M.; Codding, Robin S.

2015-01-01

The current article presents additional analyses of a classwide mathematics intervention, from a previously reported randomized controlled trial, to offer new information about the treatment and to demonstrate the utility of different types of effect sizes. Multilevel modeling was used to examine treatment effects by race, sex, socioeconomic…

An Entropy-Based Measure of Dependence between Two Groups of Random Variables. Research Report. ETS RR-07-20

ERIC Educational Resources Information Center

Kong, Nan

2007-01-01

In multivariate statistics, the linear relationship among random variables has been fully explored in the past. This paper looks into the dependence of one group of random variables on another group of random variables using (conditional) entropy. A new measure, called the K-dependence coefficient or dependence coefficient, is defined using…

Efficient sampling of complex network with modified random walk strategies

NASA Astrophysics Data System (ADS)

Xie, Yunya; Chang, Shuhua; Zhang, Zhipeng; Zhang, Mi; Yang, Lei

2018-02-01

We present two novel random walk strategies, choosing seed node (CSN) random walk and no-retracing (NR) random walk. Different from the classical random walk sampling, the CSN and NR strategies focus on the influences of the seed node choice and path overlap, respectively. Three random walk samplings are applied in the Erdös-Rényi (ER), Barabási-Albert (BA), Watts-Strogatz (WS), and the weighted USAir networks, respectively. Then, the major properties of sampled subnets, such as sampling efficiency, degree distributions, average degree and average clustering coefficient, are studied. The similar conclusions can be reached with these three random walk strategies. Firstly, the networks with small scales and simple structures are conducive to the sampling. Secondly, the average degree and the average clustering coefficient of the sampled subnet tend to the corresponding values of original networks with limited steps. And thirdly, all the degree distributions of the subnets are slightly biased to the high degree side. However, the NR strategy performs better for the average clustering coefficient of the subnet. In the real weighted USAir networks, some obvious characters like the larger clustering coefficient and the fluctuation of degree distribution are reproduced well by these random walk strategies.

The Association of Workplace Social Capital With Work Engagement of Employees in Health Care Settings: A Multilevel Cross-Sectional Analysis.

PubMed

Fujita, Sumiko; Kawakami, Norito; Ando, Emiko; Inoue, Akiomi; Tsuno, Kanami; Kurioka, Sumiko; Kawachi, Ichiro

2016-03-01

The aim of the study was to examine the cross-sectional multilevel association between unit-level workplace social capital and individual-level work engagement among employees in health care settings. The data were collected from employees of a Japanese health care corporation using a questionnaire. The analyses were limited to 440 respondents from 35 units comprising five or more respondents per unit. Unit-level workplace social capital was calculated as an average score of the Workplace Social Capital Scale for each unit. Multilevel regression analysis with a random intercept model was conducted. After adjusting for demographic variables, unit-level workplace social capital was significantly and positively associated with respondents' work engagement (P < 0.001). The association remained significant after additionally adjusting for individual-level perceptions of workplace social capital (P < 0.001). Workplace social capital might exert a positive contextual effect on work engagement of employees in health care settings.

Forward and inverse uncertainty quantification using multilevel Monte Carlo algorithms for an elliptic non-local equation

DOE PAGES

Jasra, Ajay; Law, Kody J. H.; Zhou, Yan

2016-01-01

Our paper considers uncertainty quantification for an elliptic nonlocal equation. In particular, it is assumed that the parameters which define the kernel in the nonlocal operator are uncertain and a priori distributed according to a probability measure. It is shown that the induced probability measure on some quantities of interest arising from functionals of the solution to the equation with random inputs is well-defined,s as is the posterior distribution on parameters given observations. As the elliptic nonlocal equation cannot be solved approximate posteriors are constructed. The multilevel Monte Carlo (MLMC) and multilevel sequential Monte Carlo (MLSMC) sampling algorithms are usedmore » for a priori and a posteriori estimation, respectively, of quantities of interest. Furthermore, these algorithms reduce the amount of work to estimate posterior expectations, for a given level of error, relative to Monte Carlo and i.i.d. sampling from the posterior at a given level of approximation of the solution of the elliptic nonlocal equation.« less

Forward and inverse uncertainty quantification using multilevel Monte Carlo algorithms for an elliptic non-local equation

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

Jasra, Ajay; Law, Kody J. H.; Zhou, Yan

Our paper considers uncertainty quantification for an elliptic nonlocal equation. In particular, it is assumed that the parameters which define the kernel in the nonlocal operator are uncertain and a priori distributed according to a probability measure. It is shown that the induced probability measure on some quantities of interest arising from functionals of the solution to the equation with random inputs is well-defined,s as is the posterior distribution on parameters given observations. As the elliptic nonlocal equation cannot be solved approximate posteriors are constructed. The multilevel Monte Carlo (MLMC) and multilevel sequential Monte Carlo (MLSMC) sampling algorithms are usedmore » for a priori and a posteriori estimation, respectively, of quantities of interest. Furthermore, these algorithms reduce the amount of work to estimate posterior expectations, for a given level of error, relative to Monte Carlo and i.i.d. sampling from the posterior at a given level of approximation of the solution of the elliptic nonlocal equation.« less

Affective Balance, Team Prosocial Efficacy and Team Trust: A Multilevel Analysis of Prosocial Behavior in Small Groups

PubMed Central

Cuadrado, Esther; Tabernero, Carmen

2015-01-01

Little research has focused on how individual- and team-level characteristics jointly influence, via interaction, how prosocially individuals behave in teams and few studies have considered the potential influence of team context on prosocial behavior. Using a multilevel perspective, we examined the relationships between individual (affective balance) and group (team prosocial efficacy and team trust) level variables and prosocial behavior towards team members. The participants were 123 students nested in 45 small teams. A series of multilevel random models was estimated using hierarchical linear and nonlinear modeling. Individuals were more likely to behave prosocially towards in-group members when they were feeling good. Furthermore, the relationship between positive affective balance and prosocial behavior was stronger in teams with higher team prosocial efficacy levels as well as in teams with higher team trust levels. Finally, the relevance of team trust had a stronger influence on behavior than team prosocial efficacy. PMID:26317608

Strategic Use of Random Subsample Replication and a Coefficient of Factor Replicability

ERIC Educational Resources Information Center

Katzenmeyer, William G.; Stenner, A. Jackson

1975-01-01

The problem of demonstrating replicability of factor structure across random variables is addressed. Procedures are outlined which combine the use of random subsample replication strategies with the correlations between factor score estimates across replicate pairs to generate a coefficient of replicability and confidence intervals associated with…

Note on coefficient matrices from stochastic Galerkin methods for random diffusion equations

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

Zhou Tao, E-mail: tzhou@lsec.cc.ac.c; Tang Tao, E-mail: ttang@hkbu.edu.h

2010-11-01

In a recent work by Xiu and Shen [D. Xiu, J. Shen, Efficient stochastic Galerkin methods for random diffusion equations, J. Comput. Phys. 228 (2009) 266-281], the Galerkin methods are used to solve stochastic diffusion equations in random media, where some properties for the coefficient matrix of the resulting system are provided. They also posed an open question on the properties of the coefficient matrix. In this work, we will provide some results related to the open question.

Significance tests for functional data with complex dependence structure.

PubMed

Staicu, Ana-Maria; Lahiri, Soumen N; Carroll, Raymond J

2015-01-01

We propose an L 2 -norm based global testing procedure for the null hypothesis that multiple group mean functions are equal, for functional data with complex dependence structure. Specifically, we consider the setting of functional data with a multilevel structure of the form groups-clusters or subjects-units, where the unit-level profiles are spatially correlated within the cluster, and the cluster-level data are independent. Orthogonal series expansions are used to approximate the group mean functions and the test statistic is estimated using the basis coefficients. The asymptotic null distribution of the test statistic is developed, under mild regularity conditions. To our knowledge this is the first work that studies hypothesis testing, when data have such complex multilevel functional and spatial structure. Two small-sample alternatives, including a novel block bootstrap for functional data, are proposed, and their performance is examined in simulation studies. The paper concludes with an illustration of a motivating experiment.

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

DOE PAGES

Desai, Ajit; Khalil, Mohammad; Pettit, Chris; ...

2017-09-21

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolutionmore » in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.« less

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

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

Desai, Ajit; Khalil, Mohammad; Pettit, Chris

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolutionmore » in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.« less

The relationship between sense of community in the school and students' aggressive behavior: A multilevel analysis.

PubMed

Prati, Gabriele; Albanesi, Cinzia; Cicognani, Elvira

2018-06-18

School sense of community has been associated with lower levels of students' aggressive behaviors. The main aim of the study was to examine whether the magnitude of the influence of school sense of community on students' aggressive behavior is similar or different across schools with different levels of aggressive behaviors. Participants were 1,800 Italian students attending 44 middle and high schools. Using multilevel modeling (a random intercepts and slopes model), we found that the magnitude of the negative relationship between sense of community in the school and students' aggressive behaviors was stronger in schools with high levels of aggressive behavior. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Multilevel and Latent Variable Modeling with Composite Links and Exploded Likelihoods

ERIC Educational Resources Information Center

Rabe-Hesketh, Sophia; Skrondal, Anders

2007-01-01

Composite links and exploded likelihoods are powerful yet simple tools for specifying a wide range of latent variable models. Applications considered include survival or duration models, models for rankings, small area estimation with census information, models for ordinal responses, item response models with guessing, randomized response models,…

Modeling Heterogeneous Variance-Covariance Components in Two-Level Models

ERIC Educational Resources Information Center

Leckie, George; French, Robert; Charlton, Chris; Browne, William

2014-01-01

Applications of multilevel models to continuous outcomes nearly always assume constant residual variance and constant random effects variances and covariances. However, modeling heterogeneity of variance can prove a useful indicator of model misspecification, and in some educational and behavioral studies, it may even be of direct substantive…

Optimal Design for Regression Discontinuity Studies with Clustering

ERIC Educational Resources Information Center

Rhoads, Christopher; Dye, Charles

2014-01-01

Recent years have seen an increased interest in quantitative educational research studies that use random assignment (RA) to evaluate the causal impacts of educational interventions (Angrist, 2004). The multi-level structure of the public education system in the United States often leads to experimental designs where naturally occurring clusters…

Optimal Design for Two-Level Random Assignment and Regression Discontinuity Studies

ERIC Educational Resources Information Center

Rhoads, Christopher H.; Dye, Charles

2016-01-01

An important concern when planning research studies is to obtain maximum precision of an estimate of a treatment effect given a budget constraint. When research designs have a "multilevel" or "hierarchical" structure changes in sample size at different levels of the design will impact precision differently. Furthermore, there…

Whose health is affected by income inequality? A multilevel interaction analysis of contemporaneous and lagged effects of state income inequality on individual self-rated health in the United States.

PubMed

Subramanian, S V; Kawachi, Ichiro

2006-06-01

The empirical relationship between income inequality and health has been much debated and discussed. Recent reviews suggest that the current evidence is mixed, with the relationship between state income inequality and health in the United States (US) being perhaps the most robust. In this paper, we examine the multilevel interactions between state income inequality, individual poor self-rated health, and a range of individual demographic and socioeconomic markers in the US. We use the pooled data from the 1995 and 1997 Current Population Surveys, and the data on state income inequality (represented using Gini coefficient) from the 1990, 1980, and 1970 US Censuses. Utilizing a cross-sectional multilevel design of 201,221 adults nested within 50 US states we calibrated two-level binomial hierarchical mixed models (with states specified as a random effect). Our analyses suggest that for a 0.05 change in the state income inequality, the odds ratio (OR) of reporting poor health was 1.30 (95% CI: 1.17-1.45) in a conditional model that included individual age, sex, race, marital status, education, income, and health insurance coverage as well as state median income. With few exceptions, we did not find strong statistical support for differential effects of state income inequality across different population groups. For instance, the relationship between state income inequality and poor health was steeper for whites compared to blacks (OR=1.34; 95% CI: 1.20-1.48) and for individuals with incomes greater than $75,000 compared to less affluent individuals (OR=1.65; 95% CI: 1.26-2.15). Our findings, however, primarily suggests an overall (as opposed to differential) contextual effect of state income inequality on individual self-rated poor health. To the extent that contemporaneous state income inequality differentially affects population sub-groups, our analyses suggest that the adverse impact of inequality is somewhat stronger for the relatively advantaged socioeconomic groups. This pattern was found to be consistent regardless of whether we consider contemporaneous or lagged effects of state income inequality on health. At the same time, the contemporaneous main effect of state income inequality remained statistically significant even when conditioned for past levels of income inequality and median income of states.

Variation of Care Time Between Nursing Units in Classification-Based Nurse-to-Resident Ratios: A Multilevel Analysis

PubMed Central

Planer, Katarina; Hagel, Anja

2018-01-01

A validity test was conducted to determine how care level–based nurse-to-resident ratios compare with actual daily care times per resident in Germany. Stability across different long-term care facilities was tested. Care level–based nurse-to-resident ratios were compared with the standard minimum nurse-to-resident ratios. Levels of care are determined by classification authorities in long-term care insurance programs and are used to distribute resources. Care levels are a powerful tool for classifying authorities in long-term care insurance. We used observer-based measurement of assignable direct and indirect care time in 68 nursing units for 2028 residents across 2 working days. Organizational data were collected at the end of the quarter in which the observation was made. Data were collected from January to March, 2012. We used a null multilevel model with random intercepts and multilevel models with fixed and random slopes to analyze data at both the organization and resident levels. A total of 14% of the variance in total care time per day was explained by membership in nursing units. The impact of care levels on care time differed significantly between nursing units. Forty percent of residents at the lowest care level received less than the standard minimum registered nursing time per day. For facilities that have been significantly disadvantaged in the current staffing system, a higher minimum standard will function more effectively than a complex classification system without scientific controls. PMID:29442533

Variation of Care Time Between Nursing Units in Classification-Based Nurse-to-Resident Ratios: A Multilevel Analysis.

PubMed

Brühl, Albert; Planer, Katarina; Hagel, Anja

2018-01-01

A validity test was conducted to determine how care level-based nurse-to-resident ratios compare with actual daily care times per resident in Germany. Stability across different long-term care facilities was tested. Care level-based nurse-to-resident ratios were compared with the standard minimum nurse-to-resident ratios. Levels of care are determined by classification authorities in long-term care insurance programs and are used to distribute resources. Care levels are a powerful tool for classifying authorities in long-term care insurance. We used observer-based measurement of assignable direct and indirect care time in 68 nursing units for 2028 residents across 2 working days. Organizational data were collected at the end of the quarter in which the observation was made. Data were collected from January to March, 2012. We used a null multilevel model with random intercepts and multilevel models with fixed and random slopes to analyze data at both the organization and resident levels. A total of 14% of the variance in total care time per day was explained by membership in nursing units. The impact of care levels on care time differed significantly between nursing units. Forty percent of residents at the lowest care level received less than the standard minimum registered nursing time per day. For facilities that have been significantly disadvantaged in the current staffing system, a higher minimum standard will function more effectively than a complex classification system without scientific controls.

Analyzing chromatographic data using multilevel modeling.

PubMed

Wiczling, Paweł

2018-06-01

It is relatively easy to collect chromatographic measurements for a large number of analytes, especially with gradient chromatographic methods coupled with mass spectrometry detection. Such data often have a hierarchical or clustered structure. For example, analytes with similar hydrophobicity and dissociation constant tend to be more alike in their retention than a randomly chosen set of analytes. Multilevel models recognize the existence of such data structures by assigning a model for each parameter, with its parameters also estimated from data. In this work, a multilevel model is proposed to describe retention time data obtained from a series of wide linear organic modifier gradients of different gradient duration and different mobile phase pH for a large set of acids and bases. The multilevel model consists of (1) the same deterministic equation describing the relationship between retention time and analyte-specific and instrument-specific parameters, (2) covariance relationships relating various physicochemical properties of the analyte to chromatographically specific parameters through quantitative structure-retention relationship based equations, and (3) stochastic components of intra-analyte and interanalyte variability. The model was implemented in Stan, which provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods. Graphical abstract Relationships between log k and MeOH content for acidic, basic, and neutral compounds with different log P. CI credible interval, PSA polar surface area.

Conference attendance does not correlate with emergency medicine residency in-training examination scores.

PubMed

Gene Hern, H; Wills, Charlotte; Alter, Harrison; Bowman, Steven H; Katz, Eric; Shayne, Philip; Vahidnia, Farnaz

2009-12-01

The residency review committee for emergency medicine (EM) requires residents to have greater than 70% attendance of educational conferences during residency training, but it is unknown whether attendance improves clinical competence or scores on the American Board of Emergency Medicine (ABEM) in-training examination (ITE). This study examined the relationship between conference attendance and ITE scores. The hypothesis was that greater attendance would correlate to a higher examination score. This was a multi-center retrospective cohort study using conference attendance data and examination results from residents in four large county EM residency training programs. Longitudinal multi-level models, adjusting for training site, U.S. Medical Licensing Examination (USMLE) Step 1 score, and sex were used to explore the relationship between conference attendance and in-training examination scores according to year of training. Each year of training was studied, as well as the overall effect of mean attendance as it related to examination score. Four training sites reported data on 405 residents during 2002 to 2008; 386 residents had sufficient data to analyze. In the multi-level longitudinal models, attendance at conference was not a significant predictor of in-training percentile score (coefficient = 0.005, 95% confidence interval [CI] = -0.053 to 0.063, p = 0.87). Score on the USMLE Step 1 examination was a strong predictor of ITE score (coefficient = 0.186, 95% CI = 0.155 to 0.217; p < 0.001), as was female sex (coefficient = 2.117, 95% CI = 0.987 to 3.25; p < 0.001). Greater conference attendance does not correlate with performance on an individual's ITE scores. Conference attendance may represent an important part of EM residency training but perhaps not of ITE performance. (c) 2009 by the Society for Academic Emergency Medicine.

Predictors for Physical Activity in Adolescent Girls Using Statistical Shrinkage Techniques for Hierarchical Longitudinal Mixed Effects Models

PubMed Central

Grant, Edward M.; Young, Deborah Rohm; Wu, Tong Tong

2015-01-01

We examined associations among longitudinal, multilevel variables and girls’ physical activity to determine the important predictors for physical activity change at different adolescent ages. The Trial of Activity for Adolescent Girls 2 study (Maryland) contributed participants from 8th (2009) to 11th grade (2011) (n=561). Questionnaires were used to obtain demographic, and psychosocial information (individual- and social-level variables); height, weight, and triceps skinfold to assess body composition; interviews and surveys for school-level data; and self-report for neighborhood-level variables. Moderate to vigorous physical activity minutes were assessed from accelerometers. A doubly regularized linear mixed effects model was used for the longitudinal multilevel data to identify the most important covariates for physical activity. Three fixed effects at the individual level and one random effect at the school level were chosen from an initial total of 66 variables, consisting of 47 fixed effects and 19 random effects variables, in additional to the time effect. Self-management strategies, perceived barriers, and social support from friends were the three selected fixed effects, and whether intramural or interscholastic programs were offered in middle school was the selected random effect. Psychosocial factors and friend support, plus a school’s physical activity environment, affect adolescent girl’s moderate to vigorous physical activity longitudinally. PMID:25928064

Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data

PubMed Central

Sharafi, Zahra

2017-01-01

Background The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. Methods The ordinal logistic regression (OLR) and hierarchical ordinal logistic regression (HOLR) were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter) with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 collected from 576 healthy school children were analyzed. Results Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. Conclusions The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed. PMID:29312463

Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data.

PubMed

Sharafi, Zahra; Mousavi, Amin; Ayatollahi, Seyyed Mohammad Taghi; Jafari, Peyman

2017-01-01

The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. The ordinal logistic regression (OLR) and hierarchical ordinal logistic regression (HOLR) were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter) with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 collected from 576 healthy school children were analyzed. Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed.

Writing Week-Journals to Improve the Writing Quality of Fourth-Graders' Compositions

ERIC Educational Resources Information Center

Rosário, Pedro; Högemann, Julia; Núñez, José Carlos; Vallejo, Guillermo; Cunha, Jennifer; Oliveira, Vera; Fuentes, Sonia; Rodrigues, Celestino

2017-01-01

Students' writing problems are a global educational concern and is in need of particular attention. This study aims to examine the impact of providing extra writing opportunities (i.e., writing journals) on the quality of writing compositions. A longitudinal cluster-randomized controlled design using a multilevel modeling analysis with 182 fourth…

Avoiding Boundary Estimates in Hierarchical Linear Models through Weakly Informative Priors

ERIC Educational Resources Information Center

Chung, Yeojin; Rabe-Hesketh, Sophia; Gelman, Andrew; Dorie, Vincent; Liu, Jinchen

2012-01-01

Hierarchical or multilevel linear models are widely used for longitudinal or cross-sectional data on students nested in classes and schools, and are particularly important for estimating treatment effects in cluster-randomized trials, multi-site trials, and meta-analyses. The models can allow for variation in treatment effects, as well as…

How Central Is the Alliance in Psychotherapy? A Multilevel Longitudinal Meta-Analysis

ERIC Educational Resources Information Center

Fluckiger, Christoph; Del Re, A. C.; Wampold, Bruce E.; Symonds, Dianne; Horvath, Adam O.

2012-01-01

Prior meta-analyses have found a moderate but robust relationship between alliance and outcome across a broad spectrum of treatments, presenting concerns, contexts, and measurements. However, there continues to be a lively debate about the therapeutic role of the alliance, particularly in treatments that are tested using randomized clinical trial…

Using Cluster Bootstrapping to Analyze Nested Data With a Few Clusters.

PubMed

Huang, Francis L

2018-04-01

Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials are performed with a low number of clusters (~20 groups). Although multilevel models are often used to analyze nested data, researchers may be concerned of potentially biased results due to having only a few groups under study. Cluster bootstrapping has been suggested as an alternative procedure when analyzing clustered data though it has seen very little use in educational and psychological studies. Using a Monte Carlo simulation that varied the number of clusters, average cluster size, and intraclass correlations, we compared standard errors using cluster bootstrapping with those derived using ordinary least squares regression and multilevel models. Results indicate that cluster bootstrapping, though more computationally demanding, can be used as an alternative procedure for the analysis of clustered data when treatment effects at the group level are of primary interest. Supplementary material showing how to perform cluster bootstrapped regressions using R is also provided.

Income inequality and mortality: a multilevel prospective study of 521 248 individuals in 50 US states.

PubMed

Backlund, Eric; Rowe, Geoff; Lynch, John; Wolfson, Michael C; Kaplan, George A; Sorlie, Paul D

2007-06-01

Some of the most consistent evidence in favour of an association between income inequality and health has been among US states. However, in multilevel studies of mortality, only two out of five studies have reported a positive relationship with income inequality after adjustment for the compositional characteristics of the state's inhabitants. In this study, we attempt to clarify these mixed results by analysing the relationship within age-sex groups and by applying a previously unused analytical method to a database that contains more deaths than any multilevel study to date. The US National Longitudinal Mortality Study (NLMS) was used to model the relationship between income inequality in US states and mortality using both a novel and previously used methodologies that fall into the general framework of multilevel regression. We adjust age-sex specific models for nine socioeconomic and demographic variables at the individual level and percentage black and region at the state level. The preponderance of evidence from this study suggests that 1990 state-level income inequality is associated with a 40% differential in state level mortality rates (95% CI = 26-56%) for men 25-64 years and a 14% (95% CI = 3-27%) differential for women 25-64 years after adjustment for compositional factors. No such relationship was found for men or women over 65. The relationship between income inequality and mortality is only robust to adjustment for compositional factors in men and women under 65. This explains why income inequality is not a major driver of mortality trends in the United States because most deaths occur at ages 65 and over. This analysis does suggest, however, the certain causes of death that occur primarily in the population under 65 may be associated with income inequality. Comparison of analytical techniques also suggests coefficients for income inequality in previous multilevel mortality studies may be biased, but further research is needed to provide a definitive answer.

Multilevel Hierarchical Modeling of Benthic Macroinvertebrate Responses to Urbanization in Nine Metropolitan Regions across the Conterminous United States

USGS Publications Warehouse

Kashuba, Roxolana; Cha, YoonKyung; Alameddine, Ibrahim; Lee, Boknam; Cuffney, Thomas F.

2010-01-01

Multilevel hierarchical modeling methodology has been developed for use in ecological data analysis. The effect of urbanization on stream macroinvertebrate communities was measured across a gradient of basins in each of nine metropolitan regions across the conterminous United States. The hierarchical nature of this dataset was harnessed in a multi-tiered model structure, predicting both invertebrate response at the basin scale and differences in invertebrate response at the region scale. Ordination site scores, total taxa richness, Ephemeroptera, Plecoptera, Trichoptera (EPT) taxa richness, and richness-weighted mean tolerance of organisms at a site were used to describe invertebrate responses. Percentage of urban land cover was used as a basin-level predictor variable. Regional mean precipitation, air temperature, and antecedent agriculture were used as region-level predictor variables. Multilevel hierarchical models were fit to both levels of data simultaneously, borrowing statistical strength from the complete dataset to reduce uncertainty in regional coefficient estimates. Additionally, whereas non-hierarchical regressions were only able to show differing relations between invertebrate responses and urban intensity separately for each region, the multilevel hierarchical regressions were able to explain and quantify those differences within a single model. In this way, this modeling approach directly establishes the importance of antecedent agricultural conditions in masking the response of invertebrates to urbanization in metropolitan regions such as Milwaukee-Green Bay, Wisconsin; Denver, Colorado; and Dallas-Fort Worth, Texas. Also, these models show that regions with high precipitation, such as Atlanta, Georgia; Birmingham, Alabama; and Portland, Oregon, start out with better regional background conditions of invertebrates prior to urbanization but experience faster negative rates of change with urbanization. Ultimately, this urbanization-invertebrate response example is used to detail the multilevel hierarchical construction methodology, showing how the result is a set of models that are both statistically more rigorous and ecologically more interpretable than simple linear regression models.

Prediction of random-regression coefficient for daily milk yield after 305 days in milk by using the regression-coefficient estimates from the first 305 days.

PubMed

Yamazaki, Takeshi; Takeda, Hisato; Hagiya, Koichi; Yamaguchi, Satoshi; Sasaki, Osamu

2018-03-13

Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a random regression model. We analyzed test-day milk records from 85690 Holstein cows in their first lactations and 131727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. The first-order Legendre polynomials were practical covariates of random regression for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.

Association between income inequality and dental status in Japanese older adults: Analysis of data from JAGES2013.

PubMed

Tashiro, Atsushi; Aida, Jun; Shobugawa, Yugo; Fujiyama, Yuki; Yamamoto, Tatsuo; Saito, Reiko; Kondo, Katsunori

2017-01-01

Objectives　Personal income affects dental status in older people. However, the impact of income inequality on dental status at the community level (junior high school district) is unclear. The purpose of this study was to examine the association between dental status and community level income inequity after adjust for individual socio-economic status in Japanese older adults, and to verify the relative income hypothesis, also known as the Wilkinson hypothesis.Methods　We used data from the Japan Gerontological Evaluation Study (JAGES) conducted in Niigata city. JAGES is a postal survey of functionally independent adults aged 65 years or older. We enrolled 4,983 respondents (response rate 62.3%) and used data on 3,980 of them after excluding incomplete data. We evaluated health condition and socio-economic status using questionnaires. The Gini coefficient, as an indicator of income inequality, was calculated by junior high school district (57 districts) based on the data from the questionnaire. Additionally, the Pearson's coefficient of correlation was calculated to evaluate the association between the mean number of remaining teeth and the community level Gini coefficient. Then we evaluated the mean number of remaining teeth among the groups stratified by the Gini coefficient conditions. Next, we conducted a multilevel analysis using an ordinal logistic regression model. The number of remaining teeth was set as the dependent variable, while sex, age, household size, education, smoking status, diabetes treatment, current living conditions, and equivalent income were used as independent variables at the individual level. The Gini coefficient and average equivalent income in the junior high school district were used as independent variables at the community level.Results　The Pearson's correlation coefficient for the relationship between the Gini coefficient and the mean number of remaining teeth in the junior high school district was -0.44 (P<0.01). Wider income disparity area (Gini coefficient≧0.35) revealed a significantly small number of remaining teeth (P<0.001). The multilevel analysis showed that a higher Gini coefficient and a lower average equivalent income at the community level were significantly associated with a lower number of remaining teeth, and with educational attainment, smoking status, current living conditions, and equivalent income at the individual level, after adjusting for sex and age. On the other hand, educational attainment at the individual level, and average equivalent income at the community level were not significant factors after adjusting for all individual level variables.Conclusion　This study showed that, in addition to individual socio-economic status, income inequality at the community level was significantly associated with number of remaining teeth in Japanese older adults. Although the precise mechanism of this association is still unclear, our result supports the relative income hypothesis.

Is the Non-Dipole Magnetic Field Random?

NASA Technical Reports Server (NTRS)

Walker, Andrew D.; Backus, George E.

1996-01-01

Statistical modelling of the Earth's magnetic field B has a long history. In particular, the spherical harmonic coefficients of scalar fields derived from B can be treated as Gaussian random variables. In this paper, we give examples of highly organized fields whose spherical harmonic coefficients pass tests for independent Gaussian random variables. The fact that coefficients at some depth may be usefully summarized as independent samples from a normal distribution need not imply that there really is some physical, random process at that depth. In fact, the field can be extremely structured and still be regarded for some purposes as random. In this paper, we examined the radial magnetic field B(sub r) produced by the core, but the results apply to any scalar field on the core-mantle boundary (CMB) which determines B outside the CMB.

An Integrative, Multilevel, and Transdisciplinary Research Approach to Challenges of Work, Family, and Health

PubMed Central

Bray, Jeremy W.; Kelly, Erin L.; Hammer, Leslie B.; Almeida, David M.; Dearing, James W.; King, Rosalind B.; Buxton, Orfeu M.

2013-01-01

Recognizing a need for rigorous, experimental research to support the efforts of workplaces and policymakers in improving the health and wellbeing of employees and their families, the National Institutes of Health and the Centers for Disease Control and Prevention formed the Work, Family & Health Network (WFHN). The WFHN is implementing an innovative multisite study with a rigorous experimental design (adaptive randomization, control groups), comprehensive multilevel measures, a novel and theoretically based intervention targeting the psychosocial work environment, and translational activities. This paper describes challenges and benefits of designing a multilevel and transdisciplinary research network that includes an effectiveness study to assess intervention effects on employees, families, and managers; a daily diary study to examine effects on family functioning and daily stress; a process study to understand intervention implementation; and translational research to understand and inform diffusion of innovation. Challenges were both conceptual and logistical, spanning all aspects of study design and implementation. In dealing with these challenges, however, the WFHN developed innovative, transdisciplinary, multi-method approaches to conducting workplace research that will benefit both the research and business communities. PMID:24618878

Estimation of river and stream temperature trends under haphazard sampling

USGS Publications Warehouse

Gray, Brian R.; Lyubchich, Vyacheslav; Gel, Yulia R.; Rogala, James T.; Robertson, Dale M.; Wei, Xiaoqiao

2015-01-01

Long-term temporal trends in water temperature in rivers and streams are typically estimated under the assumption of evenly-spaced space-time measurements. However, sampling times and dates associated with historical water temperature datasets and some sampling designs may be haphazard. As a result, trends in temperature may be confounded with trends in time or space of sampling which, in turn, may yield biased trend estimators and thus unreliable conclusions. We address this concern using multilevel (hierarchical) linear models, where time effects are allowed to vary randomly by day and date effects by year. We evaluate the proposed approach by Monte Carlo simulations with imbalance, sparse data and confounding by trend in time and date of sampling. Simulation results indicate unbiased trend estimators while results from a case study of temperature data from the Illinois River, USA conform to river thermal assumptions. We also propose a new nonparametric bootstrap inference on multilevel models that allows for a relatively flexible and distribution-free quantification of uncertainties. The proposed multilevel modeling approach may be elaborated to accommodate nonlinearities within days and years when sampling times or dates typically span temperature extremes.

Using Cross-Classified Multilevel Models to Disentangle School and Neighborhood Effects: An Example Focusing on Smoking Behaviors among Adolescents in the United States

PubMed Central

Dunn, Erin C.; Richmond, Tracy K.; Milliren, Carly E.; Subramanian, S.V.

2015-01-01

Background Despite much interest in understanding the influence of contexts on health, most research has focused on one context at a time despite the reality that individuals have simultaneous memberships in multiple settings. Method Using the example of smoking behavior among adolescents in the National Longitudinal Study of Adolescent Health, we applied cross-classified multilevel modeling (CCMM) to examine fixed and random effects for schools and neighborhoods. We compared the CCMM results with those obtained from a traditional multilevel model (MLM) focused on either the school and neighborhood separately. Results In the MLMs, 5.2% of the variation in smoking was due to differences between neighborhoods (when schools were ignored) and 6.3% to differences between schools (when neighborhoods were ignored). However in the CCMM examining neighborhood and school variation simultaneously, the neighborhood-level variation was reduced to 0.4%. Conclusion Results suggest that using MLM, instead of CCMM, could lead to overestimating the importance of certain contexts and could ultimately lead to targeting interventions or policies to the wrong settings. PMID:25579227

Identifying patient and practice characteristics associated with patient-reported experiences of safety problems and harm: a cross-sectional study using a multilevel modelling approach.

PubMed

Ricci-Cabello, Ignacio; Reeves, David; Bell, Brian G; Valderas, Jose M

2017-11-01

To identify patient and family practice characteristics associated with patient-reported experiences of safety problems and harm. Cross-sectional study combining data from the individual postal administration of the validated Patient Reported Experiences and Outcomes of Safety in Primary Care (PREOS-PC) questionnaire to a random sample of patients in family practices (response rate=18.4%) and practice-level data for those practices obtained from NHS Digital. We built linear multilevel multivariate regression models to model the association between patient-level (clinical and sociodemographic) and practice-level (size and case-mix, human resources, indicators of quality and safety of care, and practice safety activation) characteristics, and outcome measures. Practices distributed across five regions in the North, Centre and South of England. 1190 patients registered in 45 practices purposefully sampled (maximal variation in practice size and levels of deprivation). Self-reported safety problems, harm and overall perception of safety. Higher self-reported levels of safety problems were associated with younger age of patients (beta coefficient 0.15) and lower levels of practice safety activation (0.44). Higher self-reported levels of harm were associated with younger age (0.13) and worse self-reported health status (0.23). Lower self-reported healthcare safety was associated with lower levels of practice safety activation (0.40). The fully adjusted models explained 4.5% of the variance in experiences of safety problems, 8.6% of the variance in harm and 4.4% of the variance in perceptions of patient safety. Practices' safety activation levels and patients' age and health status are associated with patient-reported safety outcomes in English family practices. The development of interventions aimed at improving patient safety outcomes would benefit from focusing on the identified groups. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Multilevel Associations of Neighborhood Poverty, Crime, and Satisfaction With Blood Pressure in African-American Adults.

PubMed

Coulon, Sandra M; Wilson, Dawn K; Alia, Kassandra A; Van Horn, M Lee

2016-01-01

African-American adults experience the highest rates of elevated blood pressure (BP), and this disparity may be linked to socioeconomic and neighborhood-related disadvantage. Based on a bioecological stress-buffering framework, relations of poverty and neighborhood environmental perceptions with BP were assessed using multilevel regression in at-risk African-American adults. This cross-sectional study used baseline data that were collected in 2008 as part of the Positive Action for Today's Health (PATH) trial (N = 409), a community-based intervention to increase walking in low-income, high-crime neighborhoods. BP and perceived neighborhood crime and satisfaction were investigated as individual-level indicators of health and neighborhood environment. Census block groups (N = 22) served as geographic proxies for neighborhoods, and poverty was obtained using 2010 U.S. Census data, to characterize the neighborhood-level socioeconomic environment. There were no individual-level direct associations. Significant cross-product interactions demonstrated that with higher perceived crime, high satisfaction was associated with lower systolic (γ = 3.34) and diastolic (γ = -1.37) BP, but low satisfaction was associated with higher systolic (γ = 15.12) and diastolic (γ = 7.57) BP. Neighborhood-level poverty was associated with diastolic (γ = 11.48, SE = 4.08, P = 0.008) and systolic BP (γ = 12.79, SE = 6.33, P = 0.052). Variance in BP across block groups was low (intraclass correlation coefficients = 0.002-0.014) and there were no significant random effects. Results supported hypotheses, with greater neighborhood satisfaction linked to lower systolic and diastolic BP when perceived crime was high. Neighborhood poverty was also linked to higher systolic and diastolic BP. Prevention efforts should further investigate whether attending to issues of poverty and related neighborhood perceptions reduces high BP in at-risk African-American communities. © Published by Oxford University Press on behalf of American Journal of Hypertension Ltd 2015. This work is written by (a) US Government employees(s) and is in the public domain in the US.

Multilevel Associations of Neighborhood Poverty, Crime, and Satisfaction With Blood Pressure in African-American Adults

PubMed Central

Wilson, Dawn K.; Alia, Kassandra A.; Van Horn, M. Lee

2016-01-01

BACKGROUND African-American adults experience the highest rates of elevated blood pressure (BP), and this disparity may be linked to socioeconomic and neighborhood-related disadvantage. Based on a bioecological stress-buffering framework, relations of poverty and neighborhood environmental perceptions with BP were assessed using multilevel regression in at-risk African-American adults. METHODS This cross-sectional study used baseline data that were collected in 2008 as part of the Positive Action for Today’s Health (PATH) trial (N = 409), a community-based intervention to increase walking in low-income, high-crime neighborhoods. BP and perceived neighborhood crime and satisfaction were investigated as individual-level indicators of health and neighborhood environment. Census block groups (N = 22) served as geographic proxies for neighborhoods, and poverty was obtained using 2010U.S. Census data, to characterize the neighborhood-level socioeconomic environment. RESULTS There were no individual-level direct associations. Significant cross–product interactions demonstrated that with higher perceived crime, high satisfaction was associated with lower systolic (γ = 3.34) and diastolic (γ = −1.37) BP, but low satisfaction was associated with higher systolic (γ = 15.12) and diastolic (γ = 7.57) BP. Neighborhood-level poverty was associated with diastolic (γ = 11.48, SE = 4.08, P = 0.008) and systolic BP (γ = 12.79, SE = 6.33, P = 0.052). Variance in BP across block groups was low (intraclass correlation coefficients = 0.002–0.014) and there were no significant random effects. CONCLUSIONS Results supported hypotheses, with greater neighborhood satisfaction linked to lower systolic and diastolic BP when perceived crime was high. Neighborhood poverty was also linked to higher systolic and diastolic BP. Prevention efforts should further investigate whether attending to issues of poverty and related neighborhood perceptions reduces high BP in at-risk African-American communities. PMID:25917562

Cluster randomized controlled trial of a multilevel physical activity intervention for older adults.

PubMed

Kerr, Jacqueline; Rosenberg, Dori; Millstein, Rachel A; Bolling, Khalisa; Crist, Katie; Takemoto, Michelle; Godbole, Suneeta; Moran, Kevin; Natarajan, Loki; Castro-Sweet, Cynthia; Buchner, David

2018-04-02

Older adults are the least active population group. Interventions in residential settings may support a multi-level approach to behavior change. In a cluster randomized control trial, 11 San Diego retirement communities were assigned to a physical activity (PA) intervention or a healthy aging attention control condition. Participants were 307 adults over 65 years old. The multilevel PA intervention was delivered with the assistance of peer leaders, who were trained older adult from the retirement communities. Intervention components included individual counseling & self-monitoring with pedometers, group education sessions, group walks, community advocacy and pedestrian community change projects. Intervention condition by time interactions were tested using generalized mixed effects regressions. The primary outcomes was accelerometer measured physical activity. Secondary outcomes were blood pressure and objectively measured physical functioning. Over 70% of the sample were 80 years or older. PA significantly increased in the intervention condition (56 min of moderate-vigorous PA per week; 119 min of light PA) compared with the control condition and remained significantly higher across the 12 month study. Men and participants under 84 years old benefited most from the intervention. There was a significant decrease in systolic (p < .007) and diastolic (p < .02) blood pressure at 6 months. Physical functioning improved but the changes were not statistically significant. Intervention fidelity was high demonstrating feasibility. Changes in PA and blood pressure achieved were comparable to other studies with much younger participants. Men, in particular, avoided a year-long decline in PA. clincialtrials.gov Identifier: NCT01155011 .

Randomized Multilevel Intervention to Improve Outcomes of Residents in Nursing Homes in Need of Improvement

PubMed Central

Rantz, Marilyn J.; Nahm, Helen E.; Zwygart-Stauffacher, Mary; Hicks, Lanis; Mehr, David; Flesner, Marcia; Petroski, Gregory F.; Madsen, Richard W.; Scott-Cawiezell, Jill

2012-01-01

Purpose A comprehensive multilevel intervention was tested to build organizational capacity to create and sustain improvement in quality of care and subsequently improve resident outcomes in nursing homes in need of improvement. Intervention facilities (n=29) received a two-year multilevel intervention with monthly on-site consultation from expert nurses with graduate education in gerontological nursing. Attention control facilities (n=29) that also needed to improve resident outcomes received monthly information about aging and physical assessment of elders. Design and Methods Randomized clinical trial of nursing homes in need of improving resident outcomes of bladder and bowel incontinence, weight loss, pressure ulcers, and decline in activities of daily living (ADL). It was hypothesized that following the intervention, experimental facilities would have better resident outcomes, higher quality of care, higher staff retention, more organizational attributes of improved working conditions than control facilities, similar staffing and staff mix, and lower total and direct care costs. Results The intervention did improve quality of care (p=0.02); there were improvements in pressure ulcers (p=0.05), weight loss (p=0.05). Staff retention, organizational working conditions, staffing, and staff mix and most costs were not affected by the intervention. Leadership turnover was surprisingly excessive in both intervention and control groups. Implications Some facilities that are in need of improving quality of care and resident outcomes are able to build the organizational capacity to improve while not increasing staffing or costs of care. Improvement requires continuous supportive consultation and leadership willing to involve staff and work together to build the systematic improvements in care delivery needed. PMID:21816681

The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models.

PubMed

Browne, William J; Steele, Fiona; Golalizadeh, Mousa; Green, Martin J

2009-06-01

We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in many situations in the medical and social sciences and we illustrate their use through two examples that differ in terms of both substantive area and data structure. A multilevel discrete time survival analysis involves expanding the data set so that the model can be cast as a standard multilevel binary response model. For such models it has been shown that MCMC methods have advantages in terms of reducing estimate bias. However, the data expansion results in very large data sets for which MCMC estimation is often slow and can produce chains that exhibit poor mixing. Any way of improving the mixing will result in both speeding up the methods and more confidence in the estimates that are produced. The MCMC methodological literature is full of alternative algorithms designed to improve mixing of chains and we describe three reparameterization techniques that are easy to implement in available software. We consider two examples of multilevel survival analysis: incidence of mastitis in dairy cattle and contraceptive use dynamics in Indonesia. For each application we show where the reparameterization techniques can be used and assess their performance.

Risk Factors for Child Malnutrition in Bangladesh: A Multilevel Analysis of a Nationwide Population-Based Survey.

PubMed

Chowdhury, Mohammad Rocky Khan; Rahman, Mohammad Shafiur; Khan, Mohammad Mubarak Hossain; Mondal, Mohammad Nazrul Islam; Rahman, Mohammad Mosiur; Billah, Baki

2016-05-01

To identify the prevalence and risk factors of child malnutrition in Bangladesh. Data was extracted from the Bangladesh Demographic Health Survey (2011). The outcome measures were stunting, wasting, and underweight. χ(2) analysis was performed to find the association of outcome variables with selected factors. Multilevel logistic regression models with a random intercept at each of the household and community levels were used to identify the risk factors of stunting, wasting, and underweight. From the 2011 survey, 7568 children less than 5 years of age were included in the current analysis. The overall prevalence of stunting, wasting, and underweight was 41.3% (95% CI 39.0-42.9). The χ(2) test and multilevel logistic regression analysis showed that the variables age, sex, mother's body mass index, mother's educational status, father's educational status, place of residence, socioeconomic status, community status, religion, region of residence, and food security are significant factors of child malnutrition. Children with poor socioeconomic and community status were at higher risk of malnutrition. Children from food insecure families were more likely to be malnourished. Significant community- and household-level variations were found. The prevalence of child malnutrition is still high in Bangladesh, and the risk was assessed at several multilevel factors. Therefore, prevention of malnutrition should be given top priority as a major public health intervention. Copyright © 2016 Elsevier Inc. All rights reserved.

Perturbed effects at radiation physics

NASA Astrophysics Data System (ADS)

Külahcı, Fatih; Şen, Zekâi

2013-09-01

Perturbation methodology is applied in order to assess the linear attenuation coefficient, mass attenuation coefficient and cross-section behavior with random components in the basic variables such as the radiation amounts frequently used in the radiation physics and chemistry. Additionally, layer attenuation coefficient (LAC) and perturbed LAC (PLAC) are proposed for different contact materials. Perturbation methodology provides opportunity to obtain results with random deviations from the average behavior of each variable that enters the whole mathematical expression. The basic photon intensity variation expression as the inverse exponential power law (as Beer-Lambert's law) is adopted for perturbation method exposition. Perturbed results are presented not only in terms of the mean but additionally the standard deviation and the correlation coefficients. Such perturbation expressions provide one to assess small random variability in basic variables.

MCMC Sampling for a Multilevel Model with Nonindependent Residuals within and between Cluster Units

ERIC Educational Resources Information Center

Browne, William; Goldstein, Harvey

2010-01-01

In this article, we discuss the effect of removing the independence assumptions between the residuals in two-level random effect models. We first consider removing the independence between the Level 2 residuals and instead assume that the vector of all residuals at the cluster level follows a general multivariate normal distribution. We…

Teaching Groups as Foci for Evaluating Performance in Cost-Effectiveness of GCE Advanced Level Provision: Some Practical Methodological Innovations.

ERIC Educational Resources Information Center

Fielding, Antony

2002-01-01

Analyzes subject teaching-group effectiveness in English and Welsh General Certification of Education (GCE) Advanced Level prior to a linking to resources; suggests cross-classified multilevel models with weighted random effects for disentangling student, group, and teacher effects; finds that teacher effects are considerable, but cannot find…

The Friendly Schools Friendly Families Programme: Three-Year Bullying Behaviour Outcomes in Primary School Children

ERIC Educational Resources Information Center

Cross, Donna; Waters, Stacey; Pearce, Natasha; Shaw, Therese; Hall, Margaret; Erceg, Erin; Burns, Sharyn; Roberts, Clare; Hamilton, Greg

2012-01-01

Purpose: This three-year group randomized controlled trial assessed whether a multi-age, multi-level bullying prevention and intervention with staff capacity building, can reduce bullying among primary school children. Methods: This study comprised two intervention and one comparison conditions. Student self-report data were collected from 2552…

A Multilevel Study of Self-Beliefs and Student Behaviors in a Group Problem-Solving Task

ERIC Educational Resources Information Center

Hanham, José; McCormick, John

2018-01-01

Relationships among self-construal, self-efficacy, and group behaviors during a group problem-solving task with friends and acquaintances were hypothesized. The sample comprised 126 students in Grades 8-11, from 5 randomly selected government high schools, organized into 42 groups. Data collection involved self-reports and observations.…

Impact of Baltimore Healthy Eating Zones: An Environmental Intervention to Improve Diet among African American Youth

ERIC Educational Resources Information Center

Shin, Ahyoung; Surkan, Pamela J.; Coutinho, Anastasia J.; Suratkar, Sonali R.; Campbell, Rebecca K.; Rowan, Megan; Sharma, Sangita; Dennisuk, Lauren A.; Karlsen, Micaela; Gass, Anthony; Gittelsohn, Joel

2015-01-01

This study assessed the impact of a youth-targeted multilevel nutrition intervention in Baltimore City. The study used a clustered randomized design in which 7 recreation centers and 21 corner stores received interventions and 7 additional recreation centers served as comparison. The 8-month intervention aimed to increase availability and…

Examining the Effects of Jyoti Meditation on Stress and the Moderating Role of Emotional Intelligence

ERIC Educational Resources Information Center

Gutierrez, Daniel; Conley, Abigail H.; Young, Mark

2016-01-01

The authors examined whether Jyoti meditation (JM), a spiritually based meditation (Singh, 2012), influenced student counselors' (N = 60) level of stress and emotional intelligence (EI). Results from a randomized controlled trial and growth curve analysis provided a multilevel model in which JM reduced stress and EI moderated the effect.

The Performance of Methods to Test Upper-Level Mediation in the Presence of Nonnormal Data

ERIC Educational Resources Information Center

Pituch, Keenan A.; Stapleton, Laura M.

2008-01-01

A Monte Carlo study compared the statistical performance of standard and robust multilevel mediation analysis methods to test indirect effects for a cluster randomized experimental design under various departures from normality. The performance of these methods was examined for an upper-level mediation process, where the indirect effect is a fixed…

Literacy Coaching to Improve Student Reading Achievement: A Multi-Level Mediation Model

ERIC Educational Resources Information Center

Matsumura, Lindsay Clare; Garnier, Helen E.; Spybrook, Jessaca

2013-01-01

In a longitudinal group-randomized trial, we explore the key role of the quality of classroom text discussions in mediating the effects of Content-Focused Coaching (CFC) on student reading achievement (2983 students, 167 teachers). Schools in the United States serving large numbers of minority and English language learning (ELL) students from…

Influences of sampling size and pattern on the uncertainty of correlation estimation between soil water content and its influencing factors

NASA Astrophysics Data System (ADS)

Lai, Xiaoming; Zhu, Qing; Zhou, Zhiwen; Liao, Kaihua

2017-12-01

In this study, seven random combination sampling strategies were applied to investigate the uncertainties in estimating the hillslope mean soil water content (SWC) and correlation coefficients between the SWC and soil/terrain properties on a tea + bamboo hillslope. One of the sampling strategies is the global random sampling and the other six are the stratified random sampling on the top, middle, toe, top + mid, top + toe and mid + toe slope positions. When each sampling strategy was applied, sample sizes were gradually reduced and each sampling size contained 3000 replicates. Under each sampling size of each sampling strategy, the relative errors (REs) and coefficients of variation (CVs) of the estimated hillslope mean SWC and correlation coefficients between the SWC and soil/terrain properties were calculated to quantify the accuracy and uncertainty. The results showed that the uncertainty of the estimations decreased as the sampling size increasing. However, larger sample sizes were required to reduce the uncertainty in correlation coefficient estimation than in hillslope mean SWC estimation. Under global random sampling, 12 randomly sampled sites on this hillslope were adequate to estimate the hillslope mean SWC with RE and CV ≤10%. However, at least 72 randomly sampled sites were needed to ensure the estimated correlation coefficients with REs and CVs ≤10%. Comparing with all sampling strategies, reducing sampling sites on the middle slope had the least influence on the estimation of hillslope mean SWC and correlation coefficients. Under this strategy, 60 sites (10 on the middle slope and 50 on the top and toe slopes) were enough to ensure the estimated correlation coefficients with REs and CVs ≤10%. This suggested that when designing the SWC sampling, the proportion of sites on the middle slope can be reduced to 16.7% of the total number of sites. Findings of this study will be useful for the optimal SWC sampling design.

Fitting and Calibrating a Multilevel Mixed-Effects Stem Taper Model for Maritime Pine in NW Spain

PubMed Central

Arias-Rodil, Manuel; Castedo-Dorado, Fernando; Cámara-Obregón, Asunción; Diéguez-Aranda, Ulises

2015-01-01

Stem taper data are usually hierarchical (several measurements per tree, and several trees per plot), making application of a multilevel mixed-effects modelling approach essential. However, correlation between trees in the same plot/stand has often been ignored in previous studies. Fitting and calibration of a variable-exponent stem taper function were conducted using data from 420 trees felled in even-aged maritime pine (Pinus pinaster Ait.) stands in NW Spain. In the fitting step, the tree level explained much more variability than the plot level, and therefore calibration at plot level was omitted. Several stem heights were evaluated for measurement of the additional diameter needed for calibration at tree level. Calibration with an additional diameter measured at between 40 and 60% of total tree height showed the greatest improvement in volume and diameter predictions. If additional diameter measurement is not available, the fixed-effects model fitted by the ordinary least squares technique should be used. Finally, we also evaluated how the expansion of parameters with random effects affects the stem taper prediction, as we consider this a key question when applying the mixed-effects modelling approach to taper equations. The results showed that correlation between random effects should be taken into account when assessing the influence of random effects in stem taper prediction. PMID:26630156

A randomized controlled trial of a multilevel intervention to increase colorectal cancer screening among Latino immigrants in a primary care facility.

PubMed

Aragones, Abraham; Schwartz, Mark D; Shah, Nirav R; Gany, Francesca M

2010-06-01

Latino immigrants face a higher burden of colorectal cancer (CRC) and screening rates are low. To assess the effectiveness of a multilevel intervention in increasing the rate of CRC screening among Latino immigrants. A randomized controlled trial, with randomization at the physician level. Pairs of 65 primary care physicians and 65 Latino immigrant patients participated, 31 in the intervention and 34 in the control group. CRC educational video in Spanish on a portable personal digital video display device accompanied by a brochure with key information for the patient, and a patient-delivered paper-based reminder for their physician. Completed CRC screening, physician recommendation for CRC screening, and patient adherence to physician recommended CRC screening. The overall rate of completed screening for CRC was 55% for the intervention and 18% for the control group (p = 0.002). Physicians recommended CRC screening for 61% of patients in the intervention group versus 41% in the control group (p = 0.08). Of those that received a recommendation, 90% in the intervention group adhered to it versus 26% in the control group (p = 0.007). The intervention was successful in increasing rates of completed CRC screening primarily through increasing adherence after screening was recommended. Additional efforts should focus on developing new strategies to increase physician recommendation for CRC screening, while employing effective patient adherence interventions.

Improved estimates of partial volume coefficients from noisy brain MRI using spatial context.

PubMed

Manjón, José V; Tohka, Jussi; Robles, Montserrat

2010-11-01

This paper addresses the problem of accurate voxel-level estimation of tissue proportions in the human brain magnetic resonance imaging (MRI). Due to the finite resolution of acquisition systems, MRI voxels can contain contributions from more than a single tissue type. The voxel-level estimation of this fractional content is known as partial volume coefficient estimation. In the present work, two new methods to calculate the partial volume coefficients under noisy conditions are introduced and compared with current similar methods. Concretely, a novel Markov Random Field model allowing sharp transitions between partial volume coefficients of neighbouring voxels and an advanced non-local means filtering technique are proposed to reduce the errors due to random noise in the partial volume coefficient estimation. In addition, a comparison was made to find out how the different methodologies affect the measurement of the brain tissue type volumes. Based on the obtained results, the main conclusions are that (1) both Markov Random Field modelling and non-local means filtering improved the partial volume coefficient estimation results, and (2) non-local means filtering was the better of the two strategies for partial volume coefficient estimation. Copyright 2010 Elsevier Inc. All rights reserved.

Displacement Based Multilevel Structural Optimization

NASA Technical Reports Server (NTRS)

Sobieszezanski-Sobieski, J.; Striz, A. G.

1996-01-01

In the complex environment of true multidisciplinary design optimization (MDO), efficiency is one of the most desirable attributes of any approach. In the present research, a new and highly efficient methodology for the MDO subset of structural optimization is proposed and detailed, i.e., for the weight minimization of a given structure under size, strength, and displacement constraints. Specifically, finite element based multilevel optimization of structures is performed. In the system level optimization, the design variables are the coefficients of assumed polynomially based global displacement functions, and the load unbalance resulting from the solution of the global stiffness equations is minimized. In the subsystems level optimizations, the weight of each element is minimized under the action of stress constraints, with the cross sectional dimensions as design variables. The approach is expected to prove very efficient since the design task is broken down into a large number of small and efficient subtasks, each with a small number of variables, which are amenable to parallel computing.

Zero-Profile Spacer Versus Cage-Plate Construct in Anterior Cervical Diskectomy and Fusion for Multilevel Cervical Spondylotic Myelopathy: Systematic Review and Meta-Analysis.

PubMed

Tong, Min-Ji; Xiang, Guang-Heng; He, Zi-Li; Chen, De-Heng; Tang, Qian; Xu, Hua-Zi; Tian, Nai-Feng

2017-08-01

Anterior cervical diskectomy and fusion with plate-screw construct has been gradually applied for multilevel cervical spondylotic myelopathy in recent years. However, long cervical plate was associated with complications including breakage or loosening of plate and screws, trachea-esophageal injury, neurovascular injury, and postoperative dysphagia. To reduce these complications, the zero-profile spacer has been introduced. This meta-analysis was performed to compare the clinical and radiologic outcomes of zero-profile spacer versus cage-plate construct for the treatment of multilevel cervical spondylotic myelopathy. We systematically searched MEDLINE, Springer, and Web of Science databases for relevant studies that compared the clinical and radiologic outcomes of zero-profile spacer versus cage and plate for multilevel cervical spondylotic myelopathy. Risk of bias in included studies was assessed. Pooled estimates and corresponding 95% confidence intervals were calculated. On the basis of predefined inclusion criteria, 7 studies with a total of 409 patients were included in this analysis. The pooled data revealed that zero-profile spacer was associated with a decreased dysphagia rate at 2, 3, and 6 months postoperatively when compared with the cage-plate group. Both techniques had similar perioperative outcomes, functional outcome, radiologic outcome, and dysphagia rate immediately and at >1-year after operation. On the basis of available evidence, zero-profile spacer was more effective in reducing postoperative dysphagia rate for multilevel cervical spondylotic myelopathy. Both devices were safe in anterior cervical surgeries, and they had similar efficacy in improving the functional and radiologic outcomes. More randomized controlled trials are needed to compare these 2 devices. Copyright © 2017 Elsevier Inc. All rights reserved.

Electrically-controlled nonlinear switching and multi-level storage characteristics in WOx film-based memory cells

NASA Astrophysics Data System (ADS)

Duan, W. J.; Wang, J. B.; Zhong, X. L.

2018-05-01

Resistive switching random access memory (RRAM) is considered as a promising candidate for the next generation memory due to its scalability, high integration density and non-volatile storage characteristics. Here, the multiple electrical characteristics in Pt/WOx/Pt cells are investigated. Both of the nonlinear switching and multi-level storage can be achieved by setting different compliance current in the same cell. The correlations among the current, time and temperature are analyzed by using contours and 3D surfaces. The switching mechanism is explained in terms of the formation and rupture of conductive filament which is related to oxygen vacancies. The experimental results show that the non-stoichiometric WOx film-based device offers a feasible way for the applications of oxide-based RRAMs.

Income inequality, parental socioeconomic status, and birth outcomes in Japan.

PubMed

Fujiwara, Takeo; Ito, Jun; Kawachi, Ichiro

2013-05-15

The purpose of this study was to investigate the impact of income inequality and parental socioeconomic status on several birth outcomes in Japan. Data were collected on birth outcomes and parental socioeconomic status by questionnaire from Japanese parents nationwide (n = 41,499) and then linked to Gini coefficients at the prefectural level in 2001. In multilevel analysis, z scores of birth weight for gestational age decreased by 0.018 (95% confidence interval (CI): -0.029, -0.006) per 1-standard-deviation (0.018-unit) increase in the Gini coefficient, while gestational age at delivery was not associated with the Gini coefficient. For dichotomous outcomes, mothers living in prefectures with middle and high Gini coefficients were 1.24 (95% CI: 1.05, 1.47) and 1.23 (95% CI: 1.02, 1.48) times more likely, respectively, to deliver a small-for-gestational-age infant than mothers living in more egalitarian prefectures (low Gini coefficients), although preterm births were not significantly associated with income distribution. Parental educational level, but not household income, was significantly associated with the z score of birth weight for gestational age and small-for-gestational-age status. Higher income inequality at the prefectural level and parental educational level, rather than household income, were associated with intrauterine growth but not with shorter gestational age at delivery.

Probing Resilience: Daily Environmental Mastery, Self-Esteem, and Stress Appraisal.

PubMed

Montpetit, Mignon A; Tiberio, Stacey S

2016-10-01

The current study explores one way the process of resilience arises by investigating the underlying process of stress appraisal. In particular, the analyses examine how resilience resources function each day to attenuate the extent to which life experiences are perceived as threatening, and how trait-like resilience resources shape the appraisal process. Daily diary and questionnaire data from 96 participants of Successful Aging in Context: The Macroenvironment and Daily Lived Experience (SAIC; MAge = 67 years, SDAge = 4.9 years; range: 58-86 years) were analyzed using multilevel random coefficient modeling to investigate how individuals' daily perceptions of control and self-esteem impacted perceived stress on a given day. Results suggested that both self-esteem and environmental mastery help mitigate the experience of stress; furthermore, dispositional resilience and self-esteem stability predict differences between individuals in the extent to which self-esteem tempers the perception of stress each day. The results inform theoretical and empirical work on the nature of resilience, especially regarding how the process arises in ordinary life. From an application perspective, results imply that augmenting environmental mastery and self-esteem, both of which are malleable, can facilitate resilience by helping elders challenge their perceptions of stress each day. © The Author(s) 2016.

Discrimination and subjective well-being: protective influences of membership in a discriminated category.

PubMed

Hnilica, Karel

2011-03-01

Research reveals that discrimination has harmful effects on health and quality of life. Among the most frequent types of discrimination pertains gender and age discrimination. Research results show that discriminatory behaviours based on gender afflict predominantly women; age discrimination afflicts mainly older adults. At the same time, it has been found that members of these traditionally discriminated categories often use strategies that mitigate the effects of discrimination. Discrimination will have detrimental effects on subjective well-being. But its effects will be most harmful for persons who are not members of the traditionally discriminated categories. These hypotheses were tested on data from three waves of the European Social Survey that the Czech Republic also participated in. Data were analyzed in a series of multilevel random coefficients regression analyses with respondents nested within states and states nested within years of study. Both perceived gender discrimination and perceived age discrimination have negative effects on subjective well-being. However, gender discrimination had more harmful effects on the subjective well-being of men than women and age discrimination had the most harmful effects on the subjective well-being of people in their middle ages, not the elderly ones. Discrimination does not need to have most harmful effects on the quality of life of members of the categories that are discriminated against most often.

Random diffusion and leverage effect in financial markets.

PubMed

Perelló, Josep; Masoliver, Jaume

2003-03-01

We prove that Brownian market models with random diffusion coefficients provide an exact measure of the leverage effect [J-P. Bouchaud et al., Phys. Rev. Lett. 87, 228701 (2001)]. This empirical fact asserts that past returns are anticorrelated with future diffusion coefficient. Several models with random diffusion have been suggested but without a quantitative study of the leverage effect. Our analysis lets us to fully estimate all parameters involved and allows a deeper study of correlated random diffusion models that may have practical implications for many aspects of financial markets.

Statistical Power and Optimum Sample Allocation Ratio for Treatment and Control Having Unequal Costs Per Unit of Randomization

ERIC Educational Resources Information Center

Liu, Xiaofeng

2003-01-01

This article considers optimal sample allocation between the treatment and control condition in multilevel designs when the costs per sampling unit vary due to treatment assignment. Optimal unequal allocation may reduce the cost from that of a balanced design without sacrificing any power. The optimum sample allocation ratio depends only on the…

Fully Bayesian Estimation of Data from Single Case Designs

ERIC Educational Resources Information Center

Rindskopf, David

2013-01-01

Single case designs (SCDs) generally consist of a small number of short time series in two or more phases. The analysis of SCDs statistically fits in the framework of a multilevel model, or hierarchical model. The usual analysis does not take into account the uncertainty in the estimation of the random effects. This not only has an effect on the…

Employing a Multi-level Approach to Recruit a Representative Sample of Women with Recent Gestational Diabetes Mellitus into a Randomized Lifestyle Intervention Trial.

PubMed

Nicklas, Jacinda M; Skurnik, Geraldine; Zera, Chloe A; Reforma, Liberty G; Levkoff, Sue E; Seely, Ellen W

2016-02-01

The postpartum period is a window of opportunity for diabetes prevention in women with recent gestational diabetes (GDM), but recruitment for clinical trials during this period of life is a major challenge. We adapted a social-ecologic model to develop a multi-level recruitment strategy at the macro (high or institutional level), meso (mid or provider level), and micro (individual) levels. Our goal was to recruit 100 women with recent GDM into the Balance after Baby randomized controlled trial over a 17-month period. Participants were asked to attend three in-person study visits at 6 weeks, 6, and 12 months postpartum. They were randomized into a control arm or a web-based intervention arm at the end of the baseline visit at six weeks postpartum. At the end of the recruitment period, we compared population characteristics of our enrolled subjects to the entire population of women with GDM delivering at Brigham and Women's Hospital (BWH). We successfully recruited 107 of 156 (69 %) women assessed for eligibility, with the majority (92) recruited during pregnancy at a mean 30 (SD ± 5) weeks of gestation, and 15 recruited postpartum, at a mean 2 (SD ± 3) weeks postpartum. 78 subjects attended the initial baseline visit, and 75 subjects were randomized into the trial at a mean 7 (SD ± 2) weeks postpartum. The recruited subjects were similar in age and race/ethnicity to the total population of 538 GDM deliveries at BWH over the 17-month recruitment period. Our multilevel approach allowed us to successfully meet our recruitment goal and recruit a representative sample of women with recent GDM. We believe that our most successful strategies included using a dedicated in-person recruiter, integrating recruitment into clinical flow, allowing for flexibility in recruitment, minimizing barriers to participation, and using an opt-out strategy with providers. Although the majority of women were recruited while pregnant, women recruited in the early postpartum period were more likely to present for the first study visit. Given the increased challenges of recruiting postpartum women with GDM into research studies, we believe our findings will be useful to other investigators seeking to study this population.

Resonance energy transfer process in nanogap-based dual-color random lasing

NASA Astrophysics Data System (ADS)

Shi, Xiaoyu; Tong, Junhua; Liu, Dahe; Wang, Zhaona

2017-04-01

The resonance energy transfer (RET) process between Rhodamine 6G and oxazine in the nanogap-based random systems is systematically studied by revealing the variations and fluctuations of RET coefficients with pump power density. Three working regions stable fluorescence, dynamic laser, and stable laser are thus demonstrated in the dual-color random systems. The stable RET coefficients in fluorescence and lasing regions are generally different and greatly dependent on the donor concentration and the donor-acceptor ratio. These results may provide a way to reveal the energy distribution regulars in the random system and to design the tunable multi-color coherent random lasers for colorful imaging.

Engaging Latina Cancer Survivors, their Caregivers, and Community Partners in a Randomized Controlled Trial: Nueva Vida Intervention

PubMed Central

Rush, Christina L.; Darling, Margaret; Elliott, Maria Gloria; Febus-Sampayo, Ivis; Kuo, Charlene; Muñoz, Juliana; Duron, Ysabel; Torres, Migdalia; Galván, Claudia Campos; Gonzalez, Florencia; Caicedo, Larisa; Nápoles, Anna; Jensen, Roxanne E.; Anderson, Emily; Graves, Kristi D.

2014-01-01

Introduction Few studies have evaluated interventions to improve quality of life (QOL) for Latina breast cancer survivors and caregivers. Following best practices in community-based participatory research (CBPR), we established a multi-level partnership among Latina survivors, caregivers, community-based organizations (CBOs), clinicians and researchers to evaluate a survivor-caregiver QOL intervention. Methods A CBO in the mid-Atlantic region, Nueva Vida, developed a patient-caregiver program called Cuidando a mis Cuidadores (Caring for My Caregivers), to improve outcomes important to Latina cancer survivors and their families. Together with an academic partner, Nueva Vida and 3 CBOs established a multi-level team of researchers, clinicians, Latina cancer survivors, and caregivers to conduct a national randomized trial to compare the patient-caregiver program to usual care. Results Incorporating team feedback and programmatic considerations, we adapted the prior patient-caregiver program into an 8-session patient- and caregiver-centered intervention that includes skill-building workshops such as managing stress, communication, self-care, social well-being, and impact of cancer on sexual intimacy. We will measure QOL domains with the Patient-Reported Outcomes Measurement Information System (PROMIS), dyadic communication between the survivor and caregiver, and survivors’ adherence to recommended cancer care. To integrate the intervention within each CBO, we conducted interactive training on the protection of human subjects, qualitative interviewing, and intervention delivery. Conclusion The development and engagement process for our QOL intervention study is innovative because it is both informed by and directly impacts underserved Latina survivors and caregivers. The CBPR-based process demonstrates successful multi-level patient engagement through collaboration among researchers, clinicians, community partners, survivors and caregivers. PMID:25377349

Does an uneven sample size distribution across settings matter in cross-classified multilevel modeling? Results of a simulation study.

PubMed

Milliren, Carly E; Evans, Clare R; Richmond, Tracy K; Dunn, Erin C

2018-06-06

Recent advances in multilevel modeling allow for modeling non-hierarchical levels (e.g., youth in non-nested schools and neighborhoods) using cross-classified multilevel models (CCMM). Current practice is to cluster samples from one context (e.g., schools) and utilize the observations however they are distributed from the second context (e.g., neighborhoods). However, it is unknown whether an uneven distribution of sample size across these contexts leads to incorrect estimates of random effects in CCMMs. Using the school and neighborhood data structure in Add Health, we examined the effect of neighborhood sample size imbalance on the estimation of variance parameters in models predicting BMI. We differentially assigned students from a given school to neighborhoods within that school's catchment area using three scenarios of (im)balance. 1000 random datasets were simulated for each of five combinations of school- and neighborhood-level variance and imbalance scenarios, for a total of 15,000 simulated data sets. For each simulation, we calculated 95% CIs for the variance parameters to determine whether the true simulated variance fell within the interval. Across all simulations, the "true" school and neighborhood variance parameters were estimated 93-96% of the time. Only 5% of models failed to capture neighborhood variance; 6% failed to capture school variance. These results suggest that there is no systematic bias in the ability of CCMM to capture the true variance parameters regardless of the distribution of students across neighborhoods. Ongoing efforts to use CCMM are warranted and can proceed without concern for the sample imbalance across contexts. Copyright © 2018 Elsevier Ltd. All rights reserved.

Increased Survival Among HIV-Infected PWID Receiving a Multi-Level HIV Risk and Stigma Reduction Intervention: Results From a Randomized Controlled Trial.

PubMed

Go, Vivian F; Frangakis, Constantine; Le Minh, Nguyen; Ha, Tran Viet; Latkin, Carl A; Sripaipan, Teerada; Zelaya, Carla E; Davis, Wendy W; Celentano, David D; Quan, Vu Minh

2017-02-01

In Vietnam, where 58% of prevalent HIV cases are attributed to people who inject drugs, we evaluated whether a multi-level intervention could improve care outcomes and increase survival. We enrolled 455 HIV-infected males who inject drugs from 32 communes in Thai Nguyen Province. Communes were randomized to a community stigma reduction intervention or standard of care and then within each commune, to an individual enhanced counseling intervention or standard of care, resulting into 4 arms: Arm 1 (standard of care); Arm 2 (community intervention alone); Arm 3 (individual intervention alone); and Arm 4 (community + individual interventions). Follow-up was conducted at 6, 12, 18, and 24 months to assess survival. Overall mortality was 23% (n = 103/455) more than 2 years. There were no losses to follow-up for the mortality endpoint. Survival at 24 months was different across arms: Arm 4 (87%) vs Arm 1 (82%) vs Arm 2 (68%) vs Arm 3 (73%); log-rank test for comparison among arms: P = 0.001. Among those with CD4 cell count <200 cells/mm and not on antiretroviral therapy at baseline (n = 162), survival at 24 months was higher in Arm 4 (84%) compared with other arms (Arm 1: 61%; Arm 2: 50%; Arm 3: 53%; P-value = 0.002). Overall, Arm 4 (community + individual interventions) had increased uptake of antiretroviral therapy compared with Arms 1, 2, and 3. This multi-level behavioral intervention seemed to increase survival of HIV-infected participants more than a 2-year period. Relative to the standard of care, the greatest intervention effect was among those with lower CD4 cell counts.

Statistical Analysis for Multisite Trials Using Instrumental Variables with Random Coefficients

ERIC Educational Resources Information Center

Raudenbush, Stephen W.; Reardon, Sean F.; Nomi, Takako

2012-01-01

Multisite trials can clarify the average impact of a new program and the heterogeneity of impacts across sites. Unfortunately, in many applications, compliance with treatment assignment is imperfect. For these applications, we propose an instrumental variable (IV) model with person-specific and site-specific random coefficients. Site-specific IV…

Improvement of multi-level resistive switching characteristics in solution-processed AlO x -based non-volatile resistive memory using microwave irradiation

NASA Astrophysics Data System (ADS)

Kim, Seung-Tae; Cho, Won-Ju

2018-01-01

We fabricated a resistive random access memory (ReRAM) device on a Ti/AlO x /Pt structure with solution-processed AlO x switching layer using microwave irradiation (MWI), and demonstrated multi-level cell (MLC) operation. To investigate the effect of MWI power on the MLC characteristics, post-deposition annealing was performed at 600-3000 W after AlO x switching layer deposition, and the MLC operation was compared with as-deposited (as-dep) and conventional thermally annealing (CTA) treated devices. All solution-processed AlO x -based ReRAM devices exhibited bipolar resistive switching (BRS) behavior. We found that these devices have four-resistance states (2 bits) of MLC operation according to the modulation of the high-resistance state (HRSs) through reset voltage control. Particularly, compared to the as-dep and CTA ReRAM devices, the MWI-treated ReRAM devices showed a significant increase in the memory window and stable endurance for multi-level operation. Moreover, as the MWI power increased, excellent MLC characteristics were exhibited because the resistance ratio between each resistance state was increased. In addition, it exhibited reliable retention characteristics without deterioration at 25 °C and 85 °C for 10 000 s. Finally, the relationship between the chemical characteristics of the solution-processed AlO x switching layer and BRS-based multi-level operation according to the annealing method and MWI power was investigated using x-ray photoelectron spectroscopy.

Objectively measured sedentary time and academic achievement in schoolchildren.

PubMed

Lopes, Luís; Santos, Rute; Mota, Jorge; Pereira, Beatriz; Lopes, Vítor

2017-03-01

This study aimed to evaluate the relationship between objectively measured total sedentary time and academic achievement (AA) in Portuguese children. The sample comprised of 213 children (51.6% girls) aged 9.46 ± 0.43 years, from the north of Portugal. Sedentary time was measured with accelerometry, and AA was assessed using the Portuguese Language and Mathematics National Exams results. Multilevel linear regression models were fitted to assess regression coefficients predicting AA. The results showed that objectively measured total sedentary time was not associated with AA, after adjusting for potential confounders.

Community-level income inequality and HIV prevalence among persons who inject drugs in Thai Nguyen, Vietnam.

PubMed

Lim, Travis W; Frangakis, Constantine; Latkin, Carl; Ha, Tran Viet; Minh, Nguyen Le; Zelaya, Carla; Quan, Vu Minh; Go, Vivian F

2014-01-01

Socioeconomic status has a robust positive relationship with several health outcomes at the individual and population levels, but in the case of HIV prevalence, income inequality may be a better predictor than absolute level of income. Most studies showing a relationship between income inequality and HIV have used entire countries as the unit of analysis. In this study, we examine the association between income inequality at the community level and HIV prevalence in a sample of persons who inject drugs (PWID) in a concentrated epidemic setting. We recruited PWID and non-PWID community participants in Thai Nguyen, Vietnam, and administered a cross-sectional questionnaire; PWID were tested for HIV. We used ecologic regression to model HIV burden in our PWID study population on GINI indices of inequality calculated from total reported incomes of non-PWID community members in each commune. We also modeled HIV burden on interaction terms between GINI index and median commune income, and finally used a multi-level model to control for community level inequality and individual level income. HIV burden among PWID was significantly correlated with the commune GINI coefficient (r = 0.53, p = 0.002). HIV burden was also associated with GINI coefficient (β = 0.082, p = 0.008) and with median commune income (β = -0.018, p = 0.023) in ecological regression. In the multi-level model, higher GINI coefficient at the community level was associated with higher odds of individual HIV infection in PWID (OR = 1.46 per 0.01, p = 0.003) while higher personal income was associated with reduced odds of infection (OR = 0.98 per $10, p = 0.022). This study demonstrates a context where income inequality is associated with HIV prevalence at the community level in a concentrated epidemic. It further suggests that community level socioeconomic factors, both contextual and compositional, could be indirect determinants of HIV infection in PWID.

Community-Level Income Inequality and HIV Prevalence among Persons Who Inject Drugs in Thai Nguyen, Vietnam

PubMed Central

Lim, Travis W.; Frangakis, Constantine; Latkin, Carl; Ha, Tran Viet; Minh, Nguyen Le; Zelaya, Carla; Quan, Vu Minh; Go, Vivian F.

2014-01-01

Socioeconomic status has a robust positive relationship with several health outcomes at the individual and population levels, but in the case of HIV prevalence, income inequality may be a better predictor than absolute level of income. Most studies showing a relationship between income inequality and HIV have used entire countries as the unit of analysis. In this study, we examine the association between income inequality at the community level and HIV prevalence in a sample of persons who inject drugs (PWID) in a concentrated epidemic setting. We recruited PWID and non-PWID community participants in Thai Nguyen, Vietnam, and administered a cross-sectional questionnaire; PWID were tested for HIV. We used ecologic regression to model HIV burden in our PWID study population on GINI indices of inequality calculated from total reported incomes of non-PWID community members in each commune. We also modeled HIV burden on interaction terms between GINI index and median commune income, and finally used a multi-level model to control for community level inequality and individual level income. HIV burden among PWID was significantly correlated with the commune GINI coefficient (r = 0.53, p = 0.002). HIV burden was also associated with GINI coefficient (β = 0.082, p = 0.008) and with median commune income (β = −0.018, p = 0.023) in ecological regression. In the multi-level model, higher GINI coefficient at the community level was associated with higher odds of individual HIV infection in PWID (OR = 1.46 per 0.01, p = 0.003) while higher personal income was associated with reduced odds of infection (OR = 0.98 per $10, p = 0.022). This study demonstrates a context where income inequality is associated with HIV prevalence at the community level in a concentrated epidemic. It further suggests that community level socioeconomic factors, both contextual and compositional, could be indirect determinants of HIV infection in PWID. PMID:24618892

A Bayesian Multilevel Model for Microcystin Prediction in ...

EPA Pesticide Factsheets

The frequency of cyanobacteria blooms in North American lakes is increasing. A major concernwith rising cyanobacteria blooms is microcystin, a common cyanobacterial hepatotoxin. Toexplore the conditions that promote high microcystin concentrations, we analyzed the US EPANational Lake Assessment (NLA) dataset collected in the summer of 2007. The NLA datasetis reported for nine eco-regions. We used the results of random forest modeling as a means ofvariable selection from which we developed a Bayesian multilevel model of microcystin concentrations.Model parameters under a multilevel modeling framework are eco-region specific, butthey are also assumed to be exchangeable across eco-regions for broad continental scaling. Theexchangeability assumption ensures that both the common patterns and eco-region specific featureswill be reflected in the model. Furthermore, the method incorporates appropriate estimatesof uncertainty. Our preliminary results show associations between microcystin and turbidity, totalnutrients, and N:P ratios. The NLA 2012 will be used for Bayesian updating. The results willhelp develop management strategies to alleviate microcystin impacts and improve lake quality. This work provides a probabilistic framework for predicting microcystin presences in lakes. It would allow for insights to be made about how changes in nutrient concentrations could potentially change toxin levels.

Using cross-classified multilevel models to disentangle school and neighborhood effects: an example focusing on smoking behaviors among adolescents in the United States.

PubMed

Dunn, Erin C; Richmond, Tracy K; Milliren, Carly E; Subramanian, S V

2015-01-01

Despite much interest in understanding the influence of contexts on health, most research has focused on one context at a time, ignoring the reality that individuals have simultaneous memberships in multiple settings. Using the example of smoking behavior among adolescents in the National Longitudinal Study of Adolescent Health, we applied cross-classified multilevel modeling (CCMM) to examine fixed and random effects for schools and neighborhoods. We compared the CCMM results with those obtained from a traditional multilevel model (MLM) focused on either the school and neighborhood separately. In the MLMs, 5.2% of the variation in smoking was due to differences between neighborhoods (when schools were ignored) and 6.3% of the variation in smoking was due to differences between schools (when neighborhoods were ignored). However in the CCMM examining neighborhood and school variation simultaneously, the neighborhood-level variation was reduced to 0.4%. Results suggest that using MLM, instead of CCMM, could lead to overestimating the importance of certain contexts and could ultimately lead to targeting interventions or policies to the wrong settings. Copyright © 2014 Elsevier Ltd. All rights reserved.

A longitudinal multilevel CFA-MTMM model for interchangeable and structurally different methods

PubMed Central

Koch, Tobias; Schultze, Martin; Eid, Michael; Geiser, Christian

2014-01-01

One of the key interests in the social sciences is the investigation of change and stability of a given attribute. Although numerous models have been proposed in the past for analyzing longitudinal data including multilevel and/or latent variable modeling approaches, only few modeling approaches have been developed for studying the construct validity in longitudinal multitrait-multimethod (MTMM) measurement designs. The aim of the present study was to extend the spectrum of current longitudinal modeling approaches for MTMM analysis. Specifically, a new longitudinal multilevel CFA-MTMM model for measurement designs with structurally different and interchangeable methods (called Latent-State-Combination-Of-Methods model, LS-COM) is presented. Interchangeable methods are methods that are randomly sampled from a set of equivalent methods (e.g., multiple student ratings for teaching quality), whereas structurally different methods are methods that cannot be easily replaced by one another (e.g., teacher, self-ratings, principle ratings). Results of a simulation study indicate that the parameters and standard errors in the LS-COM model are well recovered even in conditions with only five observations per estimated model parameter. The advantages and limitations of the LS-COM model relative to other longitudinal MTMM modeling approaches are discussed. PMID:24860515

Effect of conductance linearity and multi-level cell characteristics of TaOx-based synapse device on pattern recognition accuracy of neuromorphic system

NASA Astrophysics Data System (ADS)

Sung, Changhyuck; Lim, Seokjae; Kim, Hyungjun; Kim, Taesu; Moon, Kibong; Song, Jeonghwan; Kim, Jae-Joon; Hwang, Hyunsang

2018-03-01

To improve the classification accuracy of an image data set (CIFAR-10) by using analog input voltage, synapse devices with excellent conductance linearity (CL) and multi-level cell (MLC) characteristics are required. We analyze the CL and MLC characteristics of TaOx-based filamentary resistive random access memory (RRAM) to implement the synapse device in neural network hardware. Our findings show that the number of oxygen vacancies in the filament constriction region of the RRAM directly controls the CL and MLC characteristics. By adopting a Ta electrode (instead of Ti) and the hot-forming step, we could form a dense conductive filament. As a result, a wide range of conductance levels with CL is achieved and significantly improved image classification accuracy is confirmed.

Rational-emotive and cognitive-behavior therapy (REBT/CBT) versus pharmacotherapy versus REBT/CBT plus pharmacotherapy in the treatment of major depressive disorder in youth; a randomized clinical trial.

PubMed

Iftene, Felicia; Predescu, Elena; Stefan, Simona; David, Daniel

2015-02-28

Major depressive disorder is a highly prevalent and debilitating condition in youth, so developing efficient treatments is a priority for mental health professionals. Psychotherapy (i.e., cognitive behavioral therapy/CBT), pharmacotherapy (i.e., SSRI medication), and their combination have been shown to be effective in treating youth depression; however, the results are still mixed and there are few studies engaging multi-level analyses (i.e., subjective, cognitive, and biological). Therefore, the aims of this randomized control study (RCT) were both theoretical - integrating psychological and biological markers of depression in a multi-level outcome analysis - and practical - testing the generalizability of previous results on depressed Romanian youth population. Eighty-eight (N=88) depressed Romanian youths were randomly allocated to one of the three treatment arms: group Rational Emotive Behavior Therapy (REBT)/CBT (i.e., a form of CBT), pharmacotherapy (i.e., sertraline), and group REBT/CBT plus pharmacotherapy. The results showed that all outcomes (i.e., subjective, cognitive, and biological) significantly change from pre to post-treatment under all treatment conditions at a similar rate and there were no significant differences among conditions at post-test. In case of categorical analysis of the clinical response rate, we found a non-significant trend favoring group REBT/CBT therapy. Results of analyses concerning outcome interrelations are discussed. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

A Randomized Controlled Trial of a Multilevel Intervention to Increase Colorectal Cancer Screening among Latino Immigrants in a Primary Care Facility

PubMed Central

Schwartz, Mark D.; Shah, Nirav R.; Gany, Francesca M.

2010-01-01

BACKGROUND Latino immigrants face a higher burden of colorectal cancer (CRC) and screening rates are low. OBJECTIVE To assess the effectiveness of a multilevel intervention in increasing the rate of CRC screening among Latino immigrants. DESIGN A randomized controlled trial, with randomization at the physician level. PARTICIPANTS Pairs of 65 primary care physicians and 65 Latino immigrant patients participated, 31 in the intervention and 34 in the control group. INTERVENTION CRC educational video in Spanish on a portable personal digital video display device accompanied by a brochure with key information for the patient, and a patient-delivered paper-based reminder for their physician. MEASUREMENTS Completed CRC screening, physician recommendation for CRC screening, and patient adherence to physician recommended CRC screening. RESULTS The overall rate of completed screening for CRC was 55% for the intervention and 18% for the control group (p = 0.002). Physicians recommended CRC screening for 61% of patients in the intervention group versus 41% in the control group (p = 0.08). Of those that received a recommendation, 90% in the intervention group adhered to it versus 26% in the control group (p = 0.007). CONCLUSIONS The intervention was successful in increasing rates of completed CRC screening primarily through increasing adherence after screening was recommended. Additional efforts should focus on developing new strategies to increase physician recommendation for CRC screening, while employing effective patient adherence interventions. PMID:20213208

A Randomized Controlled Trial of a Resilience-Based Intervention for Children Affected by Parental HIV: Educational Outcomes at 24-, 30-, and 36-Months

ERIC Educational Resources Information Center

Harrison, Sayward E.; Li, Xiaoming; Zhang, JiaJia; Zhao, Junfeng; Zhao, Guoxiang

2018-01-01

Children of parents with human immunodeficiency virus (HIV) are at-risk for a variety of negative outcomes, including poor educational achievement. The multi-level, resilience-based "ChildCARE" intervention has been found to yield short-term improvement in a number of school-related variables for children affected by parental HIV.…

Enhancing physical and social environments to reduce obesity among public housing residents: rationale, trial design, and baseline data for the Healthy Families study.

PubMed

Quintiliani, Lisa M; DeBiasse, Michele A; Branco, Jamie M; Bhosrekar, Sarah Gees; Rorie, Jo-Anna L; Bowen, Deborah J

2014-11-01

Intervention programs that change environments have the potential for greater population impact on obesity compared to individual-level programs. We began a cluster randomized, multi-component multi-level intervention to improve weight, diet, and physical activity among low-socioeconomic status public housing residents. Here we describe the rationale, intervention design, and baseline survey data. After approaching 12 developments, ten were randomized to intervention (n=5) or assessment-only control (n=5). All residents in intervention developments are welcome to attend any intervention component: health screenings, mobile food bus, walking groups, cooking demonstrations, and a social media campaign; all of which are facilitated by community health workers who are residents trained in health outreach. To evaluate weight and behavioral outcomes, a subgroup of female residents and their daughters age 8-15 were recruited into an evaluation cohort. In total, 211 households completed the survey (RR=46.44%). Respondents were Latino (63%), Black (24%), and had ≤ high school education (64%). Respondents reported ≤2 servings of fruits & vegetables/day (62%), visiting fast food restaurants 1+ times/week (32%), and drinking soft drinks daily or more (27%). The only difference between randomized groups was race/ethnicity, with more Black residents in the intervention vs. control group (28% vs. 19%, p=0.0146). Among low-socioeconomic status urban public housing residents, we successfully recruited and randomized families into a multi-level intervention targeting obesity. If successful, this intervention model could be adopted in other public housing developments or entities that also employ community health workers, such as food assistance programs or hospitals. Copyright © 2014 Elsevier Inc. All rights reserved.

Study protocol for a group randomized controlled trial of a classroom-based intervention aimed at preventing early risk factors for drug abuse: integrating effectiveness and implementation research.

PubMed

Poduska, Jeanne; Kellam, Sheppard; Brown, C Hendricks; Ford, Carla; Windham, Amy; Keegan, Natalie; Wang, Wei

2009-09-02

While a number of preventive interventions delivered within schools have shown both short-term and long-term impact in epidemiologically based randomized field trials, programs are not often sustained with high-quality implementation over time. This study was designed to support two purposes. The first purpose was to test the effectiveness of a universal classroom-based intervention, the Whole Day First Grade Program (WD), aimed at two early antecedents to drug abuse and other problem behaviors, namely, aggressive, disruptive behavior and poor academic achievement. The second purpose--the focus of this paper--was to examine the utility of a multilevel structure to support high levels of implementation during the effectiveness trial, to sustain WD practices across additional years, and to train additional teachers in WD practices. The WD intervention integrated three components, each previously tested separately: classroom behavior management; instruction, specifically reading; and family-classroom partnerships around behavior and learning. Teachers and students in 12 schools were randomly assigned to receive either the WD intervention or the standard first-grade program of the school system (SC). Three consecutive cohorts of first graders were randomized within schools to WD or SC classrooms and followed through the end of third grade to test the effectiveness of the WD intervention. Teacher practices were assessed over three years to examine the utility of the multilevel structure to support sustainability and scaling-up. The design employed in this trial appears to have considerable utility to provide data on WD effectiveness and to inform the field with regard to structures required to move evidence-based programs into practice. NCT00257088.

Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features.

PubMed

Wu, Wei; Chen, Albert Y C; Zhao, Liang; Corso, Jason J

2014-03-01

Detection and segmentation of a brain tumor such as glioblastoma multiforme (GBM) in magnetic resonance (MR) images are often challenging due to its intrinsically heterogeneous signal characteristics. A robust segmentation method for brain tumor MRI scans was developed and tested. Simple thresholds and statistical methods are unable to adequately segment the various elements of the GBM, such as local contrast enhancement, necrosis, and edema. Most voxel-based methods cannot achieve satisfactory results in larger data sets, and the methods based on generative or discriminative models have intrinsic limitations during application, such as small sample set learning and transfer. A new method was developed to overcome these challenges. Multimodal MR images are segmented into superpixels using algorithms to alleviate the sampling issue and to improve the sample representativeness. Next, features were extracted from the superpixels using multi-level Gabor wavelet filters. Based on the features, a support vector machine (SVM) model and an affinity metric model for tumors were trained to overcome the limitations of previous generative models. Based on the output of the SVM and spatial affinity models, conditional random fields theory was applied to segment the tumor in a maximum a posteriori fashion given the smoothness prior defined by our affinity model. Finally, labeling noise was removed using "structural knowledge" such as the symmetrical and continuous characteristics of the tumor in spatial domain. The system was evaluated with 20 GBM cases and the BraTS challenge data set. Dice coefficients were computed, and the results were highly consistent with those reported by Zikic et al. (MICCAI 2012, Lecture notes in computer science. vol 7512, pp 369-376, 2012). A brain tumor segmentation method using model-aware affinity demonstrates comparable performance with other state-of-the art algorithms.

Temporal behavior of the effective diffusion coefficients for transport in heterogeneous saturated aquifers

NASA Astrophysics Data System (ADS)

Suciu, N.; Vamos, C.; Vereecken, H.; Vanderborght, J.; Hardelauf, H.

2003-04-01

When the small scale transport is modeled by a Wiener process and the large scale heterogeneity by a random velocity field, the effective coefficients, Deff, can be decomposed as sums between the local coefficient, D, a contribution of the random advection, Dadv, and a contribution of the randomness of the trajectory of plume center of mass, Dcm: Deff=D+Dadv-Dcm. The coefficient Dadv is similar to that introduced by Taylor in 1921, and more recent works associate it with the thermodynamic equilibrium. The ``ergodic hypothesis'' says that over large time intervals Dcm vanishes and the effect of the heterogeneity is described by Dadv=Deff-D. In this work we investigate numerically the long time behavior of the effective coefficients as well as the validity of the ergodic hypothesis. The transport in every realization of the velocity field is modeled with the Global Random Walk Algorithm, which is able to track as many particles as necessary to achieve a statistically reliable simulation of the process. Averages over realizations are further used to estimate mean coefficients and standard deviations. In order to remain in the frame of most of the theoretical approaches, the velocity field was generated in a linear approximation and the logarithm of the hydraulic conductivity was taken to be exponential decaying correlated with variance equal to 0.1. Our results show that even in these idealized conditions, the effective coefficients tend to asymptotic constant values only when the plume travels thousands of correlations lengths (while the first order theories usually predict Fickian behavior after tens of correlations lengths) and that the ergodicity conditions are still far from being met.

Random matrix theory analysis of cross-correlations in the US stock market: Evidence from Pearson’s correlation coefficient and detrended cross-correlation coefficient

NASA Astrophysics Data System (ADS)

Wang, Gang-Jin; Xie, Chi; Chen, Shou; Yang, Jiao-Jiao; Yang, Ming-Yan

2013-09-01

In this study, we first build two empirical cross-correlation matrices in the US stock market by two different methods, namely the Pearson’s correlation coefficient and the detrended cross-correlation coefficient (DCCA coefficient). Then, combining the two matrices with the method of random matrix theory (RMT), we mainly investigate the statistical properties of cross-correlations in the US stock market. We choose the daily closing prices of 462 constituent stocks of S&P 500 index as the research objects and select the sample data from January 3, 2005 to August 31, 2012. In the empirical analysis, we examine the statistical properties of cross-correlation coefficients, the distribution of eigenvalues, the distribution of eigenvector components, and the inverse participation ratio. From the two methods, we find some new results of the cross-correlations in the US stock market in our study, which are different from the conclusions reached by previous studies. The empirical cross-correlation matrices constructed by the DCCA coefficient show several interesting properties at different time scales in the US stock market, which are useful to the risk management and optimal portfolio selection, especially to the diversity of the asset portfolio. It will be an interesting and meaningful work to find the theoretical eigenvalue distribution of a completely random matrix R for the DCCA coefficient because it does not obey the Marčenko-Pastur distribution.

Measuring multivariate association and beyond

PubMed Central

Josse, Julie; Holmes, Susan

2017-01-01

Simple correlation coefficients between two variables have been generalized to measure association between two matrices in many ways. Coefficients such as the RV coefficient, the distance covariance (dCov) coefficient and kernel based coefficients are being used by different research communities. Scientists use these coefficients to test whether two random vectors are linked. Once it has been ascertained that there is such association through testing, then a next step, often ignored, is to explore and uncover the association’s underlying patterns. This article provides a survey of various measures of dependence between random vectors and tests of independence and emphasizes the connections and differences between the various approaches. After providing definitions of the coefficients and associated tests, we present the recent improvements that enhance their statistical properties and ease of interpretation. We summarize multi-table approaches and provide scenarii where the indices can provide useful summaries of heterogeneous multi-block data. We illustrate these different strategies on several examples of real data and suggest directions for future research. PMID:29081877

The effect of social support features and gamification on a Web-based intervention for rheumatoid arthritis patients: randomized controlled trial.

PubMed

Allam, Ahmed; Kostova, Zlatina; Nakamoto, Kent; Schulz, Peter Johannes

2015-01-09

Rheumatoid arthritis (RA) is chronic systematic disease that affects people during the most productive period of their lives. Web-based health interventions have been effective in many studies; however, there is little evidence and few studies showing the effectiveness of online social support and especially gamification on patients' behavioral and health outcomes. The aim of this study was to look into the effects of a Web-based intervention that included online social support features and gamification on physical activity, health care utilization, medication overuse, empowerment, and RA knowledge of RA patients. The effect of gamification on website use was also investigated. We conducted a 5-arm parallel randomized controlled trial for RA patients in Ticino (Italian-speaking part of Switzerland). A total of 157 patients were recruited through brochures left with physicians and were randomly allocated to 1 of 4 experimental conditions with different types of access to online social support and gamification features and a control group that had no access to the website. Data were collected at 3 time points through questionnaires at baseline, posttest 2 months later, and at follow-up after another 2 months. Primary outcomes were physical activity, health care utilization, and medication overuse; secondary outcomes included empowerment and RA knowledge. All outcomes were self-reported. Intention-to-treat analysis was followed and multilevel linear mixed models were used to study the change of outcomes over time. The best-fit multilevel models (growth curve models) that described the change in the primary outcomes over the course of the intervention included time and empowerment as time-variant predictors. The growth curve analyses of experimental conditions were compared to the control group. Physical activity increased over time for patients having access to social support sections plus gaming (unstandardized beta coefficient [B]=3.39, P=.02). Health care utilization showed a significant decrease for patients accessing social support features (B=-0.41, P=.01) and patients accessing both social support features and gaming (B=-0.33, P=.03). Patients who had access to either social support sections or the gaming experience of the website gained more empowerment (B=2.59, P=.03; B=2.29, P=.05; respectively). Patients who were offered a gamified experience used the website more often than the ones without gaming (t91=-2.41, P=.02; U=812, P=.02). The Web-based intervention had a positive impact (more desirable outcomes) on intervention groups compared to the control group. Social support sections on the website decreased health care utilization and medication overuse and increased empowerment. Gamification alone or with social support increased physical activity and empowerment and decreased health care utilization. This study provides evidence demonstrating the potential positive effect of gamification and online social support on health and behavioral outcomes. International Standard Randomized Controlled Trial Number (ISRCTN): 57366516; http://www.controlled-trials. com/ISRCTN57366516 (Archived by webcite at http://www.webcitation.org/6PBvvAvvV).

The Effect of Social Support Features and Gamification on a Web-Based Intervention for Rheumatoid Arthritis Patients: Randomized Controlled Trial

PubMed Central

Kostova, Zlatina; Nakamoto, Kent; Schulz, Peter Johannes

2015-01-01

Background Rheumatoid arthritis (RA) is chronic systematic disease that affects people during the most productive period of their lives. Web-based health interventions have been effective in many studies; however, there is little evidence and few studies showing the effectiveness of online social support and especially gamification on patients’ behavioral and health outcomes. Objective The aim of this study was to look into the effects of a Web-based intervention that included online social support features and gamification on physical activity, health care utilization, medication overuse, empowerment, and RA knowledge of RA patients. The effect of gamification on website use was also investigated. Methods We conducted a 5-arm parallel randomized controlled trial for RA patients in Ticino (Italian-speaking part of Switzerland). A total of 157 patients were recruited through brochures left with physicians and were randomly allocated to 1 of 4 experimental conditions with different types of access to online social support and gamification features and a control group that had no access to the website. Data were collected at 3 time points through questionnaires at baseline, posttest 2 months later, and at follow-up after another 2 months. Primary outcomes were physical activity, health care utilization, and medication overuse; secondary outcomes included empowerment and RA knowledge. All outcomes were self-reported. Intention-to-treat analysis was followed and multilevel linear mixed models were used to study the change of outcomes over time. Results The best-fit multilevel models (growth curve models) that described the change in the primary outcomes over the course of the intervention included time and empowerment as time-variant predictors. The growth curve analyses of experimental conditions were compared to the control group. Physical activity increased over time for patients having access to social support sections plus gaming (unstandardized beta coefficient [B]=3.39, P=.02). Health care utilization showed a significant decrease for patients accessing social support features (B=–0.41, P=.01) and patients accessing both social support features and gaming (B=–0.33, P=.03). Patients who had access to either social support sections or the gaming experience of the website gained more empowerment (B=2.59, P=.03; B=2.29, P=.05; respectively). Patients who were offered a gamified experience used the website more often than the ones without gaming (t 91=–2.41, P=.02; U=812, P=.02). Conclusions The Web-based intervention had a positive impact (more desirable outcomes) on intervention groups compared to the control group. Social support sections on the website decreased health care utilization and medication overuse and increased empowerment. Gamification alone or with social support increased physical activity and empowerment and decreased health care utilization. This study provides evidence demonstrating the potential positive effect of gamification and online social support on health and behavioral outcomes. Trial Registration International Standard Randomized Controlled Trial Number (ISRCTN): 57366516; http://www.controlled-trials. com/ISRCTN57366516 (Archived by webcite at http://www.webcitation.org/6PBvvAvvV). PMID:25574939

Ciliary Body Thickness and Refractive Error in Children

PubMed Central

Bailey, Melissa D.; Sinnott, Loraine T.; Mutti, Donald O.

2010-01-01

Purpose To determine whether ciliary body thickness (CBT) is related to refractive error in school-age children. Methods Fifty-three children, 8 to 15 years of age, were recruited. CBT was measured from anterior segment OCT images (Visante; Carl Zeiss Meditec, Inc., Dublin, CA) at 1 (CBT1), 2 (CBT2) and 3 (CBT3) mm posterior to the scleral spur. Cycloplegic refractive error was measured with an autorefractor, and axial length was measured with an optical biometer. Multilevel regression models determined the relationship between CBT measurements and refractive error or axial length. A Bland-Altman analysis was used to assess the between-visit repeatability of the ciliary body measurements. Results The between-visits coefficients of repeatability for CBT1, -2, and -3 were 148.04, 165.68, and 110.90, respectively. Thicker measurements at CBT2 (r = −0.29, P = 0.03) and CBT3 (r = −0.38, P = 0.005) were associated with increasingly myopic refractive errors (multilevel model: P < 0.001). Thicker measurements at CBT2 (r = 0.40, P = 0.003) and CBT3 (r = 0.51, P < 0.001) were associated with longer axial lengths (multilevel model: P < 0.001). Conclusions Thicker ciliary body measurements were associated with myopia and a longer axial length. Future studies should determine whether this relationship is also present in animal models of myopia and determine the temporal relationship between thickening of the ciliary muscle and the onset of myopia. PMID:18566470

Using Multisite Experiments to Study Cross-Site Variation in Treatment Effects: A Hybrid Approach with Fixed Intercepts and A Random Treatment Coefficient

ERIC Educational Resources Information Center

Bloom, Howard S.; Raudenbush, Stephen W.; Weiss, Michael J.; Porter, Kristin

2017-01-01

The present article considers a fundamental question in evaluation research: "By how much do program effects vary across sites?" The article first presents a theoretical model of cross-site impact variation and a related estimation model with a random treatment coefficient and fixed site-specific intercepts. This approach eliminates…

MATIN: a random network coding based framework for high quality peer-to-peer live video streaming.

PubMed

Barekatain, Behrang; Khezrimotlagh, Dariush; Aizaini Maarof, Mohd; Ghaeini, Hamid Reza; Salleh, Shaharuddin; Quintana, Alfonso Ariza; Akbari, Behzad; Cabrera, Alicia Triviño

2013-01-01

In recent years, Random Network Coding (RNC) has emerged as a promising solution for efficient Peer-to-Peer (P2P) video multicasting over the Internet. This probably refers to this fact that RNC noticeably increases the error resiliency and throughput of the network. However, high transmission overhead arising from sending large coefficients vector as header has been the most important challenge of the RNC. Moreover, due to employing the Gauss-Jordan elimination method, considerable computational complexity can be imposed on peers in decoding the encoded blocks and checking linear dependency among the coefficients vectors. In order to address these challenges, this study introduces MATIN which is a random network coding based framework for efficient P2P video streaming. The MATIN includes a novel coefficients matrix generation method so that there is no linear dependency in the generated coefficients matrix. Using the proposed framework, each peer encapsulates one instead of n coefficients entries into the generated encoded packet which results in very low transmission overhead. It is also possible to obtain the inverted coefficients matrix using a bit number of simple arithmetic operations. In this regard, peers sustain very low computational complexities. As a result, the MATIN permits random network coding to be more efficient in P2P video streaming systems. The results obtained from simulation using OMNET++ show that it substantially outperforms the RNC which uses the Gauss-Jordan elimination method by providing better video quality on peers in terms of the four important performance metrics including video distortion, dependency distortion, End-to-End delay and Initial Startup delay.

Macroscopic damping model for structural dynamics with random polycrystalline configurations

NASA Astrophysics Data System (ADS)

Yang, Yantao; Cui, Junzhi; Yu, Yifan; Xiang, Meizhen

2018-06-01

In this paper the macroscopic damping model for dynamical behavior of the structures with random polycrystalline configurations at micro-nano scales is established. First, the global motion equation of a crystal is decomposed into a set of motion equations with independent single degree of freedom (SDOF) along normal discrete modes, and then damping behavior is introduced into each SDOF motion. Through the interpolation of discrete modes, the continuous representation of damping effects for the crystal is obtained. Second, from energy conservation law the expression of the damping coefficient is derived, and the approximate formula of damping coefficient is given. Next, the continuous damping coefficient for polycrystalline cluster is expressed, the continuous dynamical equation with damping term is obtained, and then the concrete damping coefficients for a polycrystalline Cu sample are shown. Finally, by using statistical two-scale homogenization method, the macroscopic homogenized dynamical equation containing damping term for the structures with random polycrystalline configurations at micro-nano scales is set up.

Random walk numerical simulation for hopping transport at finite carrier concentrations: diffusion coefficient and transport energy concept.

PubMed

Gonzalez-Vazquez, J P; Anta, Juan A; Bisquert, Juan

2009-11-28

The random walk numerical simulation (RWNS) method is used to compute diffusion coefficients for hopping transport in a fully disordered medium at finite carrier concentrations. We use Miller-Abrahams jumping rates and an exponential distribution of energies to compute the hopping times in the random walk simulation. The computed diffusion coefficient shows an exponential dependence with respect to Fermi-level and Arrhenius behavior with respect to temperature. This result indicates that there is a well-defined transport level implicit to the system dynamics. To establish the origin of this transport level we construct histograms to monitor the energies of the most visited sites. In addition, we construct "corrected" histograms where backward moves are removed. Since these moves do not contribute to transport, these histograms provide a better estimation of the effective transport level energy. The analysis of this concept in connection with the Fermi-level dependence of the diffusion coefficient and the regime of interest for the functioning of dye-sensitised solar cells is thoroughly discussed.

Designing Studies That Would Address the Multilayered Nature of Health Care

PubMed Central

Pennell, Michael; Rhoda, Dale; Hade, Erinn M.; Paskett, Electra D.

2010-01-01

We review design and analytic methods available for multilevel interventions in cancer research with particular attention to study design, sample size requirements, and potential to provide statistical evidence for causal inference. The most appropriate methods will depend on the stage of development of the research and whether randomization is possible. Early on, fractional factorial designs may be used to screen intervention components, particularly when randomization of individuals is possible. Quasi-experimental designs, including time-series and multiple baseline designs, can be useful once the intervention is designed because they require few sites and can provide the preliminary evidence to plan efficacy studies. In efficacy and effectiveness studies, group-randomized trials are preferred when randomization is possible and regression discontinuity designs are preferred otherwise if assignment based on a quantitative score is possible. Quasi-experimental designs may be used, especially when combined with recent developments in analytic methods to reduce bias in effect estimates. PMID:20386057

The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study.

PubMed

Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi

2015-01-01

Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.

Introducing the Event Related Fixed Interval Area (ERFIA) Multilevel Technique: a Method to Analyze the Complete Epoch of Event-Related Potentials at Single Trial Level

PubMed Central

Vossen, Catherine J.; Vossen, Helen G. M.; Marcus, Marco A. E.; van Os, Jim; Lousberg, Richel

2013-01-01

In analyzing time-locked event-related potentials (ERPs), many studies have focused on specific peaks and their differences between experimental conditions. In theory, each latency point after a stimulus contains potentially meaningful information, regardless of whether it is peak-related. Based on this assumption, we introduce a new concept which allows for flexible investigation of the whole epoch and does not primarily focus on peaks and their corresponding latencies. For each trial, the entire epoch is partitioned into event-related fixed-interval areas under the curve (ERFIAs). These ERFIAs, obtained at single trial level, act as dependent variables in a multilevel random regression analysis. The ERFIA multilevel method was tested in an existing ERP dataset of 85 healthy subjects, who underwent a rating paradigm of 150 painful and non-painful somatosensory electrical stimuli. We modeled the variability of each consecutive ERFIA with a set of predictor variables among which were stimulus intensity and stimulus number. Furthermore, we corrected for latency variations of the P2 (260 ms). With respect to known relationships between stimulus intensity, habituation, and pain-related somatosensory ERP, the ERFIA method generated highly comparable results to those of commonly used methods. Notably, effects on stimulus intensity and habituation were also observed in non-peak-related latency ranges. Further, cortical processing of actual stimulus intensity depended on the intensity of the previous stimulus, which may reflect pain-memory processing. In conclusion, the ERFIA multilevel method is a promising tool that can be used to study event-related cortical processing. PMID:24224018

Best (but oft-forgotten) practices: designing, analyzing, and reporting cluster randomized controlled trials.

PubMed

Brown, Andrew W; Li, Peng; Bohan Brown, Michelle M; Kaiser, Kathryn A; Keith, Scott W; Oakes, J Michael; Allison, David B

2015-08-01

Cluster randomized controlled trials (cRCTs; also known as group randomized trials and community-randomized trials) are multilevel experiments in which units that are randomly assigned to experimental conditions are sets of grouped individuals, whereas outcomes are recorded at the individual level. In human cRCTs, clusters that are randomly assigned are typically families, classrooms, schools, worksites, or counties. With growing interest in community-based, public health, and policy interventions to reduce obesity or improve nutrition, the use of cRCTs has increased. Errors in the design, analysis, and interpretation of cRCTs are unfortunately all too common. This situation seems to stem in part from investigator confusion about how the unit of randomization affects causal inferences and the statistical procedures required for the valid estimation and testing of effects. In this article, we provide a brief introduction and overview of the importance of cRCTs and highlight and explain important considerations for the design, analysis, and reporting of cRCTs by using published examples. © 2015 American Society for Nutrition.

Electron holography on HfO2/HfO2-x bilayer structures with multilevel resistive switching properties

NASA Astrophysics Data System (ADS)

Niu, G.; Schubert, M. A.; Sharath, S. U.; Zaumseil, P.; Vogel, S.; Wenger, C.; Hildebrandt, E.; Bhupathi, S.; Perez, E.; Alff, L.; Lehmann, M.; Schroeder, T.; Niermann, T.

2017-05-01

Unveiling the physical nature of the oxygen-deficient conductive filaments (CFs) that are responsible for the resistive switching of the HfO2-based resistive random access memory (RRAM) devices represents a challenging task due to the oxygen vacancy related defect nature and nanometer size of the CFs. As a first important step to this goal, we demonstrate in this work direct visualization and a study of physico-chemical properties of oxygen-deficient amorphous HfO2-x by carrying out transmission electron microscopy electron holography as well as energy dispersive x-ray spectroscopy on HfO2/HfO2-x bilayer heterostructures, which are realized by reactive molecular beam epitaxy. Furthermore, compared to single layer devices, Pt/HfO2/HfO2-x /TiN bilayer devices show enhanced resistive switching characteristics with multilevel behavior, indicating their potential as electronic synapses in future neuromorphic computing applications.

Socioemotional, Personality, and Biological Development: Illustrations from a Multilevel Developmental Psychopathology Perspective on Child Maltreatment.

PubMed

Cicchetti, Dante

2016-01-01

Developmental theories can be affirmed, challenged, and augmented by incorporating knowledge about atypical ontogenesis. Investigations of the biological, socioemotional, and personality development in individuals with high-risk conditions and psychopathological disorders can provide an entrée into the study of system organization, disorganization, and reorganization. This article examines child maltreatment to illustrate the benefit that can be derived from the study of individuals subjected to nonnormative caregiving experiences. Relative to an average expectable environment, which consists of a species-specific range of environmental conditions that support adaptive development among genetically normal individuals, maltreating families fail to provide many of the experiences that are required for normal development. Principles gleaned from the field of developmental psychopathology provide a framework for understanding multilevel functioning in normality and pathology. Knowledge of normative developmental processes provides the impetus to design and implement randomized control trial (RCT) interventions that can promote resilient functioning in maltreated children.

Mental health, places and people: a multilevel analysis of economic inactivity and social deprivation.

PubMed

Fone, David L; Dunstan, Frank

2006-09-01

Using data on 24,975 respondents to the Welsh Health Survey 1998 aged 17-74 years, we investigated associations between individual mental health status measured using the SF-36 instrument, social class, economic inactivity and the electoral division Townsend deprivation score. In a multilevel modelling analysis, we found mental health was significantly associated with the Townsend score after adjusting for composition, and this effect was strongest in respondents who were economically inactive. Further contextual effects were shown by significant random variability in the slopes of the relation between mental health and economic inactivity at the electoral division level. Our results suggest that the places in which people live affect their mental health, supporting NHS policy that multi-agency planning to reduce inequalities in mental health status should address the wider determinants of health, as well as services for individual patients.

An adaptive multi-level simulation algorithm for stochastic biological systems

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

Lester, C., E-mail: lesterc@maths.ox.ac.uk; Giles, M. B.; Baker, R. E.

2015-01-14

Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on stochastic simulation algorithms (SSA) to estimate system statistics. The Gillespie algorithm is exact, but computationally costly as it simulates every single reaction. As such, approximate stochastic simulation algorithms such as the tau-leap algorithm are often used. Potentially computationally more efficient, the system statistics generated suffer from significant bias unless tau is relatively small, in which case the computational time can be comparable to that of the Gillespie algorithm. The multi-level method [Anderson and Higham, “Multi-level Montemore » Carlo for continuous time Markov chains, with applications in biochemical kinetics,” SIAM Multiscale Model. Simul. 10(1), 146–179 (2012)] tackles this problem. A base estimator is computed using many (cheap) sample paths at low accuracy. The bias inherent in this estimator is then reduced using a number of corrections. Each correction term is estimated using a collection of paired sample paths where one path of each pair is generated at a higher accuracy compared to the other (and so more expensive). By sharing random variables between these paired paths, the variance of each correction estimator can be reduced. This renders the multi-level method very efficient as only a relatively small number of paired paths are required to calculate each correction term. In the original multi-level method, each sample path is simulated using the tau-leap algorithm with a fixed value of τ. This approach can result in poor performance when the reaction activity of a system changes substantially over the timescale of interest. By introducing a novel adaptive time-stepping approach where τ is chosen according to the stochastic behaviour of each sample path, we extend the applicability of the multi-level method to such cases. We demonstrate the efficiency of our method using a number of examples.« less

Using the Major Field Test for a Bachelor's Degree in Business as a Learning Outcomes Assessment: Evidence from a Review of 20 Years of Institution-Based Research

ERIC Educational Resources Information Center

Ling, Guangming; Bochenek, Jennifer; Burkander, Kri

2015-01-01

By applying multilevel models with random effects, the authors reviewed and synthesized findings from 30 studies that were published in the last 20 years exploring the relationship between the Educational Testing Service Major Field Test for a Bachelor's Degree in Business (MFTB) and related factors. The results suggest that MFTB scores correlated…

Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis.

PubMed

Crowther, Michael J; Look, Maxime P; Riley, Richard D

2014-09-28

Multilevel mixed effects survival models are used in the analysis of clustered survival data, such as repeated events, multicenter clinical trials, and individual participant data (IPD) meta-analyses, to investigate heterogeneity in baseline risk and covariate effects. In this paper, we extend parametric frailty models including the exponential, Weibull and Gompertz proportional hazards (PH) models and the log logistic, log normal, and generalized gamma accelerated failure time models to allow any number of normally distributed random effects. Furthermore, we extend the flexible parametric survival model of Royston and Parmar, modeled on the log-cumulative hazard scale using restricted cubic splines, to include random effects while also allowing for non-PH (time-dependent effects). Maximum likelihood is used to estimate the models utilizing adaptive or nonadaptive Gauss-Hermite quadrature. The methods are evaluated through simulation studies representing clinically plausible scenarios of a multicenter trial and IPD meta-analysis, showing good performance of the estimation method. The flexible parametric mixed effects model is illustrated using a dataset of patients with kidney disease and repeated times to infection and an IPD meta-analysis of prognostic factor studies in patients with breast cancer. User-friendly Stata software is provided to implement the methods. Copyright © 2014 John Wiley & Sons, Ltd.

The influence of statistical properties of Fourier coefficients on random Gaussian surfaces.

PubMed

de Castro, C P; Luković, M; Andrade, R F S; Herrmann, H J

2017-05-16

Many examples of natural systems can be described by random Gaussian surfaces. Much can be learned by analyzing the Fourier expansion of the surfaces, from which it is possible to determine the corresponding Hurst exponent and consequently establish the presence of scale invariance. We show that this symmetry is not affected by the distribution of the modulus of the Fourier coefficients. Furthermore, we investigate the role of the Fourier phases of random surfaces. In particular, we show how the surface is affected by a non-uniform distribution of phases.

Scaling up prevention of mother-to-child HIV transmission programs in sub-Saharan African countries: a multilevel assessment of site-, program- and country-level determinants of performance.

PubMed

Audureau, Etienne; Kahn, James G; Besson, Marie-Hélène; Saba, Joseph; Ladner, Joël

2013-04-01

Uptake of prevention of mother-to-child HIV transmission (PMTCT) programs remains challenging in sub-Saharan Africa because of multiple barriers operating at the individual or health facility levels. Less is known regarding the influence of program-level and contextual determinants. In this study, we explored the multilevel factors associated with coverage in single-dose nevirapine PMTCT programs. We analyzed aggregate routine data collected within the framework of the Viramune(®) Donation Programme (VDP) from 269 sites in 20 PMTCT programs and 15 sub-Saharan countries from 2002 to 2005. Site performance was measured using a nevirapine coverage ratio (NCR), defined as the reported number of women receiving nevirapine divided by the number of women who should have received nevirapine (observed HIV prevalence x number of women in antenatal care [ANC]). Data on program-level determinants were drawn from the initial application forms, and country-level determinants from the Demographic and Health Surveys (DHS) and the World Bank (World Development Indicators). Multilevel linear mixed models were used to identify independent factors associated with NCR at the site-, program- and country-level. Of 283,410 pregnant women attending ANC in the included sites, 174,312 women (61.5%) underwent HIV testing after receiving pre-test counselling, of whom 26,700 tested HIV positive (15.3%), and 22,591 were dispensed NVP (84.6%). Site performance was highly heterogeneous between and within programs. Mean NCR by site was 43.8% (interquartile range: 19.1-63.9). Multilevel analysis identified higher HIV prevalence (Beta coefficient: 25.1, 95% confidence interval [CI] 18.7 to 31.6), higher proportion of persons with knowledge of PMTCT (8.3; CI 0.5 to 16.0), higher health expenditure as a proportion of Gross Domestic Product (3.9 per %; CI 2.0 to 5.8) and lower percentage of rural population (-0.7 per %; CI -1.0 to -0.5) as significant country-level predictors of higher NCR at the p<0.05 level. A medium ANC monthly activity (30-100/month) was the only site-level predictor found (-7.6; CI -15.1 to -0.1). Heterogeneity of nevirapine coverage between sites and programs was high. Multilevel analysis identified several significant contextual determinants, which may warrant additional research to further define important multi-level and potentially modifiable determinants of performance of PMTCT programs.

Income inequality is associated with adolescent fertility in Brazil: a longitudinal multilevel analysis of 5,565 municipalities.

PubMed

Chiavegatto Filho, Alexandre D P; Kawachi, Ichiro

2015-02-07

Brazil has one of the highest adolescent fertility rates in the world. Income inequality has been frequently linked to overall adolescent health, but studies that analyzed its association with adolescent fertility have been performed only in developed countries. Brazil, in the past decade, has presented a rare combination of increasing per capita income and decreasing income inequality, which could influence future desirable pathways for other countries. We analyzed every live birth from 2000 and from 2010 in each of the 5,565 municipalities of Brazil, a total of 6,049,864 births, which included 1,247,145 (20.6%) births from women aged 15 to 19. Income inequality was assessed by the Gini Coefficient and adolescent fertility by the ratio between the number of live births from women aged 15 to 19 and the number of women aged 15 to 19, calculated for each municipality. We first applied multilevel models separately for 2000 and 2010 to test the cross-sectional association between income inequality and adolescent fertility. We then fitted longitudinal first-differences multilevel models to control for time-invariant effects. We also performed a sensitivity analysis to include only municipality with satisfactory birth record coverage. Our results indicate a consistent and positive association between income inequality and adolescent fertility. After controlling for per capita income, college access, youth homicide rate and adult fertility, higher income inequality was significantly associated with higher adolescent fertility for both 2000 and 2010. The longitudinal multilevel models found similar results. The sensitivity analysis indicated that the results for the association between income inequality and adolescent fertility were robust. Adult fertility was also significantly associated with adolescent fertility in the cross-sectional and longitudinal models. Income inequality is expected to be a leading concern for most countries in the near future. Our results suggest that changes in income inequality are positively and consistently associated with changes in adolescent fertility.

A Bayesian Multilevel Model for Microcystin Prediction in ...

EPA Pesticide Factsheets

The frequency of cyanobacteria blooms in North American lakes is increasing. A major concern with rising cyanobacteria blooms is microcystin, a common cyanobacterial hepatotoxin. To explore the conditions that promote high microcystin concentrations, we analyzed the US EPA National Lake Assessment (NLA) dataset collected in the summer of 2007. The NLA dataset is reported for nine eco-regions. We used the results of random forest modeling as a means ofvariable selection from which we developed a Bayesian multilevel model of microcystin concentrations. Model parameters under a multilevel modeling framework are eco-region specific, butthey are also assumed to be exchangeable across eco-regions for broad continental scaling. The exchangeability assumption ensures that both the common patterns and eco-region specific features will be reflected in the model. Furthermore, the method incorporates appropriate estimates of uncertainty. Our preliminary results show associations between microcystin and turbidity, total nutrients, and N:P ratios. Upon release of a comparable 2012 NLA dataset, we will apply Bayesian updating. The results will help develop management strategies to alleviate microcystin impacts and improve lake quality. This work provides a probabilistic framework for predicting microcystin presences in lakes. It would allow for insights to be made about how changes in nutrient concentrations could potentially change toxin levels.

Variability of multilevel switching in scaled hybrid RS/CMOS nanoelectronic circuits: theory

NASA Astrophysics Data System (ADS)

Heittmann, Arne; Noll, Tobias G.

2013-07-01

A theory is presented which describes the variability of multilevel switching in scaled hybrid resistive-switching/CMOS nanoelectronic circuits. Variability is quantified in terms of conductance variation using the first two moments derived from the probability density function (PDF) of the RS conductance. For RS, which are based on the electrochemical metallization effect (ECM), this variability is - to some extent - caused by discrete events such as electrochemical reactions, which occur on atomic scale and are at random. The theory shows that the conductance variation depends on the joint interaction between the programming circuit and the resistive switch (RS), and explicitly quantifies the impact of RS device parameters and parameters of the programming circuit on the conductance variance. Using a current mirror as an exemplary programming circuit an upper limit of 2-4 bits (dependent on the filament surface area) is estimated as the storage capacity exploiting the multilevel capabilities of an ECM cell. The theoretical results were verified by Monte Carlo circuit simulations on a standard circuit simulation environment using an ECM device model which models the filament growth by a Poisson process. Contribution to the Topical Issue “International Semiconductor Conference Dresden-Grenoble - ISCDG 2012”, Edited by Gérard Ghibaudo, Francis Balestra and Simon Deleonibus.

Temporal Associations Among Chronic PTSD Symptoms in U.S. Combat Veterans.

PubMed

Doron-LaMarca, Susan; Niles, Barbara L; King, Daniel W; King, Lynda A; Pless Kaiser, Anica; Lyons, Michael J

2015-10-01

The present study examined fluctuation over time in symptoms of posttraumatic stress disorder (PTSD) among 34 combat veterans (28 with diagnosed PTSD, 6 with subclinical symptoms) assessed every 2 weeks for up to 2 years (range of assessments = 13-52). Temporal relationships were examined among four PTSD symptom clusters (reexperiencing, avoidance, emotional numbing, and hyperarousal) with particular attention to the influence of hyperarousal. Multilevel cross-lagged random coefficients autoregression for intensive time series data analyses were used to model symptom fluctuation decades after combat experiences. As anticipated, hyperarousal predicted subsequent fluctuations in the 3 other PTSD symptom clusters (reexperiencing, avoidance, emotional numbing) at subsequent 2-week intervals (rs = .45, .36, and .40, respectively). Additionally, emotional numbing influenced later reexperiencing and avoidance, and reexperiencing influenced later hyperarousal (rs = .44, .40, and .34, respectively). These findings underscore the important influence of hyperarousal. Furthermore, results indicate a bidirectional relationship between hyperarousal and reexperiencing as well as a possible chaining of symptoms (hyperarousal → emotional numbing → reexperiencing → hyperarousal) and establish potential internal, intrapersonal mechanisms for the maintenance of persistent PTSD symptoms. Results suggested that clinical interventions targeting hyperarousal and emotional numbing symptoms may hold promise for PTSD of long duration. Published 2015. This article is a US Government work and is in the public domain in the USA. View this article online at wileyonlinelibrary.com.

Using the RE-AIM framework to evaluate the statewide dissemination of a school-based physical activity and nutrition curriculum: "Exercise Your Options".

PubMed

Dunton, Genevieve F; Lagloire, Renee; Robertson, Trina

2009-01-01

Examine the reach, efficacy, adoption, implementation, and maintenance of a physical activity and nutrition curriculum for middle-school students. Nonexperimental pilot evaluation of a statewide dissemination trial. California middle schools during the 2006 to 2007 school year. Sixteen classes (N = 668 students and 16 teachers) sampled from the statewide pool who used the program. An eight-lesson nutrition and physical activity curriculum, "Exercise Your Options" (EYO), including a teacher guide, video clips, a student activity booklet, and ancillary materials was made available to teachers. Program records, classroom observations, teacher surveys, and student presurveys and postsurveys (assessing physical activity, sedentary behaviors, and dietary intake). Descriptive statistics and multilevel random-coefficient modeling. The EYO program reached 234,442 middle-school students in California. During the program, total physical activity increased (p < .001), whereas watching TV/DVDs and playing electronic games/computer use decreased (p < .05). Intake of dairy products increased (p < .05), whereas consumption of sugars/sweets decreased (p < .001). Forty-two percent of eligible middle-school classrooms ordered the program materials. Eighty-six percent of sampled teachers implemented all of the lessons. Over the past 5 years, 51% of all middle-school students in California were exposed to the program. The EYO program showed its potential for moderate to high public health impact among California middle-school students.

Choice of reserve capacity by hospitals: a problem for prospective payment.

PubMed

Widmer, Philippe K; Trottmann, Maria; Zweifel, Peter

2018-06-01

This contribution analyzes the impact of prospective payment on hospital decisions with regard to reserve capacity, using Swiss hospital data covering the years 2004-2009. This data set is unique because it permits distinguishing of institutional characteristics (e.g., ownership status) from the mode of payment as determinants of hospital efficiency, due to the fact that some Swiss cantons introduced prospective payment early while others waited for federal legislation to be enacted in 2012. Since a hospital's choice of reserve capacity depends also on the risk preferences of management while affecting the cost function, heterogeneity is predicted even in the presence of identical technology and factor prices. For estimating hospitals' marginal costs, we employ the flexible representation of risk preferences by Pope and Chavas [Am J Agric Econ 76, 196-204 (1994)]. Production uncertainty is measured as the difference between actual admissions and admissions predicted by an autoregressive moving average model. Its effect on hospital cost is analyzed using a multilevel stochastic cost frontier model with random coefficients reflecting unobserved differences in technology. Public hospitals are found to opt for a higher probability of meeting unexpected demand, as predicted. Their operating cost is 1.1% higher than for private hospitals and even 1.9% higher than for teaching hospitals, creating an incentive to turn away patients or to keep them waiting for treatment.

What affects response rates in primary healthcare-based programmes? An analysis of individual and unit-related factors associated with increased odds of non-response based on HCV screening in the general population in Poland

PubMed Central

Parda, Natalia; Stępień, Małgorzata; Zakrzewska, Karolina; Madaliński, Kazimierz; Kołakowska, Agnieszka; Godzik, Paulina; Rosińska, Magdalena

2016-01-01

Objectives Response rate in public health programmes may be a limiting factor. It is important to first consider their delivery and acceptability for the target. This study aimed at determining individual and unit-related factors associated with increased odds of non-response based on hepatitis C virus screening in primary healthcare. Design Primary healthcare units (PHCUs) were extracted from the Register of Health Care Centres. Each of the PHCUs was to enrol adult patients selected on a random basis. Data on the recruitment of PHCUs and patients were analysed. Multilevel modelling was applied to investigate individual and unit-related factors associated with non-response. Multilevel logistic model was developed with fixed effects and only a random intercept for the unit. Preliminary analysis included a random effect for unit and each of the individual or PHCU covariates separately. For each of the PHCU covariates, we applied a two-level model with individual covariates, unit random effect and a single fixed effect of this unit covariate. Setting This study was conducted in primary care units in selected provinces in Poland. Participants A total of 242 PHCUs and 24 480 adults were invited. Of them, 44 PHCUs and 20 939 patients agreed to participate. Both PHCUs and patients were randomly selected. Results Data on 44 PHCUs and 24 480 patients were analysed. PHCU-level factors and recruitment strategies were important predictors of non-response. Unit random effect was significant in all models. Larger and private units reported higher non-response rates, while for those with a history of running public health programmes the odds of non-response was lower. Proactive recruitment, more working hours devoted to the project and patient resulted in higher acceptance of the project. Higher number of personnel had no such effect. Conclusions Prior to the implementation of public health programme, several factors that could hinder its execution should be addressed. PMID:27927665

What affects response rates in primary healthcare-based programmes? An analysis of individual and unit-related factors associated with increased odds of non-response based on HCV screening in the general population in Poland.

PubMed

Parda, Natalia; Stępień, Małgorzata; Zakrzewska, Karolina; Madaliński, Kazimierz; Kołakowska, Agnieszka; Godzik, Paulina; Rosińska, Magdalena

2016-12-07

Response rate in public health programmes may be a limiting factor. It is important to first consider their delivery and acceptability for the target. This study aimed at determining individual and unit-related factors associated with increased odds of non-response based on hepatitis C virus screening in primary healthcare. Primary healthcare units (PHCUs) were extracted from the Register of Health Care Centres. Each of the PHCUs was to enrol adult patients selected on a random basis. Data on the recruitment of PHCUs and patients were analysed. Multilevel modelling was applied to investigate individual and unit-related factors associated with non-response. Multilevel logistic model was developed with fixed effects and only a random intercept for the unit. Preliminary analysis included a random effect for unit and each of the individual or PHCU covariates separately. For each of the PHCU covariates, we applied a two-level model with individual covariates, unit random effect and a single fixed effect of this unit covariate. This study was conducted in primary care units in selected provinces in Poland. A total of 242 PHCUs and 24 480 adults were invited. Of them, 44 PHCUs and 20 939 patients agreed to participate. Both PHCUs and patients were randomly selected. Data on 44 PHCUs and 24 480 patients were analysed. PHCU-level factors and recruitment strategies were important predictors of non-response. Unit random effect was significant in all models. Larger and private units reported higher non-response rates, while for those with a history of running public health programmes the odds of non-response was lower. Proactive recruitment, more working hours devoted to the project and patient resulted in higher acceptance of the project. Higher number of personnel had no such effect. Prior to the implementation of public health programme, several factors that could hinder its execution should be addressed. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Neighbourhood social capital and adolescent self-reported wellbeing in New Zealand: a multilevel analysis.

PubMed

Aminzadeh, Kaveh; Denny, Simon; Utter, Jennifer; Milfont, Taciano L; Ameratunga, Shanthi; Teevale, Tasileta; Clark, Terryann

2013-05-01

The association between neighbourhood social capital and individual health and wellbeing has been explored mainly by focussing on adult outcomes. This study explores the relationship between neighbourhood social capital and adolescent subjective wellbeing, and its interaction with adolescents' socioeconomic status. Data was taken from a random sample of 9107 students who participated in a nationally representative health survey of high school students in New Zealand in 2007. Students' wellbeing was measured by questions on general mood, life satisfaction and WHO-5 Wellbeing Index. Neighbourhood social capital was assessed according to five indicators: neighbourhood social cohesion, facilities, physical disintegration, membership in community organisations, and residential stability. All neighbourhood measures were created based on students' responses aggregated to the neighbourhood level. Neighbourhood was defined as a Census Area Unit, with a median population of 2000 people. Analyses included only neighbourhoods with more than 10 students, and were conducted using cross-classified random intercept multilevel models controlling for students' age, sex, ethnicity and socioeconomic status, with both schools and neighbourhoods treated as random effects. A total of 5567 students within 262 neighbourhoods were considered in the analysis. Students living in neighbourhoods characterised by higher levels of social cohesion and membership in community organisations reported higher levels of wellbeing. The association between student self-reported wellbeing and neighbourhood membership in community organisations varied according to the individual socioeconomic status of students. Neighbourhood membership in community organisations showed a stronger protective effect for students who were more socioeconomically deprived. Copyright © 2013 Elsevier Ltd. All rights reserved.

An intervention to reduce sitting and increase light-intensity physical activity at work: Design and rationale of the ‘Stand & Move at Work’ group randomized trial

PubMed Central

Buman, Matthew P.; Mullane, Sarah L.; Toledo, Meynard J.; Rydell, Sarah A.; Gaesser, Glenn A.; Crespo, Noe C.; Hannan, Peter; Feltes, Linda; Vuong, Brenna; Pereira, Mark A

2016-01-01

Background American workers spend 70–80% of their time at work being sedentary. Traditional approaches to increase moderate-vigorous physical activity (MVPA) may be perceived to be harmful to productivity. Approaches that target reductions in sedentary behavior and/or increases in standing or light-intensity physical activity [LPA] may not interfere with productivity and may be more feasible to achieve through small changes accumulated throughout the workday. Methods/Design This group randomized trial (i.e., cluster randomized trial) will test the relative efficacy of two sedentary behavior focused interventions in 24 worksites across two states (N=720 workers). The MOVE+ intervention is a multilevel individual, social, environmental, and organizational intervention targeting increases in light-intensity physical activity in the workplace. The STAND+ intervention is the MOVE+ intervention with the addition of the installation and use of sit-stand workstations to reduce sedentary behavior and enhance light-intensity physical activity opportunities. Our primary outcome will be objectively-measured changes in sedentary behavior and light-intensity physical activity over 12 months, with additional process measures at 3 months and longer-term sustainability outcomes at 24 months. Our secondary outcomes will be a clustered cardiometabolic risk score (comprised of fasting glucose, insulin, triglycerides, HDL-cholesterol, and blood pressure), workplace productivity, and job satisfaction. Discussion This study will determine the efficacy of a multilevel workplace intervention (including the use of a sit-stand workstation) to reduce sedentary behavior and increase LPA and concomitant impact on cardiometabolic health, workplace productivity, and satisfaction. PMID:27940181

Bayesian dynamic modeling of time series of dengue disease case counts.

PubMed

Martínez-Bello, Daniel Adyro; López-Quílez, Antonio; Torres-Prieto, Alexander

2017-07-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.

MATIN: A Random Network Coding Based Framework for High Quality Peer-to-Peer Live Video Streaming

PubMed Central

Barekatain, Behrang; Khezrimotlagh, Dariush; Aizaini Maarof, Mohd; Ghaeini, Hamid Reza; Salleh, Shaharuddin; Quintana, Alfonso Ariza; Akbari, Behzad; Cabrera, Alicia Triviño

2013-01-01

In recent years, Random Network Coding (RNC) has emerged as a promising solution for efficient Peer-to-Peer (P2P) video multicasting over the Internet. This probably refers to this fact that RNC noticeably increases the error resiliency and throughput of the network. However, high transmission overhead arising from sending large coefficients vector as header has been the most important challenge of the RNC. Moreover, due to employing the Gauss-Jordan elimination method, considerable computational complexity can be imposed on peers in decoding the encoded blocks and checking linear dependency among the coefficients vectors. In order to address these challenges, this study introduces MATIN which is a random network coding based framework for efficient P2P video streaming. The MATIN includes a novel coefficients matrix generation method so that there is no linear dependency in the generated coefficients matrix. Using the proposed framework, each peer encapsulates one instead of n coefficients entries into the generated encoded packet which results in very low transmission overhead. It is also possible to obtain the inverted coefficients matrix using a bit number of simple arithmetic operations. In this regard, peers sustain very low computational complexities. As a result, the MATIN permits random network coding to be more efficient in P2P video streaming systems. The results obtained from simulation using OMNET++ show that it substantially outperforms the RNC which uses the Gauss-Jordan elimination method by providing better video quality on peers in terms of the four important performance metrics including video distortion, dependency distortion, End-to-End delay and Initial Startup delay. PMID:23940530

A pattern-mixture model approach for handling missing continuous outcome data in longitudinal cluster randomized trials.

PubMed

Fiero, Mallorie H; Hsu, Chiu-Hsieh; Bell, Melanie L

2017-11-20

We extend the pattern-mixture approach to handle missing continuous outcome data in longitudinal cluster randomized trials, which randomize groups of individuals to treatment arms, rather than the individuals themselves. Individuals who drop out at the same time point are grouped into the same dropout pattern. We approach extrapolation of the pattern-mixture model by applying multilevel multiple imputation, which imputes missing values while appropriately accounting for the hierarchical data structure found in cluster randomized trials. To assess parameters of interest under various missing data assumptions, imputed values are multiplied by a sensitivity parameter, k, which increases or decreases imputed values. Using simulated data, we show that estimates of parameters of interest can vary widely under differing missing data assumptions. We conduct a sensitivity analysis using real data from a cluster randomized trial by increasing k until the treatment effect inference changes. By performing a sensitivity analysis for missing data, researchers can assess whether certain missing data assumptions are reasonable for their cluster randomized trial. Copyright © 2017 John Wiley & Sons, Ltd.

Evaluation of Seeds of Science/Roots of Reading: Effective Tools for Developing Literacy through Science in the Early Grades-Light Energy Unit. CRESST Report 781

ERIC Educational Resources Information Center

Goldschmidt, Pete; Jung, Hyekyung

2011-01-01

This evaluation focuses on the Seeds of Science/Roots of Reading: Effective Tools for Developing Literacy through Science in the Early Grades ("Seeds/Roots") model of science-literacy integration. The evaluation is based on a cluster randomized design of 100 teachers, half of which were in the treatment group. Multi-level models are employed to…

"L"-Bivariate and "L"-Multivariate Association Coefficients. Research Report. ETS RR-08-40

ERIC Educational Resources Information Center

Kong, Nan; Lewis, Charles

2008-01-01

Given a system of multiple random variables, a new measure called the "L"-multivariate association coefficient is defined using (conditional) entropy. Unlike traditional correlation measures, the L-multivariate association coefficient measures the multiassociations or multirelations among the multiple variables in the given system; that…

Evaluating the Effectiveness of an Antimicrobial Stewardship Program on Reducing the Incidence Rate of Healthcare-Associated Clostridium difficile Infection: A Non-Randomized, Stepped Wedge, Single-Site, Observational Study.

PubMed

DiDiodato, Giulio; McArthur, Leslie

2016-01-01

The incidence rate of healthcare-associated Clostridium difficile infection (HA-CDI) is estimated at 1 in 100 patients. Antibiotic exposure is the most consistently reported risk factor for HA-CDI. Strategies to reduce the risk of HA-CDI have focused on reducing antibiotic utilization. Prospective audit and feedback is a commonly used antimicrobial stewardship intervention (ASi). The impact of this ASi on risk of HA-CDI is equivocal. This study examines the effectiveness of a prospective audit and feedback ASi on reducing the risk of HA-CDI. Single-site, 339 bed community-hospital in Barrie, Ontario, Canada. Primary outcome is HA-CDI incidence rate. Daily prospective and audit ASi is the exposure variable. ASi implemented across 6 wards in a non-randomized, stepped wedge design. Criteria for ASi; any intravenous antibiotic use for ≥ 48 hrs, any oral fluoroquinolone or oral second generation cephalosporin use for ≥ 48 hrs, or any antimicrobial use for ≥ 5 days. HA-CDI cases and model covariates were aggregated by ward, year and month starting September 2008 and ending February 2016. Multi-level mixed effect negative binomial regression analysis was used to model the primary outcome, with intercept and slope coefficients for ward-level random effects estimated. Other covariates tested for inclusion in the final model were derived from previously published risk factors. Deviance residuals were used to assess the model's goodness-of-fit. The dataset included 486 observation periods, of which 350 were control periods and 136 were intervention periods. After accounting for all other model covariates, the estimated overall ASi incidence rate ratio (IRR) was 0.48 (95% 0.30, 0.79). The ASi effect was independent of antimicrobial utilization. The ASi did not seem to reduce the risk of Clostridium difficile infection on the surgery wards (IRR 0.87, 95% CI 0.45, 1.69) compared to the medicine wards (IRR 0.42, 95% CI 0.28, 0.63). The ward-level burden of Clostridium difficile as measured by the ward's previous month's total CDI cases (CDI Lag) and the ward's current month's community-associated CDI cases (CA-CDI) was significantly associated with an increased risk of HA-CDI, with the estimated CDI Lag IRR of 1.21 (95% 1.15, 1.28) and the estimated CA-CDI IRR of 1.10 (95% CI 1.01, 1.20). The ward-level random intercept and slope coefficients were not significant. The final model demonstrated good fit. In this study, a daily prospective audit and feedback ASi resulted in a significant reduction in the risk of HA-CDI on the medicine wards, however, this effect was independent of an overall reduction in antibiotic utilization. In addition, the ward-level burden of Clostridium difficile was shown to significantly increase the risk of HA-CDI, reinforcing the importance of the environment as a source of HA-CDI.

Primary Prevention of Gestational Diabetes Mellitus and Large-for-Gestational-Age Newborns by Lifestyle Counseling: A Cluster-Randomized Controlled Trial

PubMed Central

Luoto, Riitta; Kinnunen, Tarja I.; Aittasalo, Minna; Kolu, Päivi; Raitanen, Jani; Ojala, Katriina; Mansikkamäki, Kirsi; Lamberg, Satu; Vasankari, Tommi; Komulainen, Tanja; Tulokas, Sirkku

2011-01-01

Background Our objective was to examine whether gestational diabetes mellitus (GDM) or newborns' high birthweight can be prevented by lifestyle counseling in pregnant women at high risk of GDM. Method and Findings We conducted a cluster-randomized trial, the NELLI study, in 14 municipalities in Finland, where 2,271 women were screened by oral glucose tolerance test (OGTT) at 8–12 wk gestation. Euglycemic (n = 399) women with at least one GDM risk factor (body mass index [BMI] ≥25 kg/m2, glucose intolerance or newborn's macrosomia (≥4,500 g) in any earlier pregnancy, family history of diabetes, age ≥40 y) were included. The intervention included individual intensified counseling on physical activity and diet and weight gain at five antenatal visits. Primary outcomes were incidence of GDM as assessed by OGTT (maternal outcome) and newborns' birthweight adjusted for gestational age (neonatal outcome). Secondary outcomes were maternal weight gain and the need for insulin treatment during pregnancy. Adherence to the intervention was evaluated on the basis of changes in physical activity (weekly metabolic equivalent task (MET) minutes) and diet (intake of total fat, saturated and polyunsaturated fatty acids, saccharose, and fiber). Multilevel analyses took into account cluster, maternity clinic, and nurse level influences in addition to age, education, parity, and prepregnancy BMI. 15.8% (34/216) of women in the intervention group and 12.4% (22/179) in the usual care group developed GDM (absolute effect size 1.36, 95% confidence interval [CI] 0.71–2.62, p = 0.36). Neonatal birthweight was lower in the intervention than in the usual care group (absolute effect size −133 g, 95% CI −231 to −35, p = 0.008) as was proportion of large-for-gestational-age (LGA) newborns (26/216, 12.1% versus 34/179, 19.7%, p = 0.042). Women in the intervention group increased their intake of dietary fiber (adjusted coefficient 1.83, 95% CI 0.30–3.25, p = 0.023) and polyunsaturated fatty acids (adjusted coefficient 0.37, 95% CI 0.16–0.57, p<0.001), decreased their intake of saturated fatty acids (adjusted coefficient −0.63, 95% CI −1.12 to −0.15, p = 0.01) and intake of saccharose (adjusted coefficient −0.83, 95% CI −1.55 to −0.11, p  =  0.023), and had a tendency to a smaller decrease in MET minutes/week for at least moderate intensity activity (adjusted coefficient 91, 95% CI −37 to 219, p = 0.17) than women in the usual care group. In subgroup analysis, adherent women in the intervention group (n = 55/229) had decreased risk of GDM (27.3% versus 33.0%, p = 0.43) and LGA newborns (7.3% versus 19.5%, p = 0.03) compared to women in the usual care group. Conclusions The intervention was effective in controlling birthweight of the newborns, but failed to have an effect on maternal GDM. Trial registration Current Controlled Trials ISRCTN33885819 Please see later in the article for the Editors' Summary PMID:21610860

Master equation theory applied to the redistribution of polarized radiation in the weak radiation field limit. III. Theory for the multilevel atom

NASA Astrophysics Data System (ADS)

Bommier, Véronique

2016-06-01

Context. We discuss the case of lines formed by scattering, which comprises both coherent and incoherent scattering. Both processes contribute to form the line profiles in the so-called second solar spectrum, which is the spectrum of the linear polarization of such lines observed close to the solar limb. However, most of the lines cannot be simply modeled with a two-level or two-term atom model, and we present a generalized formalism for this purpose. Aims: The aim is to obtain a formalism that is able to describe scattering in line centers (resonant scattering or incoherent scattering) and in far wings (Rayleigh/Raman scattering or coherent scattering) for a multilevel and multiline atom. Methods: The method is designed to overcome the Markov approximation, which is often performed in the atom-photon interaction description. The method was already presented in the two first papers of this series, but the final equations of those papers were for a two-level atom. Results: We present here the final equations generalized for the multilevel and multiline atom. We describe the main steps of the theoretical development, and, in particular, how we performed the series development to overcome the Markov approximation. Conclusions: The statistical equilibrium equations for the atomic density matrix and the radiative transfer equation coefficients are obtained with line profiles. The Doppler redistribution is also taken into account because we show that the statistical equilibrium equations must be solved for each atomic velocity class.

A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation.

PubMed

Wang, Rui; Zhou, Yongquan; Zhao, Chengyan; Wu, Haizhou

2015-01-01

Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu's objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu's objective values and standard deviations.

Perceived public transport infrastructure modifies the association between public transport use and mental health: Multilevel analyses from the United Kingdom.

PubMed

Feng, Xiaoqi; Feng, Zhiqiang; Astell-Burt, Thomas

2017-01-01

Investments to promote public transport utilisation are being championed to achieve sustainable development, but the potential co-benefits for mental health are comparatively under-researched. We hypothesised that frequent users of public transport would be more likely to have better mental health (possibly due to increased levels of physical activity), but among the more frequent users, less favourable perceptions of public transport infrastructure (PPTI) could have a negative influence on mental health. Multilevel linear and logistic regressions were fitted on 30,214 participants in the UK Household Longitudinal Study with lagged PPTI and confounder measures at baseline and indicators of active travel and mental health (General Health Questionnaire (GHQ), SF-12 Mental Component Scale (MCS) and the Warwick Edinburgh Mental Well Being Scale (WEMWBS)) at follow-up. Compared to participants expressing poor PPTI, those who felt it was excellent were 1.29 (95%CI 1.15, 1.45) times more likely to be frequent users of public transport and 1.53 (95%CI 1.33, 1.76) times more likely to choose to walk or cycle journeys of less than two to three miles. Frequent use of public transport was found to be consistently associated with better mental health for GHQ caseness (OR 0.85, 95%CI 0.79, 0.91), GHQ score (coefficient -0.28, 95%CI -0.41, -0.16), MCS (coefficient 0.45, 95%CI 0.23, 0.66), and WEMWBS (coefficient 0.30, 95%CI 0.19, 0.40). Among frequent users of public transport, participants expressing poor PPTI were 1.46 (95%CI 1.11, 1.93) times more likely to report poorer mental health according to the GHQ caseness indicator, compared to frequent users that regarded PPTI as excellent. Similar results were observed for the other indicators of mental health. These findings indicate that while the provision of public transport infrastructure is a necessary pre-condition for stimulating population increases in physical activity, PPTI improvements needs to be prioritised to leverage the full mental health-related co-benefits of active travel.

Perceived public transport infrastructure modifies the association between public transport use and mental health: Multilevel analyses from the United Kingdom

PubMed Central

Feng, Xiaoqi; Feng, Zhiqiang; Astell-Burt, Thomas

2017-01-01

Aims Investments to promote public transport utilisation are being championed to achieve sustainable development, but the potential co-benefits for mental health are comparatively under-researched. We hypothesised that frequent users of public transport would be more likely to have better mental health (possibly due to increased levels of physical activity), but among the more frequent users, less favourable perceptions of public transport infrastructure (PPTI) could have a negative influence on mental health. Methods Multilevel linear and logistic regressions were fitted on 30,214 participants in the UK Household Longitudinal Study with lagged PPTI and confounder measures at baseline and indicators of active travel and mental health (General Health Questionnaire (GHQ), SF-12 Mental Component Scale (MCS) and the Warwick Edinburgh Mental Well Being Scale (WEMWBS)) at follow-up. Results Compared to participants expressing poor PPTI, those who felt it was excellent were 1.29 (95%CI 1.15, 1.45) times more likely to be frequent users of public transport and 1.53 (95%CI 1.33, 1.76) times more likely to choose to walk or cycle journeys of less than two to three miles. Frequent use of public transport was found to be consistently associated with better mental health for GHQ caseness (OR 0.85, 95%CI 0.79, 0.91), GHQ score (coefficient -0.28, 95%CI -0.41, -0.16), MCS (coefficient 0.45, 95%CI 0.23, 0.66), and WEMWBS (coefficient 0.30, 95%CI 0.19, 0.40). Among frequent users of public transport, participants expressing poor PPTI were 1.46 (95%CI 1.11, 1.93) times more likely to report poorer mental health according to the GHQ caseness indicator, compared to frequent users that regarded PPTI as excellent. Similar results were observed for the other indicators of mental health. Conclusions These findings indicate that while the provision of public transport infrastructure is a necessary pre-condition for stimulating population increases in physical activity, PPTI improvements needs to be prioritised to leverage the full mental health-related co-benefits of active travel. PMID:28813422

Search for Directed Networks by Different Random Walk Strategies

NASA Astrophysics Data System (ADS)

Zhu, Zi-Qi; Jin, Xiao-Ling; Huang, Zhi-Long

2012-03-01

A comparative study is carried out on the efficiency of five different random walk strategies searching on directed networks constructed based on several typical complex networks. Due to the difference in search efficiency of the strategies rooted in network clustering, the clustering coefficient in a random walker's eye on directed networks is defined and computed to be half of the corresponding undirected networks. The search processes are performed on the directed networks based on Erdös—Rényi model, Watts—Strogatz model, Barabási—Albert model and clustered scale-free network model. It is found that self-avoiding random walk strategy is the best search strategy for such directed networks. Compared to unrestricted random walk strategy, path-iteration-avoiding random walks can also make the search process much more efficient. However, no-triangle-loop and no-quadrangle-loop random walks do not improve the search efficiency as expected, which is different from those on undirected networks since the clustering coefficient of directed networks are smaller than that of undirected networks.

Babies Living Safe & Smokefree: randomized controlled trial of a multilevel multimodal behavioral intervention to reduce low-income children's tobacco smoke exposure.

PubMed

Collins, Bradley N; Lepore, Stephen J

2017-03-14

Addressing children's tobacco smoke exposure (TSE) remains a public health priority. However, there is low uptake and ineffectiveness of treatment, particularly in low-income populations that face numerous challenges to smoking behavior change. A multilevel intervention combining system-level health messaging and advice about TSE delivered at community clinics that disseminate the Special Supplemental Nutrition Program for Women, Infants and Children (WIC), combined with nicotine replacement and intensive multimodal, individual-level behavioral intervention may improve TSE control efforts in such high-risk populations. This trial uses a randomized two-group design with three measurement points: baseline, 3-month and 12-month follow-up. The primary outcome is bioverified child TSE; the secondary outcome is bioverified maternal quit status. Smoking mothers of children less than 6 years old are recruited from WIC clinics. All participants receive WIC system-level intervention based on the "Ask, Advise, Refer (AAR)" best practices guidelines for pediatrics clinics. It includes training all WIC staff about the importance of maternal tobacco control; and detailing clinics with AAR intervention prompts in routine work flow to remind WIC nutrition counselors to ask all mothers about child TSE, advise about TSE harms and benefits of protection, and refer smokers to cessation services. After receiving the system intervention, mothers are randomized to receive 3 months of additional treatment or an attention control intervention: (1) The multimodal behavioral intervention (MBI) treatment includes telephone counseling sessions about child TSE reduction and smoking cessation, provision of nicotine replacement therapy, a mobile app to support cessation efforts, and multimedia text messages about TSE and smoking cessation; (2) The attention control intervention offers equivalent contact as the MBI and includes nutrition-focused telephone counseling, mobile app, and multimedia text messages about improving nutrition. The control condition also receives a referral to the state smoking cessation quitline. This study tests an innovative community-based, multilevel and integrated multimodal approach to reducing child TSE in a vulnerable, low-income population. The approach is sustainable and has potential for wide reach because WIC can integrate the tobacco intervention prompts into routine workflow and refer smokers to free evidence-based behavioral counseling interventions, such as state quitlines. Clinicaltrials.gov NCT02602288 . Registered 9 November 2015.

Anomalous diffusion and dynamics of fluorescence recovery after photobleaching in the random-comb model

NASA Astrophysics Data System (ADS)

Yuste, S. B.; Abad, E.; Baumgaertner, A.

2016-07-01

We address the problem of diffusion on a comb whose teeth display varying lengths. Specifically, the length ℓ of each tooth is drawn from a probability distribution displaying power law behavior at large ℓ ,P (ℓ ) ˜ℓ-(1 +α ) (α >0 ). To start with, we focus on the computation of the anomalous diffusion coefficient for the subdiffusive motion along the backbone. This quantity is subsequently used as an input to compute concentration recovery curves mimicking fluorescence recovery after photobleaching experiments in comblike geometries such as spiny dendrites. Our method is based on the mean-field description provided by the well-tested continuous time random-walk approach for the random-comb model, and the obtained analytical result for the diffusion coefficient is confirmed by numerical simulations of a random walk with finite steps in time and space along the backbone and the teeth. We subsequently incorporate retardation effects arising from binding-unbinding kinetics into our model and obtain a scaling law characterizing the corresponding change in the diffusion coefficient. Finally, we show that recovery curves obtained with the help of the analytical expression for the anomalous diffusion coefficient cannot be fitted perfectly by a model based on scaled Brownian motion, i.e., a standard diffusion equation with a time-dependent diffusion coefficient. However, differences between the exact curves and such fits are small, thereby providing justification for the practical use of models relying on scaled Brownian motion as a fitting procedure for recovery curves arising from particle diffusion in comblike systems.

A Graph Theory Practice on Transformed Image: A Random Image Steganography

PubMed Central

Thanikaiselvan, V.; Arulmozhivarman, P.; Subashanthini, S.; Amirtharajan, Rengarajan

2013-01-01

Modern day information age is enriched with the advanced network communication expertise but unfortunately at the same time encounters infinite security issues when dealing with secret and/or private information. The storage and transmission of the secret information become highly essential and have led to a deluge of research in this field. In this paper, an optimistic effort has been taken to combine graceful graph along with integer wavelet transform (IWT) to implement random image steganography for secure communication. The implementation part begins with the conversion of cover image into wavelet coefficients through IWT and is followed by embedding secret image in the randomly selected coefficients through graph theory. Finally stegoimage is obtained by applying inverse IWT. This method provides a maximum of 44 dB peak signal to noise ratio (PSNR) for 266646 bits. Thus, the proposed method gives high imperceptibility through high PSNR value and high embedding capacity in the cover image due to adaptive embedding scheme and high robustness against blind attack through graph theoretic random selection of coefficients. PMID:24453857

Biases and Standard Errors of Standardized Regression Coefficients

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Chan, Wai

2011-01-01

The paper obtains consistent standard errors (SE) and biases of order O(1/n) for the sample standardized regression coefficients with both random and given predictors. Analytical results indicate that the formulas for SEs given in popular text books are consistent only when the population value of the regression coefficient is zero. The sample…

[Associations between dormitory environment/other factors and sleep quality of medical students].

PubMed

Zheng, Bang; Wang, Kailu; Pan, Ziqi; Li, Man; Pan, Yuting; Liu, Ting; Xu, Dan; Lyu, Jun

2016-03-01

To investigate the sleep quality and related factors among medical students in China, understand the association between dormitory environment and sleep quality, and provide evidence and recommendations for sleep hygiene intervention. A total of 555 undergraduate students were selected from a medical school of an university in Beijing through stratified-cluster random-sampling to conduct a questionnaire survey by using Chinese version of Pittsburgh Sleep Quality Index (PSQI) and self-designed questionnaire. Analyses were performed by using multiple logistic regression model as well as multilevel linear regression model. The prevalence of sleep disorder was 29.1%(149/512), and 39.1%(200/512) of the students reported that the sleep quality was influenced by dormitory environment. PSQI score was negatively correlated with self-reported rating of dormitory environment (γs=-0.310, P<0.001). Logistic regression analysis showed the related factors of sleep disorder included grade, sleep regularity, self-rated health status, pressures of school work and employment, as well as dormitory environment. RESULTS of multilevel regression analysis also indicated that perception on dormitory environment (individual level) was associated with sleep quality with the dormitory level random effects under control (b=-0.619, P<0.001). The prevalence of sleep disorder was high in medical students, which was associated with multiple factors. Dormitory environment should be taken into consideration when the interventions are taken to improve the sleep quality of students.

Teachers' support and depression among Japanese adolescents: a multilevel analysis.

PubMed

Mizuta, Akiko; Suzuki, Kohta; Yamagata, Zentaro; Ojima, Toshiyuki

2017-02-01

Depression is a major cause of suicide among adolescents. Therefore, childhood and adolescent depression is an important public health concern. This study explored factors as class and individual levels that may influence depression among adolescents in Japan. A questionnaire survey among junior high school students (N = 2968) from two cities in Japan was conducted. Depression was assessed using the Depression Self-Rating Scale for Children; teachers' support was assessed using the Scale of Expectancy for Social Support. The class average score of teachers' support was calculated to indicate what we termed the "homeroom teachers' support." Multilevel analysis was applied to clarify the relation between homeroom teachers' support and depression. Finally, 2466 students completed the questionnaire without missing variables (valid response rate, 83.1%). There was no random effect of the teachers' support at the class level on depression, although there was a significant association between teachers' support and depression for 9th graders (β = -0.12, p = 0.009). Moreover, there were significant associations between economic status, having a best friend, and experiencing unforgettable stress at the individual level and depression in all grades. There was no significant random effect of homeroom teachers' support in class level although there might be marginal negative association between teacher's support and depression. It is suggested that homeroom teachers need to promote population approaches to mental health.

Experimental detection of active defects in few layers MoS2 through random telegraphic signals analysis observed in its FET characteristics

NASA Astrophysics Data System (ADS)

Fang, Nan; Nagashio, Kosuke; Toriumi, Akira

2017-03-01

Transition-metal dichalcogenides (TMDs), such as molybdenum disulfide (MoS2), are expected to be promising for next generation device applications. The existence of sulfur vacancies formed in MoS2, however, will potentially make devices unstable and problematic. Random telegraphic signals (RTSs) have often been studied in small area Si metal-oxide-semiconductor field-effect transistors (MOSFETs) to identify the carrier capture and emission processes at defects. In this paper, we have systemically analyzed RTSs observed in atomically thin layer MoS2 FETs. Several types of RTSs have been analyzed. One is the simple on/off type of telegraphic signals, the second is multilevel telegraphic signals with a superposition of the simple signals, and the third is multilevel telegraphic signals that are correlated with each other. The last one is discussed from the viewpoint of the defect-defect interaction in MoS2 FETs with a weak screening in atomically confined two-dimensional electron-gas systems. Furthermore, the position of defects causing RTSs has also been investigated by preparing MoS2 FETs with multi-probes. The electron beam was locally irradiated to intentionally generate defects in the MoS2 channel. It is clearly demonstrated that the MoS2 channel is one of the RTS origins. RTS analysis enables us to analyze the defect dynamics of TMD devices.

Towards a drift-free multi-level Phase Change Memory

NASA Astrophysics Data System (ADS)

Cinar, Ibrahim; Ozdemir, Servet; Cogulu, Egecan; Gokce, Aisha; Stipe, Barry; Katine, Jordan; Aktas, Gulen; Ozatay, Ozhan

For ultra-high density data storage applications, Phase Change Memory (PCM) is considered a potentially disruptive technology. Yet, the long-term reliability of the logic levels corresponding to the resistance states of a PCM device is an important issue for a stable device operation since the resistance levels drift uncontrollably in time. The underlying mechanism for the resistance drift is considered as the structural relaxation and spontaneous crystallization at elevated temperatures. We fabricated a nanoscale single active layer-phase change memory cell with three resistance levels corresponding to crystalline, amorphous and intermediate states by controlling the current injection site geometry. For the intermediate state and the reset state, the activation energies and the trap distances have been found to be 0.021 eV and 0.235 eV, 1.31 nm and 7.56 nm, respectively. We attribute the ultra-low and weakly temperature dependent drift coefficient of the intermediate state (ν = 0.0016) as opposed to that of the reset state (ν = 0.077) as being due to the dominant contribution of the interfacial defects in electrical transport in the case of the mixed phase. Our results indicate that the engineering of interfacial defects will enable a drift-free multi-level PCM device design.

On the predictivity of pore-scale simulations: Estimating uncertainties with multilevel Monte Carlo

NASA Astrophysics Data System (ADS)

Icardi, Matteo; Boccardo, Gianluca; Tempone, Raúl

2016-09-01

A fast method with tunable accuracy is proposed to estimate errors and uncertainties in pore-scale and Digital Rock Physics (DRP) problems. The overall predictivity of these studies can be, in fact, hindered by many factors including sample heterogeneity, computational and imaging limitations, model inadequacy and not perfectly known physical parameters. The typical objective of pore-scale studies is the estimation of macroscopic effective parameters such as permeability, effective diffusivity and hydrodynamic dispersion. However, these are often non-deterministic quantities (i.e., results obtained for specific pore-scale sample and setup are not totally reproducible by another ;equivalent; sample and setup). The stochastic nature can arise due to the multi-scale heterogeneity, the computational and experimental limitations in considering large samples, and the complexity of the physical models. These approximations, in fact, introduce an error that, being dependent on a large number of complex factors, can be modeled as random. We propose a general simulation tool, based on multilevel Monte Carlo, that can reduce drastically the computational cost needed for computing accurate statistics of effective parameters and other quantities of interest, under any of these random errors. This is, to our knowledge, the first attempt to include Uncertainty Quantification (UQ) in pore-scale physics and simulation. The method can also provide estimates of the discretization error and it is tested on three-dimensional transport problems in heterogeneous materials, where the sampling procedure is done by generation algorithms able to reproduce realistic consolidated and unconsolidated random sphere and ellipsoid packings and arrangements. A totally automatic workflow is developed in an open-source code [1], that include rigid body physics and random packing algorithms, unstructured mesh discretization, finite volume solvers, extrapolation and post-processing techniques. The proposed method can be efficiently used in many porous media applications for problems such as stochastic homogenization/upscaling, propagation of uncertainty from microscopic fluid and rock properties to macro-scale parameters, robust estimation of Representative Elementary Volume size for arbitrary physics.

Do Evidence-Based Youth Psychotherapies Outperform Usual Clinical Care? A Multilevel Meta-Analysis

PubMed Central

Weisz, John R.; Kuppens, Sofie; Eckshtain, Dikla; Ugueto, Ana M.; Hawley, Kristin M.; Jensen-Doss, Amanda

2013-01-01

Context Research across four decades has produced numerous empirically-tested evidence-based psychotherapies (EBPs) for youth psychopathology, developed to improve upon usual clinical interventions. Advocates argue that these should replace usual care; but do the EBPs produce better outcomes than usual care? Objective This question was addressed in a meta-analysis of 52 randomized trials directly comparing EBPs to usual care. Analyses assessed the overall effect of EBPs vs. usual care, and candidate moderators; multilevel analysis was used to address the dependency among effect sizes that is common but typically unaddressed in psychotherapy syntheses. Data Sources The PubMed, PsychINFO, and Dissertation Abstracts International databases were searched for studies from January 1, 1960 – December 31, 2010. Study Selection 507 randomized youth psychotherapy trials were identified. Of these, the 52 studies that compared EBPs to usual care were included in the meta-analysis. Data Extraction Sixteen variables (participant, treatment, and study characteristics) were extracted from each study, and effect sizes were calculated for all EBP versus usual care comparisons. Data Synthesis EBPs outperformed usual care. Mean effect size was 0.29; the probability was 58% that a randomly selected youth receiving an EBP would be better off after treatment than a randomly selected youth receiving usual care. Three variables moderated treatment benefit: Effect sizes decreased for studies conducted outside North America, for studies in which all participants were impaired enough to qualify for diagnoses, and for outcomes reported by people other than the youths and parents in therapy. For certain key groups (e.g., studies using clinically referred samples and diagnosed samples), significant EBP effects were not demonstrated. Conclusions EBPs outperformed usual care, but the EBP advantage was modest and moderated by youth, location, and assessment characteristics. There is room for improvement in EBPs, both in the magnitude and range of their benefit, relative to usual care. PMID:23754332

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.

Fluctuating Navier-Stokes equations for inelastic hard spheres or disks.

PubMed

Brey, J Javier; Maynar, P; de Soria, M I García

2011-04-01

Starting from the fluctuating Boltzmann equation for smooth inelastic hard spheres or disks, closed equations for the fluctuating hydrodynamic fields to Navier-Stokes order are derived. This requires deriving constitutive relations for both the fluctuating fluxes and the correlations of the random forces. The former are identified as having the same form as the macroscopic average fluxes and involving the same transport coefficients. On the other hand, the random force terms exhibit two peculiarities as compared with their elastic limit for molecular systems. First, they are not white but have some finite relaxation time. Second, their amplitude is not determined by the macroscopic transport coefficients but involves new coefficients. ©2011 American Physical Society

The Analysis of Completely Randomized Factorial Experiments When Observations Are Lost at Random.

ERIC Educational Resources Information Center

Hummel, Thomas J.

An investigation was conducted of the characteristics of two estimation procedures and corresponding test statistics used in the analysis of completely randomized factorial experiments when observations are lost at random. For one estimator, contrast coefficients for cell means did not involve the cell frequencies. For the other, contrast…

Effects of Personality Disorders on Self-Other Agreement and Favorableness in Personality Descriptions.

PubMed

Tandler, Nancy; Mosch, Alice; Wolf, Annegret; Borkenau, Peter

2016-10-01

The authors studied effects of self-reported personality disorder (PD) symptoms on interpersonal perception, particularly self-other agreement and favorableness. Using a round-robin design, 52 groups of four well-acquainted students described themselves and each other on a measure of the Five-Factor model of personality and were administered a self-report screening instrument for DSM-IV (Axis 2). Using the Social Accuracy Model, the peer reports were predicted, across items, from either (a) the target person's self-reports plus the self-report item means, or (b) the items' social desirability. This resulted in separate coefficients for each peer-target dyad, indicating either self-other agreement or favorableness. These coefficients were then predicted from the PD scores of the target and the peer, using multilevel modeling. Main findings were that persons scoring high on PD measures agreed less with their peers on their unique personality characteristics, and that such persons were described by, and described their peers, less favorably.

Improved multi-level capability in Si3N4-based resistive switching memory using continuous gradual reset switching

NASA Astrophysics Data System (ADS)

Kim, Sungjun; Park, Byung-Gook

2017-01-01

In this letter, we compare three different types of reset switching behavior in a bipolar resistive random-access memory (RRAM) system that is housed in a Ni/Si3N4/Si structure. The abrupt, step-like gradual and continuous gradual reset transitions are largely determined by the low-resistance state (LRS). For abrupt reset switching, the large conducting path shows ohmic behavior or has a weak nonlinear current-voltage (I-V) characteristics in the LRS. For gradual switching, including both the step-like and continuous reset types, trap-assisted direct tunneling is dominant in the low-voltage regime, while trap-assisted Fowler-Nordheim tunneling is dominant in the high-voltage regime, thus causing nonlinear I-V characteristics. More importantly, we evaluate the multi-level capabilities of the two different gradual switching types, including both step-like and continuous reset behavior, using identical and incremental voltage conditions. Finer control of the conductance level with good uniformity is achieved in continuous gradual reset switching when compared to that in step-like gradual reset switching. For continuous reset switching, a single conducting path, which initially has a tunneling gap, gradually responds to pulses with even and identical amplitudes, while for step-like reset switching, the multiple conducting paths only respond to incremental pulses to obtain effective multi-level states.

An intervention to reduce sitting and increase light-intensity physical activity at work: Design and rationale of the 'Stand & Move at Work' group randomized trial.

PubMed

Buman, Matthew P; Mullane, Sarah L; Toledo, Meynard J; Rydell, Sarah A; Gaesser, Glenn A; Crespo, Noe C; Hannan, Peter; Feltes, Linda; Vuong, Brenna; Pereira, Mark A

2017-02-01

American workers spend 70-80% of their time at work being sedentary. Traditional approaches to increase moderate-vigorous physical activity (MVPA) may be perceived to be harmful to productivity. Approaches that target reductions in sedentary behavior and/or increases in standing or light-intensity physical activity [LPA] may not interfere with productivity and may be more feasible to achieve through small changes accumulated throughout the workday METHODS/DESIGN: This group randomized trial (i.e., cluster randomized trial) will test the relative efficacy of two sedentary behavior focused interventions in 24 worksites across two states (N=720 workers). The MOVE+ intervention is a multilevel individual, social, environmental, and organizational intervention targeting increases in light-intensity physical activity in the workplace. The STAND+ intervention is the MOVE+ intervention with the addition of the installation and use of sit-stand workstations to reduce sedentary behavior and enhance light-intensity physical activity opportunities. Our primary outcome will be objectively-measured changes in sedentary behavior and light-intensity physical activity over 12months, with additional process measures at 3months and longer-term sustainability outcomes at 24months. Our secondary outcomes will be a clustered cardiometabolic risk score (comprised of fasting glucose, insulin, triglycerides, HDL-cholesterol, and blood pressure), workplace productivity, and job satisfaction DISCUSSION: This study will determine the efficacy of a multi-level workplace intervention (including the use of a sit-stand workstation) to reduce sedentary behavior and increase LPA and concomitant impact on cardiometabolic health, workplace productivity, and satisfaction. ClinicalTrials.gov Identifier: NCT02566317 (date of registration: 10/1/2015). Copyright © 2016 Elsevier Inc. All rights reserved.

Income Inequality and Use of Dental Services in 66 Countries.

PubMed

Bhandari, B; Newton, J T; Bernabé, E

2015-08-01

This study explored the association between income inequality and use of dental services and the role that investment in health care plays in explaining that association. We pooled individual-level data from 223,299 adults, 18 years or older, in 66 countries, who participated in the World Health Organization (WHO) World Health Surveys with country-level data from different international sources. Income inequality was measured at the national level using the Gini coefficient, and use of dental services was defined as having received treatment to address problems with mouth and/or teeth in the past year. The association between the Gini coefficient and use of dental services was examined in multilevel models controlling for a standard set of individual- and country-level confounders. The individual and joint contributions of 4 indicators of investment in health care were evaluated in sequential modeling. The Gini coefficient and use of dental services were inversely associated after adjustment for confounders. Every 10% increase in the Gini coefficient corresponded with a 15% lower odds of using dental services (odds ratio: 0.85; 95% confidence interval: 0.70-0.99). The association between the Gini coefficient and use of dental services was attenuated and became nonsignificant after individual adjustment for total health expenditure, public expenditure on health, health system responsiveness, or type of dental health system. The 4 indicators together explained 80% of the association between the Gini coefficient and use of dental services. This study suggests that more equal countries have greater use of dental services. It also supports the mediating role of investment in health care in explaining that association. © International & American Associations for Dental Research 2015.

Individual relocation decisions after tornadoes: a multi-level analysis.

PubMed

Cong, Zhen; Nejat, Ali; Liang, Daan; Pei, Yaolin; Javid, Roxana J

2018-04-01

This study examines how multi-level factors affected individuals' relocation decisions after EF4 and EF5 (Enhanced Fujita Tornado Intensity Scale) tornadoes struck the United States in 2013. A telephone survey was conducted with 536 respondents, including oversampled older adults, one year after these two disaster events. Respondents' addresses were used to associate individual information with block group-level variables recorded by the American Community Survey. Logistic regression revealed that residential damage and homeownership are important predictors of relocation. There was also significant interaction between these two variables, indicating less difference between homeowners and renters at higher damage levels. Homeownership diminished the likelihood of relocation among younger respondents. Random effects logistic regression found that the percentage of homeownership and of higher income households in the community buffered the effect of damage on relocation; the percentage of older adults reduced the likelihood of this group relocating. The findings are assessed from the standpoint of age difference, policy implications, and social capital and vulnerability. © 2018 The Author(s). Disasters © Overseas Development Institute, 2018.

Optical colour image watermarking based on phase-truncated linear canonical transform and image decomposition

NASA Astrophysics Data System (ADS)

Su, Yonggang; Tang, Chen; Li, Biyuan; Lei, Zhenkun

2018-05-01

This paper presents a novel optical colour image watermarking scheme based on phase-truncated linear canonical transform (PT-LCT) and image decomposition (ID). In this proposed scheme, a PT-LCT-based asymmetric cryptography is designed to encode the colour watermark into a noise-like pattern, and an ID-based multilevel embedding method is constructed to embed the encoded colour watermark into a colour host image. The PT-LCT-based asymmetric cryptography, which can be optically implemented by double random phase encoding with a quadratic phase system, can provide a higher security to resist various common cryptographic attacks. And the ID-based multilevel embedding method, which can be digitally implemented by a computer, can make the information of the colour watermark disperse better in the colour host image. The proposed colour image watermarking scheme possesses high security and can achieve a higher robustness while preserving the watermark’s invisibility. The good performance of the proposed scheme has been demonstrated by extensive experiments and comparison with other relevant schemes.

Evolution of cooperation in multilevel public goods games with community structures

NASA Astrophysics Data System (ADS)

Wang, Jing; Wu, Bin; Ho, Daniel W. C.; Wang, Long

2011-03-01

In a community-structured population, public goods games (PGG) occur both within and between communities. Such type of PGG is referred as multilevel public goods games (MPGG). We propose a minimalist evolutionary model of the MPGG and analytically study the evolution of cooperation. We demonstrate that in the case of sufficiently large community size and community number, if the imitation strength within community is weak, i.e., an individual imitates another one in the same community almost randomly, cooperation as well as punishment are more abundant than defection in the long run; if the imitation strength between communities is strong, i.e., the more successful strategy in two individuals from distinct communities is always imitated, cooperation and punishment are also more abundant. However, when both of the two imitation intensities are strong, defection becomes the most abundant strategy in the population. Our model provides insight into the investigation of the large-scale cooperation in public social dilemma among contemporary communities.

Weibull crack density coefficient for polydimensional stress states

NASA Technical Reports Server (NTRS)

Gross, Bernard; Gyekenyesi, John P.

1989-01-01

A structural ceramic analysis and reliability evaluation code has recently been developed encompassing volume and surface flaw induced fracture, modeled by the two-parameter Weibull probability density function. A segment of the software involves computing the Weibull polydimensional stress state crack density coefficient from uniaxial stress experimental fracture data. The relationship of the polydimensional stress coefficient to the uniaxial stress coefficient is derived for a shear-insensitive material with a random surface flaw population.

Bayesian dynamic modeling of time series of dengue disease case counts

PubMed Central

López-Quílez, Antonio; Torres-Prieto, Alexander

2017-01-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health. PMID:28671941

A comparison of confidence interval methods for the intraclass correlation coefficient in community-based cluster randomization trials with a binary outcome.

PubMed

Braschel, Melissa C; Svec, Ivana; Darlington, Gerarda A; Donner, Allan

2016-04-01

Many investigators rely on previously published point estimates of the intraclass correlation coefficient rather than on their associated confidence intervals to determine the required size of a newly planned cluster randomized trial. Although confidence interval methods for the intraclass correlation coefficient that can be applied to community-based trials have been developed for a continuous outcome variable, fewer methods exist for a binary outcome variable. The aim of this study is to evaluate confidence interval methods for the intraclass correlation coefficient applied to binary outcomes in community intervention trials enrolling a small number of large clusters. Existing methods for confidence interval construction are examined and compared to a new ad hoc approach based on dividing clusters into a large number of smaller sub-clusters and subsequently applying existing methods to the resulting data. Monte Carlo simulation is used to assess the width and coverage of confidence intervals for the intraclass correlation coefficient based on Smith's large sample approximation of the standard error of the one-way analysis of variance estimator, an inverted modified Wald test for the Fleiss-Cuzick estimator, and intervals constructed using a bootstrap-t applied to a variance-stabilizing transformation of the intraclass correlation coefficient estimate. In addition, a new approach is applied in which clusters are randomly divided into a large number of smaller sub-clusters with the same methods applied to these data (with the exception of the bootstrap-t interval, which assumes large cluster sizes). These methods are also applied to a cluster randomized trial on adolescent tobacco use for illustration. When applied to a binary outcome variable in a small number of large clusters, existing confidence interval methods for the intraclass correlation coefficient provide poor coverage. However, confidence intervals constructed using the new approach combined with Smith's method provide nominal or close to nominal coverage when the intraclass correlation coefficient is small (<0.05), as is the case in most community intervention trials. This study concludes that when a binary outcome variable is measured in a small number of large clusters, confidence intervals for the intraclass correlation coefficient may be constructed by dividing existing clusters into sub-clusters (e.g. groups of 5) and using Smith's method. The resulting confidence intervals provide nominal or close to nominal coverage across a wide range of parameters when the intraclass correlation coefficient is small (<0.05). Application of this method should provide investigators with a better understanding of the uncertainty associated with a point estimator of the intraclass correlation coefficient used for determining the sample size needed for a newly designed community-based trial. © The Author(s) 2015.

Anthropometry and blood pressure changes in a Caribbean adolescent population of African ancestry: an evaluation of longitudinal data using a multilevel mixed regression approach.

PubMed

Nichols, S; Cadogan, F

2012-10-01

The aim of this study was to determine the effect of growth pattern on blood pressure changes in an adolescent population of African ancestry based on longitudinal data and to compare this with estimates derived from cross-sectional data. Participants had measurements of weight, height, blood pressure and percentage body fat taken annually using standardized procedures. Annual blood pressure and anthropometry velocities as well as one- and three-year interval gender specific tracking coefficients were computed. We investigated whether changes in blood pressure could be explained by measures of growth using a multilevel mixed regression approach. The results showed that systolic blood pressure (SBP) increased by 1.27 and 3.09 mmHg per year among females and males, respectively. Similarly, diastolic blood pressure (DBP) increased by 1.16 and 1.92 mmHg per year among females and males, respectively. Multilevel analyses suggested that weight, body mass index, percentage body fat and height were the strongest anthropometric determinants of blood pressure change in this population. The results also suggest that there are gender differences in the relative importance of these anthropometric measures with height playing a minor role in predicting blood pressure changes among adolescent females. With the exception of DBP at 18 years among females, there were no significant differences between mean blood pressure generated from cross-sectional and longitudinal data by age in both males and females. Anthropometric measures are important covariates of age-related blood pressure changes and cross-sectional data may provide a more cost-effective and useful proxy for generating age-related blood pressure estimates in this population.

Effect of a multi-level education intervention model on knowledge and attitudes of accidental injuries in rural children in Zunyi, Southwest China.

PubMed

Cao, Bo-Ling; Shi, Xiu-Quan; Qi, Yong-Hong; Hui, Ya; Yang, Hua-Jun; Shi, Shang-Peng; Luo, Li-Rong; Zhang, Hong; Wang, Xin; Yang, Ying-Ping

2015-04-08

To explore the effect of a school-family-individual (SFI) multi-level education intervention model on knowledge and attitudes about accidental injuries among school-aged children to improve injury prevention strategies and reduce the incidence of pediatric injuries. The random sample of rural school-aged children were recruited by using a multistage, stratified, cluster sampling method in Zunyi, Southwest China from 2012 to 2014, and 2342 children were randomly divided into intervention and control groups. Then children answered a baseline survey to collect knowledge and attitude scores (KAS) of accidental injuries. In the intervention group, children, their parents/guardians and the school received a SFI multi-level education intervention, which included a children's injury-prevention poster at schools, an open letter about security instruction for parents/guardians and multiple-media health education (Microsoft PowerPoint lectures, videos, handbooks, etc.) to children. Children in the control group were given only handbook education. After 16 months, children answered a follow-up survey to collect data on accidental injury types and accidental injury-related KAS for comparing the intervention and control groups and baseline and follow-up data. The distribution of gender was not significantly different while age was different between the baseline and follow-up survey. At baseline, the mean KAS was lower for the intervention than control group (15.37 ± 3.40 and 18.35 ± 5.01; p < 0.001). At follow-up, the mean KAS was higher for the intervention than control group (21.16 ± 3.05 and 20.02 ± 3.40; p < 0.001). The increase in KAS in the intervention and control groups was significant (p < 0.001; KAS: 5.79 vs. 1.67) and suggested that children's injury-related KAS improved in the intervention group. Moreover, the KAS between the groups differed for most subtypes of incidental injuries (based on International Classification of Diseases 10, ICD-10) (p < 0.05). Before intervention, 350 children had reported their accident injury episodes, while after intervention 237 children had reported their accidental injury episodes in the follow-up survey. SFI multi-level education intervention could significantly increase KAS for accidental injuries, which should improve children's prevention-related knowledge and attitudes about such injuries. It should help children change their risk behaviors and reduce the incidence of accidental injuries. Our results highlight a new intervention model of injury prevention among school-aged children.

Social support, volunteering and health around the world: cross-national evidence from 139 countries.

PubMed

Kumar, Santosh; Calvo, Rocio; Avendano, Mauricio; Sivaramakrishnan, Kavita; Berkman, Lisa F

2012-03-01

High levels of social capital and social integration are associated with self-rated health in many developed countries. However, it is not known whether this association extends to non-western and less economically advanced countries. We examine associations between social support, volunteering, and self-rated health in 139 low-, middle- and high-income countries. Data come from the Gallup World Poll, an internationally comparable survey conducted yearly from 2005 to 2009 for those 15 and over. Volunteering was measured by self-reports of volunteering to an organization in the past month. Social support was based on self-reports of access to support from relatives and friends. We started by estimating random coefficient (multi-level) models and then used multivariate logistic regression to model health as a function of social support and volunteering, controlling for age, gender, education, marital status, and religiosity. We found statistically significant evidence of cross-national variation in the association between social capital variables and self-rated health. In the multivariate logistic model, self-rated health were significantly associated with having social support from friends and relatives and volunteering. Results from stratified analyses indicate that these associations are strikingly consistent across countries. Our results indicate that the link between social capital and health is not restricted to high-income countries but extends across many geographical regions regardless of their national-income level. Copyright © 2012 Elsevier Ltd. All rights reserved.

An investigation of associations between clinicians' ethnic or racial bias and hypertension treatment, medication adherence and blood pressure control.

PubMed

Blair, Irene V; Steiner, John F; Hanratty, Rebecca; Price, David W; Fairclough, Diane L; Daugherty, Stacie L; Bronsert, Michael; Magid, David J; Havranek, Edward P

2014-07-01

Few studies have directly investigated the association of clinicians' implicit (unconscious) bias with health care disparities in clinical settings. To determine if clinicians' implicit ethnic or racial bias is associated with processes and outcomes of treatment for hypertension among black and Latino patients, relative to white patients. Primary care clinicians completed Implicit Association Tests of ethnic and racial bias. Electronic medical records were queried for a stratified, random sample of the clinicians' black, Latino and white patients to assess treatment intensification, adherence and control of hypertension. Multilevel random coefficient models assessed the associations between clinicians' implicit biases and ethnic or racial differences in hypertension care and outcomes. Standard measures of treatment intensification and medication adherence were calculated from pharmacy refills. Hypertension control was assessed by the percentage of time that patients met blood pressure goals recorded during primary care visits. One hundred and thirty-eight primary care clinicians and 4,794 patients with hypertension participated. Black patients received equivalent treatment intensification, but had lower medication adherence and worse hypertension control than white patients; Latino patients received equivalent treatment intensification and had similar hypertension control, but lower medication adherence than white patients. Differences in treatment intensification, medication adherence and hypertension control were unrelated to clinician implicit bias for black patients (P = 0.85, P = 0.06 and P = 0.31, respectively) and for Latino patients (P = 0.55, P = 0.40 and P = 0.79, respectively). An increase in clinician bias from average to strong was associated with a relative change of less than 5 % in all outcomes for black and Latino patients. Implicit bias did not affect clinicians' provision of care to their minority patients, nor did it affect the patients' outcomes. The identification of health care contexts in which bias does not impact outcomes can assist both patients and clinicians in their efforts to build trust and partnership.

Effects of dietary counselling on food habits and dietary intake of Finnish pregnant women at increased risk for gestational diabetes - a secondary analysis of a cluster-randomized controlled trial.

PubMed

Kinnunen, Tarja I; Puhkala, Jatta; Raitanen, Jani; Ahonen, Suvi; Aittasalo, Minna; Virtanen, Suvi M; Luoto, Riitta

2014-04-01

The incidence of gestational diabetes mellitus (GDM) is increasing and GDM might be prevented by improving diet. Few interventions have assessed the effects of dietary counselling on dietary intake of pregnant women. This study examined the effects of dietary counselling on food habits and dietary intake of Finnish pregnant women as secondary outcomes of a trial primarily aiming at preventing GDM. A cluster-randomized controlled trial was conducted in 14 municipalities in Finland, including 399 pregnant women at increased risk for developing GDM. The intervention consisted of dietary counselling focusing on dietary fat, fibre and saccharose intake at four routine maternity clinic visits. Usual counselling practices were continued in the usual care municipalities. A validated 181-item food frequency questionnaire was used to assess changes in diet from baseline to 26-28 and 36-37 weeks gestation. The data were analysed using multilevel mixed-effects linear regression models. By 36-37 weeks gestation, the intervention had beneficial effects on total intake of vegetables, fruits and berries (coefficient for between-group difference in change 61.6 g day(-1), 95% confidence interval 25.7-97.6), the proportions of high-fibre bread of all bread (7.2% units, 2.5-11.9), low-fat cheeses of all cheeses (10.7% units, 2.6-18.9) and vegetable fats of all dietary fats (6.1% -units, 2.0-10.3), and the intake of saturated fatty acids (-0.67 energy-%-units, -1.16 to -0.19), polyunsaturated fatty acids (0.38 energy-%-units, 0.18-0.58), linoleic acid (764 mg day(-1), 173-1354) and fibre (2.07 g day(-1) , 0.39-3.75). The intervention improved diet towards the recommendations in pregnant women at increased risk for GDM suggesting the counselling methods could be implemented in maternity care. © 2012 John Wiley & Sons Ltd.

Income inequality and schizophrenia: increased schizophrenia incidence in countries with high levels of income inequality.

PubMed

Burns, Jonathan K; Tomita, Andrew; Kapadia, Amy S

2014-03-01

Income inequality is associated with numerous negative health outcomes. There is evidence that ecological-level socio-environmental factors may increase risk for schizophrenia. The aim was to investigate whether measures of income inequality are associated with incidence of schizophrenia at the country level. We conducted a systematic review of incidence rates for schizophrenia, reported between 1975 and 2011. For each country, national measures of income inequality (Gini coefficient) along with covariate risk factors for schizophrenia were obtained. Multi-level mixed-effects Poisson regression was performed to investigate the relationship between Gini coefficients and incidence rates of schizophrenia controlling for covariates. One hundred and seven incidence rates (from 26 countries) were included. Mean incidence of schizophrenia was 18.50 per 100,000 (SD = 11.9; range = 1.7-67). There was a significant positive relationship between incidence rate of schizophrenia and Gini coefficient (β = 1.02; Z = 2.28; p = .02; 95% CI = 1.00, 1.03). Countries characterized by a large rich-poor gap may be at increased risk of schizophrenia. We suggest that income inequality impacts negatively on social cohesion, eroding social capital, and that chronic stress associated with living in highly disparate societies places individuals at risk of schizophrenia.

Measurement of the absorption coefficient using the sound-intensity technique

NASA Technical Reports Server (NTRS)

Atwal, M.; Bernhard, R.

1984-01-01

The possibility of using the sound intensity technique to measure the absorption coefficient of a material is investigated. This technique measures the absorption coefficient by measuring the intensity incident on the sample and the net intensity reflected by the sample. Results obtained by this technique are compared with the standard techniques of measuring the change in the reverberation time and the standing wave ratio in a tube, thereby, calculating the random incident and the normal incident adsorption coefficient.

Multi-level analysis in information systems research: the case of enterprise resource planning system usage in China

NASA Astrophysics Data System (ADS)

Sun, Yuan; Bhattacherjee, Anol

2011-11-01

Information technology (IT) usage within organisations is a multi-level phenomenon that is influenced by individual-level and organisational-level variables. Yet, current theories, such as the unified theory of acceptance and use of technology, describe IT usage as solely an individual-level phenomenon. This article postulates a model of organisational IT usage that integrates salient organisational-level variables such as user training, top management support and technical support within an individual-level model to postulate a multi-level model of IT usage. The multi-level model was then empirically validated using multi-level data collected from 128 end users and 26 managers in 26 firms in China regarding their use of enterprise resource planning systems and analysed using the multi-level structural equation modelling (MSEM) technique. We demonstrate the utility of MSEM analysis of multi-level data relative to the more common structural equation modelling analysis of single-level data and show how single-level data can be aggregated to approximate multi-level analysis when multi-level data collection is not possible. We hope that this article will motivate future scholars to employ multi-level data and multi-level analysis for understanding organisational phenomena that are truly multi-level in nature.

Active microwave remote sensing of an anisotropic random medium layer

NASA Technical Reports Server (NTRS)

Lee, J. K.; Kong, J. A.

1985-01-01

A two-layer anisotropic random medium model has been developed to study the active remote sensing of the earth. The dyadic Green's function for a two-layer anisotropic medium is developed and used in conjunction with the first-order Born approximation to calculate the backscattering coefficients. It is shown that strong cross-polarization occurs in the single scattering process and is indispensable in the interpretation of radar measurements of sea ice at different frequencies, polarizations, and viewing angles. The effects of anisotropy on the angular responses of backscattering coefficients are also illustrated.

Modeling of Thermal Phase Noise in a Solid Core Photonic Crystal Fiber-Optic Gyroscope.

PubMed

Song, Ningfang; Ma, Kun; Jin, Jing; Teng, Fei; Cai, Wei

2017-10-26

A theoretical model of the thermal phase noise in a square-wave modulated solid core photonic crystal fiber-optic gyroscope has been established, and then verified by measurements. The results demonstrate a good agreement between theory and experiment. The contribution of the thermal phase noise to the random walk coefficient of the gyroscope is derived. A fiber coil with 2.8 km length is used in the experimental solid core photonic crystal fiber-optic gyroscope, showing a random walk coefficient of 9.25 × 10 -5 deg/√h.

A scattering model for forested area

NASA Technical Reports Server (NTRS)

Karam, M. A.; Fung, A. K.

1988-01-01

A forested area is modeled as a volume of randomly oriented and distributed disc-shaped, or needle-shaped leaves shading a distribution of branches modeled as randomly oriented finite-length, dielectric cylinders above an irregular soil surface. Since the radii of branches have a wide range of sizes, the model only requires the length of a branch to be large compared with its radius which may be any size relative to the incident wavelength. In addition, the model also assumes the thickness of a disc-shaped leaf or the radius of a needle-shaped leaf is much smaller than the electromagnetic wavelength. The scattering phase matrices for disc, needle, and cylinder are developed in terms of the scattering amplitudes of the corresponding fields which are computed by the forward scattering theorem. These quantities along with the Kirchoff scattering model for a randomly rough surface are used in the standard radiative transfer formulation to compute the backscattering coefficient. Numerical illustrations for the backscattering coefficient are given as a function of the shading factor, incidence angle, leaf orientation distribution, branch orientation distribution, and the number density of leaves. Also illustrated are the properties of the extinction coefficient as a function of leaf and branch orientation distributions. Comparisons are made with measured backscattering coefficients from forested areas reported in the literature.

Health-related quality of life among adults 65 years and older in the United States, 2011-2012: a multilevel small area estimation approach.

PubMed

Lin, Yu-Hsiu; McLain, Alexander C; Probst, Janice C; Bennett, Kevin J; Qureshi, Zaina P; Eberth, Jan M

2017-01-01

The purpose of this study was to develop county-level estimates of poor health-related quality of life (HRQOL) among aged 65 years and older U.S. adults and to identify spatial clusters of poor HRQOL using a multilevel, poststratification approach. Multilevel, random-intercept models were fit to HRQOL data (two domains: physical health and mental health) from the 2011-2012 Behavioral Risk Factor Surveillance System. Using a poststratification, small area estimation approach, we generated county-level probabilities of having poor HRQOL for each domain in U.S. adults aged 65 and older, and validated our model-based estimates against state and county direct estimates. County-level estimates of poor HRQOL in the United States ranged from 18.07% to 44.81% for physical health and 14.77% to 37.86% for mental health. Correlations between model-based and direct estimates were higher for physical than mental HRQOL. Counties located in the Arkansas, Kentucky, and Mississippi exhibited the worst physical HRQOL scores, but this pattern did not hold for mental HRQOL, which had the highest probability of mentally unhealthy days in Illinois, Indiana, and Vermont. Substantial geographic variation in physical and mental HRQOL scores exists among older U.S. adults. State and local policy makers should consider these local conditions in targeting interventions and policies to counties with high levels of poor HRQOL scores. Copyright © 2016 Elsevier Inc. All rights reserved.

Multilevel Intervention Research: Lessons Learned and Pathways Forward

PubMed Central

Taplin, Stephen H.; Foster, Mary K.; Fagan, Pebbles; Kaluzny, Arnold D.

2012-01-01

This summary reflects on this monograph regarding multilevel intervention (MLI) research to 1) assess its added value; 2) discuss what has been learned to date about its challenges in cancer care delivery; and 3) identify specific ways to improve its scientific soundness, feasibility, policy relevance, and research agenda. The 12 submitted chapters, and discussion of them at the March 2011 multilevel meeting, were reviewed and discussed among the authors to elicit key findings and results addressing the questions raised at the outset of this effort. MLI research is underrepresented as an explicit focus in the cancer literature but may improve implementation of studies of cancer care delivery if they assess contextual, organizational, and environmental factors important to understanding behavioral and/or system-level interventions. The field lacks a single unifying theory, although several psychological or biological theories are useful, and an ecological model helps conceptualize and communicate interventions. MLI research designs are often complex, involving nonlinear and nonhierarchical relationships that may not be optimally studied in randomized designs. Simulation modeling and pilot studies may be necessary to evaluate MLI interventions. Measurement and evaluation of team and organizational interventions are especially needed in cancer care, as are attention to the context of health-care reform, eHealth technology, and genomics-based medicine. Future progress in MLI research requires greater attention to developing and supporting relevant metrics of level effects and interactions and evaluating MLI interventions. MLI research holds an unrealized promise for understanding how to improve cancer care delivery. PMID:22623606

Neighbourhood effects on body constitution-A case study of Hong Kong.

PubMed

Low, Chien Tat; Lai, Poh Chin; Li, Han Dong; Ho, Wai Kit; Wong, Paulina; Chen, Si; Wong, Wing Cheung

2016-06-01

Traditional Chinese Medicine (TCM) has long perceived environment as an integral part of the development of body constitution, which is a personal state of health closely related to disease presence. Despite of the ever-growing studies on the clinical effectiveness of TCM and the scientific linking between body constitution and diseases, the geographical influence on body constitution has yet remained an unexplored territory. This study sought to investigate whether the neighbourhood environment is relevant to the composition of body type of a population through statistical multilevel and Geographic Information Systems modelling. The analysis comprised 3277 participants who had completed their body type assessment between 2009 and 2012 inclusive. The multilevel analysis also took simultaneous accounts of both individual-level (gender, age, BMI, type of housing) and area-level (percent greenery, percent road surface, total road intersection, sky view factor, temperature, relative humidity, rainfall and social deprivation index) characteristics to explain geographical variation by body types. Significant random or place effects (p < 0.001) were identified in the multilevel models. The spatial variation of body constitution involved the dynamic interplay between individual and environmental factors. The findings amassed the first scientific indications to back the common belief that place does play a role in the development of body constitution and is worthy of further investigation. By considering spatial and personal attributes simultaneously, the study can yield valuable insights into the patterning of area variation in body constitution and disease presence. Copyright © 2016 Elsevier Ltd. All rights reserved.

The relationship between self-management abilities, quality of chronic care delivery, and wellbeing among patients with chronic obstructive pulmonary disease in The Netherlands

PubMed Central

Cramm, Jane Murray; Nieboer, Anna Petra

2013-01-01

Background This cross-sectional study aimed to identify the relationship between quality of chronic care delivery, self-management abilities, and wellbeing among patients with chronic obstructive pulmonary disease (COPD). Methods The study was conducted in 2012 and included 548 (out of 1303; 42% response rate) patients with COPD enrolled in a COPD care program in the region of Noord-Kennemerland in The Netherlands. We employed a multilevel random-effects model (548 patients nested in 47 healthcare practices) to investigate the relationship between quality of chronic care delivery, self-management abilities, and patients’ wellbeing. In the multilevel analyses we controlled for patients’ background characteristics and health behaviors. Results Multilevel analyses clearly showed a significant relationship between quality of chronic care delivery and wellbeing of patients with COPD (P ≤ 0.001). When self-management abilities were included in the equation while controlling for background characteristics, health behaviors, and quality of chronic care delivery, these abilities were found to have a strong positive relationship with patients’ wellbeing (P ≤ 0.001). Low educational level, single marital status, and physical exercise were not significantly associated with wellbeing when self-management abilities were included in the equation. Conclusion Self-management abilities and the quality of chronic care delivery are important for the wellbeing of patients with COPD. Furthermore, self-management abilities acted as mediators between wellbeing and low educational level, single status, and physical exercise among these patients. PMID:23641152

Random attractor of non-autonomous stochastic Boussinesq lattice system

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

Zhao, Min, E-mail: zhaomin1223@126.com; Zhou, Shengfan, E-mail: zhoushengfan@yahoo.com

2015-09-15

In this paper, we first consider the existence of tempered random attractor for second-order non-autonomous stochastic lattice dynamical system of nonlinear Boussinesq equations effected by time-dependent coupled coefficients and deterministic forces and multiplicative white noise. Then, we establish the upper semicontinuity of random attractors as the intensity of noise approaches zero.

Random walk study of electron motion in helium in crossed electromagnetic fields

NASA Technical Reports Server (NTRS)

Englert, G. W.

1972-01-01

Random walk theory, previously adapted to electron motion in the presence of an electric field, is extended to include a transverse magnetic field. In principle, the random walk approach avoids mathematical complexity and concomitant simplifying assumptions and permits determination of energy distributions and transport coefficients within the accuracy of available collisional cross section data. Application is made to a weakly ionized helium gas. Time of relaxation of electron energy distribution, determined by the random walk, is described by simple expressions based on energy exchange between the electron and an effective electric field. The restrictive effect of the magnetic field on electron motion, which increases the required number of collisions per walk to reach a terminal steady state condition, as well as the effect of the magnetic field on electron transport coefficients and mean energy can be quite adequately described by expressions involving only the Hall parameter.

Hurdle models for multilevel zero-inflated data via h-likelihood.

PubMed

Molas, Marek; Lesaffre, Emmanuel

2010-12-30

Count data often exhibit overdispersion. One type of overdispersion arises when there is an excess of zeros in comparison with the standard Poisson distribution. Zero-inflated Poisson and hurdle models have been proposed to perform a valid likelihood-based analysis to account for the surplus of zeros. Further, data often arise in clustered, longitudinal or multiple-membership settings. The proper analysis needs to reflect the design of a study. Typically random effects are used to account for dependencies in the data. We examine the h-likelihood estimation and inference framework for hurdle models with random effects for complex designs. We extend the h-likelihood procedures to fit hurdle models, thereby extending h-likelihood to truncated distributions. Two applications of the methodology are presented. Copyright © 2010 John Wiley & Sons, Ltd.

Random telegraph noise in 2D hexagonal boron nitride dielectric films

NASA Astrophysics Data System (ADS)

Ranjan, A.; Puglisi, F. M.; Raghavan, N.; O'Shea, S. J.; Shubhakar, K.; Pavan, P.; Padovani, A.; Larcher, L.; Pey, K. L.

2018-03-01

This study reports the observation of low frequency random telegraph noise (RTN) in a 2D layered hexagonal boron nitride dielectric film in the pre- and post-soft breakdown phases using conductive atomic force microscopy as a nanoscale spectroscopy tool. The RTN traces of the virgin and electrically stressed dielectric (after percolation breakdown) were compared, and the signal features were statistically analyzed using the Factorial Hidden Markov Model technique. We observe a combination of both two-level and multi-level RTN signals in h-BN, akin to the trends commonly observed for bulk oxides such as SiO2 and HfO2. Experimental evidence suggests frequent occurrence of unstable and anomalous RTN traces in 2D dielectrics which makes extraction of defect energetics challenging.

Efficiency Improvements to the Displacement Based Multilevel Structural Optimization Algorithm

NASA Technical Reports Server (NTRS)

Plunkett, C. L.; Striz, A. G.; Sobieszczanski-Sobieski, J.

2001-01-01

Multilevel Structural Optimization (MSO) continues to be an area of research interest in engineering optimization. In the present project, the weight optimization of beams and trusses using Displacement based Multilevel Structural Optimization (DMSO), a member of the MSO set of methodologies, is investigated. In the DMSO approach, the optimization task is subdivided into a single system and multiple subsystems level optimizations. The system level optimization minimizes the load unbalance resulting from the use of displacement functions to approximate the structural displacements. The function coefficients are then the design variables. Alternately, the system level optimization can be solved using the displacements themselves as design variables, as was shown in previous research. Both approaches ensure that the calculated loads match the applied loads. In the subsystems level, the weight of the structure is minimized using the element dimensions as design variables. The approach is expected to be very efficient for large structures, since parallel computing can be utilized in the different levels of the problem. In this paper, the method is applied to a one-dimensional beam and a large three-dimensional truss. The beam was tested to study possible simplifications to the system level optimization. In previous research, polynomials were used to approximate the global nodal displacements. The number of coefficients of the polynomials equally matched the number of degrees of freedom of the problem. Here it was desired to see if it is possible to only match a subset of the degrees of freedom in the system level. This would lead to a simplification of the system level, with a resulting increase in overall efficiency. However, the methods tested for this type of system level simplification did not yield positive results. The large truss was utilized to test further improvements in the efficiency of DMSO. In previous work, parallel processing was applied to the subsystems level, where the derivative verification feature of the optimizer NPSOL had been utilized in the optimizations. This resulted in large runtimes. In this paper, the optimizations were repeated without using the derivative verification, and the results are compared to those from the previous work. Also, the optimizations were run on both, a network of SUN workstations using the MPICH implementation of the Message Passing Interface (MPI) and on the faster Beowulf cluster at ICASE, NASA Langley Research Center, using the LAM implementation of UP]. The results on both systems were consistent and showed that it is not necessary to verify the derivatives and that this gives a large increase in efficiency of the DMSO algorithm.

A Combined Cognitive Stimulation and Physical Exercise Programme (MINDVital) in Early Dementia: Differential Effects on Single- and Dual-Task Gait Performance.

PubMed

Tay, Laura; Lim, Wee Shiong; Chan, Mark; Ali, Noorhazlina; Chong, Mei Sian

2016-01-01

Gait disorders are common in early dementia, with particularly pronounced dual-task deficits, contributing to the increased fall risk and mobility decline associated with cognitive impairment. This study examines the effects of a combined cognitive stimulation and physical exercise programme (MINDVital) on gait performance under single- and dual-task conditions in older adults with mild dementia. Thirty-nine patients with early dementia participated in a multi-disciplinary rehabilitation programme comprising both physical exercise and cognitive stimulation. The programme was conducted in 8-week cycles with participants attending once weekly, and all participants completed 2 successive cycles. Cognitive, functional performance and behavioural symptoms were assessed at baseline and at the end of each 8-week cycle. Gait speed was examined under both single- (Timed Up and Go and 6-metre walk tests) and dual-task (animal category and serial counting) conditions. A random effects model was performed for the independent effect of MINDVital on the primary outcome variable of gait speed under dual-task conditions. The mean age of patients enroled in the rehabilitation programme was 79 ± 6.2 years; 25 (64.1%) had a diagnosis of Alzheimer's dementia, and 26 (66.7%) were receiving a cognitive enhancer therapy. There was a significant improvement in cognitive performance [random effects coefficient (standard error) = 0.90 (0.31), p = 0.003] and gait speed under both dual-task situations [animal category: random effects coefficient = 0.04 (0.02), p = 0.039; serial counting: random effects coefficient = 0.05 (0.02), p = 0.013], with reduced dual-task cost for gait speed [serial counting: random effects coefficient = -4.05 (2.35), p = 0.086] following successive MINDVital cycles. No significant improvement in single-task gait speed was observed. Improved cognitive performance over time was a significant determinant of changes in dual-task gait speed [random effects coefficients = 0.01 (0.005), p = 0.048, and 0.02 (0.005), p = 0.003 for category fluency and counting backwards, respectively]. A combined physical and cognitive rehabilitation programme leads to significant improvements in dual-task walking in early dementia, which may be contributed by improvement in cognitive performance, as single-task gait performance remained stable. © 2016 S. Karger AG, Basel.

Social and individual risk factors for suicide ideation among Chinese children and adolescents: A multilevel analysis.

PubMed

Tan, Ling; Xia, Tiansheng; Reece, Christy

2018-04-01

The objective of this study was to investigate the prevalence and predictors of suicide ideation among primary, middle and high school students. We used multilevel modelling to investigate suicide ideation among 12,733 Chinese children and adolescents aged 9-18 years from wide range of areas across China. Approximately, 32.09% of children and adolescents reported suicide ideation, with females were more likely to report suicide ideation than males (38.09% vs. 29.95%). Our results showed that the risk factors in primary school students were different from middle and high school student groups, whereas significant risk factors for middle and high school students were similar. The city's standard of living as indicated by the Engel coefficient and the city's divorce rate were positively associated with the prevalence of suicide ideation; in contrast, the school's pupil-to-teacher ratio was negatively correlated with elevated suicide ideation. Significant risk factors for suicide ideation included study anxiety, self-accusation tendency, impulsive tendency, terror tendency and physical symptoms. These results have important implications for the prevention of suicide, suggesting that both contextual (city-level) and compositional (individual-level) factors could be important targets for prevention and intervention for children and adolescents at risk of suicide ideation. © 2016 International Union of Psychological Science.

Personal, relational and school factors associated with involvement in fights with weapons among school-age youth in Brazil: a multilevel ecological approach.

PubMed

Peres, Maria Fernanda Tourinho; Azeredo, Catarina Machado; de Rezende, Leandro Fórnias Machado; Zucchi, Eliana Miura; Franca-Junior, Ivan; Luiz, Olinda do Carmo; Levy, Renata Bertazzi

2018-06-08

To investigate the association between personal, relational and school factors with involvement in fights with weapon among Brazilian school-age youth. Using data from the Adolescent School-Based Health Survey 2015 (n = 102.072), we conducted multilevel logistic regression models. IFW was associated with female sex (OR = 0.45), and with older age (OR = 1.15), previous involvement in physical violence (OR = 2.05), history of peer verbal (OR = 1.14) and domestic victimization (OR = 2.11), alcohol use (OR = 2.42) and drug use (OR = 3.23). The relational variables (e.g., parent's supervision) were mostly negatively associated with IFW. At the school level, attending public school and attending schools in violent surroundings were both positively associated with IFW. The intraclass correlation coefficient estimated in the empty model showed that 5.77% of the variance of IFW was at school level. When all individual- and school-level variables were included in the model, the proportional changes in variance were 61.7 and 71.55%, respectively. IFW is associated with personal, relational and school factors. Part of the variance in IFW by school is explained by characteristics of the school context.

Space versus Place in Complex Human-Natural Systems: Spatial and Multi-level Models of Tropical Land Use and Cover Change (LUCC) in Guatemala

PubMed Central

López-Carr, David; Davis, Jason; Jankowska, Marta; Grant, Laura; López-Carr, Anna Carla; Clark, Matthew

2013-01-01

The relative role of space and place has long been debated in geography. Yet modeling efforts applied to coupled human-natural systems seemingly favor models assuming continuous spatial relationships. We examine the relative importance of placebased hierarchical versus spatial clustering influences in tropical land use/cover change (LUCC). Guatemala was chosen as our study site given its high rural population growth and deforestation in recent decades. We test predictors of 2009 forest cover and forest cover change from 2001-2009 across Guatemala's 331 municipalities and 22 departments using spatial and multi-level statistical models. Our results indicate the emergence of several socio-economic predictors of LUCC regardless of model choice. Hierarchical model results suggest that significant differences exist at the municipal and departmental levels but largely maintain the magnitude and direction of single-level model coefficient estimates. They are also intervention-relevant since policies tend to be applicable to distinct political units rather than to continuous space. Spatial models complement hierarchical approaches by indicating where and to what magnitude significant negative and positive clustering associations emerge. Appreciating the comparative advantages and limitations of spatial and nested models enhances a holistic approach to geographical analysis of tropical LUCC and human-environment interactions. PMID:24013908

An investigation of emotion dynamics in major depressive disorder patients and healthy persons using sparse longitudinal networks.

PubMed

de Vos, Stijn; Wardenaar, Klaas J; Bos, Elisabeth H; Wit, Ernst C; Bouwmans, Mara E J; de Jonge, Peter

2017-01-01

Differences in within-person emotion dynamics may be an important source of heterogeneity in depression. To investigate these dynamics, researchers have previously combined multilevel regression analyses with network representations. However, sparse network methods, specifically developed for longitudinal network analyses, have not been applied. Therefore, this study used this approach to investigate population-level and individual-level emotion dynamics in healthy and depressed persons and compared this method with the multilevel approach. Time-series data were collected in pair-matched healthy persons and major depressive disorder (MDD) patients (n = 54). Seven positive affect (PA) and seven negative affect (NA) items were administered electronically at 90 times (30 days; thrice per day). The population-level (healthy vs. MDD) and individual-level time series were analyzed using a sparse longitudinal network model based on vector autoregression. The population-level model was also estimated with a multilevel approach. Effects of different preprocessing steps were evaluated as well. The characteristics of the longitudinal networks were investigated to gain insight into the emotion dynamics. In the population-level networks, longitudinal network connectivity was strongest in the healthy group, with nodes showing more and stronger longitudinal associations with each other. Individually estimated networks varied strongly across individuals. Individual variations in network connectivity were unrelated to baseline characteristics (depression status, neuroticism, severity). A multilevel approach applied to the same data showed higher connectivity in the MDD group, which seemed partly related to the preprocessing approach. The sparse network approach can be useful for the estimation of networks with multiple nodes, where overparameterization is an issue, and for individual-level networks. However, its current inability to model random effects makes it less useful as a population-level approach in case of large heterogeneity. Different preprocessing strategies appeared to strongly influence the results, complicating inferences about network density.

Multilevel Interventions: Measurement and Measures

PubMed Central

Charns, Martin P.; Alligood, Elaine C.; Benzer, Justin K.; Burgess, James F.; Mcintosh, Nathalie M.; Burness, Allison; Partin, Melissa R.; Clauser, Steven B.

2012-01-01

Background Multilevel intervention research holds the promise of more accurately representing real-life situations and, thus, with proper research design and measurement approaches, facilitating effective and efficient resolution of health-care system challenges. However, taking a multilevel approach to cancer care interventions creates both measurement challenges and opportunities. Methods One-thousand seventy two cancer care articles from 2005 to 2010 were reviewed to examine the state of measurement in the multilevel intervention cancer care literature. Ultimately, 234 multilevel articles, 40 involving cancer care interventions, were identified. Additionally, literature from health services, social psychology, and organizational behavior was reviewed to identify measures that might be useful in multilevel intervention research. Results The vast majority of measures used in multilevel cancer intervention studies were individual level measures. Group-, organization-, and community-level measures were rarely used. Discussion of the independence, validity, and reliability of measures was scant. Discussion Measurement issues may be especially complex when conducting multilevel intervention research. Measurement considerations that are associated with multilevel intervention research include those related to independence, reliability, validity, sample size, and power. Furthermore, multilevel intervention research requires identification of key constructs and measures by level and consideration of interactions within and across levels. Thus, multilevel intervention research benefits from thoughtful theory-driven planning and design, an interdisciplinary approach, and mixed methods measurement and analysis. PMID:22623598

On the Multilevel Nature of Meta-Analysis: A Tutorial, Comparison of Software Programs, and Discussion of Analytic Choices.

PubMed

Pastor, Dena A; Lazowski, Rory A

2018-01-01

The term "multilevel meta-analysis" is encountered not only in applied research studies, but in multilevel resources comparing traditional meta-analysis to multilevel meta-analysis. In this tutorial, we argue that the term "multilevel meta-analysis" is redundant since all meta-analysis can be formulated as a special kind of multilevel model. To clarify the multilevel nature of meta-analysis the four standard meta-analytic models are presented using multilevel equations and fit to an example data set using four software programs: two specific to meta-analysis (metafor in R and SPSS macros) and two specific to multilevel modeling (PROC MIXED in SAS and HLM). The same parameter estimates are obtained across programs underscoring that all meta-analyses are multilevel in nature. Despite the equivalent results, not all software programs are alike and differences are noted in the output provided and estimators available. This tutorial also recasts distinctions made in the literature between traditional and multilevel meta-analysis as differences between meta-analytic choices, not between meta-analytic models, and provides guidance to inform choices in estimators, significance tests, moderator analyses, and modeling sequence. The extent to which the software programs allow flexibility with respect to these decisions is noted, with metafor emerging as the most favorable program reviewed.

Single-image super-resolution based on Markov random field and contourlet transform

NASA Astrophysics Data System (ADS)

Wu, Wei; Liu, Zheng; Gueaieb, Wail; He, Xiaohai

2011-04-01

Learning-based methods are well adopted in image super-resolution. In this paper, we propose a new learning-based approach using contourlet transform and Markov random field. The proposed algorithm employs contourlet transform rather than the conventional wavelet to represent image features and takes into account the correlation between adjacent pixels or image patches through the Markov random field (MRF) model. The input low-resolution (LR) image is decomposed with the contourlet transform and fed to the MRF model together with the contourlet transform coefficients from the low- and high-resolution image pairs in the training set. The unknown high-frequency components/coefficients for the input low-resolution image are inferred by a belief propagation algorithm. Finally, the inverse contourlet transform converts the LR input and the inferred high-frequency coefficients into the super-resolved image. The effectiveness of the proposed method is demonstrated with the experiments on facial, vehicle plate, and real scene images. A better visual quality is achieved in terms of peak signal to noise ratio and the image structural similarity measurement.

PET-CT image fusion using random forest and à-trous wavelet transform.

PubMed

Seal, Ayan; Bhattacharjee, Debotosh; Nasipuri, Mita; Rodríguez-Esparragón, Dionisio; Menasalvas, Ernestina; Gonzalo-Martin, Consuelo

2018-03-01

New image fusion rules for multimodal medical images are proposed in this work. Image fusion rules are defined by random forest learning algorithm and a translation-invariant à-trous wavelet transform (AWT). The proposed method is threefold. First, source images are decomposed into approximation and detail coefficients using AWT. Second, random forest is used to choose pixels from the approximation and detail coefficients for forming the approximation and detail coefficients of the fused image. Lastly, inverse AWT is applied to reconstruct fused image. All experiments have been performed on 198 slices of both computed tomography and positron emission tomography images of a patient. A traditional fusion method based on Mallat wavelet transform has also been implemented on these slices. A new image fusion performance measure along with 4 existing measures has been presented, which helps to compare the performance of 2 pixel level fusion methods. The experimental results clearly indicate that the proposed method outperforms the traditional method in terms of visual and quantitative qualities and the new measure is meaningful. Copyright © 2017 John Wiley & Sons, Ltd.

Multilevel model to estimate county-level untreated dental caries among US children aged 6-9years using the National Health and Nutrition Examination Survey.

PubMed

Lin, Mei; Zhang, Xingyou; Holt, James B; Robison, Valerie; Li, Chien-Hsun; Griffin, Susan O

2018-06-01

Because conducting population-based oral health screening is resource intensive, oral health data at small-area levels (e.g., county-level) are not commonly available. We applied the multilevel logistic regression and poststratification method to estimate county-level prevalence of untreated dental caries among children aged 6-9years in the United States using data from the National Health and Nutrition Examination Survey (NHANES) 2005-2010 linked with various area-level data at census tract, county and state levels. We validated model-based national estimates against direct estimates from NHANES. We also compared model-based estimates with direct estimates from select State Oral Health Surveys (SOHS) at state and county levels. The model with individual-level covariates only and the model with individual-, census tract- and county-level covariates explained 7.2% and 96.3% respectively of overall county-level variation in untreated caries. Model-based county-level prevalence estimates ranged from 4.9% to 65.2% with median of 22.1%. The model-based national estimate (19.9%) matched the NHANES direct estimate (19.8%). We found significantly positive correlations between model-based estimates for 8-year-olds and direct estimates from the third-grade State Oral Health Surveys (SOHS) at state level for 34 states (Pearson coefficient: 0.54, P=0.001) and SOHS estimates at county level for 53 New York counties (Pearson coefficient: 0.38, P=0.006). This methodology could be a useful tool to characterize county-level disparities in untreated dental caries among children aged 6-9years and complement oral health surveillance to inform public health programs especially when local-level data are not available although the lack of external validation due to data unavailability should be acknowledged. Published by Elsevier Inc.

Translating multilevel theory into multilevel research: Challenges and opportunities for understanding the social determinants of psychiatric disorders

PubMed Central

Dunn, Erin C.; Masyn, Katherine E.; Yudron, Monica; Jones, Stephanie M.; Subramanian, S.V.

2014-01-01

The observation that features of the social environment, including family, school, and neighborhood characteristics, are associated with individual-level outcomes has spurred the development of dozens of multilevel or ecological theoretical frameworks in epidemiology, public health, psychology, and sociology, among other disciplines. Despite the widespread use of such theories in etiological, intervention, and policy studies, challenges remain in bridging multilevel theory and empirical research. This paper set out to synthesize these challenges and provide specific examples of methodological and analytical strategies researchers are using to gain a more nuanced understanding of the social determinants of psychiatric disorders, with a focus on children’s mental health. To accomplish this goal, we begin by describing multilevel theories, defining their core elements, and discussing what these theories suggest is needed in empirical work. In the second part, we outline the main challenges researchers face in translating multilevel theory into research. These challenges are presented for each stage of the research process. In the third section, we describe two methods being used as alternatives to traditional multilevel modeling techniques to better bridge multilevel theory and multilevel research. These are: (1) multilevel factor analysis and multilevel structural equation modeling; and (2) dynamic systems approaches. Through its review of multilevel theory, assessment of existing strategies, and examination of emerging methodologies, this paper offers a framework to evaluate and guide empirical studies on the social determinants of child psychiatric disorders as well as health across the lifecourse. PMID:24469555

Investigation of resistive switching behaviours in WO3-based RRAM devices

NASA Astrophysics Data System (ADS)

Li, Ying-Tao; Long, Shi-Bing; Lü, Hang-Bing; Liu, Qi; Wang, Qin; Wang, Yan; Zhang, Sen; Lian, Wen-Tai; Liu, Su; Liu, Ming

2011-01-01

In this paper, a WO3-based resistive random access memory device composed of a thin film of WO3 sandwiched between a copper top and a platinum bottom electrodes is fabricated by electron beam evaporation at room temperature. The reproducible resistive switching, low power consumption, multilevel storage possibility, and good data retention characteristics demonstrate that the Cu/WO3/Pt memory device is very promising for future nonvolatile memory applications. The formation and rupture of localised conductive filaments is suggested to be responsible for the observed resistive switching behaviours.

Friendship Dissolution Within Social Networks Modeled Through Multilevel Event History Analysis

PubMed Central

Dean, Danielle O.; Bauer, Daniel J.; Prinstein, Mitchell J.

2018-01-01

A social network perspective can bring important insight into the processes that shape human behavior. Longitudinal social network data, measuring relations between individuals over time, has become increasingly common—as have the methods available to analyze such data. A friendship duration model utilizing discrete-time multilevel survival analysis with a multiple membership random effect structure is developed and applied here to study the processes leading to undirected friendship dissolution within a larger social network. While the modeling framework is introduced in terms of understanding friendship dissolution, it can be used to understand microlevel dynamics of a social network more generally. These models can be fit with standard generalized linear mixed-model software, after transforming the data to a pair-period data set. An empirical example highlights how the model can be applied to understand the processes leading to friendship dissolution between high school students, and a simulation study is used to test the use of the modeling framework under representative conditions that would be found in social network data. Advantages of the modeling framework are highlighted, and potential limitations and future directions are discussed. PMID:28463022

Social capital and neo-materialist contextual determinants of sense of insecurity in the neighbourhood: a multilevel analysis in Southern Sweden.

PubMed

Lindström, Martin; Lindström, Christine; Moghaddassi, Mahnaz; Merlo, Juan

2006-12-01

The aim of this study was to investigate the influence of contextual (social capital and neo-materialist) and individual factors on sense of insecurity in the neighbourhood. The 2000 public health survey in Scania is a cross-sectional study. A total of 13,715 persons answered a postal questionnaire, which is 59% of the random sample. A multilevel logistic regression model, with individuals at the first level and municipalities at the second, was performed. The effect (median odds ratios, intra-class correlation, cross-level modification and odds ratios) of individual and municipality/city quarter (social capital and police district) factors on sense of insecurity was analysed. The crude variance between municipalities/city quarters was not affected by individual factors. The introduction of administrative police district in the model reduced the municipality variance, although some of the significant variance between municipalities remained. The introduction of social capital did not affect the municipality variance. This study suggests that the neo-materialist factor administrative police district may partly explain the individual's sense of insecurity in the neighbourhood.

Multilevel modeling of single-case data: A comparison of maximum likelihood and Bayesian estimation.

PubMed

Moeyaert, Mariola; Rindskopf, David; Onghena, Patrick; Van den Noortgate, Wim

2017-12-01

The focus of this article is to describe Bayesian estimation, including construction of prior distributions, and to compare parameter recovery under the Bayesian framework (using weakly informative priors) and the maximum likelihood (ML) framework in the context of multilevel modeling of single-case experimental data. Bayesian estimation results were found similar to ML estimation results in terms of the treatment effect estimates, regardless of the functional form and degree of information included in the prior specification in the Bayesian framework. In terms of the variance component estimates, both the ML and Bayesian estimation procedures result in biased and less precise variance estimates when the number of participants is small (i.e., 3). By increasing the number of participants to 5 or 7, the relative bias is close to 5% and more precise estimates are obtained for all approaches, except for the inverse-Wishart prior using the identity matrix. When a more informative prior was added, more precise estimates for the fixed effects and random effects were obtained, even when only 3 participants were included. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Towards multi-level biomonitoring of nematodes to assess risk of nitrogen and phosphorus pollution in Jinchuan Wetland of Northeast China.

PubMed

Wang, Yunbiao; Qiao, Jie; He, Chunguang; Wang, Zhongqiang; Luo, Wenbo; Sheng, Lianxi

2015-12-01

Cultivation for agricultural production often poses threats to nearby wetlands ecosystems in fertile landscapes. In this study, nematode ecological indexes were assessed through the main soil properties of the wetlands, farmlands, and edges of wetlands and farmlands in Jinchuan Wetland by the random sampling. Behavior and reproduction in Caenorhabditis elegans (C. elegans) exposed to the sampled waters were also examined. Stress proteins Hsp70 and Hsp90 were measured both in the living field samples of C. elegans and the lab-tested C. elegans. Our results suggested that disturbance to wetland ecosystems by nitrogen and phosphorus reduced nematode richness and proportions of bacterivore nematodes. Bacterivore nematode diversity and plant-parasitic ecological index were proven to be sensitive indicators of the ecological health of wetlands. Nematode Hsp70 were useful biosensors to monitor and assess the levels of nitrogen and phosphorus pollutions in wetlands. Furthermore, multi-level soil faunal assessments by canonical correspondence analysis showed that Jinchuan Wetland is threatened with non-point source pollution from nearby farmlands.

Results of a Multi-level Intervention to Prevent and Control Childhood Obesity among Latino Children: The Aventuras Para Niños Study

PubMed Central

Crespo, Noe C.; Elder, John P.; Ayala, Guadalupe X.; Campbell, Nadia R.; Arredondo, Elva M.; Slymen, Donald J.; Baquero, Barbara; Sallis, James F.; McKenzie, Thomas L.

2014-01-01

Background Community-based behavioral interventions are needed to reduce the burden of childhood obesity. Purpose This study evaluated the impact of a multi-level promotora-based (Community Health Advisor) intervention to promote healthy eating and physical activity (PA) and prevent excess weight gain among Latino children. Methods Thirteen elementary schools were randomized to one of four intervention conditions: individual and family level (Fam-only), school and community level (Comm-only), combined Fam+Comm intervention, or a measurement-only condition. Participants were 808 Latino parents and their children enrolled in kindergarten through 2nd grade. Measures included parent and child BMI and a self-administered parent survey that assessed several parent and child behaviors. Results There were no intervention effects on children's BMI z-score. The Fam-only and Fam+Comm interventions changed several obesity-related child behaviors and these were mediated by changes in parenting variables. Discussion A promotora-based behavioral intervention was efficacious at changing parental factors and child obesity-related health behaviors. PMID:22215470

Effects of an online alcohol education course among college freshmen: an investigation of potential mediators.

PubMed

Paschall, Mallie J; Ringwalt, Chris; Wyatt, Todd; Dejong, William

2014-04-01

The authors investigated possible mediating effects of psychosocial variables (perceived drinking norms, positive and negative alcohol expectancies, personal approval of alcohol use, protective behavioral strategies) targeted by an online alcohol education course (AlcoholEdu for College) as part of a 30-campus randomized trial with 2,400 first-year students. Previous multilevel analyses have found significant effects of the AlcoholEdu course on the frequency of past-30-day alcohol use and binge drinking during the fall semester, and the most common types of alcohol-related problems. Exposure to the online AlcoholEdu course was inversely related to perceived drinking norms but was not related to any of the other psychosocial variables. Multilevel analyses indicated at least partial mediating effects of perceived drinking norms on behavioral outcomes. Findings of this study suggest that AlcoholEdu for College affects alcohol use and related consequences indirectly through its effect on student perceptions of drinking norms. Further research is needed to better understand why this online course did not appear to affect other targeted psychosocial variables.

Typhoon air-sea drag coefficient in coastal regions

NASA Astrophysics Data System (ADS)

Zhao, Zhong-Kuo; Liu, Chun-Xia; Li, Qi; Dai, Guang-Feng; Song, Qing-Tao; Lv, Wei-Hua

2015-02-01

The air-sea drag during typhoon landfalls is investigated for a 10 m wind speed as high as U10 ≈ 42 m s-1, based on multilevel wind measurements from a coastal tower located in the South China Sea. The drag coefficient (CD) plotted against the typhoon wind speed is similar to that of open ocean conditions; however, the CD curve shifts toward a regime of lower winds, and CD increases by a factor of approximately 0.5 relative to the open ocean. Our results indicate that the critical wind speed at which CD peaks is approximately 24 m s-1, which is 5-15 m s-1 lower than that from deep water. Shoaling effects are invoked to explain the findings. Based on our results, the proposed CD formulation, which depends on both water depth and wind speed, is applied to a typhoon forecast model. The forecasts of typhoon track and surface wind speed are improved. Therefore, a water-depth-dependence formulation of CD may be particularly pertinent for parameterizing air-sea momentum exchanges over shallow water.

The reliability of in-training assessment when performance improvement is taken into account.

PubMed

van Lohuizen, Mirjam T; Kuks, Jan B M; van Hell, Elisabeth A; Raat, A N; Stewart, Roy E; Cohen-Schotanus, Janke

2010-12-01

During in-training assessment students are frequently assessed over a longer period of time and therefore it can be expected that their performance will improve. We studied whether there really is a measurable performance improvement when students are assessed over an extended period of time and how this improvement affects the reliability of the overall judgement. In-training assessment results were obtained from 104 students on rotation at our university hospital or at one of the six affiliated hospitals. Generalisability theory was used in combination with multilevel analysis to obtain reliability coefficients and to estimate the number of assessments needed for reliable overall judgement, both including and excluding performance improvement. Students' clinical performance ratings improved significantly from a mean of 7.6 at the start to a mean of 7.8 at the end of their clerkship. When taking performance improvement into account, reliability coefficients were higher. The number of assessments needed to achieve a reliability of 0.80 or higher decreased from 17 to 11. Therefore, when studying reliability of in-training assessment, performance improvement should be considered.

A wideband fast multipole boundary element method for half-space/plane-symmetric acoustic wave problems

NASA Astrophysics Data System (ADS)

Zheng, Chang-Jun; Chen, Hai-Bo; Chen, Lei-Lei

2013-04-01

This paper presents a novel wideband fast multipole boundary element approach to 3D half-space/plane-symmetric acoustic wave problems. The half-space fundamental solution is employed in the boundary integral equations so that the tree structure required in the fast multipole algorithm is constructed for the boundary elements in the real domain only. Moreover, a set of symmetric relations between the multipole expansion coefficients of the real and image domains are derived, and the half-space fundamental solution is modified for the purpose of applying such relations to avoid calculating, translating and saving the multipole/local expansion coefficients of the image domain. The wideband adaptive multilevel fast multipole algorithm associated with the iterative solver GMRES is employed so that the present method is accurate and efficient for both lowand high-frequency acoustic wave problems. As for exterior acoustic problems, the Burton-Miller method is adopted to tackle the fictitious eigenfrequency problem involved in the conventional boundary integral equation method. Details on the implementation of the present method are described, and numerical examples are given to demonstrate its accuracy and efficiency.

Non-linear effects and thermoelectric efficiency of quantum dot-based single-electron transistors.

PubMed

Talbo, Vincent; Saint-Martin, Jérôme; Retailleau, Sylvie; Dollfus, Philippe

2017-11-01

By means of advanced numerical simulation, the thermoelectric properties of a Si-quantum dot-based single-electron transistor operating in sequential tunneling regime are investigated in terms of figure of merit, efficiency and power. By taking into account the phonon-induced collisional broadening of energy levels in the quantum dot, both heat and electrical currents are computed in a voltage range beyond the linear response. Using our homemade code consisting in a 3D Poisson-Schrödinger solver and the resolution of the Master equation, the Seebeck coefficient at low bias voltage appears to be material independent and nearly independent on the level broadening, which makes this device promising for metrology applications as a nanoscale standard of Seebeck coefficient. Besides, at higher voltage bias, the non-linear characteristics of the heat current are shown to be related to the multi-level effects. Finally, when considering only the electronic contribution to the thermal conductance, the single-electron transistor operating in generator regime is shown to exhibit very good efficiency at maximum power.

Income inequality, alcohol use, and alcohol-related problems.

PubMed

Karriker-Jaffe, Katherine J; Roberts, Sarah C M; Bond, Jason

2013-04-01

We examined the relationship between state-level income inequality and alcohol outcomes and sought to determine whether associations of inequality with alcohol consumption and problems would be more evident with between-race inequality measures than with the Gini coefficient. We also sought to determine whether inequality would be most detrimental for disadvantaged individuals. Data from 2 nationally representative samples of adults (n = 13,997) from the 2000 and 2005 National Alcohol Surveys were merged with state-level inequality and neighborhood disadvantage indicators from the 2000 US Census. We measured income inequality using the Gini coefficient and between-race poverty ratios (Black-White and Hispanic-White). Multilevel models accounted for clustering of respondents within states. Inequality measured by poverty ratios was positively associated with light and heavy drinking. Associations between poverty ratios and alcohol problems were strongest for Blacks and Hispanics compared with Whites. Household poverty did not moderate associations with income inequality. Poverty ratios were associated with alcohol use and problems, whereas overall income inequality was not. Higher levels of alcohol problems in high-inequality states may be partly due to social context.

Income Inequality, Alcohol Use, and Alcohol-Related Problems

PubMed Central

C. M. Roberts, Sarah; Bond, Jason

2013-01-01

Objectives. We examined the relationship between state-level income inequality and alcohol outcomes and sought to determine whether associations of inequality with alcohol consumption and problems would be more evident with between-race inequality measures than with the Gini coefficient. We also sought to determine whether inequality would be most detrimental for disadvantaged individuals. Methods. Data from 2 nationally representative samples of adults (n = 13 997) from the 2000 and 2005 National Alcohol Surveys were merged with state-level inequality and neighborhood disadvantage indicators from the 2000 US Census. We measured income inequality using the Gini coefficient and between-race poverty ratios (Black–White and Hispanic–White). Multilevel models accounted for clustering of respondents within states. Results. Inequality measured by poverty ratios was positively associated with light and heavy drinking. Associations between poverty ratios and alcohol problems were strongest for Blacks and Hispanics compared with Whites. Household poverty did not moderate associations with income inequality. Conclusions. Poverty ratios were associated with alcohol use and problems, whereas overall income inequality was not. Higher levels of alcohol problems in high-inequality states may be partly due to social context. PMID:23237183

Neither fixed nor random: weighted least squares meta-regression.

PubMed

Stanley, T D; Doucouliagos, Hristos

2017-03-01

Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of 'mixed-effects' or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the 'true' regression coefficient. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Do stand-alone interbody spacers with integrated screws provide adequate segmental stability for multilevel cervical arthrodesis?

PubMed

Paik, Haines; Kang, Daniel G; Lehman, Ronald A; Cardoso, Mario J; Gaume, Rachel E; Ambati, Divya V; Dmitriev, Anton E

2014-08-01

Some postoperative complications after anterior cervical fusions have been attributed to anterior cervical plate (ACP) profiles and the necessary wide operative exposure for their insertion. Consequently, low-profile stand-alone interbody spacers with integrated screws (SIS) have been developed. Although SIS constructs have demonstrated similar biomechanical stability to the ACP in single-level fusions, their role as a stand-alone device in multilevel reconstructions has not been thoroughly evaluated. To evaluate the acute segmental stability afforded by an SIS device compared with the traditional ACP in the setting of a multilevel cervical arthrodesis. In vitro human cadaveric biomechanical analysis. Thirteen human cadaveric cervical spines (C2-T1) were nondestructively tested with a custom 6 df spine simulator under axial rotation, flexion-extension, and lateral bending loading. After intact analysis, eight single-levels (C4-C5/C6-C7) from four specimens were instrumented and tested with ACP and SIS. Nine specimens were tested with C5-C7 SIS, C5-C7 ACP, C4-C7 ACP, C4-C7 ACP+posterior fixation, C4-C7 SIS, and C4-C7 SIS+posterior fixation. Testing order was randomized with each additional level instrumented. Full range of motion (ROM) data were obtained and analyzed by each loading modality, using mean comparisons with repeated measures analysis of variance. Paired t tests were used for post hoc analysis with Sidak correction for multiple comparisons. No significant difference in ROM was noted between the ACP and SIS for single-level fixation (p>.05). For multisegment reconstructions (two and three levels), the ACP proved superior to SIS and intact condition, with significantly lower ROM in all planes (p<.05). When either the three-level SIS or ACP constructs were supplemented with posterior lateral mass fixation, there was a greater than 80% reduction in ROM under all testing modalities (p<.05), with no significant difference between the ACP and SIS constructs (p>.05). The SIS device may be a reasonable option as a stand-alone device for single-level fixation. However, SIS devices should be used with careful consideration in the setting of multilevel cervical fusion. However, when supplemented with posterior fixation, SIS devices are a sound biomechanical alternative to ACP for multilevel fusion constructs. Published by Elsevier Inc.

Identifying factors associated with the uptake of prevention of mother to child HIV transmission programme in Tigray region, Ethiopia: a multilevel modeling approach.

PubMed

Lerebo, Wondwossen; Callens, Steven; Jackson, Debra; Zarowsky, Christina; Temmerman, Marleen

2014-04-23

Prevention of mother to child HIV transmission (PMTCT) remains a challenge in low and middle-income countries. Determinants of utilization occur--and often interact--at both individual and community levels, but most studies do not address how determinants interact across levels. Multilevel models allow for the importance of both groups and individuals in understanding health outcomes and provide one way to link the traditionally distinct ecological- and individual-level studies. This study examined individual and community level determinants of mother and child receiving PMTCT services in Tigray region, Ethiopia. A multistage probability sampling method was used for this 2011 cross-sectional study of 220 HIV positive post-partum women attending child immunization services at 50 health facilities in 46 districts. In view of the nested nature of the data, we used multilevel modeling methods and assessed macro level random effects. Seventy nine percent of mothers and 55.7% of their children had received PMTCT services. Multivariate multilevel modeling found that mothers who delivered at a health facility were 18 times (AOR = 18.21; 95% CI 4.37,75.91) and children born at a health facility were 5 times (AOR = 4.77; 95% CI 1.21,18.83) more likely to receive PMTCT services, compared to mothers delivering at home. For every addition of one nurse per 1500 people, the likelihood of getting PMTCT services for a mother increases by 7.22 fold (AOR = 7.22; 95% CI 1.02,51.26), when other individual and community level factors were controlled simultaneously. In addition, district-level variation was low for mothers receiving PMTCT services (0.6% between districts) but higher for children (27.2% variation between districts). This study, using a multilevel modeling approach, was able to identify factors operating at both individual and community levels that affect mothers and children getting PMTCT services. This may allow differentiating and accentuating approaches for different settings in Ethiopia. Increasing health facility delivery and HCT coverage could increase mother-child pairs who are getting PMTCT. Reducing the distance to health facility and increasing the number of nurses and laboratory technicians are also important variables to be considered by the government.

A randomized-control trial for the teachers' diploma programme on psychosocial care, support and protection in Zambian government primary schools.

PubMed

Kaljee, Linda; Zhang, Liying; Langhaug, Lisa; Munjile, Kelvin; Tembo, Stephen; Menon, Anitha; Stanton, Bonita; Li, Xiaoming; Malungo, Jacob

2017-04-01

Orphaned and vulnerable children (OVC) experience poverty, stigma, and abuse resulting in poor physical, emotional, and psychological outcomes. The Teachers' Diploma Programme on Psychosocial Care, Support, and Protection is a child-centered 15-month long-distance learning program focused on providing teachers with the knowledge and skills to enhance their school environments, foster psychosocial support, and facilitate school-community relationships. A randomized controlled trial was implemented in 2013-2014. Both teachers (n=325) and students (n=1378) were assessed at baseline and 15-months post-intervention from randomly assigned primary schools in Lusaka and Eastern Provinces, Zambia. Multilevel linear mixed models (MLM) indicate positive significant changes for intervention teachers on outcomes related to self-care, teaching resources, safety, social support, and gender equity. Positive outcomes for intervention students related to future orientation, respect, support, safety, sexual abuse, and bullying. Outcomes support the hypothesis that teachers and students benefit from a program designed to enhance teachers' psychosocial skills and knowledge.

FIBER AND INTEGRAL OPTICS: Mode composition of radiation in graded-index waveguides with random microbending of the axis

NASA Astrophysics Data System (ADS)

Valyaev, A. B.; Krivoshlykov, S. G.

1989-06-01

It is shown that the problem of investigating the mode composition of a partly coherent radiation beam in a randomly inhomogeneous medium can be reduced to a study of evolution of the energy of individual modes and of the coefficients of correlations between the modes. General expressions are obtained for the coupling coefficients of modes in a parabolic waveguide with a random microbending of the axis and an analysis is made of their evolution as a function of the excitation conditions. An estimate is obtained of the distance in which a steady-state energy distribution between the modes is established. Explicit expressions are obtained for the correlation function in the case when a waveguide is excited by off-axial Gaussian beams or Gauss-Hermite modes.

Modeling of Thermal Phase Noise in a Solid Core Photonic Crystal Fiber-Optic Gyroscope

PubMed Central

Song, Ningfang; Ma, Kun; Jin, Jing; Teng, Fei; Cai, Wei

2017-01-01

A theoretical model of the thermal phase noise in a square-wave modulated solid core photonic crystal fiber-optic gyroscope has been established, and then verified by measurements. The results demonstrate a good agreement between theory and experiment. The contribution of the thermal phase noise to the random walk coefficient of the gyroscope is derived. A fiber coil with 2.8 km length is used in the experimental solid core photonic crystal fiber-optic gyroscope, showing a random walk coefficient of 9.25 × 10−5 deg/h. PMID:29072605

Recovering DC coefficients in block-based DCT.

PubMed

Uehara, Takeyuki; Safavi-Naini, Reihaneh; Ogunbona, Philip

2006-11-01

It is a common approach for JPEG and MPEG encryption systems to provide higher protection for dc coefficients and less protection for ac coefficients. Some authors have employed a cryptographic encryption algorithm for the dc coefficients and left the ac coefficients to techniques based on random permutation lists which are known to be weak against known-plaintext and chosen-ciphertext attacks. In this paper we show that in block-based DCT, it is possible to recover dc coefficients from ac coefficients with reasonable image quality and show the insecurity of image encryption methods which rely on the encryption of dc values using a cryptoalgorithm. The method proposed in this paper combines dc recovery from ac coefficients and the fact that ac coefficients can be recovered using a chosen ciphertext attack. We demonstrate that a method proposed by Tang to encrypt and decrypt MPEG video can be completely broken.

School related factors and 1yr change in physical activity amongst 9-11 year old English schoolchildren.

PubMed

Mantjes, Joyce A; Jones, Andrew P; Corder, Kirsten; Jones, Natalia R; Harrison, Flo; Griffin, Simon J; van Sluijs, Esther M F

2012-12-31

Activity levels are known to decline with age and there is growing evidence of associations between the school environment and physical activity. In this study we investigated how objectively measured one-year changes in physical activity may be associated with school-related factors in 9- to 10-year-old British children. Data were analysed from 839 children attending 89 schools in the SPEEDY (Sport, Physical Activity, and Eating behaviours: Environmental Determinants in Young People) study. Outcomes variables were one year changes in objectively measured sedentary, moderate, and vigorous physical activity, with baseline measures taken when the children were 9-10 years old. School characteristics hypothesised to be associated with change in physical activity were identified from questionnaires, grounds audits, and computer mapping. Associations were examined using simple and multivariable multilevel regression models for both school (9 am - 3 pm) and travel (8-9 am and 3-4 pm) time. Significant associations during school time included the length of the morning break which was found to be supportive of moderate (β coefficient: 0.68 [p: 0.003]) and vigorous (β coefficient: 0.52 [p: 0.002]) activities and helps to prevent adverse changes in sedentary time (β coefficient: -2.52 [p: 0.001]). During travel time, positive associations were found between the presence of safe places to cross roads around the school and changes in moderate (β coefficient: 0.83 [p:0.022]) and vigorous (β coefficient: 0.56 [p:0.001]) activity, as well as sedentary time (β coefficient: -1.61 [p:0.005]). This study suggests that having longer morning school breaks and providing road safety features such as cycling infrastructure, a crossing guard, and safe places for children to cross the road may have a role to play in supporting the maintenance of moderate and vigorous activity behaviours, and preventing the development of sedentary behaviours in children.

UNSTEADY DISPERSION IN RANDOM INTERMITTENT FLOW

EPA Science Inventory

The longitudinal dispersion coefficient of a conservative tracer was calculated from flow tests in a dead-end pipe loop system. Flow conditions for these tests ranged from laminar to transitional flow, and from steady to intermittent and random. Two static mixers linked in series...

A multilevel preconditioner for domain decomposition boundary systems

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

Bramble, J.H.; Pasciak, J.E.; Xu, Jinchao.

1991-12-11

In this note, we consider multilevel preconditioning of the reduced boundary systems which arise in non-overlapping domain decomposition methods. It will be shown that the resulting preconditioned systems have condition numbers which be bounded in the case of multilevel spaces on the whole domain and grow at most proportional to the number of levels in the case of multilevel boundary spaces without multilevel extensions into the interior.

Equimolar mixture of nitroux oxyde and oxygen during post-operative physiotherapy in patients with cerebral palsy: A randomized, double-blind, placebo-controlled study.

PubMed

Delafontaine, A; Presedo, A; Mohamed, D; Lopes, D; Wood, C; Alberti, C

2017-11-01

The administration of an equimolar mixture of nitrous oxide and oxygen (N2O) is recommended during painful procedures. However, the evaluation of its use during physiotherapy after surgery has not been reported, although pain may hamper physiotherapy efficiency. This study investigated whether the use of N2O improves the efficacy of post-operative physiotherapy after multilevel surgery in patients with cerebral palsy. It was a randomized 1:1, double-blind, placebo-controlled study. All patients had post-operative physiotherapy starting the day after surgery. Patients received either N2O or placebo gas during the rehabilitation sessions. All patients had post-operative pain management protocol, including pain medication as needed for acute pain. The primary objective was to reach angles of knee flexion of 110° combined with hip extension of 10°, with the patient lying prone, within six or less physiotherapy sessions. Secondary evaluation criteria were the number of sessions required to reach the targeted angles, the session-related pain intensity and the analgesics consumption for managing post-operative pain. Sixty-four patients were enrolled. Targeted angles were achieved more often in the N2O group (23 of 32, 72%, vs. Placebo: 13/ of 32, 41%; p = 0.01). The administration of N2O during post-operative physiotherapy can help to achieve more quickly an improved range of motion, and, although not significant in our study, to alleviate the need for pain medication. Further studies evaluating the administration of N2O in various settings are warranted. During this randomized placebo-controlled double-blind study, children receiving nitrous oxide and oxygen (N2O) achieved more often the targeted range of motion during physiotherapy sessions after multilevel surgery. Compared to placebo, nitrous oxide and oxygen (N2O) enabled a better management of acute pain related to physiotherapy procedures. © 2017 European Pain Federation - EFIC®.

Fast Multilevel Solvers for a Class of Discrete Fourth Order Parabolic Problems

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

Zheng, Bin; Chen, Luoping; Hu, Xiaozhe

2016-03-05

In this paper, we study fast iterative solvers for the solution of fourth order parabolic equations discretized by mixed finite element methods. We propose to use consistent mass matrix in the discretization and use lumped mass matrix to construct efficient preconditioners. We provide eigenvalue analysis for the preconditioned system and estimate the convergence rate of the preconditioned GMRes method. Furthermore, we show that these preconditioners only need to be solved inexactly by optimal multigrid algorithms. Our numerical examples indicate that the proposed preconditioners are very efficient and robust with respect to both discretization parameters and diffusion coefficients. We also investigatemore » the performance of multigrid algorithms with either collective smoothers or distributive smoothers when solving the preconditioner systems.« less

Aggregated N-of-1 randomized controlled trials: modern data analytics applied to a clinically valid method of intervention effectiveness.

PubMed

Cushing, Christopher C; Walters, Ryan W; Hoffman, Lesa

2014-03-01

Aggregated N-of-1 randomized controlled trials (RCTs) combined with multilevel modeling represent a methodological advancement that may help bridge science and practice in pediatric psychology. The purpose of this article is to offer a primer for pediatric psychologists interested in conducting aggregated N-of-1 RCTs. An overview of N-of-1 RCT methodology is provided and 2 simulated data sets are analyzed to demonstrate the clinical and research potential of the methodology. The simulated data example demonstrates the utility of aggregated N-of-1 RCTs for understanding the clinical impact of an intervention for a given individual and the modeling of covariates to explain why an intervention worked for one patient and not another. Aggregated N-of-1 RCTs hold potential for improving the science and practice of pediatric psychology.

Extensively Parameterized Mutation-Selection Models Reliably Capture Site-Specific Selective Constraint.

PubMed

Spielman, Stephanie J; Wilke, Claus O

2016-11-01

The mutation-selection model of coding sequence evolution has received renewed attention for its use in estimating site-specific amino acid propensities and selection coefficient distributions. Two computationally tractable mutation-selection inference frameworks have been introduced: One framework employs a fixed-effects, highly parameterized maximum likelihood approach, whereas the other employs a random-effects Bayesian Dirichlet Process approach. While both implementations follow the same model, they appear to make distinct predictions about the distribution of selection coefficients. The fixed-effects framework estimates a large proportion of highly deleterious substitutions, whereas the random-effects framework estimates that all substitutions are either nearly neutral or weakly deleterious. It remains unknown, however, how accurately each method infers evolutionary constraints at individual sites. Indeed, selection coefficient distributions pool all site-specific inferences, thereby obscuring a precise assessment of site-specific estimates. Therefore, in this study, we use a simulation-based strategy to determine how accurately each approach recapitulates the selective constraint at individual sites. We find that the fixed-effects approach, despite its extensive parameterization, consistently and accurately estimates site-specific evolutionary constraint. By contrast, the random-effects Bayesian approach systematically underestimates the strength of natural selection, particularly for slowly evolving sites. We also find that, despite the strong differences between their inferred selection coefficient distributions, the fixed- and random-effects approaches yield surprisingly similar inferences of site-specific selective constraint. We conclude that the fixed-effects mutation-selection framework provides the more reliable software platform for model application and future development. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Thermoelectric properties of an interacting quantum dot based heat engine

NASA Astrophysics Data System (ADS)

Erdman, Paolo Andrea; Mazza, Francesco; Bosisio, Riccardo; Benenti, Giuliano; Fazio, Rosario; Taddei, Fabio

2017-06-01

We study the thermoelectric properties and heat-to-work conversion performance of an interacting, multilevel quantum dot (QD) weakly coupled to electronic reservoirs. We focus on the sequential tunneling regime. The dynamics of the charge in the QD is studied by means of master equations for the probabilities of occupation. From here we compute the charge and heat currents in the linear response regime. Assuming a generic multiterminal setup, and for low temperatures (quantum limit), we obtain analytical expressions for the transport coefficients which account for the interplay between interactions (charging energy) and level quantization. In the case of systems with two and three terminals we derive formulas for the power factor Q and the figure of merit Z T for a QD-based heat engine, identifying optimal working conditions which maximize output power and efficiency of heat-to-work conversion. Beyond the linear response we concentrate on the two-terminal setup. We first study the thermoelectric nonlinear coefficients assessing the consequences of large temperature and voltage biases, focusing on the breakdown of the Onsager reciprocal relation between thermopower and Peltier coefficient. We then investigate the conditions which optimize the performance of a heat engine, finding that in the quantum limit output power and efficiency at maximum power can almost be simultaneously maximized by choosing appropriate values of electrochemical potential and bias voltage. At last we study how energy level degeneracy can increase the output power.

New evidence favoring multilevel decomposition and optimization

NASA Technical Reports Server (NTRS)

Padula, Sharon L.; Polignone, Debra A.

1990-01-01

The issue of the utility of multilevel decomposition and optimization remains controversial. To date, only the structural optimization community has actively developed and promoted multilevel optimization techniques. However, even this community acknowledges that multilevel optimization is ideally suited for a rather limited set of problems. It is warned that decomposition typically requires eliminating local variables by using global variables and that this in turn causes ill-conditioning of the multilevel optimization by adding equality constraints. The purpose is to suggest a new multilevel optimization technique. This technique uses behavior variables, in addition to design variables and constraints, to decompose the problem. The new technique removes the need for equality constraints, simplifies the decomposition of the design problem, simplifies the programming task, and improves the convergence speed of multilevel optimization compared to conventional optimization.

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

PubMed

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

2016-07-30

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

Manual therapy compared with physical therapy in patients with non-specific neck pain: a randomized controlled trial.

PubMed

Groeneweg, Ruud; van Assen, Luite; Kropman, Hans; Leopold, Huco; Mulder, Jan; Smits-Engelsman, Bouwien C M; Ostelo, Raymond W J G; Oostendorp, Rob A B; van Tulder, Maurits W

2017-01-01

Manual therapy according to the School of Manual Therapy Utrecht (MTU) is a specific type of passive manual joint mobilization. MTU has not yet been systematically compared to other manual therapies and physical therapy. In this study the effectiveness of MTU is compared to physical therapy, particularly active exercise therapy (PT) in patients with non-specific neck pain. Patients neck pain, aged between 18-70 years, were included in a pragmatic randomized controlled trial with a one-year follow-up. Primary outcome measures were global perceived effect and functioning (Neck Disability Index), the secondary outcome was pain intensity (Numeric Rating Scale for Pain). Outcomes were measured at 3, 7, 13, 26 and 52 weeks. Multilevel analyses (intention-to-treat) were the primary analyses for overall between-group differences. Additional to the primary and secondary outcomes the number of treatment sessions of the MTU group and PT group was analyzed. Data were collected from September 2008 to February 2011. A total of 181 patients were included. Multilevel analyses showed no statistically significant overall differences at one year between the MTU and PT groups on any of the primary and secondary outcomes. The MTU group showed significantly lower treatment sessions compared to the PT group (respectively 3.1 vs. 5.9 after 7 weeks; 6.1 vs. 10.0 after 52 weeks). Patients with neck pain improved in both groups without statistical significantly or clinically relevant differences between the MTU and PT groups during one-year follow-up. ClinicalTrials.gov Identifier: NCT00713843.

Polynomials with Restricted Coefficients and Their Applications

DTIC Science & Technology

1987-01-01

sums of exponentials of quadratics, he reduced such ýzums to exponentials of linears (geometric sums!) by simplg multiplying by their conjugates...n, the same algebraic manipulations as before lead to rn V`-~ v ie ? --8-- el4V’ .fk ts with 𔄃 = a+(2r+l)t, A = a+(2r+2m+l)t. To estimate the right...coefficients. These random polynomials represent the deviation in frequency response of a linear , equispaced antenna array cauised by coefficient

Mapping Regional Impervious Surface Distribution from Night Time Light: The Variability across Global Cities

NASA Astrophysics Data System (ADS)

Lin, M.; Yang, Z.; Park, H.; Qian, S.; Chen, J.; Fan, P.

2017-12-01

Impervious surface area (ISA) has become an important indicator for studying urban environments, but mapping ISA at the regional or global scale is still challenging due to the complexity of impervious surface features. The Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light data is (NTL) and Resolution Imaging Spectroradiometer (MODIS) are the major remote sensing data source for regional ISA mapping. A single regression relationship between fractional ISA and NTL or various index derived based on NTL and MODIS vegetation index (NDVI) data was established in many previous studies for regional ISA mapping. However, due to the varying geographical, climatic, and socio-economic characteristics of different cities, the same regression relationship may vary significantly across different cities in the same region in terms of both fitting performance (i.e. R2) and the rate of change (Slope). In this study, we examined the regression relationship between fractional ISA and Vegetation Adjusted Nighttime light Urban Index (VANUI) for 120 randomly selected cities around the world with a multilevel regression model. We found that indeed there is substantial variability of both the R2 (0.68±0.29) and slopes (0.64±0.40) among individual regressions, which suggests that multilevel/hierarchical models are needed for accuracy improvement of future regional ISA mapping .Further analysis also let us find the this substantial variability are affected by climate conditions, socio-economic status, and urban spatial structures. However, all these effects are nonlinear rather than linear, thus could not modeled explicitly in multilevel linear regression models.

Workplace interventions to improve work ability: A systematic review and meta-analysis of their effectiveness.

PubMed

Oakman, Jodi; Neupane, Subas; Proper, Karin I; Kinsman, Natasha; Nygård, Clas-Håkan

2018-03-01

Objective Extended working lives due to an ageing population will necessitate the maintenance of work ability across the life course. This systematic review aimed to analyze whether workplace interventions positively impact work ability. Methods We searched Medline, PsycINFO, CINAHL and Embase databases using relevant terms. Work-based interventions were those focused on individuals, the workplace, or multilevel (combination). Work ability - measured using the work ability index (WAI) or the single-item work ability score (WAS) - was the outcome measure. Grading of Recommendations Assessment, Development & Evaluation (GRADE) criteria was used to assess evidence quality, and impact statements were developed to synthesize the results. Meta-analysis was undertaken where appropriate. Results We reviewed 17 randomized control trials (comprising 22 articles). Multilevel interventions (N=5) included changes to work arrangements and liaisons with supervisors, whilst individual-focused interventions (N=12) involved behavior change or exercise programs. We identified only evidence of a moderate quality for either individual or multilevel interventions aiming to improve work ability. The meta-analysis of 13 studies found a small positive significant effect for interventions on work ability [overall pooled mean 0.12, 95% confidence interval (CI) 0.03-0.21] with no heterogeneity for the effect size (Chi 2 =11.28, P=0.51; I 2 =0%). Conclusions The meta-analysis showed a small positive effect, suggesting that workplace interventions might improve work ability. However, the quality of the evidence base was only moderate, precluding any firm conclusion. Further high quality studies are require to establish the role of interventions on work ability.

Evolution of the “Drivers” of Translational Cancer Epidemiology: Analysis of Funded Grants and the Literature

PubMed Central

Lam, Tram Kim; Chang, Christine Q.; Rogers, Scott D.; Khoury, Muin J.; Schully, Sheri D.

2015-01-01

Concurrently with a workshop sponsored by the National Cancer Institute, we identified key “drivers” for accelerating cancer epidemiology across the translational research continuum in the 21st century: emerging technologies, a multilevel approach, knowledge integration, and team science. To map the evolution of these “drivers” and translational phases (T0–T4) in the past decade, we analyzed cancer epidemiology grants funded by the National Cancer Institute and published literature for 2000, 2005, and 2010. For each year, we evaluated the aims of all new/competing grants and abstracts of randomly selected PubMed articles. Compared with grants based on a single institution, consortium-based grants were more likely to incorporate contemporary technologies (P = 0.012), engage in multilevel analyses (P = 0.010), and incorporate elements of knowledge integration (P = 0.036). Approximately 74% of analyzed grants and publications involved discovery (T0) or characterization (T1) research, suggesting a need for more translational (T2–T4) research. Our evaluation indicated limited research in 1) a multilevel approach that incorporates molecular, individual, social, and environmental determinants and 2) knowledge integration that evaluates the robustness of scientific evidence. Cancer epidemiology is at the cusp of a paradigm shift, and the field will need to accelerate the pace of translating scientific discoveries in order to impart population health benefits. While multi-institutional and technology-driven collaboration is happening, concerted efforts to incorporate other key elements are warranted for the discipline to meet future challenges. PMID:25767265

Reduced food access due to a lack of money, inability to lift and lack of access to a car for food shopping: a multilevel study in Melbourne, Victoria.

PubMed

Burns, Cate; Bentley, Rebecca; Thornton, Lukar; Kavanagh, Anne

2011-06-01

To describe associations between demographic and individual and area-level socio-economic variables and restricted household food access due to lack of money, inability to lift groceries and lack of access to a car to do food shopping. Multilevel study of three measures of restricted food access, i.e. running out of money to buy food, inability to lift groceries and lack of access to a car for food shopping. Multilevel logistic regression was conducted to examine the risk of each of these outcomes according to demographic and socio-economic variables. Random selection of households from fifty small areas in Melbourne, Australia, in 2003. The main food shoppers in each household (n 2564). A lack of money was significantly more likely among the young and in households with single adults. Difficulty lifting was more likely among the elderly and those born overseas. The youngest and highest age groups both reported reduced car access, as did those born overseas and single-adult households. All three factors were most likely among those with a lower individual or household socio-economic position. Increased levels of area disadvantage were independently associated with difficulty lifting and reduced car access. In Melbourne, households with lower individual socio-economic position and area disadvantage have restricted access to food because of a lack of money and/or having physical limitations due difficulty lifting or lack of access to a car for food shopping. Further research is required to explore the relationship between physical restrictions and food access.

Variation in prostate cancer treatment associated with population density of the county of residence.

PubMed

Cary, C; Odisho, A Y; Cooperberg, M R

2016-06-01

We sought to assess variation in the primary treatment of prostate cancer by examining the effect of population density of the county of residence on treatment for clinically localized prostate cancer and quantify variation in primary treatment attributable to the county and state level. A total 138 226 men with clinically localized prostate cancer in the Surveillance, Epidemiology and End Result (SEER) database in 2005 through 2008 were analyzed. The main association of interest was between prostate cancer treatment and population density using multilevel hierarchical logit models while accounting for the random effects of counties nested within SEER regions. To quantify the effect of county and SEER region on individual treatment, the percent of total variance in treatment attributable to county of residence and SEER site was estimated with residual intraclass correlation coefficients. Men with localized prostate cancer in metropolitan counties had 23% higher odds of being treated with surgery or radiation compared with men in rural counties, controlling for number of urologists per county as well as clinical and sociodemographic characteristics. Three percent (95% confidence interval (CI): 1.2-6.2%) of the total variation in treatment was attributable to SEER site, while 6% (95% CI: 4.3-9.0%) of variation was attributable to county of residence, adjusting for clinical and sociodemographic characteristics. Variation in treatment for localized prostate cancer exists for men living in different population-dense counties of the country. These findings highlight the importance of comparative effectiveness research to improve understanding of this variation and lead to a reduction in unwarranted variation.

Undifferentiated negative affect and impulsivity in borderline personality and depressive disorders: A momentary perspective.

PubMed

Tomko, Rachel L; Lane, Sean P; Pronove, Lisa M; Treloar, Hayley R; Brown, Whitney C; Solhan, Marika B; Wood, Phillip K; Trull, Timothy J

2015-08-01

Individuals with borderline personality disorder (BPD) often report experiencing several negative emotions simultaneously, an indicator of "undifferentiated" negative affect. The current study examined the relationship between undifferentiated negative affect and impulsivity. Participants with a current BPD (n = 67) or depressive disorder (DD; n = 38) diagnosis carried an electronic diary for 28 days, reporting on emotions and impulsivity when randomly prompted (up to 6 times per day). Undifferentiated negative affect was quantified using momentary intraclass correlation coefficients, which indicated how consistently negative emotion items were rated across fear, hostility, and sadness subscales. Undifferentiated negative affect at the occasion-level, day-level, and across 28 days was used to predict occasion-level impulsivity. Multilevel modeling was used to test the hypothesis that undifferentiated negative emotion would be a significant predictor of momentary impulsivity above and beyond levels of overall negative affect. Undifferentiated negative affect at the occasion and day levels were significant predictors of occasion-level impulsivity, but undifferentiated negative affect across the 28-day study period was only marginally significant. Results did not differ depending on BPD or DD status, though individuals with BPD did report significantly greater momentary impulsivity and undifferentiated negative affect. Undifferentiated negative affect may increase risk for impulsivity among individuals with BPD and depressive disorders, and the current data suggest that this process can be relatively immediate as well as cumulative over the course of a day. This research supports the consideration of undifferentiated negative affect as a transdiagnostic construct, but one that may be particularly relevant for those with BPD. (c) 2015 APA, all rights reserved).

Modeling naturalistic craving, withdrawal, and affect during early nicotine abstinence: A pilot ecological momentary assessment study.

PubMed

Bujarski, Spencer; Roche, Daniel J O; Sheets, Erin S; Krull, Jennifer L; Guzman, Iris; Ray, Lara A

2015-04-01

Despite the critical role of withdrawal, craving, and positive affect (PA) and negative affect (NA) in smoking relapse, relatively little is known about the temporal and predictive relationship between these constructs within the first day of abstinence. This pilot study aims to characterize dynamic changes in withdrawal, craving, and affect over the course of early abstinence using ecological momentary assessment. Beginning immediately after smoking, moderate and heavy smoking participants (n = 15 per group) responded to hourly surveys assessing craving, withdrawal, NA, and PA. Univariate and multivariate multilevel random coefficient modeling was used to describe the progression of craving, withdrawal/NA, and PA and to test correlations between these constructs at the subject level over the course of early abstinence. Heavy smokers reported greater craving from 1-4 hr of abstinence and greater withdrawal/NA after 3 or more hours as compared with moderate smokers. Level of withdrawal/NA was strongly positively associated with craving, and PA was negatively correlated with craving; however, the temporal dynamics of these correlations differed substantially. The association between withdrawal/NA and craving decreased over early abstinence, whereas the reverse was observed for PA. These findings can inform experimental studies of nicotine abstinence as well as their clinical applications to smoking cessation efforts. In particular, these results help to elucidate the role of PA in nicotine abstinence by demonstrating its independent association with nicotine craving over and above withdrawal/NA. If supported by future studies, these findings can refine experimental methods and clinical approaches for smoking cessation. (c) 2015 APA, all rights reserved).

Undifferentiated Negative Affect and Impulsivity in Borderline Personality and Depressive Disorders: A Momentary Perspective

PubMed Central

Pronove, Lisa M.; Treloar, Hayley R.; Brown, Whitney C.; Solhan, Marika B.; Wood, Phillip K.; Trull, Timothy J.

2015-01-01

Individuals with borderline personality disorder (BPD) often report experiencing several negative emotions simultaneously, an indicator of “undifferentiated” negative affect. The current study examined the relationship between undifferentiated negative affect and impulsivity. Participants with a current BPD (n = 67) or depressive disorder (DD; n = 38) diagnosis carried an electronic diary for 28 days, reporting on emotions and impulsivity when randomly prompted (up to 6 times per day). Undifferentiated negative affect was quantified using momentary intraclass correlation coefficients, which indicated how consistently negative emotion items were rated across fear, hostility, and sadness subscales. Undifferentiated negative affect at the occasion-level, day-level, and across 28 days was used to predict occasion-level impulsivity. Multilevel modeling was used to test the hypothesis that undifferentiated negative emotion would be a significant predictor of momentary impulsivity above and beyond levels of overall negative affect. Undifferentiated negative affect at the occasion and day levels were significant predictors of occasion-level impulsivity, but undifferentiated negative affect across the 28-day study period was only marginally significant. Results did not differ depending on BPD or DD status, though BPD individuals did report significantly greater momentary impulsivity and undifferentiated negative affect. Undifferentiated negative affect may increase risk for impulsivity among individuals with BPD and depressive disorders, and the current data suggest that this process can be relatively immediate as well as cumulative over the course of a day. This research supports the consideration of undifferentiated negative affect as a transdiagnostic construct, but one that may be particularly relevant for those with BPD. PMID:26147324

Interpretation of diffusion coefficients in nanostructured materials from random walk numerical simulation.

PubMed

Anta, Juan A; Mora-Seró, Iván; Dittrich, Thomas; Bisquert, Juan

2008-08-14

We make use of the numerical simulation random walk (RWNS) method to compute the "jump" diffusion coefficient of electrons in nanostructured materials via mean-square displacement. First, a summary of analytical results is given that relates the diffusion coefficient obtained from RWNS to those in the multiple-trapping (MT) and hopping models. Simulations are performed in a three-dimensional lattice of trap sites with energies distributed according to an exponential distribution and with a step-function distribution centered at the Fermi level. It is observed that once the stationary state is reached, the ensemble of particles follow Fermi-Dirac statistics with a well-defined Fermi level. In this stationary situation the diffusion coefficient obeys the theoretical predictions so that RWNS effectively reproduces the MT model. Mobilities can be also computed when an electrical bias is applied and they are observed to comply with the Einstein relation when compared with steady-state diffusion coefficients. The evolution of the system towards the stationary situation is also studied. When the diffusion coefficients are monitored along simulation time a transition from anomalous to trap-limited transport is observed. The nature of this transition is discussed in terms of the evolution of electron distribution and the Fermi level. All these results will facilitate the use of RW simulation and related methods to interpret steady-state as well as transient experimental techniques.

Backscattering from a randomly rough dielectric surface

NASA Technical Reports Server (NTRS)

Fung, Adrian K.; Li, Zongqian; Chen, K. S.

1992-01-01

A backscattering model for scattering from a randomly rough dielectric surface is developed based on an approximate solution of a pair of integral equations for the tangential surface fields. Both like and cross-polarized scattering coefficients are obtained. It is found that the like polarized scattering coefficients contain two types of terms: single scattering terms and multiple scattering terms. The single scattering terms in like polarized scattering are shown to reduce the first-order solutions derived from the small perturbation method when the roughness parameters satisfy the slightly rough conditions. When surface roughnesses are large but the surface slope is small, only a single scattering term corresponding to the standard Kirchhoff model is significant. If the surface slope is large, the multiple scattering term will also be significant. The cross-polarized backscattering coefficients satisfy reciprocity and contain only multiple scattering terms. The difference between vertical and horizontal scattering coefficients is found to increase with the dielectric constant and is generally smaller than that predicted by the first-order small perturbation model. Good agreements are obtained between this model and measurements from statistically known surfaces.

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach.

PubMed

Xu, Pengpeng; Huang, Helai; Dong, Ni; Wong, S C

2017-01-01

This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness. Copyright © 2016 Elsevier Ltd. All rights reserved.

Long-range correlations and charge transport properties of DNA sequences

NASA Astrophysics Data System (ADS)

Liu, Xiao-liang; Ren, Yi; Xie, Qiong-tao; Deng, Chao-sheng; Xu, Hui

2010-04-01

By using Hurst's analysis and transfer approach, the rescaled range functions and Hurst exponents of human chromosome 22 and enterobacteria phage lambda DNA sequences are investigated and the transmission coefficients, Landauer resistances and Lyapunov coefficients of finite segments based on above genomic DNA sequences are calculated. In a comparison with quasiperiodic and random artificial DNA sequences, we find that λ-DNA exhibits anticorrelation behavior characterized by a Hurst exponent 0.5

Electromagnetic wave extinction within a forested canopy

NASA Technical Reports Server (NTRS)

Karam, M. A.; Fung, A. K.

1989-01-01

A forested canopy is modeled by a collection of randomly oriented finite-length cylinders shaded by randomly oriented and distributed disk- or needle-shaped leaves. For a plane wave exciting the forested canopy, the extinction coefficient is formulated in terms of the extinction cross sections (ECSs) in the local frame of each forest component and the Eulerian angles of orientation (used to describe the orientation of each component). The ECSs in the local frame for the finite-length cylinders used to model the branches are obtained by using the forward-scattering theorem. ECSs in the local frame for the disk- and needle-shaped leaves are obtained by the summation of the absorption and scattering cross-sections. The behavior of the extinction coefficients with the incidence angle is investigated numerically for both deciduous and coniferous forest. The dependencies of the extinction coefficients on the orientation of the leaves are illustrated numerically.

Fault Detection of Aircraft System with Random Forest Algorithm and Similarity Measure

PubMed Central

Park, Wookje; Jung, Sikhang

2014-01-01

Research on fault detection algorithm was developed with the similarity measure and random forest algorithm. The organized algorithm was applied to unmanned aircraft vehicle (UAV) that was readied by us. Similarity measure was designed by the help of distance information, and its usefulness was also verified by proof. Fault decision was carried out by calculation of weighted similarity measure. Twelve available coefficients among healthy and faulty status data group were used to determine the decision. Similarity measure weighting was done and obtained through random forest algorithm (RFA); RF provides data priority. In order to get a fast response of decision, a limited number of coefficients was also considered. Relation of detection rate and amount of feature data were analyzed and illustrated. By repeated trial of similarity calculation, useful data amount was obtained. PMID:25057508

The episodic random utility model unifies time trade-off and discrete choice approaches in health state valuation

PubMed Central

Craig, Benjamin M; Busschbach, Jan JV

2009-01-01

Background To present an episodic random utility model that unifies time trade-off and discrete choice approaches in health state valuation. Methods First, we introduce two alternative random utility models (RUMs) for health preferences: the episodic RUM and the more common instant RUM. For the interpretation of time trade-off (TTO) responses, we show that the episodic model implies a coefficient estimator, and the instant model implies a mean slope estimator. Secondly, we demonstrate these estimators and the differences between the estimates for 42 health states using TTO responses from the seminal Measurement and Valuation in Health (MVH) study conducted in the United Kingdom. Mean slopes are estimates with and without Dolan's transformation of worse-than-death (WTD) responses. Finally, we demonstrate an exploded probit estimator, an extension of the coefficient estimator for discrete choice data that accommodates both TTO and rank responses. Results By construction, mean slopes are less than or equal to coefficients, because slopes are fractions and, therefore, magnify downward errors in WTD responses. The Dolan transformation of WTD responses causes mean slopes to increase in similarity to coefficient estimates, yet they are not equivalent (i.e., absolute mean difference = 0.179). Unlike mean slopes, coefficient estimates demonstrate strong concordance with rank-based predictions (Lin's rho = 0.91). Combining TTO and rank responses under the exploded probit model improves the identification of health state values, decreasing the average width of confidence intervals from 0.057 to 0.041 compared to TTO only results. Conclusion The episodic RUM expands upon the theoretical framework underlying health state valuation and contributes to health econometrics by motivating the selection of coefficient and exploded probit estimators for the analysis of TTO and rank responses. In future MVH surveys, sample size requirements may be reduced through the incorporation of multiple responses under a single estimator. PMID:19144115

Multilevel SEM Strategies for Evaluating Mediation in Three-Level Data

ERIC Educational Resources Information Center

Preacher, Kristopher J.

2011-01-01

Strategies for modeling mediation effects in multilevel data have proliferated over the past decade, keeping pace with the demands of applied research. Approaches for testing mediation hypotheses with 2-level clustered data were first proposed using multilevel modeling (MLM) and subsequently using multilevel structural equation modeling (MSEM) to…

Permeability of model porous medium formed by random discs

NASA Astrophysics Data System (ADS)

Gubaidullin, A. A.; Gubkin, A. S.; Igoshin, D. E.; Ignatev, P. A.

2018-03-01

Two-dimension model of the porous medium with skeleton of randomly located overlapping discs is proposed. The geometry and computational grid are built in open package Salome. Flow of Newtonian liquid in longitudinal and transverse directions is calculated and its flow rate is defined. The numerical solution of the Navier-Stokes equations for a given pressure drop at the boundaries of the area is realized in the open package OpenFOAM. Calculated value of flow rate is used for defining of permeability coefficient on the base of Darcy law. For evaluating of representativeness of computational domain the permeability coefficients in longitudinal and transverse directions are compered.

The Bayesian group lasso for confounded spatial data

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Hanks, Ephraim M.; Russell, Robin E.; Walsh, Daniel P.

2017-01-01

Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.

A Experimental Investigation of Hydrodynamic Forces on Circular Cylinders in Sinusoidal and Random Oscillating Flow

NASA Astrophysics Data System (ADS)

Longoria, Raul Gilberto

An experimental apparatus has been developed which can be used to generate a general time-dependent planar flow across a cylinder. A mass of water enclosed with no free surface within a square cross-section tank and two spring pre-loaded pistons is oscillated using a hydraulic actuator. A circular cylinder is suspended horizontally in the tank by two X-Y force transducers used to simultaneously measure the total in-line and transverse forces. Fluid motion is measured using a differential pressure transducer for instantaneous acceleration and an LVDT for displacement. This investigation provides measurement of forces on cylinders subjected to planar fluid flow velocity with a time (and frequency) dependence which more accurately represent the random conditions encountered in a natural ocean environment. The use of the same apparatus for both sinusoidal and random experiments provides a quantified assessment of the applicability of sinusoidal planar oscillatory flow data in offshore structure design methods. The drag and inertia coefficients for a Morison equation representation of the inline force are presented for both sinusoidal and random flow. Comparison of the sinusoidal results is favorable with those of previous investigations. The results from random experiments illustrates the difference in the force mechanism by contrasting the force transfer coefficients for the inline and transverse forces. It is found that application of sinusoidal results to random hydrodynamic inline force prediction using the Morison equation wrongly weighs the drag and inertia components, and the transverse force is overpredicted. The use of random planar oscillatory flow in the laboratory, contrasted with sinusoidal planar oscillatory flow, quantifies the accepted belief that the force transfer coefficients from sinusoidal flow experiments are conservative for prediction of forces on cylindrical structures subjected to random sea waves and the ensuing forces. Further analysis of data is conducted in the frequency domain to illustrate models used for predicting the power spectral density of the inline force including a nonlinear describing function method. It is postulated that the large-scale vortex activity prominent in sinusoidal oscillatory flow is subdued in random flow conditions.

Effects of intermode nonlinearity and intramode nonlinearity on modulation instability in randomly birefringent two-mode optical fibers

NASA Astrophysics Data System (ADS)

Li, Jin Hua; Xu, Hui; Sun, Ting Ting; Pei, Shi Xin; Ren, Hai Dong

2018-05-01

We analyze in detail the effects of the intermode nonlinearity (IEMN) and intramode nonlinearity (IRMN) on modulation instability (MI) in randomly birefringent two-mode optical fibers (RB-TMFs). In the anomalous dispersion regime, the MI gain enhances significantly as the IEMN and IRMN coefficients increases. In the normal dispersion regime, MI can be generated without the differential mode group delay (DMGD) effect, as long as the IEMN coefficient between two distinct modes is above a critical value, or the IRMN coefficient inside a mode is below a critical value. This critical IEMN (IRMN) coefficient depends strongly on the given IRMN (IEMN) coefficient and DMGD for a given nonlinear RB-TMF structure, and is independent on the input total power, the power ratio distribution and the group velocity dispersion (GVD) ratio between the two modes. On the other hand, in contrast to the MI band arising from the pure effect of DMGD in the normal dispersion regime, where MI vanishes after a critical total power, the generated MI band under the combined effects of IEMN and IRMN without DMGD exists for any total power and enhances with the total power. The MI analysis is verified numerically by launching perturbed continuous waves (CWs) with wave propagation method.

Note: On the relation between Lifson-Jackson and Derrida formulas for effective diffusion coefficient

NASA Astrophysics Data System (ADS)

Kalnin, Juris R.; Berezhkovskii, Alexander M.

2013-11-01

The Lifson-Jackson formula provides the effective free diffusion coefficient for a particle diffusing in an arbitrary one-dimensional periodic potential. Its counterpart, when the underlying dynamics is described in terms of an unbiased nearest-neighbor Markovian random walk on a one-dimensional periodic lattice is given by the formula obtained by Derrida. It is shown that the latter formula can be considered as a discretized version of the Lifson-Jackson formula with correctly chosen position-dependent diffusion coefficient.

Societal characteristics and health in the former communist countries of Central and Eastern Europe and the former Soviet Union: a multilevel analysis.

PubMed

Bobak, Martin; Murphy, Mike; Rose, Richard; Marmot, Michael

2007-11-01

To examine whether, in former communist countries that have undergone profound social and economic transformation, health status is associated with income inequality and other societal characteristics, and whether this represents something more than the association of health status with individual socioeconomic circumstances. Multilevel analysis of cross-sectional data. 13 Countries from Central and Eastern Europe and the former Soviet Union. Population samples aged 18+ years (a total of 15 331 respondents). Poor self-rated health. There were marked differences among participating countries in rates of poor health (a greater than twofold difference between the countries with the highest and lowest rates of poor health), gross domestic product per capita adjusted for purchasing power parity (a greater than threefold difference), the Gini coefficient of income inequality (twofold difference), corruption index (twofold difference) and homicide rates (20-fold difference). Ecologically, the age- and sex-standardised prevalence of poor self-rated health correlated strongly with life expectancy at age 15 (r = -0.73). In multilevel analyses, societal (country-level) measures of income inequality were not associated with poor health. Corruption and gross domestic product per capita were associated with poor health after controlling for individuals' socioeconomic circumstances (education, household income, marital status and ownership of household items); the odds ratios were 1.15 (95% confidence interval 1.03 to 1.29) per 1 unit (on a 10-point scale) increase in the corruption index and 0.79 (95% confidence interval 0.68 to 0.93) per $5000 increase in gross domestic product per capita. The effects of gross domestic product and corruption were virtually identical in people whose household income was below and above the median. Societal measures of prosperity and corruption, but not income inequalities, were associated with health independently of individual-level socioeconomic characteristics. The finding that these effects were similar in persons with lower and higher income suggests that these factors do not operate exclusively through poverty.

Revisiting causal neighborhood effects on individual ischemic heart disease risk: a quasi-experimental multilevel analysis among Swedish siblings.

PubMed

Merlo, Juan; Ohlsson, Henrik; Chaix, Basile; Lichtenstein, Paul; Kawachi, Ichiro; Subramanian, S V

2013-01-01

Neighborhood socioeconomic disadvantage is associated to increased individual risk of ischemic heart disease (IHD). However, the value of this association for causal inference is uncertain. Moreover, neighborhoods are often defined by available administrative boundaries without evaluating in which degree these boundaries embrace a relevant socio-geographical context that condition individual differences in IHD risk. Therefore, we performed an analysis of variance, and also compared the associations obtained by conventional multilevel analyses and by quasi-experimental family-based design that provides stronger evidence for causal inference. Linking the Swedish Multi-Generation Register to several other national registers, we analyzed 184,931 families embracing 415,540 full brothers 45-64 years old in 2004, and residing in 8408 small-area market statistics (SAMS) considered as "neighborhoods" in our study. We investigated the association between low neighborhood income (categorized in groups by deciles) and IHD risk in the next four years. We distinguished between family mean and intrafamilial-centered low neighborhood income, which allowed us to investigate both unrelated individuals from different families and full brothers within families. We applied multilevel logistic regression techniques to obtain odds ratios (OR), variance partition coefficients (VPC) and 95% credible intervals (CI). In unrelated individuals a decile unit increase of low neighborhood income increased individual IHD risk (OR = 1.04, 95% CI: 1.03-1.07). In the intrafamilial analysis this association was reduced (OR = 1.02, 95% CI: 1.02-1.04). Low neighborhood income seems associated with IHD risk in middle-aged men. However, despite the family-based design, we cannot exclude residual confounding by genetic and non-shared environmental factors. Besides, the low neighborhood level VPC = 1.5% suggest that the SAMS are a rather inappropriate construct of the socio-geographic context that conditions individual variance in IHD risk. In contrast the high family level VPC = 20.1% confirms the relevance of the family context for understanding IHD risk. Copyright © 2012 Elsevier Ltd. All rights reserved.

Observed intra-cluster correlation coefficients in a cluster survey sample of patient encounters in general practice in Australia

PubMed Central

Knox, Stephanie A; Chondros, Patty

2004-01-01

Background Cluster sample study designs are cost effective, however cluster samples violate the simple random sample assumption of independence of observations. Failure to account for the intra-cluster correlation of observations when sampling through clusters may lead to an under-powered study. Researchers therefore need estimates of intra-cluster correlation for a range of outcomes to calculate sample size. We report intra-cluster correlation coefficients observed within a large-scale cross-sectional study of general practice in Australia, where the general practitioner (GP) was the primary sampling unit and the patient encounter was the unit of inference. Methods Each year the Bettering the Evaluation and Care of Health (BEACH) study recruits a random sample of approximately 1,000 GPs across Australia. Each GP completes details of 100 consecutive patient encounters. Intra-cluster correlation coefficients were estimated for patient demographics, morbidity managed and treatments received. Intra-cluster correlation coefficients were estimated for descriptive outcomes and for associations between outcomes and predictors and were compared across two independent samples of GPs drawn three years apart. Results Between April 1999 and March 2000, a random sample of 1,047 Australian general practitioners recorded details of 104,700 patient encounters. Intra-cluster correlation coefficients for patient demographics ranged from 0.055 for patient sex to 0.451 for language spoken at home. Intra-cluster correlations for morbidity variables ranged from 0.005 for the management of eye problems to 0.059 for management of psychological problems. Intra-cluster correlation for the association between two variables was smaller than the descriptive intra-cluster correlation of each variable. When compared with the April 2002 to March 2003 sample (1,008 GPs) the estimated intra-cluster correlation coefficients were found to be consistent across samples. Conclusions The demonstrated precision and reliability of the estimated intra-cluster correlations indicate that these coefficients will be useful for calculating sample sizes in future general practice surveys that use the GP as the primary sampling unit. PMID:15613248

A multilevel simulation approach to derive the slip boundary condition of the solid phase in two-fluid models

NASA Astrophysics Data System (ADS)

Feng, Zhi-Gang; Michaelides, Efstathios; Mao, Shaolin

2011-11-01

The simulation of particulate flows for industrial applications often requires the use of a two-fluid model (TFM), where the solid particles are considered as a separate continuous phase. One of the underlining uncertainties in the use of aTFM in multiphase computations comes from the boundary condition of the solid phase. The no-slip condition at a solid boundary is not a valid assumption for the solid phase. Instead, several researchers advocate a slip condition as a more appropriate boundary condition. However, the question on the selection of an exact slip length or a slip velocity coefficient is still unanswered. In the present work we propose a multilevel simulation approach to compute the slip length that is applicable to a TFM. We investigate the motion of a number of particles near a vertical solid wall, while the particles are in fluidization using a direct numerical simulation (DNS); the positions and velocities of the particles are being tracked and analyzed at each time step. It is found that the time- and vertical-space averaged values of the particle velocities converge, yielding velocity profiles that can be used to deduce the particle slip length close to a solid wall. This work was supported by a grant from the DOE-NETL (DE-NT0008064) and by a grant from NSF (HRD-0932339).

Formulation and Application of the Generalized Multilevel Facets Model

ERIC Educational Resources Information Center

Wang, Wen-Chung; Liu, Chih-Yu

2007-01-01

In this study, the authors develop a generalized multilevel facets model, which is not only a multilevel and two-parameter generalization of the facets model, but also a multilevel and facet generalization of the generalized partial credit model. Because the new model is formulated within a framework of nonlinear mixed models, no efforts are…

A Geometrical Framework for Covariance Matrices of Continuous and Categorical Variables

ERIC Educational Resources Information Center

Vernizzi, Graziano; Nakai, Miki

2015-01-01

It is well known that a categorical random variable can be represented geometrically by a simplex. Accordingly, several measures of association between categorical variables have been proposed and discussed in the literature. Moreover, the standard definitions of covariance and correlation coefficient for continuous random variables have been…

A Methodological Review of Statistical Methods for Handling Multilevel Non-Nested Longitudinal Data in Educational Research

ERIC Educational Resources Information Center

Sun, Shuyan; Pan, Wei

2014-01-01

As applications of multilevel modelling in educational research increase, researchers realize that multilevel data collected in many educational settings are often not purely nested. The most common multilevel non-nested data structure is one that involves student mobility in longitudinal studies. This article provides a methodological review of…

Detecting Differential Item Discrimination (DID) and the Consequences of Ignoring DID in Multilevel Item Response Models

ERIC Educational Resources Information Center

Lee, Woo-yeol; Cho, Sun-Joo

2017-01-01

Cross-level invariance in a multilevel item response model can be investigated by testing whether the within-level item discriminations are equal to the between-level item discriminations. Testing the cross-level invariance assumption is important to understand constructs in multilevel data. However, in most multilevel item response model…

Alternative Methods for Assessing Mediation in Multilevel Data: The Advantages of Multilevel SEM

ERIC Educational Resources Information Center

Preacher, Kristopher J.; Zhang, Zhen; Zyphur, Michael J.

2011-01-01

Multilevel modeling (MLM) is a popular way of assessing mediation effects with clustered data. Two important limitations of this approach have been identified in prior research and a theoretical rationale has been provided for why multilevel structural equation modeling (MSEM) should be preferred. However, to date, no empirical evidence of MSEM's…

Basis adaptation and domain decomposition for steady partial differential equations with random coefficients

DOE PAGES

Tipireddy, R.; Stinis, P.; Tartakovsky, A. M.

2017-09-04

In this paper, we present a novel approach for solving steady-state stochastic partial differential equations (PDEs) with high-dimensional random parameter space. The proposed approach combines spatial domain decomposition with basis adaptation for each subdomain. The basis adaptation is used to address the curse of dimensionality by constructing an accurate low-dimensional representation of the stochastic PDE solution (probability density function and/or its leading statistical moments) in each subdomain. Restricting the basis adaptation to a specific subdomain affords finding a locally accurate solution. Then, the solutions from all of the subdomains are stitched together to provide a global solution. We support ourmore » construction with numerical experiments for a steady-state diffusion equation with a random spatially dependent coefficient. Lastly, our results show that highly accurate global solutions can be obtained with significantly reduced computational costs.« less

Stochastic Resonance and Safe Basin of Single-Walled Carbon Nanotubes with Strongly Nonlinear Stiffness under Random Magnetic Field.

PubMed

Xu, Jia; Li, Chao; Li, Yiran; Lim, Chee Wah; Zhu, Zhiwen

2018-05-04

In this paper, a kind of single-walled carbon nanotube nonlinear model is developed and the strongly nonlinear dynamic characteristics of such carbon nanotubes subjected to random magnetic field are studied. The nonlocal effect of the microstructure is considered based on Eringen’s differential constitutive model. The natural frequency of the strongly nonlinear dynamic system is obtained by the energy function method, the drift coefficient and the diffusion coefficient are verified. The stationary probability density function of the system dynamic response is given and the fractal boundary of the safe basin is provided. Theoretical analysis and numerical simulation show that stochastic resonance occurs when varying the random magnetic field intensity. The boundary of safe basin has fractal characteristics and the area of safe basin decreases when the intensity of the magnetic field permeability increases.

Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients

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

Tipireddy, R.; Stinis, P.; Tartakovsky, A. M.

We present a novel approach for solving steady-state stochastic partial differential equations (PDEs) with high-dimensional random parameter space. The proposed approach combines spatial domain decomposition with basis adaptation for each subdomain. The basis adaptation is used to address the curse of dimensionality by constructing an accurate low-dimensional representation of the stochastic PDE solution (probability density function and/or its leading statistical moments) in each subdomain. Restricting the basis adaptation to a specific subdomain affords finding a locally accurate solution. Then, the solutions from all of the subdomains are stitched together to provide a global solution. We support our construction with numericalmore » experiments for a steady-state diffusion equation with a random spatially dependent coefficient. Our results show that highly accurate global solutions can be obtained with significantly reduced computational costs.« less

The Norwegian Healthier Goats program--modeling lactation curves using a multilevel cubic spline regression model.

PubMed

Nagel-Alne, G E; Krontveit, R; Bohlin, J; Valle, P S; Skjerve, E; Sølverød, L S

2014-07-01

In 2001, the Norwegian Goat Health Service initiated the Healthier Goats program (HG), with the aim of eradicating caprine arthritis encephalitis, caseous lymphadenitis, and Johne's disease (caprine paratuberculosis) in Norwegian goat herds. The aim of the present study was to explore how control and eradication of the above-mentioned diseases by enrolling in HG affected milk yield by comparison with herds not enrolled in HG. Lactation curves were modeled using a multilevel cubic spline regression model where farm, goat, and lactation were included as random effect parameters. The data material contained 135,446 registrations of daily milk yield from 28,829 lactations in 43 herds. The multilevel cubic spline regression model was applied to 4 categories of data: enrolled early, control early, enrolled late, and control late. For enrolled herds, the early and late notations refer to the situation before and after enrolling in HG; for nonenrolled herds (controls), they refer to development over time, independent of HG. Total milk yield increased in the enrolled herds after eradication: the total milk yields in the fourth lactation were 634.2 and 873.3 kg in enrolled early and enrolled late herds, respectively, and 613.2 and 701.4 kg in the control early and control late herds, respectively. Day of peak yield differed between enrolled and control herds. The day of peak yield came on d 6 of lactation for the control early category for parities 2, 3, and 4, indicating an inability of the goats to further increase their milk yield from the initial level. For enrolled herds, on the other hand, peak yield came between d 49 and 56, indicating a gradual increase in milk yield after kidding. Our results indicate that enrollment in the HG disease eradication program improved the milk yield of dairy goats considerably, and that the multilevel cubic spline regression was a suitable model for exploring effects of disease control and eradication on milk yield. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Human cooperation by lethal group competition.

PubMed

Egas, Martijn; Kats, Ralph; van der Sar, Xander; Reuben, Ernesto; Sabelis, Maurice W

2013-01-01

Why humans are prone to cooperate puzzles biologists, psychologists and economists alike. Between-group conflict has been hypothesized to drive within-group cooperation. However, such conflicts did not have lasting effects in laboratory experiments, because they were about luxury goods, not needed for survival ("looting"). Here, we find within-group cooperation to last when between-group conflict is implemented as "all-out war" (eliminating the weakest groups). Human subjects invested in helping group members to avoid having the lowest collective pay-off, whereas they failed to cooperate in control treatments with random group elimination or with no subdivision in groups. When the game was repeated, experience was found to promote helping. Thus, not within-group interactions alone, not random group elimination, but pay-off-dependent group elimination was found to drive within-group cooperation in our experiment. We suggest that some forms of human cooperation are maintained by multi-level selection: reciprocity within groups and lethal competition among groups acting together.

Toward a theory of multilevel evolution: long-term information integration shapes the mutational landscape and enhances evolvability.

PubMed

Hogeweg, Paulien

2012-01-01

Most of evolutionary theory has abstracted away from how information is coded in the genome and how this information is transformed into traits on which selection takes place. While in the earliest stages of biological evolution, in the RNA world, the mapping from the genotype into function was largely predefined by the physical-chemical properties of the evolving entities (RNA replicators, e.g. from sequence to folded structure and catalytic sites), in present-day organisms, the mapping itself is the result of evolution. I will review results of several in silico evolutionary studies which examine the consequences of evolving the genetic coding, and the ways this information is transformed, while adapting to prevailing environments. Such multilevel evolution leads to long-term information integration. Through genome, network, and dynamical structuring, the occurrence and/or effect of random mutations becomes nonrandom, and facilitates rapid adaptation. This is what does happen in the in silico experiments. Is it also what did happen in biological evolution? I will discuss some data that suggest that it did. In any case, these results provide us with novel search images to tackle the wealth of biological data.

Travel time to maternity care and its effect on utilization in rural Ghana: a multilevel analysis.

PubMed

Masters, Samuel H; Burstein, Roy; Amofah, George; Abaogye, Patrick; Kumar, Santosh; Hanlon, Michael

2013-09-01

Rates of neonatal and maternal mortality are high in Ghana. In-facility delivery and other maternal services could reduce this burden, yet utilization rates of key maternal services are relatively low, especially in rural areas. We tested a theoretical implication that travel time negatively affects the use of in-facility delivery and other maternal services. Empirically, we used geospatial techniques to estimate travel times between populations and health facilities. To account for uncertainty in Ghana Demographic and Health Survey cluster locations, we adopted a novel approach of treating the location selection as an imputation problem. We estimated a multilevel random-intercept logistic regression model. For rural households, we found that travel time had a significant effect on the likelihood of in-facility delivery and antenatal care visits, holding constant education, wealth, maternal age, facility capacity, female autonomy, and the season of birth. In contrast, a facility's capacity to provide sophisticated maternity care had no detectable effect on utilization. As the Ghanaian health network expands, our results suggest that increasing the availability of basic obstetric services and improving transport infrastructure may be important interventions. Copyright © 2013 Elsevier Ltd. All rights reserved.

Evolution of neuroarchitecture, multi-level analyses and calibrative reductionism

PubMed Central

Berntson, Gary G.; Norman, Greg J.; Hawkley, Louise C.; Cacioppo, John T.

2012-01-01

Evolution has sculpted the incredibly complex human nervous system, among the most complex functions of which extend beyond the individual to an intricate social structure. Although these functions are deterministic, those determinants are legion, heavily interacting and dependent on a specific evolutionary trajectory. That trajectory was directed by the adaptive significance of quasi-random genetic variations, but was also influenced by chance and caprice. With a different evolutionary pathway, the same neural elements could subserve functions distinctly different from what they do in extant human brains. Consequently, the properties of higher level neural networks cannot be derived readily from the properties of the lower level constituent elements, without studying these elements in the aggregate. Thus, a multi-level approach to integrative neuroscience may offer an optimal strategy. Moreover, the process of calibrative reductionism, by which concepts and understandings from one level of organization or analysis can mutually inform and ‘calibrate’ those from other levels (both higher and lower), may represent a viable approach to the application of reductionism in science. This is especially relevant in social neuroscience, where the basic subject matter of interest is defined by interacting organisms across diverse environments. PMID:23386961

Construction of Covariance Functions with Variable Length Fields

NASA Technical Reports Server (NTRS)

Gaspari, Gregory; Cohn, Stephen E.; Guo, Jing; Pawson, Steven

2005-01-01

This article focuses on construction, directly in physical space, of three-dimensional covariance functions parametrized by a tunable length field, and on an application of this theory to reproduce the Quasi-Biennial Oscillation (QBO) in the Goddard Earth Observing System, Version 4 (GEOS-4) data assimilation system. These Covariance models are referred to as multi-level or nonseparable, to associate them with the application where a multi-level covariance with a large troposphere to stratosphere length field gradient is used to reproduce the QBO from sparse radiosonde observations in the tropical lower stratosphere. The multi-level covariance functions extend well-known single level covariance functions depending only on a length scale. Generalizations of the first- and third-order autoregressive covariances in three dimensions are given, providing multi-level covariances with zero and three derivatives at zero separation, respectively. Multi-level piecewise rational covariances with two continuous derivatives at zero separation are also provided. Multi-level powerlaw covariances are constructed with continuous derivatives of all orders. Additional multi-level covariance functions are constructed using the Schur product of single and multi-level covariance functions. A multi-level powerlaw covariance used to reproduce the QBO in GEOS-4 is described along with details of the assimilation experiments. The new covariance model is shown to represent the vertical wind shear associated with the QBO much more effectively than in the baseline GEOS-4 system.

A Multilevel Algorithm for the Solution of Second Order Elliptic Differential Equations on Sparse Grids

NASA Technical Reports Server (NTRS)

Pflaum, Christoph

1996-01-01

A multilevel algorithm is presented that solves general second order elliptic partial differential equations on adaptive sparse grids. The multilevel algorithm consists of several V-cycles. Suitable discretizations provide that the discrete equation system can be solved in an efficient way. Numerical experiments show a convergence rate of order Omicron(1) for the multilevel algorithm.

Multiple imputation by chained equations for systematically and sporadically missing multilevel data.

PubMed

Resche-Rigon, Matthieu; White, Ian R

2018-06-01

In multilevel settings such as individual participant data meta-analysis, a variable is 'systematically missing' if it is wholly missing in some clusters and 'sporadically missing' if it is partly missing in some clusters. Previously proposed methods to impute incomplete multilevel data handle either systematically or sporadically missing data, but frequently both patterns are observed. We describe a new multiple imputation by chained equations (MICE) algorithm for multilevel data with arbitrary patterns of systematically and sporadically missing variables. The algorithm is described for multilevel normal data but can easily be extended for other variable types. We first propose two methods for imputing a single incomplete variable: an extension of an existing method and a new two-stage method which conveniently allows for heteroscedastic data. We then discuss the difficulties of imputing missing values in several variables in multilevel data using MICE, and show that even the simplest joint multilevel model implies conditional models which involve cluster means and heteroscedasticity. However, a simulation study finds that the proposed methods can be successfully combined in a multilevel MICE procedure, even when cluster means are not included in the imputation models.

DC-DC Type High-Frequency Link DC for Improved Power Quality of Cascaded Multilevel Inverter

NASA Astrophysics Data System (ADS)

Sadikin, Muhammad; Senjyu, Tomonobu; Yona, Atsushi

2013-06-01

Multilevel inverters are emerging as a new breed of power converter options for power system applications. Recent advances in power switching devices enabled the suitability of multilevel inverters for high voltage and high power applications because they are connecting several devices in series without the need of component matching. Usually, a transformerless battery energy storage system, based on a cascaded multilevel inverter, is used as a measure for voltage and frequency deviations. System can be reduced in size, weight, and cost of energy storage system. High-frequency link circuit topology is advantageous in realizing compact and light-weight power converters for uninterruptible power supply systems, new energy systems using photovoltaic-cells, fuel-cells and so on. This paper presents a DC-DC type high-frequency link DC (HFLDC) cascaded multilevel inverter. Each converter cell is implemented a control strategy for two H-bridge inverters that are controlled with the same multicarrier pulse width modulation (PWM) technique. The proposed cascaded multilevel inverter generates lower voltage total harmonic distortion (THD) in comparison with conventional cascaded multilevel inverter. Digital simulations are carried out using PSCAD/EMTDC to validate the performance of the proposed cascaded multilevel inverter.

Dissecting random and systematic differences between noisy composite data sets.

PubMed

Diederichs, Kay

2017-04-01

Composite data sets measured on different objects are usually affected by random errors, but may also be influenced by systematic (genuine) differences in the objects themselves, or the experimental conditions. If the individual measurements forming each data set are quantitative and approximately normally distributed, a correlation coefficient is often used to compare data sets. However, the relations between data sets are not obvious from the matrix of pairwise correlations since the numerical value of the correlation coefficient is lowered by both random and systematic differences between the data sets. This work presents a multidimensional scaling analysis of the pairwise correlation coefficients which places data sets into a unit sphere within low-dimensional space, at a position given by their CC* values [as defined by Karplus & Diederichs (2012), Science, 336, 1030-1033] in the radial direction and by their systematic differences in one or more angular directions. This dimensionality reduction can not only be used for classification purposes, but also to derive data-set relations on a continuous scale. Projecting the arrangement of data sets onto the subspace spanned by systematic differences (the surface of a unit sphere) allows, irrespective of the random-error levels, the identification of clusters of closely related data sets. The method gains power with increasing numbers of data sets. It is illustrated with an example from low signal-to-noise ratio image processing, and an application in macromolecular crystallography is shown, but the approach is completely general and thus should be widely applicable.

Processing of meteorological data with ultrasonic thermoanemometers

NASA Astrophysics Data System (ADS)

Telminov, A. E.; Bogushevich, A. Ya.; Korolkov, V. A.; Botygin, I. A.

2017-11-01

The article describes a software system intended for supporting scientific researches of the atmosphere during the processing of data gathered by multi-level ultrasonic complexes for automated monitoring of meteorological and turbulent parameters in the ground layer of the atmosphere. The system allows to process files containing data sets of temperature instantaneous values, three orthogonal components of wind speed, humidity and pressure. The processing task execution is done in multiple stages. During the first stage, the system executes researcher's query for meteorological parameters. At the second stage, the system computes series of standard statistical meteorological field properties, such as averages, dispersion, standard deviation, asymmetry coefficients, excess, correlation etc. The third stage is necessary to prepare for computing the parameters of atmospheric turbulence. The computation results are displayed to user and stored at hard drive.

Clarifying beliefs underlying hunter intentions to support a ban on lead shot

USGS Publications Warehouse

Schroeder, Susan A.; Fulton, David C.; Doncarlos, Kathy

2016-01-01

Shot from hunting adds toxic lead to environments worldwide. Existing lead shot regulations have been instituted with little understanding of hunter beliefs and attitudes. This study applied the Theory of Reasoned Action, using a multilevel, multivariate approach, to clarify how positive and negative beliefs relate to attitudes about a ban on lead shot. Structure coefficients and commonality analysis were employed to further examine relationships between beliefs and attitudes. Results suggest that while both positive and negative outcomes influence attitudes, positive outcomes were more influential for supporters and negative beliefs for opposers. Management may need to focus on the results from hunters who indicated that they would be unlikely to support a ban, as these hunters include those who may actively oppose additional efforts to regulate lead.

A randomized controlled trial comparing EMDR and CBT for obsessive-compulsive disorder.

PubMed

Marsden, Zoe; Lovell, Karina; Blore, David; Ali, Shehzad; Delgadillo, Jaime

2018-01-01

This study aimed to evaluate eye movement desensitization and reprocessing (EMDR) as a treatment for obsessive-compulsive disorder (OCD), by comparison to cognitive behavioural therapy (CBT) based on exposure and response prevention. This was a pragmatic, feasibility randomized controlled trial in which 55 participants with OCD were randomized to EMDR (n = 29) or CBT (n = 26). The Yale-Brown obsessive-compulsive scale was completed at baseline, after treatment and at 6 months follow-up. Treatment completion and response rates were compared using chi-square tests. Effect size was examined using Cohen's d and multilevel modelling. Overall, 61.8% completed treatment and 30.2% attained reliable and clinically significant improvement in OCD symptoms, with no significant differences between groups (p > .05). There were no significant differences between groups in Yale-Brown obsessive-compulsive scale severity post-treatment (d = -0.24, p = .38) or at 6 months follow-up (d = -0.03, p = .90). EMDR and CBT had comparable completion rates and clinical outcomes. Copyright © 2017 John Wiley & Sons, Ltd.

Evaluating the effectiveness of selected community-level interventions on key maternal, child health, and prevention of mother-to-child transmission of HIV outcomes in three countries (the ACCLAIM Project): a study protocol for a randomized controlled trial.

PubMed

Woelk, Godfrey B; Kieffer, Mary Pat; Walker, Damilola; Mpofu, Daphne; Machekano, Rhoderick

2016-02-16

Efforts to scale up and improve programs for prevention of mother-to-child transmission of HIV (PMTCT) have focused primarily at the health facility level, and limited attention has been paid to defining an effective set of community interventions to improve demand and uptake of services and retention. Many barriers to PMTCT are also barriers to pregnancy, childbirth, and postnatal care faced by mothers regardless of HIV status. Demand for maternal and child health (MCH) and PMTCT services can be limited by critical social, cultural, and structural barriers. Yet, rigorous evaluation has shown limited evidence of effectiveness of multilevel community-wide interventions aimed at improving MCH and HIV outcomes for pregnant women living with HIV. We propose to assess the effect of a package of multilevel community interventions: a social learning and action component, community dialogues, and peer-led discussion groups, on the demand for, uptake of, and retention of HIV positive pregnant/postpartum women in MCH/PMTCT services. This study will undertake a three-arm randomized trial in Swaziland, Uganda, and Zimbabwe. Districts/regions (n = 9) with 45 PMTCT-implementing health facilities and their catchment areas (populations 7,300-27,500) will be randomly allocated to three intervention arms: 1) community leader engagement, 2) community leader engagement with community days, or 3) community leader engagement with community days and male and female community peer groups. The primary study outcome is HIV exposed infants (HEIs) returning to the health facility within 2 months for early infant diagnosis (EID) of HIV. Secondary study outcomes include gestational age of women attending for first antenatal care, male partners tested for HIV, and HEIs receiving nevirapine prophylaxis at birth. Changes in community knowledge, attitudes, practices, and beliefs on MCH/PMTCT will be assessed through household surveys. Implementation of the protocol necessitated changes in the original study design. We purposively selected facilities in the districts/regions though originally the study clusters were to be randomly selected. Lifelong antiretroviral therapy for all HIV positive pregnant and lactating women, Option B+, was implemented in the three countries during the study period, with the potential for a differential impact by study arm. Implementation however, was rapidly done across the districts/regions, so that there is unlikely be this potential confounding. We developed a system of monitoring and documentation of potential confounding activities or actions, and these data will be incorporated into analyses at the conclusion of the project. Strengthens of the study are that it tests multilevel interventions, utilizes program as well as study specific and individual data, and it is conducted under "real conditions" leading to more robust findings. Limitations of the protocol include the lack of a true control arm and inadequate control for the potential effect of Option B+, such as the intensification of messages as the importance of early ANC and male partner testing. ClinicalTrials.gov (study ID: NCT01971710) Protocol version 5, 30 July 2013, registered 13 August 2013.

How children explore the phonological network in child-directed speech: A survival analysis of children’s first word productions

PubMed Central

Carlson, Matthew T.; Sonderegger, Morgan; Bane, Max

2014-01-01

We explored how phonological network structure influences the age of words’ first appearance in children’s (14–50 months) speech, using a large, longitudinal corpus of spontaneous child-caregiver interactions. We represent the caregiver lexicon as a network in which each word is connected to all of its phonological neighbors, and consider both words’ local neighborhood density (degree), and also their embeddedness among interconnected neighborhoods (clustering coefficient and coreness). The larger-scale structure reflected in the latter two measures is implicated in current theories of lexical development and processing, but its role in lexical development has not yet been explored. Multilevel discrete-time survival analysis revealed that children are more likely to produce new words whose network properties support lexical access for production: high degree, but low clustering coefficient and coreness. These effects appear to be strongest at earlier ages and largely absent from 30 months on. These results suggest that both a word’s local connectivity in the lexicon and its position in the lexicon as a whole influences when it is learned, and they underscore how general lexical processing mechanisms contribute to productive vocabulary development. PMID:25089073

The method of micro-motion cycle feature extraction based on confidence coefficient evaluation criteria

NASA Astrophysics Data System (ADS)

Tang, Chuanzi; Ren, Hongmei; Bo, Li; Jing, Huang

2017-11-01

In radar target recognition, the micro motion characteristics of target is one of the characteristics that researchers pay attention to at home and abroad, in which the characteristics of target precession cycle is one of the important characteristics of target movement characteristics. Periodic feature extraction methods have been studied for years, the complex shape of the target and the scattering center stack lead to random fluctuations of the RCS. These random fluctuations also exist certain periodicity, which has a great influence on the target recognition result. In order to solve the problem, this paper proposes a extraction method of micro-motion cycle feature based on confidence coefficient evaluation criteria.

Multilevel predictors of colorectal cancer testing modality among publicly and privately insured people turning 50.

PubMed

Wheeler, Stephanie B; Kuo, Tzy-Mey; Meyer, Anne Marie; Martens, Christa E; Hassmiller Lich, Kristen M; Tangka, Florence K L; Richardson, Lisa C; Hall, Ingrid J; Smith, Judith Lee; Mayorga, Maria E; Brown, Paul; Crutchfield, Trisha M; Pignone, Michael P

2017-06-01

Understanding multilevel predictors of colorectal cancer (CRC) screening test modality can help inform screening program design and implementation. We used North Carolina Medicare, Medicaid, and private, commercially available, health plan insurance claims data from 2003 to 2008 to ascertain CRC test modality among people who received CRC screening around their 50th birthday, when guidelines recommend that screening should commence for normal risk individuals. We ascertained receipt of colonoscopy, fecal occult blood test (FOBT) and fecal immunochemical test (FIT) from billing codes. Person-level and county-level contextual variables were included in multilevel random intercepts models to understand predictors of CRC test modality, stratified by insurance type. Of 12,570 publicly-insured persons turning 50 during the study period who received CRC testing, 57% received colonoscopy, whereas 43% received FOBT/FIT, with significant regional variation. In multivariable models, females with public insurance had lower odds of colonoscopy than males (odds ratio [OR] = 0.68; p < 0.05). Of 56,151 privately-insured persons turning 50 years old who received CRC testing, 42% received colonoscopy, whereas 58% received FOBT/FIT, with significant regional variation. In multivariable models, females with private insurance had lower odds of colonoscopy than males (OR = 0.43; p < 0.05). People living 10-15 miles away from endoscopy facilities also had lower odds of colonoscopy than those living within 5 miles (OR = 0.91; p < 0.05). Both colonoscopy and FOBT/FIT are widely used in North Carolina among insured persons newly age-eligible for screening. The high level of FOBT/FIT use among privately insured persons and women suggests that renewed emphasis on FOBT/FIT as a viable screening alternative to colonoscopy may be important.

Trauma cognitions are related to symptoms up to 10 years after cognitive behavioral treatment for posttraumatic stress disorder.

PubMed

Scher, Christine D; Suvak, Michael K; Resick, Patricia A

2017-11-01

This study examined (a) relationships between trauma-related cognitions and posttraumatic stress disorder (PTSD) symptoms from pretreatment through a long-term period after cognitive-behavioral therapy (CBT) for PTSD and (b) whether these relationships were impacted by treatment type. Participants were 171 women randomized into treatment for PTSD after rape. Measures of self-reported trauma-related cognitions and interviewer-assessed PTSD symptoms (i.e., Posttraumatic Maladaptive Beliefs Scale, Trauma-Related Guilt Inventory, and Clinician-Administered PTSD Scale) were obtained at pretreatment, posttreatment, and 3-month, 9-month, and 5-10 year follow-ups. Multilevel regression analyses were used to examine relationships between trauma-related cognitions and PTSD symptoms throughout the study period and whether these relationships differed as a function of treatment type (i.e., Cognitive Processing Therapy or Prolonged Exposure). Initial multilevel regression analyses that examined mean within-participant associations suggested that beliefs regarding Reliability and Trustworthiness of Others, Self-Worth and Judgment, Threat of Harm, and Guilt were related to PTSD symptoms throughout follow-up. Growth curve modeling suggested that patterns of belief change throughout follow-up were similar to those previously observed in PTSD symptoms over the same time period. Finally, multilevel mediation analyses that incorporated time further suggested that change in beliefs was related to change in symptoms throughout follow-up. With 1 minor exception, relationships between beliefs and symptoms were not moderated by treatment type. These data suggest that trauma-related cognitions are a potential mechanism for long-term maintenance of treatment gains after CBT for PTSD. Moreover, these cognitions may be a common, rather than specific, treatment maintenance mechanism. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Multilevel Spatial Structure Impacts on the Pollination Services of Comarum palustre (Rosaceae)

PubMed Central

Somme, Laurent; Mayer, Carolin; Jacquemart, Anne-Laure

2014-01-01

Habitat destruction and fragmentation accelerate pollinator decline, consequently disrupting ecosystem processes such as pollination. To date, the impacts of multilevel spatial structure on pollination services have rarely been addressed. We focused on the effects of population spatial structure on the pollination services of Comarum palustre at three levels (i.e. within-population, between-populations and landscape). For three years, we investigated 14 Belgian populations, which differed in their within-population flower density, population surface, closure (i.e. proportion of the population edge that consisted of woody elements) and isolation (i.e. percentage of woody area cover within a 500 m radius from the population centre). We tested whether these spatial characteristics impact on pollinator abundance and visitation rate and thus, reproductive success of C. palustre. Insects were observed in 15 randomly-chosen plots in each population. We tested for pollen limitation with supplemental hand-cross pollination. Bumble bees and solitary bees were the major pollinators through all populations. Within populations, plots with high flower densities attracted high numbers of bumble bees and other insects. High bumble bee and solitary bee abundance was observed in populations presenting high proportions of woody edges and in populations within landscapes presenting high proportions of woody areas. Seed set resulting from open pollination varied with bumble bee and solitary bee visitation rate, leading to increased pollen limitation when pollinators were scarce. Since the reproductive success depended on the visitation rate of the main pollinators, which depended on multilevel spatial structure, wetland management plans should pay special attention to favour a mosaic of biotopes, including nesting sites and food resources for insects. This study particularly supports the relevance of a mix wetlands and woody habitats to bees. PMID:24915450

Situational drinking in private and public locations: A multilevel analysis of blood alcohol level in Finnish drinking occasions.

PubMed

Mustonen, Heli; Mäkelä, Pia; Lintonen, Tomi

2016-11-01

The first aim was to estimate the extent to which the variation in alcohol use across specific drinking occasions arises from variation at the occasion level and from variation at the drinker level. The second aim was to identify characteristics of drinking situations that moderate or increase situational alcohol use beyond the influence of drinker-level characteristics. The general population aged 15-69 years in Finland was sampled randomly in 2008. The multilevel analysis was based on data from 1511 drinkers and 2933 drinking occasions that occurred in the 7 days before the interview. Alcohol use was operationalised as estimated blood alcohol level (BAL). Characteristics of drinking occasions included location, circumstance, company and timing. Drinker-level data included demographic and drinking pattern variables. Fifty-three percent of the variance in BAL was between occasions and 47% between respondents, for both women and men. With drinking patterns and demographic characteristics controlled for, the dominant characteristics of drinking occasions predisposing to greater intoxication were late-night drinking, across locations and for both genders. For private locations, predisposing characteristics included drinking on weekends for both genders and drinking with friends for men. Situational and drinker levels are equally important in determining the BAL in drinking occasions; therefore, prevention efforts should be targeted at both risky individuals and risky drinking occasions. Occasions occurring late at night, often at home and with friends, are a central challenge for targeting preventive efforts related to situational drinking.[Mustonen H, Mäkelä P, Lintonen T. Situational drinking in private and public locations: A multilevel analysis of blood alcohol level in Finnish drinking occasions. Drug Alcohol Rev 2016;35:772-784]. © 2016 Australasian Professional Society on Alcohol and other Drugs.

Individual and contextual factors associated with community health workers' performance in Nyanza Province, Kenya: a multilevel analysis.

PubMed

Kawakatsu, Yoshito; Sugishita, Tomohiko; Tsutsui, Junya; Oruenjo, Kennedy; Wakhule, Stephen; Kibosia, Kennedy; Were, Eric; Honda, Sumihisa

2015-10-01

Several African and South Asian countries are currently investing in new cadres of community health workers (CHWs) as a major part of strategies aimed at reaching the Millennium Development Goals. However, one review concluded that community health workers did not consistently provide services likely to have substantial effects on health and that quality was usually poor. The objective of this research was to assess the CHWs' performance in Western Kenya and describe determinants of that performance using a multilevel analysis of the two levels, individual and supervisor/community. This study conducted three surveys between August and September 2011 in Nyanza Province, Kenya. The participants of the three surveys were all 1,788 active CHWs, all their supervisors, and 2,560 randomly selected mothers who had children aged 12 to 23 months. CHW performance was generated by three indicators: reporting rate, health knowledge and household coverage. Multilevel analysis was performed to describe the determinants of that performance. The significant factors associated with the CHWs' performance were their marital status, educational level, the size of their household, their work experience, personal sanitation practice, number of supervisions received and the interaction between their supervisors' better health knowledge and the number of supervisions. A high quality of routine supervisions is one of the key interventions in sustaining a CHW's performance. In addition, decreasing the dropout rate of CHWs is important both for sustaining their performance and for avoiding the additional cost of replacing them. As for the selection criteria of new CHWs, good educational status, availability of supporters for household chores and good sanitation practices are all important in selecting CHWs who can maintain their high performance level.

Multilevel spatial structure impacts on the pollination services of Comarum palustre (Rosaceae).

PubMed

Somme, Laurent; Mayer, Carolin; Jacquemart, Anne-Laure

2014-01-01

Habitat destruction and fragmentation accelerate pollinator decline, consequently disrupting ecosystem processes such as pollination. To date, the impacts of multilevel spatial structure on pollination services have rarely been addressed. We focused on the effects of population spatial structure on the pollination services of Comarum palustre at three levels (i.e. within-population, between-populations and landscape). For three years, we investigated 14 Belgian populations, which differed in their within-population flower density, population surface, closure (i.e. proportion of the population edge that consisted of woody elements) and isolation (i.e. percentage of woody area cover within a 500 m radius from the population centre). We tested whether these spatial characteristics impact on pollinator abundance and visitation rate and thus, reproductive success of C. palustre. Insects were observed in 15 randomly-chosen plots in each population. We tested for pollen limitation with supplemental hand-cross pollination. Bumble bees and solitary bees were the major pollinators through all populations. Within populations, plots with high flower densities attracted high numbers of bumble bees and other insects. High bumble bee and solitary bee abundance was observed in populations presenting high proportions of woody edges and in populations within landscapes presenting high proportions of woody areas. Seed set resulting from open pollination varied with bumble bee and solitary bee visitation rate, leading to increased pollen limitation when pollinators were scarce. Since the reproductive success depended on the visitation rate of the main pollinators, which depended on multilevel spatial structure, wetland management plans should pay special attention to favour a mosaic of biotopes, including nesting sites and food resources for insects. This study particularly supports the relevance of a mix wetlands and woody habitats to bees.

Effect of Items Direction (Positive or Negative) on the Reliability in Likert Scale. Paper-11

ERIC Educational Resources Information Center

Gul, Showkeen Bilal Ahmad; Qasem, Mamun Ali Naji; Bhat, Mehraj Ahmad

2015-01-01

In this paper an attempt was made to analyze the effect of items direction (positive or negative) on the Alpha Cronbach reliability coefficient and the Split Half reliability coefficient in Likert scale. The descriptive survey research method was used for the study and sample of 510 undergraduate students were selected by used random sampling…

Counseling African Americans to Control Hypertension (CAATCH) Trial: A Multi-level Intervention to Improve Blood Pressure Control in Hypertensive African Americans

PubMed Central

Ogedegbe, Gbenga; Tobin, Jonathan N.; Fernandez, Senaida; Gerin, William; Diaz-Gloster, Marleny; Cassells, Andrea; Khalida, Chamanara; Pickering, Thomas; Schoenthaler, Antoinette; Ravenell, Joseph

2009-01-01

Background Despite strong evidence of effective interventions targeted at blood pressure (BP) control, there is little evidence on the translation of these approaches to routine clinical practice in care of hypertensive African Americans. The goal of this study is to evaluate the effectiveness of a multi-level, multi-component, evidence-based intervention compared to usual care in improving BP control among hypertensive African Americans who receive care in Community Health Centers (CHCs). The primary outcomes are BP control rate at 12 months; and maintenance of intervention one year after the trial. The secondary outcomes are within-patient change in BP from baseline to 12 months and cost effectiveness of the intervention. Methods and Results Counseling African Americans to Control Hypertension (CAATCH) is a group randomized clinical trial with two conditions: Intervention Condition (IC) and Usual Care (UC). Thirty CHCs were randomly assigned equally to the IC group (N=15) or the UC group (N=15). The intervention is comprised of three components targeted at patients (interactive computerized hypertension education; home BP monitoring; and monthly behavioral counseling on lifestyle modification) and two components targeted at physicians (monthly case rounds based on JNC-7 guidelines; chart audit and provision of feedback on clinical performance and patients’ home BP readings). All outcomes are assessed at quarterly study visits for one year. Chart review is conducted at 24 months to evaluate maintenance of intervention effects and sustainability of the intervention. Conclusions Poor BP control is one of the major reasons for the mortality gap between African Americans and whites. Findings from this study, if successful, will provide salient information needed for translation and dissemination of evidence-based interventions targeted at BP control into clinical practice for this high-risk population. PMID:20031845

Mixed-Method Quasi-Experimental Study of Outcomes of a Large-Scale Multilevel Economic and Food Security Intervention on HIV Vulnerability in Rural Malawi.

PubMed

Weinhardt, Lance S; Galvao, Loren W; Yan, Alice F; Stevens, Patricia; Mwenyekonde, Thokozani Ng'ombe; Ngui, Emmanuel; Emer, Lindsay; Grande, Katarina M; Mkandawire-Valhmu, Lucy; Watkins, Susan C

2017-03-01

The objective of the Savings, Agriculture, Governance, and Empowerment for Health (SAGE4Health) study was to evaluate the impact of a large-scale multi-level economic and food security intervention on health outcomes and HIV vulnerability in rural Malawi. The study employed a quasi-experimental non-equivalent control group design to compare intervention participants (n = 598) with people participating in unrelated programs in distinct but similar geographical areas (control, n = 301). We conducted participant interviews at baseline, 18-, and 36-months on HIV vulnerability and related health outcomes, food security, and economic vulnerability. Randomly selected households (n = 1002) were interviewed in the intervention and control areas at baseline and 36 months. Compared to the control group, the intervention led to increased HIV testing (OR 1.90; 95 % CI 1.29-2.78) and HIV case finding (OR = 2.13; 95 % CI 1.07-4.22); decreased food insecurity (OR = 0.74; 95 % CI 0.63-0.87), increased nutritional diversity, and improved economic resilience to shocks. Most effects were sustained over a 3-year period. Further, no significant differences in change were found over the 3-year study period on surveys of randomly selected households in the intervention and control areas. Although there were general trends toward improvement in the study area, only intervention participants' outcomes were significantly better. Results indicate the intervention can improve economic and food security and HIV vulnerability through increased testing and case finding. Leveraging the resources of economic development NGOs to deliver locally-developed programs with scientific funding to conduct controlled evaluations has the potential to accelerate the scientific evidence base for the effects of economic development programs on health.

Strong Hearts, Healthy Communities: A Community-Based Randomized Trial for Rural Women.

PubMed

Seguin, Rebecca A; Paul, Lynn; Folta, Sara C; Nelson, Miriam E; Strogatz, David; Graham, Meredith L; Diffenderfer, Anna; Eldridge, Galen; Parry, Stephen A

2018-05-01

The aim of this study was to evaluate a multilevel cardiovascular disease (CVD) prevention program for rural women. This 6-month, community-based, randomized trial enrolled 194 sedentary rural women aged 40 or older with BMI ≥ 25 kg/m 2 . Intervention participants attended 6 months of twice-weekly exercise, nutrition, and heart health classes (48 total) that included individual-, social-, and environment-level components. An education-only control program included didactic healthy lifestyle classes once a month (six total). The primary outcome measures were change in BMI and weight. Within-group and between-group multivariate analyses revealed that only intervention participants decreased BMI (-0.85 units; 95% CI: -1.32 to -0.39; P = 0.001) and weight (-2.24 kg; 95% CI: -3.49 to -0.99; P = 0.002). Compared with controls, intervention participants decreased BMI (difference: -0.71 units; 95% CI: -1.35 to -0.08; P = 0.03) and weight (1.85 kg; 95% CI: -3.55 to -0.16; P = 0.03) and improved C-reactive protein (difference: -1.15 mg/L; 95% CI: -2.16 to -0.15; P = 0.03) and Simple 7, a composite CVD risk score (difference: 0.67; 95% CI: 0.14 to 1.21; P = 0.01). Cholesterol decreased among controls but increased in the intervention group (-7.85 vs. 3.92 mg/dL; difference: 11.77; 95% CI: 0.57 to 22.96; P = 0.04). The multilevel intervention demonstrated modest but superior and meaningful improvements in BMI and other CVD risk factors compared with the control program. © 2018 The Obesity Society.

Mechanisms of change: Testing how preventative interventions impact psychological and physiological stress functioning in mothers in neglectful families.

PubMed

Toth, Sheree L; Sturge-Apple, Melissa L; Rogosch, Fred A; Cicchetti, Dante

2015-11-01

The present study applies a multilevel approach to an examination of the effect of two randomized preventive interventions with mothers in neglectful families who are also contending with elevated levels of impoverishment and ecological risk. Specifically, we examined how participation in either child-parent psychotherapy (CPP) or psychoeducational parenting intervention (PPI) was associated with reductions in maternal psychological parenting stress and in turn physiological stress system functioning when compared to mothers involved in standard community services as well as a demographic comparison group of nonmaltreating mothers. The resulting group sizes in the current investigation were 44 for CPP, 34 for PPI, 27 for community services, and 52 for nonmaltreating mothers. Mothers and their 13-month-old infants were randomly assigned to intervention group at baseline. Mothers completed assessments on stress within the parenting role at baseline and postintervention. Basal cortisol was sampled at postintervention and 1-year follow-up. Latent difference score analyses examined change in these constructs over time. Results suggested that mothers within the CPP intervention experienced significant declines in child-related parenting stress, while mothers in the PPI intervention reported declines in parent-related parenting stress. In turn, significant decreases in stress within the CPP mothers were further associated with adaptive basal cortisol functioning at 1-year postintervention. The results highlight the value of delineating how participation in preventive interventions aimed at ameliorating child maltreatment in neglectful families within the context of poverty may operate through improvements in psychological and physiological stress functioning. Findings are discussed with respect to the importance of multilevel assessments of intervention process and outcome.

School-based intervention for childhood disruptive behavior in disadvantaged settings: a randomized controlled trial with and without active teacher support.

PubMed

Liber, Juliette M; De Boo, Gerly M; Huizenga, Hilde; Prins, Pier J M

2013-12-01

In this randomized controlled trial, we investigated the effectiveness of a school-based targeted intervention program for disruptive behavior. A child-focused cognitive behavioral therapy (CBT) program was introduced at schools in disadvantaged settings and with active teacher support (ATS) versus educational teacher support (ETS) (CBT + ATS vs. CBT + ETS). Screening (n = 1,929) and assessment (n = 224) led to the inclusion of 173 children ages 8-12 years from 17 elementary schools. Most of the children were boys (n = 136, 79%) of low or low-to-middle class socioeconomic status (87%); the sample was ethnically diverse (63% of non-Western origin). Children received CBT + ATS (n = 29) or CBT + ETS (n = 41) or were entered into a waitlist control condition (n = 103) to be treated afterward (CBT + ATS, n = 39, and CBT + ETS, n = 64). Effect sizes (ES), clinical significance (reliable change), and the results of multilevel modeling are reported. Ninety-seven percent of children completed treatment. Teachers and parents reported positive posttreatment effects (mean ES = .31) for CBT compared with the waitlist control condition on disruptive behavior. Multilevel modeling showed similar results. Clinical significance was modest. Changes had remained stable or had increased at 3-months follow-up (mean ES = .39). No consistent effect of teacher condition was found at posttreatment; however, at follow-up, children who received ETS fared significantly better. This study shows that a school-based CBT program is beneficial for difficult-to-reach children with disruptive behavior: The completion rate was remarkably high, ESs (mean ES = .31) matched those of previous studies with targeted intervention, and effects were maintained or had increased at follow-up.

Mechanisms of Change: Testing how Preventative Interventions Impact Psychological and Physiological Stress Functioning in Mothers in Neglectful Families

PubMed Central

Toth, Sheree L.; Sturge-Apple, Melissa L.; Rogosch, Fred A.; Cicchetti, Dante

2015-01-01

The present study applies a multilevel approach to an examination of the effect of two randomized preventative interventions with mothers in neglectful families who are also contending with elevated levels of impoverishment and ecological risk. Specifically, we examined how participation in either Child-Parent Psychotherapy (CPP) or Psychoeducational Parenting (PPI) interventions was associated with reductions in maternal psychological parenting stress and in turn physiological stress system functioning when compared to mothers involved in standard community services (CS) as well as a demographic comparison group of nonmaltreating mothers (NC). The resulting group sizes in the current investigation were: CPP (n = 44), PPI (n = 34), CS (n = 27), and NC (n = 52). Mothers and infants who were 13-months of age were randomly assigned to intervention group at baseline. Mothers completed assessments on stress within the parenting role at baseline and post-intervention. Basal cortisol was sampled at post-intervention and 1-year follow-up. Latent difference score analyses examined change in these constructs over time. Results suggested that mothers within the CPP intervention experienced significant declines in child-related parenting stress while mothers in the PPI intervention reported declines in parent-related parenting stress. In turn, significant decreases in stress within the CPP mothers were further associated with adaptive basal cortisol functioning at 1-year post-intervention. Results highlight the value of delineating how participation in preventtive interventions aimed at ameliorating child maltreatment in neglectful families within the context of poverty may operate through improvements in psychological and physiological stress functioning. Findings are discussed with respect to the importance of multi-level assessments of intervention process and outcome. PMID:26535951

Sample Size Estimation in Cluster Randomized Educational Trials: An Empirical Bayes Approach

ERIC Educational Resources Information Center

Rotondi, Michael A.; Donner, Allan

2009-01-01

The educational field has now accumulated an extensive literature reporting on values of the intraclass correlation coefficient, a parameter essential to determining the required size of a planned cluster randomized trial. We propose here a simple simulation-based approach including all relevant information that can facilitate this task. An…

Hájek-Rényi inequality for m-asymptotically almost negatively associated random vectors in Hilbert space and applications.

PubMed

Ko, Mi-Hwa

2018-01-01

In this paper, we obtain the Hájek-Rényi inequality and, as an application, we study the strong law of large numbers for H -valued m -asymptotically almost negatively associated random vectors with mixing coefficients [Formula: see text] such that [Formula: see text].

Migration of lymphocytes on fibronectin-coated surfaces: temporal evolution of migratory parameters

NASA Technical Reports Server (NTRS)

Bergman, A. J.; Zygourakis, K.; McIntire, L. V. (Principal Investigator)

1999-01-01

Lymphocytes typically interact with implanted biomaterials through adsorbed exogenous proteins. To provide a more complete characterization of these interactions, analysis of lymphocyte migration on adsorbed extracellular matrix proteins must accompany the commonly performed adhesion studies. We report here a comparison of the migratory and adhesion behavior of Jurkat cells (a T lymphoblastoid cell line) on tissue culture treated and untreated polystyrene surfaces coated with various concentrations of fibronectin. The average speed of cell locomotion showed a biphasic response to substrate adhesiveness for cells migrating on untreated polystyrene and a monotonic decrease for cells migrating on tissue culture-treated polystyrene. A modified approach to the persistent random walk model was implemented to determine the time dependence of cell migration parameters. The random motility coefficient showed significant increases with time when cells migrated on tissue culture-treated polystyrene surfaces, while it remained relatively constant for experiments with untreated polystyrene plates. Finally, a cell migration computer model was developed to verify our modified persistent random walk analysis. Simulation results suggest that our experimental data were consistent with temporally increasing random motility coefficients.

A two-level stochastic collocation method for semilinear elliptic equations with random coefficients

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

Chen, Luoping; Zheng, Bin; Lin, Guang

In this work, we propose a novel two-level discretization for solving semilinear elliptic equations with random coefficients. Motivated by the two-grid method for deterministic partial differential equations (PDEs) introduced by Xu, our two-level stochastic collocation method utilizes a two-grid finite element discretization in the physical space and a two-level collocation method in the random domain. In particular, we solve semilinear equations on a coarse meshmore » $$\\mathcal{T}_H$$ with a low level stochastic collocation (corresponding to the polynomial space $$\\mathcal{P}_{P}$$) and solve linearized equations on a fine mesh $$\\mathcal{T}_h$$ using high level stochastic collocation (corresponding to the polynomial space $$\\mathcal{P}_p$$). We prove that the approximated solution obtained from this method achieves the same order of accuracy as that from solving the original semilinear problem directly by stochastic collocation method with $$\\mathcal{T}_h$$ and $$\\mathcal{P}_p$$. The two-level method is computationally more efficient, especially for nonlinear problems with high random dimensions. Numerical experiments are also provided to verify the theoretical results.« less

Mixed-effects varying-coefficient model with skewed distribution coupled with cause-specific varying-coefficient hazard model with random-effects for longitudinal-competing risks data analysis.

PubMed

Lu, Tao; Wang, Min; Liu, Guangying; Dong, Guang-Hui; Qian, Feng

2016-01-01

It is well known that there is strong relationship between HIV viral load and CD4 cell counts in AIDS studies. However, the relationship between them changes during the course of treatment and may vary among individuals. During treatments, some individuals may experience terminal events such as death. Because the terminal event may be related to the individual's viral load measurements, the terminal mechanism is non-ignorable. Furthermore, there exists competing risks from multiple types of events, such as AIDS-related death and other death. Most joint models for the analysis of longitudinal-survival data developed in literatures have focused on constant coefficients and assume symmetric distribution for the endpoints, which does not meet the needs for investigating the nature of varying relationship between HIV viral load and CD4 cell counts in practice. We develop a mixed-effects varying-coefficient model with skewed distribution coupled with cause-specific varying-coefficient hazard model with random-effects to deal with varying relationship between the two endpoints for longitudinal-competing risks survival data. A fully Bayesian inference procedure is established to estimate parameters in the joint model. The proposed method is applied to a multicenter AIDS cohort study. Various scenarios-based potential models that account for partial data features are compared. Some interesting findings are presented.

Failure tolerance of spike phase synchronization in coupled neural networks

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2011-09-01

Neuronal synchronization plays an important role in the various functionality of nervous system such as binding, cognition, information processing, and computation. In this paper, we investigated how random and intentional failures in the nodes of a network influence its phase synchronization properties. We considered both artificially constructed networks using models such as preferential attachment, Watts-Strogatz, and Erdős-Rényi as well as a number of real neuronal networks. The failure strategy was either random or intentional based on properties of the nodes such as degree, clustering coefficient, betweenness centrality, and vulnerability. Hindmarsh-Rose model was considered as the mathematical model for the individual neurons, and the phase synchronization of the spike trains was monitored as a function of the percentage/number of removed nodes. The numerical simulations were supplemented by considering coupled non-identical Kuramoto oscillators. Failures based on the clustering coefficient, i.e., removing the nodes with high values of the clustering coefficient, had the least effect on the spike synchrony in all of the networks. This was followed by errors where the nodes were removed randomly. However, the behavior of the other three attack strategies was not uniform across the networks, and different strategies were the most influential in different network structure.

Multilevel Interventions: Study Design and Analysis Issues

PubMed Central

Gross, Cary P.; Zaslavsky, Alan M.; Taplin, Stephen H.

2012-01-01

Multilevel interventions, implemented at the individual, physician, clinic, health-care organization, and/or community level, increasingly are proposed and used in the belief that they will lead to more substantial and sustained changes in behaviors related to cancer prevention, detection, and treatment than would single-level interventions. It is important to understand how intervention components are related to patient outcomes and identify barriers to implementation. Designs that permit such assessments are uncommon, however. Thus, an important way of expanding our knowledge about multilevel interventions would be to assess the impact of interventions at different levels on patients as well as the independent and synergistic effects of influences from different levels. It also would be useful to assess the impact of interventions on outcomes at different levels. Multilevel interventions are much more expensive and complicated to implement and evaluate than are single-level interventions. Given how little evidence there is about the value of multilevel interventions, however, it is incumbent upon those arguing for this approach to do multilevel research that explicates the contributions that interventions at different levels make to the desired outcomes. Only then will we know whether multilevel interventions are better than more focused interventions and gain greater insights into the kinds of interventions that can be implemented effectively and efficiently to improve health and health care for individuals with cancer. This chapter reviews designs for assessing multilevel interventions and analytic ways of controlling for potentially confounding variables that can account for the complex structure of multilevel data. PMID:22623596

Box-Cox Mixed Logit Model for Travel Behavior Analysis

NASA Astrophysics Data System (ADS)

Orro, Alfonso; Novales, Margarita; Benitez, Francisco G.

2010-09-01

To represent the behavior of travelers when they are deciding how they are going to get to their destination, discrete choice models, based on the random utility theory, have become one of the most widely used tools. The field in which these models were developed was halfway between econometrics and transport engineering, although the latter now constitutes one of their principal areas of application. In the transport field, they have mainly been applied to mode choice, but also to the selection of destination, route, and other important decisions such as the vehicle ownership. In usual practice, the most frequently employed discrete choice models implement a fixed coefficient utility function that is linear in the parameters. The principal aim of this paper is to present the viability of specifying utility functions with random coefficients that are nonlinear in the parameters, in applications of discrete choice models to transport. Nonlinear specifications in the parameters were present in discrete choice theory at its outset, although they have seldom been used in practice until recently. The specification of random coefficients, however, began with the probit and the hedonic models in the 1970s, and, after a period of apparent little practical interest, has burgeoned into a field of intense activity in recent years with the new generation of mixed logit models. In this communication, we present a Box-Cox mixed logit model, original of the authors. It includes the estimation of the Box-Cox exponents in addition to the parameters of the random coefficients distribution. Probability of choose an alternative is an integral that will be calculated by simulation. The estimation of the model is carried out by maximizing the simulated log-likelihood of a sample of observed individual choices between alternatives. The differences between the predictions yielded by models that are inconsistent with real behavior have been studied with simulation experiments.

Freak waves in random oceanic sea states.

PubMed

Onorato, M; Osborne, A R; Serio, M; Bertone, S

2001-06-18

Freak waves are very large, rare events in a random ocean wave train. Here we study their generation in a random sea state characterized by the Joint North Sea Wave Project spectrum. We assume, to cubic order in nonlinearity, that the wave dynamics are governed by the nonlinear Schrödinger (NLS) equation. We show from extensive numerical simulations of the NLS equation how freak waves in a random sea state are more likely to occur for large values of the Phillips parameter alpha and the enhancement coefficient gamma. Comparison with linear simulations is also reported.

Photon diffusion coefficient in scattering and absorbing media.

PubMed

Pierrat, Romain; Greffet, Jean-Jacques; Carminati, Rémi

2006-05-01

We present a unified derivation of the photon diffusion coefficient for both steady-state and time-dependent transport in disordered absorbing media. The derivation is based on a modal analysis of the time-dependent radiative transfer equation. This approach confirms that the dynamic diffusion coefficient is given by the random-walk result D = cl(*)/3, where l(*) is the transport mean free path and c is the energy velocity, independent of the level of absorption. It also shows that the diffusion coefficient for steady-state transport, often used in biomedical optics, depends on absorption, in agreement with recent theoretical and experimental works. These two results resolve a recurrent controversy in light propagation and imaging in scattering media.

Higher-order clustering in networks

NASA Astrophysics Data System (ADS)

Yin, Hao; Benson, Austin R.; Leskovec, Jure

2018-05-01

A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order cliques beyond triangles are crucial to understanding complex networks, and the clustering behavior with respect to such higher-order network structures is not well understood. Here we introduce higher-order clustering coefficients that measure the closure probability of higher-order network cliques and provide a more comprehensive view of how the edges of complex networks cluster. Our higher-order clustering coefficients are a natural generalization of the traditional clustering coefficient. We derive several properties about higher-order clustering coefficients and analyze them under common random graph models. Finally, we use higher-order clustering coefficients to gain new insights into the structure of real-world networks from several domains.

Automatic Classification of Aerial Imagery for Urban Hydrological Applications

NASA Astrophysics Data System (ADS)

Paul, A.; Yang, C.; Breitkopf, U.; Liu, Y.; Wang, Z.; Rottensteiner, F.; Wallner, M.; Verworn, A.; Heipke, C.

2018-04-01

In this paper we investigate the potential of automatic supervised classification for urban hydrological applications. In particular, we contribute to runoff simulations using hydrodynamic urban drainage models. In order to assess whether the capacity of the sewers is sufficient to avoid surcharge within certain return periods, precipitation is transformed into runoff. The transformation of precipitation into runoff requires knowledge about the proportion of drainage-effective areas and their spatial distribution in the catchment area. Common simulation methods use the coefficient of imperviousness as an important parameter to estimate the overland flow, which subsequently contributes to the pipe flow. The coefficient of imperviousness is the percentage of area covered by impervious surfaces such as roofs or road surfaces. It is still common practice to assign the coefficient of imperviousness for each particular land parcel manually by visual interpretation of aerial images. Based on classification results of these imagery we contribute to an objective automatic determination of the coefficient of imperviousness. In this context we compare two classification techniques: Random Forests (RF) and Conditional Random Fields (CRF). Experimental results performed on an urban test area show good results and confirm that the automated derivation of the coefficient of imperviousness, apart from being more objective and, thus, reproducible, delivers more accurate results than the interactive estimation. We achieve an overall accuracy of about 85 % for both classifiers. The root mean square error of the differences of the coefficient of imperviousness compared to the reference is 4.4 % for the CRF-based classification, and 3.8 % for the RF-based classification.

Relationship Factors and Trajectories of Intimate Partner Violence among South African Women during Pregnancy and the Postpartum Period

PubMed Central

Groves, Allison K.; McNaughton-Reyes, H. Luz; Foshee, Vangie A.; Moodley, Dhayendre; Maman, Suzanne

2014-01-01

Intimate partner violence (IPV) is a significant public health problem in South Africa. However, there is limited research on whether and how IPV changes during pregnancy and the postpartum period and on the factors that might affect women's risk during this time. In this study, we describe the mean trajectories of physical and psychological IPV during pregnancy and the postpartum period and examine whether relationship power, partner social support, and relationship stress are associated with women's trajectories of IPV. Data come from a longitudinal study with 1,480 women recruited during pregnancy between May 2008 and June 2010 at a public clinic in Durban. Women completed behavioral assessments at their first antenatal visit, at fourteen weeks and at nine months postpartum. Women's experiences of IPV were measured at all three time points and relationship power, partner social support and relationship stress were each measured at the baseline assessment. We used multilevel random coefficients growth modeling to build our models. The mean trajectory for both types of IPV was flat which means that, on average, there was not significant change in levels of IPV over pregnancy and the postpartum period. However, there was significant individual variability in trajectories of IPV over the study period. Women who had higher relationship power had lower levels of physical and psychological IPV over time than women with lower relationship power. Additionally, women with higher relationship stress and lower partner support had higher levels of psychological IPV at pregnancy. Interventions that maximize women's relationship power and partner social support and minimize relationship stress during this transformative time are needed. PMID:25268363

Association of job demands with work engagement of Japanese employees: comparison of challenges with hindrances (J-HOPE).

PubMed

Inoue, Akiomi; Kawakami, Norito; Tsutsumi, Akizumi; Shimazu, Akihito; Miyaki, Koichi; Takahashi, Masaya; Kurioka, Sumiko; Eguchi, Hisashi; Tsuchiya, Masao; Enta, Kazuhiko; Kosugi, Yuki; Sakata, Tomoko; Totsuzaki, Takafumi

2014-01-01

Recent epidemiological research in Europe has reported that two groups of job demands, i.e., challenges and hindrances, are differently associated with work engagement. The purpose of the present study was to replicate the cross-sectional association of workload and time pressure (as a challenge) and role ambiguity (as a hindrance) with work engagement among Japanese employees. Between October 2010 and December 2011, a total of 9,134 employees (7,101 men and 1,673 women) from 12 companies in Japan were surveyed using a self-administered questionnaire comprising the Job Content Questionnaire, National Institute for Occupational Safety and Health Generic Job Stress Questionnaire, short 10-item version of the Effort-Reward Imbalance Questionnaire, short nine-item version of the Utrecht Work Engagement Scale, and demographic characteristics. Multilevel regression analyses with a random intercept model were conducted. After adjusting for demographic characteristics, workload and time pressure showed a positive association with work engagement with a small effect size (standardized coefficient [β] = 0.102, Cohen's d [d] = 0.240) while role ambiguity showed a negative association with a large effect size (β = -0.429, d = 1.011). After additionally adjusting for job resources (i.e., decision latitude, supervisor support, co-worker support, and extrinsic reward), the effect size of workload and time pressure was not attenuated (β = 0.093, d = 0.234) while that of role ambiguity was attenuated but still medium (β = -0.242, d = 0.609). Among Japanese employees, challenges such as having higher levels of workload and time pressure may enhance work engagement but hindrances, such as role ambiguity, may reduce it.

The Role of Occupational Status in the Association between Job Strain and Ambulatory Blood Pressure during Working and Nonworking Days

PubMed Central

Joseph, Nataria T.; Muldoon, Matthew F.; Manuck, Stephen B.; Matthews, Karen A.; MacDonald, Leslie A.; Grosch, James; Kamarck, Thomas W.

2016-01-01

Objective The objectives of this study were to determine whether job strain is more strongly associated with higher ambulatory blood pressure (ABP) among blue-collar workers compared to white-collar workers; to examine whether this pattern generalizes across working and nonworking days and across sex; and to examine whether this pattern is accounted for by psychosocial factors or health behaviors during daily life. Methods 480 healthy workers (mean age = 43; 53% female)in the Adult Health and Behavior Project – Phase 2 (AHAB-II)completed ABP monitoring during 3 working days and 1 nonworking day. Job strain was operationalized as high psychological demand (> sample median) combined with low decision latitude (< sample median) (Karasek model; Job Content Questionnaire). Results Covariate-adjusted multilevel random coefficients regressions demonstrated that associations between job strain and systolic and diastolic ABP were stronger among blue-collar workers compared to white-collar workers (b = 6.53, F(1, 464)= 3.89, p = .049 and b = 5.25, F(1, 464)= 6.09, p = .014, respectively). This pattern did not vary by sex but diastolic ABP findings were stronger when participants were at work. The stronger association between job strain and ABP among blue-collar workers was not accounted for by education, momentary physical activity or substance use, but was partially accounted for by covariation between higher hostility and blue-collar status. Conclusions Job strain is associated with ABP among blue-collar workers. These results extend previous findings to a mixed-sex sample and nonworking days and provide, for the first time, comprehensive exploration of several behavioral and psychosocial explanations for this finding. PMID:27359177

Neighborhood effects on birthweight: an exploration of psychosocial and behavioral pathways in Baltimore, 1995--1996.

PubMed

Schempf, Ashley; Strobino, Donna; O'Campo, Patricia

2009-01-01

Neighborhood characteristics have been proposed to influence birth outcomes through psychosocial and behavioral pathways, yet empirical evidence is lacking. Using data from an urban, low-income sample, this study examined the impact of the neighborhood environment on birthweight and evaluated mediation by psychosocial and behavioral factors. The sample included 726 women who delivered a live birth at Johns Hopkins Hospital in Baltimore, Maryland, USA between 1995 and 1996. Census-tract data were used to create a principal component index of neighborhood risk based on racial and economic stratification (% Black, % poverty), social disorder (violent crime rate), and physical deterioration (% boarded-up housing) (alpha=0.82). Information on sociodemographic, psychosocial, and behavioral factors was gathered from a postpartum interview and medical records. Random intercept multilevel models were used to estimate neighborhood effects and assess potential mediation. Controlling for sociodemographic characteristics, a standard deviation increase in neighborhood risk conferred a 76g birthweight decrement. This represents an approximate 300g difference between the best and worst neighborhoods. Although stress (daily hassles), perceived locus-of-control, and social support were related to birthweight, their adjustment reduced the neighborhood coefficient by only 12%. In contrast, the neighborhood effect was reduced by an additional 30% and was no longer statistically significant after adjustment for the behavioral factors of smoking, drug use, and delayed prenatal care. These findings suggest that neighborhood factors may influence birthweight by shaping maternal behavioral risks. Thus, neighborhood level interventions should be considered to address multiple maternal and infant health risks. Future studies should examine more direct measures of neighborhood stress, such as perceived neighborhood disorder, and evaluate alternative mechanisms by which neighborhood factors influence behavior (e.g., social norms and access to goods and services).

Eating habits of preschool children with high migrant status in Switzerland according to a new food frequency questionnaire.

PubMed

Ebenegger, Vincent; Marques-Vidal, Pedro; Barral, Jérôme; Kriemler, Susi; Puder, Jardena J; Nydegger, Andreas

2010-02-01

Assessment of eating habits in young children from multicultural backgrounds has seldom been conducted. Our objectives were to study the reproducibility and the results of a food frequency questionnaire (FFQ) developed to assess changes in eating habits of preschool children with a high migrant population, in the context of a multidisciplinary multilevel lifestyle intervention. Three kindergarten classes (53% from migrant backgrounds) in French-speaking Switzerland were randomly selected and included 16 girls and 28 boys (mean age +/- SD, 5.4 +/- 0.7 years). The FFQ was filled out twice within a 4-week interval by the parents. Spearman rank correlations between the first and the second FFQ for the 39 items of the food questions were as follows: low (r < 0.50) for 8 (7 P < .05 and 1 nonsignificant), moderate (0.50 or= 0.70) for 9 (all P < .01). In addition, 28 of 39 intraclass correlation coefficients were high (>0.50, all P < .01). Eighty-six percent of the children ate breakfast at home daily, but only 67% had lunch at home. The percentages of children eating at least once a week in front of the TV were as follows: 50% for breakfast, 33% for lunch, 38% for dinner, and 48% for snacks. Forty percent of children asked their parents to buy food previously seen in advertisements and ate fast food between once a week and once a month. Children generally consumed foods with a high-energy content. The FFQ yielded good test-retest reproducibility for most items of the food questions and gave relevant findings about the eating habits of preschool children in areas with a high migrant population. Copyright 2010 Elsevier Inc. All rights reserved.

Association of Job Demands with Work Engagement of Japanese Employees: Comparison of Challenges with Hindrances (J-HOPE)

PubMed Central

Inoue, Akiomi; Kawakami, Norito; Tsutsumi, Akizumi; Shimazu, Akihito; Miyaki, Koichi; Takahashi, Masaya; Kurioka, Sumiko; Eguchi, Hisashi; Tsuchiya, Masao; Enta, Kazuhiko; Kosugi, Yuki; Sakata, Tomoko; Totsuzaki, Takafumi

2014-01-01

Objectives Recent epidemiological research in Europe has reported that two groups of job demands, i.e., challenges and hindrances, are differently associated with work engagement. The purpose of the present study was to replicate the cross-sectional association of workload and time pressure (as a challenge) and role ambiguity (as a hindrance) with work engagement among Japanese employees. Methods Between October 2010 and December 2011, a total of 9,134 employees (7,101 men and 1,673 women) from 12 companies in Japan were surveyed using a self-administered questionnaire comprising the Job Content Questionnaire, National Institute for Occupational Safety and Health Generic Job Stress Questionnaire, short 10-item version of the Effort-Reward Imbalance Questionnaire, short nine-item version of the Utrecht Work Engagement Scale, and demographic characteristics. Multilevel regression analyses with a random intercept model were conducted. Results After adjusting for demographic characteristics, workload and time pressure showed a positive association with work engagement with a small effect size (standardized coefficient [β] = 0.102, Cohen’s d [d] = 0.240) while role ambiguity showed a negative association with a large effect size (β = −0.429, d = 1.011). After additionally adjusting for job resources (i.e., decision latitude, supervisor support, co-worker support, and extrinsic reward), the effect size of workload and time pressure was not attenuated (β = 0.093, d = 0.234) while that of role ambiguity was attenuated but still medium (β = −0.242, d = 0.609). Conclusions Among Japanese employees, challenges such as having higher levels of workload and time pressure may enhance work engagement but hindrances, such as role ambiguity, may reduce it. PMID:24614682

A European benchmarking system to evaluate in-hospital mortality rates in acute coronary syndrome: the EURHOBOP project.

PubMed

Dégano, Irene R; Subirana, Isaac; Torre, Marina; Grau, María; Vila, Joan; Fusco, Danilo; Kirchberger, Inge; Ferrières, Jean; Malmivaara, Antti; Azevedo, Ana; Meisinger, Christa; Bongard, Vanina; Farmakis, Dimitros; Davoli, Marina; Häkkinen, Unto; Araújo, Carla; Lekakis, John; Elosua, Roberto; Marrugat, Jaume

2015-03-01

Hospital performance models in acute myocardial infarction (AMI) are useful to assess patient management. While models are available for individual countries, mainly US, cross-European performance models are lacking. Thus, we aimed to develop a system to benchmark European hospitals in AMI and percutaneous coronary intervention (PCI), based on predicted in-hospital mortality. We used the EURopean HOspital Benchmarking by Outcomes in ACS Processes (EURHOBOP) cohort to develop the models, which included 11,631 AMI patients and 8276 acute coronary syndrome (ACS) patients who underwent PCI. Models were validated with a cohort of 55,955 European ACS patients. Multilevel logistic regression was used to predict in-hospital mortality in European hospitals for AMI and PCI. Administrative and clinical models were constructed with patient- and hospital-level covariates, as well as hospital- and country-based random effects. Internal cross-validation and external validation showed good discrimination at the patient level and good calibration at the hospital level, based on the C-index (0.736-0.819) and the concordance correlation coefficient (55.4%-80.3%). Mortality ratios (MRs) showed excellent concordance between administrative and clinical models (97.5% for AMI and 91.6% for PCI). Exclusion of transfers and hospital stays ≤1day did not affect in-hospital mortality prediction in sensitivity analyses, as shown by MR concordance (80.9%-85.4%). Models were used to develop a benchmarking system to compare in-hospital mortality rates of European hospitals with similar characteristics. The developed system, based on the EURHOBOP models, is a simple and reliable tool to compare in-hospital mortality rates between European hospitals in AMI and PCI. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Variable-speed wind power system with improved energy capture via multilevel conversion

DOEpatents

Erickson, Robert W.; Al-Naseem, Osama A.; Fingersh, Lee Jay

2005-05-31

A system and method for efficiently capturing electrical energy from a variable-speed generator are disclosed. The system includes a matrix converter using full-bridge, multilevel switch cells, in which semiconductor devices are clamped to a known constant DC voltage of a capacitor. The multilevel matrix converter is capable of generating multilevel voltage wave waveform of arbitrary magnitude and frequencies. The matrix converter can be controlled by using space vector modulation.

Self-balanced modulation and magnetic rebalancing method for parallel multilevel inverters

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

Li, Hui; Shi, Yanjun

A self-balanced modulation method and a closed-loop magnetic flux rebalancing control method for parallel multilevel inverters. The combination of the two methods provides for balancing of the magnetic flux of the inter-cell transformers (ICTs) of the parallel multilevel inverters without deteriorating the quality of the output voltage. In various embodiments a parallel multi-level inverter modulator is provide including a multi-channel comparator to generate a multiplexed digitized ideal waveform for a parallel multi-level inverter and a finite state machine (FSM) module coupled to the parallel multi-channel comparator, the FSM module to receive the multiplexed digitized ideal waveform and to generate amore » pulse width modulated gate-drive signal for each switching device of the parallel multi-level inverter. The system and method provides for optimization of the output voltage spectrum without influence the magnetic balancing.« less

Complex-valued multistate associative memory with nonlinear multilevel functions for gray-level image reconstruction.

PubMed

Tanaka, Gouhei; Aihara, Kazuyuki

2009-09-01

A widely used complex-valued activation function for complex-valued multistate Hopfield networks is revealed to be essentially based on a multilevel step function. By replacing the multilevel step function with other multilevel characteristics, we present two alternative complex-valued activation functions. One is based on a multilevel sigmoid function, while the other on a characteristic of a multistate bifurcating neuron. Numerical experiments show that both modifications to the complex-valued activation function bring about improvements in network performance for a multistate associative memory. The advantage of the proposed networks over the complex-valued Hopfield networks with the multilevel step function is more outstanding when a complex-valued neuron represents a larger number of multivalued states. Further, the performance of the proposed networks in reconstructing noisy 256 gray-level images is demonstrated in comparison with other recent associative memories to clarify their advantages and disadvantages.

Long bone reconstruction using multilevel lengthening of bone defect fragments.

PubMed

Borzunov, Dmitry Y

2012-08-01

This paper presents experimental findings to substantiate the use of multilevel bone fragment lengthening for managing extensive long bone defects caused by diverse aetiologies and shows its clinical introduction which could provide a solution for the problem of reducing the total treatment time. Both experimental and clinical multilevel lengthening to bridge bone defect gaps was performed with the use of the Ilizarov method only. The experimental findings and clinical outcomes showed that multilevel defect fragment lengthening could provide sufficient bone formation and reduction of the total osteosynthesis time in one stage as compared to traditional Ilizarov bone transport. The method of multilevel regeneration enabled management of critical-size defects that measured on average 13.5 ± 0.7 cm in 78 patients. The experimental and clinical results proved the efficiency of the Ilizarov non-free multilevel bone plasty that can be recommended for practical use.

Measuring monotony in two-dimensional samples

NASA Astrophysics Data System (ADS)

Kachapova, Farida; Kachapov, Ilias

2010-04-01

This note introduces a monotony coefficient as a new measure of the monotone dependence in a two-dimensional sample. Some properties of this measure are derived. In particular, it is shown that the absolute value of the monotony coefficient for a two-dimensional sample is between |r| and 1, where r is the Pearson's correlation coefficient for the sample; that the monotony coefficient equals 1 for any monotone increasing sample and equals -1 for any monotone decreasing sample. This article contains a few examples demonstrating that the monotony coefficient is a more accurate measure of the degree of monotone dependence for a non-linear relationship than the Pearson's, Spearman's and Kendall's correlation coefficients. The monotony coefficient is a tool that can be applied to samples in order to find dependencies between random variables; it is especially useful in finding couples of dependent variables in a big dataset of many variables. Undergraduate students in mathematics and science would benefit from learning and applying this measure of monotone dependence.

Firm-Related Training Tracks: A Random Effects Ordered Probit Model

ERIC Educational Resources Information Center

Groot, Wim; van den Brink, Henriette Maassen

2003-01-01

A random effects ordered response model of training is estimated to analyze the existence of training tracks and time varying coefficients in training frequency. Two waves of a Dutch panel survey of workers are used covering the period 1992-1996. The amount of training received by workers increased during the period 1994-1996 compared to…

Mechanical equivalent of quantum heat engines.

PubMed

Arnaud, Jacques; Chusseau, Laurent; Philippe, Fabrice

2008-06-01

Quantum heat engines employ as working agents multilevel systems instead of classical gases. We show that under some conditions quantum heat engines are equivalent to a series of reservoirs at different altitudes containing balls of various weights. A cycle consists of picking up at random a ball from one reservoir and carrying it to the next, thereby performing or absorbing some work. In particular, quantum heat engines, employing two-level atoms as working agents, are modeled by reservoirs containing balls of weight 0 or 1. The mechanical model helps us prove that the maximum efficiency of quantum heat engines is the Carnot efficiency. Heat pumps and negative temperatures are considered.

Methodological considerations in using complex survey data: an applied example with the Head Start Family and Child Experiences Survey.

PubMed

Hahs-Vaughn, Debbie L; McWayne, Christine M; Bulotsky-Shearer, Rebecca J; Wen, Xiaoli; Faria, Ann-Marie

2011-06-01

Complex survey data are collected by means other than simple random samples. This creates two analytical issues: nonindependence and unequal selection probability. Failing to address these issues results in underestimated standard errors and biased parameter estimates. Using data from the nationally representative Head Start Family and Child Experiences Survey (FACES; 1997 and 2000 cohorts), three diverse multilevel models are presented that illustrate differences in results depending on addressing or ignoring the complex sampling issues. Limitations of using complex survey data are reported, along with recommendations for reporting complex sample results. © The Author(s) 2011

Multi-sensor physical activity recognition in free-living.

PubMed

Ellis, Katherine; Godbole, Suneeta; Kerr, Jacqueline; Lanckriet, Gert

Physical activity monitoring in free-living populations has many applications for public health research, weight-loss interventions, context-aware recommendation systems and assistive technologies. We present a system for physical activity recognition that is learned from a free-living dataset of 40 women who wore multiple sensors for seven days. The multi-level classification system first learns low-level codebook representations for each sensor and uses a random forest classifier to produce minute-level probabilities for each activity class. Then a higher-level HMM layer learns patterns of transitions and durations of activities over time to smooth the minute-level predictions. [Formula: see text].

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

PubMed

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

2015-06-14

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

Microwave scattering and emission from a half-space anisotropic random medium

NASA Astrophysics Data System (ADS)

Mudaliar, Saba; Lee, Jay Kyoon

1990-12-01

This paper is a sequel to an earlier paper (Lee and Mudaliar, 1988) where the backscattering coefficients of a half-space anisotropic random medium were obtained. Here the bistatic scattering coefficients are calculated by solving the modified radiative transfer equations under a first-order approximation. The effects of multiple scattering on the results are observed. Emissivities are calculated and compared with those obtained using the Born approximation (single scattering). Several interesting properties of the model are brought to notice using numerical examples. Finally, as an application, the theory is used to interpret the passive remote sensing data of multiyear sea ice in the microwave frequency range. A quite close agreement between theoretical prediction and the measured data is found.

Giant mesoscopic fluctuations of the elastic cotunneling thermopower of a single-electron transistor

NASA Astrophysics Data System (ADS)

Vasenko, A. S.; Basko, D. M.; Hekking, F. W. J.

2015-02-01

We study the thermoelectric transport of a small metallic island weakly coupled to two electrodes by tunnel junctions. In the Coulomb blockade regime, in the case when the ground state of the system corresponds to an even number of electrons on the island, the main mechanism of electron transport at the lowest temperatures is elastic cotunneling. In this regime, the transport coefficients strongly depend on the realization of the random impurity potential or the shape of the island. Using random-matrix theory, we calculate the thermopower and the thermoelectric kinetic coefficient and study the statistics of their mesoscopic fluctuations in the elastic cotunneling regime. The fluctuations of the thermopower turn out to be much larger than the average value.

Time domain simulation of the response of geometrically nonlinear panels subjected to random loading

NASA Technical Reports Server (NTRS)

Moyer, E. Thomas, Jr.

1988-01-01

The response of composite panels subjected to random pressure loads large enough to cause geometrically nonlinear responses is studied. A time domain simulation is employed to solve the equations of motion. An adaptive time stepping algorithm is employed to minimize intermittent transients. A modified algorithm for the prediction of response spectral density is presented which predicts smooth spectral peaks for discrete time histories. Results are presented for a number of input pressure levels and damping coefficients. Response distributions are calculated and compared with the analytical solution of the Fokker-Planck equations. RMS response is reported as a function of input pressure level and damping coefficient. Spectral densities are calculated for a number of examples.

On S.N. Bernstein's derivation of Mendel's Law and 'rediscovery' of the Hardy-Weinberg distribution.

PubMed

Stark, Alan; Seneta, Eugene

2012-04-01

Around 1923 the soon-to-be famous Soviet mathematician and probabilist Sergei N. Bernstein started to construct an axiomatic foundation of a theory of heredity. He began from the premise of stationarity (constancy of type proportions) from the first generation of offspring. This led him to derive the Mendelian coefficients of heredity. It appears that he had no direct influence on the subsequent development of population genetics. A basic assumption of Bernstein was that parents coupled randomly to produce offspring. This paper shows that a simple model of non-random mating, which nevertheless embodies a feature of the Hardy-Weinberg Law, can produce Mendelian coefficients of heredity while maintaining the population distribution. How W. Johannsen's monograph influenced Bernstein is discussed.

A spatially filtered multilevel model to account for spatial dependency: application to self-rated health status in South Korea

PubMed Central

2014-01-01

Background This study aims to suggest an approach that integrates multilevel models and eigenvector spatial filtering methods and apply it to a case study of self-rated health status in South Korea. In many previous health-related studies, multilevel models and single-level spatial regression are used separately. However, the two methods should be used in conjunction because the objectives of both approaches are important in health-related analyses. The multilevel model enables the simultaneous analysis of both individual and neighborhood factors influencing health outcomes. However, the results of conventional multilevel models are potentially misleading when spatial dependency across neighborhoods exists. Spatial dependency in health-related data indicates that health outcomes in nearby neighborhoods are more similar to each other than those in distant neighborhoods. Spatial regression models can address this problem by modeling spatial dependency. This study explores the possibility of integrating a multilevel model and eigenvector spatial filtering, an advanced spatial regression for addressing spatial dependency in datasets. Methods In this spatially filtered multilevel model, eigenvectors function as additional explanatory variables accounting for unexplained spatial dependency within the neighborhood-level error. The specification addresses the inability of conventional multilevel models to account for spatial dependency, and thereby, generates more robust outputs. Results The findings show that sex, employment status, monthly household income, and perceived levels of stress are significantly associated with self-rated health status. Residents living in neighborhoods with low deprivation and a high doctor-to-resident ratio tend to report higher health status. The spatially filtered multilevel model provides unbiased estimations and improves the explanatory power of the model compared to conventional multilevel models although there are no changes in the signs of parameters and the significance levels between the two models in this case study. Conclusions The integrated approach proposed in this paper is a useful tool for understanding the geographical distribution of self-rated health status within a multilevel framework. In future research, it would be useful to apply the spatially filtered multilevel model to other datasets in order to clarify the differences between the two models. It is anticipated that this integrated method will also out-perform conventional models when it is used in other contexts. PMID:24571639

Simulator for multilevel optimization research

NASA Technical Reports Server (NTRS)

Padula, S. L.; Young, K. C.

1986-01-01

A computer program designed to simulate and improve multilevel optimization techniques is described. By using simple analytic functions to represent complex engineering analyses, the simulator can generate and test a large variety of multilevel decomposition strategies in a relatively short time. This type of research is an essential step toward routine optimization of large aerospace systems. The paper discusses the types of optimization problems handled by the simulator and gives input and output listings and plots for a sample problem. It also describes multilevel implementation techniques which have value beyond the present computer program. Thus, this document serves as a user's manual for the simulator and as a guide for building future multilevel optimization applications.

A hand hygiene intervention to decrease infections among children attending day care centers: design of a cluster randomized controlled trial.

PubMed

Zomer, Tizza P; Erasmus, Vicki; Vlaar, Nico; van Beeck, Ed F; Tjon-A-Tsien, Aimée; Richardus, Jan Hendrik; Voeten, Hélène A C M

2013-06-03

Day care center attendance has been recognized as a risk factor for acquiring gastrointestinal and respiratory infections, which can be prevented with adequate hand hygiene (HH). Based on previous studies on environmental and sociocognitive determinants of caregivers' compliance with HH guidelines in day care centers (DCCs), an intervention has been developed aiming to improve caregivers' and children's HH compliance and decrease infections among children attending DCCs. The aim of this paper is to describe the design of a cluster randomized controlled trial to evaluate the effectiveness of this intervention. The intervention will be evaluated in a two-arm cluster randomized controlled trial among 71 DCCs in the Netherlands. In total, 36 DCCs will receive the intervention consisting of four components: 1) HH products (dispensers and refills for paper towels, soap, alcohol-based hand sanitizer, and hand cream); 2) training to educate about the Dutch national HH guidelines; 3) two team training sessions aimed at goal setting and formulating specific HH improvement activities; and 4) reminders and cues to action (posters/stickers). Intervention DCCs will be compared to 35 control DCCs continuing usual practice. The primary outcome measure will be observed HH compliance of caregivers and children, measured at baseline and one, three, and six months after start of the intervention. The secondary outcome measure will be the incidence of gastrointestinal and respiratory infections in 600 children attending DCCs, monitored over six months by parents using a calendar to mark the days their child has diarrhea and/or a cold. Multilevel logistic regression will be performed to assess the effect of the intervention on HH compliance. Multilevel poisson regression will be performed to assess the incidence of gastrointestinal and respiratory infections in children attending DCCs. This is one of the first DCC intervention studies to assess HH compliance of both caregivers and children, as well as the incidence of gastrointestinal and respiratory infections in children, as outcome measures. When an effect of the intervention on improving HH compliance and/or reducing incidence of infections is shown, (inter)national dissemination of the intervention in other DCCs may be considered. Netherlands trial registry: NTR3000.

Random walks on cubic lattices with bond disorder

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

Ernst, M.H.; van Velthoven, P.F.J.

1986-12-01

The authors consider diffusive systems with static disorder, such as Lorentz gases, lattice percolation, ants in a labyrinth, termite problems, random resistor networks, etc. In the case of diluted randomness the authors can apply the methods of kinetic theory to obtain systematic expansions of dc and ac transport properties in powers of the impurity concentration c. The method is applied to a hopping model on a d-dimensional cubic lattice having two types of bonds with conductivity sigma and sigma/sub 0/ = 1, with concentrations c and 1-c, respectively. For the square lattice the authors explicitly calculate the diffusion coefficient D(c,sigma)more » as a function of c, to O(c/sup 2/) terms included for different ratios of the bond conductivity sigma. The probability of return at long times is given by P/sub 0/(t) approx. (4..pi..D(c,sigma)t)/sup -d/2/, which is determined by the diffusion coefficient of the disordered system.« less

Multilevel Mixture Kalman Filter

NASA Astrophysics Data System (ADS)

Guo, Dong; Wang, Xiaodong; Chen, Rong

2004-12-01

The mixture Kalman filter is a general sequential Monte Carlo technique for conditional linear dynamic systems. It generates samples of some indicator variables recursively based on sequential importance sampling (SIS) and integrates out the linear and Gaussian state variables conditioned on these indicators. Due to the marginalization process, the complexity of the mixture Kalman filter is quite high if the dimension of the indicator sampling space is high. In this paper, we address this difficulty by developing a new Monte Carlo sampling scheme, namely, the multilevel mixture Kalman filter. The basic idea is to make use of the multilevel or hierarchical structure of the space from which the indicator variables take values. That is, we draw samples in a multilevel fashion, beginning with sampling from the highest-level sampling space and then draw samples from the associate subspace of the newly drawn samples in a lower-level sampling space, until reaching the desired sampling space. Such a multilevel sampling scheme can be used in conjunction with the delayed estimation method, such as the delayed-sample method, resulting in delayed multilevel mixture Kalman filter. Examples in wireless communication, specifically the coherent and noncoherent 16-QAM over flat-fading channels, are provided to demonstrate the performance of the proposed multilevel mixture Kalman filter.

Multilevel Green's function interpolation method for scattering from composite metallic and dielectric objects.

PubMed

Shi, Yan; Wang, Hao Gang; Li, Long; Chan, Chi Hou

2008-10-01

A multilevel Green's function interpolation method based on two kinds of multilevel partitioning schemes--the quasi-2D and the hybrid partitioning scheme--is proposed for analyzing electromagnetic scattering from objects comprising both conducting and dielectric parts. The problem is formulated using the surface integral equation for homogeneous dielectric and conducting bodies. A quasi-2D multilevel partitioning scheme is devised to improve the efficiency of the Green's function interpolation. In contrast to previous multilevel partitioning schemes, noncubic groups are introduced to discretize the whole EM structure in this quasi-2D multilevel partitioning scheme. Based on the detailed analysis of the dimension of the group in this partitioning scheme, a hybrid quasi-2D/3D multilevel partitioning scheme is proposed to effectively handle objects with fine local structures. Selection criteria for some key parameters relating to the interpolation technique are given. The proposed algorithm is ideal for the solution of problems involving objects such as missiles, microstrip antenna arrays, photonic bandgap structures, etc. Numerical examples are presented to show that CPU time is between O(N) and O(N log N) while the computer memory requirement is O(N).

Genetic parameters for stayability to consecutive calvings in Zebu cattle.

PubMed

Silva, D O; Santana, M L; Ayres, D R; Menezes, G R O; Silva, L O C; Nobre, P R C; Pereira, R J

2017-12-22

Longer-lived cows tend to be more profitable and the stayability trait is a selection criterion correlated to longevity. An alternative to the traditional approach to evaluate stayability is its definition based on consecutive calvings, whose main advantage is the more accurate evaluation of young bulls. However, no study using this alternative approach has been conducted for Zebu breeds. Therefore, the objective of this study was to compare linear random regression models to fit stayability to consecutive calvings of Guzerá, Nelore and Tabapuã cows and to estimate genetic parameters for this trait in the respective breeds. Data up to the eighth calving were used. The models included the fixed effects of age at first calving and year-season of birth of the cow and the random effects of contemporary group, additive genetic, permanent environmental and residual. Random regressions were modeled by orthogonal Legendre polynomials of order 1 to 4 (2 to 5 coefficients) for contemporary group, additive genetic and permanent environmental effects. Using Deviance Information Criterion as the selection criterion, the model with 4 regression coefficients for each effect was the most adequate for the Nelore and Tabapuã breeds and the model with 5 coefficients is recommended for the Guzerá breed. For Guzerá, heritabilities ranged from 0.05 to 0.08, showing a quadratic trend with a peak between the fourth and sixth calving. For the Nelore and Tabapuã breeds, the estimates ranged from 0.03 to 0.07 and from 0.03 to 0.08, respectively, and increased with increasing calving number. The additive genetic correlations exhibited a similar trend among breeds and were higher for stayability between closer calvings. Even between more distant calvings (second v. eighth), stayability showed a moderate to high genetic correlation, which was 0.77, 0.57 and 0.79 for the Guzerá, Nelore and Tabapuã breeds, respectively. For Guzerá, when the models with 4 or 5 regression coefficients were compared, the rank correlations between predicted breeding values for the intercept were always higher than 0.99, indicating the possibility of practical application of the least parameterized model. In conclusion, the model with 4 random regression coefficients is recommended for the genetic evaluation of stayability to consecutive calvings in Zebu cattle.

Scattering properties of electromagnetic waves from metal object in the lower terahertz region

NASA Astrophysics Data System (ADS)

Chen, Gang; Dang, H. X.; Hu, T. Y.; Su, Xiang; Lv, R. C.; Li, Hao; Tan, X. M.; Cui, T. J.

2018-01-01

An efficient hybrid algorithm is proposed to analyze the electromagnetic scattering properties of metal objects in the lower terahertz (THz) frequency. The metal object can be viewed as perfectly electrical conducting object with a slightly rough surface in the lower THz region. Hence the THz scattered field from metal object can be divided into coherent and incoherent parts. The physical optics and truncated-wedge incremental-length diffraction coefficients methods are combined to compute the coherent part; while the small perturbation method is used for the incoherent part. With the MonteCarlo method, the radar cross section of the rough metal surface is computed by the multilevel fast multipole algorithm and the proposed hybrid algorithm, respectively. The numerical results show that the proposed algorithm has good accuracy to simulate the scattering properties rapidly in the lower THz region.