Development and validation of a gender-balanced measure of aggression-relevant social cognition.

PubMed

Hughes, Jan N; Webster-Stratton, Barbara T; Cavell, Timothy A

2004-06-01

This study examined the psychometric properties of the Social-Cognitive Assessment Profile (SCAP), a gender-balanced measure of social information processing (SIP) in a sample of 371 (139 girls, 232 boys) 2nd- to 4th-grade children. The SCAP assesses 4 dimensions of SIP (Inferring Hostile Intent, Constructing Hostile Goals, Generating Aggressive Solutions, and Anticipating Positive Outcomes for Aggression) in the context of peer conflict involving relational and overt provocation. Confirmatory factor analyses indicated that the 4 latent factors provided a good fit to the data for girls and boys and for African American and non-African American children. Regression analyses in which teacher and peer evaluations of aggression and peer evaluations of social competencies were regressed on each of the 4 SCAP scales supported the test's convergent and discriminant validity. These results suggest that the SCAP is an easily administered and brief measure of SIP that is appropriate for racially diverse populations of elementary boys and girls.

Urinary inorganic arsenic concentrations and semen quality of male partners of subfertile couples in Tokyo.

PubMed

Oguri, Tomoko; Yoshinaga, Jun; Toshima, Hiroki; Mizumoto, Yoshifumi; Hatakeyama, Shota; Tokuoka, Susumu

2016-01-01

Inorganic arsenic (iAs) has been known as a testicular toxicant in experimental rodents. Possible association between iAs exposure and semen quality (semen volume, sperm concentration, and sperm motility) was explored in male partners of couples (n = 42) who visited a gynecology clinic in Tokyo for infertility consultation. Semen parameters were measured according to WHO guideline at the clinic, and urinary iAs and methylarsonic acid (MMA), and dimethylarsinic acid concentrations were determined by liquid chromatography-hydride generation-ICP mass spectrometry. Biological attributes, dietary habits, and exposure levels to other chemicals with known effects on semen parameters were taken into consideration as covariates. Multiple regression analyses and logistic regression analyses did not find iAs exposure as significant contributor to semen parameters. Lower exposure level of subjects (estimated to be 0.5 μg kg(-1) day(-1)) was considered a reason of the absence of adverse effects on semen parameters, which were seen in rodents dosed with 4-7.5 mg kg(-1).

Interdependency of the maximum range of flexion-extension of hand metacarpophalangeal joints.

PubMed

Gracia-Ibáñez, V; Vergara, M; Sancho-Bru, J-L

2016-12-01

Mobility of the fingers metacarpophalangeal (MCP) joints depends on the posture of the adjacent ones. Current Biomechanical hand models consider fixed ranges of movement at joints, regardless of the posture, thus allowing for non-realistic postures, generating wrong results in reach studies and forward dynamic analyses. This study provides data for more realistic hand models. The maximum voluntary extension (MVE) and flexion (MVF) of different combinations of MCP joints were measured covering their range of motion. Dependency of the MVF and MVE on the posture of the adjacent MCP joints was confirmed and mathematical models obtained through regression analyses (RMSE 7.7°).

Robust inference under the beta regression model with application to health care studies.

PubMed

Ghosh, Abhik

2017-01-01

Data on rates, percentages, or proportions arise frequently in many different applied disciplines like medical biology, health care, psychology, and several others. In this paper, we develop a robust inference procedure for the beta regression model, which is used to describe such response variables taking values in (0, 1) through some related explanatory variables. In relation to the beta regression model, the issue of robustness has been largely ignored in the literature so far. The existing maximum likelihood-based inference has serious lack of robustness against outliers in data and generate drastically different (erroneous) inference in the presence of data contamination. Here, we develop the robust minimum density power divergence estimator and a class of robust Wald-type tests for the beta regression model along with several applications. We derive their asymptotic properties and describe their robustness theoretically through the influence function analyses. Finite sample performances of the proposed estimators and tests are examined through suitable simulation studies and real data applications in the context of health care and psychology. Although we primarily focus on the beta regression models with a fixed dispersion parameter, some indications are also provided for extension to the variable dispersion beta regression models with an application.

Displacement efficiency of alternative energy and trans-provincial imported electricity in China.

PubMed

Hu, Yuanan; Cheng, Hefa

2017-02-17

China has invested heavily on alternative energy, but the effectiveness of such energy sources at substituting the dominant coal-fired generation remains unknown. Here we analyse the displacement of fossil-fuel-generated electricity by alternative energy, primarily hydropower, and by trans-provincial imported electricity in China between 1995 and 2014 using two-way fixed-effects panel regression models. Nationwide, each unit of alternative energy displaces nearly one-quarter of a unit of fossil-fuel-generated electricity, while each unit of imported electricity (regardless of the generation source) displaces ∼0.3 unit of fossil-fuel electricity generated locally. Results from the six regional grids indicate that significant displacement of fossil-fuel-generated electricity occurs once the share of alternative energy in the electricity supply mix exceeds ∼10%, which is accompanied by 10-50% rebound in the consumption of fossil-fuel-generated electricity. These findings indicate the need for a policy that integrates carbon taxation, alternative energy and energy efficiency to facilitate China's transition towards a low-carbon economy.

Displacement efficiency of alternative energy and trans-provincial imported electricity in China

NASA Astrophysics Data System (ADS)

Hu, Yuanan; Cheng, Hefa

2017-02-01

China has invested heavily on alternative energy, but the effectiveness of such energy sources at substituting the dominant coal-fired generation remains unknown. Here we analyse the displacement of fossil-fuel-generated electricity by alternative energy, primarily hydropower, and by trans-provincial imported electricity in China between 1995 and 2014 using two-way fixed-effects panel regression models. Nationwide, each unit of alternative energy displaces nearly one-quarter of a unit of fossil-fuel-generated electricity, while each unit of imported electricity (regardless of the generation source) displaces ~0.3 unit of fossil-fuel electricity generated locally. Results from the six regional grids indicate that significant displacement of fossil-fuel-generated electricity occurs once the share of alternative energy in the electricity supply mix exceeds ~10%, which is accompanied by 10-50% rebound in the consumption of fossil-fuel-generated electricity. These findings indicate the need for a policy that integrates carbon taxation, alternative energy and energy efficiency to facilitate China's transition towards a low-carbon economy.

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

PubMed

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

2009-11-01

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

Linear regression metamodeling as a tool to summarize and present simulation model results.

PubMed

Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M

2013-10-01

Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

The role of attitudes and culture in family caregiving for older adults.

PubMed

Anngela-Cole, Linda; Hilton, Jeanne M

2009-01-01

This study evaluated cultural differences in attitudes toward caregiving and the stress levels of family caregivers. Participants included 98 Japanese American and 86 Caucasian American family caregivers caring for frail elders. Analyses using MANOVA and multiple regression analyses revealed that the Caucasian caregivers had more positive attitudes and provided more hours of care than the Japanese caregivers but that both groups had elevated levels of caregiver stress. The stress that family caregivers currently experience could lead to a future generation of care recipients who enter old age in worse condition than their predecessors. Professionals need to work together to develop culturally appropriate, evidence-based interventions to address this issue.

Generation, language, body mass index, and activity patterns in Hispanic children.

PubMed

Taverno, Sharon E; Rollins, Brandi Y; Francis, Lori A

2010-02-01

The acculturation hypothesis proposes an overall disadvantage in health outcomes for Hispanic immigrants with more time spent living in the U.S., but little is known about how generational status and language may influence Hispanic children's relative weight and activity patterns. To investigate associations among generation and language with relative weight (BMI z-scores), physical activity, screen time, and participation in extracurricular activities (i.e., sports, clubs) in a U.S.-based, nationally representative sample of Hispanic children. Participants included 2012 Hispanic children aged 6-11 years from the cross-sectional 2003 National Survey of Children's Health. Children were grouped according to generational status (first, second, or third), and the primary language spoken in the home (English versus non-English). Primary analyses included adjusted logistic and multinomial logistic regression to examine the relationships among variables; all analyses were conducted between 2008 and 2009. Compared to third-generation, English speakers, first- and second-generation, non-English speakers were more than two times more likely to be obese. Moreover, first-generation, non-English speakers were half as likely to engage in regular physical activity and sports. Both first- and second-generation, non-English speakers were less likely to participate in clubs compared to second- and third-generation, English speakers. Overall, non-English-speaking groups reported less screen time compared to third-generation, English speakers. The hypothesis that Hispanics lose their health protection with more time spent in the U.S. was not supported in this sample of Hispanic children. Copyright 2010 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

NASA Astrophysics Data System (ADS)

Duman, T. Y.; Can, T.; Gokceoglu, C.; Nefeslioglu, H. A.; Sonmez, H.

2006-11-01

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.

Inhibitory saccadic dysfunction is associated with cerebellar injury in multiple sclerosis.

PubMed

Kolbe, Scott C; Kilpatrick, Trevor J; Mitchell, Peter J; White, Owen; Egan, Gary F; Fielding, Joanne

2014-05-01

Cognitive dysfunction is common in patients with multiple sclerosis (MS). Saccadic eye movement paradigms such as antisaccades (AS) can sensitively interrogate cognitive function, in particular, the executive and attentional processes of response selection and inhibition. Although we have previously demonstrated significant deficits in the generation of AS in MS patients, the neuropathological changes underlying these deficits were not elucidated. In this study, 24 patients with relapsing-remitting MS underwent testing using an AS paradigm. Rank correlation and multiple regression analyses were subsequently used to determine whether AS errors in these patients were associated with: (i) neurological and radiological abnormalities, as measured by standard clinical techniques, (ii) cognitive dysfunction, and (iii) regionally specific cerebral white and gray-matter damage. Although AS error rates in MS patients did not correlate with clinical disability (using the Expanded Disability Status Score), T2 lesion load or brain parenchymal fraction, AS error rate did correlate with performance on the Paced Auditory Serial Addition Task and the Symbol Digit Modalities Test, neuropsychological tests commonly used in MS. Further, voxel-wise regression analyses revealed associations between AS errors and reduced fractional anisotropy throughout most of the cerebellum, and increased mean diffusivity in the cerebellar vermis. Region-wise regression analyses confirmed that AS errors also correlated with gray-matter atrophy in the cerebellum right VI subregion. These results support the use of the AS paradigm as a marker for cognitive dysfunction in MS and implicate structural and microstructural changes to the cerebellum as a contributing mechanism for AS deficits in these patients. Copyright © 2013 Wiley Periodicals, Inc.

Research Costs Investigated: A Study Into the Budgets of Dutch Publicly Funded Drug-Related Research.

PubMed

van Asselt, Thea; Ramaekers, Bram; Corro Ramos, Isaac; Joore, Manuela; Al, Maiwenn; Lesman-Leegte, Ivonne; Postma, Maarten; Vemer, Pepijn; Feenstra, Talitha

2018-01-01

The costs of performing research are an important input in value of information (VOI) analyses but are difficult to assess. The aim of this study was to investigate the costs of research, serving two purposes: (1) estimating research costs for use in VOI analyses; and (2) developing a costing tool to support reviewers of grant proposals in assessing whether the proposed budget is realistic. For granted study proposals from the Netherlands Organization for Health Research and Development (ZonMw), type of study, potential cost drivers, proposed budget, and general characteristics were extracted. Regression analysis was conducted in an attempt to generate a 'predicted budget' for certain combinations of cost drivers, for implementation in the costing tool. Of 133 drug-related research grant proposals, 74 were included for complete data extraction. Because an association between cost drivers and budgets was not confirmed, we could not generate a predicted budget based on regression analysis, but only historic reference budgets given certain study characteristics. The costing tool was designed accordingly, i.e. with given selection criteria the tool returns the range of budgets in comparable studies. This range can be used in VOI analysis to estimate whether the expected net benefit of sampling will be positive to decide upon the net value of future research. The absence of association between study characteristics and budgets may indicate inconsistencies in the budgeting or granting process. Nonetheless, the tool generates useful information on historical budgets, and the option to formally relate VOI to budgets. To our knowledge, this is the first attempt at creating such a tool, which can be complemented with new studies being granted, enlarging the underlying database and keeping estimates up to date.

Implementation of a multi-variable regression analysis in the assessment of the generation rate and composition of hospital solid waste for the design of a sustainable management system in developing countries.

PubMed

Al-Khatib, Issam A; Abu Fkhidah, Ismail; Khatib, Jumana I; Kontogianni, Stamatia

2016-03-01

Forecasting of hospital solid waste generation is a critical challenge for future planning. The composition and generation rate of hospital solid waste in hospital units was the field where the proposed methodology of the present article was applied in order to validate the results and secure the outcomes of the management plan in national hospitals. A set of three multiple-variable regression models has been derived for estimating the daily total hospital waste, general hospital waste, and total hazardous waste as a function of number of inpatients, number of total patients, and number of beds. The application of several key indicators and validation procedures indicates the high significance and reliability of the developed models in predicting the hospital solid waste of any hospital. Methodology data were drawn from existent scientific literature. Also, useful raw data were retrieved from international organisations and the investigated hospitals' personnel. The primal generation outcomes are compared with other local hospitals and also with hospitals from other countries. The main outcome, which is the developed model results, are presented and analysed thoroughly. The goal is this model to act as leverage in the discussions among governmental authorities on the implementation of a national plan for safe hospital waste management in Palestine. © The Author(s) 2016.

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

Using Classification and Regression Trees (CART) and random forests to analyze attrition: Results from two simulations.

PubMed

Hayes, Timothy; Usami, Satoshi; Jacobucci, Ross; McArdle, John J

2015-12-01

In this article, we describe a recent development in the analysis of attrition: using classification and regression trees (CART) and random forest methods to generate inverse sampling weights. These flexible machine learning techniques have the potential to capture complex nonlinear, interactive selection models, yet to our knowledge, their performance in the missing data analysis context has never been evaluated. To assess the potential benefits of these methods, we compare their performance with commonly employed multiple imputation and complete case techniques in 2 simulations. These initial results suggest that weights computed from pruned CART analyses performed well in terms of both bias and efficiency when compared with other methods. We discuss the implications of these findings for applied researchers. (c) 2015 APA, all rights reserved).

Using Classification and Regression Trees (CART) and Random Forests to Analyze Attrition: Results From Two Simulations

PubMed Central

Hayes, Timothy; Usami, Satoshi; Jacobucci, Ross; McArdle, John J.

2016-01-01

In this article, we describe a recent development in the analysis of attrition: using classification and regression trees (CART) and random forest methods to generate inverse sampling weights. These flexible machine learning techniques have the potential to capture complex nonlinear, interactive selection models, yet to our knowledge, their performance in the missing data analysis context has never been evaluated. To assess the potential benefits of these methods, we compare their performance with commonly employed multiple imputation and complete case techniques in 2 simulations. These initial results suggest that weights computed from pruned CART analyses performed well in terms of both bias and efficiency when compared with other methods. We discuss the implications of these findings for applied researchers. PMID:26389526

Student-generated questions during chemistry lectures: Patterns, self-appraisals, and relations with motivational beliefs and achievement

NASA Astrophysics Data System (ADS)

Bergey, Bradley W.

Self-generated questions are a central mechanism for learning, yet students' questions are often infrequent during classroom instruction. As a result, little is known about the nature of student questioning during typical instructional contexts such as listening to a lecture, including the extent and nature of student-generated questions, how students evaluate their questions, and the relations among questions, motivations, and achievement. This study examined the questions undergraduate students (N = 103) generated during 8 lectures in an introductory chemistry course. Students recorded and appraised their question in daily question logs and reported lecture-specific self-efficacy beliefs. Self-efficacy, personal interest, goal orientations, and other motivational self-beliefs were measured before and after the unit. Primary analyses included testing path models, multiple regressions, and latent class analyses. Overall, results indicated that several characteristics of student questioning during lectures were significantly related to various motivations and achievement. Higher end-of-class self-efficacy was associated with fewer procedural questions and more questions that reflected smaller knowledge deficits. Lower exam scores were associated with questions reflecting broader knowledge deficits and students' appraisals that their questions had less value for others than for themselves. Individual goal orientations collectively and positively predicted question appraisals. The questions students generated and their relations with motivational variables and achievement are discussed in light of the learning task and academic context.

Application of Response Surface Methodology for characterization of ozone production from Multi-Cylinder Reactor in non-thermal plasma device

NASA Astrophysics Data System (ADS)

Lian See, Tan; Zulazlan Shah Zulkifli, Ahmad; Mook Tzeng, Lim

2018-04-01

Ozone is a reactant which can be applied for various environmental treatment processes. It can be generated via atmospheric air non-thermal plasmas when sufficient voltages are applied through a combination of electrodes and dielectric materials. In this study, the concentration of ozone generated via two different configurations of multi-cylinder dielectric barrier discharge (DBD) reactor (3 x 40 mm and 10 x 10 mm) was investigated. The influence of the voltage and the duty cycle to the concentration of ozone generated by each configuration was analysed using response surface methodology. Voltage was identified as significant factor to the ozone production process. However, the regressed model was biased towards one of the configuration, leaving the predicted results of another configuration to be out of range.

Displacement efficiency of alternative energy and trans-provincial imported electricity in China

PubMed Central

Hu, Yuanan; Cheng, Hefa

2017-01-01

China has invested heavily on alternative energy, but the effectiveness of such energy sources at substituting the dominant coal-fired generation remains unknown. Here we analyse the displacement of fossil-fuel-generated electricity by alternative energy, primarily hydropower, and by trans-provincial imported electricity in China between 1995 and 2014 using two-way fixed-effects panel regression models. Nationwide, each unit of alternative energy displaces nearly one-quarter of a unit of fossil-fuel-generated electricity, while each unit of imported electricity (regardless of the generation source) displaces ∼0.3 unit of fossil-fuel electricity generated locally. Results from the six regional grids indicate that significant displacement of fossil-fuel-generated electricity occurs once the share of alternative energy in the electricity supply mix exceeds ∼10%, which is accompanied by 10–50% rebound in the consumption of fossil-fuel-generated electricity. These findings indicate the need for a policy that integrates carbon taxation, alternative energy and energy efficiency to facilitate China's transition towards a low-carbon economy. PMID:28211467

The moderating role of emotional competence in suicidal ideation among Chinese university students.

PubMed

Kwok, Sylvia Y C L

2014-04-01

To explore the relationship among perceived family functioning, emotional competence and suicidal ideation and to examine the moderating role of emotional competence in suicidal ideation. Previous studies have highlighted that poor family relationships and emotional symptoms are significant predictors of suicidal ideation. However, the roles of perceived family functioning and emotional competence in predicting suicidal ideation have not been given adequate attention. A cross-sectional survey using convenience sampling. A questionnaire was administered to 302 university students from February-April in 2011 in Hong Kong. The means, standard deviations and Cronbach's alphas of the variables were computed. Pearson correlation analyses and hierarchical regression analyses were performed. Hierarchical regression analyses showed that perceived high family functioning and emotional competence were significant negative predictors of suicidal ideation. Further analyses showed that parental concern, parental control and creative use of emotions were significant predictors of suicidal ideation. Emotional competence, specifically creative use of emotions, was found to moderate the relationship between perceived family functioning and suicidal ideation. The findings support the family ecological framework and provide evidence for emotional competence as a resilience factor that buffers low family functioning on suicidal ideation. Suggested measures to decrease suicidal ideation include enhancing parental concern, lessening parental control, developing students' awareness, regulation and management of their own emotions, fostering empathy towards others' emotional expression, enhancing social skills in sharing and influencing others' emotions and increasing the positive use of emotions for the evaluation and generation of new ideas. © 2013 John Wiley & Sons Ltd.

Roles of Smartphone App Use in Improving Social Capital and Reducing Social Isolation.

PubMed

Cho, Jaehee

2015-06-01

This study investigated the relationships among smartphone app use, social capital, and social isolation. It focused on two different smartphone apps--communication and social networking site (SNS) apps--and their effects on bonding and bridging social capital. Generational differences in smartphone use were also considered. Results from hierarchical regression analyses indicated that individuals' use of communication apps was helpful for increasing social capital and that this effect of using communication apps was stronger among those of the millennial generation than among older users. Moreover, bonding and bridging social capital was found to reduce individuals' social isolation significantly. These results imply the notable role of smartphone apps in reducing social isolation and improving the personal lives of individuals.

Patterns of medicinal plant use: an examination of the Ecuadorian Shuar medicinal flora using contingency table and binomial analyses.

PubMed

Bennett, Bradley C; Husby, Chad E

2008-03-28

Botanical pharmacopoeias are non-random subsets of floras, with some taxonomic groups over- or under-represented. Moerman [Moerman, D.E., 1979. Symbols and selectivity: a statistical analysis of Native American medical ethnobotany, Journal of Ethnopharmacology 1, 111-119] introduced linear regression/residual analysis to examine these patterns. However, regression, the commonly-employed analysis, suffers from several statistical flaws. We use contingency table and binomial analyses to examine patterns of Shuar medicinal plant use (from Amazonian Ecuador). We first analyzed the Shuar data using Moerman's approach, modified to better meet requirements of linear regression analysis. Second, we assessed the exact randomization contingency table test for goodness of fit. Third, we developed a binomial model to test for non-random selection of plants in individual families. Modified regression models (which accommodated assumptions of linear regression) reduced R(2) to from 0.59 to 0.38, but did not eliminate all problems associated with regression analyses. Contingency table analyses revealed that the entire flora departs from the null model of equal proportions of medicinal plants in all families. In the binomial analysis, only 10 angiosperm families (of 115) differed significantly from the null model. These 10 families are largely responsible for patterns seen at higher taxonomic levels. Contingency table and binomial analyses offer an easy and statistically valid alternative to the regression approach.

Neutrophil/lymphocyte ratio and platelet/lymphocyte ratio in mood disorders: A meta-analysis.

PubMed

Mazza, Mario Gennaro; Lucchi, Sara; Tringali, Agnese Grazia Maria; Rossetti, Aurora; Botti, Eugenia Rossana; Clerici, Massimo

2018-06-08

The immune and inflammatory system is involved in the etiology of mood disorders. Neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR) and monocyte/lymphocyte ratio (MLR) are inexpensive and reproducible biomarkers of inflammation. This is the first meta-analysis exploring the role of NLR and PLR in mood disorder. We identified 11 studies according to our inclusion criteria from the main Electronic Databases. Meta-analyses were carried out generating pooled standardized mean differences (SMDs) between index and healthy controls (HC). Heterogeneity was estimated. Relevant sensitivity and meta-regression analyses were conducted. Subjects with bipolar disorder (BD) had higher NLR and PLR as compared with HC (respectively SMD = 0.672; p < 0.001; I 2  = 82.4% and SMD = 0.425; p = 0.048; I 2  = 86.53%). Heterogeneity-based sensitivity analyses confirmed these findings. Subgroup analysis evidenced an influence of bipolar phase on the overall estimate whit studies including subjects in manic and any bipolar phase showing a significantly higher NLR and PLR as compared with HC whereas the effect was not significant among studies including only euthymic bipolar subjects. Meta-regression showed that age and sex influenced the relationship between BD and NLR but not the relationship between BD and PLR. Meta-analysis was not carried out for MLR because our search identified only one study when comparing BD to HC, and only one study when comparing MDD to HC. Subjects with major depressive disorder (MDD) had higher NLR as compared with HC (SMD = 0.670; p = 0.028; I 2  = 89.931%). Heterogeneity-based sensitivity analyses and meta-regression confirmed these findings. Our meta-analysis supports the hypothesis that an inflammatory activation occurs in mood disorders and NLR and PLR may be useful to detect this activation. More researches including comparison of NLR, PLR and MLR between different bipolar phases and between BD and MDD are needed. Copyright © 2018 Elsevier Inc. All rights reserved.

Life-span development of self-esteem and its effects on important life outcomes.

PubMed

Orth, Ulrich; Robins, Richard W; Widaman, Keith F

2012-06-01

We examined the life-span development of self-esteem and tested whether self-esteem influences the development of important life outcomes, including relationship satisfaction, job satisfaction, occupational status, salary, positive and negative affect, depression, and physical health. Data came from the Longitudinal Study of Generations. Analyses were based on 5 assessments across a 12-year period of a sample of 1,824 individuals ages 16 to 97 years. First, growth curve analyses indicated that self-esteem increases from adolescence to middle adulthood, reaches a peak at about age 50 years, and then decreases in old age. Second, cross-lagged regression analyses indicated that self-esteem is best modeled as a cause rather than a consequence of life outcomes. Third, growth curve analyses, with self-esteem as a time-varying covariate, suggested that self-esteem has medium-sized effects on life-span trajectories of affect and depression, small to medium-sized effects on trajectories of relationship and job satisfaction, a very small effect on the trajectory of health, and no effect on the trajectory of occupational status. These findings replicated across 4 generations of participants--children, parents, grandparents, and their great-grandparents. Together, the results suggest that self-esteem has a significant prospective impact on real-world life experiences and that high and low self-esteem are not mere epiphenomena of success and failure in important life domains. 2012 APA, all rights reserved

Dynamic Dimensionality Selection for Bayesian Classifier Ensembles

DTIC Science & Technology

2015-03-19

learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but much more...classifier, Generative learning, Discriminative learning, Naïve Bayes, Feature selection, Logistic regression , higher order attribute independence 16...discriminative learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but

Predictors of Prosocial Behavior: Differences in Middle Aged and Older Adults

PubMed Central

Wenner, Jennifer R.; Randall, Brandy A.

2016-01-01

Generativity, contributing to the next generation, is important for well-being throughout middle and late life. Therefore, it is crucial to understand what contributes to generativity during these life stages. Parenting and work are common, but not the only, ways people engage generatively; prosocial behavior is another. A community connection may encourage generative contributions in adults. However, older adults may face obstacles to being generative, and may need an additional drive to engage in these behaviors. Given this, it was expected that community cohesion would predict prosocial behavior despite age, and that grit would provide motivation for older adults, so the current study examined whether age moderated the relation between grit and prosocial behavior. Data were used from 188 upper-Midwest adults (aged 37-89). Multiple regression analyses showed that age moderated the relation between grit and prosocial behavior such that grit predicted prosocial behavior in older adults but not middle age adults. A sense of community cohesion was predictive of prosocial behavior despite age. While grit may promote generative acts in different ways depending on age, a sense of community cohesion may foster community contributions despite age. The discussion focuses on future directions and ways to promote generativity using this research. PMID:28163344

Predictors of Prosocial Behavior: Differences in Middle Aged and Older Adults.

PubMed

Wenner, Jennifer R; Randall, Brandy A

2016-10-01

Generativity, contributing to the next generation, is important for well-being throughout middle and late life. Therefore, it is crucial to understand what contributes to generativity during these life stages. Parenting and work are common, but not the only, ways people engage generatively; prosocial behavior is another. A community connection may encourage generative contributions in adults. However, older adults may face obstacles to being generative, and may need an additional drive to engage in these behaviors. Given this, it was expected that community cohesion would predict prosocial behavior despite age, and that grit would provide motivation for older adults, so the current study examined whether age moderated the relation between grit and prosocial behavior. Data were used from 188 upper-Midwest adults (aged 37-89). Multiple regression analyses showed that age moderated the relation between grit and prosocial behavior such that grit predicted prosocial behavior in older adults but not middle age adults. A sense of community cohesion was predictive of prosocial behavior despite age. While grit may promote generative acts in different ways depending on age, a sense of community cohesion may foster community contributions despite age. The discussion focuses on future directions and ways to promote generativity using this research.

Methodological reporting of randomized trials in five leading Chinese nursing journals.

PubMed

Shi, Chunhu; Tian, Jinhui; Ren, Dan; Wei, Hongli; Zhang, Lihuan; Wang, Quan; Yang, Kehu

2014-01-01

Randomized controlled trials (RCTs) are not always well reported, especially in terms of their methodological descriptions. This study aimed to investigate the adherence of methodological reporting complying with CONSORT and explore associated trial level variables in the Chinese nursing care field. In June 2012, we identified RCTs published in five leading Chinese nursing journals and included trials with details of randomized methods. The quality of methodological reporting was measured through the methods section of the CONSORT checklist and the overall CONSORT methodological items score was calculated and expressed as a percentage. Meanwhile, we hypothesized that some general and methodological characteristics were associated with reporting quality and conducted a regression with these data to explore the correlation. The descriptive and regression statistics were calculated via SPSS 13.0. In total, 680 RCTs were included. The overall CONSORT methodological items score was 6.34 ± 0.97 (Mean ± SD). No RCT reported descriptions and changes in "trial design," changes in "outcomes" and "implementation," or descriptions of the similarity of interventions for "blinding." Poor reporting was found in detailing the "settings of participants" (13.1%), "type of randomization sequence generation" (1.8%), calculation methods of "sample size" (0.4%), explanation of any interim analyses and stopping guidelines for "sample size" (0.3%), "allocation concealment mechanism" (0.3%), additional analyses in "statistical methods" (2.1%), and targeted subjects and methods of "blinding" (5.9%). More than 50% of trials described randomization sequence generation, the eligibility criteria of "participants," "interventions," and definitions of the "outcomes" and "statistical methods." The regression analysis found that publication year and ITT analysis were weakly associated with CONSORT score. The completeness of methodological reporting of RCTs in the Chinese nursing care field is poor, especially with regard to the reporting of trial design, changes in outcomes, sample size calculation, allocation concealment, blinding, and statistical methods.

Use of Multiple Regression and Use-Availability Analyses in Determining Habitat Selection by Gray Squirrels (Sciurus Carolinensis)

Treesearch

John W. Edwards; Susan C. Loeb; David C. Guynn

1994-01-01

Multiple regression and use-availability analyses are two methods for examining habitat selection. Use-availability analysis is commonly used to evaluate macrohabitat selection whereas multiple regression analysis can be used to determine microhabitat selection. We compared these techniques using behavioral observations (n = 5534) and telemetry locations (n = 2089) of...

Socioeconomic determinants of childhood overweight and obesity in China: the long arm of institutional power

PubMed Central

Fu, Qiang; George, Linda K.

2018-01-01

Abstract Previous studies have widely reported that the association between socioeconomic status (SES) and childhood overweight and obesity in China is significant and positive, which lends little support to the fundamental-cause perspective. Using multiple waves (1997, 2000, 2004 and 2006) of the China Health and Nutrition Survey (CHNS) (N = 2,556, 2,063, 1,431 and 1,242, respectively) and continuous BMI cut-points obtained from a polynomial method, (mixed-effect) logistic regression analyses show that parental state-sector employment, an important, yet overlooked, indicator of political power during the market transformation has changed from a risk factor for childhood overweight/obesity in 1997 to a protective factor for childhood overweight/obesity in 2006. Results from quantile regression analyses generate the same conclusions and demonstrate that the protective effect of parental state sector employment at high percentiles of BMI is robust under different estimation strategies. By bridging the fundamental causes perspective and theories of market transformation, this research not only documents the effect of political power on childhood overweight/obesity but also calls for the use of multifaceted, culturally-relevant stratification measures in testing the fundamental cause perspective across time and space. PMID:26178452

Prognostic value of inflammation-based scores in patients with osteosarcoma

PubMed Central

Liu, Bangjian; Huang, Yujing; Sun, Yuanjue; Zhang, Jianjun; Yao, Yang; Shen, Zan; Xiang, Dongxi; He, Aina

2016-01-01

Systemic inflammation responses have been associated with cancer development and progression. C-reactive protein (CRP), Glasgow prognostic score (GPS), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), and neutrophil-platelet score (NPS) have been shown to be independent risk factors in various types of malignant tumors. This retrospective analysis of 162 osteosarcoma cases was performed to estimate their predictive value of survival in osteosarcoma. All statistical analyses were performed by SPSS statistical software. Receiver operating characteristic (ROC) analysis was generated to set optimal thresholds; area under the curve (AUC) was used to show the discriminatory abilities of inflammation-based scores; Kaplan-Meier analysis was performed to plot the survival curve; cox regression models were employed to determine the independent prognostic factors. The optimal cut-off points of NLR, PLR, and LMR were 2.57, 123.5 and 4.73, respectively. GPS and NLR had a markedly larger AUC than CRP, PLR and LMR. High levels of CRP, GPS, NLR, PLR, and low level of LMR were significantly associated with adverse prognosis (P < 0.05). Multivariate Cox regression analyses revealed that GPS, NLR, and occurrence of metastasis were top risk factors associated with death of osteosarcoma patients. PMID:28008988

Regression Verification Using Impact Summaries

NASA Technical Reports Server (NTRS)

Backes, John; Person, Suzette J.; Rungta, Neha; Thachuk, Oksana

2013-01-01

Regression verification techniques are used to prove equivalence of syntactically similar programs. Checking equivalence of large programs, however, can be computationally expensive. Existing regression verification techniques rely on abstraction and decomposition techniques to reduce the computational effort of checking equivalence of the entire program. These techniques are sound but not complete. In this work, we propose a novel approach to improve scalability of regression verification by classifying the program behaviors generated during symbolic execution as either impacted or unimpacted. Our technique uses a combination of static analysis and symbolic execution to generate summaries of impacted program behaviors. The impact summaries are then checked for equivalence using an o-the-shelf decision procedure. We prove that our approach is both sound and complete for sequential programs, with respect to the depth bound of symbolic execution. Our evaluation on a set of sequential C artifacts shows that reducing the size of the summaries can help reduce the cost of software equivalence checking. Various reduction, abstraction, and compositional techniques have been developed to help scale software verification techniques to industrial-sized systems. Although such techniques have greatly increased the size and complexity of systems that can be checked, analysis of large software systems remains costly. Regression analysis techniques, e.g., regression testing [16], regression model checking [22], and regression verification [19], restrict the scope of the analysis by leveraging the differences between program versions. These techniques are based on the idea that if code is checked early in development, then subsequent versions can be checked against a prior (checked) version, leveraging the results of the previous analysis to reduce analysis cost of the current version. Regression verification addresses the problem of proving equivalence of closely related program versions [19]. These techniques compare two programs with a large degree of syntactic similarity to prove that portions of one program version are equivalent to the other. Regression verification can be used for guaranteeing backward compatibility, and for showing behavioral equivalence in programs with syntactic differences, e.g., when a program is refactored to improve its performance, maintainability, or readability. Existing regression verification techniques leverage similarities between program versions by using abstraction and decomposition techniques to improve scalability of the analysis [10, 12, 19]. The abstractions and decomposition in the these techniques, e.g., summaries of unchanged code [12] or semantically equivalent methods [19], compute an over-approximation of the program behaviors. The equivalence checking results of these techniques are sound but not complete-they may characterize programs as not functionally equivalent when, in fact, they are equivalent. In this work we describe a novel approach that leverages the impact of the differences between two programs for scaling regression verification. We partition program behaviors of each version into (a) behaviors impacted by the changes and (b) behaviors not impacted (unimpacted) by the changes. Only the impacted program behaviors are used during equivalence checking. We then prove that checking equivalence of the impacted program behaviors is equivalent to checking equivalence of all program behaviors for a given depth bound. In this work we use symbolic execution to generate the program behaviors and leverage control- and data-dependence information to facilitate the partitioning of program behaviors. The impacted program behaviors are termed as impact summaries. The dependence analyses that facilitate the generation of the impact summaries, we believe, could be used in conjunction with other abstraction and decomposition based approaches, [10, 12], as a complementary reduction technique. An evaluation of our regression verification technique shows that our approach is capable of leveraging similarities between program versions to reduce the size of the queries and the time required to check for logical equivalence. The main contributions of this work are: - A regression verification technique to generate impact summaries that can be checked for functional equivalence using an off-the-shelf decision procedure. - A proof that our approach is sound and complete with respect to the depth bound of symbolic execution. - An implementation of our technique using the LLVMcompiler infrastructure, the klee Symbolic Virtual Machine [4], and a variety of Satisfiability Modulo Theory (SMT) solvers, e.g., STP [7] and Z3 [6]. - An empirical evaluation on a set of C artifacts which shows that the use of impact summaries can reduce the cost of regression verification.

Spatial patterns of species richness in New World coral snakes and the metabolic theory of ecology

NASA Astrophysics Data System (ADS)

Terribile, Levi Carina; Diniz-Filho, José Alexandre Felizola

2009-03-01

The metabolic theory of ecology (MTE) has attracted great interest because it proposes an explanation for species diversity gradients based on temperature-metabolism relationships of organisms. Here we analyse the spatial richness pattern of 73 coral snake species from the New World in the context of MTE. We first analysed the association between ln-transformed richness and environmental variables, including the inverse transformation of annual temperature (1/ kT). We used eigenvector-based spatial filtering to remove the residual spatial autocorrelation in the data and geographically weighted regression to account for non-stationarity in data. In a model I regression (OLS), the observed slope between ln-richness and 1/ kT was -0.626 ( r2 = 0.413), but a model II regression generated a much steeper slope (-0.975). When we added additional environmental correlates and the spatial filters in the OLS model, the R2 increased to 0.863 and the partial regression coefficient of 1/ kT was -0.676. The GWR detected highly significant non-stationarity, in data, and the median of local slopes of ln-richness against 1/ kT was -0.38. Our results expose several problems regarding the assumptions needed to test MTE: although the slope of OLS fell within that predicted by the theory and the dataset complied with the assumption of temperature-independence of average body size, the fact that coral snakes consist of a restricted taxonomic group and the non-stationarity of slopes across geographical space makes MTE invalid to explain richness in this case. Also, it is clear that other ecological and historical factors are important drivers of species richness patterns and must be taken into account both in theoretical modeling and data analysis.

Antibiotic Resistances in Livestock: A Comparative Approach to Identify an Appropriate Regression Model for Count Data

PubMed Central

Hüls, Anke; Frömke, Cornelia; Ickstadt, Katja; Hille, Katja; Hering, Johanna; von Münchhausen, Christiane; Hartmann, Maria; Kreienbrock, Lothar

2017-01-01

Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial resistance in livestock holdings. In general, regression models are used to describe these relationships between environmental factors and resistance outcome. Besides the study design, the correlation structures of the different outcomes of antibiotic resistance and structural zero measurements on the resistance outcome as well as on the exposure side are challenges for the epidemiological model building process. The use of appropriate regression models that acknowledge these complexities is essential to assure valid epidemiological interpretations. The aims of this paper are (i) to explain the model building process comparing several competing models for count data (negative binomial model, quasi-Poisson model, zero-inflated model, and hurdle model) and (ii) to compare these models using data from a cross-sectional study on antibiotic resistance in animal husbandry. These goals are essential to evaluate which model is most suitable to identify potential prevention measures. The dataset used as an example in our analyses was generated initially to study the prevalence and associated factors for the appearance of cefotaxime-resistant Escherichia coli in 48 German fattening pig farms. For each farm, the outcome was the count of samples with resistant bacteria. There was almost no overdispersion and only moderate evidence of excess zeros in the data. Our analyses show that it is essential to evaluate regression models in studies analyzing the relationship between environmental factors and antibiotic resistances in livestock. After model comparison based on evaluation of model predictions, Akaike information criterion, and Pearson residuals, here the hurdle model was judged to be the most appropriate model. PMID:28620609

Ultrasound predictors of placental invasion: the Placenta Accreta Index.

PubMed

Rac, Martha W F; Dashe, Jodi S; Wells, C Edward; Moschos, Elysia; McIntire, Donald D; Twickler, Diane M

2015-03-01

We sought to apply a standardized evaluation of ultrasound parameters for the prediction of placental invasion in a high-risk population. This was a retrospective review of gravidas with ≥1 prior cesarean delivery who received an ultrasound diagnosis of placenta previa or low-lying placenta in the third trimester at our institution from 1997 through 2011. Sonographic images were reviewed by an investigator blinded to pregnancy outcome and sonography reports. Parameters assessed included loss of retroplacental clear zone, irregularity and width of uterine-bladder interface, smallest myometrial thickness, presence of lacunar spaces, and bridging vessels. Diagnosis of placental invasion was based on histologic confirmation. Statistical analyses were performed using linear logistic regression and multiparametric analyses to generate a predictive equation evaluated using a receiver operating characteristic curve. Of 184 gravidas who met inclusion criteria, 54 (29%) had invasion confirmed on hysterectomy specimen. All sonographic parameters were associated with placental invasion (P < .001). Constructing a receiver operating characteristic curve, the combination of smallest sagittal myometrial thickness, lacunae, and bridging vessels, in addition to number of cesarean deliveries and placental location, yielded an area under the curve of 0.87 (95% confidence interval, 0.80-0.95). Using logistic regression, a predictive equation was generated, termed the "Placenta Accreta Index." Each parameter was weighted to create a 9-point scale in which a score of 0-9 provided a probability of invasion that ranged from 2-96%, respectively. Assignment of the Placenta Accreta Index may be helpful in predicting individual patient risk for morbidly adherent placenta. Copyright © 2015 Elsevier Inc. All rights reserved.

Individual, Psychosocial, and Social Correlates of Unprotected Anal Intercourse in a New Generation of Young Men Who Have Sex With Men in New York City

PubMed Central

Kapadia, Farzana; Siconolfi, Daniel E.; Moeller, Robert W.; Figueroa, Rafael Perez; Barton, Staci C.; Blachman-Forshay, Jaclyn

2013-01-01

Objectives. We examined associations of individual, psychosocial, and social factors with unprotected anal intercourse (UAI) among young men who have sex with men in New York City. Methods. Using baseline assessment data from 592 young men who have sex with men participating in an ongoing prospective cohort study, we conducted multivariable logistic regression analyses to examine the associations between covariates and likelihood of recently engaging in UAI with same-sex partners. Results. Nineteen percent reported recent UAI with a same-sex partner. In multivariable models, being in a current relationship with another man (adjusted odds ratio [AOR] = 4.87), an arrest history (AOR = 2.01), greater residential instability (AOR = 1.75), and unstable housing or homelessness (AOR = 3.10) was associated with recent UAI. Although high levels of gay community affinity and low internalized homophobia were associated with engaging in UAI in bivariate analyses, these associations did not persist in multivariable analyses. Conclusions. Associations of psychosocial and socially produced conditions with UAI among a new generation of young men who have sex with men warrant that HIV prevention programs and policies address structural factors that predispose sexual risk behaviors. PMID:23488487

Spatial Autocorrelation of Cancer Incidence in Saudi Arabia

PubMed Central

Al-Ahmadi, Khalid; Al-Zahrani, Ali

2013-01-01

Little is known about the geographic distribution of common cancers in Saudi Arabia. We explored the spatial incidence patterns of common cancers in Saudi Arabia using spatial autocorrelation analyses, employing the global Moran’s I and Anselin’s local Moran’s I statistics to detect nonrandom incidence patterns. Global ordinary least squares (OLS) regression and local geographically-weighted regression (GWR) were applied to examine the spatial correlation of cancer incidences at the city level. Population-based records of cancers diagnosed between 1998 and 2004 were used. Male lung cancer and female breast cancer exhibited positive statistically significant global Moran’s I index values, indicating a tendency toward clustering. The Anselin’s local Moran’s I analyses revealed small significant clusters of lung cancer, prostate cancer and Hodgkin’s disease among males in the Eastern region and significant clusters of thyroid cancers in females in the Eastern and Riyadh regions. Additionally, both regression methods found significant associations among various cancers. For example, OLS and GWR revealed significant spatial associations among NHL, leukemia and Hodgkin’s disease (r² = 0.49–0.67 using OLS and r² = 0.52–0.68 using GWR) and between breast and prostate cancer (r² = 0.53 OLS and 0.57 GWR) in Saudi Arabian cities. These findings may help to generate etiologic hypotheses of cancer causation and identify spatial anomalies in cancer incidence in Saudi Arabia. Our findings should stimulate further research on the possible causes underlying these clusters and associations. PMID:24351742

Non-metallic coating thickness prediction using artificial neural network and support vector machine with time resolved thermography

NASA Astrophysics Data System (ADS)

Wang, Hongjin; Hsieh, Sheng-Jen; Peng, Bo; Zhou, Xunfei

2016-07-01

A method without requirements on knowledge about thermal properties of coatings or those of substrates will be interested in the industrial application. Supervised machine learning regressions may provide possible solution to the problem. This paper compares the performances of two regression models (artificial neural networks (ANN) and support vector machines for regression (SVM)) with respect to coating thickness estimations made based on surface temperature increments collected via time resolved thermography. We describe SVM roles in coating thickness prediction. Non-dimensional analyses are conducted to illustrate the effects of coating thicknesses and various factors on surface temperature increments. It's theoretically possible to correlate coating thickness with surface increment. Based on the analyses, the laser power is selected in such a way: during the heating, the temperature increment is high enough to determine the coating thickness variance but low enough to avoid surface melting. Sixty-one pain-coated samples with coating thicknesses varying from 63.5 μm to 571 μm are used to train models. Hyper-parameters of the models are optimized by 10-folder cross validation. Another 28 sets of data are then collected to test the performance of the three methods. The study shows that SVM can provide reliable predictions of unknown data, due to its deterministic characteristics, and it works well when used for a small input data group. The SVM model generates more accurate coating thickness estimates than the ANN model.

Modeling the energy content of combustible ship-scrapping waste at Alang-Sosiya, India, using multiple regression analysis.

PubMed

Reddy, M Srinivasa; Basha, Shaik; Joshi, H V; Sravan Kumar, V G; Jha, B; Ghosh, P K

2005-01-01

Alang-Sosiya is the largest ship-scrapping yard in the world, established in 1982. Every year an average of 171 ships having a mean weight of 2.10 x 10(6)(+/-7.82 x 10(5)) of light dead weight tonnage (LDT) being scrapped. Apart from scrapped metals, this yard generates a massive amount of combustible solid waste in the form of waste wood, plastic, insulation material, paper, glass wool, thermocol pieces (polyurethane foam material), sponge, oiled rope, cotton waste, rubber, etc. In this study multiple regression analysis was used to develop predictive models for energy content of combustible ship-scrapping solid wastes. The scope of work comprised qualitative and quantitative estimation of solid waste samples and performing a sequential selection procedure for isolating variables. Three regression models were developed to correlate the energy content (net calorific values (LHV)) with variables derived from material composition, proximate and ultimate analyses. The performance of these models for this particular waste complies well with the equations developed by other researchers (Dulong, Steuer, Scheurer-Kestner and Bento's) for estimating energy content of municipal solid waste.

When your smoking is not just about you: antismoking advertising, interpersonal pressure, and quitting outcomes.

PubMed

Dunlop, Sally M; Cotter, Trish; Perez, Donna

2014-01-01

The authors investigated the potential for antismoking advertising to generate interpersonal pressure on smokers to quit using the Cancer Institute NSW's Tobacco Tracking Survey, a telephone tracking survey of adult smokers conducted throughout the year with approximately 50 interviews per week (N = 5,448). The survey includes questions relating to recently broadcast antismoking advertisements, including whether smokers have received pressure from family and friends as a result of their seeing the advertisements. The authors conducted multivariate logistic regression analyses to predict: (a) receiving ad-stimulated interpersonal pressure; and (b) quitting outcomes. All analyses controlled for smoker characteristics and potential exposure to the advertisements. Compared with ads coded as having a low level of emotion (by independent coders), ads coded as highly emotional were more likely to have generated interpersonal pressure. Ad-stimulated interpersonal pressure was associated with an increased likelihood of recent quit attempts and with salient quitting thoughts, with a greater effect on quitting thoughts for interpersonal pressure generated by highly and moderately emotional ads. These results support previous research suggesting that highly emotional antismoking ads with personal stories or graphic imagery are effective in promoting smoking cessation, and these results help to identify communication processes that contribute to the ads' success.

Linear models for calculating digestibile energy for sheep diets.

PubMed

Fonnesbeck, P V; Christiansen, M L; Harris, L E

1981-05-01

Equations for estimating the digestible energy (DE) content of sheep diets were generated from the chemical contents and a factorial description of diets fed to lambs in digestion trials. The diet factors were two forages (alfalfa and grass hay), harvested at three stages of maturity (late vegetative, early bloom and full bloom), fed in two ingredient combinations (all hay or a 50:50 hay and corn grain mixture) and prepared by two forage texture processes (coarsely chopped or finely chopped and pelleted). The 2 x 3 x 2 x 2 factorial arrangement produced 24 diet treatments. These were replicated twice, for a total of 48 lamb digestion trials. In model 1 regression equations, DE was calculated directly from chemical composition of the diet. In model 2, regression equations predicted the percentage of digested nutrient from the chemical contents of the diet and then DE of the diet was calculated as the sum of the gross energy of the digested organic components. Expanded forms of model 1 and model 2 were also developed that included diet factors as qualitative indicator variables to adjust the regression constant and regression coefficients for the diet description. The expanded forms of the equations accounted for significantly more variation in DE than did the simple models and more accurately estimated DE of the diet. Information provided by the diet description proved as useful as chemical analyses for the prediction of digestibility of nutrients. The statistics indicate that, with model 1, neutral detergent fiber and plant cell wall analyses provided as much information for the estimation of DE as did model 2 with the combined information from crude protein, available carbohydrate, total lipid, cellulose and hemicellulose. Regression equations are presented for estimating DE with the most currently analyzed organic components, including linear and curvilinear variables and diet factors that significantly reduce the standard error of the estimate. To estimate De of a diet, the user utilizes the equation that uses the chemical analysis information and diet description most effectively.

Regression Discontinuity for Causal Effect Estimation in Epidemiology.

PubMed

Oldenburg, Catherine E; Moscoe, Ellen; Bärnighausen, Till

Regression discontinuity analyses can generate estimates of the causal effects of an exposure when a continuously measured variable is used to assign the exposure to individuals based on a threshold rule. Individuals just above the threshold are expected to be similar in their distribution of measured and unmeasured baseline covariates to individuals just below the threshold, resulting in exchangeability. At the threshold exchangeability is guaranteed if there is random variation in the continuous assignment variable, e.g., due to random measurement error. Under exchangeability, causal effects can be identified at the threshold. The regression discontinuity intention-to-treat (RD-ITT) effect on an outcome can be estimated as the difference in the outcome between individuals just above (or below) versus just below (or above) the threshold. This effect is analogous to the ITT effect in a randomized controlled trial. Instrumental variable methods can be used to estimate the effect of exposure itself utilizing the threshold as the instrument. We review the recent epidemiologic literature reporting regression discontinuity studies and find that while regression discontinuity designs are beginning to be utilized in a variety of applications in epidemiology, they are still relatively rare, and analytic and reporting practices vary. Regression discontinuity has the potential to greatly contribute to the evidence base in epidemiology, in particular on the real-life and long-term effects and side-effects of medical treatments that are provided based on threshold rules - such as treatments for low birth weight, hypertension or diabetes.

Quasi-Likelihood Techniques in a Logistic Regression Equation for Identifying Simulium damnosum s.l. Larval Habitats Intra-cluster Covariates in Togo.

PubMed

Jacob, Benjamin G; Novak, Robert J; Toe, Laurent; Sanfo, Moussa S; Afriyie, Abena N; Ibrahim, Mohammed A; Griffith, Daniel A; Unnasch, Thomas R

2012-01-01

The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l. a major black-fly vector of Onchoceriasis, postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects. Generally, this correlation comes from two sources: (1) the design of the random effects and their assumed covariance from the multiple levels within the regression model; and, (2) the correlation structure of the residuals. Unfortunately, inconspicuous errors in residual intra-cluster correlation estimates can overstate precision in forecasted S.damnosum s.l. riverine larval habitat explanatory attributes regardless how they are treated (e.g., independent, autoregressive, Toeplitz, etc). In this research, the geographical locations for multiple riverine-based S. damnosum s.l. larval ecosystem habitats sampled from 2 pre-established epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially the data was aggregated into proc genmod. An agglomerative hierarchical residual cluster-based analysis was then performed. The sampled clustered study site data was then analyzed for statistical correlations using Monthly Biting Rates (MBR). Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS. A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by Annual Biting Rates (ABR). This data was overlain onto multitemporal sub-meter pixel resolution satellite data (i.e., QuickBird 0.61m wavbands ). Orthogonal spatial filter eigenvectors were then generated in SAS/GIS. Univariate and non-linear regression-based models (i.e., Logistic, Poisson and Negative Binomial) were also employed to determine probability distributions and to identify statistically significant parameter estimators from the sampled data. Thereafter, Durbin-Watson test statistics were used to test the null hypothesis that the regression residuals were not autocorrelated against the alternative that the residuals followed an autoregressive process in AUTOREG. Bayesian uncertainty matrices were also constructed employing normal priors for each of the sampled estimators in PROC MCMC. The residuals revealed both spatially structured and unstructured error effects in the high and low ABR-stratified clusters. The analyses also revealed that the estimators, levels of turbidity and presence of rocks were statistically significant for the high-ABR-stratified clusters, while the estimators distance between habitats and floating vegetation were important for the low-ABR-stratified cluster. Varying and constant coefficient regression models, ABR- stratified GIS-generated clusters, sub-meter resolution satellite imagery, a robust residual intra-cluster diagnostic test, MBR-based histograms, eigendecomposition spatial filter algorithms and Bayesian matrices can enable accurate autoregressive estimation of latent uncertainity affects and other residual error probabilities (i.e., heteroskedasticity) for testing correlations between georeferenced S. damnosum s.l. riverine larval habitat estimators. The asymptotic distribution of the resulting residual adjusted intra-cluster predictor error autocovariate coefficients can thereafter be established while estimates of the asymptotic variance can lead to the construction of approximate confidence intervals for accurately targeting productive S. damnosum s.l habitats based on spatiotemporal field-sampled count data.

Segregation analysis of prostate cancer in France: evidence for autosomal dominant inheritance and residual brother-brother dependence.

PubMed

Valeri, A; Briollais, L; Azzouzi, R; Fournier, G; Mangin, P; Berthon, P; Cussenot, O; Demenais, F

2003-03-01

Four segregation analyses concerning prostate cancer (CaP), three conducted in the United States and one in Northern Europe, have shown evidence for a dominant major gene but with different parameter estimates. A recent segregation analysis of Australian pedigrees has found a better fit of a two-locus model than single-locus models. This model included a dominantly inherited increased risk that was greater at younger ages and a recessively inherited or X-linked increased risk that was greater at older ages. Recent linkage analyses have led to the detection of at least 8 CaP predisposing genes, suggesting a complex inheritance and genetic heterogeneity. To assess the nature of familial aggregation of prostate cancer in France, segregation analysis was conducted in 691 families ascertained through 691 CaP patients, recruited from three French hospitals and unselected with respect to age at diagnosis, clinical stage or family history. This mode of family inclusion, without any particular selection of the probands, is unique, as probands from all previous analyses were selected according to various criteria. Segregation analysis was carried out using the logistic hazard regressive model, as incorporated in the REGRESS program, which can accommodate a major gene effect, residual familial dependences of any origin (genetic and/or environmental), and covariates, while including survival analysis concepts. Segregation analysis showed evidence for the segregation of an autosomal dominant gene (allele frequency of 0.03%) with an additional brother-brother dependence. The estimated cumulative risks of prostate cancer by age 85 years, among subjects with the at-risk genotype, were 86% in the fathers' generation and 99% in the probands' generation. This study supports the model of Mendelian transmission of a rare autosomal dominant gene with high penetrance, and demonstrates that additional genetic and/or common sibling environmental factors are involved to account for the familial clustering of CaP.

Analysis of response to 20 generations of selection for body composition in mice: fit to infinitesimal model assumptions

PubMed Central

Martinez, Victor; Bünger, Lutz; Hill, William G

2000-01-01

Data were analysed from a divergent selection experiment for an indicator of body composition in the mouse, the ratio of gonadal fat pad to body weight (GFPR). Lines were selected for 20 generations for fat (F), lean (L) or were unselected (C), with three replicates of each. Selection was within full-sib families, 16 families per replicate for the first seven generations, eight subsequently. At generation 20, GFPR in the F lines was twice and in the L lines half that of C. A log transformation removed both asymmetry of response and heterogeneity of variance among lines, and so was used throughout. Estimates of genetic variance and heritability (approximately 50%) obtained using REML with an animal model were very similar, whether estimated from the first few generations of selection, or from all 20 generations, or from late generations having fitted pedigree. The estimates were also similar when estimated from selected or control lines. Estimates from REML also agreed with estimates of realised heritability. The results all accord with expectations under the infinitesimal model, despite the four-fold changes in mean. Relaxed selection lines, derived from generation 20, showed little regression in fatness after 40 generations without selection. PMID:14736404

The use of segmented regression in analysing interrupted time series studies: an example in pre-hospital ambulance care.

PubMed

Taljaard, Monica; McKenzie, Joanne E; Ramsay, Craig R; Grimshaw, Jeremy M

2014-06-19

An interrupted time series design is a powerful quasi-experimental approach for evaluating effects of interventions introduced at a specific point in time. To utilize the strength of this design, a modification to standard regression analysis, such as segmented regression, is required. In segmented regression analysis, the change in intercept and/or slope from pre- to post-intervention is estimated and used to test causal hypotheses about the intervention. We illustrate segmented regression using data from a previously published study that evaluated the effectiveness of a collaborative intervention to improve quality in pre-hospital ambulance care for acute myocardial infarction (AMI) and stroke. In the original analysis, a standard regression model was used with time as a continuous variable. We contrast the results from this standard regression analysis with those from segmented regression analysis. We discuss the limitations of the former and advantages of the latter, as well as the challenges of using segmented regression in analysing complex quality improvement interventions. Based on the estimated change in intercept and slope from pre- to post-intervention using segmented regression, we found insufficient evidence of a statistically significant effect on quality of care for stroke, although potential clinically important effects for AMI cannot be ruled out. Segmented regression analysis is the recommended approach for analysing data from an interrupted time series study. Several modifications to the basic segmented regression analysis approach are available to deal with challenges arising in the evaluation of complex quality improvement interventions.

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

PubMed Central

Nowicka, Malgorzata; Krieg, Carsten; Weber, Lukas M.; Hartmann, Felix J.; Guglietta, Silvia; Becher, Burkhard; Levesque, Mitchell P.; Robinson, Mark D.

2017-01-01

High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals). PMID:28663787

Primary healthcare usage and use of medications among immigrant children according to age of arrival to Norway: a population-based study

PubMed Central

Diaz, Esperanza

2017-01-01

Background Morbidity, use of healthcare and medication use have been reported to vary across groups of migrants and according to the different phases of migration, but little is known about children with immigrant background. In this study, we investigate whether the immigrant children's age of arrival predicts differences in usage of primary healthcare (PHC) and in use of prescribed medication. Methods This nationwide, population-based study used information for children under 18 years of age in 2008 from three linked registers in Norway. Use of medication was assessed with logistic regression analyses presented with ORs with 95% CIs. Results Of 1 168 365 children, 119 251 had immigrant background. The mean number of PHC visits among children aged 10–18 years, was 1.19 for non-immigrants, 1.17 among second generation immigrants, 1.12, 1.05 and 0.83 among first immigrant children who were <5, 5–9 and ≥10 years at arrival in Norway, respectively. Patterns were similar for younger immigrants, and were confirmed with regression models adjusting for age and sex. First generation immigrant children used less of nearly all groups of prescribed medication compared to non-immigrants when adjusting for age and sex (overall OR 0.48 (0.47 to 0.49)), and medication was also generally less used among second generation immigrant children (overall OR 0.92 (0.91 to 0.94)). Conclusions Age of arrival predicted PHC usage among children among first-generation children. First-generation immigrant children, particularly those arriving later in adolescence, used PHC less than age corresponding non-immigrant children. Immigrant children used less prescribed medication compared to non-immigrants after adjustment for age and sex. PMID:28148537

Gender Gaps in Mathematics, Science and Reading Achievements in Muslim Countries: Evidence from Quantile Regression Analyses

ERIC Educational Resources Information Center

Shafiq, M. Najeeb

2011-01-01

Using quantile regression analyses, this study examines gender gaps in mathematics, science, and reading in Azerbaijan, Indonesia, Jordan, the Kyrgyz Republic, Qatar, Tunisia, and Turkey among 15 year-old students. The analyses show that girls in Azerbaijan achieve as well as boys in mathematics and science and overachieve in reading. In Jordan,…

A general framework for the use of logistic regression models in meta-analysis.

PubMed

Simmonds, Mark C; Higgins, Julian Pt

2016-12-01

Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, "one-stage" random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally distributed. A second benefit of using this model is that it may be applied, with only minor modification, in a range of meta-analytic scenarios, including meta-regression, network meta-analyses and meta-analyses of diagnostic test accuracy. This single model can potentially replace the variety of often complex methods used in these areas. This paper considers, with a range of meta-analysis examples, how random-effects logistic regression models may be used in a number of different types of meta-analyses. This one-stage approach is compared with widely used meta-analysis methods including Bayesian network meta-analysis and the bivariate and hierarchical summary receiver operating characteristic (ROC) models for meta-analyses of diagnostic test accuracy. © The Author(s) 2014.

Statistical Analyses of Raw Material Data for MTM45-1/CF7442A-36% RW: CMH Cure Cycle

NASA Technical Reports Server (NTRS)

Coroneos, Rula; Pai, Shantaram, S.; Murthy, Pappu

2013-01-01

This report describes statistical characterization of physical properties of the composite material system MTM45-1/CF7442A, which has been tested and is currently being considered for use on spacecraft structures. This composite system is made of 6K plain weave graphite fibers in a highly toughened resin system. This report summarizes the distribution types and statistical details of the tests and the conditions for the experimental data generated. These distributions will be used in multivariate regression analyses to help determine material and design allowables for similar material systems and to establish a procedure for other material systems. Additionally, these distributions will be used in future probabilistic analyses of spacecraft structures. The specific properties that are characterized are the ultimate strength, modulus, and Poisson??s ratio by using a commercially available statistical package. Results are displayed using graphical and semigraphical methods and are included in the accompanying appendixes.

What We Have Learned from the Recent Meta-analyses on Diagnostic Methods for Atherosclerotic Plaque Regression.

PubMed

Biondi-Zoccai, Giuseppe; Mastrangeli, Simona; Romagnoli, Enrico; Peruzzi, Mariangela; Frati, Giacomo; Roever, Leonardo; Giordano, Arturo

2018-01-17

Atherosclerosis has major morbidity and mortality implications globally. While it has often been considered an irreversible degenerative process, recent evidence provides compelling proof that atherosclerosis can be reversed. Plaque regression is however difficult to appraise and quantify, with competing diagnostic methods available. Given the potential of evidence synthesis to provide clinical guidance, we aimed to review recent meta-analyses on diagnostic methods for atherosclerotic plaque regression. We identified 8 meta-analyses published between 2015 and 2017, including 79 studies and 14,442 patients, followed for a median of 12 months. They reported on atherosclerotic plaque regression appraised with carotid duplex ultrasound, coronary computed tomography, carotid magnetic resonance, coronary intravascular ultrasound, and coronary optical coherence tomography. Overall, all meta-analyses showed significant atherosclerotic plaque regression with lipid-lowering therapy, with the most notable effects on echogenicity, lipid-rich necrotic core volume, wall/plaque volume, dense calcium volume, and fibrous cap thickness. Significant interactions were found with concomitant changes in low density lipoprotein cholesterol, high density lipoprotein cholesterol, and C-reactive protein levels, and with ethnicity. Atherosclerotic plaque regression and conversion to a stable phenotype is possible with intensive medical therapy and can be demonstrated in patients using a variety of non-invasive and invasive imaging modalities.

Modelling of capital asset pricing by considering the lagged effects

NASA Astrophysics Data System (ADS)

Sukono; Hidayat, Y.; Bon, A. Talib bin; Supian, S.

2017-01-01

In this paper the problem of modelling the Capital Asset Pricing Model (CAPM) with the effect of the lagged is discussed. It is assumed that asset returns are analysed influenced by the market return and the return of risk-free assets. To analyse the relationship between asset returns, the market return, and the return of risk-free assets, it is conducted by using a regression equation of CAPM, and regression equation of lagged distributed CAPM. Associated with the regression equation lagged CAPM distributed, this paper also developed a regression equation of Koyck transformation CAPM. Results of development show that the regression equation of Koyck transformation CAPM has advantages, namely simple as it only requires three parameters, compared with regression equation of lagged distributed CAPM.

Gambling prevalence rates among immigrants: a multigenerational examination.

PubMed

Wilson, Alyssa N; Salas-Wright, Christopher P; Vaughn, Michael G; Maynard, Brandy R

2015-03-01

The present study employed data from Waves I and II of the National Epidemiologic Survey of Alcohol and Related Conditions (NESARC) to compare gambling prevalence rates across gender and world regions (e.g., Africa, Asia, Europe, and Latin America). Responses from first generation (n=5363), second generation (n=4826), third generation (n=4746), and native-born Americans (n=19,715) were subjected to a series of multinomial regression analyses, after controlling for sociodemographic variables such as age, gender, race/ethnicity, household income, education level, region of the United States, and urbanicity. The prevalence of gambling and problem gambling was markedly lower among first-generation immigrants than that of native-born Americans and second and third-generation immigrants. Results also point to inter- and intra-generational dynamics related to gender, age of arrival and duration in the United States, and world region from which participants emigrated. Additionally, we found that second-generation immigrants and nonimmigrants were significantly more likely to meet criteria for disordered gambling compared to first-generation immigrants in general. Compared to first-generation immigrants, male and female immigrants of subsequent generations and nonimmigrants were significantly more likely to report involvement in all problem gambling behaviors examined. Findings suggest that gambling prevalence rates increase across subsequent generations, and are more likely to occur in women than among men. Copyright © 2014 Elsevier Ltd. All rights reserved.

Use of principal-component, correlation, and stepwise multiple-regression analyses to investigate selected physical and hydraulic properties of carbonate-rock aquifers

USGS Publications Warehouse

Brown, C. Erwin

1993-01-01

Correlation analysis in conjunction with principal-component and multiple-regression analyses were applied to laboratory chemical and petrographic data to assess the usefulness of these techniques in evaluating selected physical and hydraulic properties of carbonate-rock aquifers in central Pennsylvania. Correlation and principal-component analyses were used to establish relations and associations among variables, to determine dimensions of property variation of samples, and to filter the variables containing similar information. Principal-component and correlation analyses showed that porosity is related to other measured variables and that permeability is most related to porosity and grain size. Four principal components are found to be significant in explaining the variance of data. Stepwise multiple-regression analysis was used to see how well the measured variables could predict porosity and (or) permeability for this suite of rocks. The variation in permeability and porosity is not totally predicted by the other variables, but the regression is significant at the 5% significance level. ?? 1993.

Association of acculturative stress, Islamic practices, and internalizing symptoms among Arab American adolescents.

PubMed

Goforth, Anisa N; Pham, Andy V; Chun, Heejung; Castro-Olivo, Sara M; Yosai, Erin R

2016-06-01

Although the numbers of Arab American immigrant youth in schools is increasing, there is little understanding of their mental health and the sociocultural factors that might influence it. This study examined the relationship between 2 sociocultural factors (i.e., acculturative stress and religious practices) and internalizing symptoms in first- and second-generation Muslim Arab American adolescents. Adolescents (n = 88) ages 11 to 18 completed measures related to acculturative stress, religious practices, internalizing symptoms, and general demographic information. Results of multiple regression analyses found that acculturative stress significantly predicted internalizing symptoms. Gender was found to moderate this association. No differences in the reported acculturative stress and internalizing symptoms were found between youth of different generational status (i.e., first- vs. second-generation). Finally, adolescents' organizational religious practices, but not their private religious practices, were found to be associated with lower acculturative stress. Implications are discussed related to how school psychologists can provide culturally responsive services to this population. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Dynamic modelling of n-of-1 data: powerful and flexible data analytics applied to individualised studies.

PubMed

Vieira, Rute; McDonald, Suzanne; Araújo-Soares, Vera; Sniehotta, Falko F; Henderson, Robin

2017-09-01

N-of-1 studies are based on repeated observations within an individual or unit over time and are acknowledged as an important research method for generating scientific evidence about the health or behaviour of an individual. Statistical analyses of n-of-1 data require accurate modelling of the outcome while accounting for its distribution, time-related trend and error structures (e.g., autocorrelation) as well as reporting readily usable contextualised effect sizes for decision-making. A number of statistical approaches have been documented but no consensus exists on which method is most appropriate for which type of n-of-1 design. We discuss the statistical considerations for analysing n-of-1 studies and briefly review some currently used methodologies. We describe dynamic regression modelling as a flexible and powerful approach, adaptable to different types of outcomes and capable of dealing with the different challenges inherent to n-of-1 statistical modelling. Dynamic modelling borrows ideas from longitudinal and event history methodologies which explicitly incorporate the role of time and the influence of past on future. We also present an illustrative example of the use of dynamic regression on monitoring physical activity during the retirement transition. Dynamic modelling has the potential to expand researchers' access to robust and user-friendly statistical methods for individualised studies.

Work, sleep, and cholesterol levels of U.S. long-haul truck drivers

PubMed Central

LEMKE, Michael K.; APOSTOLOPOULOS, Yorghos; HEGE, Adam; WIDEMAN, Laurie; SÖNMEZ, Sevil

2016-01-01

Long-haul truck drivers in the United States experience elevated cardiovascular health risks, possibly due to hypercholesterolemia. The current study has two objectives: 1) to generate a cholesterol profile for U.S. long-haul truck drivers; and 2) to determine the influence of work organization characteristics and sleep quality and duration on cholesterol levels of long-haul truck drivers. Survey and biometric data were collected from 262 long-haul truck drivers. Descriptive analyses were performed for demographic, work organization, sleep, and cholesterol measures. Linear regression and ordinal logistic regression analyses were conducted to examine for possible predictive relationships between demographic, work organization, and sleep variables, and cholesterol outcomes. The majority (66.4%) of drivers had a low HDL (<40 mg/dL), and nearly 42% of drivers had a high-risk total cholesterol to HDL cholesterol ratio. Sleep quality was associated with HDL, LDL, and total cholesterol, and daily work hours were associated with LDL cholesterol. Workday sleep duration was associated with non-HDL cholesterol, and driving experience and sleep quality were associated with cholesterol ratio. Long-haul truck drivers have a high risk cholesterol profile, and sleep quality and work organization factors may induce these cholesterol outcomes. Targeted worksite health promotion programs are needed to curb these atherosclerotic risks. PMID:28049935

Socioeconomic determinants of childhood overweight and obesity in China: the long arm of institutional power.

PubMed

Fu, Qiang; George, Linda K

2015-07-01

Previous studies have widely reported that the association between socioeconomic status (SES) and childhood overweight and obesity in China is significant and positive, which lends little support to the fundamental-cause perspective. Using multiple waves (1997, 2000, 2004 and 2006) of the China Health and Nutrition Survey (CHNS) (N = 2,556, 2,063, 1,431 and 1,242, respectively) and continuous BMI cut-points obtained from a polynomial method, (mixed-effect) logistic regression analyses show that parental state-sector employment, an important, yet overlooked, indicator of political power during the market transformation has changed from a risk factor for childhood overweight/obesity in 1997 to a protective factor for childhood overweight/obesity in 2006. Results from quantile regression analyses generate the same conclusions and demonstrate that the protective effect of parental state sector employment at high percentiles of BMI is robust under different estimation strategies. By bridging the fundamental causes perspective and theories of market transformation, this research not only documents the effect of political power on childhood overweight/obesity but also calls for the use of multifaceted, culturally-relevant stratification measures in testing the fundamental cause perspective across time and space. © 2015 Foundation for the Sociology of Health & Illness.

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

Multiple regression and inverse moments improve the characterization of the spatial scaling behavior of daily streamflows in the Southeast United States

USGS Publications Warehouse

Farmer, William H.; Over, Thomas M.; Vogel, Richard M.

2015-01-01

Understanding the spatial structure of daily streamflow is essential for managing freshwater resources, especially in poorly-gaged regions. Spatial scaling assumptions are common in flood frequency prediction (e.g., index-flood method) and the prediction of continuous streamflow at ungaged sites (e.g. drainage-area ratio), with simple scaling by drainage area being the most common assumption. In this study, scaling analyses of daily streamflow from 173 streamgages in the southeastern US resulted in three important findings. First, the use of only positive integer moment orders, as has been done in most previous studies, captures only the probabilistic and spatial scaling behavior of flows above an exceedance probability near the median; negative moment orders (inverse moments) are needed for lower streamflows. Second, assessing scaling by using drainage area alone is shown to result in a high degree of omitted-variable bias, masking the true spatial scaling behavior. Multiple regression is shown to mitigate this bias, controlling for regional heterogeneity of basin attributes, especially those correlated with drainage area. Previous univariate scaling analyses have neglected the scaling of low-flow events and may have produced biased estimates of the spatial scaling exponent. Third, the multiple regression results show that mean flows scale with an exponent of one, low flows scale with spatial scaling exponents greater than one, and high flows scale with exponents less than one. The relationship between scaling exponents and exceedance probabilities may be a fundamental signature of regional streamflow. This signature may improve our understanding of the physical processes generating streamflow at different exceedance probabilities. 

Development of the Forensically Important Beetle Creophilus maxillosus (Coleoptera: Staphylinidae) at Constant Temperatures.

PubMed

Wang, Y; Yang, J B; Wang, J F; Li, L L; Wang, M; Yang, L J; Tao, L Y; Chu, J; Hou, Y D

2017-03-01

Creophilus maxillosus (L., 1758) is a common and widely distributed beetle species found on corpses, and its development duration is far longer than species belonging to the genus Calliphoridae and Sarcophagidae. Therefore, C. maxillosus can be used as a supplementary indicator to estimate minimum postmortem interval (PMImin), and could greatly extend the range of PMImin when the primary colonizers are no longer associated with the corpse or have emerged from pupae. Better descriptions of C. maxillosus development are needed to apply this species for forensic investigations. In this study, the development of C. maxillosus at seven constant temperatures ranging from 17.5-32.5 °C was studied. Through regression analyses, the simulation equations of larval body length variation with time after hatching were obtained. Isomegalen diagrams of the changes of larval body length over time at specific temperatures, and the isomorphen diagrams on the duration of different developmental milestones at specific temperatures were generated. In addition, thermal summation models of different developmental stages and the overall development process of C. maxillosus were generated through regression analysis, by estimating the development threshold temperatures (D0) and the thermal summation constants (K). These results provide important tools for forensic investigations to generate a long-range of PMImin estimation based on the development of C. maxillosus. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Caregiving Practice Patterns of Asian, Hispanic, and Non-Hispanic White American Family Caregivers of Older Adults Across Generations.

PubMed

Miyawaki, Christina E

2016-03-01

This study is a cross-sectional investigation of caregiving practice patterns among Asian, Hispanic and non-Hispanic White American family caregivers of older adults across three immigrant generations. The 2009 California Health Interview Survey (CHIS) dataset was used, and 591 Asian, 989 Hispanic and 6537 non-Hispanic White American caregivers of older adults were selected. First, descriptive analyses of caregivers' characteristics, caregiving situations and practice patterns were examined by racial/ethnic groups and immigrant generations. Practice patterns measured were respite care use, hours and length of caregiving. Three hypotheses on caregiving patterns based on assimilation theory were tested and analyzed using logistic regression and generalized linear models by racial/ethnic groups and generations. Caregiving patterns of non-Hispanic White caregivers supported all three hypotheses regarding respite care use, caregiving hours and caregiving duration, showing less caregiving involvement in later generations. However, Asian and Hispanic counterparts showed mixed results. Third generation Asian and Hispanic caregivers used respite care the least and spent the most caregiving hours per week and had the longest caregiving duration compared to earlier generations. These caregiving patterns revealed underlying cultural values related to filial responsibility, even among later generations of caregivers of color. Findings suggest the importance of considering the cultural values of each racial/ethnic group regardless of generation when working with racially and ethnically diverse populations of family caregivers of older adults.

Transgenerational effect of neighborhood poverty on low birth weight among African Americans in Cook County, Illinois.

PubMed

Collins, James W; David, Richard J; Rankin, Kristin M; Desireddi, Jennifer R

2009-03-15

In perinatal epidemiology, transgenerational risk factors are defined as conditions experienced by one generation that affect the pregnancy outcomes of the next generation. The authors investigated the transgenerational effect of neighborhood poverty on infant birth weight among African Americans. Stratified and multilevel logistic regression analyses were performed on an Illinois transgenerational data set with appended US Census income information. Singleton African-American infants (n = 40,648) born in 1989-1991 were considered index births. The mothers of index infants had been born in 1956-1976. The maternal grandmothers of index infants were identified. Rates of infant low birth weight (<2,500 g) rose as maternal grandmother's residential environment during her pregnancy deteriorated, independently of mother's residential environment during her pregnancy. In a multilevel logistic regression model that accounted for clustering by maternal grandmother's residential environment, the adjusted odds ratio (controlling for mother's age, education, prenatal care, cigarette smoking status, and residential environment) for infant low birth weight for maternal grandmother's residence in a poor neighborhood (compared with an affluent neighborhood) equaled 1.3 (95% confidence interval: 1.1, 1.4). This study suggests that maternal grandmother's exposure to neighborhood poverty during her pregnancy is a risk factor for infant low birth weight among African Americans.

imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel.

PubMed

Grapov, Dmitry; Newman, John W

2012-09-01

Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010).

The challenge of staying happier: testing the Hedonic Adaptation Prevention model.

PubMed

Sheldon, Kennon M; Lyubomirsky, Sonja

2012-05-01

The happiness that comes from a particular success or change in fortune abates with time. The Hedonic Adaptation Prevention (HAP) model specifies two routes by which the well-being gains derived from a positive life change are eroded--the first involving bottom-up processes (i.e., declining positive emotions generated by the positive change) and the second involving top-down processes (i.e., increased aspirations for even more positivity). The model also specifies two moderators that can forestall these processes--continued appreciation of the original life change and continued variety in change-related experiences. The authors formally tested the predictions of the HAP model in a 3-month three-wave longitudinal study of 481 students. Temporal path analyses and moderated regression analyses provided good support for the model. Implications for the stability of well-being, the feasibility of "the pursuit of happiness," and the appeal of overconsumption are discussed.

Study of the uses of Information and Communication Technologies by Pain Treatment Unit Physicians.

PubMed

Muriel Fernandez, Jorge; Sánchez Ledesma, María José; López Millan, Manuel; García Cenador, María Begoña

2017-05-01

Adequate use of Information and Communication Technologies (ICTs) in health has been shown to save the patient and caregiver time, improve access to the health system, improve diagnosis and control of disease or treatment. All this results in cost savings, and more importantly, they help improve the quality of service and the lives of patients. The purpose of this study is to analyse the differences in the uses of this ICTs between those physicians that belong to Pain Treatment Units (PU) and other physicians that work in pain not linked to these PUs. An online survey, generated by Netquest online survey tool, was sent to both groups of professionals and the data collected was statistical analysed through a logistic regression methodology which is the Logit binomial model. Our results show that those physicians that belong to PUs use ICTs more frequently and consider it more relevant to their clinical practice.

Discovery of potent NEK2 inhibitors as potential anticancer agents using structure-based exploration of NEK2 pharmacophoric space coupled with QSAR analyses.

PubMed

Khanfar, Mohammad A; Banat, Fahmy; Alabed, Shada; Alqtaishat, Saja

2017-02-01

High expression of Nek2 has been detected in several types of cancer and it represents a novel target for human cancer. In the current study, structure-based pharmacophore modeling combined with multiple linear regression (MLR)-based QSAR analyses was applied to disclose the structural requirements for NEK2 inhibition. Generated pharmacophoric models were initially validated with receiver operating characteristic (ROC) curve, and optimum models were subsequently implemented in QSAR modeling with other physiochemical descriptors. QSAR-selected models were implied as 3D search filters to mine the National Cancer Institute (NCI) database for novel NEK2 inhibitors, whereas the associated QSAR model prioritized the bioactivities of captured hits for in vitro evaluation. Experimental validation identified several potent NEK2 inhibitors of novel structural scaffolds. The most potent captured hit exhibited an [Formula: see text] value of 237 nM.

Shrinkage regression-based methods for microarray missing value imputation.

PubMed

Wang, Hsiuying; Chiu, Chia-Chun; Wu, Yi-Ching; Wu, Wei-Sheng

2013-01-01

Missing values commonly occur in the microarray data, which usually contain more than 5% missing values with up to 90% of genes affected. Inaccurate missing value estimation results in reducing the power of downstream microarray data analyses. Many types of methods have been developed to estimate missing values. Among them, the regression-based methods are very popular and have been shown to perform better than the other types of methods in many testing microarray datasets. To further improve the performances of the regression-based methods, we propose shrinkage regression-based methods. Our methods take the advantage of the correlation structure in the microarray data and select similar genes for the target gene by Pearson correlation coefficients. Besides, our methods incorporate the least squares principle, utilize a shrinkage estimation approach to adjust the coefficients of the regression model, and then use the new coefficients to estimate missing values. Simulation results show that the proposed methods provide more accurate missing value estimation in six testing microarray datasets than the existing regression-based methods do. Imputation of missing values is a very important aspect of microarray data analyses because most of the downstream analyses require a complete dataset. Therefore, exploring accurate and efficient methods for estimating missing values has become an essential issue. Since our proposed shrinkage regression-based methods can provide accurate missing value estimation, they are competitive alternatives to the existing regression-based methods.

Covariate Imbalance and Adjustment for Logistic Regression Analysis of Clinical Trial Data

PubMed Central

Ciolino, Jody D.; Martin, Reneé H.; Zhao, Wenle; Jauch, Edward C.; Hill, Michael D.; Palesch, Yuko Y.

2014-01-01

In logistic regression analysis for binary clinical trial data, adjusted treatment effect estimates are often not equivalent to unadjusted estimates in the presence of influential covariates. This paper uses simulation to quantify the benefit of covariate adjustment in logistic regression. However, International Conference on Harmonization guidelines suggest that covariate adjustment be pre-specified. Unplanned adjusted analyses should be considered secondary. Results suggest that that if adjustment is not possible or unplanned in a logistic setting, balance in continuous covariates can alleviate some (but never all) of the shortcomings of unadjusted analyses. The case of log binomial regression is also explored. PMID:24138438

An Empirical Study of Eight Nonparametric Tests in Hierarchical Regression.

ERIC Educational Resources Information Center

Harwell, Michael; Serlin, Ronald C.

When normality does not hold, nonparametric tests represent an important data-analytic alternative to parametric tests. However, the use of nonparametric tests in educational research has been limited by the absence of easily performed tests for complex experimental designs and analyses, such as factorial designs and multiple regression analyses,…

How Many Subjects Does It Take to Do a Regression Analysis?

ERIC Educational Resources Information Center

Green, Samuel B.

1991-01-01

An evaluation of the rules-of-thumb used to determine the minimum number of subjects required to conduct multiple regression analyses suggests that researchers who use a rule of thumb rather than power analyses trade simplicity of use for accuracy and specificity of response. Insufficient power is likely to result. (SLD)

Hydrology and trout populations of cold-water rivers of Michigan and Wisconsin

USGS Publications Warehouse

Hendrickson, G.E.; Knutilla, R.L.

1974-01-01

Statistical multiple-regression analyses showed significant relationships between trout populations and hydrologic parameters. Parameters showing the higher levels of significance were temperature, hardness of water, percentage of gravel bottom, percentage of bottom vegetation, variability of streamflow, and discharge per unit drainage area. Trout populations increase with lower levels of annual maximum water temperatures, with increase in water hardness, and with increase in percentage of gravel and bottom vegetation. Trout populations also increase with decrease in variability of streamflow, and with increase in discharge per unit drainage area. Most hydrologic parameters were significant when evaluated collectively, but no parameter, by itself, showed a high degree of correlation with trout populations in regression analyses that included all the streams sampled. Regression analyses of stream segments that were restricted to certain limits of hardness, temperature, or percentage of gravel bottom showed improvements in correlation. Analyses of trout populations, in pounds per acre and pounds per mile and hydrologic parameters resulted in regression equations from which trout populations could be estimated with standard errors of 89 and 84 per cent, respectively.

Hybrid propulsion technology program

NASA Technical Reports Server (NTRS)

1990-01-01

Technology was identified which will enable application of hybrid propulsion to manned and unmanned space launch vehicles. Two design concepts are proposed. The first is a hybrid propulsion system using the classical method of regression (classical hybrid) resulting from the flow of oxidizer across a fuel grain surface. The second system uses a self-sustaining gas generator (gas generator hybrid) to produce a fuel rich exhaust that was mixed with oxidizer in a separate combustor. Both systems offer cost and reliability improvement over the existing solid rocket booster and proposed liquid boosters. The designs were evaluated using life cycle cost and reliability. The program consisted of: (1) identification and evaluation of candidate oxidizers and fuels; (2) preliminary evaluation of booster design concepts; (3) preparation of a detailed point design including life cycle costs and reliability analyses; (4) identification of those hybrid specific technologies needing improvement; and (5) preperation of a technology acquisition plan and large scale demonstration plan.

Exploring the Academic Benefits of Friendship Ties for Latino Boys and Girls*

PubMed

Riegle-Crumb, Catherine; Callahan, Rebecca M

2009-09-01

OBJECTIVES: We examine how the racial/ethnic and generational status composition of Latino students' friendship groups is related to their academic achievement and whether there are differential effects by gender. METHODS: We use multivariate regression analyses to examine the effects of friends' characteristics on Latino students' end of high school grades, utilizing data from the Adolescent Health and Academic Achievement Study (AHAA), and its parent survey, the National Longitudinal Study of Adolescent Health (Add Health). RESULTS: For Latina girls, there are positive effects of having more friendship ties to third-plus-generation Latino peers in contrast to dominant culture peers; yet Latino boys benefit academically from ties to all co-ethnic peers. Having friends with higher parental education promotes achievement of both genders. CONCLUSION: Our results counter notions of a pervasive negative peer influence of minority youth and suggest that co-ethnic ties are an important source of social capital for Latino students' achievement.

BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES1

PubMed Central

Zhu, Xiang; Stephens, Matthew

2017-01-01

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a “Regression with Summary Statistics” (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss. PMID:29399241

Bayesian Unimodal Density Regression for Causal Inference

ERIC Educational Resources Information Center

Karabatsos, George; Walker, Stephen G.

2011-01-01

Karabatsos and Walker (2011) introduced a new Bayesian nonparametric (BNP) regression model. Through analyses of real and simulated data, they showed that the BNP regression model outperforms other parametric and nonparametric regression models of common use, in terms of predictive accuracy of the outcome (dependent) variable. The other,…

Methodological uncertainties in multi-regression analyses of middle-atmospheric data series.

PubMed

Kerzenmacher, Tobias E; Keckhut, Philippe; Hauchecorne, Alain; Chanin, Marie-Lise

2006-07-01

Multi-regression analyses have often been used recently to detect trends, in particular in ozone or temperature data sets in the stratosphere. The confidence in detecting trends depends on a number of factors which generate uncertainties. Part of these uncertainties comes from the random variability and these are what is usually considered. They can be statistically estimated from residual deviations between the data and the fitting model. However, interferences between different sources of variability affecting the data set, such as the Quasi-Biennal Oscillation (QBO), volcanic aerosols, solar flux variability and the trend can also be a critical source of errors. This type of error has hitherto not been well quantified. In this work an artificial data series has been generated to carry out such estimates. The sources of errors considered here are: the length of the data series, the dependence on the choice of parameters used in the fitting model and the time evolution of the trend in the data series. Curves provided here, will permit future studies to test the magnitude of the methodological bias expected for a given case, as shown in several real examples. It is found that, if the data series is shorter than a decade, the uncertainties are very large, whatever factors are chosen to identify the source of the variability. However the errors can be limited when dealing with natural variability, if a sufficient number of periods (for periodic forcings) are covered by the analysed dataset. However when analysing the trend, the response to volcanic eruption induces a bias, whatever the length of the data series. The signal to noise ratio is a key factor: doubling the noise increases the period for which data is required in order to obtain an error smaller than 10%, from 1 to 3-4 decades. Moreover, if non-linear trends are superimposed on the data, and if the length of the series is longer than five years, a non-linear function has to be used to estimate trends. When applied to real data series, and when a breakpoint in the series occurs, the study reveals that data extending over 5 years are needed to detect a significant change in the slope of the ozone trends at mid-latitudes.

Logistic regression applied to natural hazards: rare event logistic regression with replications

NASA Astrophysics Data System (ADS)

Guns, M.; Vanacker, V.

2012-06-01

Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

Peak oxygen consumption measured during the stair-climbing test in lung resection candidates.

PubMed

Brunelli, Alessandro; Xiumé, Francesco; Refai, Majed; Salati, Michele; Di Nunzio, Luca; Pompili, Cecilia; Sabbatini, Armando

2010-01-01

The stair-climbing test is commonly used in the preoperative evaluation of lung resection candidates, but it is difficult to standardize and provides little physiologic information on the performance. To verify the association between the altitude and the V(O2peak) measured during the stair-climbing test. 109 consecutive candidates for lung resection performed a symptom-limited stair-climbing test with direct breath-by-breath measurement of V(O2peak) by a portable gas analyzer. Stepwise logistic regression and bootstrap analyses were used to verify the association of several perioperative variables with a V(O2peak) <15 ml/kg/min. Subsequently, multiple regression analysis was also performed to develop an equation to estimate V(O2peak) from stair-climbing parameters and other patient-related variables. 56% of patients climbing <14 m had a V(O2peak) <15 ml/kg/min, whereas 98% of those climbing >22 m had a V(O2peak) >15 ml/kg/min. The altitude reached at stair-climbing test resulted in the only significant predictor of a V(O2peak) <15 ml/kg/min after logistic regression analysis. Multiple regression analysis yielded an equation to estimate V(O2peak) factoring altitude (p < 0.0001), speed of ascent (p = 0.005) and body mass index (p = 0.0008). There was an association between altitude and V(O2peak) measured during the stair-climbing test. Most of the patients climbing more than 22 m are able to generate high values of V(O2peak) and can proceed to surgery without any additional tests. All others need to be referred for a formal cardiopulmonary exercise test. In addition, we were able to generate an equation to estimate V(O2peak), which could assist in streamlining the preoperative workup and could be used across different settings to standardize this test. Copyright (c) 2010 S. Karger AG, Basel.

Total nucleated cell and leukocyte differential counts in canine pleural and peritoneal fluid and equine synovial fluid samples: comparison of automated and manual methods.

PubMed

Brudvig, Jean M; Swenson, Cheryl L

2015-12-01

Rapid and precise measurement of total and differential nucleated cell counts is a crucial diagnostic component of cavitary and synovial fluid analyses. The objectives of this study included (1) evaluation of reliability and precision of canine and equine fluid total nucleated cell count (TNCC) determined by the benchtop Abaxis VetScan HM5, in comparison with the automated reference instruments ADVIA 120 and the scil Vet abc, respectively, and (2) comparison of automated with manual canine differential nucleated cell counts. The TNCC and differential counts in canine pleural and peritoneal, and equine synovial fluids were determined on the Abaxis VetScan HM5 and compared with the ADVIA 120 and Vet abc analyzer, respectively. Statistical analyses included correlation, least squares fit linear regression, Passing-Bablok regression, and Bland-Altman difference plots. In addition, precision of the total cell count generated by the VetScan HM5 was determined. Agreement was excellent without significant constant or proportional bias for canine cavitary fluid TNCC. Automated and manual differential counts had R(2)  < .5 for individual cell types (least squares fit linear regression). Equine synovial fluid TNCC agreed but with some bias due to the VetScan HM5 overestimating TNCC compared to the Vet abc. Intra-assay precision of the VetScan HM5 in 3 fluid samples was 2-31%. The Abaxis VetScan HM5 provided rapid, reliable TNCC for canine and equine fluid samples. The differential nucleated cell count should be verified microscopically as counts from the VetScan HM5 and also from the ADVIA 120 were often incorrect in canine fluid samples. © 2015 American Society for Veterinary Clinical Pathology.

Causal relationship between the AHSG gene and BMD through fetuin-A and BMI: multiple mediation analysis.

PubMed

Sritara, C; Thakkinstian, A; Ongphiphadhanakul, B; Chailurkit, L; Chanprasertyothin, S; Ratanachaiwong, W; Vathesatogkit, P; Sritara, P

2014-05-01

Using mediation analysis, a causal relationship between the AHSG gene and bone mineral density (BMD) through fetuin-A and body mass index (BMI) mediators was suggested. Fetuin-A, a multifunctional protein of hepatic origin, is associated with bone mineral density. It is unclear if this association is causal. This study aimed at clarification of this issue. A cross-sectional study was conducted among 1,741 healthy workers from the Electricity Generating Authority of Thailand (EGAT) cohort. The alpha-2-Heremans-Schmid glycoprotein (AHSG) rs2248690 gene was genotyped. Three mediation models were constructed using seemingly unrelated regression analysis. First, the ln[fetuin-A] group was regressed on the AHSG gene. Second, the BMI group was regressed on the AHSG gene and the ln[fetuin-A] group. Finally, the BMD model was constructed by fitting BMD on two mediators (ln[fetuin-A] and BMI) and the independent AHSG variable. All three analyses were adjusted for confounders. The prevalence of the minor T allele for the AHSG locus was 15.2%. The AHSG locus was highly related to serum fetuin-A levels (P < 0.001). Multiple mediation analyses showed that AHSG was significantly associated with BMD through the ln[fetuin-A] and BMI pathway, with beta coefficients of 0.0060 (95% CI 0.0038, 0.0083) and 0.0030 (95% CI 0.0020, 0.0045) at the total hip and lumbar spine, respectively. About 27.3 and 26.0% of total genetic effects on hip and spine BMD, respectively, were explained by the mediation effects of fetuin-A and BMI. Our study suggested evidence of a causal relationship between the AHSG gene and BMD through fetuin-A and BMI mediators.

Relationships among body weight, joint moments generated during functional activities, and hip bone mass in older adults

PubMed Central

Wang, Man-Ying; Flanagan, Sean P.; Song, Joo-Eun; Greendale, Gail A.; Salem, George J.

2012-01-01

Objective To investigate the relationships among hip joint moments produced during functional activities and hip bone mass in sedentary older adults. Methods Eight male and eight female older adults (70–85 yr) performed functional activities including walking, chair sit–stand–sit, and stair stepping at a self-selected pace while instrumented for biomechanical analysis. Bone mass at proximal femur, femoral neck, and greater trochanter were measured by dual-energy X-ray absorptiometry. Three-dimensional hip moments were obtained using a six-camera motion analysis system, force platforms, and inverse dynamics techniques. Pearson’s correlation coefficients were employed to assess the relationships among hip bone mass, height, weight, age, and joint moments. Stepwise regression analyses were performed to determine the factors that significantly predicted bone mass using all significant variables identified in the correlation analysis. Findings Hip bone mass was not significantly correlated with moments during activities in men. Conversely, in women bone mass at all sites were significantly correlated with weight, moments generated with stepping, and moments generated with walking (p < 0.05 to p < 0.001). Regression analysis results further indicated that the overall moments during stepping independently predicted up to 93% of the variability in bone mass at femoral neck and proximal femur; whereas weight independently predicted up to 92% of the variability in bone mass at greater trochanter. Interpretation Submaximal loading events produced during functional activities were highly correlated with hip bone mass in sedentary older women, but not men. The findings may ultimately be used to modify exercise prescription for the preservation of bone mass. PMID:16631283

Regression modeling plan for 29 biochemical indicators of diet and nutrition measured in NHANES 2003-2006.

PubMed

Sternberg, Maya R; Schleicher, Rosemary L; Pfeiffer, Christine M

2013-06-01

The collection of articles in this supplement issue provides insight into the association of various covariates with concentrations of biochemical indicators of diet and nutrition (biomarkers), beyond age, race, and sex, using linear regression. We studied 10 specific sociodemographic and lifestyle covariates in combination with 29 biomarkers from NHANES 2003-2006 for persons aged ≥ 20 y. The covariates were organized into 2 sets or "chunks": sociodemographic (age, sex, race-ethnicity, education, and income) and lifestyle (dietary supplement use, smoking, alcohol consumption, BMI, and physical activity) and fit in hierarchical fashion by using each category or set of related variables to determine how covariates, jointly, are related to biomarker concentrations. In contrast to many regression modeling applications, all variables were retained in a full regression model regardless of significance to preserve the interpretation of the statistical properties of β coefficients, P values, and CIs and to keep the interpretation consistent across a set of biomarkers. The variables were preselected before data analysis, and the data analysis plan was designed at the outset to minimize the reporting of false-positive findings by limiting the amount of preliminary hypothesis testing. Although we generally found that demographic differences seen in biomarkers were over- or underestimated when ignoring other key covariates, the demographic differences generally remained significant after adjusting for sociodemographic and lifestyle variables. These articles are intended to provide a foundation to researchers to help them generate hypotheses for future studies or data analyses and/or develop predictive regression models using the wealth of NHANES data.

Tympanic ear thermometer assessment of body temperature among patients with cognitive disturbances. An acceptable and ethically desirable alternative?

PubMed

Aadal, Lena; Fog, Lisbet; Pedersen, Asger Roer

2016-12-01

Investigation of a possible relation between body temperature measurements by the current generation of tympanic ear and rectal thermometers. In Denmark, a national guideline recommends the rectal measurement. Subsequently, the rectal thermometers and tympanic ear devices are the most frequently used and first choice in Danish hospital wards. Cognitive changes constitute challenges with cooperating in rectal temperature assessments. With regard to diagnosing, ethics, safety and the patients' dignity, the tympanic ear thermometer might comprise a desirable alternative to rectal noninvasive measurement of body temperature during in-hospital-based neurorehabilitation. A prospective, descriptive cohort study. Consecutive inclusion of 27 patients. Linear regression models were used to analyse 284 simultaneous temperature measurements. Ethical approval for this study was granted by the Danish Data Protection Agency, and the study was completed in accordance with the Helsinki Declaration 2008. About 284 simultaneous rectal and ear temperature measurements on 27 patients were analysed. The patient-wise variability of measured temperatures was significantly higher for the ear measurements. Patient-wise linear regressions for the 25 patients with at least three pairs of simultaneous ear and rectal temperature measurements showed large interpatient variability of the association. A linear relationship between the rectal body temperature assessment and the temperature assessment employing the tympanic thermometer is weak. Both measuring methods reflect variance in temperature, but ear measurements showed larger variation. © 2016 Nordic College of Caring Science.

Reanalysis of the start of the UK 1967 to 1968 foot-and-mouth disease epidemic to calculate airborne transmission probabilities.

PubMed

Sanson, R L; Gloster, J; Burgin, L

2011-09-24

The aims of this study were to statistically reassess the likelihood that windborne spread of foot-and-mouth disease (FMD) virus (FMDV) occurred at the start of the UK 1967 to 1968 FMD epidemic at Oswestry, Shropshire, and to derive dose-response probability of infection curves for farms exposed to airborne FMDV. To enable this, data on all farms present in 1967 in the parishes near Oswestry were assembled. Cases were infected premises whose date of appearance of first clinical signs was within 14 days of the depopulation of the index farm. Logistic regression was used to evaluate the association between infection status and distance and direction from the index farm. The UK Met Office's NAME atmospheric dispersion model (ADM) was used to generate plumes for each day that FMDV was excreted from the index farm based on actual historical weather records from October 1967. Daily airborne FMDV exposure rates for all farms in the study area were calculated using a geographical information system. Probit analyses were used to calculate dose-response probability of infection curves to FMDV, using relative exposure rates on case and control farms. Both the logistic regression and probit analyses gave strong statistical support to the hypothesis that airborne spread occurred. There was some evidence that incubation period was inversely proportional to the exposure rate.

Between-centre variability in transfer function analysis, a widely used method for linear quantification of the dynamic pressure–flow relation: The CARNet study

PubMed Central

Meel-van den Abeelen, Aisha S.S.; Simpson, David M.; Wang, Lotte J.Y.; Slump, Cornelis H.; Zhang, Rong; Tarumi, Takashi; Rickards, Caroline A.; Payne, Stephen; Mitsis, Georgios D.; Kostoglou, Kyriaki; Marmarelis, Vasilis; Shin, Dae; Tzeng, Yu-Chieh; Ainslie, Philip N.; Gommer, Erik; Müller, Martin; Dorado, Alexander C.; Smielewski, Peter; Yelicich, Bernardo; Puppo, Corina; Liu, Xiuyun; Czosnyka, Marek; Wang, Cheng-Yen; Novak, Vera; Panerai, Ronney B.; Claassen, Jurgen A.H.R.

2014-01-01

Transfer function analysis (TFA) is a frequently used method to assess dynamic cerebral autoregulation (CA) using spontaneous oscillations in blood pressure (BP) and cerebral blood flow velocity (CBFV). However, controversies and variations exist in how research groups utilise TFA, causing high variability in interpretation. The objective of this study was to evaluate between-centre variability in TFA outcome metrics. 15 centres analysed the same 70 BP and CBFV datasets from healthy subjects (n = 50 rest; n = 20 during hypercapnia); 10 additional datasets were computer-generated. Each centre used their in-house TFA methods; however, certain parameters were specified to reduce a priori between-centre variability. Hypercapnia was used to assess discriminatory performance and synthetic data to evaluate effects of parameter settings. Results were analysed using the Mann–Whitney test and logistic regression. A large non-homogeneous variation was found in TFA outcome metrics between the centres. Logistic regression demonstrated that 11 centres were able to distinguish between normal and impaired CA with an AUC > 0.85. Further analysis identified TFA settings that are associated with large variation in outcome measures. These results indicate the need for standardisation of TFA settings in order to reduce between-centre variability and to allow accurate comparison between studies. Suggestions on optimal signal processing methods are proposed. PMID:24725709

Genetic Programming Transforms in Linear Regression Situations

NASA Astrophysics Data System (ADS)

Castillo, Flor; Kordon, Arthur; Villa, Carlos

The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.

Shergottite Lead Isotope Signature in Chassigny and the Nakhlites

NASA Technical Reports Server (NTRS)

Jones, J. H.; Simon, J. I.

2017-01-01

The nakhlites/chassignites and the shergottites represent two differing suites of basaltic martian meteorites. The shergottites have ages less than or equal to 0.6 Ga and a large range of initial Sr-/Sr-86 and epsilon (Nd-143) ratios. Conversely, the nakhlites and chassignites cluster at 1.3-1.4 Ga and have a limited range of initial Sr-87/Sr-86 and epsilon (Nd-143). More importantly, the shergottites have epsilon (W-182) less than 1, whereas the nakhlites and chassignites have epsilon (W-182) approximately 3. This latter observation precludes the extraction of both meteorite groups from a single source region. However, recent Pb isotopic analyses indicate that there may have been interaction between shergottite and nakhlite/chassignite Pb reservoirs.Pb Analyses of Chassigny: Two different studies haveinvestigated 207Pb/204Pb vs. 206Pb/204Pb in Chassigny: (i)TIMS bulk-rock analyses of successive leaches and theirresidue [3]; and (ii) SIMS analysis of individual minerals[4]. The bulk-rock analyses fall along a regression of SIMSplagioclase analyses that define an errorchron that is olderthan the Solar System (4.61±0.1 Ga); i.e., these define amixing line between Chassigny’s principal Pb isotopic components(Fig. 1). Augites and olivines in Chassingy (notshown) also fall along or near the plagioclase regression [4].This agreement indicates that the whole-rock leachateslikely measure indigenous, martian Pb, not terrestrial contamination[5]. SIMS analyses of K-spars and sulfides definea separate, sub-parallel trend having higher 207Pb/206Pbvalues ([4]; Fig. 1). The good agreement between the bulkrockanalyses and the SIMS analyses of plagioclases alsoindicates that the Pb in the K-spars and sulfides cannot be amajor component of Chassigny.The depleted reservoir sampled by Chassigny plagioclaseis not the same as the solar system initial (PAT) andrequires a multi-stage origin. Here we show a two-stagemodel (Fig. 1) with a 238U/204Pb (µ) of 0.5 for 4.5-2.4 Gaand a µ of 7 for 2.4-1.4 Ga. This is not a unique model butdoes produce a Pb composition that falls on the plagioclaseregression at 1.4 Ga, the approximate igneous age of Chassigny [1]. It should be noted that low-µ single-stage modelsare not capable of producing sufficiently radiogenic 206Pb/204Pb at 1.4 Ga.Relation to Shergottites: The Chassigny K-spars and sulfides fall along a second mixing line defined by leachesand residues of depleted and intermediate shergottites [6]. This mixing line falls above the plagioclase regression.Therefore, we also interpret the radiogenic component of this mixing line to represent indigenous martian Pb. It ispossible that the depleted and intermediate shergottites and the Chassigny plagioclases sample radiogenic Pb from thethe same source, i.e., the mixing lines may intersect at high 206Pb/204Pb.Both K-spar and sulfide are late-stage phases. At the time of their crystallization, the Chassigny system appearsto have remained open to a depleted shergottite Pb reservoir. The depleted component of the shergottite mixing linecan be generated by a single-stage evolution from PAT (4.5 to 1.4 Ga) in a reservoir having a µ 2. A similar modelfor the most depleted shergottites is also possible: µ = 1.5 for 4.5 to 0.3 Ga.Nakhlites: Nakhlite analyses plot between the shergottite and Chassigny plagioclase regressions [3]. So again,members of the nakhlite/chassignite suite show affinities to shergottite Pb.

Gender Gaps in Mathematics, Science and Reading Achievements in Muslim Countries: A Quantile Regression Approach

ERIC Educational Resources Information Center

Shafiq, M. Najeeb

2013-01-01

Using quantile regression analyses, this study examines gender gaps in mathematics, science, and reading in Azerbaijan, Indonesia, Jordan, the Kyrgyz Republic, Qatar, Tunisia, and Turkey among 15-year-old students. The analyses show that girls in Azerbaijan achieve as well as boys in mathematics and science and overachieve in reading. In Jordan,…

The impact of intuition and supervisor-nurse relationships on empowerment and affective commitment by generation.

PubMed

Farr-Wharton, Rod; Brunetto, Yvonne; Shacklock, Kate

2012-06-01

  This article reports a generational cohort and leader-member exchange theoretical frameworks-guided study of the influence of the supervisor-subordinate relationship on three generational nurse cohorts' use of intuition, perceptions of empowerment and affective commitment.   Within a global context of nurse shortages, knowledge about factors influencing nurse retention is urgently sought. We postulated that nurses' use of intuition is the key to their empowerment and consequent commitment to the organization, and that impact would vary among the three large nurse generations.   A self-report survey was used to gather data in 2008, which were then analysed using correlations, regression analysis, manova and path analysis. Data were obtained from 900 Baby Boomer and Generations X and Y nurses, randomly chosen from seven private hospitals across Australia.   The findings confirm the important impact of supervisor-nurse relationships upon all three generations' use of intuition. The findings add new knowledge about the differing importance of using intuition for Generation X, Generation Y and Baby Boomer nurses' perceptions of empowerment, suggesting it is more important to Baby Boomers and Generation X than to Generation Y. Further, the impact of using intuition differs significantly among the generational cohorts. The findings suggest the need for a more differentiated tailored style - sensitive to varying needs of the generations. Improving supervisor-nurse relationships is also critical, because of their impact upon nurses' use of intuition, perceptions of empowerment and affective commitment. Poor relationships lead to increased nurse replacement costs. © 2011 Blackwell Publishing Ltd.

The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?

PubMed Central

Murphy, Kevin; Birn, Rasmus M.; Handwerker, Daniel A.; Jones, Tyler B.; Bandettini, Peter A.

2009-01-01

Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step. PMID:18976716

The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?

PubMed

Murphy, Kevin; Birn, Rasmus M; Handwerker, Daniel A; Jones, Tyler B; Bandettini, Peter A

2009-02-01

Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step.

Analysis of Palm Oil Production, Export, and Government Consumption to Gross Domestic Product of Five Districts in West Kalimantan by Panel Regression

NASA Astrophysics Data System (ADS)

Sulistianingsih, E.; Kiftiah, M.; Rosadi, D.; Wahyuni, H.

2017-04-01

Gross Domestic Product (GDP) is an indicator of economic growth in a region. GDP is a panel data, which consists of cross-section and time series data. Meanwhile, panel regression is a tool which can be utilised to analyse panel data. There are three models in panel regression, namely Common Effect Model (CEM), Fixed Effect Model (FEM) and Random Effect Model (REM). The models will be chosen based on results of Chow Test, Hausman Test and Lagrange Multiplier Test. This research analyses palm oil about production, export, and government consumption to five district GDP are in West Kalimantan, namely Sanggau, Sintang, Sambas, Ketapang and Bengkayang by panel regression. Based on the results of analyses, it concluded that REM, which adjusted-determination-coefficient is 0,823, is the best model in this case. Also, according to the result, only Export and Government Consumption that influence GDP of the districts.

Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.

Item parameters dissociate between expectation formats: a regression analysis of time-frequency decomposed EEG data

PubMed Central

Monsalve, Irene F.; Pérez, Alejandro; Molinaro, Nicola

2014-01-01

During language comprehension, semantic contextual information is used to generate expectations about upcoming items. This has been commonly studied through the N400 event-related potential (ERP), as a measure of facilitated lexical retrieval. However, the associative relationships in multi-word expressions (MWE) may enable the generation of a categorical expectation, leading to lexical retrieval before target word onset. Processing of the target word would thus reflect a target-identification mechanism, possibly indexed by a P3 ERP component. However, given their time overlap (200–500 ms post-stimulus onset), differentiating between N400/P3 ERP responses (averaged over multiple linguistically variable trials) is problematic. In the present study, we analyzed EEG data from a previous experiment, which compared ERP responses to highly expected words that were placed either in a MWE or a regular non-fixed compositional context, and to low predictability controls. We focused on oscillatory dynamics and regression analyses, in order to dissociate between the two contexts by modeling the electrophysiological response as a function of item-level parameters. A significant interaction between word position and condition was found in the regression model for power in a theta range (~7–9 Hz), providing evidence for the presence of qualitative differences between conditions. Power levels within this band were lower for MWE than compositional contexts when the target word appeared later on in the sentence, confirming that in the former lexical retrieval would have taken place before word onset. On the other hand, gamma-power (~50–70 Hz) was also modulated by predictability of the item in all conditions, which is interpreted as an index of a similar “matching” sub-step for both types of contexts, binding an expected representation and the external input. PMID:25161630

Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis

ERIC Educational Resources Information Center

Kim, Rae Seon

2011-01-01

When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…

Immigrants' health in Europe: a cross-classified multilevel approach to examine origin country, destination country, and community effects.

PubMed

Huijts, Tim; Kraaykamp, Gerbert

2012-01-01

In this study, we examined origin, destination, and community effects on first- and second-generation immigrants' health in Europe. We used information from the European Social Surveys (2002–2008) on 19,210 immigrants from 123 countries of origin, living in 31 European countries. Cross-classified multilevel regression analyses reveal that political suppression in the origin country and living in countries with large numbers of immigrant peers have a detrimental influence on immigrants' health. Originating from predominantly Islamic countries and good average health among natives in the destination country appear to be beneficial. Additionally, the results point toward health selection mechanisms into migration.

Can longitudinal generalized estimating equation models distinguish network influence and homophily? An agent-based modeling approach to measurement characteristics.

PubMed

Sauser Zachrison, Kori; Iwashyna, Theodore J; Gebremariam, Achamyeleh; Hutchins, Meghan; Lee, Joyce M

2016-12-28

Connected individuals (or nodes) in a network are more likely to be similar than two randomly selected nodes due to homophily and/or network influence. Distinguishing between these two influences is an important goal in network analysis, and generalized estimating equation (GEE) analyses of longitudinal dyadic network data are an attractive approach. It is not known to what extent such regressions can accurately extract underlying data generating processes. Therefore our primary objective is to determine to what extent, and under what conditions, does the GEE-approach recreate the actual dynamics in an agent-based model. We generated simulated cohorts with pre-specified network characteristics and attachments in both static and dynamic networks, and we varied the presence of homophily and network influence. We then used statistical regression and examined the GEE model performance in each cohort to determine whether the model was able to detect the presence of homophily and network influence. In cohorts with both static and dynamic networks, we find that the GEE models have excellent sensitivity and reasonable specificity for determining the presence or absence of network influence, but little ability to distinguish whether or not homophily is present. The GEE models are a valuable tool to examine for the presence of network influence in longitudinal data, but are quite limited with respect to homophily.

Random regression analyses using B-splines to model growth of Australian Angus cattle

PubMed Central

Meyer, Karin

2005-01-01

Regression on the basis function of B-splines has been advocated as an alternative to orthogonal polynomials in random regression analyses. Basic theory of splines in mixed model analyses is reviewed, and estimates from analyses of weights of Australian Angus cattle from birth to 820 days of age are presented. Data comprised 84 533 records on 20 731 animals in 43 herds, with a high proportion of animals with 4 or more weights recorded. Changes in weights with age were modelled through B-splines of age at recording. A total of thirteen analyses, considering different combinations of linear, quadratic and cubic B-splines and up to six knots, were carried out. Results showed good agreement for all ages with many records, but fluctuated where data were sparse. On the whole, analyses using B-splines appeared more robust against "end-of-range" problems and yielded more consistent and accurate estimates of the first eigenfunctions than previous, polynomial analyses. A model fitting quadratic B-splines, with knots at 0, 200, 400, 600 and 821 days and a total of 91 covariance components, appeared to be a good compromise between detailedness of the model, number of parameters to be estimated, plausibility of results, and fit, measured as residual mean square error. PMID:16093011

Refining cost-effectiveness analyses using the net benefit approach and econometric methods: an example from a trial of anti-depressant treatment.

PubMed

Sabes-Figuera, Ramon; McCrone, Paul; Kendricks, Antony

2013-04-01

Economic evaluation analyses can be enhanced by employing regression methods, allowing for the identification of important sub-groups and to adjust for imperfect randomisation in clinical trials or to analyse non-randomised data. To explore the benefits of combining regression techniques and the standard Bayesian approach to refine cost-effectiveness analyses using data from randomised clinical trials. Data from a randomised trial of anti-depressant treatment were analysed and a regression model was used to explore the factors that have an impact on the net benefit (NB) statistic with the aim of using these findings to adjust the cost-effectiveness acceptability curves. Exploratory sub-samples' analyses were carried out to explore possible differences in cost-effectiveness. Results The analysis found that having suffered a previous similar depression is strongly correlated with a lower NB, independent of the outcome measure or follow-up point. In patients with previous similar depression, adding an selective serotonin reuptake inhibitors (SSRI) to supportive care for mild-to-moderate depression is probably cost-effective at the level used by the English National Institute for Health and Clinical Excellence to make recommendations. This analysis highlights the need for incorporation of econometric methods into cost-effectiveness analyses using the NB approach.

A comparison of Cox and logistic regression for use in genome-wide association studies of cohort and case-cohort design.

PubMed

Staley, James R; Jones, Edmund; Kaptoge, Stephen; Butterworth, Adam S; Sweeting, Michael J; Wood, Angela M; Howson, Joanna M M

2017-06-01

Logistic regression is often used instead of Cox regression to analyse genome-wide association studies (GWAS) of single-nucleotide polymorphisms (SNPs) and disease outcomes with cohort and case-cohort designs, as it is less computationally expensive. Although Cox and logistic regression models have been compared previously in cohort studies, this work does not completely cover the GWAS setting nor extend to the case-cohort study design. Here, we evaluated Cox and logistic regression applied to cohort and case-cohort genetic association studies using simulated data and genetic data from the EPIC-CVD study. In the cohort setting, there was a modest improvement in power to detect SNP-disease associations using Cox regression compared with logistic regression, which increased as the disease incidence increased. In contrast, logistic regression had more power than (Prentice weighted) Cox regression in the case-cohort setting. Logistic regression yielded inflated effect estimates (assuming the hazard ratio is the underlying measure of association) for both study designs, especially for SNPs with greater effect on disease. Given logistic regression is substantially more computationally efficient than Cox regression in both settings, we propose a two-step approach to GWAS in cohort and case-cohort studies. First to analyse all SNPs with logistic regression to identify associated variants below a pre-defined P-value threshold, and second to fit Cox regression (appropriately weighted in case-cohort studies) to those identified SNPs to ensure accurate estimation of association with disease.

Regression and multivariate models for predicting particulate matter concentration level.

PubMed

Nazif, Amina; Mohammed, Nurul Izma; Malakahmad, Amirhossein; Abualqumboz, Motasem S

2018-01-01

The devastating health effects of particulate matter (PM 10 ) exposure by susceptible populace has made it necessary to evaluate PM 10 pollution. Meteorological parameters and seasonal variation increases PM 10 concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM 10 concentration levels. The analyses were carried out using daily average PM 10 concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM 10 concentration levels having coefficient of determination (R 2 ) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R 2 result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R 2 result from 0.50 to 0.60. While, PCR models had R 2 result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies.

Does the stress generation hypothesis apply to eating disorders?: an examination of stress generation in eating, depressive, and anxiety symptoms.

PubMed

Bodell, Lindsay P; Hames, Jennifer L; Holm-Denoma, Jill M; Smith, April R; Gordon, Kathryn H; Joiner, Thomas E

2012-12-15

The stress generation hypothesis posits that individuals actively contribute to stress in their lives. Although stress generation has been studied frequently in the context of depression, few studies have examined whether this stress generation process is unique to depression or whether it occurs in other disorders. Although evidence suggests that stress contributes to the development of eating disorders, it is unclear whether eating disorders contribute to subsequent stress. A prospective design was used to examine the influence of eating disorder symptoms on negative life stressors. Two hundred and ninety female undergraduates completed questionnaires at two time points that examined eating disorder, depressive and anxiety symptoms and the presence of negative life events. Regression analyses found that while eating disorder symptoms (i.e. bulimic symptoms and drive for thinness) were independent, significant predictors of negative life events, they did not predict negative life events above and beyond symptoms of depression. Limitations include the use of self-report measures and a college-based sample, which may limit generalizability of the results. Findings suggest that if stress generation is present in individuals with symptoms of eating disorders, it is likely attributable to symptoms of depression. Thus, it may be important for clinicians to target depressive symptoms in order to reduce the frequency of negative life stressors among individuals with eating disorders. Copyright © 2012 Elsevier B.V. All rights reserved.

A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

PubMed

Karabatsos, George

2017-02-01

Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected functionals and values of covariates. The software is illustrated through the BNP regression analysis of real data.

PARAMETRIC AND NON PARAMETRIC (MARS: MULTIVARIATE ADDITIVE REGRESSION SPLINES) LOGISTIC REGRESSIONS FOR PREDICTION OF A DICHOTOMOUS RESPONSE VARIABLE WITH AN EXAMPLE FOR PRESENCE/ABSENCE OF AMPHIBIANS

EPA Science Inventory

The purpose of this report is to provide a reference manual that could be used by investigators for making informed use of logistic regression using two methods (standard logistic regression and MARS). The details for analyses of relationships between a dependent binary response ...

A Practical Guide to Conducting a Systematic Review and Meta-analysis of Health State Utility Values.

PubMed

Petrou, Stavros; Kwon, Joseph; Madan, Jason

2018-05-10

Economic analysts are increasingly likely to rely on systematic reviews and meta-analyses of health state utility values to inform the parameter inputs of decision-analytic modelling-based economic evaluations. Beyond the context of economic evaluation, evidence from systematic reviews and meta-analyses of health state utility values can be used to inform broader health policy decisions. This paper provides practical guidance on how to conduct a systematic review and meta-analysis of health state utility values. The paper outlines a number of stages in conducting a systematic review, including identifying the appropriate evidence, study selection, data extraction and presentation, and quality and relevance assessment. The paper outlines three broad approaches that can be used to synthesise multiple estimates of health utilities for a given health state or condition, namely fixed-effect meta-analysis, random-effects meta-analysis and mixed-effects meta-regression. Each approach is illustrated by a synthesis of utility values for a hypothetical decision problem, and software code is provided. The paper highlights a number of methodological issues pertinent to the conduct of meta-analysis or meta-regression. These include the importance of limiting synthesis to 'comparable' utility estimates, for example those derived using common utility measurement approaches and sources of valuation; the effects of reliance on limited or poorly reported published data from primary utility assessment studies; the use of aggregate outcomes within analyses; approaches to generating measures of uncertainty; handling of median utility values; challenges surrounding the disentanglement of utility estimates collected serially within the context of prospective observational studies or prospective randomised trials; challenges surrounding the disentanglement of intervention effects; and approaches to measuring model validity. Areas of methodological debate and avenues for future research are highlighted.

A Simulation Investigation of Principal Component Regression.

ERIC Educational Resources Information Center

Allen, David E.

Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…

Artificial Neural Network for the Prediction of Chromosomal Abnormalities in Azoospermic Males.

PubMed

Akinsal, Emre Can; Haznedar, Bulent; Baydilli, Numan; Kalinli, Adem; Ozturk, Ahmet; Ekmekçioğlu, Oğuz

2018-02-04

To evaluate whether an artifical neural network helps to diagnose any chromosomal abnormalities in azoospermic males. The data of azoospermic males attending to a tertiary academic referral center were evaluated retrospectively. Height, total testicular volume, follicle stimulating hormone, luteinising hormone, total testosterone and ejaculate volume of the patients were used for the analyses. In artificial neural network, the data of 310 azoospermics were used as the education and 115 as the test set. Logistic regression analyses and discriminant analyses were performed for statistical analyses. The tests were re-analysed with a neural network. Both logistic regression analyses and artificial neural network predicted the presence or absence of chromosomal abnormalities with more than 95% accuracy. The use of artificial neural network model has yielded satisfactory results in terms of distinguishing patients whether they have any chromosomal abnormality or not.

Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma. A systematic review and meta-regression analysis.

PubMed

Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos

2017-06-01

We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel

PubMed Central

Grapov, Dmitry; Newman, John W.

2012-01-01

Summary: Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Availability and implementation: Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010). Contact: John.Newman@ars.usda.gov Supplementary Information: Installation instructions, tutorials and users manual are available at http://sourceforge.net/projects/imdev/. PMID:22815358

Estimating population diversity with CatchAll

PubMed Central

Bunge, John; Woodard, Linda; Böhning, Dankmar; Foster, James A.; Connolly, Sean; Allen, Heather K.

2012-01-01

Motivation: The massive data produced by next-generation sequencing require advanced statistical tools. We address estimating the total diversity or species richness in a population. To date, only relatively simple methods have been implemented in available software. There is a need for software employing modern, computationally intensive statistical analyses including error, goodness-of-fit and robustness assessments. Results: We present CatchAll, a fast, easy-to-use, platform-independent program that computes maximum likelihood estimates for finite-mixture models, weighted linear regression-based analyses and coverage-based non-parametric methods, along with outlier diagnostics. Given sample ‘frequency count’ data, CatchAll computes 12 different diversity estimates and applies a model-selection algorithm. CatchAll also derives discounted diversity estimates to adjust for possibly uncertain low-frequency counts. It is accompanied by an Excel-based graphics program. Availability: Free executable downloads for Linux, Windows and Mac OS, with manual and source code, at www.northeastern.edu/catchall. Contact: jab18@cornell.edu PMID:22333246

Network effects across the earnings distribution: payoffs to visible and invisible job finding assistance.

PubMed

McDonald, Steve

2015-01-01

This study makes three critical contributions to the "Do Contacts Matter?" debate. First, the widely reported null relationship between informal job searching and wages is shown to be mostly the artifact of a coding error and sample selection restrictions. Second, previous analyses examined only active informal job searching without fully considering the benefits derived from unsolicited network assistance (the "invisible hand of social capital") - thereby underestimating the network effect. Third, wage returns to networks are examined across the earnings distribution. Longitudinal data from the NLSY reveal significant wage returns for network-based job finding over formal job searching, especially for individuals who were informally recruited into their jobs (non-searchers). Fixed effects quantile regression analyses show that contacts generate wage premiums among middle and high wage jobs, but not low wage jobs. These findings challenge conventional wisdom on contact effects and advance understanding of how social networks affect wage attainment and inequality. Copyright © 2014 Elsevier Inc. All rights reserved.

Biometrics-based service marketing issues: exploring acceptability and risk factors of iris scans associated with registered travel programmes.

PubMed

Smith, Alan D

2008-01-01

The marketability and viability of biometric technologies by companies marketing their own versions of pre-approved registered travel programmes have generated a number of controversies. Data were collected and analysed to formulate graphs, run regression and correlation analyses, and use Chi-square to formally test basic research propositions on a sample of 241 professionals in the Pittsburgh area. It was found that there was a significant relationship between the respondents' familiarity with new technology (namely web-enabled and internet sophistication) and knowledge of biometrics, in particular iris scans. Participants who frequently use the internet are more comfortable with innovative technology; although individuals with higher income levels have less trust in the government, it appeared that virtually everyone is concerned about trusting the government with their personal information. Healthcare professionals need to document the safety, CRM-related factors, and provide leadership in the international collaboration of biometric-related personal identification technologies, since they will be one of the main beneficiaries of the implementation of such technologies.

Predicting Word Reading Ability: A Quantile Regression Study

ERIC Educational Resources Information Center

McIlraith, Autumn L.

2018-01-01

Predictors of early word reading are well established. However, it is unclear if these predictors hold for readers across a range of word reading abilities. This study used quantile regression to investigate predictive relationships at different points in the distribution of word reading. Quantile regression analyses used preschool and…

An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology

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

Lynch, Cary D.; Hartin, Corinne A.; Bond-Lamberty, Benjamin

Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squared regression methods. We exploremore » the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60-90°N/S). Bias and mean errors between modeled and pattern predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5°C, but choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. As a result, this paper describes our library of least squared regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns.« less

An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology

DOE PAGES

Lynch, Cary D.; Hartin, Corinne A.; Bond-Lamberty, Benjamin; ...

2017-05-15

Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squared regression methods. We exploremore » the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60-90°N/S). Bias and mean errors between modeled and pattern predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5°C, but choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. As a result, this paper describes our library of least squared regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns.« less

An open-access CMIP5 pattern library for temperature and precipitation: description and methodology

NASA Astrophysics Data System (ADS)

Lynch, Cary; Hartin, Corinne; Bond-Lamberty, Ben; Kravitz, Ben

2017-05-01

Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squares regression methods. We explore the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60-90° N/S). Bias and mean errors between modeled and pattern-predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5 °C, but the choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. This paper describes our library of least squares regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns. The dataset and netCDF data generation code are available at doi:10.5281/zenodo.495632.

Missing heritability in the tails of quantitative traits? A simulation study on the impact of slightly altered true genetic models.

PubMed

Pütter, Carolin; Pechlivanis, Sonali; Nöthen, Markus M; Jöckel, Karl-Heinz; Wichmann, Heinz-Erich; Scherag, André

2011-01-01

Genome-wide association studies have identified robust associations between single nucleotide polymorphisms and complex traits. As the proportion of phenotypic variance explained is still limited for most of the traits, larger and larger meta-analyses are being conducted to detect additional associations. Here we investigate the impact of the study design and the underlying assumption about the true genetic effect in a bimodal mixture situation on the power to detect associations. We performed simulations of quantitative phenotypes analysed by standard linear regression and dichotomized case-control data sets from the extremes of the quantitative trait analysed by standard logistic regression. Using linear regression, markers with an effect in the extremes of the traits were almost undetectable, whereas analysing extremes by case-control design had superior power even for much smaller sample sizes. Two real data examples are provided to support our theoretical findings and to explore our mixture and parameter assumption. Our findings support the idea to re-analyse the available meta-analysis data sets to detect new loci in the extremes. Moreover, our investigation offers an explanation for discrepant findings when analysing quantitative traits in the general population and in the extremes. Copyright © 2011 S. Karger AG, Basel.

A method for fitting regression splines with varying polynomial order in the linear mixed model.

PubMed

Edwards, Lloyd J; Stewart, Paul W; MacDougall, James E; Helms, Ronald W

2006-02-15

The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial regression splines with varying polynomial order in the fixed effects and/or random effects of the linear mixed model. The polynomial segments are explicitly constrained by side conditions for continuity and some smoothness at the points where they join. By using a reparameterization of this explicitly constrained linear mixed model, an implicitly constrained linear mixed model is constructed that simplifies implementation of fixed-knot regression splines. The proposed approach is relatively simple, handles splines in one variable or multiple variables, and can be easily programmed using existing commercial software such as SAS or S-plus. The method is illustrated using two examples: an analysis of longitudinal viral load data from a study of subjects with acute HIV-1 infection and an analysis of 24-hour ambulatory blood pressure profiles.

Integrative eQTL analysis of tumor and host omics data in individuals with bladder cancer.

PubMed

Pineda, Silvia; Van Steen, Kristel; Malats, Núria

2017-09-01

Integrative analyses of several omics data are emerging. The data are usually generated from the same source material (i.e., tumor sample) representing one level of regulation. However, integrating different regulatory levels (i.e., blood) with those from tumor may also reveal important knowledge about the human genetic architecture. To model this multilevel structure, an integrative-expression quantitative trait loci (eQTL) analysis applying two-stage regression (2SR) was proposed. This approach first regressed tumor gene expression levels with tumor markers and the adjusted residuals from the previous model were then regressed with the germline genotypes measured in blood. Previously, we demonstrated that penalized regression methods in combination with a permutation-based MaxT method (Global-LASSO) is a promising tool to fix some of the challenges that high-throughput omics data analysis imposes. Here, we assessed whether Global-LASSO can also be applied when tumor and blood omics data are integrated. We further compared our strategy with two 2SR-approaches, one using multiple linear regression (2SR-MLR) and other using LASSO (2SR-LASSO). We applied the three models to integrate genomic, epigenomic, and transcriptomic data from tumor tissue with blood germline genotypes from 181 individuals with bladder cancer included in the TCGA Consortium. Global-LASSO provided a larger list of eQTLs than the 2SR methods, identified a previously reported eQTLs in prostate stem cell antigen (PSCA), and provided further clues on the complexity of APBEC3B loci, with a minimal false-positive rate not achieved by 2SR-MLR. It also represents an important contribution for omics integrative analysis because it is easy to apply and adaptable to any type of data. © 2017 WILEY PERIODICALS, INC.

Current suicidal ideation in treatment-seeking individuals in the United Kingdom with gambling problems.

PubMed

Ronzitti, Silvia; Soldini, Emiliano; Smith, Neil; Potenza, Marc N; Clerici, Massimo; Bowden-Jones, Henrietta

2017-11-01

Studies show higher lifetime prevalence of suicidality in individuals with pathological gambling. However, less is known about the relationship between pathological gambling and current suicidal ideation. We investigated socio-demographic, clinical and gambling-related variables associated with suicidality in treatment-seeking individuals. Bivariate analyses and logistic regression models were generated on data from 903 individuals to identify measures associated with aspects of suicidality. Forty-six percent of patients reported current suicidal ideation. People with current suicidal thoughts were more likely to report greater problem-gambling severity (p<0.001), depression (p<0.001) and anxiety (p<0.001) compared to those without suicidality. Logistic regression models suggested that past suicidal ideation (p<0.001) and higher anxiety (p<0.05) may be predictive factors of current suicidality. Our findings suggest that the severity of anxiety disorder, along with a lifetime history of suicidal ideation, may help to identify treatment-seeking individuals with pathological gambling with a higher risk of suicidality, highlighting the importance of assessing suicidal ideation in clinical settings. Copyright © 2017 Elsevier Ltd. All rights reserved.

Three-dimensional kinematic correlates of ball velocity during maximal instep soccer kicking in males.

PubMed

Sinclair, Jonathan; Fewtrell, David; Taylor, Paul John; Bottoms, Lindsay; Atkins, Stephen; Hobbs, Sarah Jane

2014-01-01

Achieving a high ball velocity is important during soccer shooting, as it gives the goalkeeper less time to react, thus improving a player's chance of scoring. This study aimed to identify important technical aspects of kicking linked to the generation of ball velocity using regression analyses. Maximal instep kicks were obtained from 22 academy-level soccer players using a 10-camera motion capture system sampling at 500 Hz. Three-dimensional kinematics of the lower extremity segments were obtained. Regression analysis was used to identify the kinematic parameters associated with the development of ball velocity. A single biomechanical parameter; knee extension velocity of the kicking limb at ball contact Adjusted R(2) = 0.39, p ≤ 0.01 was obtained as a significant predictor of ball-velocity. This study suggests that sagittal plane knee extension velocity is the strongest contributor to ball velocity and potentially overall kicking performance. It is conceivable therefore that players may benefit from exposure to coaching and strength techniques geared towards the improvement of knee extension angular velocity as highlighted in this study.

Herd-specific random regression carcass profiles for beef cattle after adjustment for animal genetic merit.

PubMed

Englishby, Tanya M; Moore, Kirsty L; Berry, Donagh P; Coffey, Mike P; Banos, Georgios

2017-07-01

Abattoir data are an important source of information for the genetic evaluation of carcass traits, but also for on-farm management purposes. The present study aimed to quantify the contribution of herd environment to beef carcass characteristics (weight, conformation score and fat score) with particular emphasis on generating finishing herd-specific profiles for these traits across different ages at slaughter. Abattoir records from 46,115 heifers and 78,790 steers aged between 360 and 900days, and from 22,971 young bulls aged between 360 and 720days, were analysed. Finishing herd-year and animal genetic (co)variance components for each trait were estimated using random regression models. Across slaughter age and gender, the ratio of finishing herd-year to total phenotypic variance ranged from 0.31 to 0.72 for carcass weight, 0.21 to 0.57 for carcass conformation and 0.11 to 0.44 for carcass fat score. These parameters indicate that the finishing herd environment is an important contributor to carcass trait variability and amenable to improvement with management practices. Copyright © 2017 Elsevier Ltd. All rights reserved.

Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia.

PubMed

Henrard, S; Speybroeck, N; Hermans, C

2015-11-01

Haemophilia is a rare genetic haemorrhagic disease characterized by partial or complete deficiency of coagulation factor VIII, for haemophilia A, or IX, for haemophilia B. As in any other medical research domain, the field of haemophilia research is increasingly concerned with finding factors associated with binary or continuous outcomes through multivariable models. Traditional models include multiple logistic regressions, for binary outcomes, and multiple linear regressions for continuous outcomes. Yet these regression models are at times difficult to implement, especially for non-statisticians, and can be difficult to interpret. The present paper sought to didactically explain how, why, and when to use classification and regression tree (CART) analysis for haemophilia research. The CART method is non-parametric and non-linear, based on the repeated partitioning of a sample into subgroups based on a certain criterion. Breiman developed this method in 1984. Classification trees (CTs) are used to analyse categorical outcomes and regression trees (RTs) to analyse continuous ones. The CART methodology has become increasingly popular in the medical field, yet only a few examples of studies using this methodology specifically in haemophilia have to date been published. Two examples using CART analysis and previously published in this field are didactically explained in details. There is increasing interest in using CART analysis in the health domain, primarily due to its ease of implementation, use, and interpretation, thus facilitating medical decision-making. This method should be promoted for analysing continuous or categorical outcomes in haemophilia, when applicable. © 2015 John Wiley & Sons Ltd.

Gender and migration on the labour market: Additive or interacting disadvantages in Germany?

PubMed

Fleischmann, Fenella; Höhne, Jutta

2013-09-01

Despite substantial differences in labour market attainment according to gender and migration status, gender and ethnic differences in labour market behaviour are most often studied separately. In contrast, this study describes and analyses interactions between gender, ethnic background and immigrant generation with regard to labour market participation, part-time work, and occupational status. The double comparison aims to reveal whether gender gaps in these labour market outcomes among the majority population generalise to ethnic minorities. Moreover, we ask whether variation in gender gaps in labour market behaviour follows the patterns in migrants' origin countries, and whether gender gaps show signs of intergenerational assimilation. Our heterogeneous choice and OLS regressions of 2009 German Microcensus data reveal considerable variation in gender gaps in labour market behaviour between East and West Germany, across ethnic groups and across generations. Intergenerational comparisons show that most ethnic minorities assimilate towards German patterns of gendered labour market attainment. Copyright © 2013 Elsevier Inc. All rights reserved.

Emotional balances in experimental consumer choices.

PubMed

Mengov, George; Egbert, Henrik; Pulov, Stefan; Georgiev, Kalin

2008-11-01

This paper presents an experiment, which builds a bridge over the gap between neuroscience and the analysis of economic behaviour. We apply the mathematical theory of Pavlovian conditioning, known as Recurrent Associative Gated Dipole (READ), to analyse consumer choices in a computer-based experiment. Supplier reputations, consumer satisfaction, and customer reactions are operationally defined and, together with prices, related to READ's neural dynamics. We recorded our participants' decisions with their timing, and then mapped those decisions on a sequence of events generated by the READ model. To achieve this, all constants in the differential equations were determined using simulated annealing with data from 129 people. READ predicted correctly 96% of all consumer choices in a calibration sample (n=1290), and 87% in a test sample (n=903), thus outperforming logit models. The rank correlations between self-assessed and dipole-generated consumer satisfactions were 89% in the calibration sample and 78% in the test sample, surpassing by a wide margin the best linear regression model.

Regression Analysis: Legal Applications in Institutional Research

ERIC Educational Resources Information Center

Frizell, Julie A.; Shippen, Benjamin S., Jr.; Luna, Andrew L.

2008-01-01

This article reviews multiple regression analysis, describes how its results should be interpreted, and instructs institutional researchers on how to conduct such analyses using an example focused on faculty pay equity between men and women. The use of multiple regression analysis will be presented as a method with which to compare salaries of…

Regression Effects in Angoff Ratings: Examples from Credentialing Exams

ERIC Educational Resources Information Center

Wyse, Adam E.

2018-01-01

This article discusses regression effects that are commonly observed in Angoff ratings where panelists tend to think that hard items are easier than they are and easy items are more difficult than they are in comparison to estimated item difficulties. Analyses of data from two credentialing exams illustrate these regression effects and the…

Effects of cereal fiber on bowel function: A systematic review of intervention trials

PubMed Central

de Vries, Jan; Miller, Paige E; Verbeke, Kristin

2015-01-01

AIM: To comprehensively review and quantitatively summarize results from intervention studies that examined the effects of intact cereal dietary fiber on parameters of bowel function. METHODS: A systematic literature search was conducted using PubMed and EMBASE. Supplementary literature searches included screening reference lists from relevant studies and reviews. Eligible outcomes were stool wet and dry weight, percentage water in stools, stool frequency and consistency, and total transit time. Weighted regression analyses generated mean change (± SD) in these measures per g/d of dietary fiber. RESULTS: Sixty-five intervention studies among generally healthy populations were identified. A quantitative examination of the effects of non-wheat sources of intact cereal dietary fibers was not possible due to an insufficient number of studies. Weighted regression analyses demonstrated that each extra g/d of wheat fiber increased total stool weight by 3.7 ± 0.09 g/d (P < 0.0001; 95%CI: 3.50-3.84), dry stool weight by 0.75 ± 0.03 g/d (P < 0.0001; 95%CI: 0.69-0.82), and stool frequency by 0.004 ± 0.002 times/d (P = 0.0346; 95%CI: 0.0003-0.0078). Transit time decreased by 0.78 ± 0.13 h per additional g/d (P < 0.0001; 95%CI: 0.53-1.04) of wheat fiber among those with an initial transit time greater than 48 h. CONCLUSION: Wheat dietary fiber, and predominately wheat bran dietary fiber, improves measures of bowel function. PMID:26269686

Regionalization of subsurface stormflow parameters of hydrologic models: Derivation from regional analysis of streamflow recession curves

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

Ye, Sheng; Li, Hongyi; Huang, Maoyi

2014-07-21

Subsurface stormflow is an important component of the rainfall–runoff response, especially in steep terrain. Its contribution to total runoff is, however, poorly represented in the current generation of land surface models. The lack of physical basis of these common parameterizations precludes a priori estimation of the stormflow (i.e. without calibration), which is a major drawback for prediction in ungauged basins, or for use in global land surface models. This paper is aimed at deriving regionalized parameterizations of the storage–discharge relationship relating to subsurface stormflow from a top–down empirical data analysis of streamflow recession curves extracted from 50 eastern United Statesmore » catchments. Detailed regression analyses were performed between parameters of the empirical storage–discharge relationships and the controlling climate, soil and topographic characteristics. The regression analyses performed on empirical recession curves at catchment scale indicated that the coefficient of the power-law form storage–discharge relationship is closely related to the catchment hydrologic characteristics, which is consistent with the hydraulic theory derived mainly at the hillslope scale. As for the exponent, besides the role of field scale soil hydraulic properties as suggested by hydraulic theory, it is found to be more strongly affected by climate (aridity) at the catchment scale. At a fundamental level these results point to the need for more detailed exploration of the co-dependence of soil, vegetation and topography with climate.« less

The importance of family factors and generation status: mental health service use among Latino and Asian Americans.

PubMed

Chang, Janet; Natsuaki, Misaki N; Chen, Chih-Nan

2013-07-01

The present study utilized data from the National Latino and Asian American Study to examine ethnic and generational differences in family cultural conflict and family cohesion and how the effects of such family conflict and cohesion on lifetime service use vary by generation status for Latino Americans (n = 2,554) and Asian Americans (n = 2,095). Findings revealed that first-generation Asian Americans reported greater family cultural conflict than their Latino counterparts, but third-generation Latino Americans had higher family conflict than their Asian American counterparts. First-generation Latino and Asian Americans had the highest levels of family cohesion. Results from logistic regression analyses indicated that Latino Americans who reported higher family cultural conflict and lower family cohesion were more likely to use mental health services. For Asian Americans, family cultural conflict, but not family cohesion, was associated with service use. Relative to third-generation Asian Americans, second-generation Asian Americans with higher family cultural conflict were more likely to use mental health services. Given that cohesive familial bonds appear to discourage service use on the part of Latino Americans irrespective of generation status, further research is needed to ascertain the extent to which this tendency stems from greater reliance on family support as opposed to the stigma associated with mental health treatment. Mental health providers and treatment programs need to address the role of family cultural conflict in the lives of Asian Americans, particularly second generation, and Latino Americans across generations, because conflictual family ties may motivate help-seeking behaviors and reveal substantial underlying distress. PsycINFO Database Record (c) 2013 APA, all rights reserved.

An examination of the prospective impact of bulimic symptoms and dietary restraint on life hassles and social support.

PubMed

Kwan, Mun Yee; Gordon, Kathryn H

2016-02-01

The stress generation hypothesis posits that individuals with psychopathology engage in maladaptive behaviors that create stress. Although extensively researched in the depression literature, few studies have investigated whether the stress generation hypothesis applies to eating disorders. This study examined whether bulimic symptoms and dietary restraint predict future life hassles and low social support among undergraduate students. Three hundred seventy-four undergraduate students participated in this two-part prospective study through a secure online system. They completed questionnaires assessing depressive symptoms, bulimic symptoms, dietary restraint, life hassles, and social support. Regression analyses revealed that baseline bulimic symptoms predicted greater life hassles but not lower social support one month later, after statistically controlling for baseline measures. Baseline dietary restraint did not predict future life hassles or social support. Limitations include use of self-report measures, suboptimal response rates at the follow-up assessment, and use of a non-clinical sample with primarily White participants. These results provide preliminary support for the stress generation hypothesis in relation to bulimic symptoms. Individuals with bulimic symptoms may generate stressors similar to those experiencing depressive symptoms. Our findings suggest that emphasizing stress management in the treatment of individuals with bulimic symptoms could potentially improve treatment outcomes. Copyright © 2015 Elsevier B.V. All rights reserved.

A Statistical Analysis of the Economic Drivers of Battery Energy Storage in Commercial Buildings: Preprint

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

Long, Matthew; Simpkins, Travis; Cutler, Dylan

There is significant interest in using battery energy storage systems (BESS) to reduce peak demand charges, and therefore the life cycle cost of electricity, in commercial buildings. This paper explores the drivers of economic viability of BESS in commercial buildings through statistical analysis. A sample population of buildings was generated, a techno-economic optimization model was used to size and dispatch the BESS, and the resulting optimal BESS sizes were analyzed for relevant predictor variables. Explanatory regression analyses were used to demonstrate that peak demand charges are the most significant predictor of an economically viable battery, and that the shape ofmore » the load profile is the most significant predictor of the size of the battery.« less

A Statistical Analysis of the Economic Drivers of Battery Energy Storage in Commercial Buildings

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

Long, Matthew; Simpkins, Travis; Cutler, Dylan

There is significant interest in using battery energy storage systems (BESS) to reduce peak demand charges, and therefore the life cycle cost of electricity, in commercial buildings. This paper explores the drivers of economic viability of BESS in commercial buildings through statistical analysis. A sample population of buildings was generated, a techno-economic optimization model was used to size and dispatch the BESS, and the resulting optimal BESS sizes were analyzed for relevant predictor variables. Explanatory regression analyses were used to demonstrate that peak demand charges are the most significant predictor of an economically viable battery, and that the shape ofmore » the load profile is the most significant predictor of the size of the battery.« less

The impact of dependent-care responsibility and gender on work attitudes.

PubMed

Buffardi, L C; Smith, J L; O'Brien, A S; Erdwins, C J

1999-10-01

On the basis of a survey of 18,120 federal employees in dual-income households, six 5-stage hierarchical multiple regression analyses, controlling for 10 demographic variables, assessed the impact of child care, elder care, and gender on work-family balance and various facets of job satisfaction. Elder-care responsibility was associated with lower levels of satisfaction with perceived organizational support, pay, leave benefits, and work-family balance, whereas the negative main effects of child care were limited to leave benefits and work-family balance. However, child-care responsibility also interacted with gender: Its negative influence was greater on women's work-family balance and leave satisfaction. Decrements in satisfaction associated with dependent care on the "sandwich generation" were additive, not interactive.

RN work engagement in generational cohorts: the view from rural US hospitals.

PubMed

Sullivan Havens, Donna; Warshawsky, Nora E; Vasey, Joseph

2013-10-01

To describe staff nurse work engagement, identify predictors by generational cohort, present implications for nurse managers and suggest future research. A global nurse shortage looms. While an adequate supply of nurses is needed to ensure access to care, access to quality care may be enhanced by an adequate supply of highly engaged nurses-those who are dedicated, energized, and absorbed. Nurses have long reported the presence of energy depleting practice environments. Nurses practicing in professional practice environments may be more engaged. A non-experimental survey design was executed. Direct care Registered Nurses (n = 747) working in five rural acute care hospitals completed questionnaires to assess work engagement (Utrecht Work Engagement Scale-9), decisional involvement (Decisional Involvement Scale), relational coordination (Relational Coordination Survey) and the nursing practice environment (Practice Environment Scale of the Nursing Work Index). Descriptive, correlational and regression analyses examined work engagement and predictors by generational cohort. With the exception of the absorption component, no statistically significant differences in engagement emerged across generational cohorts. Predictors of engagement differed by cohort, however across all cohorts, professional nursing practice environments predicted nurse work engagement. Professional nursing practice environments are significantly associated with nurse work engagement. Enhancing nurse work engagement is a complex challenge. Generational cohorts may respond to different strategies to enhance engagement. © 2013 John Wiley & Sons Ltd.

An empirical study using permutation-based resampling in meta-regression

PubMed Central

2012-01-01

Background In meta-regression, as the number of trials in the analyses decreases, the risk of false positives or false negatives increases. This is partly due to the assumption of normality that may not hold in small samples. Creation of a distribution from the observed trials using permutation methods to calculate P values may allow for less spurious findings. Permutation has not been empirically tested in meta-regression. The objective of this study was to perform an empirical investigation to explore the differences in results for meta-analyses on a small number of trials using standard large sample approaches verses permutation-based methods for meta-regression. Methods We isolated a sample of randomized controlled clinical trials (RCTs) for interventions that have a small number of trials (herbal medicine trials). Trials were then grouped by herbal species and condition and assessed for methodological quality using the Jadad scale, and data were extracted for each outcome. Finally, we performed meta-analyses on the primary outcome of each group of trials and meta-regression for methodological quality subgroups within each meta-analysis. We used large sample methods and permutation methods in our meta-regression modeling. We then compared final models and final P values between methods. Results We collected 110 trials across 5 intervention/outcome pairings and 5 to 10 trials per covariate. When applying large sample methods and permutation-based methods in our backwards stepwise regression the covariates in the final models were identical in all cases. The P values for the covariates in the final model were larger in 78% (7/9) of the cases for permutation and identical for 22% (2/9) of the cases. Conclusions We present empirical evidence that permutation-based resampling may not change final models when using backwards stepwise regression, but may increase P values in meta-regression of multiple covariates for relatively small amount of trials. PMID:22587815

Publication bias in obesity treatment trials?

PubMed

Allison, D B; Faith, M S; Gorman, B S

1996-10-01

The present investigation examined the extent of publication bias (namely the tendency to publish significant findings and file away non-significant findings) within the obesity treatment literature. Quantitative literature synthesis of four published meta-analyses from the obesity treatment literature. Interventions in these studies included pharmacological, educational, child, and couples treatments. To assess publication bias, several regression procedures (for example weighted least-squares, random-effects multi-level modeling, and robust regression methods) were used to regress effect sizes onto their standard errors, or proxies thereof, within each of the four meta-analysis. A significant positive beta weight in these analyses signified publication bias. There was evidence for publication bias within two of the four published meta-analyses, such that reviews of published studies were likely to overestimate clinical efficacy. The lack of evidence for publication bias within the two other meta-analyses might have been due to insufficient statistical power rather than the absence of selection bias. As in other disciplines, publication bias appears to exist in the obesity treatment literature. Suggestions are offered for managing publication bias once identified or reducing its likelihood in the first place.

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

PubMed

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

2018-01-01

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

CADDIS Volume 4. Data Analysis: Basic Analyses

EPA Pesticide Factsheets

Use of statistical tests to determine if an observation is outside the normal range of expected values. Details of CART, regression analysis, use of quantile regression analysis, CART in causal analysis, simplifying or pruning resulting trees.

A systematic review and meta-analysis of the effects of antibiotic consumption on antibiotic resistance

PubMed Central

2014-01-01

Background Greater use of antibiotics during the past 50 years has exerted selective pressure on susceptible bacteria and may have favoured the survival of resistant strains. Existing information on antibiotic resistance patterns from pathogens circulating among community-based patients is substantially less than from hospitalized patients on whom guidelines are often based. We therefore chose to assess the relationship between the antibiotic resistance pattern of bacteria circulating in the community and the consumption of antibiotics in the community. Methods Both gray literature and published scientific literature in English and other European languages was examined. Multiple regression analysis was used to analyse whether studies found a positive relationship between antibiotic consumption and resistance. A subsequent meta-analysis and meta-regression was conducted for studies for which a common effect size measure (odds ratio) could be calculated. Results Electronic searches identified 974 studies but only 243 studies were considered eligible for inclusion by the two independent reviewers who extracted the data. A binomial test revealed a positive relationship between antibiotic consumption and resistance (p < .001) but multiple regression modelling did not produce any significant predictors of study outcome. The meta-analysis generated a significant pooled odds ratio of 2.3 (95% confidence interval 2.2 to 2.5) with a meta-regression producing several significant predictors (F(10,77) = 5.82, p < .01). Countries in southern Europe produced a stronger link between consumption and resistance than other regions. Conclusions Using a large set of studies we found that antibiotic consumption is associated with the development of antibiotic resistance. A subsequent meta-analysis, with a subsample of the studies, generated several significant predictors. Countries in southern Europe produced a stronger link between consumption and resistance than other regions so efforts at reducing antibiotic consumption may need to be strengthened in this area. Increased consumption of antibiotics may not only produce greater resistance at the individual patient level but may also produce greater resistance at the community, country, and regional levels, which can harm individual patients. PMID:24405683

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

PubMed Central

Dipnall, Joanna F.

2016-01-01

Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. Results After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). Conclusion The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin. PMID:26848571

Comparing least-squares and quantile regression approaches to analyzing median hospital charges.

PubMed

Olsen, Cody S; Clark, Amy E; Thomas, Andrea M; Cook, Lawrence J

2012-07-01

Emergency department (ED) and hospital charges obtained from administrative data sets are useful descriptors of injury severity and the burden to EDs and the health care system. However, charges are typically positively skewed due to costly procedures, long hospital stays, and complicated or prolonged treatment for few patients. The median is not affected by extreme observations and is useful in describing and comparing distributions of hospital charges. A least-squares analysis employing a log transformation is one approach for estimating median hospital charges, corresponding confidence intervals (CIs), and differences between groups; however, this method requires certain distributional properties. An alternate method is quantile regression, which allows estimation and inference related to the median without making distributional assumptions. The objective was to compare the log-transformation least-squares method to the quantile regression approach for estimating median hospital charges, differences in median charges between groups, and associated CIs. The authors performed simulations using repeated sampling of observed statewide ED and hospital charges and charges randomly generated from a hypothetical lognormal distribution. The median and 95% CI and the multiplicative difference between the median charges of two groups were estimated using both least-squares and quantile regression methods. Performance of the two methods was evaluated. In contrast to least squares, quantile regression produced estimates that were unbiased and had smaller mean square errors in simulations of observed ED and hospital charges. Both methods performed well in simulations of hypothetical charges that met least-squares method assumptions. When the data did not follow the assumed distribution, least-squares estimates were often biased, and the associated CIs had lower than expected coverage as sample size increased. Quantile regression analyses of hospital charges provide unbiased estimates even when lognormal and equal variance assumptions are violated. These methods may be particularly useful in describing and analyzing hospital charges from administrative data sets. © 2012 by the Society for Academic Emergency Medicine.

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

PubMed

Dipnall, Joanna F; Pasco, Julie A; Berk, Michael; Williams, Lana J; Dodd, Seetal; Jacka, Felice N; Meyer, Denny

2016-01-01

Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

Alcohol consumption among first- and second-generation immigrant and native adolescents in 23 countries: Testing the importance of origin and receiving country alcohol prevalence rates.

PubMed

Barsties, Lisa S; Walsh, Sophie D; Huijts, Tim; Bendtsen, Pernille; Molcho, Michal; Buijs, Thomas; Vieno, Alessio; Elgar, Frank J; Stevens, Gonneke W J M

2017-11-01

This internationally comparative study examines differences in alcohol consumption between first- and second-generation immigrant and native adolescents. We also investigate to what extent origin and receiving country alcohol per capita consumption (APCC) rates and proportions of heavy episodic drinkers (HED) are associated with immigrant adolescents' alcohol consumption. We used cross-sectional survey data from the 2013/2014 Health Behaviour in School-aged Children study. Applying multilevel regression analyses, we investigated the lifetime frequency of alcohol use and drunkenness in 69 842 13- to 15-year-olds in 23 receiving countries, with immigrants from over 130 origin countries (82% natives, 6% first-generation immigrants and 12% second-generation immigrants). The lifetime frequency of alcohol use was higher among natives than among first- and second-generation immigrants, while no differences were found between the latter two. Lifetime drunkenness was more frequent among first-generation immigrants than among natives and second-generation immigrants. Higher origin country APCC and HED were associated with more frequent lifetime alcohol use and drunkenness among immigrant adolescents. Cross-level interactions revealed that for lifetime frequency of alcohol use, the origin country HED effects were stronger for first- than for second-generation immigrant adolescents. Further, especially for first-generation immigrants, a higher receiving country HED was related to lower lifetime frequencies of alcohol use and drunkenness. Our results suggest differences in lifetime frequencies of alcohol use and drunkenness between natives and first- and second-generation immigrant adolescents. Origin country APCC and HED seem to affect immigrant adolescents' alcohol consumption differently than receiving country APCC and HED. © 2017 Australasian Professional Society on Alcohol and other Drugs.

Testing the vulnerability and scar models of self-esteem and depressive symptoms from adolescence to middle adulthood and across generations.

PubMed

Steiger, Andrea E; Fend, Helmut A; Allemand, Mathias

2015-02-01

The vulnerability model states that low self-esteem functions as a predictor for the development of depressive symptoms whereas the scar model assumes that these symptoms leave scars in individuals resulting in lower self-esteem. Both models have received empirical support, however, they have only been tested within individuals and not across generations (i.e., between family members). Thus, we tested the scope of these competing models by (a) investigating whether the effects hold from adolescence to middle adulthood (long-term vulnerability and scar effects), (b) whether the effects hold across generations (intergenerational vulnerability and scar effects), and (c) whether intergenerational effects are mediated by parental self-esteem and depressive symptoms and parent-child discord. We used longitudinal data from adolescence to middle adulthood (N = 1,359) and from Generation 1 adolescents (G1) to Generation 2 adolescents (G2) (N = 572 parent-child pairs). Results from latent cross-lagged regression analyses demonstrated that both adolescent self-esteem and depressive symptoms were prospectively related to adult self-esteem and depressive symptoms 3 decades later. That is, both the vulnerability and scar models are valid over decades with stronger effects for the vulnerability model. Across generations, we found a substantial direct transmission effect from G1 to G2 adolescent depressive symptoms but no evidence for the proposed intergenerational vulnerability and scar effect or for any of the proposed mediating mechanisms. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Comparative study of outcome measures and analysis methods for traumatic brain injury trials.

PubMed

Alali, Aziz S; Vavrek, Darcy; Barber, Jason; Dikmen, Sureyya; Nathens, Avery B; Temkin, Nancy R

2015-04-15

Batteries of functional and cognitive measures have been proposed as alternatives to the Extended Glasgow Outcome Scale (GOSE) as the primary outcome for traumatic brain injury (TBI) trials. We evaluated several approaches to analyzing GOSE and a battery of four functional and cognitive measures. Using data from a randomized trial, we created a "super" dataset of 16,550 subjects from patients with complete data (n=331) and then simulated multiple treatment effects across multiple outcome measures. Patients were sampled with replacement (bootstrapping) to generate 10,000 samples for each treatment effect (n=400 patients/group). The percentage of samples where the null hypothesis was rejected estimates the power. All analytic techniques had appropriate rates of type I error (≤5%). Accounting for baseline prognosis either by using sliding dichotomy for GOSE or using regression-based methods substantially increased the power over the corresponding analysis without accounting for prognosis. Analyzing GOSE using multivariate proportional odds regression or analyzing the four-outcome battery with regression-based adjustments had the highest power, assuming equal treatment effect across all components. Analyzing GOSE using a fixed dichotomy provided the lowest power for both unadjusted and regression-adjusted analyses. We assumed an equal treatment effect for all measures. This may not be true in an actual clinical trial. Accounting for baseline prognosis is critical to attaining high power in Phase III TBI trials. The choice of primary outcome for future trials should be guided by power, the domain of brain function that an intervention is likely to impact, and the feasibility of collecting outcome data.

Comparative Study of Outcome Measures and Analysis Methods for Traumatic Brain Injury Trials

PubMed Central

Alali, Aziz S.; Vavrek, Darcy; Barber, Jason; Dikmen, Sureyya; Nathens, Avery B.

2015-01-01

Abstract Batteries of functional and cognitive measures have been proposed as alternatives to the Extended Glasgow Outcome Scale (GOSE) as the primary outcome for traumatic brain injury (TBI) trials. We evaluated several approaches to analyzing GOSE and a battery of four functional and cognitive measures. Using data from a randomized trial, we created a “super” dataset of 16,550 subjects from patients with complete data (n=331) and then simulated multiple treatment effects across multiple outcome measures. Patients were sampled with replacement (bootstrapping) to generate 10,000 samples for each treatment effect (n=400 patients/group). The percentage of samples where the null hypothesis was rejected estimates the power. All analytic techniques had appropriate rates of type I error (≤5%). Accounting for baseline prognosis either by using sliding dichotomy for GOSE or using regression-based methods substantially increased the power over the corresponding analysis without accounting for prognosis. Analyzing GOSE using multivariate proportional odds regression or analyzing the four-outcome battery with regression-based adjustments had the highest power, assuming equal treatment effect across all components. Analyzing GOSE using a fixed dichotomy provided the lowest power for both unadjusted and regression-adjusted analyses. We assumed an equal treatment effect for all measures. This may not be true in an actual clinical trial. Accounting for baseline prognosis is critical to attaining high power in Phase III TBI trials. The choice of primary outcome for future trials should be guided by power, the domain of brain function that an intervention is likely to impact, and the feasibility of collecting outcome data. PMID:25317951

Fragile--Handle with Care: Regression Analyses That Include Categorical Data.

ERIC Educational Resources Information Center

Brown, Diane Peacock

In education and the social sciences, problems of interest to researchers and users of research often involve variables that do not meet the assumptions of regression in the area of an equal interval scale relative to a zero point. Various coding schemes exist that allow the use of regression while still answering the researcher's questions of…

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery.

PubMed

Tan, Kok Chooi; Lim, Hwee San; Matjafri, Mohd Zubir; Abdullah, Khiruddin

2012-06-01

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.

The influence of tree morphology on stemflow generation in a tropical lowland rainforest

NASA Astrophysics Data System (ADS)

Uber, Magdalena; Levia, Delphis F.; Zimmermann, Beate; Zimmermann, Alexander

2014-05-01

Even though stemflow usually accounts for only a small proportion of rainfall, it is an important point source of water and ion input to forest floors and may, for instance, influence soil moisture patterns and groundwater recharge. Previous studies showed that the generation of stemflow depends on a multitude of meteorological and biological factors. Interestingly, despite the tremendous progress in stemflow research during the last decades it is still largely unknown which combination of tree characteristics determines stemflow volumes in species-rich tropical forests. This knowledge gap motivated us to analyse the influence of tree characteristics on stemflow volumes in a 1 hectare plot located in a Panamanian lowland rainforest. Our study comprised stemflow measurements in six randomly selected 10 m by 10 m subplots. In each subplot we measured stemflow of all trees with a diameter at breast height (DBH) > 5 cm on an event-basis for a period of six weeks. Additionally, we identified all tree species and determined a set of tree characteristics including DBH, crown diameter, bark roughness, bark furrowing, epiphyte coverage, tree architecture, stem inclination, and crown position. During the sampling period, we collected 985 L of stemflow (0.98 % of total rainfall). Based on regression analyses and comparisons among plant functional groups we show that palms were most efficient in yielding stemflow due to their large inclined fronds. Trees with large emergent crowns also produced relatively large amounts of stemflow. Due to their abundance, understory trees contribute much to stemflow yield not on individual but on the plot scale. Even though parameters such as crown diameter, branch inclination and position of the crown influence stemflow generation to some extent, these parameters explain less than 30 % of the variation in stemflow volumes. In contrast to published results from temperate forests, we did not detect a negative correlation between bark roughness and stemflow volume. This is because other parameters such as crown diameter obscured this relationship. Due to multicollinearity and poor correlations between single tree characteristics with stemflow volume, an assessment of stemflow volumes based on forest characteristics remains cumbersome in highly diverse ecosystems. Instead of relying on regression relationships, we therefore advocate a total sampling of trees in several plots to determine stand-scale stemflow yield in tropical forests.

Regression Simulation of Turbine Engine Performance - Accuracy Improvement (TASK IV)

DTIC Science & Technology

1978-09-30

33 21 Generalized Form of the Regression Equation for the Optimized Polynomial Exponent M ethod...altitude, Mach number and power setting combinations were generated during the ARES evaluation. The orthogonal Latin Square selection procedure...pattern. In data generation , the low (L), mid (M), and high (H) values of a variable are not always the same. At some of the corner points where

Spatial quantile regression using INLA with applications to childhood overweight in Malawi.

PubMed

Mtambo, Owen P L; Masangwi, Salule J; Kazembe, Lawrence N M

2015-04-01

Analyses of childhood overweight have mainly used mean regression. However, using quantile regression is more appropriate as it provides flexibility to analyse the determinants of overweight corresponding to quantiles of interest. The main objective of this study was to fit a Bayesian additive quantile regression model with structured spatial effects for childhood overweight in Malawi using the 2010 Malawi DHS data. Inference was fully Bayesian using R-INLA package. The significant determinants of childhood overweight ranged from socio-demographic factors such as type of residence to child and maternal factors such as child age and maternal BMI. We observed significant positive structured spatial effects on childhood overweight in some districts of Malawi. We recommended that the childhood malnutrition policy makers should consider timely interventions based on risk factors as identified in this paper including spatial targets of interventions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Variable selection and model choice in geoadditive regression models.

PubMed

Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

2009-06-01

Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses.

PubMed

Prunier, J G; Colyn, M; Legendre, X; Nimon, K F; Flamand, M C

2015-01-01

Direct gradient analyses in spatial genetics provide unique opportunities to describe the inherent complexity of genetic variation in wildlife species and are the object of many methodological developments. However, multicollinearity among explanatory variables is a systemic issue in multivariate regression analyses and is likely to cause serious difficulties in properly interpreting results of direct gradient analyses, with the risk of erroneous conclusions, misdirected research and inefficient or counterproductive conservation measures. Using simulated data sets along with linear and logistic regressions on distance matrices, we illustrate how commonality analysis (CA), a detailed variance-partitioning procedure that was recently introduced in the field of ecology, can be used to deal with nonindependence among spatial predictors. By decomposing model fit indices into unique and common (or shared) variance components, CA allows identifying the location and magnitude of multicollinearity, revealing spurious correlations and thus thoroughly improving the interpretation of multivariate regressions. Despite a few inherent limitations, especially in the case of resistance model optimization, this review highlights the great potential of CA to account for complex multicollinearity patterns in spatial genetics and identifies future applications and lines of research. We strongly urge spatial geneticists to systematically investigate commonalities when performing direct gradient analyses. © 2014 John Wiley & Sons Ltd.

What is the strength of evidence for heart failure disease-management programs?

PubMed

Clark, Alexander M; Savard, Lori A; Thompson, David R

2009-07-28

Heart failure (HF) disease-management programs are increasingly common. However, some large and recent trials of programs have not reported positive findings. There have also been parallel recent advances in reporting standards and theory around complex nonpharmacological interventions. These developments compel reconsideration in this Viewpoint of how research into HF-management programs should be evaluated, the quality, specificity, and usefulness of this evidence, and the recommendations for future research. Addressing the main determinants of intervention effectiveness by using the PICO (Patient, Intervention, Comparison, and Outcome) approach and the recent CONSORT (Consolidated Standards of Reporting Trials) statement on nonpharmacological trials, we will argue that in both current trials and meta-analyses, interventions and comparisons are not sufficiently well described; that complex programs have been excessively oversimplified; and that potentially salient differences in programs, populations, and settings are not incorporated into analyses. In preference to more general meta-analyses of programs, adequate descriptions are first needed of populations, interventions, comparisons, and outcomes in past and future trials. This could be achieved via a systematic survey of study authors based on the CONSORT statement. These more detailed data on studies should be incorporated into future meta-analyses of comparable trials and used with other techniques such as patient-based outcomes data and meta-regression. Although trials and meta-analyses continue to have potential to generate useful evidence, a more specific evidence base is needed to support the development of effective programs for different populations and settings.

Who Will Win?: Predicting the Presidential Election Using Linear Regression

ERIC Educational Resources Information Center

Lamb, John H.

2007-01-01

This article outlines a linear regression activity that engages learners, uses technology, and fosters cooperation. Students generated least-squares linear regression equations using TI-83 Plus[TM] graphing calculators, Microsoft[C] Excel, and paper-and-pencil calculations using derived normal equations to predict the 2004 presidential election.…

Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling

NASA Astrophysics Data System (ADS)

Moura, Ricardo; Sinha, Bimal; Coelho, Carlos A.

2017-06-01

The recent popularity of the use of synthetic data as a Statistical Disclosure Control technique has enabled the development of several methods of generating and analyzing such data, but almost always relying in asymptotic distributions and in consequence being not adequate for small sample datasets. Thus, a likelihood-based exact inference procedure is derived for the matrix of regression coefficients of the multivariate regression model, for multiply imputed synthetic data generated via Posterior Predictive Sampling. Since it is based in exact distributions this procedure may even be used in small sample datasets. Simulation studies compare the results obtained from the proposed exact inferential procedure with the results obtained from an adaptation of Reiters combination rule to multiply imputed synthetic datasets and an application to the 2000 Current Population Survey is discussed.

A Comparison between the Use of Beta Weights and Structure Coefficients in Interpreting Regression Results

ERIC Educational Resources Information Center

Tong, Fuhui

2006-01-01

Background: An extensive body of researches has favored the use of regression over other parametric analyses that are based on OVA. In case of noteworthy regression results, researchers tend to explore magnitude of beta weights for the respective predictors. Purpose: The purpose of this paper is to examine both beta weights and structure…

A tutorial on the piecewise regression approach applied to bedload transport data

Treesearch

Sandra E. Ryan; Laurie S. Porth

2007-01-01

This tutorial demonstrates the application of piecewise regression to bedload data to define a shift in phase of transport so that the reader may perform similar analyses on available data. The use of piecewise regression analysis implicitly recognizes different functions fit to bedload data over varying ranges of flow. The transition from primarily low rates of sand...

A Statistical Multimodel Ensemble Approach to Improving Long-Range Forecasting in Pakistan

DTIC Science & Technology

2012-03-01

Impact of global warming on monsoon variability in Pakistan. J. Anim. Pl. Sci., 21, no. 1, 107–110. Gillies, S., T. Murphree, and D. Meyer, 2012...are generated by multiple regression models that relate globally distributed oceanic and atmospheric predictors to local predictands. The...generated by multiple regression models that relate globally distributed oceanic and atmospheric predictors to local predictands. The predictands are

Ecologic regression analysis and the study of the influence of air quality on mortality.

PubMed Central

Selvin, S; Merrill, D; Wong, L; Sacks, S T

1984-01-01

This presentation focuses entirely on the use and evaluation of regression analysis applied to ecologic data as a method to study the effects of ambient air pollution on mortality rates. Using extensive national data on mortality, air quality and socio-economic status regression analyses are used to study the influence of air quality on mortality. The analytic methods and data are selected in such a way that direct comparisons can be made with other ecologic regression studies of mortality and air quality. Analyses are performed by use of two types of geographic areas, age-specific mortality of both males and females and three pollutants (total suspended particulates, sulfur dioxide and nitrogen dioxide). The overall results indicate no persuasive evidence exists of a link between air quality and general mortality levels. Additionally, a lack of consistency between the present results and previous published work is noted. Overall, it is concluded that linear regression analysis applied to nationally collected ecologic data cannot be used to usefully infer a causal relationship between air quality and mortality which is in direct contradiction to other major published studies. PMID:6734568

Second generation anticoagulant rodenticides in predatory birds: Probabilistic characterisation of toxic liver concentrations and implications for predatory bird populations in Canada.

PubMed

Thomas, Philippe J; Mineau, Pierre; Shore, Richard F; Champoux, Louise; Martin, Pamela A; Wilson, Laurie K; Fitzgerald, Guy; Elliott, John E

2011-07-01

Second-generation anticoagulant rodenticides (SGARs) are widely used to control rodent pests but exposure and poisonings occur in non-target species, such as birds of prey. Liver residues are often analysed to detect exposure in birds found dead but their use to assess toxicity of SGARs is problematic. We analysed published data on hepatic rodenticide residues and associated symptoms of anticoagulant poisoning from 270 birds of prey using logistic regression to estimate the probability of toxicosis associated with different liver SGAR residues. We also evaluated exposure to SGARs on a national level in Canada by analysing 196 livers from great horned owls (Bubo virginianus) and red-tailed hawks (Buteo jamaicensis) found dead at locations across the country. Analysis of a broader sample of raptor species from Quebec also helped define the taxonomic breadth of contamination. Calculated probability curves suggest significant species differences in sensitivity to SGARs and significant likelihood of toxicosis below previously suggested concentrations of concern (<0.1mg/kg). Analysis of birds from Quebec showed that a broad range of raptor species are exposed to SGARs, indicating that generalised terrestrial food chains could be contaminated in the vicinity of the sampled areas. Of the two species for which we had samples from across Canada, great horned owls are exposed to SGARs to a greater extent than red-tailed hawks and the liver residue levels were also higher. Using our probability estimates of effect, we estimate that a minimum of 11% of the sampled great horned owl population is at risk of being directly killed by SGARs. This is the first time the potential mortality impact of SGARs on a raptor population has been estimated. Crown Copyright © 2011. Published by Elsevier Ltd. All rights reserved.

Locally linear regression for pose-invariant face recognition.

PubMed

Chai, Xiujuan; Shan, Shiguang; Chen, Xilin; Gao, Wen

2007-07-01

The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given nonfrontal view to obtain a virtual gallery/probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the virtual frontal view from a given nonfrontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a nonfrontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the nonfrontal face image to obtain many overlapped local patches. Then, the linear regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated. The experimental results on the CMU PIE database show distinct advantage of the proposed method over Eigen light-field method.

A Methodology for Generating Placement Rules that Utilizes Logistic Regression

ERIC Educational Resources Information Center

Wurtz, Keith

2008-01-01

The purpose of this article is to provide the necessary tools for institutional researchers to conduct a logistic regression analysis and interpret the results. Aspects of the logistic regression procedure that are necessary to evaluate models are presented and discussed with an emphasis on cutoff values and choosing the appropriate number of…

Cell-phone vs microphone recordings: Judging emotion in the voice.

PubMed

Green, Joshua J; Eigsti, Inge-Marie

2017-09-01

Emotional states can be conveyed by vocal cues such as pitch and intensity. Despite the ubiquity of cellular telephones, there is limited information on how vocal emotional states are perceived during cell-phone transmissions. Emotional utterances (neutral, happy, angry) were elicited from two female talkers and simultaneously recorded via microphone and cell-phone. Ten-step continua (neutral to happy, neutral to angry) were generated using the straight algorithm. Analyses compared reaction time (RT) and emotion judgment as a function of recording type (microphone vs cell-phone). Logistic regression revealed no judgment differences between recording types, though there were interactions with emotion type. Multi-level model analyses indicated that RT data were best fit by a quadratic model, with slower RT at the middle of each continuum, suggesting greater ambiguity, and slower RT for cell-phone stimuli across blocks. While preliminary, results suggest that critical acoustic cues to emotion are largely retained in cell-phone transmissions, though with effects of recording source on RT, and support the methodological utility of collecting speech samples by phone.

The link between discrimination and telomere length in African American adults.

PubMed

Lee, Daniel B; Kim, Eric S; Neblett, Enrique W

2017-05-01

Prior work shows that discrimination is associated with a wide array of negative health outcomes. However, the biological mechanisms through which this link occurs require more study. We evaluated the association between discrimination and leukocyte telomere length (LTL; a biological marker of systemic aging). Cross-sectional data were from the Health and Retirement study, a study of people aged 51+ in the United States, and included 595 African American males and females. Multiple regression analyses were used to evaluate whether discrimination was independently associated with LTL. We also considered the role of potential confounders including sociodemographic factors, health factors, depressive symptoms, and stress. High discrimination was associated with shorter LTL after controlling for sociodemographic factors (b = -.034, SE = 0.14, p = .017). This association persisted in analyses that further adjusted for health factors, depressive symptoms, and stress. Results suggest that discrimination experiences accelerate biological aging in older African American males and females, alike. This finding helps advance our understanding of how discrimination generates greater disease vulnerability and premature death in African Americans. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Alignment-Independent Comparisons of Human Gastrointestinal Tract Microbial Communities in a Multidimensional 16S rRNA Gene Evolutionary Space▿

PubMed Central

Rudi, Knut; Zimonja, Monika; Kvenshagen, Bente; Rugtveit, Jarle; Midtvedt, Tore; Eggesbø, Merete

2007-01-01

We present a novel approach for comparing 16S rRNA gene clone libraries that is independent of both DNA sequence alignment and definition of bacterial phylogroups. These steps are the major bottlenecks in current microbial comparative analyses. We used direct comparisons of taxon density distributions in an absolute evolutionary coordinate space. The coordinate space was generated by using alignment-independent bilinear multivariate modeling. Statistical analyses for clone library comparisons were based on multivariate analysis of variance, partial least-squares regression, and permutations. Clone libraries from both adult and infant gastrointestinal tract microbial communities were used as biological models. We reanalyzed a library consisting of 11,831 clones covering complete colons from three healthy adults in addition to a smaller 390-clone library from infant feces. We show that it is possible to extract detailed information about microbial community structures using our alignment-independent method. Our density distribution analysis is also very efficient with respect to computer operation time, meeting the future requirements of large-scale screenings to understand the diversity and dynamics of microbial communities. PMID:17337554

Gas chromatography-mass spectrometry and high-performance liquid chromatography-diode array detection for dating of paper ink.

PubMed

Díaz-Santana, Oscar; Vega-Moreno, Daura; Conde-Hardisson, Francisco

2017-09-15

An extraction and determination method is shown for the analysis of dyes and solvents present in two types of ballpoint pen inks that are deposited onto paper. Ink extracts are analysed using a combination of gas chromatography with mass spectrometry (GC-MS), and high-pressure liquid chromatography with photodiode array detection (HPLC-DAD), within a single sample extraction procedure. Seventeen solvents and thirteen dyes contained in two Montblanc ® inks (black and blue) were monitored for 45 months at monthly intervals, in order to determine variations in the concentrations of the compounds over time. We also studied the relative variations between different compounds and the generation of degradation products such as phenol. The concentration data obtained from these compounds during their exposure have been analysed and a multiple regression model is developed for each ink type that allows an estimate of the exposure time of the ink on paper with a maximum error of between 4 and 7 months. Copyright © 2017 Elsevier B.V. All rights reserved.

Trauma-Related Dissociation Is Linked With Maladaptive Personality Functioning

PubMed Central

Granieri, Antonella; Guglielmucci, Fanny; Costanzo, Antonino; Caretti, Vincenzo; Schimmenti, Adriano

2018-01-01

Background: Extensive research has demonstrated the positive associations among the exposure to traumatic experiences, the levels of dissociation, and the severity of psychiatric symptoms in adults. However, it has been hypothesized in clinical literature that an excessive activation of the dissociative processes following multiple traumatic experiences may jeopardize the psychological and behavioral functioning of the individuals, fostering higher levels of maladaptive personality functioning. Methods: The study involved 322 adult volunteers from Italy. Participants completed measures on traumatic experiences, dissociation, and maladaptive personality traits. Results: The number of traumatic experiences reported by participants were positively associated with dissociation scores and maladaptive personality scores. Mediation analyses showed that dissociation acted as a partial mediator in the relationship between traumatic experiences and overall maladaptive personality functioning. Regression curve analyses showed that the positive association between maladaptive personality functioning and dissociation was stronger among participants with higher exposure to traumatic experiences. Conclusion: Exposure to multiple traumatic experiences may increase the risk for an excessive activation of the dissociative processes, which in turn may generate severe impairments in multiple domains of personality functioning. PMID:29887807

Quality of search strategies reported in systematic reviews published in stereotactic radiosurgery.

PubMed

Faggion, Clovis M; Wu, Yun-Chun; Tu, Yu-Kang; Wasiak, Jason

2016-06-01

Systematic reviews require comprehensive literature search strategies to avoid publication bias. This study aimed to assess and evaluate the reporting quality of search strategies within systematic reviews published in the field of stereotactic radiosurgery (SRS). Three electronic databases (Ovid MEDLINE(®), Ovid EMBASE(®) and the Cochrane Library) were searched to identify systematic reviews addressing SRS interventions, with the last search performed in October 2014. Manual searches of the reference lists of included systematic reviews were conducted. The search strategies of the included systematic reviews were assessed using a standardized nine-question form based on the Cochrane Collaboration guidelines and Assessment of Multiple Systematic Reviews checklist. Multiple linear regression analyses were performed to identify the important predictors of search quality. A total of 85 systematic reviews were included. The median quality score of search strategies was 2 (interquartile range = 2). Whilst 89% of systematic reviews reported the use of search terms, only 14% of systematic reviews reported searching the grey literature. Multiple linear regression analyses identified publication year (continuous variable), meta-analysis performance and journal impact factor (continuous variable) as predictors of higher mean quality scores. This study identified the urgent need to improve the quality of search strategies within systematic reviews published in the field of SRS. This study is the first to address how authors performed searches to select clinical studies for inclusion in their systematic reviews. Comprehensive and well-implemented search strategies are pivotal to reduce the chance of publication bias and consequently generate more reliable systematic review findings.

Application of random forests methods to diabetic retinopathy classification analyses.

PubMed

Casanova, Ramon; Saldana, Santiago; Chew, Emily Y; Danis, Ronald P; Greven, Craig M; Ambrosius, Walter T

2014-01-01

Diabetic retinopathy (DR) is one of the leading causes of blindness in the United States and world-wide. DR is a silent disease that may go unnoticed until it is too late for effective treatment. Therefore, early detection could improve the chances of therapeutic interventions that would alleviate its effects. Graded fundus photography and systemic data from 3443 ACCORD-Eye Study participants were used to estimate Random Forest (RF) and logistic regression classifiers. We studied the impact of sample size on classifier performance and the possibility of using RF generated class conditional probabilities as metrics describing DR risk. RF measures of variable importance are used to detect factors that affect classification performance. Both types of data were informative when discriminating participants with or without DR. RF based models produced much higher classification accuracy than those based on logistic regression. Combining both types of data did not increase accuracy but did increase statistical discrimination of healthy participants who subsequently did or did not have DR events during four years of follow-up. RF variable importance criteria revealed that microaneurysms counts in both eyes seemed to play the most important role in discrimination among the graded fundus variables, while the number of medicines and diabetes duration were the most relevant among the systemic variables. We have introduced RF methods to DR classification analyses based on fundus photography data. In addition, we propose an approach to DR risk assessment based on metrics derived from graded fundus photography and systemic data. Our results suggest that RF methods could be a valuable tool to diagnose DR diagnosis and evaluate its progression.

[Being online without a purpose -- study of background variables of problematic internet use].

PubMed

Prievara, Dóra Katalin; Pikó, Bettina

2016-01-01

These days, use of the Internet is unavoidable for the younger generations. The online world is the primary source of infomation and quick communication, and these activities can take many hours per day. The main goal of the present study was to examine the correlations among problematic internet use, social factors, stress and life satisfaction. Data collection was going online during the first semester of the year 2014 (N= 386 girls). The anonymous questionnaire contained items on perceived social support and the amount of online activites beyond sociodemographics. After descriptive statistics, factor, correlation and multiple linear regression analyses were applied to detect interrelationships. According to our data, 78% of the participants spent daily at least 2 hours, 40% more than 4 hours online. Using factor analysis, four factors of online activities were identified: Social networking-surfing, News-information, Risky and Lonely game factors. Only the News-information factor was not related to the problematic internet use. Based on multiple regression analyses, we may conclude that shyness, stress, loneliness and two factors, the Social networking-surfing and the Risky factors acted as background variables for problematic internet use. As a summary we may conclude that the internet has an important role in the everyday life of the participants. In case of the direct aim of the online activities the problematic use did not appear. These activities were mostly searching for information and news. In introduction of prevention, education about the correct use of the internet may be reasonable as early as possible.

Environmental risk factors for cancers of the brain and nervous system: the use of ecological data to generate hypotheses.

PubMed

de Vocht, Frank; Hannam, Kimberly; Buchan, Iain

2013-05-01

There is a public health need to balance timely generation of hypotheses with cautious causal inference. For rare cancers this is particularly challenging because standard epidemiological study designs may not be able to elucidate causal factors in an early period of newly emerging risks. Alternative methodologies need to be considered for generating and shaping hypotheses prior to definitive investigation. To evaluate whether open-access databases can be used to explore links between potential risk factors and cancers at an ecological level, using the case study of brain and nervous system cancers as an example. National age-adjusted cancer incidence rates were obtained from the GLOBOCAN 2008 resource and combined with data from the United Nations Development Report and the World Bank list of development indicators. Data were analysed using multivariate regression models. Cancer rates, potential confounders and environmental risk factors were available for 165 of 208 countries. 2008 national incidences of brain and nervous system cancers were associated with continent, gross national income in 2008 and Human Development Index Score. The only exogenous risk factor consistently associated with higher incidence was the penetration rate of mobile/cellular telecommunications subscriptions, although other factors were highlighted. According to these ecological results the latency period is at least 11-12 years, but probably more than 20 years. Missing data on cancer incidence and for other potential risk factors prohibit more detailed investigation of exposure-response associations and/or explore other hypotheses. Readily available ecological data may be underused, particularly for the study of risk factors for rare diseases and those with long latencies. The results of ecological analyses in general should not be overinterpreted in causal inference, but equally they should not be ignored where alternative signals of aetiology are lacking.

Effects of a school-based sexuality education program on peer educators: the Teen PEP model.

PubMed

Jennings, J M; Howard, S; Perotte, C L

2014-04-01

This study evaluated the impact of the Teen Prevention Education Program (Teen PEP), a peer-led sexuality education program designed to prevent unintended pregnancy and sexually transmitted infections (STIs) including HIV among high school students. The study design was a quasi-experimental, nonrandomized design conducted from May 2007 to May 2008. The sample consisted of 96 intervention (i.e. Teen PEP peer educators) and 61 comparison students from five high schools in New Jersey. Baseline and 12-month follow-up surveys were conducted. Summary statistics were generated and multiple regression analyses were conducted. In the primary intent-to-treat analyses, and secondary non-intent-to-treat analyses, Teen PEP peer educators (versus comparison students) reported significantly greater opportunities to practice sexual risk reduction skills and higher intentions to talk with friends, parents, and sex partners about sex and birth control, set boundaries with sex partners, and ask a partner to be tested for STIs including HIV. In addition in the secondary analysis, Teen PEP peer educators (as compared with the comparison students) had significantly higher scores on knowledge of sexual health issues and ability to refuse risky sexual situations. School-based sexuality education programs offering comprehensive training to peer educators may improve sexual risk behavior knowledge, attitudes and behaviors among high school students.

Effects of a school-based sexuality education program on peer educators: the Teen PEP model

PubMed Central

Jennings, J. M.; Howard, S.; Perotte, C. L.

2014-01-01

This study evaluated the impact of the Teen Prevention Education Program (Teen PEP), a peer-led sexuality education program designed to prevent unintended pregnancy and sexually transmitted infections (STIs) including HIV among high school students. The study design was a quasi-experimental, nonrandomized design conducted from May 2007 to May 2008. The sample consisted of 96 intervention (i.e. Teen PEP peer educators) and 61 comparison students from five high schools in New Jersey. Baseline and 12-month follow-up surveys were conducted. Summary statistics were generated and multiple regression analyses were conducted. In the primary intent-to-treat analyses, and secondary non-intent-to-treat analyses, Teen PEP peer educators (versus comparison students) reported significantly greater opportunities to practice sexual risk reduction skills and higher intentions to talk with friends, parents, and sex partners about sex and birth control, set boundaries with sex partners, and ask a partner to be tested for STIs including HIV. In addition in the secondary analysis, Teen PEP peer educators (as compared with the comparison students) had significantly higher scores on knowledge of sexual health issues and ability to refuse risky sexual situations. School-based sexuality education programs offering comprehensive training to peer educators may improve sexual risk behavior knowledge, attitudes and behaviors among high school students. PMID:24488649

Forecasting municipal solid waste generation using prognostic tools and regression analysis.

PubMed

Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Câmpean, Teofil; Curteanu, Silvia; Gavrilescu, Maria

2016-11-01

For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction. Copyright © 2016 Elsevier Ltd. All rights reserved.

ReQON: a Bioconductor package for recalibrating quality scores from next-generation sequencing data

PubMed Central

2012-01-01

Background Next-generation sequencing technologies have become important tools for genome-wide studies. However, the quality scores that are assigned to each base have been shown to be inaccurate. If the quality scores are used in downstream analyses, these inaccuracies can have a significant impact on the results. Results Here we present ReQON, a tool that recalibrates the base quality scores from an input BAM file of aligned sequencing data using logistic regression. ReQON also generates diagnostic plots showing the effectiveness of the recalibration. We show that ReQON produces quality scores that are both more accurate, in the sense that they more closely correspond to the probability of a sequencing error, and do a better job of discriminating between sequencing errors and non-errors than the original quality scores. We also compare ReQON to other available recalibration tools and show that ReQON is less biased and performs favorably in terms of quality score accuracy. Conclusion ReQON is an open source software package, written in R and available through Bioconductor, for recalibrating base quality scores for next-generation sequencing data. ReQON produces a new BAM file with more accurate quality scores, which can improve the results of downstream analysis, and produces several diagnostic plots showing the effectiveness of the recalibration. PMID:22946927

Perceived stress and resilience in Alzheimer's disease caregivers: testing moderation and mediation models of social support.

PubMed

Wilks, Scott E; Croom, Beth

2008-05-01

The study examined whether social support functioned as a protective, resilience factor among Alzheimer's disease (AD) caregivers. Moderation and mediation models were used to test social support amid stress and resilience. A cross-sectional analysis of self-reported data was conducted. Measures of demographics, perceived stress, family support, friend support, overall social support, and resilience were administered to caregiver attendees (N=229) of two AD caregiver conferences. Hierarchical regression analysis showed the compounded impact of predictors on resilience. Odds ratios generated probability of high resilience given high stress and social supports. Social support moderation and mediation were tested via distinct series of regression equations. Path analyses illustrated effects on the models for significant moderation and/or mediation. Stress negatively influenced and accounted for most variation in resilience. Social support positively influenced resilience, and caregivers with high family support had the highest probability of elevated resilience. Moderation was observed among all support factors. No social support fulfilled the complete mediation criteria. Evidence of social support as a protective, moderating factor yields implications for health care practitioners who deliver services to assist AD caregivers, particularly the promotion of identification and utilization of supportive familial and peer relations.

Laboratory Headphone Studies of Human Response to Low-Amplitude Sonic Booms and Rattle Heard Indoors

NASA Technical Reports Server (NTRS)

Loubeau, Alexandra; Sullivan, Brenda M.; Klos, Jacob; Rathsam, Jonathan; Gavin, Joseph R.

2013-01-01

Human response to sonic booms heard indoors is affected by the generation of contact-induced rattle noise. The annoyance caused by sonic boom-induced rattle noise was studied in a series of psychoacoustics tests. Stimuli were divided into three categories and presented in three different studies: isolated rattles at the same calculated Perceived Level (PL), sonic booms combined with rattles with the mixed sound at a single PL, and sonic booms combined with rattles with the mixed sound at three different PL. Subjects listened to sounds over headphones and were asked to report their annoyance. Annoyance to different rattles was shown to vary significantly according to rattle object size. In addition, the combination of low-amplitude sonic booms and rattles can be more annoying than the sonic boom alone. Correlations and regression analyses for the combined sonic boom and rattle sounds identified the Moore and Glasberg Stationary Loudness (MGSL) metric as a primary predictor of annoyance for the tested sounds. Multiple linear regression models were developed to describe annoyance to the tested sounds, and simplifications for applicability to a wider range of sounds are presented.

Modeling and Analysis of Process Parameters for Evaluating Shrinkage Problems During Plastic Injection Molding of a DVD-ROM Cover

NASA Astrophysics Data System (ADS)

Öktem, H.

2012-01-01

Plastic injection molding plays a key role in the production of high-quality plastic parts. Shrinkage is one of the most significant problems of a plastic part in terms of quality in the plastic injection molding. This article focuses on the study of the modeling and analysis of the effects of process parameters on the shrinkage by evaluating the quality of the plastic part of a DVD-ROM cover made with Acrylonitrile Butadiene Styrene (ABS) polymer material. An effective regression model was developed to determine the mathematical relationship between the process parameters (mold temperature, melt temperature, injection pressure, injection time, and cooling time) and the volumetric shrinkage by utilizing the analysis data. Finite element (FE) analyses designed by Taguchi (L27) orthogonal arrays were run in the Moldflow simulation program. Analysis of variance (ANOVA) was then performed to check the adequacy of the regression model and to determine the effect of the process parameters on the shrinkage. Experiments were conducted to control the accuracy of the regression model with the FE analyses obtained from Moldflow. The results show that the regression model agrees very well with the FE analyses and the experiments. From this, it can be concluded that this study succeeded in modeling the shrinkage problem in our application.

Aging, not menopause, is associated with higher prevalence of hyperuricemia among older women.

PubMed

Krishnan, Eswar; Bennett, Mihoko; Chen, Linjun

2014-11-01

This work aims to study the associations, if any, of hyperuricemia, gout, and menopause status in the US population. Using multiyear data from the National Health and Nutrition Examination Survey, we performed unmatched comparisons and one to three age-matched comparisons of women aged 20 to 70 years with and without hyperuricemia (serum urate ≥6 mg/dL). Analyses were performed using survey-weighted multiple logistic regression and conditional logistic regression, respectively. Overall, there were 1,477 women with hyperuricemia. Age and serum urate were significantly correlated. In unmatched analyses (n = 9,573 controls), postmenopausal women were older, were heavier, and had higher prevalence of renal impairment, hypertension, diabetes, and hyperlipidemia. In multivariable regression, after accounting for age, body mass index, glomerular filtration rate, and diuretic use, menopause was associated with hyperuricemia (odds ratio, 1.36; 95% CI, 1.05-1.76; P = 0.002). In corresponding multivariable regression using age-matched data (n = 4,431 controls), the odds ratio for menopause was 0.94 (95% CI, 0.83-1.06). Current use of hormone therapy was not associated with prevalent hyperuricemia in both unmatched and matched analyses. Age is a better statistical explanation for the higher prevalence of hyperuricemia among older women than menopause status.

Cognitive, emotive, and cognitive-behavioral correlates of suicidal ideation among Chinese adolescents in Hong Kong.

PubMed

Kwok, Sylvia Lai Yuk Ching; Shek, Daniel Tan Lei

2010-03-05

Utilizing Daniel Goleman's theory of emotional competence, Beck's cognitive theory, and Rudd's cognitive-behavioral theory of suicidality, the relationships between hopelessness (cognitive component), social problem solving (cognitive-behavioral component), emotional competence (emotive component), and adolescent suicidal ideation were examined. Based on the responses of 5,557 Secondary 1 to Secondary 4 students from 42 secondary schools in Hong Kong, results showed that suicidal ideation was positively related to adolescent hopelessness, but negatively related to emotional competence and social problem solving. While standard regression analyses showed that all the above variables were significant predictors of suicidal ideation, hierarchical regression analyses showed that hopelessness was the most important predictor of suicidal ideation, followed by social problem solving and emotional competence. Further regression analyses found that all four subscales of emotional competence, i.e., empathy, social skills, self-management of emotions, and utilization of emotions, were important predictors of male adolescent suicidal ideation. However, the subscale of social skills was not a significant predictor of female adolescent suicidal ideation. Standard regression analysis also revealed that all three subscales of social problem solving, i.e., negative problem orientation, rational problem solving, and impulsiveness/carelessness style, were important predictors of suicidal ideation. Theoretical and practice implications of the findings are discussed.

Risk factors for autistic regression: results of an ambispective cohort study.

PubMed

Zhang, Ying; Xu, Qiong; Liu, Jing; Li, She-chang; Xu, Xiu

2012-08-01

A subgroup of children diagnosed with autism experience developmental regression featured by a loss of previously acquired abilities. The pathogeny of autistic regression is unknown, although many risk factors likely exist. To better characterize autistic regression and investigate the association between autistic regression and potential influencing factors in Chinese autistic children, we conducted an ambispective study with a cohort of 170 autistic subjects. Analyses by multiple logistic regression showed significant correlations between autistic regression and febrile seizures (OR = 3.53, 95% CI = 1.17-10.65, P = .025), as well as with a family history of neuropsychiatric disorders (OR = 3.62, 95% CI = 1.35-9.71, P = .011). This study suggests that febrile seizures and family history of neuropsychiatric disorders are correlated with autistic regression.

Space shuttle propulsion parameter estimation using optional estimation techniques

NASA Technical Reports Server (NTRS)

1983-01-01

A regression analyses on tabular aerodynamic data provided. A representative aerodynamic model for coefficient estimation. It also reduced the storage requirements for the "normal' model used to check out the estimation algorithms. The results of the regression analyses are presented. The computer routines for the filter portion of the estimation algorithm and the :"bringing-up' of the SRB predictive program on the computer was developed. For the filter program, approximately 54 routines were developed. The routines were highly subsegmented to facilitate overlaying program segments within the partitioned storage space on the computer.

Assessment of Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk

PubMed Central

Czarnota, Jenna; Gennings, Chris; Wheeler, David C

2015-01-01

In evaluation of cancer risk related to environmental chemical exposures, the effect of many chemicals on disease is ultimately of interest. However, because of potentially strong correlations among chemicals that occur together, traditional regression methods suffer from collinearity effects, including regression coefficient sign reversal and variance inflation. In addition, penalized regression methods designed to remediate collinearity may have limitations in selecting the truly bad actors among many correlated components. The recently proposed method of weighted quantile sum (WQS) regression attempts to overcome these problems by estimating a body burden index, which identifies important chemicals in a mixture of correlated environmental chemicals. Our focus was on assessing through simulation studies the accuracy of WQS regression in detecting subsets of chemicals associated with health outcomes (binary and continuous) in site-specific analyses and in non-site-specific analyses. We also evaluated the performance of the penalized regression methods of lasso, adaptive lasso, and elastic net in correctly classifying chemicals as bad actors or unrelated to the outcome. We based the simulation study on data from the National Cancer Institute Surveillance Epidemiology and End Results Program (NCI-SEER) case–control study of non-Hodgkin lymphoma (NHL) to achieve realistic exposure situations. Our results showed that WQS regression had good sensitivity and specificity across a variety of conditions considered in this study. The shrinkage methods had a tendency to incorrectly identify a large number of components, especially in the case of strong association with the outcome. PMID:26005323

Assessment of weighted quantile sum regression for modeling chemical mixtures and cancer risk.

PubMed

Czarnota, Jenna; Gennings, Chris; Wheeler, David C

2015-01-01

In evaluation of cancer risk related to environmental chemical exposures, the effect of many chemicals on disease is ultimately of interest. However, because of potentially strong correlations among chemicals that occur together, traditional regression methods suffer from collinearity effects, including regression coefficient sign reversal and variance inflation. In addition, penalized regression methods designed to remediate collinearity may have limitations in selecting the truly bad actors among many correlated components. The recently proposed method of weighted quantile sum (WQS) regression attempts to overcome these problems by estimating a body burden index, which identifies important chemicals in a mixture of correlated environmental chemicals. Our focus was on assessing through simulation studies the accuracy of WQS regression in detecting subsets of chemicals associated with health outcomes (binary and continuous) in site-specific analyses and in non-site-specific analyses. We also evaluated the performance of the penalized regression methods of lasso, adaptive lasso, and elastic net in correctly classifying chemicals as bad actors or unrelated to the outcome. We based the simulation study on data from the National Cancer Institute Surveillance Epidemiology and End Results Program (NCI-SEER) case-control study of non-Hodgkin lymphoma (NHL) to achieve realistic exposure situations. Our results showed that WQS regression had good sensitivity and specificity across a variety of conditions considered in this study. The shrinkage methods had a tendency to incorrectly identify a large number of components, especially in the case of strong association with the outcome.

Subgrid spatial variability of soil hydraulic functions for hydrological modelling

NASA Astrophysics Data System (ADS)

Kreye, Phillip; Meon, Günter

2016-07-01

State-of-the-art hydrological applications require a process-based, spatially distributed hydrological model. Runoff characteristics are demanded to be well reproduced by the model. Despite that, the model should be able to describe the processes at a subcatchment scale in a physically credible way. The objective of this study is to present a robust procedure to generate various sets of parameterisations of soil hydraulic functions for the description of soil heterogeneity on a subgrid scale. Relations between Rosetta-generated values of saturated hydraulic conductivity (Ks) and van Genuchten's parameters of soil hydraulic functions were statistically analysed. An universal function that is valid for the complete bandwidth of Ks values could not be found. After concentrating on natural texture classes, strong correlations were identified for all parameters. The obtained regression results were used to parameterise sets of hydraulic functions for each soil class. The methodology presented in this study is applicable on a wide range of spatial scales and does not need input data from field studies. The developments were implemented into a hydrological modelling system.

Access, treatment and outcomes of care: a study of ethnic minorities in Europe.

PubMed

Hanssens, Lise G M; Detollenaere, Jens; Hardyns, Wim; Willems, Sara J T

2016-05-01

Recent research has shown that ethnic minorities still have less access to medical care and are less satisfied with the treatment they receive and the outcomes of the health care process. This article assesses how migrants in Europe experience access, treatment and outcomes in the European health care systems. Data were obtained from the QUALICOPC study (Quality and Costs of Primary Care in Europe). Regression analyses were used to estimate the access, treatment and outcomes of care for ethnic minorities. In several countries, migrants experience that the opening hours of their GP practice were too limited and indicate that the practice was too far away from their work or home (lower access). They are more likely to report negative patient-doctor communication and less continuity of care than native patients (worse treatment). In addition, they are less satisfied with the care they received and are more likely to postpone care (worse outcomes). In general, migrants are still disadvantaged during the health care process. However, our results also indicate that satisfaction with the health care process improves for second-generation migrants in comparison with first-generation migrants.

Examining Arguments Generated by Year 5, 7, and 10 Students in Science Classrooms

NASA Astrophysics Data System (ADS)

Choi, Aeran; Notebaert, Andrew; Diaz, Juan; Hand, Brian

2010-03-01

A critical component of science is the role of inquiry and argument in moving scientific knowledge forward. However, while students are expected to engage in inquiry activities in science classrooms, there is not always a similar emphasis on the role of argument within the inquiry activities. Building from previous studies on the Science Writing Heuristic (SWH), we were keen to find out if the writing structure used in the SWH approach helped students in Year 5, 7, and 10 to create well constructed arguments. We were also interested in examining which argument components were important for the quality of arguments generated by these students. Two hundred and ninety six writing samples were scored using an analysis framework to evaluate the quality of arguments. Step-wise multiple regression analyses were conducted to determine important argument components. The results of this study suggest that the SWH approach is useful in assisting students to develop reasonable arguments. The critical element determining the quality of the arguments is the relationship between the student’s written claims and his or her evidence.

Do sexual risk behaviour, risk perception and testing behaviour differ across generations of migrants?

PubMed

Kramer, Merlijn A; van Veen, Maaike G; Op de Coul, Eline L M; Coutinho, Roel A; Prins, Maria

2014-02-01

Behaviour and related health outcomes of migrants have been suggested to shift towards the practices of the indigenous population of the host country. To investigate this, we studied generational differences in sexual behaviour between first- and second-generation migrants (FGMs and SGMs) in The Netherlands. In 2003-05, persons aged 16-70 years with origins in Surinam, the Antilles and Aruba were interviewed on their sexual behaviour in The Netherlands and their country of origin. The relationship of generation, age at migration and sexual behaviour was studied by multinomial logistic regression analyses. Generational differences were observed regarding concurrent partnerships, anal sex and history of sexually transmitted infection. Compared with FGMs who migrated at an age >25 years, those who migrated between 10 and 25 years of age were more likely to report concurrency [odds ratio (OR): 1.52, 95% confidence interval (CI): 1.14-2.04], whereas SGMs were less likely to report concurrency (OR: 0.65, 95% CI: 0.43-0.98). FGMs who migrated before the age of 10 were more likely to have had anal sex (OR: 1.90, 95% CI: 1.34-2.71) or a sexually transmitted infection diagnosis (OR: 1.80, 95% CI: 1.20-2.71) than those who had migrated at >25 years of age. Our study shows that not only SGMs but also FGMs who migrated at an early age tend to differ from the sexual patterns of FGMs who migrated at an older age. Generational differences in sexual behaviour could be explained by acculturation and increased identity with the values of the host country.

Influence of Social Support on Health-Related Quality of Life in New-Generation Migrant Workers in Eastern China.

PubMed

Xing, Haiyan; Yu, Wei; Chen, Sanmei; Zhang, Dengke; Tan, Rongmei

2013-08-01

The World Health Organization Quality of Life-BREF (WHOQOL-BREF) has generally been used for patients, few studies in migrants who move from rural to urban within one country. Many studies asserted that social isolation presents a risk to individual health. Poor social networks are associated with worse QOL. This study examined health-related quality of life (HRQOL) and social support in new-generation migrant workers and compared it with urban workers. Nine hundred thirty new-generation migrant workers and 939 urban controls completed the WHOQOL-BREF questionnaire and Social Support Rating Scale (SSRS) by stratified sampling in 2011. Spearman's correlation was performed to clarify the relationship between social support and HRQOL in migrants. Multiple linear regression analyses were used to identify the variables that were associated with HRQOL. The general health, psychological health, and environmental scores of QOL in new-generation migrant workers were lower than in urban workers. New-generation migrants had poorer social support compared with urban controls with regard to general support, objective support, and support utilization. A positive correlation was found between social support and HRQOL. Workers with a higher level of education achieved better psychological, environmental, and general scores than workers with a primary education. Physical, social, environmental, and general health was also closely connected with the age factor. Physical health scores were higher in males than in females. These data suggest that new-generation migrant workers have significant impairment in HRQOL and receive less social support. HRQOL may be affected by social support, education, age, and gender.

Association between prospective registration and overall reporting and methodological quality of systematic reviews: a meta-epidemiological study.

PubMed

Ge, Long; Tian, Jin-Hui; Li, Ya-Nan; Pan, Jia-Xue; Li, Ge; Wei, Dang; Xing, Xin; Pan, Bei; Chen, Yao-Long; Song, Fu-Jian; Yang, Ke-Hu

2018-01-01

The aim of this study was to investigate the differences in main characteristics, reporting and methodological quality between prospectively registered and nonregistered systematic reviews. PubMed was searched to identify systematic reviews of randomized controlled trials published in 2015 in English. After title and abstract screening, potentially relevant reviews were divided into three groups: registered non-Cochrane reviews, Cochrane reviews, and nonregistered reviews. For each group, random number tables were generated in Microsoft Excel, and the first 50 eligible studies from each group were randomly selected. Data of interest from systematic reviews were extracted. Regression analyses were conducted to explore the association between total Revised Assessment of Multiple Systematic Review (R-AMSTAR) or Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) scores and the selected characteristics of systematic reviews. The conducting and reporting of literature search in registered reviews were superior to nonregistered reviews. Differences in 9 of the 11 R-AMSTAR items were statistically significant between registered and nonregistered reviews. The total R-AMSTAR score of registered reviews was higher than nonregistered reviews [mean difference (MD) = 4.82, 95% confidence interval (CI): 3.70, 5.94]. Sensitivity analysis by excluding the registration-related item presented similar result (MD = 4.34, 95% CI: 3.28, 5.40). Total PRISMA scores of registered reviews were significantly higher than nonregistered reviews (all reviews: MD = 1.47, 95% CI: 0.64-2.30; non-Cochrane reviews: MD = 1.49, 95% CI: 0.56-2.42). However, the difference in the total PRISMA score was no longer statistically significant after excluding the item related to registration (item 5). Regression analyses showed similar results. Prospective registration may at least indirectly improve the overall methodological quality of systematic reviews, although its impact on the overall reporting quality was not significant. Copyright © 2017 Elsevier Inc. All rights reserved.

A Combined Pathway and Regional Heritability Analysis Indicates NETRIN1 Pathway Is Associated With Major Depressive Disorder.

PubMed

Zeng, Yanni; Navarro, Pau; Fernandez-Pujals, Ana M; Hall, Lynsey S; Clarke, Toni-Kim; Thomson, Pippa A; Smith, Blair H; Hocking, Lynne J; Padmanabhan, Sandosh; Hayward, Caroline; MacIntyre, Donald J; Wray, Naomi R; Deary, Ian J; Porteous, David J; Haley, Chris S; McIntosh, Andrew M

2017-02-15

Genome-wide association studies (GWASs) of major depressive disorder (MDD) have identified few significant associations. Testing the aggregation of genetic variants, in particular biological pathways, may be more powerful. Regional heritability analysis can be used to detect genomic regions that contribute to disease risk. We integrated pathway analysis and multilevel regional heritability analyses in a pipeline designed to identify MDD-associated pathways. The pipeline was applied to two independent GWAS samples [Generation Scotland: The Scottish Family Health Study (GS:SFHS, N = 6455) and Psychiatric Genomics Consortium (PGC:MDD) (N = 18,759)]. A polygenic risk score (PRS) composed of single nucleotide polymorphisms from the pathway most consistently associated with MDD was created, and its accuracy to predict MDD, using area under the curve, logistic regression, and linear mixed model analyses, was tested. In GS:SFHS, four pathways were significantly associated with MDD, and two of these explained a significant amount of pathway-level regional heritability. In PGC:MDD, one pathway was significantly associated with MDD. Pathway-level regional heritability was significant in this pathway in one subset of PGC:MDD. For both samples the regional heritabilities were further localized to the gene and subregion levels. The NETRIN1 signaling pathway showed the most consistent association with MDD across the two samples. PRSs from this pathway showed competitive predictive accuracy compared with the whole-genome PRSs when using area under the curve statistics, logistic regression, and linear mixed model. These post-GWAS analyses highlight the value of combining multiple methods on multiple GWAS data for the identification of risk pathways for MDD. The NETRIN1 signaling pathway is identified as a candidate pathway for MDD and should be explored in further large population studies. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Normative reference values for strength and flexibility of 1,000 children and adults.

PubMed

McKay, Marnee J; Baldwin, Jennifer N; Ferreira, Paulo; Simic, Milena; Vanicek, Natalie; Burns, Joshua

2017-01-03

To establish reference values for isometric strength of 12 muscle groups and flexibility of 13 joint movements in 1,000 children and adults and investigate the influence of demographic and anthropometric factors. A standardized reliable protocol of hand-held and fixed dynamometry for isometric strength of ankle, knee, hip, elbow, and shoulder musculature as well as goniometry for flexibility of the ankle, knee, hip, elbow, shoulder, and cervical spine was performed in an observational study investigating 1,000 healthy male and female participants aged 3-101 years. Correlation and multiple regression analyses were performed to identify factors independently associated with strength and flexibility of children, adolescents, adults, and older adults. Normative reference values of 25 strength and flexibility measures were generated. Strong linear correlations between age and strength were identified in the first 2 decades of life. Muscle strength significantly decreased with age in older adults. Regression modeling identified increasing height as the most significant predictor of strength in children, higher body mass in adolescents, and male sex in adults and older adults. Joint flexibility gradually decreased with age, with little sex difference. Waist circumference was a significant predictor of variability in joint flexibility in adolescents, adults, and older adults. Reference values and associated age- and sex-stratified z scores generated from this study can be used to determine the presence and extent of impairments associated with neuromuscular and other neurologic disorders, monitor disease progression over time in natural history studies, and evaluate the effect of new treatments in clinical trials. © 2016 American Academy of Neurology.

AGSuite: Software to conduct feature analysis of artificial grammar learning performance.

PubMed

Cook, Matthew T; Chubala, Chrissy M; Jamieson, Randall K

2017-10-01

To simplify the problem of studying how people learn natural language, researchers use the artificial grammar learning (AGL) task. In this task, participants study letter strings constructed according to the rules of an artificial grammar and subsequently attempt to discriminate grammatical from ungrammatical test strings. Although the data from these experiments are usually analyzed by comparing the mean discrimination performance between experimental conditions, this practice discards information about the individual items and participants that could otherwise help uncover the particular features of strings associated with grammaticality judgments. However, feature analysis is tedious to compute, often complicated, and ill-defined in the literature. Moreover, the data violate the assumption of independence underlying standard linear regression models, leading to Type I error inflation. To solve these problems, we present AGSuite, a free Shiny application for researchers studying AGL. The suite's intuitive Web-based user interface allows researchers to generate strings from a database of published grammars, compute feature measures (e.g., Levenshtein distance) for each letter string, and conduct a feature analysis on the strings using linear mixed effects (LME) analyses. The LME analysis solves the inflation of Type I errors that afflicts more common methods of repeated measures regression analysis. Finally, the software can generate a number of graphical representations of the data to support an accurate interpretation of results. We hope the ease and availability of these tools will encourage researchers to take full advantage of item-level variance in their datasets in the study of AGL. We moreover discuss the broader applicability of the tools for researchers looking to conduct feature analysis in any field.

Remote Sensing as a Landscape Epidemiologic Tool to Identify Villages at High Risk for Malaria Transmission

NASA Technical Reports Server (NTRS)

Beck, Louisa R.; Rodriquez, Mario H.; Dister, Sheri W.; Rodriquez, Americo D.; Rejmankova, Eliska; Ulloa, Armando; Meza, Rosa A.; Roberts, Donald R.; Paris, Jack F.; Spanner, Michael A.;

Temporal Synchronization Analysis for Improving Regression Modeling of Fecal Indicator Bacteria Levels

EPA Science Inventory

Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...

Random regression analyses using B-splines functions to model growth from birth to adult age in Canchim cattle.

PubMed

Baldi, F; Alencar, M M; Albuquerque, L G

2010-12-01

The objective of this work was to estimate covariance functions using random regression models on B-splines functions of animal age, for weights from birth to adult age in Canchim cattle. Data comprised 49,011 records on 2435 females. The model of analysis included fixed effects of contemporary groups, age of dam as quadratic covariable and the population mean trend taken into account by a cubic regression on orthogonal polynomials of animal age. Residual variances were modelled through a step function with four classes. The direct and maternal additive genetic effects, and animal and maternal permanent environmental effects were included as random effects in the model. A total of seventeen analyses, considering linear, quadratic and cubic B-splines functions and up to seven knots, were carried out. B-spline functions of the same order were considered for all random effects. Random regression models on B-splines functions were compared to a random regression model on Legendre polynomials and with a multitrait model. Results from different models of analyses were compared using the REML form of the Akaike Information criterion and Schwarz' Bayesian Information criterion. In addition, the variance components and genetic parameters estimated for each random regression model were also used as criteria to choose the most adequate model to describe the covariance structure of the data. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most adequate to describe the covariance structure of the data. Random regression models using B-spline functions as base functions fitted the data better than Legendre polynomials, especially at mature ages, but higher number of parameters need to be estimated with B-splines functions. © 2010 Blackwell Verlag GmbH.

The Relationship Between Surface Curvature and Abdominal Aortic Aneurysm Wall Stress.

PubMed

de Galarreta, Sergio Ruiz; Cazón, Aitor; Antón, Raúl; Finol, Ender A

2017-08-01

The maximum diameter (MD) criterion is the most important factor when predicting risk of rupture of abdominal aortic aneurysms (AAAs). An elevated wall stress has also been linked to a high risk of aneurysm rupture, yet is an uncommon clinical practice to compute AAA wall stress. The purpose of this study is to assess whether other characteristics of the AAA geometry are statistically correlated with wall stress. Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis (FEA). These models were subsequently used to estimate wall stress and maximum diameter and to evaluate the spatial distributions of wall thickness, cross-sectional diameter, mean curvature, and Gaussian curvature. Data analysis consisted of statistical correlations of the aforementioned geometry metrics with wall stress for the 30 AAA inner and outer wall surfaces. In addition, a linear regression analysis was performed with all the AAA wall surfaces to quantify the relationship of the geometric indices with wall stress. These analyses indicated that while all the geometry metrics have statistically significant correlations with wall stress, the local mean curvature (LMC) exhibits the highest average Pearson's correlation coefficient for both inner and outer wall surfaces. The linear regression analysis revealed coefficients of determination for the outer and inner wall surfaces of 0.712 and 0.516, respectively, with LMC having the largest effect on the linear regression equation with wall stress. This work underscores the importance of evaluating AAA mean wall curvature as a potential surrogate for wall stress.

Identifying neonates at a very high risk for mortality among children with congenital diaphragmatic hernia managed with extracorporeal membrane oxygenation.

PubMed

Haricharan, Ramanath N; Barnhart, Douglas C; Cheng, Hong; Delzell, Elizabeth

2009-01-01

The purpose of this study was to identify mortality risk factors in children with congenital diaphragmatic hernia (CDH) treated with extracorporeal membrane oxygenation (ECMO) and generate a prediction score for those at a very high risk for mortality. Data on first ECMO runs of all neonates with CDH, between January 1997 and June 2007, were obtained from the Extracorporeal Life Support Organization registry (N = 2678). The data were split into "training data (TD)" (n = 2006) and "validation data" (n = 672). The primary outcome analyzed was in-hospital mortality. Modified Poisson regression was used for analyses. Overall in-hospital mortality among 2678 neonates (males, 57%; median age at ECMO, 1 day) was 52%. The univariate and multivariable analyses were performed using TD. An empirically weighted mortality prediction score was generated with possible scores ranging from 0 to 35 points. Of 69 who scored 14 or higher in the TD, 62 died (positive predictive value [PPV], 90%), of 37 with 15 or higher, 35 died (PPV, 95%), of 23 with 16 or higher, 22 died (PPV, 96%). A cut-off point of 15 was chosen and was tested using the separate validation dataset. In validation data, the cut-off point 15 had a PPV of 96% (23 died of 24). Scoring 15 or higher on the prediction score identifies neonates with CDH at a very high risk for mortality among those managed with ECMO and could be used in surgical decision making and counseling.

Estimates of Flow Duration, Mean Flow, and Peak-Discharge Frequency Values for Kansas Stream Locations

USGS Publications Warehouse

Perry, Charles A.; Wolock, David M.; Artman, Joshua C.

2004-01-01

Streamflow statistics of flow duration and peak-discharge frequency were estimated for 4,771 individual locations on streams listed on the 1999 Kansas Surface Water Register. These statistics included the flow-duration values of 90, 75, 50, 25, and 10 percent, as well as the mean flow value. Peak-discharge frequency values were estimated for the 2-, 5-, 10-, 25-, 50-, and 100-year floods. Least-squares multiple regression techniques were used, along with Tobit analyses, to develop equations for estimating flow-duration values of 90, 75, 50, 25, and 10 percent and the mean flow for uncontrolled flow stream locations. The contributing-drainage areas of 149 U.S. Geological Survey streamflow-gaging stations in Kansas and parts of surrounding States that had flow uncontrolled by Federal reservoirs and used in the regression analyses ranged from 2.06 to 12,004 square miles. Logarithmic transformations of climatic and basin data were performed to yield the best linear relation for developing equations to compute flow durations and mean flow. In the regression analyses, the significant climatic and basin characteristics, in order of importance, were contributing-drainage area, mean annual precipitation, mean basin permeability, and mean basin slope. The analyses yielded a model standard error of prediction range of 0.43 logarithmic units for the 90-percent duration analysis to 0.15 logarithmic units for the 10-percent duration analysis. The model standard error of prediction was 0.14 logarithmic units for the mean flow. Regression equations used to estimate peak-discharge frequency values were obtained from a previous report, and estimates for the 2-, 5-, 10-, 25-, 50-, and 100-year floods were determined for this report. The regression equations and an interpolation procedure were used to compute flow durations, mean flow, and estimates of peak-discharge frequency for locations along uncontrolled flow streams on the 1999 Kansas Surface Water Register. Flow durations, mean flow, and peak-discharge frequency values determined at available gaging stations were used to interpolate the regression-estimated flows for the stream locations where available. Streamflow statistics for locations that had uncontrolled flow were interpolated using data from gaging stations weighted according to the drainage area and the bias between the regression-estimated and gaged flow information. On controlled reaches of Kansas streams, the streamflow statistics were interpolated between gaging stations using only gaged data weighted by drainage area.

Comparison of enzyme-linked immunosorbent assay and gas chromatography procedures for the detection of cyanazine and metolachlor in surface water samples

USGS Publications Warehouse

Schraer, S.M.; Shaw, D.R.; Boyette, M.; Coupe, R.H.; Thurman, E.M.

2000-01-01

Enzyme-linked immunosorbent assay (ELISA) data from surface water reconnaissance were compared to data from samples analyzed by gas chromatography for the pesticide residues cyanazine (2-[[4-chloro-6-(ethylamino)-l,3,5-triazin-2-yl]amino]-2-methylpropanenitrile ) and metolachlor (2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl)acetamide). When ELISA analyses were duplicated, cyanazine and metolachlor detection was found to have highly reproducible results; adjusted R2s were 0.97 and 0.94, respectively. When ELISA results for cyanazine were regressed against gas chromatography results, the models effectively predicted cyanazine concentrations from ELISA analyses (adjusted R2s ranging from 0.76 to 0.81). The intercepts and slopes for these models were not different from 0 and 1, respectively. This indicates that cyanazine analysis by ELISA is expected to give the same results as analysis by gas chromatography. However, regressing ELISA analyses for metolachlor against gas chromatography data provided more variable results (adjusted R2s ranged from 0.67 to 0.94). Regression models for metolachlor analyses had two of three intercepts that were not different from 0. Slopes for all metolachlor regression models were significantly different from 1. This indicates that as metolachlor concentrations increase, ELISA will over- or under-estimate metolachlor concentration, depending on the method of comparison. ELISA can be effectively used to detect cyanazine and metolachlor in surface water samples. However, when detections of metolachlor have significant consequences or implications it may be necessary to use other analytical methods.

Application of Partial Least Square (PLS) Regression to Determine Landscape-Scale Aquatic Resources Vulnerability in the Ozark Mountains

EPA Science Inventory

Partial least squares (PLS) analysis offers a number of advantages over the more traditionally used regression analyses applied in landscape ecology, particularly for determining the associations among multiple constituents of surface water and landscape configuration. Common dat...

Cross-border Ties and Arab American Mental Health

PubMed Central

Samari, Goleen

2016-01-01

Due to increasing discrimination and marginalization, Arab Americans are at a greater risk for mental health disorders. Social networks that include ties to the country of origin could help promote mental well-being in the face of discrimination. The role of countries of origin in immigrant mental health receives little attention compared to adjustment in destination contexts. This study addresses this gap by analyzing the relationship between nativity, cross-border ties, and psychological distress and happiness for Arab Americans living in the greater Detroit Metropolitan Area (N=896). I expect that first generation Arab Americans will have more psychological distress compared to one and half, second, and third generations, and Arab Americans with more cross-border ties will have less psychological distress and more happiness. Data come from the 2003 Detroit Arab American Study, which includes measures of nativity, cross-border ties – attitudes, social ties, media consumption, and community organizations, and the Kessler-10 scale of psychological distress and self-reported happiness. Ordered logistic regression analyses suggest that psychological distress and happiness do not vary much by nativity alone. However, cross-border ties have both adverse and protective effects on psychological distress and happiness. For all generations of Arab Americans, cross-border attitudes and social ties are associated with greater odds of psychological distress and for first generation Arab Americans, media consumption is associated with greater odds of unhappiness. In contrast, for all generations, involvement in cross-border community organizations is associated with less psychological distress and for the third generation, positive cross-border attitudes are associated with higher odds of happiness. These findings show the complex relationship between cross-border ties and psychological distress and happiness for different generations of Arab Americans. PMID:26999416

Cross-border ties and Arab American mental health.

PubMed

Samari, Goleen

2016-04-01

Due to increasing discrimination and marginalization, Arab Americans are at a greater risk for mental health disorders. Social networks that include ties to the country of origin could help promote mental well-being in the face of discrimination. The role of countries of origin in immigrant mental health receives little attention compared to adjustment in destination contexts. This study addresses this gap by analyzing the relationship between nativity, cross-border ties, and psychological distress and happiness for Arab Americans living in the greater Detroit Metropolitan Area (N = 896). I expect that first generation Arab Americans will have more psychological distress compared to one and half, second, and third generations, and Arab Americans with more cross-border ties will have less psychological distress and more happiness. Data come from the 2003 Detroit Arab American Study, which includes measures of nativity, cross-border ties--attitudes, social ties, media consumption, and community organizations, and the Kessler-10 scale of psychological distress and self-reported happiness. Ordered logistic regression analyses suggest that psychological distress and happiness do not vary much by nativity alone. However, cross-border ties have both adverse and protective effects on psychological distress and happiness. For all generations of Arab Americans, cross-border attitudes and social ties are associated with greater odds of psychological distress and for first generation Arab Americans, media consumption is associated with greater odds of unhappiness. In contrast, for all generations, involvement in cross-border community organizations is associated with less psychological distress and for the third generation, positive cross-border attitudes are associated with higher odds of happiness. These findings show the complex relationship between cross-border ties and psychological distress and happiness for different generations of Arab Americans. Copyright © 2016 Elsevier Ltd. All rights reserved.

Adjusting data to body size: a comparison of methods as applied to quantitative trait loci analysis of musculoskeletal phenotypes.

PubMed

Lang, Dean H; Sharkey, Neil A; Lionikas, Arimantas; Mack, Holly A; Larsson, Lars; Vogler, George P; Vandenbergh, David J; Blizard, David A; Stout, Joseph T; Stitt, Joseph P; McClearn, Gerald E

2005-05-01

The aim of this study was to compare three methods of adjusting skeletal data for body size and examine their use in QTL analyses. It was found that dividing skeletal phenotypes by body mass index induced erroneous QTL results. The preferred method of body size adjustment was multiple regression. Many skeletal studies have reported strong correlations between phenotypes for muscle, bone, and body size, and these correlations add to the difficulty in identifying genetic influence on skeletal traits that are not mediated through overall body size. Quantitative trait loci (QTL) identified for skeletal phenotypes often map to the same chromosome regions as QTLs for body size. The actions of a QTL identified as influencing BMD could therefore be mediated through the generalized actions of growth on body size or muscle mass. Three methods of adjusting skeletal phenotypes to body size were performed on morphologic, structural, and compositional measurements of the femur and tibia in 200-day-old C57BL/6J x DBA/2 (BXD) second generation (F(2)) mice (n = 400). A common method of removing the size effect has been through the use of ratios. This technique and two alternative techniques using simple and multiple regression were performed on muscle and skeletal data before QTL analyses, and the differences in QTL results were examined. The use of ratios to remove the size effect was shown to increase the size effect by inducing spurious correlations, thereby leading to inaccurate QTL results. Adjustments for body size using multiple regression eliminated these problems. Multiple regression should be used to remove the variance of co-factors related to skeletal phenotypes to allow for the study of genetic influence independent of correlated phenotypes. However, to better understand the genetic influence, adjusted and unadjusted skeletal QTL results should be compared. Additional insight can be gained by observing the difference in LOD score between the adjusted and nonadjusted phenotypes. Identifying QTLs that exert their effects on skeletal phenotypes through body size-related pathways as well as those having a more direct and independent influence on bone are equally important in deciphering the complex physiologic pathways responsible for the maintenance of bone health.

VBM-DTI correlates of verbal intelligence: a potential link to Broca's area.

PubMed

Konrad, Andreas; Vucurevic, Goran; Musso, Francesco; Winterer, Georg

2012-04-01

Human brain lesion studies first investigated the biological roots of cognitive functions including language in the late 1800s. Neuroimaging studies have reported correlation findings with general intelligence predominantly in fronto-parietal cortical areas. However, there is still little evidence about the relationship between verbal intelligence and structural properties of the brain. We predicted that verbal performance is related to language regions of Broca's and Wernicke's areas. Verbal intelligence quotient (vIQ) was assessed in 30 healthy young subjects. T1-weighted MRI and diffusion tensor imaging data sets were acquired. Voxel-wise regression analyses were used to correlate fractional anisotropy (FA) and mean diffusivity values with vIQ. Moreover, regression analyses of regional brain volume with vIQ were performed adopting voxel-based morphometry (VBM) and ROI methodology. Our analyses revealed a significant negative correlation between vIQ and FA and a significant positive correlation between vIQ and mean diffusivity in the left-hemispheric Broca's area. VBM regression analyses did not show significant results, whereas a subsequent ROI analysis of Broca's area FA peak cluster demonstrated a positive correlation of gray matter volume and vIQ. These findings suggest that cortical thickness in Broca's area contributes to verbal intelligence. Diffusion parameters predicted gray matter ratio in Broca's area more sensitive than VBM methodology.

Association between cardiovascular risk factors and carotid intima-media thickness in prepubertal Brazilian children.

PubMed

Gazolla, Fernanda Mussi; Neves Bordallo, Maria Alice; Madeira, Isabel Rey; de Miranda Carvalho, Cecilia Noronha; Vieira Monteiro, Alexandra Maria; Pinheiro Rodrigues, Nádia Cristina; Borges, Marcos Antonio; Collett-Solberg, Paulo Ferrez; Muniz, Bruna Moreira; de Oliveira, Cecilia Lacroix; Pinheiro, Suellen Martins; de Queiroz Ribeiro, Rebeca Mathias

2015-05-01

Early exposure to cardiovascular risk factors creates a chronic inflammatory state that could damage the endothelium followed by thickening of the carotid intima-media. To investigate the association of cardiovascular risk factors and thickening of the carotid intima. Media in prepubertal children. In this cross-sectional study, carotid intima-media thickness (cIMT) and cardiovascular risk factors were assessed in 129 prepubertal children aged from 5 to 10 year. Association was assessed by simple and multivariate logistic regression analyses. In simple logistic regression analyses, body mass index (BMI) z-score, waist circumference, and systolic blood pressure (SBP) were positively associated with increased left, right, and average cIMT, whereas diastolic blood pressure was positively associated only with increased left and average cIMT (p<0.05). In multivariate logistic regression analyses increased left cIMT was positively associated to BMI z-score and SBP, and increased average cIMT was only positively associated to SBP (p<0.05). BMI z-score and SBP were the strongest risk factors for increased cIMT.

Rex fortran 4 system for combinatorial screening or conventional analysis of multivariate regressions

Treesearch

L.R. Grosenbaugh

1967-01-01

Describes an expansible computerized system that provides data needed in regression or covariance analysis of as many as 50 variables, 8 of which may be dependent. Alternatively, it can screen variously generated combinations of independent variables to find the regression with the smallest mean-squared-residual, which will be fitted if desired. The user can easily...

Spatial interpolation schemes of daily precipitation for hydrologic modeling

USGS Publications Warehouse

Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

2012-01-01

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

Parental education predicts change in intelligence quotient after childhood epilepsy surgery.

PubMed

Meekes, Joost; van Schooneveld, Monique M J; Braams, Olga B; Jennekens-Schinkel, Aag; van Rijen, Peter C; Hendriks, Marc P H; Braun, Kees P J; van Nieuwenhuizen, Onno

2015-04-01

To know whether change in the intelligence quotient (IQ) of children who undergo epilepsy surgery is associated with the educational level of their parents. Retrospective analysis of data obtained from a cohort of children who underwent epilepsy surgery between January 1996 and September 2010. We performed simple and multiple regression analyses to identify predictors associated with IQ change after surgery. In addition to parental education, six variables previously demonstrated to be associated with IQ change after surgery were included as predictors: age at surgery, duration of epilepsy, etiology, presurgical IQ, reduction of antiepileptic drugs, and seizure freedom. We used delta IQ (IQ 2 years after surgery minus IQ shortly before surgery) as the primary outcome variable, but also performed analyses with pre- and postsurgical IQ as outcome variables to support our findings. To validate the results we performed simple regression analysis with parental education as the predictor in specific subgroups. The sample for regression analysis included 118 children (60 male; median age at surgery 9.73 years). Parental education was significantly associated with delta IQ in simple regression analysis (p = 0.004), and also contributed significantly to postsurgical IQ in multiple regression analysis (p = 0.008). Additional analyses demonstrated that parental education made a unique contribution to prediction of delta IQ, that is, it could not be replaced by the illness-related variables. Subgroup analyses confirmed the association of parental education with IQ change after surgery for most groups. Children whose parents had higher education demonstrate on average a greater increase in IQ after surgery and a higher postsurgical--but not presurgical--IQ than children whose parents completed at most lower secondary education. Parental education--and perhaps other environmental variables--should be considered in the prognosis of cognitive function after childhood epilepsy surgery. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

Comparison of Xenon-Enhanced Area-Detector CT and Krypton Ventilation SPECT/CT for Assessment of Pulmonary Functional Loss and Disease Severity in Smokers.

PubMed

Ohno, Yoshiharu; Fujisawa, Yasuko; Takenaka, Daisuke; Kaminaga, Shigeo; Seki, Shinichiro; Sugihara, Naoki; Yoshikawa, Takeshi

2018-02-01

The objective of this study was to compare the capability of xenon-enhanced area-detector CT (ADCT) performed with a subtraction technique and coregistered 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity in smokers. Forty-six consecutive smokers (32 men and 14 women; mean age, 67.0 years) underwent prospective unenhanced and xenon-enhanced ADCT, 81m Kr-ventilation SPECT/CT, and pulmonary function tests. Disease severity was evaluated according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification. CT-based functional lung volume (FLV), the percentage of wall area to total airway area (WA%), and ventilated FLV on xenon-enhanced ADCT and SPECT/CT were calculated for each smoker. All indexes were correlated with percentage of forced expiratory volume in 1 second (%FEV 1 ) using step-wise regression analyses, and univariate and multivariate logistic regression analyses were performed. In addition, the diagnostic accuracy of the proposed model was compared with that of each radiologic index by means of McNemar analysis. Multivariate logistic regression showed that %FEV 1 was significantly affected (r = 0.77, r 2 = 0.59) by two factors: the first factor, ventilated FLV on xenon-enhanced ADCT (p < 0.0001); and the second factor, WA% (p = 0.004). Univariate logistic regression analyses indicated that all indexes significantly affected GOLD classification (p < 0.05). Multivariate logistic regression analyses revealed that ventilated FLV on xenon-enhanced ADCT and CT-based FLV significantly influenced GOLD classification (p < 0.0001). The diagnostic accuracy of the proposed model was significantly higher than that of ventilated FLV on SPECT/CT (p = 0.03) and WA% (p = 0.008). Xenon-enhanced ADCT is more effective than 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity.

Smartphone data as an electronic biomarker of illness activity in bipolar disorder.

PubMed

Faurholt-Jepsen, Maria; Vinberg, Maj; Frost, Mads; Christensen, Ellen Margrethe; Bardram, Jakob E; Kessing, Lars Vedel

2015-11-01

Objective methods are lacking for continuous monitoring of illness activity in bipolar disorder. Smartphones offer unique opportunities for continuous monitoring and automatic collection of real-time data. The objectives of the paper were to test the hypotheses that (i) daily electronic self-monitored data and (ii) automatically generated objective data collected using smartphones correlate with clinical ratings of depressive and manic symptoms in patients with bipolar disorder. Software for smartphones (the MONARCA I system) that collects automatically generated objective data and self-monitored data on illness activity in patients with bipolar disorder was developed by the authors. A total of 61 patients aged 18-60 years and with a diagnosis of bipolar disorder according to ICD-10 used the MONARCA I system for six months. Depressive and manic symptoms were assessed monthly using the Hamilton Depression Rating Scale 17-item (HDRS-17) and the Young Mania Rating Scale (YMRS), respectively. Data are representative of over 400 clinical ratings. Analyses were computed using linear mixed-effect regression models allowing for both between individual variation and within individual variation over time. Analyses showed significant positive correlations between the duration of incoming and outgoing calls/day and scores on the HDRS-17, and significant positive correlations between the number and duration of incoming calls/day and scores on the YMRS; the number of and duration of outgoing calls/day and scores on the YMRS; and the number of outgoing text messages/day and scores on the YMRS. Analyses showed significant negative correlations between self-monitored data (i.e., mood and activity) and scores on the HDRS-17, and significant positive correlations between self-monitored data (i.e., mood and activity) and scores on the YMRS. Finally, the automatically generated objective data were able to discriminate between affective states. Automatically generated objective data and self-monitored data collected using smartphones correlate with clinically rated depressive and manic symptoms and differ between affective states in patients with bipolar disorder. Smartphone apps represent an easy and objective way to monitor illness activity with real-time data in bipolar disorder and may serve as an electronic biomarker of illness activity. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Reproductive efficiency of captive Chinese- and Indian-origin rhesus macaque (Macaca mulatta) females

PubMed Central

Kubisch, H. Michael; Falkenstein, Kathrine P.; Deroche, Chelsea B.; Franke, Donald E.

2011-01-01

Reproductive and survival records (n = 2,913) from 313 Chinese-origin and 365 Indian-derived rhesus macaques at the Tulane National Primate Research Center spanning 3 generations were studied. Least-squares analysis of variance procedures were used to compare reproductive and infant survival traits while proportional hazards regression procedures were used to study female age at death, number of infants born per female and time from last birth to death. Chinese females were older at first parturition than Indian-females because they were older when placed with males, but the two subspecies had similar first and lifetime post-partum birth intervals. Females that gave birth to stillborn infants had shorter first post-partum birth intervals than females giving birth to live infants. Post-partum birth intervals decreased in females from 3 to 12 years of age but then increased again with advancing age. Chinese infants had a greater survival rate than Indian infants at 30 d, 6 mo and 1yr of age. Five hundred and forty-three females (80.01 %) had uncensored, or true records for age at death, number of infants born per female, and time from the birth until death whereas 135 females (19.91 %) had censored records for these traits. Low and high uncensored observations for age at death were 3 and 26 years of age for Chinese and 3 and 23 years of age for Indian females. Uncensored number of infants born per female ranged from 1 to 15 for Chinese females and 1 to 18 for Indian females. Each of these traits was significantly influenced by the origin × generation interaction in the proportional hazards regression analyses, indicating that probabilities associated with age at death, number of infants born per female and time from last birth to death for Chinese and Indian females did not rank the same across generations. PMID:22512021

Application of Partial Least Squares (PLS) Regression to Determine Landscape-Scale Aquatic Resource Vulnerability in the Ozark Mountains

EPA Science Inventory

Partial least squares (PLS) analysis offers a number of advantages over the more traditionally used regression analyses applied in landscape ecology to study the associations among constituents of surface water and landscapes. Common data problems in ecological studies include: s...

Infantile hemangioma-like vascular lesion in a 26-year-old woman after abortion.

PubMed

Lu, Yang; Wang, Shu Jun; Li, Xin; Hu, Li; Zhang, Wen Jie; Li, Wei

2014-01-01

A 26-year-old woman (G2P1A1) presented with a 5-week history of multiple red marks on her body after a therapeutic abortion. A physical examination found 15 palpable red marks on her head, neck, chest, arms and legs. Proliferating endothelial cells, which expressed CD31, CD34, von Willebrand factor, but not Glut-1 and merosin, were observed in the lesional area by histopathological analyses. Histocompatibility antigen typing of 2 lesions was identical to a sample from peripheral blood. Accelerated regression was observed in 2 lesions treated by intralesional injection of betamethasone, while spontaneous regression was observed within 9 months in the remaining lesions without any treatment. Rapid growth, spontaneous regression and histological analyses in this case support the diagnosis of 'infantile hemangioma-like vascular lesion'.

Applications of genetic algorithms on the structure-activity relationship analysis of some cinnamamides.

PubMed

Hou, T J; Wang, J M; Liao, N; Xu, X J

1999-01-01

Quantitative structure-activity relationships (QSARs) for 35 cinnamamides were studied. By using a genetic algorithm (GA), a group of multiple regression models with high fitness scores was generated. From the statistical analyses of the descriptors used in the evolution procedure, the principal features affecting the anticonvulsant activity were found. The significant descriptors include the partition coefficient, the molar refraction, the Hammet sigma constant of the substituents on the benzene ring, and the formation energy of the molecules. It could be found that the steric complementarity and the hydrophobic interaction between the inhibitors and the receptor were very important to the biological activity, while the contribution of the electronic effect was not so obvious. Moreover, by construction of the spline models for these four principal descriptors, the effective range for each descriptor was identified.

Gridded rainfall estimation for distributed modeling in western mountainous areas

NASA Astrophysics Data System (ADS)

Moreda, F.; Cong, S.; Schaake, J.; Smith, M.

2006-05-01

Estimation of precipitation in mountainous areas continues to be problematic. It is well known that radar-based methods are limited due to beam blockage. In these areas, in order to run a distributed model that accounts for spatially variable precipitation, we have generated hourly gridded rainfall estimates from gauge observations. These estimates will be used as basic data sets to support the second phase of the NWS-sponsored Distributed Hydrologic Model Intercomparison Project (DMIP 2). One of the major foci of DMIP 2 is to better understand the modeling and data issues in western mountainous areas in order to provide better water resources products and services to the Nation. We derive precipitation estimates using three data sources for the period of 1987-2002: 1) hourly cooperative observer (coop) gauges, 2) daily total coop gauges and 3) SNOw pack TELemetry (SNOTEL) daily gauges. The daily values are disaggregated using the hourly gauge values and then interpolated to approximately 4km grids using an inverse-distance method. Following this, the estimates are adjusted to match monthly mean values from the Parameter-elevation Regressions on Independent Slopes Model (PRISM). Several analyses are performed to evaluate the gridded estimates for DMIP 2 experiments. These gridded inputs are used to generate mean areal precipitation (MAPX) time series for comparison to the traditional mean areal precipitation (MAP) time series derived by the NWS' California-Nevada River Forecast Center for model calibration. We use two of the DMIP 2 basins in California and Nevada: the North Fork of the American River (catchment area 885 sq. km) and the East Fork of the Carson River (catchment area 922 sq. km) as test areas. The basins are sub-divided into elevation zones. The North Fork American basin is divided into two zones above and below an elevation threshold. Likewise, the Carson River basin is subdivided in to four zones. For each zone, the analyses include: a) overall difference, b) annual difference, c) typical year monthly comparison, and d) regression fit of the MAPX and MAP data. In terms of mean areal precipitation, overall differences between the MAP and MAPX time series are very small for the North Fork American River elevation zones. For the East Fork Carson River zones, the over all difference is up to 10 percent. The difference tends to be high when the elevation zones are small in area. In our presentation, we will show the results of our analyses and discuss future evaluations of these precipitation estimates using distributed and lumped hydrologic models.

Addressing the identification problem in age-period-cohort analysis: a tutorial on the use of partial least squares and principal components analysis.

PubMed

Tu, Yu-Kang; Krämer, Nicole; Lee, Wen-Chung

2012-07-01

In the analysis of trends in health outcomes, an ongoing issue is how to separate and estimate the effects of age, period, and cohort. As these 3 variables are perfectly collinear by definition, regression coefficients in a general linear model are not unique. In this tutorial, we review why identification is a problem, and how this problem may be tackled using partial least squares and principal components regression analyses. Both methods produce regression coefficients that fulfill the same collinearity constraint as the variables age, period, and cohort. We show that, because the constraint imposed by partial least squares and principal components regression is inherent in the mathematical relation among the 3 variables, this leads to more interpretable results. We use one dataset from a Taiwanese health-screening program to illustrate how to use partial least squares regression to analyze the trends in body heights with 3 continuous variables for age, period, and cohort. We then use another dataset of hepatocellular carcinoma mortality rates for Taiwanese men to illustrate how to use partial least squares regression to analyze tables with aggregated data. We use the second dataset to show the relation between the intrinsic estimator, a recently proposed method for the age-period-cohort analysis, and partial least squares regression. We also show that the inclusion of all indicator variables provides a more consistent approach. R code for our analyses is provided in the eAppendix.

Validation of a two-generational reproduction test in Daphnia magna: An interlaboratory exercise.

PubMed

Barata, Carlos; Campos, Bruno; Rivetti, Claudia; LeBlanc, Gerald A; Eytcheson, Stephanie; McKnight, Stephanie; Tobor-Kaplon, Marysia; de Vries Buitenweg, Selinda; Choi, Suhyon; Choi, Jinhee; Sarapultseva, Elena I; Coutellec, Marie-Agnès; Coke, Maïra; Pandard, Pascal; Chaumot, Arnaud; Quéau, Hervé; Delorme, Nicolas; Geffard, Olivier; Martínez-Jerónimo, Fernando; Watanabe, Haruna; Tatarazako, Norihisa; Lopes, Isabel; Pestana, João L T; Soares, Amadeu M V M; Pereira, Cecilia Manuela; De Schamphelaere, Karel

2017-02-01

Effects observed within one generation disregard potential detrimental effects that may appear across generations. Previously we have developed a two generation Daphnia magna reproduction test using the OECD TG 211 protocol with a few amendments, including initiating the second generation with third brood neonates produced from first generation individuals. Here we showed the results of an inter-laboratory calibration exercise among 12 partners that aimed to test the robustness and consistency of a two generation Daphnia magna reproduction test. Pyperonyl butoxide (PBO) was used as a test compound. Following experiments, PBO residues were determined by TQD-LC/MS/MS. Chemical analysis denoted minor deviations of measured PBO concentrations in freshly prepared and old test solutions and between real and nominal concentrations in all labs. Other test conditions (water, food, D. magna clone, type of test vessel) varied across partners as allowed in the OECD test guidelines. Cumulative fecundity and intrinsic population growth rates (r) were used to estimate "No observed effect concentrations "NOEC using the solvent control as the control treatment. EC 10 and EC- 50 values were obtained regression analyses. Eleven of the twelve labs succeeded in meeting the OECD criteria of producing >60 offspring per female in control treatments during 21days in each of the two consecutive generations. Analysis of variance partitioning of cumulative fecundity indicated a relatively good performance of most labs with most of the variance accounted for by PBO (56.4%) and PBO by interlaboratory interactions (20.2%), with multigenerational effects within and across PBO concentrations explaining about 6% of the variance. EC 50 values for reproduction and population growth rates were on average 16.6 and 20.8% lower among second generation individuals, respectively. In summary these results suggest that the proposed assay is reproducible but cumulative toxicity in the second generation cannot reliably be detected with this assay. Copyright © 2016 Elsevier B.V. All rights reserved.

Logistic regression for circular data

NASA Astrophysics Data System (ADS)

Al-Daffaie, Kadhem; Khan, Shahjahan

2017-05-01

This paper considers the relationship between a binary response and a circular predictor. It develops the logistic regression model by employing the linear-circular regression approach. The maximum likelihood method is used to estimate the parameters. The Newton-Raphson numerical method is used to find the estimated values of the parameters. A data set from weather records of Toowoomba city is analysed by the proposed methods. Moreover, a simulation study is considered. The R software is used for all computations and simulations.

Estimates of Median Flows for Streams on the 1999 Kansas Surface Water Register

USGS Publications Warehouse

Perry, Charles A.; Wolock, David M.; Artman, Joshua C.

2004-01-01

The Kansas State Legislature, by enacting Kansas Statute KSA 82a?2001 et. seq., mandated the criteria for determining which Kansas stream segments would be subject to classification by the State. One criterion for the selection as a classified stream segment is based on the statistic of median flow being equal to or greater than 1 cubic foot per second. As specified by KSA 82a?2001 et. seq., median flows were determined from U.S. Geological Survey streamflow-gaging-station data by using the most-recent 10 years of gaged data (KSA) for each streamflow-gaging station. Median flows also were determined by using gaged data from the entire period of record (all-available hydrology, AAH). Least-squares multiple regression techniques were used, along with Tobit analyses, to develop equations for estimating median flows for uncontrolled stream segments. The drainage area of the gaging stations on uncontrolled stream segments used in the regression analyses ranged from 2.06 to 12,004 square miles. A logarithmic transformation of the data was needed to develop the best linear relation for computing median flows. In the regression analyses, the significant climatic and basin characteristics, in order of importance, were drainage area, mean annual precipitation, mean basin permeability, and mean basin slope. Tobit analyses of KSA data yielded a model standard error of prediction of 0.285 logarithmic units, and the best equations using Tobit analyses of AAH data had a model standard error of prediction of 0.250 logarithmic units. These regression equations and an interpolation procedure were used to compute median flows for the uncontrolled stream segments on the 1999 Kansas Surface Water Register. Measured median flows from gaging stations were incorporated into the regression-estimated median flows along the stream segments where available. The segments that were uncontrolled were interpolated using gaged data weighted according to the drainage area and the bias between the regression-estimated and gaged flow information. On controlled segments of Kansas streams, the median flow information was interpolated between gaging stations using only gaged data weighted by drainage area. Of the 2,232 total stream segments on the Kansas Surface Water Register, 34.5 percent of the segments had an estimated median streamflow of less than 1 cubic foot per second when the KSA analysis was used. When the AAH analysis was used, 36.2 percent of the segments had an estimated median streamflow of less than 1 cubic foot per second. This report supercedes U.S. Geological Survey Water-Resources Investigations Report 02?4292.

Advanced Statistics for Exotic Animal Practitioners.

PubMed

Hodsoll, John; Hellier, Jennifer M; Ryan, Elizabeth G

2017-09-01

Correlation and regression assess the association between 2 or more variables. This article reviews the core knowledge needed to understand these analyses, moving from visual analysis in scatter plots through correlation, simple and multiple linear regression, and logistic regression. Correlation estimates the strength and direction of a relationship between 2 variables. Regression can be considered more general and quantifies the numerical relationships between an outcome and 1 or multiple variables in terms of a best-fit line, allowing predictions to be made. Each technique is discussed with examples and the statistical assumptions underlying their correct application. Copyright © 2017 Elsevier Inc. All rights reserved.

An evaluation of the relative quality of dike pools for benthic macroinvertebrates in the Lower Missouri River, USA

USGS Publications Warehouse

Poulton, B.C.; Allert, A.L.

2012-01-01

A habitat-based aquatic macroinvertebrate study was initiated in the Lower Missouri River to evaluate relative quality and biological condition of dike pool habitats. Water-quality and sediment-quality parameters and macroinvertebrate assemblage structure were measured from depositional substrates at 18 sites. Sediment porewater was analysed for ammonia, sulphide, pH and oxidation-reduction potential. Whole sediments were analysed for particle-size distribution, organic carbon and contaminants. Field water-quality parameters were measured at subsurface and at the sediment-water interface. Pool area adjacent and downstream from each dike was estimated from aerial photography. Macroinvertebrate biotic condition scores were determined by integrating the following indicator response metrics: % of Ephemeroptera (mayflies), % of Oligochaeta worms, Shannon Diversity Index and total taxa richness. Regression models were developed for predicting macroinvertebrate scores based on individual water-quality and sediment-quality variables and a water/sediment-quality score that integrated all variables. Macroinvertebrate scores generated significant determination coefficients with dike pool area (R2=0.56), oxidation–reduction potential (R2=0.81) and water/sediment-quality score (R2=0.71). Dissolved oxygen saturation, oxidation-reduction potential and total ammonia in sediment porewater were most important in explaining variation in macroinvertebrate scores. The best two-variable regression models included dike pool size + the water/sediment-quality score (R2=0.84) and dike pool size + oxidation-reduction potential (R2=0.93). Results indicate that dike pool size and chemistry of sediments and overlying water can be used to evaluate dike pool quality and identify environmental conditions necessary for optimizing diversity and productivity of important aquatic macroinvertebrates. A combination of these variables could be utilized for measuring the success of habitat enhancement activities currently being implemented in this system.

Did Groundwater Processes Shape the Saharan Landscape during the Previous Wet Periods? a Remote Sensing and Geostatistical Approach

NASA Astrophysics Data System (ADS)

Farag, A. Z. A.; Sultan, M.; Elkadiri, R.; Abdelhalim, A.

2014-12-01

An integrated approach using remote sensing, landscape analysis and statistical methods was conducted to assess the role of groundwater sapping in shaping the Saharan landscape. A GIS-based logistic regression model was constructed to automatically delineate the spatial distribution of the sapping features over areas occupied by the Nubian Sandstone Aquifer System (NSAS): (1) an inventory was compiled of known locations of sapping features identified either in the field or from satellite datasets (e.g. Orbview-3 and Google Earth Digital Globe imagery); (2) spatial analyses were conducted in a GIS environment and seven geomorphological and geological predisposing factors (i.e. slope, stream density, cross-sectional and profile curvature, minimum and maximum curvature, and lithology) were identified; (3) a binary logistic regression model was constructed, optimized and validated to describe the relationship between the sapping locations and the set of controlling factors and (4) the generated model (prediction accuracy: 90.1%) was used to produce a regional sapping map over the NSAS. Model outputs indicate: (1) groundwater discharge and structural control played an important role in excavating the Saharan natural depressions as evidenced by the wide distribution of sapping features (areal extent: 1180 km2) along the fault-controlled escarpments of the Libyan Plateau; (2) proximity of mapped sapping features to reported paleolake and tufa deposits suggesting a causal effect. Our preliminary observations (from satellite imagery) and statistical analyses together with previous studies in the North Western Sahara Aquifer System (North Africa), Sinai Peninsula, Negev Desert, and The Plateau of Najd (Saudi Arabia) indicate extensive occurrence of sapping features along the escarpments bordering the northern margins of the Saharan-Arabian Desert; these areas share similar hydrologic settings with the NSAS domains and they too witnessed wet climatic periods in the Mid-Late Quaternary.

Antibiotic use from conception to diagnosis of child leukaemia as compared to the background population: A nested case-control study.

PubMed

Gradel, Kim Oren; Kaerlev, Linda

2015-07-01

The role of infection in the aetiology of childhood leukaemia is unknown. We used prescriptions of antibiotics from Danish pharmacies as a proxy measure for the occurrence of infections. We investigated the association between exposure to antibiotics, from conception to leukaemia diagnosis, and the risk of leukaemia. Incident cases of leukaemia among children in Denmark, 1995-2008, with mothers having their earliest conception date in 1995, were individually matched to population controls by age, sex and municipality. Conditional logistic regression analyses assessed antibiotic redemptions in different time periods from conception up to 6 months before the diagnoses of all leukaemia types, acute lymphoblastic leukaemia [ALL] and ALL in 2- to 5-year-old children, adjusting for several potential confounders. A total of 120/360 (33.3%) leukaemia mothers and 1,081/3,509 (30.8%) control mothers redeemed antibiotics during pregnancy (P = 0.32). For children, the equivalent numbers were 276 (76.7%) and 2,665 (75.9%) (P = 0.76). Histograms of antibiotic redemptions showed no temporal differences between leukaemia mothers/children and controls, which was confirmed in adjusted regression analyses (OR [95% CI]: 1.02 [0.75-1.38]). Only antibiotics redeemed during the first year after birth differed from this (OR [95% CI] for ALL diagnosed in 2- to 5-year-old children: 0.46 [0.31-0.66]). In this hypothesis generating study, the similar amount and pattern of antibiotic redemptions in children with and without leukaemia indicate that infections play a minor role in the aetiology of childhood leukaemia. However, less antibiotic redemptions during the first year of life conform to Greaves' 'delayed infection hypothesis'. © 2015 Wiley Periodicals, Inc.

Application of Random Forests Methods to Diabetic Retinopathy Classification Analyses

PubMed Central

Casanova, Ramon; Saldana, Santiago; Chew, Emily Y.; Danis, Ronald P.; Greven, Craig M.; Ambrosius, Walter T.

2014-01-01

Background Diabetic retinopathy (DR) is one of the leading causes of blindness in the United States and world-wide. DR is a silent disease that may go unnoticed until it is too late for effective treatment. Therefore, early detection could improve the chances of therapeutic interventions that would alleviate its effects. Methodology Graded fundus photography and systemic data from 3443 ACCORD-Eye Study participants were used to estimate Random Forest (RF) and logistic regression classifiers. We studied the impact of sample size on classifier performance and the possibility of using RF generated class conditional probabilities as metrics describing DR risk. RF measures of variable importance are used to detect factors that affect classification performance. Principal Findings Both types of data were informative when discriminating participants with or without DR. RF based models produced much higher classification accuracy than those based on logistic regression. Combining both types of data did not increase accuracy but did increase statistical discrimination of healthy participants who subsequently did or did not have DR events during four years of follow-up. RF variable importance criteria revealed that microaneurysms counts in both eyes seemed to play the most important role in discrimination among the graded fundus variables, while the number of medicines and diabetes duration were the most relevant among the systemic variables. Conclusions and Significance We have introduced RF methods to DR classification analyses based on fundus photography data. In addition, we propose an approach to DR risk assessment based on metrics derived from graded fundus photography and systemic data. Our results suggest that RF methods could be a valuable tool to diagnose DR diagnosis and evaluate its progression. PMID:24940623

Cost-utility of a specific collaborative group intervention for patients with functional somatic syndromes.

PubMed

Konnopka, Alexander; König, Hans-Helmut; Kaufmann, Claudia; Egger, Nina; Wild, Beate; Szecsenyi, Joachim; Herzog, Wolfgang; Schellberg, Dieter; Schaefert, Rainer

2016-11-01

Collaborative group intervention (CGI) in patients with functional somatic syndromes (FSS) has been shown to improve mental quality of life. To analyse incremental cost-utility of CGI compared to enhanced medical care in patients with FSS. An economic evaluation alongside a cluster-randomised controlled trial was performed. 35 general practitioners (GPs) recruited 300 FSS patients. Patients in the CGI arm were offered 10 group sessions within 3months and 2 booster sessions 6 and 12months after baseline. Costs were assessed via questionnaire. Quality adjusted life years (QALYs) were calculated using the SF-6D index, derived from the 36-item short-form health survey (SF-36). We calculated patients' net-monetary-benefit (NMB), estimated the treatment effect via regression, and generated cost-effectiveness acceptability curves. Using intention-to-treat analysis, total costs during the 12-month study period were 5777EUR in the intervention, and 6858EUR in the control group. Controlling for possible confounders, we found a small, but significant positive intervention effect on QALYs (+0.017; p=0.019) and an insignificant cost saving resulting from a cost-increase in the control group (-10.5%; p=0.278). NMB regression showed that the probability of CGI to be cost-effective was 69% for a willingness to pay (WTP) of 0EUR/QALY, increased to 92% for a WTP of 50,000EUR/QALY and reached the level of 95% at a WTP of 70,375EUR/QALY. Subgroup analyses yielded that CGI was only cost-effective in severe somatic symptom severity (PHQ-15≥15). CGI has a high probability to be a cost-effective treatment for FSS, in particular for patients with severe somatic symptom severity. Copyright © 2016 Elsevier Inc. All rights reserved.

Detection and Measurement of Unhealthy, Environment-Derived Aerosol Materials in an Emergency Department.

PubMed

Choi, Bryan Y; Kobayashi, Leo; Pathania, Shivany; Miller, Courtney B; Locke, Emma R; Stearns, Branden C; Hudepohl, Nathan J; Patefield, Scott S; Suner, Selim; Williams, Kenneth A; Machan, Jason T; Jay, Gregory D

2015-01-01

To measure unhealthy aerosol materials in an Emergency Department (ED) and identify their sources for mitigation efforts. Based on pilot findings of elevated ED particulate matter (PM) levels, investigators hypothesized that unhealthy aerosol materials derive from exogenous (vehicular) sources at ambulance receiving entrances. The Aerosol Environmental Toxicity in Healthcare-related Exposure and Risk program was conducted as an observational study. Calibrated sensors monitored PM and toxic gases at Ambulance Triage Exterior (ATE), Ambulance Triage Desk (ATD), and control Public Triage Desk (PTD) on a 3/3/3-day cycle. Cassette sampling characterized PM; meteorological and ambulance traffic data were logged. Descriptive and multiple linear regression analyses assessed for interactions between aerosol material levels, location, temporal variables, ambulance activity, and meteorological factors. Sensors acquired 93,682 PM0.3, 90,250 PM2.5, and 93,768 PM5 measurements over 366 days to generate a data set representing at least 85.6% of planned measurements. PM0.3, PM2.5, and PM5 mean counts were lowest in PTD; 56%, 224%, and 223% higher in ATD; and 996%, 200%, and 63% higher in ATE, respectively (all p < .001). Qualitative analyses showed similar PM compositions in ATD and ATE. On multiple linear regression analysis, PM0.3 counts correlated primarily with location; PM2.5 and PM5 counts correlated most strongly with location and ambulance presence. PM < 2.5 and toxic gas concentrations at ATD and PTD patient care areas did not exceed hazard levels; PM0.3 counts did not have formal safety thresholds for comparison. Higher levels of PM were linked with ED ambulance areas, although their health impact is unclear. © The Author(s) 2015.

Midlife use of written Japanese and protection from late life dementia

PubMed Central

Crane, Paul K.; Gibbons, Laura E.; Arani, Keerthi; Nguyen, Viet; Rhoads, Kristoffer; McCurry, Susan M.; Launer, Lenore; Masaki, Kamal; White, Lon

2011-01-01

Background The cognitive reserve hypothesis would predict that use of written Japanese should confer protection against dementia because of the complexity of its ideograms compared with written English. We sought to test this hypothesis in analyses from a longitudinal study of Japanese-American men. Methods Participants were second-generation Japanese-American men (Nisei) on the island of Oahu, Hawaii, who were seen in 1965 and in subsequent examinations to detect dementia beginning in 1991-1993. Use of spoken and written Japanese was self-reported in 1965 (Analyses 1 and 2), and mid-life use of written Japanese and written English was self-reported in 1994-1996 (Analysis 3). We analyzed prevalent dementia outcomes in 1991-1993 (Analysis 1, n=3,139) using logistic regression, and incident dementia outcomes in 1994-2002 (Analysis 2, n=2,299) and in 1997-2002 (Analysis 3, n=1,655) using Cox proportional hazards regression. Dementia outcomes included all-cause dementia, probable and possible Alzheimer disease, and probable vascular dementia. We adjusted models for probable and possible confounders. Results Participants who reported proficiency with written Japanese were older and had lower incomes. For Analysis 1, there were 154 prevalent cases of dementia, 74 of Alzheimer disease, and 43 of vascular dementia; for Analysis 2, 236 incident cases of dementia, 138 of Alzheimer disease, and 45 of vascular dementia; and for Analysis 3, 125 incident cases of dementia, 80 of Alzheimer disease, and 20 of vascular dementia. There was no relationship in adjusted models between self-reported proficiency with written Japanese and any dementia outcomes. Conclusions Proficiency with written Japanese does not appear to be protective for dementia. PMID:19593152

The association of serum prolactin concentration with inflammatory biomarkers - cross-sectional findings from the population-based Study of Health in Pomerania.

PubMed

Friedrich, Nele; Schneider, Harald J; Spielhagen, Christin; Markus, Marcello Ricardo Paulista; Haring, Robin; Grabe, Hans J; Buchfelder, Michael; Wallaschofski, Henri; Nauck, Matthias

2011-10-01

Prolactin (PRL) is involved in immune regulation and may contribute to an atherogenic phenotype. Previous results on the association of PRL with inflammatory biomarkers have been conflicting and limited by small patient studies. Therefore, we used data from a large population-based sample to assess the cross-sectional associations between serum PRL concentration and high-sensitivity C-reactive protein (hsCRP), fibrinogen, interleukin-6 (IL-6), and white blood cell (WBC) count. From the population-based Study of Health in Pomerania (SHIP), a total of 3744 subjects were available for the present analyses. PRL and inflammatory biomarkers were measured. Linear and logistic regression models adjusted for age, sex, body-mass-index, total cholesterol and glucose were analysed. Multivariable linear regression models revealed a positive association of PRL with WBC. Multivariable logistic regression analyses showed a significant association of PRL with increased IL-6 in non-smokers [highest vs lowest quintile: odds ratio 1·69 (95% confidence interval 1·10-2·58), P = 0·02] and smokers [OR 2·06 (95%-CI 1·10-3·89), P = 0·02]. Similar results were found for WBC in non-smokers [highest vs lowest quintile: OR 2·09 (95%-CI 1·21-3·61), P = 0·01)] but not in smokers. Linear and logistic regression analyses revealed no significant associations of PRL with hsCRP or fibrinogen. Serum PRL concentrations are associated with inflammatory biomarkers including IL-6 and WBC, but not hsCRP or fibrinogen. The suggested role of PRL in inflammation needs further investigation in future prospective studies. © 2011 Blackwell Publishing Ltd.

Using Marginal Structural Modeling to Estimate the Cumulative Impact of an Unconditional Tax Credit on Self-Rated Health.

PubMed

Pega, Frank; Blakely, Tony; Glymour, M Maria; Carter, Kristie N; Kawachi, Ichiro

2016-02-15

In previous studies, researchers estimated short-term relationships between financial credits and health outcomes using conventional regression analyses, but they did not account for time-varying confounders affected by prior treatment (CAPTs) or the credits' cumulative impacts over time. In this study, we examined the association between total number of years of receiving New Zealand's Family Tax Credit (FTC) and self-rated health (SRH) in 6,900 working-age parents using 7 waves of New Zealand longitudinal data (2002-2009). We conducted conventional linear regression analyses, both unadjusted and adjusted for time-invariant and time-varying confounders measured at baseline, and fitted marginal structural models (MSMs) that more fully adjusted for confounders, including CAPTs. Of all participants, 5.1%-6.8% received the FTC for 1-3 years and 1.8%-3.6% for 4-7 years. In unadjusted and adjusted conventional regression analyses, each additional year of receiving the FTC was associated with 0.033 (95% confidence interval (CI): -0.047, -0.019) and 0.026 (95% CI: -0.041, -0.010) units worse SRH (on a 5-unit scale). In the MSMs, the average causal treatment effect also reflected a small decrease in SRH (unstabilized weights: β = -0.039 unit, 95% CI: -0.058, -0.020; stabilized weights: β = -0.031 unit, 95% CI: -0.050, -0.007). Cumulatively receiving the FTC marginally reduced SRH. Conventional regression analyses and MSMs produced similar estimates, suggesting little bias from CAPTs. © The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Testing Mediation Using Multiple Regression and Structural Equation Modeling Analyses in Secondary Data

ERIC Educational Resources Information Center

Li, Spencer D.

2011-01-01

Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…

An Exploratory Study of Face-to-Face and Cyberbullying in Sixth Grade Students

ERIC Educational Resources Information Center

Accordino, Denise B.; Accordino, Michael P.

2011-01-01

In a pilot study, sixth grade students (N = 124) completed a questionnaire assessing students' experience with bullying and cyberbullying, demographic information, quality of parent-child relationship, and ways they have dealt with bullying/cyberbullying in the past. Two multiple regression analyses were conducted. The multiple regression analysis…

Intake of key chronic disease-related nutrients among baby boomers.

PubMed

King, Dana E; Xiang, Jun; Brown, Alexander

2014-06-01

The dietary habits of baby boomers (people born between 1946 and 1964) undoubtedly will have a substantial impact on their future health; however, dietary information regarding the intake of key chronic disease-related nutrients is lacking for this generation. The objective of this study was to compare the dietary intake of key chronic disease-related nutrients of the baby boomer generation with the previous generation of middle-aged adults. National cross-sectional study comparison analyzing data from the National Health and Nutrition Examination Survey (NHANES) including NHANES III (1988-1994) and the NHANES for 2007-2010, focused on adult respondents ages 46 to 64 years who were not institutionalized at the time of each survey. The two cohorts were compared with regard to dietary intake of key nutritional components. The main outcome measures were intake of total calories, sodium, cholesterol, fat, fruits, vegetables, vitamin C, water, and fiber. The baby boomers' average daily intake of nutrients exceeded that of the previous generation of middle-aged adults for total calories (2118/1999), total fat (82/76 g), sodium (3513/3291 mg), and cholesterol (294/262 g; all P < 0.001). The intake of vitamin C (105/89 g), water (1208/1001 g), and vegetables (199/229 g) was less than that of the previous generation (P < 0.001), and the dietary intake of fruit and fiber was unchanged. In regression analyses, dietary changes remained significant after controlling for age, race, sex, and socioeconomic status (all P < 0.01). The study findings document higher dietary intake of key chronic disease-related nutrients along with reduced vegetable intake among baby boomers compared with the previous generation of middle-aged adults. These findings are indicative of a diet that may contribute to increased rates of chronic disease among individuals in this age group.

Analyzing industrial energy use through ordinary least squares regression models

NASA Astrophysics Data System (ADS)

Golden, Allyson Katherine

Extensive research has been performed using regression analysis and calibrated simulations to create baseline energy consumption models for residential buildings and commercial institutions. However, few attempts have been made to discuss the applicability of these methodologies to establish baseline energy consumption models for industrial manufacturing facilities. In the few studies of industrial facilities, the presented linear change-point and degree-day regression analyses illustrate ideal cases. It follows that there is a need in the established literature to discuss the methodologies and to determine their applicability for establishing baseline energy consumption models of industrial manufacturing facilities. The thesis determines the effectiveness of simple inverse linear statistical regression models when establishing baseline energy consumption models for industrial manufacturing facilities. Ordinary least squares change-point and degree-day regression methods are used to create baseline energy consumption models for nine different case studies of industrial manufacturing facilities located in the southeastern United States. The influence of ambient dry-bulb temperature and production on total facility energy consumption is observed. The energy consumption behavior of industrial manufacturing facilities is only sometimes sufficiently explained by temperature, production, or a combination of the two variables. This thesis also provides methods for generating baseline energy models that are straightforward and accessible to anyone in the industrial manufacturing community. The methods outlined in this thesis may be easily replicated by anyone that possesses basic spreadsheet software and general knowledge of the relationship between energy consumption and weather, production, or other influential variables. With the help of simple inverse linear regression models, industrial manufacturing facilities may better understand their energy consumption and production behavior, and identify opportunities for energy and cost savings. This thesis study also utilizes change-point and degree-day baseline energy models to disaggregate facility annual energy consumption into separate industrial end-user categories. The baseline energy model provides a suitable and economical alternative to sub-metering individual manufacturing equipment. One case study describes the conjoined use of baseline energy models and facility information gathered during a one-day onsite visit to perform an end-point energy analysis of an injection molding facility conducted by the Alabama Industrial Assessment Center. Applying baseline regression model results to the end-point energy analysis allowed the AIAC to better approximate the annual energy consumption of the facility's HVAC system.

The Use of a Poisson Regression to Evaluate Antihistamines and Fatal Aircraft Mishaps in Instrument Meteorological Conditions.

PubMed

Gildea, Kevin M; Hileman, Christy R; Rogers, Paul; Salazar, Guillermo J; Paskoff, Lawrence N

2018-04-01

Research indicates that first-generation antihistamine usage may impair pilot performance by increasing the likelihood of vestibular illusions, spatial disorientation, and/or cognitive impairment. Second- and third-generation antihistamines generally have fewer impairing side effects and are approved for pilot use. We hypothesized that toxicological findings positive for second- and third-generation antihistamines are less likely to be associated with pilots involved in fatal mishaps than first-generation antihistamines. The evaluated population consisted of 1475 U.S. civil pilots fatally injured between September 30, 2008, and October 1, 2014. Mishap factors evaluated included year, weather conditions, airman rating, recent airman flight time, quarter of year, and time of day. Due to the low prevalence of positive antihistamine findings, a count-based model was selected, which can account for rare outcomes. The means and variances were close for both regression models supporting the assumption that the data follow a Poisson distribution; first-generation antihistamine mishap airmen (N = 582, M = 0.17, S2 = 0.17) with second- and third-generation antihistamine mishap airmen (N = 116, M = 0.20, S2 = 0.18). The data indicate fewer airmen with second- and third-generation antihistamines than first-generation antihistamines in their system are fatally injured while flying in IMC conditions. Whether the lower incidence is a factor of greater usage of first-generation antihistamines versus second- and third-generation antihistamines by the pilot population or fewer deleterious side effects with second- and third-generation antihistamines is unclear. These results engender cautious optimism, but additional research is necessary to determine why these differences exist.Gildea KM, Hileman CR, Rogers P, Salazar GJ, Paskoff LN. The use of a Poisson regression to evaluate antihistamines and fatal aircraft mishaps in instrument meteorological conditions. Aerosp Med Hum Perform. 2018; 89(4):389-395.

Waste generated in high-rise buildings construction: a quantification model based on statistical multiple regression.

PubMed

Parisi Kern, Andrea; Ferreira Dias, Michele; Piva Kulakowski, Marlova; Paulo Gomes, Luciana

2015-05-01

Reducing construction waste is becoming a key environmental issue in the construction industry. The quantification of waste generation rates in the construction sector is an invaluable management tool in supporting mitigation actions. However, the quantification of waste can be a difficult process because of the specific characteristics and the wide range of materials used in different construction projects. Large variations are observed in the methods used to predict the amount of waste generated because of the range of variables involved in construction processes and the different contexts in which these methods are employed. This paper proposes a statistical model to determine the amount of waste generated in the construction of high-rise buildings by assessing the influence of design process and production system, often mentioned as the major culprits behind the generation of waste in construction. Multiple regression was used to conduct a case study based on multiple sources of data of eighteen residential buildings. The resulting statistical model produced dependent (i.e. amount of waste generated) and independent variables associated with the design and the production system used. The best regression model obtained from the sample data resulted in an adjusted R(2) value of 0.694, which means that it predicts approximately 69% of the factors involved in the generation of waste in similar constructions. Most independent variables showed a low determination coefficient when assessed in isolation, which emphasizes the importance of assessing their joint influence on the response (dependent) variable. Copyright © 2015 Elsevier Ltd. All rights reserved.

Intratumoral heterogeneity analysis reveals hidden associations between protein expression losses and patient survival in clear cell renal cell carcinoma

PubMed Central

Devarajan, Karthik; Parsons, Theodore; Wang, Qiong; O'Neill, Raymond; Solomides, Charalambos; Peiper, Stephen C.; Testa, Joseph R.; Uzzo, Robert; Yang, Haifeng

2017-01-01

Intratumoral heterogeneity (ITH) is a prominent feature of kidney cancer. It is not known whether it has utility in finding associations between protein expression and clinical parameters. We used ITH that is detected by immunohistochemistry (IHC) to aid the association analysis between the loss of SWI/SNF components and clinical parameters.160 ccRCC tumors (40 per tumor stage) were used to generate tissue microarray (TMA). Four foci from different regions of each tumor were selected. IHC was performed against PBRM1, ARID1A, SETD2, SMARCA4, and SMARCA2. Statistical analyses were performed to correlate biomarker losses with patho-clinical parameters. Categorical variables were compared between groups using Fisher's exact tests. Univariate and multivariable analyses were used to correlate biomarker changes and patient survivals. Multivariable analyses were performed by constructing decision trees using the classification and regression trees (CART) methodology. IHC detected widespread ITH in ccRCC tumors. The statistical analysis of the “Truncal loss” (root loss) found additional correlations between biomarker losses and tumor stages than the traditional “Loss in tumor (total)”. Losses of SMARCA4 or SMARCA2 significantly improved prognosis for overall survival (OS). Losses of PBRM1, ARID1A or SETD2 had the opposite effect. Thus “Truncal Loss” analysis revealed hidden links between protein losses and patient survival in ccRCC. PMID:28445125

Selecting risk factors: a comparison of discriminant analysis, logistic regression and Cox's regression model using data from the Tromsø Heart Study.

PubMed

Brenn, T; Arnesen, E

1985-01-01

For comparative evaluation, discriminant analysis, logistic regression and Cox's model were used to select risk factors for total and coronary deaths among 6595 men aged 20-49 followed for 9 years. Groups with mortality between 5 and 93 per 1000 were considered. Discriminant analysis selected variable sets only marginally different from the logistic and Cox methods which always selected the same sets. A time-saving option, offered for both the logistic and Cox selection, showed no advantage compared with discriminant analysis. Analysing more than 3800 subjects, the logistic and Cox methods consumed, respectively, 80 and 10 times more computer time than discriminant analysis. When including the same set of variables in non-stepwise analyses, all methods estimated coefficients that in most cases were almost identical. In conclusion, discriminant analysis is advocated for preliminary or stepwise analysis, otherwise Cox's method should be used.

Hypnotism as a Function of Trance State Effects, Expectancy, and Suggestibility: An Italian Replication.

PubMed

Pekala, Ronald J; Baglio, Francesca; Cabinio, Monia; Lipari, Susanna; Baglio, Gisella; Mendozzi, Laura; Cecconi, Pietro; Pugnetti, Luigi; Sciaky, Riccardo

2017-01-01

Previous research using stepwise regression analyses found self-reported hypnotic depth (srHD) to be a function of suggestibility, trance state effects, and expectancy. This study sought to replicate and expand that research using a general state measure of hypnotic responsivity, the Phenomenology of Consciousness Inventory: Hypnotic Assessment Procedure (PCI-HAP). Ninety-five participants completed an Italian translation of the PCI-HAP, with srHD scores predicted from the PCI-HAP assessment items. The regression analysis replicated the previous research results. Additionally, stepwise regression analyses were able to predict the srHD score equally well using only the PCI dimension scores. These results not only replicated prior research but suggest how this methodology to assess hypnotic responsivity, when combined with more traditional neurophysiological and cognitive-behavioral methodologies, may allow for a more comprehensive understanding of that enigma called hypnosis.

Machine learning in the string landscape

NASA Astrophysics Data System (ADS)

Carifio, Jonathan; Halverson, James; Krioukov, Dmitri; Nelson, Brent D.

2017-09-01

We utilize machine learning to study the string landscape. Deep data dives and conjecture generation are proposed as useful frameworks for utilizing machine learning in the landscape, and examples of each are presented. A decision tree accurately predicts the number of weak Fano toric threefolds arising from reflexive polytopes, each of which determines a smooth F-theory compactification, and linear regression generates a previously proven conjecture for the gauge group rank in an ensemble of 4/3× 2.96× {10}^{755} F-theory compactifications. Logistic regression generates a new conjecture for when E 6 arises in the large ensemble of F-theory compactifications, which is then rigorously proven. This result may be relevant for the appearance of visible sectors in the ensemble. Through conjecture generation, machine learning is useful not only for numerics, but also for rigorous results.

Quantitative assessment of cervical vertebral maturation using cone beam computed tomography in Korean girls.

PubMed

Byun, Bo-Ram; Kim, Yong-Il; Yamaguchi, Tetsutaro; Maki, Koutaro; Son, Woo-Sung

2015-01-01

This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6-18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R (2) had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status.

Methodological Reporting of Randomized Trials in Five Leading Chinese Nursing Journals

PubMed Central

Shi, Chunhu; Tian, Jinhui; Ren, Dan; Wei, Hongli; Zhang, Lihuan; Wang, Quan; Yang, Kehu

2014-01-01

Background Randomized controlled trials (RCTs) are not always well reported, especially in terms of their methodological descriptions. This study aimed to investigate the adherence of methodological reporting complying with CONSORT and explore associated trial level variables in the Chinese nursing care field. Methods In June 2012, we identified RCTs published in five leading Chinese nursing journals and included trials with details of randomized methods. The quality of methodological reporting was measured through the methods section of the CONSORT checklist and the overall CONSORT methodological items score was calculated and expressed as a percentage. Meanwhile, we hypothesized that some general and methodological characteristics were associated with reporting quality and conducted a regression with these data to explore the correlation. The descriptive and regression statistics were calculated via SPSS 13.0. Results In total, 680 RCTs were included. The overall CONSORT methodological items score was 6.34±0.97 (Mean ± SD). No RCT reported descriptions and changes in “trial design,” changes in “outcomes” and “implementation,” or descriptions of the similarity of interventions for “blinding.” Poor reporting was found in detailing the “settings of participants” (13.1%), “type of randomization sequence generation” (1.8%), calculation methods of “sample size” (0.4%), explanation of any interim analyses and stopping guidelines for “sample size” (0.3%), “allocation concealment mechanism” (0.3%), additional analyses in “statistical methods” (2.1%), and targeted subjects and methods of “blinding” (5.9%). More than 50% of trials described randomization sequence generation, the eligibility criteria of “participants,” “interventions,” and definitions of the “outcomes” and “statistical methods.” The regression analysis found that publication year and ITT analysis were weakly associated with CONSORT score. Conclusions The completeness of methodological reporting of RCTs in the Chinese nursing care field is poor, especially with regard to the reporting of trial design, changes in outcomes, sample size calculation, allocation concealment, blinding, and statistical methods. PMID:25415382

Pennies for Milk: Using a First-Hand Descent into Randomness to Illustrate the Risks of Regression to the Mean for Marketing Researchers and Managers

ERIC Educational Resources Information Center

Posavac, Steven S.; Posavac, Emil J.

2017-01-01

The authors describe the Pennies for Milk exercise, a participative classroom experience in which students generate a regression to the mean effect within the context of simulated household milk purchases. Regression to the mean is a ubiquitous threat for marketing researchers and managers but is often hard for students to understand. The Pennies…

Kindergarten stressors and cumulative adrenocortical activation: the "first straws" of allostatic load?

PubMed

Bush, Nicole R; Obradović, Jelena; Adler, Nancy; Boyce, W Thomas

2011-11-01

Using an ethnically diverse longitudinal sample of 338 kindergarten children, this study examined the effects of cumulative contextual stressors on children's developing hypothalamic-pituitary-adrenocortical (HPA) axis regulation as an early life indicator of allostatic load. Chronic HPA axis regulation was assessed using cumulative, multiday measures of cortisol in both the fall and spring seasons of the kindergarten year. Hierarchical linear regression analyses revealed that contextual stressors related to ethnic minority status, socioeconomic status, and family adversity each uniquely predicted children's daily HPA activity and that some of those associations were curvilinear in conformation. Results showed that the quadratic, U-shaped influences of family socioeconomic status and family adversity operate in different directions to predict children's HPA axis regulation. Results further suggested that these associations differ for White and ethnic minority children. In total, this study revealed that early childhood experiences contribute to shifts in one of the principal neurobiological systems thought to generate allostatic load, confirming the importance of early prevention and intervention efforts. Moreover, findings suggested that analyses of allostatic load and developmental theories accounting for its accrual would benefit from an inclusion of curvilinear associations in tested predictive models.

A profile of students receiving counselling services at a university in post-apartheid South Africa.

PubMed

Bowman, Brett; Payne, Jarrod

2011-12-01

The purpose of this study was to describe a profile of students seeking counselling at a racially diverse university in post-apartheid South Africa as a means to demonstrate the importance of routinely collecting and analysing student counselling data at university-based centres across the country. Student data were extracted from the only two counselling centres based at the University of the Witwatersrand in Johannesburg that provided services to 831 students during 2008. The 26 243 students that did not seek counselling during this period formed the comparison group. These data were analysed using logistic regression. Black, female and students within the 21-25 year age category were more likely to receive counselling, and presenting problems varied by population group. Given the country's past and continued levels of social asymmetry, we argue that the development of standardised university-based reporting systems able to describe the characteristics and presenting problems of students seeking counselling across South African universities should be prioritised by its higher education sector. Timely access to information of this kind is crucial to the generation of evidence-based mental health interventions in a population that is especially important to the country's development vision.

Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy.

PubMed

Ribeiro, J S; Ferreira, M M C; Salva, T J G

2011-02-15

Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PLS) were used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline, 5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1 - the lipids and proteins were closely related to the attribute body; 2 - the caffeine and chlorogenic acids were related to bitterness; 3 - the chlorogenic acids were related to acidity and flavour; 4 - the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein. Copyright © 2010 Elsevier B.V. All rights reserved.

Design and Development of a Model to Simulate 0-G Treadmill Running Using the European Space Agency's Subject Loading System

NASA Technical Reports Server (NTRS)

Caldwell, E. C.; Cowley, M. S.; Scott-Pandorf, M. M.

2010-01-01

Develop a model that simulates a human running in 0 G using the European Space Agency s (ESA) Subject Loading System (SLS). The model provides ground reaction forces (GRF) based on speed and pull-down forces (PDF). DESIGN The theoretical basis for the Running Model was based on a simple spring-mass model. The dynamic properties of the spring-mass model express theoretical vertical GRF (GRFv) and shear GRF in the posterior-anterior direction (GRFsh) during running gait. ADAMs VIEW software was used to build the model, which has a pelvis, thigh segment, shank segment, and a spring foot (see Figure 1).the model s movement simulates the joint kinematics of a human running at Earth gravity with the aim of generating GRF data. DEVELOPMENT & VERIFICATION ESA provided parabolic flight data of subjects running while using the SLS, for further characterization of the model s GRF. Peak GRF data were fit to a linear regression line dependent on PDF and speed. Interpolation and extrapolation of the regression equation provided a theoretical data matrix, which is used to drive the model s motion equations. Verification of the model was conducted by running the model at 4 different speeds, with each speed accounting for 3 different PDF. The model s GRF data fell within a 1-standard-deviation boundary derived from the empirical ESA data. CONCLUSION The Running Model aids in conducting various simulations (potential scenarios include a fatigued runner or a powerful runner generating high loads at a fast cadence) to determine limitations for the T2 vibration isolation system (VIS) aboard the International Space Station. This model can predict how running with the ESA SLS affects the T2 VIS and may be used for other exercise analyses in the future.

Morphometric analysis of the relationships between intervertebral disc and vertebral body heights: an anatomical and radiographic study of the human thoracic spine

PubMed Central

Kunkel, Maria E; Herkommer, Andrea; Reinehr, Michael; Böckers, Tobias M; Wilke, Hans-Joachim

2011-01-01

The main aim of this study was to provide anatomical data on the heights of the human intervertebral discs for all levels of the thoracic spine by direct and radiographic measurements. Additionally, the heights of the neighboring vertebral bodies were measured, and the prediction of the disc heights based only on the size of the vertebral bodies was investigated. The anterior (ADH), middle (MDH) and posterior heights (PDH) of the discs were measured directly and on radiographs of 72 spine segments from 30 donors (age 57.43 ± 11.27 years). The radiographic measurement error and the reliability of the measurements were calculated. Linear and non-linear regression analyses were employed for investigation of statistical correlations between the heights of the thoracic disc and vertebrae. Radiographic measurements displayed lower repeatability and were shorter than the anatomical ones (approximately 9% for ADH and 37% for PDH). The thickness of the discs varied from 4.5 to 7.2 mm, with the MDH approximately 22.7% greater. The disc heights showed good correlations with the vertebral body heights (R2, 0.659–0.835, P-values < 0.005; anova), allowing the generation of 10 prediction equations. New data on thoracic disc morphometry were provided in this study. The generated set of regression equations could be used to predict thoracic disc heights from radiographic measurement of the vertebral body height posterior. For the creation of parameterized models of the human thoracic discs, the use of the prediction equations could eliminate the need for direct measurement on intervertebral discs. Moreover, the error produced by radiographic measurements could be reduced at least for the PDH. PMID:21615399

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.

REGRESSION MODELS OF RESIDENTIAL EXPOSURE TO CHLORPYRIFOS AND DIAZINON

EPA Science Inventory

This study examines the ability of regression models to predict residential exposures to chlorpyrifos and diazinon, based on the information from the NHEXAS-AZ database. The robust method was used to generate "fill-in" values for samples that are below the detection l...

Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey.

PubMed

Erdogan, Saffet

2009-10-01

The aim of the study is to describe the inter-province differences in traffic accidents and mortality on roads of Turkey. Two different risk indicators were used to evaluate the road safety performance of the provinces in Turkey. These indicators are the ratios between the number of persons killed in road traffic accidents (1) and the number of accidents (2) (nominators) and their exposure to traffic risk (denominator). Population and the number of registered motor vehicles in the provinces were used as denominators individually. Spatial analyses were performed to the mean annual rate of deaths and to the number of fatal accidents that were calculated for the period of 2001-2006. Empirical Bayes smoothing was used to remove background noise from the raw death and accident rates because of the sparsely populated provinces and small number of accident and death rates of provinces. Global and local spatial autocorrelation analyses were performed to show whether the provinces with high rates of deaths-accidents show clustering or are located closer by chance. The spatial distribution of provinces with high rates of deaths and accidents was nonrandom and detected as clustered with significance of P<0.05 with spatial autocorrelation analyses. Regions with high concentration of fatal accidents and deaths were located in the provinces that contain the roads connecting the Istanbul, Ankara, and Antalya provinces. Accident and death rates were also modeled with some independent variables such as number of motor vehicles, length of roads, and so forth using geographically weighted regression analysis with forward step-wise elimination. The level of statistical significance was taken as P<0.05. Large differences were found between the rates of deaths and accidents according to denominators in the provinces. The geographically weighted regression analyses did significantly better predictions for both accident rates and death rates than did ordinary least regressions, as indicated by adjusted R(2) values. Geographically weighted regression provided values of 0.89-0.99 adjusted R(2) for death and accident rates, compared with 0.88-0.95, respectively, by ordinary least regressions. Geographically weighted regression has the potential to reveal local patterns in the spatial distribution of rates, which would be ignored by the ordinary least regression approach. The application of spatial analysis and modeling of accident statistics and death rates at provincial level in Turkey will help to identification of provinces with outstandingly high accident and death rates. This could help more efficient road safety management in Turkey.

Modeling potential habitats for alien species Dreissena polymorpha in continental USA

USGS Publications Warehouse

Mingyang, Li; Yunwei, Ju; Kumar, Sunil; Stohlgren, Thomas J.

2008-01-01

The effective measure to minimize the damage of invasive species is to block the potential invasive species to enter into suitable areas. 1864 occurrence points with GPS coordinates and 34 environmental variables from Daymet datasets were gathered, and 4 modeling methods, i.e., Logistic Regression (LR), Classification and Regression Trees (CART), Genetic Algorithm for Rule-Set Prediction (GARP), and maximum entropy method (Maxent), were introduced to generate potential geographic distributions for invasive species Dreissena polymorpha in Continental USA. Then 3 statistical criteria of the area under the Receiver Operating Characteristic curve (AUC), Pearson correlation (COR) and Kappa value were calculated to evaluate the performance of the models, followed by analyses on major contribution variables. Results showed that in terms of the 3 statistical criteria, the prediction results of the 4 ecological niche models were either excellent or outstanding, in which Maxent outperformed the others in 3 aspects of predicting current distribution habitats, selecting major contribution factors, and quantifying the influence of environmental variables on habitats. Distance to water, elevation, frequency of precipitation and solar radiation were 4 environmental forcing factors. The method suggested in the paper can have some reference meaning for modeling habitats of alien species in China and provide a direction to prevent Mytilopsis sallei on the Chinese coast line.

Visuoconstructional Impairment in Subtypes of Mild Cognitive Impairment

PubMed Central

Ahmed, Samrah; Brennan, Laura; Eppig, Joel; Price, Catherine C.; Lamar, Melissa; Delano-Wood, Lisa; Bangen, Katherine J.; Edmonds, Emily C.; Clark, Lindsey; Nation, Daniel A.; Jak, Amy; Au, Rhoda; Swenson, Rodney; Bondi, Mark W.; Libon, David J.

2018-01-01

Clock Drawing Test performance was examined alongside other neuropsychological tests in mild cognitive impairment (MCI). We tested the hypothesis that clock-drawing errors are related to executive impairment. The current research examined 86 patients with MCI for whom, in prior research, cluster analysis was used to sort patients into dysexecutive (dMCI, n=22), amnestic (aMCI, n=13), and multi-domain (mMCI, n=51) subtypes. First, principal components analysis (PCA) and linear regression examined relations between clock-drawing errors and neuropsychological test performance independent of MCI subtype. Second, between-group differences were assessed with analysis of variance (ANOVA) where MCI subgroups were compared to normal controls (NC). PCA yielded a 3-group solution. Contrary to expectations, clock-drawing errors loaded with lower performance on naming/lexical retrieval, rather than with executive tests. Regression analyses found increasing clock-drawing errors to command were associated with worse performance only on naming/lexical retrieval tests. ANOVAs revealed no differences in clock-drawing errors between dMCI versus mMCI or aMCI versus NCs. Both the dMCI and mMCI groups generated more clock-drawing errors than the aMCI and NC groups in the command condition. In MCI, language-related skills contribute to clock-drawing impairment. PMID:26397732

Bacterial diversity in saliva and oral health-related conditions: the Hisayama Study

NASA Astrophysics Data System (ADS)

Takeshita, Toru; Kageyama, Shinya; Furuta, Michiko; Tsuboi, Hidenori; Takeuchi, Kenji; Shibata, Yukie; Shimazaki, Yoshihiro; Akifusa, Sumio; Ninomiya, Toshiharu; Kiyohara, Yutaka; Yamashita, Yoshihisa

2016-02-01

This population-based study determined the salivary microbiota composition of 2,343 adult residents of Hisayama town, Japan, using 16S rRNA gene next-generation high-throughput sequencing. Of 550 identified species-level operational taxonomic units (OTUs), 72 were common, in ≥75% of all individuals, as well as in ≥75% of the individuals in the lowest quintile of phylogenetic diversity (PD). These “core” OTUs constituted 90.9 ± 6.1% of each microbiome. The relative abundance profiles of 22 of the core OTUs with mean relative abundances ≥1% were stratified into community type I and community type II by partitioning around medoids clustering. Multiple regression analysis revealed that a lower PD was associated with better conditions for oral health, including a lower plaque index, absence of decayed teeth, less gingival bleeding, shallower periodontal pockets and not smoking, and was also associated with tooth loss. By contrast, multiple Poisson regression analysis demonstrated that community type II, as characterized by a higher ratio of the nine dominant core OTUs, including Neisseria flavescens, was implicated in younger age, lower body mass index, fewer teeth with caries experience, and not smoking. Our large-scale data analyses reveal variation in the salivary microbiome among Japanese adults and oral health-related conditions associated with the salivary microbiome.

Energy, water and large-scale patterns of reptile and amphibian species richness in Europe

NASA Astrophysics Data System (ADS)

Rodríguez, Miguel Á.; Belmontes, Juan Alfonso; Hawkins, Bradford A.

2005-07-01

We used regression analyses to examine the relationships between reptile and amphibian species richness in Europe and 11 environmental variables related to five hypotheses for geographical patterns of species richness: (1) productivity; (2) ambient energy; (3) water-energy balance, (4) habitat heterogeneity; and (5) climatic variability. For reptiles, annual potential evapotranspiration (PET), a measure of the amount of atmospheric energy, explained 71% of the variance, with variability in log elevation explaining an additional 6%. For amphibians, annual actual evapotranspiration (AET), a measure of the joint availability of energy and water in the environment, and the global vegetation index, an estimate of plant biomass generated through satellite remote sensing, both described similar proportions of the variance (61% and 60%, respectively) and had partially independent effects on richness as indicated by multiple regression. The two-factor environmental models successfully removed most of the statistically detectable spatial autocorrelation in the richness data of both groups. Our results are consistent with reptile and amphibian environmental requirements, where the former depend strongly on solar energy and the latter require both warmth and moisture for reproduction. We conclude that ambient energy explains the reptile richness pattern, whereas for amphibians a combination of water-energy balance and productivity best explain the pattern.

Influences on call outcomes among Veteran callers to the National Veterans Crisis Line

PubMed Central

Britton, Peter C.; Bossarte, Robert M.; Thompson, Caitlin; Kemp, Janet; Conner, Kenneth R.

2016-01-01

This evaluation examined the association of caller and call characteristics with proximal outcomes of Veterans Crisis Line calls. From October 1-7, 2010, 665 Veterans with recent suicidal ideation or a history of attempted suicide called the Veterans Crisis Line, 646 had complete data and were included in the analyses. A multivariable multinomial logistic regression was conducted to identify correlates of a favorable outcome, either a resolution or a referral, when compared to an unfavorable outcome, no resolution or referral. A multivariable logistic regression was used to identify correlates of responder-rated caller risk in a subset of calls. Approximately 84% of calls ended with a favorable outcome, 25% with a resolution and 59% with a referral to a local health care provider. Calls from high-risk callers had greater odds of ending with a referral than without a resolution or referral, as did weekday calls (6:00 am to 5:59 pm EST, Monday through Friday). Responders used caller intent to die and the absence of future plans to determine caller risk. Findings suggest that the Veterans Crisis Line is a useful mechanism for generating referrals for high-risk Veteran callers. Responders appeared to use known risk and protective factors to determine caller risk. PMID:23611446

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

PubMed

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

2016-09-01

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

The theory of reasoned action and intention to seek cancer information.

PubMed

Ross, Levi; Kohler, Connie L; Grimley, Diane M; Anderson-Lewis, Charkarra

2007-01-01

To evaluate the applicability of the theory of reasoned action to explain men's intentions to seek prostate cancer information. Three hundred randomly selected African American men participated in telephone interviews. Correlational and regression analyses were conducted to examine relationships among measures. All relationships were significant in regression analyses. Attitudes and subjective norm were significantly related to intentions. Indirect measures of beliefs derived from elicitation research were associated with direct measures of attitude and subjective norms. The data are sufficiently clear to support the applicability of the theory for this behavioral domain with African American men and suggest several important areas for future research.

An Investigation of the Relations Between Student Knowledge, Personal Contact, and Attitudes Toward Individuals with Schizophrenia

PubMed Central

Eack, Shaun M.; Newhill, Christina E.

2013-01-01

A survey of 118 MSW students was conducted to examine the relationship between social work students’ knowledge about, contact with, and attitudes toward persons with schizophrenia. Hierarchical regression analyses indicated that students’ knowledge about and contact with persons with schizophrenia were significantly related to better attitudes toward this population. Moderated multiple regression analyses revealed a significant interaction between knowledge about and contact with persons with schizophrenia, such that knowledge was only related to positive attitudes among students who had more personal contact with persons with the illness. Implications for social work training in severe mental illness are discussed (99 words). PMID:24353396

Application and evaluation of forecasting methods for municipal solid waste generation in an Eastern-European city.

PubMed

Rimaityte, Ingrida; Ruzgas, Tomas; Denafas, Gintaras; Racys, Viktoras; Martuzevicius, Dainius

2012-01-01

Forecasting of generation of municipal solid waste (MSW) in developing countries is often a challenging task due to the lack of data and selection of suitable forecasting method. This article aimed to select and evaluate several methods for MSW forecasting in a medium-scaled Eastern European city (Kaunas, Lithuania) with rapidly developing economics, with respect to affluence-related and seasonal impacts. The MSW generation was forecast with respect to the economic activity of the city (regression modelling) and using time series analysis. The modelling based on social-economic indicators (regression implemented in LCA-IWM model) showed particular sensitivity (deviation from actual data in the range from 2.2 to 20.6%) to external factors, such as the synergetic effects of affluence parameters or changes in MSW collection system. For the time series analysis, the combination of autoregressive integrated moving average (ARIMA) and seasonal exponential smoothing (SES) techniques were found to be the most accurate (mean absolute percentage error equalled to 6.5). Time series analysis method was very valuable for forecasting the weekly variation of waste generation data (r (2) > 0.87), but the forecast yearly increase should be verified against the data obtained by regression modelling. The methods and findings of this study may assist the experts, decision-makers and scientists performing forecasts of MSW generation, especially in developing countries.

Financial ties and concordance between results and conclusions in meta-analyses: retrospective cohort study.

PubMed

Yank, Veronica; Rennie, Drummond; Bero, Lisa A

2007-12-08

To determine whether financial ties to one drug company are associated with favourable results or conclusions in meta-analyses on antihypertensive drugs. Retrospective cohort study. Meta-analyses published up to December 2004 that were not duplicates and evaluated the effects of antihypertensive drugs compared with any comparator on clinical end points in adults. Financial ties were categorised as one drug company compared with all others. The main outcomes were the results and conclusions of meta-analyses, with both outcomes separately categorised as being favourable or not favourable towards the study drug. We also collected data on characteristics of meta-analyses that the literature suggested might be associated with favourable results or conclusions. 124 meta-analyses were included in the study, 49 (40%) of which had financial ties to one drug company. On univariate logistic regression analyses, meta-analyses of better methodological quality were more likely to have favourable results (odds ratio 1.16, 95% confidence interval 1.07 to 1.27). Although financial ties to one drug company were not associated with favourable results, such ties constituted the only characteristic significantly associated with favourable conclusions (4.09, 1.30 to 12.83). When controlling for other characteristics of meta-analyses in multiple logistic regression analyses, meta-analyses that had financial ties to one drug company remained more likely to report favourable conclusions (5.11, 1.54 to 16.92). Meta-analyses on antihypertensive drugs and with financial ties to one drug company are not associated with favourable results but are associated with favourable conclusions.

The relationship between the C-statistic of a risk-adjustment model and the accuracy of hospital report cards: a Monte Carlo Study.

PubMed

Austin, Peter C; Reeves, Mathew J

2013-03-01

Hospital report cards, in which outcomes following the provision of medical or surgical care are compared across health care providers, are being published with increasing frequency. Essential to the production of these reports is risk-adjustment, which allows investigators to account for differences in the distribution of patient illness severity across different hospitals. Logistic regression models are frequently used for risk adjustment in hospital report cards. Many applied researchers use the c-statistic (equivalent to the area under the receiver operating characteristic curve) of the logistic regression model as a measure of the credibility and accuracy of hospital report cards. To determine the relationship between the c-statistic of a risk-adjustment model and the accuracy of hospital report cards. Monte Carlo simulations were used to examine this issue. We examined the influence of 3 factors on the accuracy of hospital report cards: the c-statistic of the logistic regression model used for risk adjustment, the number of hospitals, and the number of patients treated at each hospital. The parameters used to generate the simulated datasets came from analyses of patients hospitalized with a diagnosis of acute myocardial infarction in Ontario, Canada. The c-statistic of the risk-adjustment model had, at most, a very modest impact on the accuracy of hospital report cards, whereas the number of patients treated at each hospital had a much greater impact. The c-statistic of a risk-adjustment model should not be used to assess the accuracy of a hospital report card.

The relationship between the c-statistic of a risk-adjustment model and the accuracy of hospital report cards: A Monte Carlo study

PubMed Central

Austin, Peter C.; Reeves, Mathew J.

2015-01-01

Background Hospital report cards, in which outcomes following the provision of medical or surgical care are compared across health care providers, are being published with increasing frequency. Essential to the production of these reports is risk-adjustment, which allows investigators to account for differences in the distribution of patient illness severity across different hospitals. Logistic regression models are frequently used for risk-adjustment in hospital report cards. Many applied researchers use the c-statistic (equivalent to the area under the receiver operating characteristic curve) of the logistic regression model as a measure of the credibility and accuracy of hospital report cards. Objectives To determine the relationship between the c-statistic of a risk-adjustment model and the accuracy of hospital report cards. Research Design Monte Carlo simulations were used to examine this issue. We examined the influence of three factors on the accuracy of hospital report cards: the c-statistic of the logistic regression model used for risk-adjustment, the number of hospitals, and the number of patients treated at each hospital. The parameters used to generate the simulated datasets came from analyses of patients hospitalized with a diagnosis of acute myocardial infarction in Ontario, Canada. Results The c-statistic of the risk-adjustment model had, at most, a very modest impact on the accuracy of hospital report cards, whereas the number of patients treated at each hospital had a much greater impact. Conclusions The c-statistic of a risk-adjustment model should not be used to assess the accuracy of a hospital report card. PMID:23295579

Predictive and Feedback Performance Errors are Signaled in the Simple Spike Discharge of Individual Purkinje Cells

PubMed Central

Popa, Laurentiu S.; Hewitt, Angela L.; Ebner, Timothy J.

2012-01-01

The cerebellum has been implicated in processing motor errors required for online control of movement and motor learning. The dominant view is that Purkinje cell complex spike discharge signals motor errors. This study investigated whether errors are encoded in the simple spike discharge of Purkinje cells in monkeys trained to manually track a pseudo-randomly moving target. Four task error signals were evaluated based on cursor movement relative to target movement. Linear regression analyses based on firing residuals ensured that the modulation with a specific error parameter was independent of the other error parameters and kinematics. The results demonstrate that simple spike firing in lobules IV–VI is significantly correlated with position, distance and directional errors. Independent of the error signals, the same Purkinje cells encode kinematics. The strongest error modulation occurs at feedback timing. However, in 72% of cells at least one of the R2 temporal profiles resulting from regressing firing with individual errors exhibit two peak R2 values. For these bimodal profiles, the first peak is at a negative τ (lead) and a second peak at a positive τ (lag), implying that Purkinje cells encode both prediction and feedback about an error. For the majority of the bimodal profiles, the signs of the regression coefficients or preferred directions reverse at the times of the peaks. The sign reversal results in opposing simple spike modulation for the predictive and feedback components. Dual error representations may provide the signals needed to generate sensory prediction errors used to update a forward internal model. PMID:23115173

Collagen Triple Helix Repeat Containing-1 (CTHRC1) Expression in Oral Squamous Cell Carcinoma (OSCC): Prognostic Value and Clinico-Pathological Implications

PubMed Central

Lee, Chia Ee; Vincent-Chong, Vui King; Ramanathan, Anand; Kallarakkal, Thomas George; Karen-Ng, Lee Peng; Ghani, Wan Maria Nabillah; Rahman, Zainal Ariff Abdul; Ismail, Siti Mazlipah; Abraham, Mannil Thomas; Tay, Keng Kiong; Mustafa, Wan Mahadzir Wan; Cheong, Sok Ching; Zain, Rosnah Binti

2015-01-01

BACKGROUND: Collagen Triple Helix Repeat Containing 1 (CTHRC1) is a protein often found to be over-expressed in various types of human cancers. However, correlation between CTHRC1 expression level with clinico-pathological characteristics and prognosis in oral cancer remains unclear. Therefore, this study aimed to determine mRNA and protein expression of CTHRC1 in oral squamous cell carcinoma (OSCC) and to evaluate the clinical and prognostic impact of CTHRC1 in OSCC. METHODS: In this study, mRNA and protein expression of CTHRC1 in OSCCs were determined by quantitative PCR and immunohistochemistry, respectively. The association between CTHRC1 and clinico-pathological parameters were evaluated by univariate and multivariate binary logistic regression analyses. Correlation between CTHRC1 protein expressions with survival were analysed using Kaplan-Meier and Cox regression models. RESULTS: Current study demonstrated CTHRC1 was significantly overexpressed at the mRNA level in OSCC. Univariate analyses indicated a high-expression of CTHRC1 that was significantly associated with advanced stage pTNM staging, tumour size ≥ 4 cm and positive lymph node metastasis (LNM). However, only positive LNM remained significant after adjusting with other confounder factors in multivariate logistic regression analyses. Kaplan-Meier survival analyses and Cox model demonstrated that patients with high-expression of CTHRC1 protein were associated with poor prognosis and is an independent prognostic factor in OSCC. CONCLUSION: This study indicated that over-expression of CTHRC1 potentially as an independent predictor for positive LNM and poor prognosis in OSCC. PMID:26664254

Partial Least Square Analyses of Landscape and Surface Water Biota Associations in the Savannah River Basin

EPA Science Inventory

Ecologists are often faced with problem of small sample size, correlated and large number of predictors, and high noise-to-signal relationships. This necessitates excluding important variables from the model when applying standard multiple or multivariate regression analyses. In ...

Differences in Causal Estimates from Longitudinal Analyses of Residualized versus Simple Gain Scores: Contrasting Controls for Selection and Regression Artifacts

ERIC Educational Resources Information Center

Larzelere, Robert E.; Ferrer, Emilio; Kuhn, Brett R.; Danelia, Ketevan

2010-01-01

This study estimates the causal effects of six corrective actions for children's problem behaviors, comparing four types of longitudinal analyses that correct for pre-existing differences in a cohort of 1,464 4- and 5-year-olds from Canadian National Longitudinal Survey of Children and Youth (NLSCY) data. Analyses of residualized gain scores found…

Identification of Sexually Abused Female Adolescents at Risk for Suicidal Ideations: A Classification and Regression Tree Analysis

ERIC Educational Resources Information Center

Brabant, Marie-Eve; Hebert, Martine; Chagnon, Francois

2013-01-01

This study explored the clinical profiles of 77 female teenager survivors of sexual abuse and examined the association of abuse-related and personal variables with suicidal ideations. Analyses revealed that 64% of participants experienced suicidal ideations. Findings from classification and regression tree analysis indicated that depression,…

Medial prefrontal functional connectivity--relation to memory self-appraisal accuracy in older adults with and without memory disorders.

PubMed

Ries, Michele L; McLaren, Donald G; Bendlin, Barbara B; Guofanxu; Rowley, Howard A; Birn, Rasmus; Kastman, Erik K; Sager, Mark A; Asthana, Sanjay; Johnson, Sterling C

2012-04-01

It is tentatively estimated that 25% of people with early Alzheimer's disease (AD) show impaired awareness of disease-related changes in their own cognition. Research examining both normative self-awareness and altered awareness resulting from brain disease or injury points to the central role of the medial prefrontal cortex (MPFC) in generating accurate self-appraisals. The current project builds on this work - examining changes in MPFC functional connectivity that correspond to impaired self-appraisal accuracy early in the AD time course. Our behavioral focus was self-appraisal accuracy for everyday memory function, and this was measured using the Memory Function Scale of the Memory Awareness Rating Scale - an instrument psychometrically validated for this purpose. Using regression analysis of data from people with healthy memory (n=12) and people with impaired memory due to amnestic mild cognitive impairment or early AD (n=12), we tested the hypothesis that altered MPFC functional connectivity - particularly with other cortical midline structures and dorsolateral prefrontal cortex - explains variation in memory self-appraisal accuracy. We spatially constrained (i.e., explicitly masked) our regression analyses to those regions that work in conjunction with the MPFC to evoke self-appraisals in a normative group. This empirically derived explicit mask was generated from the result of a psychophysiological interaction analysis of fMRI self-appraisal task data in a separate, large group of cognitively healthy individuals. Results of our primary analysis (i.e., the regression of memory self-appraisal accuracy on MPFC functional connectivity) were generally consistent with our hypothesis: people who were less accurate in making memory self-appraisals showed attenuated functional connectivity between the MPFC seed region and proximal areas within the MPFC (including subgenual anterior cingulate cortex), bilateral dorsolateral prefrontal cortex, bilateral caudate, and left posterior hippocampus. Contrary to our expectations, MPFC functional connectivity with the posterior cingulate was not significantly related to accuracy of memory self-appraisals. Results reported here corroborate findings of variable memory self-appraisal accuracy during the earliest emergence of AD symptoms and reveal alterations in MPFC functional connectivity that correspond to impaired memory self-appraisal. Copyright © 2012 Elsevier Ltd. All rights reserved.

Regression Model for Light Weight and Crashworthiness Enhancement Design of Automotive Parts in Frontal CAR Crash

NASA Astrophysics Data System (ADS)

Bae, Gihyun; Huh, Hoon; Park, Sungho

This paper deals with a regression model for light weight and crashworthiness enhancement design of automotive parts in frontal car crash. The ULSAB-AVC model is employed for the crash analysis and effective parts are selected based on the amount of energy absorption during the crash behavior. Finite element analyses are carried out for designated design cases in order to investigate the crashworthiness and weight according to the material and thickness of main energy absorption parts. Based on simulations results, a regression analysis is performed to construct a regression model utilized for light weight and crashworthiness enhancement design of automotive parts. An example for weight reduction of main energy absorption parts demonstrates the validity of a regression model constructed.

A spline-based regression parameter set for creating customized DARTEL MRI brain templates from infancy to old age.

PubMed

Wilke, Marko

2018-02-01

This dataset contains the regression parameters derived by analyzing segmented brain MRI images (gray matter and white matter) from a large population of healthy subjects, using a multivariate adaptive regression splines approach. A total of 1919 MRI datasets ranging in age from 1-75 years from four publicly available datasets (NIH, C-MIND, fCONN, and IXI) were segmented using the CAT12 segmentation framework, writing out gray matter and white matter images normalized using an affine-only spatial normalization approach. These images were then subjected to a six-step DARTEL procedure, employing an iterative non-linear registration approach and yielding increasingly crisp intermediate images. The resulting six datasets per tissue class were then analyzed using multivariate adaptive regression splines, using the CerebroMatic toolbox. This approach allows for flexibly modelling smoothly varying trajectories while taking into account demographic (age, gender) as well as technical (field strength, data quality) predictors. The resulting regression parameters described here can be used to generate matched DARTEL or SHOOT templates for a given population under study, from infancy to old age. The dataset and the algorithm used to generate it are publicly available at https://irc.cchmc.org/software/cerebromatic.php.

A cycle of violence? Examining family-of-origin violence, attitudes, and intimate partner violence perpetration.

PubMed

Eriksson, Li; Mazerolle, Paul

2015-03-01

Exposure to violence in the family-of-origin has consistently been linked to intimate partner violence (IPV) perpetration in adulthood. However, whether the transmission of violence across generations is role- and gender-specific still remains unclear. The current study examined the effects of experiencing child abuse and observing parental violence on IPV perpetration among a sample of male arrestees (N = 303). The differential effects of observing violence perpetrated by same-sex (father to mother), opposite-sex (mother to father), and both parents on subsequent IPV perpetration were examined. Logistic regression analyses showed that while observing father-only violence and bidirectional interparental violence was predictive of IPV perpetration, observing mother-only violence and direct experiences of child abuse was not. These findings suggest that the transmission of violence across generations is both role- and gender-specific and highlight the importance of examining unique dimensions of partner violence to assess influences on children. The study further examined whether attitudes justifying wife beating mediate the effect of exposure to violence and subsequent IPV perpetration. Results showed that although attitudes were predictive of perpetration, these attitudes did not mediate the relationship. © The Author(s) 2014.

Academic Procrastination and Goal Accomplishment: A Combined Experimental and Individual Differences Investigation

PubMed Central

Gustavson, Daniel E.; Miyake, Akira

2017-01-01

This study examined the relationship between academic procrastination and goal accomplishment in two novel ways. First, we experimentally tested whether undergraduate students (N = 177) could reduce their academic procrastination over a course of three weeks after performing goal-related exercises to set so-called SMART goals and/or to prepare those students with specific strategies to resist their temptations (forming implementation intentions). Second, we conducted systematic regression analyses to examine whether academic procrastination at baseline uniquely predicts later goal-related outcomes, controlling for various correlated variables, including personality traits (e.g., impulsivity), motivational factors (e.g., motivation for the generated goals), and situational factors (e.g., memory for the goals). Results indicated that neither the SMART-goal nor implementation-intention intervention significantly reduced academic procrastination in the three-week interval, even when relevant moderating variables were examined. Initial levels of academic procrastination, however, were predictive of the success of accomplishing the goals generated during the initial exercises, above and beyond a wide range of other candidate correlates. These results provided new correlational evidence for the association between academic procrastination and goal accomplishment, but suggest a need for further research to understand what interventions are effective at reducing academic procrastination. PMID:28943742

Academic Procrastination and Goal Accomplishment: A Combined Experimental and Individual Differences Investigation.

PubMed

Gustavson, Daniel E; Miyake, Akira

2017-02-01

This study examined the relationship between academic procrastination and goal accomplishment in two novel ways. First, we experimentally tested whether undergraduate students ( N = 177) could reduce their academic procrastination over a course of three weeks after performing goal-related exercises to set so-called SMART goals and/or to prepare those students with specific strategies to resist their temptations (forming implementation intentions). Second, we conducted systematic regression analyses to examine whether academic procrastination at baseline uniquely predicts later goal-related outcomes, controlling for various correlated variables, including personality traits (e.g., impulsivity), motivational factors (e.g., motivation for the generated goals), and situational factors (e.g., memory for the goals). Results indicated that neither the SMART-goal nor implementation-intention intervention significantly reduced academic procrastination in the three-week interval, even when relevant moderating variables were examined. Initial levels of academic procrastination, however, were predictive of the success of accomplishing the goals generated during the initial exercises, above and beyond a wide range of other candidate correlates. These results provided new correlational evidence for the association between academic procrastination and goal accomplishment, but suggest a need for further research to understand what interventions are effective at reducing academic procrastination.

Fungible weights in logistic regression.

PubMed

Jones, Jeff A; Waller, Niels G

2016-06-01

In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

What Are Friends for? Friendships and Loneliness Over the Lifespan-From 18 to 79 Years.

PubMed

Nicolaisen, Magnhild; Thorsen, Kirsten

2017-01-01

Preventing and reducing loneliness is crucial to well-being and good health. Social relationships protect people against loneliness. Friendship qualities like intimacy and frequency of contact may vary throughout a person's lifespan. This study explores aspects of friendship and loneliness among people in different age groups: 18 to 29, 30 to 49, 50 to 64, and 65 to 79 years old. Data are from the Norwegian Life Course, Gender and Generations study (N = 14,725). Young people see their friends most often. The proportion of people without confidant friends is higher among older age groups. However, older age groups report higher levels of satisfaction with their contact with friends. Multivariate regression analyses show that the aspiration for contact with friends is more significant to loneliness than actual contact in all age groups. Dissatisfaction with contact with friends is strongly related to loneliness in all age groups. © The Author(s) 2016.

Social-cognitive functioning and schizotypal characteristics.

PubMed

Meyer, Joseph; Shean, Glenn

2006-05-01

The authors examined the relationship between social cognition and a feature of schizotypal personality referred to as magical ideation, defined broadly as the presence and intensity of illogical beliefs about causality and the nature of reality. The measures of social cognition used in this study were the Character Intention Task (CIT) and the adult version of the Reading the Mind in the Eyes Test. Regression analyses indicated that understanding of character intentions, as measured by CIT scores, and ability to identify emotions on the Eyes test were related to non-realistic beliefs. Principal components analysis of the Magical Ideation Scale generated 3 factors: Occult Beliefs, Non-Realism, and New Age Ideas. Results indicated that impaired understanding of character intentions and ability to identify emotions on the Eyes test were related to non-realistic beliefs. Understanding the cognitive impairments associated with schizotypal characteristics can facilitate development of more targeted therapeutic interventions.

Prospective Associations of Actual and Perceived Descriptive Norms with Drinking Among Emerging Adults.

PubMed

Simons-Morton, Bruce; Haynie, Denise; Bible, Joe; Liu, Danping

2018-02-05

Descriptive norms are commonly associated with participant drinking. However, study participants may incorrectly perceive that their peers drink about the same amount as they do, which would bias estimates of drinking homogeneity. This research examined the magnitude of associations between emerging adults' reports of their own drinking and peer drinking measured the previous year by measures of (1) participants' perceptions of friends' drinking; and (2) actual drinking reported by nominated peers. The data are from annual surveys conducted in 2014 and 2015, Waves 4 and 5 (the first 2 years after high school) of 7 annual assessments as part of the NEXT Generation Health Study (n = 323). Associations of participant alcohol use with perceived friend use (five closest, closest male, and closest female friends), and with actual peer use. Logistic regression analyses estimated the magnitudes of prospective associations between each measure of peer drinking at W4 and participant drinking at W5.

Fetal size in mid- and late pregnancy is related to infant alertness: the generation R study.

PubMed

Henrichs, Jens; Schenk, Jacqueline J; Schmidt, Henk G; Arends, Lidia R; Steegers, Eric A P; Hofman, Albert; Jaddoe, Vincent W V; Verhulst, Frank C; Tiemeier, Henning

2009-03-01

The vulnerability for behavioral problems is partly shaped in fetal life. Numerous studies have related indicators of intrauterine growth, for example, birth weight and body size, to behavioral development. We investigated whether fetal size in mid- and late pregnancy is related to infant irritability and alertness. In a population-based birth cohort of 4,255 singleton full-term infants ultrasound measurements of fetal head and abdominal circumference in mid- and late pregnancy were performed. Infant irritability and alertness scores were obtained by the Mother and Baby Scales at 3 months and z-standardized. Multiple linear regression analyses revealed curvilinear associations (inverted J-shape) of measures of fetal size in both mid- and late pregnancy with infant alertness. Fetal size characteristics were not associated with infant irritability. These results suggest that alterations of intrauterine growth affecting infant alertness are already detectable from mid-pregnancy onwards.

Examining the use of HIT functions among physicians serving minority populations.

PubMed

Tarver, Will; Menachemi, Nir

2014-02-01

The Institute of Medicine highlighted the fact that the U.S. health care system does not provide consistent, high quality medical care to all people. The routine use of health information technology (HIT) that includes certain key functions may be critical in reducing such disparities. We used logistic regression analyses to examine differences when it comes to the routine use of key HIT functions that are linked to improvements in clinical care. Physicians predominantly serving Black patients were more likely than physicians predominantly serving White patients to routinely use HIT to generate reminders for clinicians and patients about preventive services. Similarly, physicians predominantly serving Hispanic patients were more likely than physicians predominantly serving White patients to routinely use HIT to access patients' preferred language. Importantly, although minority-serving institutions have lower adoption rates overall, differences exist in the routine use of key HIT functions that have the potential to reduce health disparities.

Community Characteristics and Qualified Health Plan Selection during the First Open Enrollment Period.

PubMed

Boudreaux, Michel; Blewett, Lynn A; Fried, Brett; Hempstead, Katherine; Karaca-Mandic, Pinar

2017-06-01

To examine state and community factors that contributed to geographic variation in qualified health plan selection during the first open enrollment period. Administrative data on qualified health plan selections at the ZIP code area merged with survey estimates from the American Community Survey. Descriptive and regression analyses. Data were generated by healthcare.gov and from a household survey. Thirty-one percent of the variation in qualified health plan selection ratios resulted from between-state differences, and the rest was driven by local area differences. Education, language, age, gender, and the ethnic composition of communities contributed to disparate levels of plan selection. Medicaid expansion states had a qualified health plan selection ratio that was 4.4 points lower than non-Medicaid expansion states, controlling for covariates. Our results suggest community-level differences in the intensity or receptiveness to outreach and enrollment activities during the first open enrollment period. © Health Research and Educational Trust.

Suicide among immigrant population in Norway: a national register-based study.

PubMed

Puzo, Q; Mehlum, L; Qin, P

2017-06-01

To investigate differences in suicide risk among immigrant population in Norway compared with native Norwegians, with respect to associated country group of origin. Based on the entire national population, a nested case-control design was adopted using Norwegian national longitudinal registers to obtain 23 073 suicide cases having occurred in 1969-2012 and 373 178 controls. Odds ratios (ORs) for suicide were estimated using conditional logistic regression analysis adjusting for socio-economic factors. Compared with native Norwegians, suicide risk was significantly lower in first- and second-generation immigrants but higher in Norwegian-born with one foreign-born parent and foreign-born individuals with at least one Norwegian-born parent. When stratifying data by country group of origin, first-generation immigrants had lower ORs in most of the strata. Subjects born in Asia and in Central and South America with at least one Norwegian-born parent had a significantly higher risk of suicide. The observed results remained mostly unchanged in the analyses controlled for socio-economic status. Suicide risk is lower in first- and second-generation immigrants but higher in subjects born in Norway with one foreign-born parent and those born abroad with at least one Norwegian-born parent, with notable differences by country group of origin. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Discrimination, arrest history, and major depressive disorder in the U.S. Black population.

PubMed

Anglin, Deidre M; Lighty, Quenesha; Yang, Lawrence H; Greenspoon, Michelle; Miles, Rashun J; Slonim, Tzachi; Isaac, Kathleen; Brown, Monique J

2014-09-30

Everyday discrimination contributes negatively to depressive symptomatology among Blacks in the US and being arrested could add to this depression. Using data from the National Survey on American Life, the present study determined the association between an arrest history and major depressive disorder (MDD), while accounting for discrimination among African Americans, US-born Afro-Caribbeans and first-generation Black immigrants. Findings from logistic regression analyses adjusted for discrimination suggested an arrest history is associated with 12-month MDD (Adjusted OR=1.47; 95% CI=1.02-2.10) and lifetime MDD (Adjusted OR=1.56 CI=1.17-2.09). Accounting for drug and alcohol dependence attenuated the association between arrest history and 12-month MDD, but not lifetime MDD. The associations between arrest history and both 12-month and lifetime MDD, and discrimination and lifetime MDD varied by ethnic/immigrant group. Specifically, while the association between arrest history and MDD (both 12-month and lifetime) was strongest among US-born Afro-Caribbeans, evidence consistent with the immigrant paradox, the association between discrimination and lifetime MDD was particularly relevant for first-generation Black immigrants, suggesting discrimination may hinder the protection of first-generation status. Mental health prevention and treatment programs should target the stress associated with being arrested and experiencing discrimination among US Blacks. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Using the Quantile Mapping to improve a weather generator

NASA Astrophysics Data System (ADS)

Chen, Y.; Themessl, M.; Gobiet, A.

2012-04-01

We developed a weather generator (WG) by using statistical and stochastic methods, among them are quantile mapping (QM), Monte-Carlo, auto-regression, empirical orthogonal function (EOF). One of the important steps in the WG is using QM, through which all the variables, no matter what distribution they originally are, are transformed into normal distributed variables. Therefore, the WG can work on normally distributed variables, which greatly facilitates the treatment of random numbers in the WG. Monte-Carlo and auto-regression are used to generate the realization; EOFs are employed for preserving spatial relationships and the relationships between different meteorological variables. We have established a complete model named WGQM (weather generator and quantile mapping), which can be applied flexibly to generate daily or hourly time series. For example, with 30-year daily (hourly) data and 100-year monthly (daily) data as input, the 100-year daily (hourly) data would be relatively reasonably produced. Some evaluation experiments with WGQM have been carried out in the area of Austria and the evaluation results will be presented.

Interest in Low-Threshold Employment among People who Inject Illicit Drugs: Implications for Street Disorder

PubMed Central

DeBeck, Kora; Wood, Evan; Qi, Jiezhi; Fu, Eric; McArthur, Doug; Montaner, Julio; Kerr, Thomas

2011-01-01

Background Income generation opportunities available to people who use illicit drugs have been associated with street disorder. Among a cohort of injection drug users (IDU) we sought to examine street-based income generation practices and willingness to forgo these sources of income if other low-threshold work opportunities were made available. Methods Data were derived from a prospective community recruited cohort of IDU. We assessed the prevalence of engaging in disorderly street-based income generation activities, including sex work, drug dealing, panhandling, and recycling/salvaging/vending. Using multivariate logistic regressions based on Akaike information criterion and the best subset selection procedure, we identified factors associated with disorderly income generation activities, and assessed willingness to forgo these sources of income during the period of November 2008 to July 2009. Results Among our sample of 874 IDU, 418 (48%) reported engaging in a disorderly income generation activity in the previous six months. In multivariate analyses, engaging in disorderly income generation activities was independently associated with high intensity stimulant use, as well as binge drug use, having encounters with police, being a victim of violence, sharing used syringes, and injecting in public areas. Among those engaged in disorderly income generation, 198 (47%) reported a willingness to forgo these income sources if given opportunities for low-threshold employment, with sex workers being most willing to engage in alternative employment. Conclusion Engagement in disorderly street-based income generation activities was associated with high intensity stimulant drug use and various markers of risk. We found that a high proportion of illicit drug users were willing to cease engagement in these activities if they had options for causal low-threshold employment. These findings indicate that there is a high demand for low-threshold employment that may offer important opportunities to reduce drug-related street disorder and associated harms. PMID:21684142

Interest in low-threshold employment among people who inject illicit drugs: implications for street disorder.

PubMed

Debeck, Kora; Wood, Evan; Qi, Jiezhi; Fu, Eric; McArthur, Doug; Montaner, Julio; Kerr, Thomas

2011-09-01

Income generation opportunities available to people who use illicit drugs have been associated with street disorder. Among a cohort of injection drug users (IDU) we sought to examine street-based income generation practices and willingness to forgo these sources of income if other low-threshold work opportunities were made available. Data were derived from a prospective community recruited cohort of IDU. We assessed the prevalence of engaging in disorderly street-based income generation activities, including sex work, drug dealing, panhandling, and recycling/salvaging/vending. Using multivariate logistic regressions based on Akaike information criterion and the best subset selection procedure, we identified factors associated with disorderly income generation activities, and assessed willingness to forgo these sources of income during the period of November 2008 to July 2009. Among our sample of 874 IDU, 418 (48%) reported engaging in a disorderly income generation activity in the previous six months. In multivariate analyses, engaging in disorderly income generation activities was independently associated with high intensity stimulant use, as well as binge drug use, having encounters with police, being a victim of violence, sharing used syringes, and injecting in public areas. Among those engaged in disorderly income generation, 198 (47%) reported a willingness to forgo these income sources if given opportunities for low-threshold employment, with sex workers being most willing to engage in alternative employment. Engagement in disorderly street-based income generation activities was associated with high intensity stimulant drug use and various markers of risk. We found that a high proportion of illicit drug users were willing to cease engagement in these activities if they had options for causal low-threshold employment. These findings indicate that there is a high demand for low-threshold employment that may offer important opportunities to reduce drug-related street disorder and associated harms. Copyright © 2011 Elsevier B.V. All rights reserved.

Geodesic least squares regression for scaling studies in magnetic confinement fusion

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

Verdoolaege, Geert

In regression analyses for deriving scaling laws that occur in various scientific disciplines, usually standard regression methods have been applied, of which ordinary least squares (OLS) is the most popular. However, concerns have been raised with respect to several assumptions underlying OLS in its application to scaling laws. We here discuss a new regression method that is robust in the presence of significant uncertainty on both the data and the regression model. The method, which we call geodesic least squares regression (GLS), is based on minimization of the Rao geodesic distance on a probabilistic manifold. We demonstrate the superiority ofmore » the method using synthetic data and we present an application to the scaling law for the power threshold for the transition to the high confinement regime in magnetic confinement fusion devices.« less

Drivers of land use/land cover changes in Munessa-Shashemene landscape of the south-central highlands of Ethiopia.

PubMed

Kindu, Mengistie; Schneider, Thomas; Teketay, Demel; Knoke, Thomas

2015-07-01

Understanding drivers of changes in land use/land cover (LULC) is essential for modeling future dynamics or development of management strategies to ameliorate or prevent further decline of natural resources. In this study, an attempt has been made to identify the main drivers behind the LULC changes that had occurred in the past four decades in Munessa-Shashemene landscape of the south-central highlands of Ethiopia. The datasets required for the study were generated through both primary and secondary sources. Combination of techniques, including descriptive statistics, GIS-based processing, and regression analyses were employed for data analyses. Changes triggered by the interplay of more than 12 drivers were identified related to social, economic, environmental, policy/institutional, and technological factors. Specifically, population growth, expansion of cultivated lands and settlements, livestock ranching, cutting of woody species for fuelwood, and charcoal making were the top six important drivers of LULC change as viewed by the local people and confirmed by quantitative analyses. Differences in respondents' perceptions related to environmental (i.e., location specific) and socioeconomic determinants (e.g., age and literacy) about drivers were statically significant (P = 0.001). LULC changes were also determined by distances to major drivers (e.g., the further a pixel is from the road, the less likelihood of changes) as shown by the landscape level analyses. Further studies are suggested targeting these drivers to explore the consequences and future options and formulate intervention strategies for sustainable development in the studied landscape and elsewhere with similar geographic settings.

Social and cultural factors underlying generational differences in overweight: a cross-sectional study among ethnic minorities in the Netherlands.

PubMed

Hosper, Karen; Nicolaou, Mary; van Valkengoed, Irene; Nierkens, Vera; Stronks, Karien

2011-02-16

The prevalence of overweight appears to vary in people of first and second generation ethnic minority groups. Insight into the factors that underlie these weight differences might help in understanding the health transition that is taking place across generations following migration. We studied the role of social and cultural factors associated with generational differences in overweight among young Turkish and Moroccan men and women in the Netherlands. Cross-sectional data were derived from the LASER-study in which information on health-related behaviour and socio-demographic factors, level of education, occupational status, acculturation (cultural orientation and social contacts), religious and migration-related factors was gathered among Turkish and Moroccan men (n = 334) and women (n = 339) aged 15-30 years. Participants were interviewed during a home visit. Overweight was defined as a Body Mass Index ≥ 25 kg/m2. Using logistic regression analyses, we tested whether the measured social and cultural factors could explain differences in overweight between first and second generation ethnic groups. Second generation women were less often overweight than first generation women (21.8% and 45.0% respectively), but this association was no longer significant when adjusting for the socioeconomic position (i.e. higher level of education) of second generation women (Odds Ratio (OR) = 0.77, 95%, Confidence Interval (CI) 0.40-1.46). In men, we observed a reversed pattern: second generation men were more often overweight than first generation men (32.7% and 27.8%). This association (OR = 1.89, 95% CI 1.09-3.24) could not be explained by the social and cultural factors because none of these factors were associated with overweight among men. The higher socio-economic position of second generation Turkish and Moroccan women may partly account for the lower prevalence of overweight in this group compared to first generation women. Further research is necessary to elucidate whether any postulated socio-biological or other processes are relevant to the opposite pattern of overweight among men.

Social and cultural factors underlying generational differences in overweight: a cross-sectional study among ethnic minorities in the Netherlands

PubMed Central

2011-01-01

Background The prevalence of overweight appears to vary in people of first and second generation ethnic minority groups. Insight into the factors that underlie these weight differences might help in understanding the health transition that is taking place across generations following migration. We studied the role of social and cultural factors associated with generational differences in overweight among young Turkish and Moroccan men and women in the Netherlands. Methods Cross-sectional data were derived from the LASER-study in which information on health-related behaviour and socio-demographic factors, level of education, occupational status, acculturation (cultural orientation and social contacts), religious and migration-related factors was gathered among Turkish and Moroccan men (n = 334) and women (n = 339) aged 15-30 years. Participants were interviewed during a home visit. Overweight was defined as a Body Mass Index ≥ 25 kg/m2. Using logistic regression analyses, we tested whether the measured social and cultural factors could explain differences in overweight between first and second generation ethnic groups. Results Second generation women were less often overweight than first generation women (21.8% and 45.0% respectively), but this association was no longer significant when adjusting for the socioeconomic position (i.e. higher level of education) of second generation women (Odds Ratio (OR) = 0.77, 95%, Confidence Interval (CI) 0.40-1.46). In men, we observed a reversed pattern: second generation men were more often overweight than first generation men (32.7% and 27.8%). This association (OR = 1.89, 95% CI 1.09-3.24) could not be explained by the social and cultural factors because none of these factors were associated with overweight among men. Conclusions The higher socio-economic position of second generation Turkish and Moroccan women may partly account for the lower prevalence of overweight in this group compared to first generation women. Further research is necessary to elucidate whether any postulated socio-biological or other processes are relevant to the opposite pattern of overweight among men. PMID:21324156

Advantages of the net benefit regression framework for economic evaluations of interventions in the workplace: a case study of the cost-effectiveness of a collaborative mental health care program for people receiving short-term disability benefits for psychiatric disorders.

PubMed

Hoch, Jeffrey S; Dewa, Carolyn S

2014-04-01

Economic evaluations commonly accompany trials of new treatments or interventions; however, regression methods and their corresponding advantages for the analysis of cost-effectiveness data are not well known. To illustrate regression-based economic evaluation, we present a case study investigating the cost-effectiveness of a collaborative mental health care program for people receiving short-term disability benefits for psychiatric disorders. We implement net benefit regression to illustrate its strengths and limitations. Net benefit regression offers a simple option for cost-effectiveness analyses of person-level data. By placing economic evaluation in a regression framework, regression-based techniques can facilitate the analysis and provide simple solutions to commonly encountered challenges. Economic evaluations of person-level data (eg, from a clinical trial) should use net benefit regression to facilitate analysis and enhance results.

Morphological Awareness and Children's Writing: Accuracy, Error, and Invention

PubMed Central

McCutchen, Deborah; Stull, Sara

2014-01-01

This study examined the relationship between children's morphological awareness and their ability to produce accurate morphological derivations in writing. Fifth-grade U.S. students (n = 175) completed two writing tasks that invited or required morphological manipulation of words. We examined both accuracy and error, specifically errors in spelling and errors of the sort we termed morphological inventions, which entailed inappropriate, novel pairings of stems and suffixes. Regressions were used to determine the relationship between morphological awareness, morphological accuracy, and spelling accuracy, as well as between morphological awareness and morphological inventions. Linear regressions revealed that morphological awareness uniquely predicted children's generation of accurate morphological derivations, regardless of whether or not accurate spelling was required. A logistic regression indicated that morphological awareness was also uniquely predictive of morphological invention, with higher morphological awareness increasing the probability of morphological invention. These findings suggest that morphological knowledge may not only assist children with spelling during writing, but may also assist with word production via generative experimentation with morphological rules during sentence generation. Implications are discussed for the development of children's morphological knowledge and relationships with writing. PMID:25663748

Simulation of CO2 Solubility in Polystyrene-b-Polybutadieneb-Polystyrene (SEBS) by artificial intelligence network (ANN) method

NASA Astrophysics Data System (ADS)

Sharudin, R. W.; AbdulBari Ali, S.; Zulkarnain, M.; Shukri, M. A.

2018-05-01

This study reports on the integration of Artificial Neural Network (ANNs) with experimental data in predicting the solubility of carbon dioxide (CO2) blowing agent in SEBS by generating highest possible value for Regression coefficient (R2). Basically, foaming of thermoplastic elastomer with CO2 is highly affected by the CO2 solubility. The ability of ANN in predicting interpolated data of CO2 solubility was investigated by comparing training results via different method of network training. Regards to the final prediction result for CO2 solubility by ANN, the prediction trend (output generate) was corroborated with the experimental results. The obtained result of different method of training showed the trend of output generated by Gradient Descent with Momentum & Adaptive LR (traingdx) required longer training time and required more accurate input to produce better output with final Regression Value of 0.88. However, it goes vice versa with Levenberg-Marquardt (trainlm) technique as it produced better output in quick detention time with final Regression Value of 0.91.

Variance Estimation Using Replication Methods in Structural Equation Modeling with Complex Sample Data

ERIC Educational Resources Information Center

Stapleton, Laura M.

2008-01-01

This article discusses replication sampling variance estimation techniques that are often applied in analyses using data from complex sampling designs: jackknife repeated replication, balanced repeated replication, and bootstrapping. These techniques are used with traditional analyses such as regression, but are currently not used with structural…

A Two-Step Method to Select Major Surge-Producing Extratropical Cyclones from a 10,000-Year Stochastic Catalog

NASA Astrophysics Data System (ADS)

Keshtpoor, M.; Carnacina, I.; Yablonsky, R. M.

2016-12-01

Extratropical cyclones (ETCs) are the primary driver of storm surge events along the UK and northwest mainland Europe coastlines. In an effort to evaluate the storm surge risk in coastal communities in this region, a stochastic catalog is developed by perturbing the historical storm seeds of European ETCs to account for 10,000 years of possible ETCs. Numerical simulation of the storm surge generated by the full 10,000-year stochastic catalog, however, is computationally expensive and may take several months to complete with available computational resources. A new statistical regression model is developed to select the major surge-generating events from the stochastic ETC catalog. This regression model is based on the maximum storm surge, obtained via numerical simulations using a calibrated version of the Delft3D-FM hydrodynamic model with a relatively coarse mesh, of 1750 historical ETC events that occurred over the past 38 years in Europe. These numerically-simulated surge values were regressed to the local sea level pressure and the U and V components of the wind field at the location of 196 tide gauge stations near the UK and northwest mainland Europe coastal areas. The regression model suggests that storm surge values in the area of interest are highly correlated to the U- and V-component of wind speed, as well as the sea level pressure. Based on these correlations, the regression model was then used to select surge-generating storms from the 10,000-year stochastic catalog. Results suggest that roughly 105,000 events out of 480,000 stochastic storms are surge-generating events and need to be considered for numerical simulation using a hydrodynamic model. The selected stochastic storms were then simulated in Delft3D-FM, and the final refinement of the storm population was performed based on return period analysis of the 1750 historical event simulations at each of the 196 tide gauges in preparation for Delft3D-FM fine mesh simulations.

Heterogeneous Suppression of Sequential Effects in Random Sequence Generation, but Not in Operant Learning.

PubMed

Shteingart, Hanan; Loewenstein, Yonatan

2016-01-01

There is a long history of experiments in which participants are instructed to generate a long sequence of binary random numbers. The scope of this line of research has shifted over the years from identifying the basic psychological principles and/or the heuristics that lead to deviations from randomness, to one of predicting future choices. In this paper, we used generalized linear regression and the framework of Reinforcement Learning in order to address both points. In particular, we used logistic regression analysis in order to characterize the temporal sequence of participants' choices. Surprisingly, a population analysis indicated that the contribution of the most recent trial has only a weak effect on behavior, compared to more preceding trials, a result that seems irreconcilable with standard sequential effects that decay monotonously with the delay. However, when considering each participant separately, we found that the magnitudes of the sequential effect are a monotonous decreasing function of the delay, yet these individual sequential effects are largely averaged out in a population analysis because of heterogeneity. The substantial behavioral heterogeneity in this task is further demonstrated quantitatively by considering the predictive power of the model. We show that a heterogeneous model of sequential dependencies captures the structure available in random sequence generation. Finally, we show that the results of the logistic regression analysis can be interpreted in the framework of reinforcement learning, allowing us to compare the sequential effects in the random sequence generation task to those in an operant learning task. We show that in contrast to the random sequence generation task, sequential effects in operant learning are far more homogenous across the population. These results suggest that in the random sequence generation task, different participants adopt different cognitive strategies to suppress sequential dependencies when generating the "random" sequences.

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.

Logistic Regression in the Identification of Hazards in Construction

NASA Astrophysics Data System (ADS)

Drozd, Wojciech

2017-10-01

The construction site and its elements create circumstances that are conducive to the formation of risks to safety during the execution of works. Analysis indicates the critical importance of these factors in the set of characteristics that describe the causes of accidents in the construction industry. This article attempts to analyse the characteristics related to the construction site, in order to indicate their importance in defining the circumstances of accidents at work. The study includes sites inspected in 2014 - 2016 by the employees of the District Labour Inspectorate in Krakow (Poland). The analysed set of detailed (disaggregated) data includes both quantitative and qualitative characteristics. The substantive task focused on classification modelling in the identification of hazards in construction and identifying those of the analysed characteristics that are important in an accident. In terms of methodology, resource data analysis using statistical classifiers, in the form of logistic regression, was the method used.

Parental employment status and adolescents' health: the role of financial situation, parent-adolescent relationship and adolescents' resilience.

PubMed

Bacikova-Sleskova, Maria; Benka, Jozef; Orosova, Olga

2015-01-01

The paper deals with parental employment status and its relationship to adolescents' self-reported health. It studies the role of the financial situation, parent-adolescent relationship and adolescent resilience in the relationship between parental employment status and adolescents' self-rated health, vitality and mental health. Multiple regression analyses were used to analyse questionnaire data obtained from 2799 adolescents (mean age 14.3) in 2006. The results show a negative association of the father's, but not mother's unemployment or non-employment with adolescents' health. Regression analyses showed that neither financial strain nor a poor parent-adolescent relationship or a low score in resilience accounted for the relationship between the father's unemployment or non-employment and poorer adolescent health. Furthermore, resilience did not work as a buffer against the negative impact of fathers' unemployment on adolescents' health.

Applications of MIDAS regression in analysing trends in water quality

NASA Astrophysics Data System (ADS)

Penev, Spiridon; Leonte, Daniela; Lazarov, Zdravetz; Mann, Rob A.

2014-04-01

We discuss novel statistical methods in analysing trends in water quality. Such analysis uses complex data sets of different classes of variables, including water quality, hydrological and meteorological. We analyse the effect of rainfall and flow on trends in water quality utilising a flexible model called Mixed Data Sampling (MIDAS). This model arises because of the mixed frequency in the data collection. Typically, water quality variables are sampled fortnightly, whereas the rain data is sampled daily. The advantage of using MIDAS regression is in the flexible and parsimonious modelling of the influence of the rain and flow on trends in water quality variables. We discuss the model and its implementation on a data set from the Shoalhaven Supply System and Catchments in the state of New South Wales, Australia. Information criteria indicate that MIDAS modelling improves upon simplistic approaches that do not utilise the mixed data sampling nature of the data.

Increased Heat Generation in Postcardiac Arrest Patients During Targeted Temperature Management Is Associated With Better Outcomes.

PubMed

Uber, Amy J; Perman, Sarah M; Cocchi, Michael N; Patel, Parth V; Ganley, Sarah E; Portmann, Jocelyn M; Donnino, Michael W; Grossestreuer, Anne V

2018-04-03

Assess if amount of heat generated by postcardiac arrest patients to reach target temperature (Ttarget) during targeted temperature management is associated with outcomes by serving as a proxy for thermoregulatory ability, and whether it modifies the relationship between time to Ttarget and outcomes. Retrospective cohort study. Urban tertiary-care hospital. Successfully resuscitated targeted temperature management-treated adult postarrest patients between 2008 and 2015 with serial temperature data and Ttarget less than or equal to 34°C. None. Time to Ttarget was defined as time from targeted temperature management initiation to first recorded patient temperature less than or equal to 34°C. Patient heat generation ("heat units") was calculated as inverse of average water temperature × hours between initiation and Ttarget × 100. Primary outcome was neurologic status measured by Cerebral Performance Category score; secondary outcome was survival, both at hospital discharge. Univariate analyses were performed using Wilcoxon rank-sum tests; multivariate analyses used logistic regression. Of 203 patients included, those with Cerebral Performance Category score 3-5 generated less heat before reaching Ttarget (median, 8.1 heat units [interquartile range, 3.6-21.6 heat units] vs median, 20.0 heat units [interquartile range, 9.0-33.5 heat units]; p = 0.001) and reached Ttarget quicker (median, 2.3 hr [interquartile range, 1.5-4.0 hr] vs median, 3.6 hr [interquartile range, 2.0-5.0 hr]; p = 0.01) than patients with Cerebral Performance Category score 1-2. Nonsurvivors generated less heat than survivors (median, 8.1 heat units [interquartile range, 3.6-20.8 heat units] vs median, 19.0 heat units [interquartile range, 6.5-33.5 heat units]; p = 0.001) and reached Ttarget quicker (median, 2.2 hr [interquartile range, 1.5-3.8 hr] vs median, 3.6 hr [interquartile range, 2.0-5.0 hr]; p = 0.01). Controlling for average water temperature between initiation and Ttarget, the relationship between outcomes and time to Ttarget was no longer significant. Controlling for location, witnessed arrest, age, initial rhythm, and neuromuscular blockade use, increased heat generation was associated with better neurologic (adjusted odds ratio, 1.01 [95% CI, 1.00-1.03]; p = 0.039) and survival (adjusted odds ratio, 1.01 [95% CI, 1.00-1.03]; p = 0.045) outcomes. Increased heat generation during targeted temperature management initiation is associated with better outcomes at hospital discharge and may affect the relationship between time to Ttarget and outcomes.

Competition Between Biosimilars and Patented Biologics: Learning from European and Japanese Experience.

PubMed

Bocquet, François; Loubière, Anaïs; Fusier, Isabelle; Cordonnier, Anne-Laure; Paubel, Pascal

2016-11-01

The expiry of patents for costly biologics is creating new momentum on the pharmaceutical market for biosimilars (copies of off-patent biologics) and paving the way for their development. However, little is known about the competitiveness of biosimilars versus their originators and other biologics belonging to the same therapeutic class. The main goal of this study was to analyse the type of competition generated by the first biosimilars commercialised on key global biologic markets and to grasp their economic model. The secondary goal was to distinguish the main factors likely to influence the uptake of biosimilars on national markets. To be included in this study, countries had to meet three conditions: the regulatory framework for the development of biosimilars closely resembled that in Europe, biosimilars were marketed in 2014, and the value of the biologics market was >US$3 billion. We analysed granulocyte colony-stimulating factors (GCSFs) and erythropoietins (EPOs) over the period 2007-2014 because these are the two main therapeutic classes that have been 'biosimilarised' and thus have many years of experience available. We assessed market sizes, retail/hospital distribution mixes, incentives for using biosimilars and price discounts for originators versus biosimilars. We conducted a linear regression analysis to assess the relationship between uptakes of biosimilars and the market shares of other biologics. The EU-5 (France, Germany, Italy, Spain and the UK) and Japanese GCSF and EPO markets are highly-country-specific. Uptake of biosimilars seems to depend on retail/hospital distribution mixes and on medical practice. Depending on the therapeutic class and the market sector (retail or hospital), biosimilars may compete with first-generation or second-generation products or both. Some incentives implemented to encourage the use of biosimilars had mixed results. Overall, discounts for biosimilars versus originators are not factors that determine global uptake of biosimilars. Unlike generics, there appears to be no unique economic model for biosimilars. Moreover, a new phenomenon occurs with biosimilars: sometimes, they are able to take market shares from subsequent generations of biologics.

Regression methods for spatially correlated data: an example using beetle attacks in a seed orchard

Treesearch

Preisler Haiganoush; Nancy G. Rappaport; David L. Wood

1997-01-01

We present a statistical procedure for studying the simultaneous effects of observed covariates and unmeasured spatial variables on responses of interest. The procedure uses regression type analyses that can be used with existing statistical software packages. An example using the rate of twig beetle attacks on Douglas-fir trees in a seed orchard illustrates the...

How Variables Uncorrelated with the Dependent Variable Can Actually Make Excellent Predictors: The Important Suppressor Variable Case.

ERIC Educational Resources Information Center

Woolley, Kristin K.

Many researchers are unfamiliar with suppressor variables and how they operate in multiple regression analyses. This paper describes the role suppressor variables play in a multiple regression model and provides practical examples that explain how they can change research results. A variable that when added as another predictor increases the total…

Per capita community-level effects of an invasive grass, Microstegium vimineum, on vegetation in mesic forests in northern Mississippi (USA)

Treesearch

J. Stephen Brewer

2010-01-01

Quantifying per capita impacts of invasive species on resident communities requires integrating regression analyses with experiments under natural conditions. Using multivariate and univariate approaches, I regressed the abundance of 105 resident species of groundcover plants and tree seedlings against the abundance and height of an invasive grass, Microstegium...

Two-Year versus One-Year Head Start Program Impact: Addressing Selection Bias by Comparing Regression Modeling with Propensity Score Analysis

ERIC Educational Resources Information Center

Leow, Christine; Wen, Xiaoli; Korfmacher, Jon

2015-01-01

This article compares regression modeling and propensity score analysis as different types of statistical techniques used in addressing selection bias when estimating the impact of two-year versus one-year Head Start on children's school readiness. The analyses were based on the national Head Start secondary dataset. After controlling for…

The Impact of Being Labeled as a Persistently Lowest Achieving School: Regression Discontinuity Evidence on Consequential School Labeling

ERIC Educational Resources Information Center

Saw, Guan; Schneider, Barbara; Frank, Kenneth; Chen, I-Chien; Keesler, Venessa; Martineau, Joseph

2017-01-01

Since the No Child Left Behind Act was enacted, grading and labeling of schools as low performing have been increasingly used as means to incentivize failing schools to raise student achievement. Using statewide high school data from Michigan, our regression discontinuity analyses show that the bottom 5% of schools identified as persistently…

Regression Analyses on the Butterfly Ballot Effect: A Statistical Perspective of the US 2000 Election

ERIC Educational Resources Information Center

Wu, Dane W.

2002-01-01

The year 2000 US presidential election between Al Gore and George Bush has been the most intriguing and controversial one in American history. The state of Florida was the trigger for the controversy, mainly, due to the use of the misleading "butterfly ballot". Using prediction (or confidence) intervals for least squares regression lines…

Erosion and soil displacement related to timber harvesting in northwestern California, U.S.A.

Treesearch

R.M. Rice; D.J. Furbish

1984-01-01

The relationship between measures of site disturbance and erosion resulting from timber harvest was studied by regression analyses. None of the 12 regression models developed and tested yielded a coefficient of determination (R2) greater than 0.60. The results indicated that the poor fits to the data were due, in part, to unexplained qualitative...

"Erosion and soil displacement related to timber harvesting in northwestern California, U.S.A."

Treesearch

R. M. Rice; D. J. Furbish

1984-01-01

The relationship between measures of site disturbance and erosion resulting from timber harvest was studied by regression analyses. None of the 12 regression models developed and tested yielded a coefficient of determination (R 2) greater than 0.60. The results indicated that the poor fits to the data were due, in part, to unexplained qualitative differences in...

Regressive Imagery in Creative Problem-Solving: Comparing Verbal Protocols of Expert and Novice Visual Artists and Computer Programmers

ERIC Educational Resources Information Center

Kozbelt, Aaron; Dexter, Scott; Dolese, Melissa; Meredith, Daniel; Ostrofsky, Justin

2015-01-01

We applied computer-based text analyses of regressive imagery to verbal protocols of individuals engaged in creative problem-solving in two domains: visual art (23 experts, 23 novices) and computer programming (14 experts, 14 novices). Percentages of words involving primary process and secondary process thought, plus emotion-related words, were…

Using Genetic Variation to Explore the Causal Effect of Maternal Pregnancy Adiposity on Future Offspring Adiposity: A Mendelian Randomisation Study

PubMed Central

Felix, Janine F.; Gaillard, Romy; McMahon, George

2017-01-01

Background It has been suggested that greater maternal adiposity during pregnancy affects lifelong risk of offspring fatness via intrauterine mechanisms. Our aim was to use Mendelian randomisation (MR) to investigate the causal effect of intrauterine exposure to greater maternal body mass index (BMI) on offspring BMI and fat mass from childhood to early adulthood. Methods and Findings We used maternal genetic variants as instrumental variables (IVs) to test the causal effect of maternal BMI in pregnancy on offspring fatness (BMI and dual-energy X-ray absorptiometry [DXA] determined fat mass index [FMI]) in a MR approach. This was investigated, with repeat measurements, from ages 7 to 18 in the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 2,521 to 3,720 for different ages). We then sought to replicate findings with results for BMI at age 6 in Generation R (n = 2,337 for replication sample; n = 6,057 for total pooled sample). In confounder-adjusted multivariable regression in ALSPAC, a 1 standard deviation (SD, equivalent of 3.7 kg/m2) increase in maternal BMI was associated with a 0.25 SD (95% CI 0.21–0.29) increase in offspring BMI at age 7, with similar results at later ages and when FMI was used as the outcome. A weighted genetic risk score was generated from 32 genetic variants robustly associated with BMI (minimum F-statistic = 45 in ALSPAC). The MR results using this genetic risk score as an IV in ALSPAC were close to the null at all ages (e.g., 0.04 SD (95% CI -0.21–0.30) at age 7 and 0.03 SD (95% CI -0.26–0.32) at age 18 per SD increase in maternal BMI), which was similar when a 97 variant generic risk score was used in ALSPAC. When findings from age 7 in ALSPAC were meta-analysed with those from age 6 in Generation R, the pooled confounder-adjusted multivariable regression association was 0.22 SD (95% CI 0.19–0.25) per SD increase in maternal BMI and the pooled MR effect (pooling the 97 variant score results from ALSPAC with the 32 variant score results from Generation R) was 0.05 SD (95%CI -0.11–0.21) per SD increase in maternal BMI (p-value for difference between the two results = 0.05). A number of sensitivity analyses exploring violation of the MR results supported our main findings. However, power was limited for some of the sensitivity tests and further studies with relevant data on maternal, offspring, and paternal genotype are required to obtain more precise (and unbiased) causal estimates. Conclusions Our findings provide little evidence to support a strong causal intrauterine effect of incrementally greater maternal BMI resulting in greater offspring adiposity. PMID:28118352

Quantitative Assessment of Cervical Vertebral Maturation Using Cone Beam Computed Tomography in Korean Girls

PubMed Central

Byun, Bo-Ram; Kim, Yong-Il; Maki, Koutaro; Son, Woo-Sung

2015-01-01

This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6–18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R 2 had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status. PMID:25878721

Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

PubMed

Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

2008-01-01

This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

A classical regression framework for mediation analysis: fitting one model to estimate mediation effects.

PubMed

Saunders, Christina T; Blume, Jeffrey D

2017-10-26

Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. © The Author 2017. Published by Oxford University Press.

Procedures for adjusting regional regression models of urban-runoff quality using local data

USGS Publications Warehouse

Hoos, A.B.; Sisolak, J.K.

1993-01-01

Statistical operations termed model-adjustment procedures (MAP?s) can be used to incorporate local data into existing regression models to improve the prediction of urban-runoff quality. Each MAP is a form of regression analysis in which the local data base is used as a calibration data set. Regression coefficients are determined from the local data base, and the resulting `adjusted? regression models can then be used to predict storm-runoff quality at unmonitored sites. The response variable in the regression analyses is the observed load or mean concentration of a constituent in storm runoff for a single storm. The set of explanatory variables used in the regression analyses is different for each MAP, but always includes the predicted value of load or mean concentration from a regional regression model. The four MAP?s examined in this study were: single-factor regression against the regional model prediction, P, (termed MAP-lF-P), regression against P,, (termed MAP-R-P), regression against P, and additional local variables (termed MAP-R-P+nV), and a weighted combination of P, and a local-regression prediction (termed MAP-W). The procedures were tested by means of split-sample analysis, using data from three cities included in the Nationwide Urban Runoff Program: Denver, Colorado; Bellevue, Washington; and Knoxville, Tennessee. The MAP that provided the greatest predictive accuracy for the verification data set differed among the three test data bases and among model types (MAP-W for Denver and Knoxville, MAP-lF-P and MAP-R-P for Bellevue load models, and MAP-R-P+nV for Bellevue concentration models) and, in many cases, was not clearly indicated by the values of standard error of estimate for the calibration data set. A scheme to guide MAP selection, based on exploratory data analysis of the calibration data set, is presented and tested. The MAP?s were tested for sensitivity to the size of a calibration data set. As expected, predictive accuracy of all MAP?s for the verification data set decreased as the calibration data-set size decreased, but predictive accuracy was not as sensitive for the MAP?s as it was for the local regression models.

Mortality rates in OECD countries converged during the period 1990-2010.

PubMed

Bremberg, Sven G

2017-06-01

Since the scientific revolution of the 18th century, human health has gradually improved, but there is no unifying theory that explains this improvement in health. Studies of macrodeterminants have produced conflicting results. Most studies have analysed health at a given point in time as the outcome; however, the rate of improvement in health might be a more appropriate outcome. Twenty-eight OECD member countries were selected for analysis in the period 1990-2010. The main outcomes studied, in six age groups, were the national rates of decrease in mortality in the period 1990-2010. The effects of seven potential determinants on the rates of decrease in mortality were analysed in linear multiple regression models using least squares, controlling for country-specific history constants, which represent the mortality rate in 1990. The multiple regression analyses started with models that only included mortality rates in 1990 as determinants. These models explained 87% of the intercountry variation in the children aged 1-4 years and 51% in adults aged 55-74 years. When added to the regression equations, the seven determinants did not seem to significantly increase the explanatory power of the equations. The analyses indicated a decrease in mortality in all nations and in all age groups. The development of mortality rates in the different nations demonstrated significant catch-up effects. Therefore an important objective of the national public health sector seems to be to reduce the delay between international research findings and the universal implementation of relevant innovations.

Auditory-evoked cortical activity: contribution of brain noise, phase locking, and spectral power

PubMed Central

Harris, Kelly C.; Vaden, Kenneth I.; Dubno, Judy R.

2017-01-01

Background The N1-P2 is an obligatory cortical response that can reflect the representation of spectral and temporal characteristics of an auditory stimulus. Traditionally, mean amplitudes and latencies of the prominent peaks in the averaged response are compared across experimental conditions. Analyses of the peaks in the averaged response only reflect a subset of the data contained within the electroencephalogram (EEG) signal. We used single-trial analyses techniques to identify the contribution of brain noise, neural synchrony, and spectral power to the generation of P2 amplitude and how these variables may change across age group. This information is important for appropriate interpretation of event-related potentials (ERPs) results and in understanding of age-related neural pathologies. Methods EEG was measured from 25 younger and 25 older normal hearing adults. Age-related and individual differences in P2 response amplitudes, and variability in brain noise, phase locking value (PLV), and spectral power (4–8 Hz) were assessed from electrode FCz. Model testing and linear regression were used to determine the extent to which brain noise, PLV, and spectral power uniquely predicted P2 amplitudes and varied by age group. Results Younger adults had significantly larger P2 amplitudes, PLV, and power compared to older adults. Brain noise did not differ between age groups. The results of regression testing revealed that brain noise and PLV, but not spectral power were unique predictors of P2 amplitudes. Model fit was significantly better in younger than in older adults. Conclusions ERP analyses are intended to provide a better understanding of the underlying neural mechanisms that contribute to individual and group differences in behavior. The current results support that age-related declines in neural synchrony contribute to smaller P2 amplitudes in older normal hearing adults. Based on our results, we discuss potential models in which differences in neural synchrony and brain noise can account for associations with P2 amplitudes and behavior and potentially provide a better explanation of the neural mechanisms that underlie declines in auditory processing and training benefits. PMID:25046314

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.

The effects of stage-specific selection on the development of benzimidazole resistance in Haemonchus contortus in sheep.

PubMed

Taylor, M A; Hunt, K R; Goodyear, K L

2002-10-16

Resistance to the benzimidazole (BDZ) class of anthelmintics in nematodes of sheep has become a common and global phenomenon. The rate at which the selection process and development of resistance occurs is influenced by a number of factors. Of these, the effects of stage-specific exposures to anthelmintic were investigated with a BDZ-resistant strain of Haemonchus contortus (HCR) over five parasite generations. Sheep were infected at each generation with the HCR strain and were treated with thiabendazole (TBZ), either 5 days post-infection (p.i.) (larval line), 21 days p.i. (adult line), or left untreated (no selection line). Additionally eggs from each generation were exposed to TBZ (egg line). Geometric worm burdens were calculated from post-mortem worm counts, both at the start of the study, and after the final selection studies for each of the selection lines. Egg hatch assays (EHAs) were also conducted throughout the study. All data relating to worm burdens and EHAs for each generation were analysed by linear regression to produce dose titration curves and lethal dose(50) (LD(50)) values for each of the selection lines. Over the five generations, LD(50) values on dose-response were increased and worm survival occurred at higher dose rates of TBZ irrespective of the parasite stage exposed to treatment. A similar picture was seen with ED(50) values, which showed a fluctuating but generally upward trend for each of the three selection lines. In contrast, LD(50) and ED(50) values were decreased in the no selection line, indicating some degree of reversion albeit to levels still considered to be BDZ-resistant.

The Impact of Engagement in Street-based Income Generation Activities on Stimulant Drug Use Cessation among People who Inject Drugs

PubMed Central

Ti, Lianping; Richardson, Lindsey; DeBeck, Kora; Nguyen, Paul; Montaner, Julio; Wood, Evan; Kerr, Thomas

2014-01-01

Background Despite the growing prevalence of illicit stimulant drug use internationally, and the widespread involvement of people who inject drugs (IDU) within street-based drug markets, little is known about the impact of different types of street-based income generation activities on the cessation of stimulant use among IDU. Methods Data were derived from an open prospective cohort of IDU in Vancouver, Canada. We used Kaplan-Meier methods and Cox proportional hazards regression to examine the effect of different types of street-based income generation activities (e.g., sex work, drug dealing, and scavenging) on time to cessation of stimulant use. Results Between December, 2005 and November, 2012, 887 IDU who use stimulant drugs (cocaine, crack cocaine, or crystal methamphetamine) were prospectively followed-up for a median duration of 47 months. In Kaplan-Meier analyses, compared to those who did not engage in street-based income generation activities, participants who reported sex work, drug dealing, scavenging, or more than one of these activities were significantly less likely to report stimulant drug use cessation (all p<0.001). When considered as time-updated variables and adjusted for potential confounders in a multivariable model, each type of street-based income generation activity remained significantly associated with a slower time to stimulant drug cessation (all p<0.005). Conclusions Our findings highlight the urgent need for strategies to address stimulant dependence, including novel pharmacotherapies. Also important, structural interventions, such as low-threshold employment opportunities, availability of supportive housing, legal reforms regarding drug use, and evidence-based approaches that reduce harm among IDU are urgently required. PMID:24909853

The impact of engagement in street-based income generation activities on stimulant drug use cessation among people who inject drugs.

PubMed

Ti, Lianping; Richardson, Lindsey; DeBeck, Kora; Nguyen, Paul; Montaner, Julio; Wood, Evan; Kerr, Thomas

2014-08-01

Despite the growing prevalence of illicit stimulant drug use internationally, and the widespread involvement of people who inject drugs (IDU) within street-based drug markets, little is known about the impact of different types of street-based income generation activities on the cessation of stimulant use among IDU. Data were derived from an open prospective cohort of IDU in Vancouver, Canada. We used Kaplan-Meier methods and Cox proportional hazards regression to examine the effect of different types of street-based income generation activities (e.g., sex work, drug dealing, and scavenging) on time to cessation of stimulant use. Between December, 2005 and November, 2012, 887 IDU who use stimulant drugs (cocaine, crack cocaine, or crystal methamphetamine) were prospectively followed-up for a median duration of 47 months. In Kaplan-Meier analyses, compared to those who did not engage in street-based income generation activities, participants who reported sex work, drug dealing, scavenging, or more than one of these activities were significantly less likely to report stimulant drug use cessation (all p<0.001). When considered as time-updated variables and adjusted for potential confounders in a multivariable model, each type of street-based income generation activity remained significantly associated with a slower time to stimulant drug cessation (all p<0.005). Our findings highlight the urgent need for strategies to address stimulant dependence, including novel pharmacotherapies. Also important, structural interventions, such as low-threshold employment opportunities, availability of supportive housing, legal reforms regarding drug use, and evidence-based approaches that reduce harm among IDU are urgently required. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Influence of parental and grandparental major depressive disorder on behavior problems in early childhood: a three-generation study.

PubMed

Olino, Thomas M; Pettit, Jeremy W; Klein, Daniel N; Allen, Nicholas B; Seeley, John R; Lewinsohn, Peter M

2008-01-01

This aim of this study was to examine the influence of grandparental (G1) and parental (G2) major depressive disorder (MDD) and other forms of psychopathology on behavior problems in very young offspring (G3). Oregon Adolescent Depression Project (OADP) participants who had children over a 3-year period were invited to participate in a study of infant and child development. We attempted to collect diagnostic history from the original OADP (G2) participants, their coparents, the parents of the original OADP participants (G1), and the parents of the coparents. Child (G3) outcomes at 24 months of age were based on parent reports of behavior problems. Univariate correlations indicated that G1 and G2 familial loadings for MDD were associated with higher levels of G3 internalizing and externalizing behavior problems. Multiple regression analyses revealed a significant interaction between G1 and G2 MDD on G3 internalizing (but not externalizing) behavior problems. A higher familial loading for MDD in either the parental or grandparental generation was associated with elevated grandchild internalizing problems, but higher loadings for MDD in both generations did not convey additional risk. Parental MDD and grandparental MDD are both associated with elevated levels of internalizing problems in young grandchildren, but MDD in both the G1 and G2 generations does not confer additional risk. One important implication is that MDD in the grandparental generation is associated with increased risk to grandchildren even in the absence of parental MDD. Future studies should examine the mechanisms through which grandparental psychopathology influences behavior problems in grandchildren.

Evaluation of Cox's model and logistic regression for matched case-control data with time-dependent covariates: a simulation study.

PubMed

Leffondré, Karen; Abrahamowicz, Michal; Siemiatycki, Jack

2003-12-30

Case-control studies are typically analysed using the conventional logistic model, which does not directly account for changes in the covariate values over time. Yet, many exposures may vary over time. The most natural alternative to handle such exposures would be to use the Cox model with time-dependent covariates. However, its application to case-control data opens the question of how to manipulate the risk sets. Through a simulation study, we investigate how the accuracy of the estimates of Cox's model depends on the operational definition of risk sets and/or on some aspects of the time-varying exposure. We also assess the estimates obtained from conventional logistic regression. The lifetime experience of a hypothetical population is first generated, and a matched case-control study is then simulated from this population. We control the frequency, the age at initiation, and the total duration of exposure, as well as the strengths of their effects. All models considered include a fixed-in-time covariate and one or two time-dependent covariate(s): the indicator of current exposure and/or the exposure duration. Simulation results show that none of the models always performs well. The discrepancies between the odds ratios yielded by logistic regression and the 'true' hazard ratio depend on both the type of the covariate and the strength of its effect. In addition, it seems that logistic regression has difficulty separating the effects of inter-correlated time-dependent covariates. By contrast, each of the two versions of Cox's model systematically induces either a serious under-estimation or a moderate over-estimation bias. The magnitude of the latter bias is proportional to the true effect, suggesting that an improved manipulation of the risk sets may eliminate, or at least reduce, the bias. Copyright 2003 JohnWiley & Sons, Ltd.

Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity

PubMed Central

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K.

2012-01-01

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses. PMID:22457655

Tools to support interpreting multiple regression in the face of multicollinearity.

PubMed

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K

2012-01-01

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data.

PubMed

Holsclaw, Tracy; Hallgren, Kevin A; Steyvers, Mark; Smyth, Padhraic; Atkins, David C

2015-12-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased Type I and Type II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in online supplemental materials. (c) 2016 APA, all rights reserved).

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data

PubMed Central

Holsclaw, Tracy; Hallgren, Kevin A.; Steyvers, Mark; Smyth, Padhraic; Atkins, David C.

2015-01-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non-normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased type-I and type-II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally-technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in supplementary materials. PMID:26098126

Ceramic-on-ceramic bearing fractures in total hip arthroplasty: an analysis of data from the National Joint Registry.

PubMed

Howard, D P; Wall, P D H; Fernandez, M A; Parsons, H; Howard, P W

2017-08-01

Ceramic-on-ceramic (CoC) bearings in total hip arthroplasty (THA) are commonly used, but concerns exist regarding ceramic fracture. This study aims to report the risk of revision for fracture of modern CoC bearings and identify factors that might influence this risk, using data from the National Joint Registry (NJR) for England, Wales, Northern Ireland and the Isle of Man. We analysed data on 223 362 bearings from 111 681 primary CoC THAs and 182 linked revisions for bearing fracture recorded in the NJR. We used implant codes to identify ceramic bearing composition and generated Kaplan-Meier estimates for implant survivorship. Logistic regression analyses were performed for implant size and patient specific variables to determine any associated risks for revision. A total of 222 852 bearings (99.8%) were CeramTec Biolox products. Revisions for fracture were linked to seven of 79 442 (0.009%) Biolox Delta heads, 38 of 31 982 (0.119%) Biolox Forte heads, 101 of 80 170 (0.126%) Biolox Delta liners and 35 of 31 258 (0.112%) Biolox Forte liners. Regression analysis of implant size revealed smaller heads had significantly higher odds of fracture (chi-squared 68.0, p < 0.001). The highest fracture risk was observed in the 28 mm Biolox Forte subgroup (0.382%). There were no fractures in the 40 mm head group for either ceramic type. Liner thickness was not predictive of fracture (p = 0.67). Body mass index (BMI) was independently associated with revision for both head fractures (odds ratio (OR) 1.09 per unit increase, p = 0.031) and liner fractures (OR 1.06 per unit increase, p = 0.006). We report the largest independent study of CoC bearing fractures to date. The risk of revision for CoC bearing fracture is very low but previous studies have underestimated this risk. There is good evidence that the latest generation of ceramic has greatly reduced the odds of head fracture but not of liner fracture. Small head size and high patient BMI are associated with an increased risk of ceramic bearing fracture. Cite this article: Bone Joint J 2017;99-B:1012-19. ©2017 The British Editorial Society of Bone & Joint Surgery.

Impact of new technologies on stress, attrition and well-being in emergency call centers: the NextGeneration 9-1-1 study protocol.

PubMed

Baseman, Janet; Revere, Debra; Painter, Ian; Stangenes, Scott; Lilly, Michelle; Beaton, Randal; Calhoun, Rebecca; Meischke, Hendrika

2018-05-04

Our public health emergency response system relies on the "first of the first responders"-the emergency call center workforce that handles the emergency needs of a public in distress. Call centers across the United States have been preparing for the "Next Generation 9-1-1" initiative, which will allow citizens to place 9-1-1 calls using a variety of digital technologies. The impacts of this initiative on a workforce that is already highly stressed is unknown. There is concern that these technology changes will increase stress, reduce job performance, contribute to maladaptive coping strategies, lower employee retention, or change morale in the workplace. Understanding these impacts to inform approaches for mitigating the health and performance risks associated with new technologies is crucial for ensuring the 911 system fulfills its mission of providing optimal emergency response to the public. Our project is an observational, prospective cohort study framed by the first new technology that will be implemented: text-to-911 calling. Emergency center call takers will be recruited nationwide. Data will be collected by online surveys distributed at each center before text-to-911 implementation; within the first month of implementation; and 6 months after implementation. Primary outcome measures are stress as measured by the Calgary Symptoms of Stress Index, use of sick leave, job performance, and job satisfaction. Primary analyses will use mixed effects regression models and mixed effects logistic regression models to estimate the change in outcome variables associated with text-to-911 implementation. Multiple secondary analyses will examine effects of stress on absenteeism; associations between technology attitudes and stress; effects of implementation on attitudes towards technology; and mitigating effects of job demands, job satisfaction, attitudes towards workplace technology and workplace support on change in stress. Our public health dependence on this workforce for our security and safety makes it imperative that the impact of technological changes such as text-to-911 are researched so appropriate intervention efforts to can be developed. Failing to protect our 9-1-1 call takers from predictable health risks would be similar to knowingly exposing field emergency responders to a toxic situation without following OSHA required training and practice standards assuring their protection.

Sexual possibility situations and sexual behaviors among young adolescents: the moderating role of protective factors.

PubMed

DiLorio, Colleen; Dudley, William N; Soet, Johanna E; McCarty, Frances

2004-12-01

To examine sexual possibility situations (SPS) and protective practices associated with involvement in intimate sexual behaviors and the initiation of sexual intercourse among young adolescents and to determine if protective factors moderate the relationship between SPS and sexual behaviors. Data for these analyses were obtained from the baseline assessment for adolescents conducted as part of an HIV prevention study called "Keepin' it R.E.A.L.!" The study was conducted with a community-based organization (CBO) in an urban area serving a predominantly African-American population. In addition to items assessing SPS, intimate sexual behaviors, and initiation of sexual intercourse, adolescents provided information on the following protective factors: educational goals, self-concept, future time perspective, orientation to health, self-efficacy, outcome expectations, parenting, communication, values, and prosocial activities. Background personal information, including age and gender, was also collected. The analyses were conducted on data from 491 predominantly African-American adolescents, 61% of whom were boys. Variables were combined to form SPS and protective indices that were used in the first set of regression analyses. In a second set of analyses, the indices were unbundled and individual variables were entered into regression analyses. Both SPS and protective indices explained significant portions of variance in intimate sexual behaviors, and the SPS index explained a significant portion of variance in the initiation of sexual intercourse. The regression analysis using the unbundled SPS and protective factors revealed the following statistically significant predictors for intimate sexual behaviors: age, gender, time alone with groups of peers, time alone with a member of the opposite sex, behavior self-concept, popularity self-concept, self-efficacy for abstinence, outcome expectations for abstinence, parental control, personal values, and parental values. A similar regression analysis revealed that age, time alone with a member of the opposite sex, and personal values were significant predictors of initiation of sexual intercourse. These results provide evidence for the important role of protective factors in explaining early involvement in sexual behaviors and show that protective factors extend beyond personal characteristics to include both familial and peer factors.

Power, effects, confidence, and significance: an investigation of statistical practices in nursing research.

PubMed

Gaskin, Cadeyrn J; Happell, Brenda

2014-05-01

To (a) assess the statistical power of nursing research to detect small, medium, and large effect sizes; (b) estimate the experiment-wise Type I error rate in these studies; and (c) assess the extent to which (i) a priori power analyses, (ii) effect sizes (and interpretations thereof), and (iii) confidence intervals were reported. Statistical review. Papers published in the 2011 volumes of the 10 highest ranked nursing journals, based on their 5-year impact factors. Papers were assessed for statistical power, control of experiment-wise Type I error, reporting of a priori power analyses, reporting and interpretation of effect sizes, and reporting of confidence intervals. The analyses were based on 333 papers, from which 10,337 inferential statistics were identified. The median power to detect small, medium, and large effect sizes was .40 (interquartile range [IQR]=.24-.71), .98 (IQR=.85-1.00), and 1.00 (IQR=1.00-1.00), respectively. The median experiment-wise Type I error rate was .54 (IQR=.26-.80). A priori power analyses were reported in 28% of papers. Effect sizes were routinely reported for Spearman's rank correlations (100% of papers in which this test was used), Poisson regressions (100%), odds ratios (100%), Kendall's tau correlations (100%), Pearson's correlations (99%), logistic regressions (98%), structural equation modelling/confirmatory factor analyses/path analyses (97%), and linear regressions (83%), but were reported less often for two-proportion z tests (50%), analyses of variance/analyses of covariance/multivariate analyses of variance (18%), t tests (8%), Wilcoxon's tests (8%), Chi-squared tests (8%), and Fisher's exact tests (7%), and not reported for sign tests, Friedman's tests, McNemar's tests, multi-level models, and Kruskal-Wallis tests. Effect sizes were infrequently interpreted. Confidence intervals were reported in 28% of papers. The use, reporting, and interpretation of inferential statistics in nursing research need substantial improvement. Most importantly, researchers should abandon the misleading practice of interpreting the results from inferential tests based solely on whether they are statistically significant (or not) and, instead, focus on reporting and interpreting effect sizes, confidence intervals, and significance levels. Nursing researchers also need to conduct and report a priori power analyses, and to address the issue of Type I experiment-wise error inflation in their studies. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Re-examining the link between prenatal maternal anxiety and child emotional difficulties, using a sibling design.

PubMed

Bekkhus, Mona; Lee, Yunsung; Nordhagen, Rannveig; Magnus, Per; Samuelsen, Sven O; Borge, Anne I H

2018-02-01

Prenatal exposure to maternal anxiety has been associated with child emotional difficulties in a number of epidemiological studies. One key concern, however, is that this link is vulnerable to confounding by pleiotropic genes or environmental family factors. Data on 82 383 mothers and children from the population-based Mother and Child Cohort Study and data on 21 980 siblings were used in this study. Mothers filled out questionnaires for each unique pregnancy, for infant difficulties at 6 months and for emotional difficulties at 36 months. The link between prenatal maternal anxiety and child difficulties were examined using logistic regression analyses and multiple linear regression analyses for the full study sample and the sibling sample. In the conventional full-cohort analyses, prenatal exposure to maternal anxiety was associated with child difficulties at both 6 months [odds ratio (OR) = 2.1 (1.94-2.27)] and 36 months [OR = 2.72 (2.47-2.99)]. The findings were essentially the same whether we examined difficulties at 6 months or at 36 months. However, these associations were no longer present once we controlled for potential social and genetic confounders in the sibling comparison analyses, either at 6 months [OR = 1.32 (0.91-1.90)] or at 36 months [OR = 1.28 (0.63-2.60)]. Findings from multiple regression analyses with continuous measures were essentially the same. Our finding lends little support for there being an independent prenatal effect on child emotional difficulties; rather, our findings suggest that the link between prenatal maternal anxiety and child difficulties could be confounded by pleiotropic genes or environmental family factors. © The Author 2017; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association

Survival Data and Regression Models

NASA Astrophysics Data System (ADS)

Grégoire, G.

2014-12-01

We start this chapter by introducing some basic elements for the analysis of censored survival data. Then we focus on right censored data and develop two types of regression models. The first one concerns the so-called accelerated failure time models (AFT), which are parametric models where a function of a parameter depends linearly on the covariables. The second one is a semiparametric model, where the covariables enter in a multiplicative form in the expression of the hazard rate function. The main statistical tool for analysing these regression models is the maximum likelihood methodology and, in spite we recall some essential results about the ML theory, we refer to the chapter "Logistic Regression" for a more detailed presentation.

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies

PubMed Central

Vatcheva, Kristina P.; Lee, MinJae; McCormick, Joseph B.; Rahbar, Mohammad H.

2016-01-01

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis. PMID:27274911

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies.

PubMed

Vatcheva, Kristina P; Lee, MinJae; McCormick, Joseph B; Rahbar, Mohammad H

2016-04-01

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.

Does Bootstrap Procedure Provide Biased Estimates? An Empirical Examination for a Case of Multiple Regression.

ERIC Educational Resources Information Center

Fan, Xitao

This paper empirically and systematically assessed the performance of bootstrap resampling procedure as it was applied to a regression model. Parameter estimates from Monte Carlo experiments (repeated sampling from population) and bootstrap experiments (repeated resampling from one original bootstrap sample) were generated and compared. Sample…

On the use of log-transformation vs. nonlinear regression for analyzing biological power laws.

PubMed

Xiao, Xiao; White, Ethan P; Hooten, Mevin B; Durham, Susan L

2011-10-01

Power-law relationships are among the most well-studied functional relationships in biology. Recently the common practice of fitting power laws using linear regression (LR) on log-transformed data has been criticized, calling into question the conclusions of hundreds of studies. It has been suggested that nonlinear regression (NLR) is preferable, but no rigorous comparison of these two methods has been conducted. Using Monte Carlo simulations, we demonstrate that the error distribution determines which method performs better, with NLR better characterizing data with additive, homoscedastic, normal error and LR better characterizing data with multiplicative, heteroscedastic, lognormal error. Analysis of 471 biological power laws shows that both forms of error occur in nature. While previous analyses based on log-transformation appear to be generally valid, future analyses should choose methods based on a combination of biological plausibility and analysis of the error distribution. We provide detailed guidelines and associated computer code for doing so, including a model averaging approach for cases where the error structure is uncertain.

Association between social contact frequency and negative symptoms, psychosocial functioning and quality of life in patients with schizophrenia.

PubMed

Siegrist, Karin; Millier, Aurelie; Amri, Ikbal; Aballéa, Samuel; Toumi, Mondher

2015-12-30

The lack of social contacts may be an important element in the presumed vicious circle aggravating, or at least stabilising negative symptoms in patients with schizophrenia. A European 2-year cohort study collected negative symptom scores, psychosocial functioning scores, objective social contact frequency scores and quality of life scores every 6 months. Bivariate analyses, correlation analyses, multivariate regressions and random effects regressions were conducted to describe relations between social contact and outcomes of interest and to gain a better understanding of this relation over time. Using data from 1208 patients with schizophrenia, a link between social contact frequency and negative symptom scores, functioning and quality of life at baseline was established. Regression models confirmed the significant association between social contact and negative symptoms as well as psychosocial functioning. This study aimed at demonstrating the importance of social contact for deficient behavioural aspects of schizophrenia. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

On the equivalence of case-crossover and time series methods in environmental epidemiology.

PubMed

Lu, Yun; Zeger, Scott L

2007-04-01

The case-crossover design was introduced in epidemiology 15 years ago as a method for studying the effects of a risk factor on a health event using only cases. The idea is to compare a case's exposure immediately prior to or during the case-defining event with that same person's exposure at otherwise similar "reference" times. An alternative approach to the analysis of daily exposure and case-only data is time series analysis. Here, log-linear regression models express the expected total number of events on each day as a function of the exposure level and potential confounding variables. In time series analyses of air pollution, smooth functions of time and weather are the main confounders. Time series and case-crossover methods are often viewed as competing methods. In this paper, we show that case-crossover using conditional logistic regression is a special case of time series analysis when there is a common exposure such as in air pollution studies. This equivalence provides computational convenience for case-crossover analyses and a better understanding of time series models. Time series log-linear regression accounts for overdispersion of the Poisson variance, while case-crossover analyses typically do not. This equivalence also permits model checking for case-crossover data using standard log-linear model diagnostics.

Has Adult Sleep Duration Declined Over the Last 50+ Years?

PubMed Central

Youngstedt, Shawn D.; Goff, Eric E.; Reynolds, Alex M.; Kripke, Daniel F.; Irwin, Michael R.; Bootzin, Richard R.; Khan, Nidha; Jean-Louis, Girardin

2015-01-01

Summary The common assumption that population sleep duration has declined in the past few decades has not been supported by recent reviews, which have been limited to self-reported data. The aim of this review was to assess whether there has been a reduction in objectively recorded sleep duration over the last 50+ years. The literature was searched for studies published from 1960–2013, which assessed objective sleep duration (TST) in healthy normal-sleeping adults. The search found 168 studies that met inclusion criteria, with 257 data points representing 6,052 individuals ages 18–88 years. Data were assessed by comparing the regression lines of age vs. TST in studies conducted between 1960–1989 vs. 1990–2013. Weighted regression analyses assessed the association of year of study with age-adjusted TST across all data points. Regression analyses also assessed the association of year of study with TST separately for 10-year age categories (e.g., ages 18–27 years), and separately for polysomnographic and actigraphic data, and for studies involving a fixed sleep schedule and participants’ customary sleep schedules. Analyses revealed no significant association of sleep duration with study year. The results are consistent with recent reviews of subjective data, which have challenged the notion of a modern epidemic of insufficient sleep. PMID:26478985

The Use of Linear Instrumental Variables Methods in Health Services Research and Health Economics: A Cautionary Note

PubMed Central

Terza, Joseph V; Bradford, W David; Dismuke, Clara E

2008-01-01

Objective To investigate potential bias in the use of the conventional linear instrumental variables (IV) method for the estimation of causal effects in inherently nonlinear regression settings. Data Sources Smoking Supplement to the 1979 National Health Interview Survey, National Longitudinal Alcohol Epidemiologic Survey, and simulated data. Study Design Potential bias from the use of the linear IV method in nonlinear models is assessed via simulation studies and real world data analyses in two commonly encountered regression setting: (1) models with a nonnegative outcome (e.g., a count) and a continuous endogenous regressor; and (2) models with a binary outcome and a binary endogenous regressor. Principle Findings The simulation analyses show that substantial bias in the estimation of causal effects can result from applying the conventional IV method in inherently nonlinear regression settings. Moreover, the bias is not attenuated as the sample size increases. This point is further illustrated in the survey data analyses in which IV-based estimates of the relevant causal effects diverge substantially from those obtained with appropriate nonlinear estimation methods. Conclusions We offer this research as a cautionary note to those who would opt for the use of linear specifications in inherently nonlinear settings involving endogeneity. PMID:18546544

Factors Affecting Female Teachers' Attitudes toward Help-Seeking or Help-Avoidance in Coping with Behavioral Problems

ERIC Educational Resources Information Center

Inbar-Furst, Hagit; Gumpel, Thomas P.

2015-01-01

Questionnaires were given to 392 elementary school teachers to examine help-seeking or help-avoidance in dealing with classroom behavioral problems. Scale validity was examined through a series of exploratory and confirmatory factor analyses. Using a series of multivariate regression analyses and structural equation modeling, we identified…

Recycling and Ambivalence: Quantitative and Qualitative Analyses of Household Recycling among Young Adults

ERIC Educational Resources Information Center

Ojala, Maria

2008-01-01

Theories about ambivalence, as well as quantitative and qualitative empirical approaches, are applied to obtain an understanding of recycling among young adults. A questionnaire was mailed to 422 Swedish young people. Regression analyses showed that a mix of negative emotions (worry) and positive emotions (hope and joy) about the environmental…

Alcohol and tobacco use and cognitive-motivational variables in school settings: effects on academic performance in Spanish adolescents.

PubMed

Inglés, Cándido J; Torregrosa, María S; Rodríguez-Marín, Jesús; García del Castillo, José A; Gázquez, José J; García-Fernández, José M; Delgado, Beatriz

2013-01-01

The aim of the present study was to analyze: (a) the relationship between alcohol and tobacco use and academic performance, and (b) the predictive role of psycho-educational factors and alcohol and tobacco abuse on academic performance in a sample of 352 Spanish adolescents from grades 8 to 10 of Compulsory Secondary Education. The Self-Description Questionnaire-II, the Sydney Attribution Scale, and the Achievement Goal Tendencies Questionnaire were administered in order to analyze cognitive-motivational variables. Alcohol and tobacco abuse, sex, and grade retention were also measured using self-reported questions. Academic performance was measured by school records. Frequency analyses and logistic regression analyses were used. Frequency analyses revealed that students who abuse of tobacco and alcohol show a higher rate of poor academic performance. Logistic regression analyses showed that health behaviours, and educational and cognitive-motivational variables exert a different effect on academic performance depending on the academic area analyzed. These results point out that not only academic, but also health variables should be address to improve academic performance in adolescence.

Methods for estimating annual exceedance-probability discharges and largest recorded floods for unregulated streams in rural Missouri

USGS Publications Warehouse

Southard, Rodney E.; Veilleux, Andrea G.

2014-01-01

Regression analysis techniques were used to develop a set of equations for rural ungaged stream sites for estimating discharges with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities, which are equivalent to annual flood-frequency recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. Basin and climatic characteristics were computed using geographic information software and digital geospatial data. A total of 35 characteristics were computed for use in preliminary statewide and regional regression analyses. Annual exceedance-probability discharge estimates were computed for 278 streamgages by using the expected moments algorithm to fit a log-Pearson Type III distribution to the logarithms of annual peak discharges for each streamgage using annual peak-discharge data from water year 1844 to 2012. Low-outlier and historic information were incorporated into the annual exceedance-probability analyses, and a generalized multiple Grubbs-Beck test was used to detect potentially influential low floods. Annual peak flows less than a minimum recordable discharge at a streamgage were incorporated into the at-site station analyses. An updated regional skew coefficient was determined for the State of Missouri using Bayesian weighted least-squares/generalized least squares regression analyses. At-site skew estimates for 108 long-term streamgages with 30 or more years of record and the 35 basin characteristics defined for this study were used to estimate the regional variability in skew. However, a constant generalized-skew value of -0.30 and a mean square error of 0.14 were determined in this study. Previous flood studies indicated that the distinct physical features of the three physiographic provinces have a pronounced effect on the magnitude of flood peaks. Trends in the magnitudes of the residuals from preliminary statewide regression analyses from previous studies confirmed that regional analyses in this study were similar and related to three primary physiographic provinces. The final regional regression analyses resulted in three sets of equations. For Regions 1 and 2, the basin characteristics of drainage area and basin shape factor were statistically significant. For Region 3, because of the small amount of data from streamgages, only drainage area was statistically significant. Average standard errors of prediction ranged from 28.7 to 38.4 percent for flood region 1, 24.1 to 43.5 percent for flood region 2, and 25.8 to 30.5 percent for region 3. The regional regression equations are only applicable to stream sites in Missouri with flows not significantly affected by regulation, channelization, backwater, diversion, or urbanization. Basins with about 5 percent or less impervious area were considered to be rural. Applicability of the equations are limited to the basin characteristic values that range from 0.11 to 8,212.38 square miles (mi2) and basin shape from 2.25 to 26.59 for Region 1, 0.17 to 4,008.92 mi2 and basin shape 2.04 to 26.89 for Region 2, and 2.12 to 2,177.58 mi2 for Region 3. Annual peak data from streamgages were used to qualitatively assess the largest floods recorded at streamgages in Missouri since the 1915 water year. Based on existing streamgage data, the 1983 flood event was the largest flood event on record since 1915. The next five largest flood events, in descending order, took place in 1993, 1973, 2008, 1994 and 1915. Since 1915, five of six of the largest floods on record occurred from 1973 to 2012.

Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting

NASA Astrophysics Data System (ADS)

Wu, Cheng; Zhen Yu, Jian

2018-03-01

Linear regression techniques are widely used in atmospheric science, but they are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The five techniques are ordinary least squares (OLS), Deming regression (DR), orthogonal distance regression (ODR), weighted ODR (WODR), and York regression (YR). We first introduce a new data generation scheme that employs the Mersenne twister (MT) pseudorandom number generator. The numerical simulations are also improved by (a) refining the parameterization of nonlinear measurement uncertainties, (b) inclusion of a linear measurement uncertainty, and (c) inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R2 XY dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found that an improper λ in DR can lead to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the XY data during the measurement stage. If a priori error in one of the variables is unknown, or the measurement error described cannot be trusted, DR, WODR and YR can provide the least biases in slope and intercept among all tested regression techniques. For these reasons, DR, WODR and YR are recommended for atmospheric studies when both X and Y data have measurement errors. An Igor Pro-based program (Scatter Plot) was developed to facilitate the implementation of error-in-variables regressions.

Point-of-care testing of electrolytes and calcium using blood gas analysers: it is time we trusted the results.

PubMed

Mirzazadeh, Mehdi; Morovat, Alireza; James, Tim; Smith, Ian; Kirby, Justin; Shine, Brian

2016-03-01

Point-of-care testing allows rapid analysis of samples to facilitate prompt clinical decisions. Electrolyte and calcium abnormalities are common in acutely ill patients and can be associated with life-threatening consequences. There is uncertainty whether clinical decisions can be based on the results obtained from blood gas analysers or if laboratory results should be awaited. To assess the agreement between sodium, potassium and calcium results from blood gas and laboratory mainstream analysers in a tertiary centre, with a network consisting of one referral and two peripheral hospitals, consisting of three networked clinical biochemistry laboratories. Using the laboratory information management system database and over 11 000 paired samples in three hospital sites, the results of sodium, potassium and ionised calcium on blood gas analysers were studied over a 5-year period and compared with the corresponding laboratory results from the same patients booked in the laboratory within 1 h. The Pearson's linear correlation coefficient between laboratory and blood gas results for sodium, potassium and calcium were 0.92, 0.84 and 0.78, respectively. Deming regression analysis showed a slope of 1.04 and an intercept of -5.7 for sodium, slope of 0.93 and an intercept of 0.22 for potassium and a slope of 1.23 with an intercept of -0.55 for calcium. With some strict statistical assumptions, percentages of results lying outside the least significant difference were 9%, 26.7% and 20.8% for sodium, potassium and calcium, respectively. Most clinicians wait for the laboratory confirmation of results generated by blood gas analysers. In a large retrospective study we have shown that there is sufficient agreement between the results obtained from the blood gas and laboratory analysers to enable prompt clinical decisions to be made. 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/

Heterogeneity in drug abuse among juvenile offenders: is mixture regression more informative than standard regression?

PubMed

Montgomery, Katherine L; Vaughn, Michael G; Thompson, Sanna J; Howard, Matthew O

2013-11-01

Research on juvenile offenders has largely treated this population as a homogeneous group. However, recent findings suggest that this at-risk population may be considerably more heterogeneous than previously believed. This study compared mixture regression analyses with standard regression techniques in an effort to explain how known factors such as distress, trauma, and personality are associated with drug abuse among juvenile offenders. Researchers recruited 728 juvenile offenders from Missouri juvenile correctional facilities for participation in this study. Researchers investigated past-year substance use in relation to the following variables: demographic characteristics (gender, ethnicity, age, familial use of public assistance), antisocial behavior, and mental illness symptoms (psychopathic traits, psychiatric distress, and prior trauma). Results indicated that standard and mixed regression approaches identified significant variables related to past-year substance use among this population; however, the mixture regression methods provided greater specificity in results. Mixture regression analytic methods may help policy makers and practitioners better understand and intervene with the substance-related subgroups of juvenile offenders.

Sample size determination for logistic regression on a logit-normal distribution.

PubMed

Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance

2017-06-01

Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for [Formula: see text] for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.

Estimation of subsurface thermal structure using sea surface height and sea surface temperature

NASA Technical Reports Server (NTRS)

Kang, Yong Q. (Inventor); Jo, Young-Heon (Inventor); Yan, Xiao-Hai (Inventor)

2012-01-01

A method of determining a subsurface temperature in a body of water is disclosed. The method includes obtaining surface temperature anomaly data and surface height anomaly data of the body of water for a region of interest, and also obtaining subsurface temperature anomaly data for the region of interest at a plurality of depths. The method further includes regressing the obtained surface temperature anomaly data and surface height anomaly data for the region of interest with the obtained subsurface temperature anomaly data for the plurality of depths to generate regression coefficients, estimating a subsurface temperature at one or more other depths for the region of interest based on the generated regression coefficients and outputting the estimated subsurface temperature at the one or more other depths. Using the estimated subsurface temperature, signal propagation times and trajectories of marine life in the body of water are determined.

Breeding value accuracy estimates for growth traits using random regression and multi-trait models in Nelore cattle.

PubMed

Boligon, A A; Baldi, F; Mercadante, M E Z; Lobo, R B; Pereira, R J; Albuquerque, L G

2011-06-28

We quantified the potential increase in accuracy of expected breeding value for weights of Nelore cattle, from birth to mature age, using multi-trait and random regression models on Legendre polynomials and B-spline functions. A total of 87,712 weight records from 8144 females were used, recorded every three months from birth to mature age from the Nelore Brazil Program. For random regression analyses, all female weight records from birth to eight years of age (data set I) were considered. From this general data set, a subset was created (data set II), which included only nine weight records: at birth, weaning, 365 and 550 days of age, and 2, 3, 4, 5, and 6 years of age. Data set II was analyzed using random regression and multi-trait models. The model of analysis included the contemporary group as fixed effects and age of dam as a linear and quadratic covariable. In the random regression analyses, average growth trends were modeled using a cubic regression on orthogonal polynomials of age. Residual variances were modeled by a step function with five classes. Legendre polynomials of fourth and sixth order were utilized to model the direct genetic and animal permanent environmental effects, respectively, while third-order Legendre polynomials were considered for maternal genetic and maternal permanent environmental effects. Quadratic polynomials were applied to model all random effects in random regression models on B-spline functions. Direct genetic and animal permanent environmental effects were modeled using three segments or five coefficients, and genetic maternal and maternal permanent environmental effects were modeled with one segment or three coefficients in the random regression models on B-spline functions. For both data sets (I and II), animals ranked differently according to expected breeding value obtained by random regression or multi-trait models. With random regression models, the highest gains in accuracy were obtained at ages with a low number of weight records. The results indicate that random regression models provide more accurate expected breeding values than the traditionally finite multi-trait models. Thus, higher genetic responses are expected for beef cattle growth traits by replacing a multi-trait model with random regression models for genetic evaluation. B-spline functions could be applied as an alternative to Legendre polynomials to model covariance functions for weights from birth to mature age.

A modeling approach to compare ΣPCB concentrations between congener-specific analyses

USGS Publications Warehouse

Gibson, Polly P.; Mills, Marc A.; Kraus, Johanna M.; Walters, David M.

2017-01-01

Changes in analytical methods over time pose problems for assessing long-term trends in environmental contamination by polychlorinated biphenyls (PCBs). Congener-specific analyses vary widely in the number and identity of the 209 distinct PCB chemical configurations (congeners) that are quantified, leading to inconsistencies among summed PCB concentrations (ΣPCB) reported by different studies. Here we present a modeling approach using linear regression to compare ΣPCB concentrations derived from different congener-specific analyses measuring different co-eluting groups. The approach can be used to develop a specific conversion model between any two sets of congener-specific analytical data from similar samples (similar matrix and geographic origin). We demonstrate the method by developing a conversion model for an example data set that includes data from two different analytical methods, a low resolution method quantifying 119 congeners and a high resolution method quantifying all 209 congeners. We used the model to show that the 119-congener set captured most (93%) of the total PCB concentration (i.e., Σ209PCB) in sediment and biological samples. ΣPCB concentrations estimated using the model closely matched measured values (mean relative percent difference = 9.6). General applications of the modeling approach include (a) generating comparable ΣPCB concentrations for samples that were analyzed for different congener sets; and (b) estimating the proportional contribution of different congener sets to ΣPCB. This approach may be especially valuable for enabling comparison of long-term remediation monitoring results even as analytical methods change over time. 

Template based rotation: A method for functional connectivity analysis with a priori templates☆

PubMed Central

Schultz, Aaron P.; Chhatwal, Jasmeer P.; Huijbers, Willem; Hedden, Trey; van Dijk, Koene R.A.; McLaren, Donald G.; Ward, Andrew M.; Wigman, Sarah; Sperling, Reisa A.

2014-01-01

Functional connectivity magnetic resonance imaging (fcMRI) is a powerful tool for understanding the network level organization of the brain in research settings and is increasingly being used to study large-scale neuronal network degeneration in clinical trial settings. Presently, a variety of techniques, including seed-based correlation analysis and group independent components analysis (with either dual regression or back projection) are commonly employed to compute functional connectivity metrics. In the present report, we introduce template based rotation,1 a novel analytic approach optimized for use with a priori network parcellations, which may be particularly useful in clinical trial settings. Template based rotation was designed to leverage the stable spatial patterns of intrinsic connectivity derived from out-of-sample datasets by mapping data from novel sessions onto the previously defined a priori templates. We first demonstrate the feasibility of using previously defined a priori templates in connectivity analyses, and then compare the performance of template based rotation to seed based and dual regression methods by applying these analytic approaches to an fMRI dataset of normal young and elderly subjects. We observed that template based rotation and dual regression are approximately equivalent in detecting fcMRI differences between young and old subjects, demonstrating similar effect sizes for group differences and similar reliability metrics across 12 cortical networks. Both template based rotation and dual-regression demonstrated larger effect sizes and comparable reliabilities as compared to seed based correlation analysis, though all three methods yielded similar patterns of network differences. When performing inter-network and sub-network connectivity analyses, we observed that template based rotation offered greater flexibility, larger group differences, and more stable connectivity estimates as compared to dual regression and seed based analyses. This flexibility owes to the reduced spatial and temporal orthogonality constraints of template based rotation as compared to dual regression. These results suggest that template based rotation can provide a useful alternative to existing fcMRI analytic methods, particularly in clinical trial settings where predefined outcome measures and conserved network descriptions across groups are at a premium. PMID:25150630

Neural Network and Regression Soft Model Extended for PAX-300 Aircraft Engine

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Hopkins, Dale A.

2002-01-01

In fiscal year 2001, the neural network and regression capabilities of NASA Glenn Research Center's COMETBOARDS design optimization testbed were extended to generate approximate models for the PAX-300 aircraft engine. The analytical model of the engine is defined through nine variables: the fan efficiency factor, the low pressure of the compressor, the high pressure of the compressor, the high pressure of the turbine, the low pressure of the turbine, the operating pressure, and three critical temperatures (T(sub 4), T(sub vane), and T(sub metal)). Numerical Propulsion System Simulation (NPSS) calculations of the specific fuel consumption (TSFC), as a function of the variables can become time consuming, and numerical instabilities can occur during these design calculations. "Soft" models can alleviate both deficiencies. These approximate models are generated from a set of high-fidelity input-output pairs obtained from the NPSS code and a design of the experiment strategy. A neural network and a regression model with 45 weight factors were trained for the input/output pairs. Then, the trained models were validated through a comparison with the original NPSS code. Comparisons of TSFC versus the operating pressure and of TSFC versus the three temperatures (T(sub 4), T(sub vane), and T(sub metal)) are depicted in the figures. The overall performance was satisfactory for both the regression and the neural network model. The regression model required fewer calculations than the neural network model, and it produced marginally superior results. Training the approximate methods is time consuming. Once trained, the approximate methods generated the solution with only a trivial computational effort, reducing the solution time from hours to less than a minute.

Magnitude and frequency of floods in Arkansas

USGS Publications Warehouse

Hodge, Scott A.; Tasker, Gary D.

1995-01-01

Methods are presented for estimating the magnitude and frequency of peak discharges of streams in Arkansas. Regression analyses were developed in which a stream's physical and flood characteristics were related. Four sets of regional regression equations were derived to predict peak discharges with selected recurrence intervals of 2, 5, 10, 25, 50, 100, and 500 years on streams draining less than 7,770 square kilometers. The regression analyses indicate that size of drainage area, main channel slope, mean basin elevation, and the basin shape factor were the most significant basin characteristics that affect magnitude and frequency of floods. The region of influence method is included in this report. This method is still being improved and is to be considered only as a second alternative to the standard method of producing regional regression equations. This method estimates unique regression equations for each recurrence interval for each ungaged site. The regression analyses indicate that size of drainage area, main channel slope, mean annual precipitation, mean basin elevation, and the basin shape factor were the most significant basin and climatic characteristics that affect magnitude and frequency of floods for this method. Certain recommendations on the use of this method are provided. A method is described for estimating the magnitude and frequency of peak discharges of streams for urban areas in Arkansas. The method is from a nationwide U.S. Geeological Survey flood frequency report which uses urban basin characteristics combined with rural discharges to estimate urban discharges. Annual peak discharges from 204 gaging stations, with drainage areas less than 7,770 square kilometers and at least 10 years of unregulated record, were used in the analysis. These data provide the basis for this analysis and are published in the Appendix of this report as supplemental data. Large rivers such as the Red, Arkansas, White, Black, St. Francis, Mississippi, and Ouachita Rivers have floodflow characteristics that differ from those of smaller tributary streams and were treated individually. Regional regression equations are not applicable to these large rivers. The magnitude and frequency of floods along these rivers are based on specific station data. This section is provided in the Appendix and has not been updated since the last Arkansas flood frequency report (1987b), but is included at the request of the cooperator.

The relationship between attendance at birth and maternal mortality rates: an exploration of United Nations' data sets including the ratios of physicians and nurses to population, GNP per capita and female literacy.

PubMed

Robinson, J J; Wharrad, H

2001-05-01

The relationship between attendance at birth and maternal mortality rates: an exploration of United Nations' data sets including the ratios of physicians and nurses to population, GNP per capita and female literacy. This is the third and final paper drawing on data taken from United Nations (UN) data sets. The first paper examined the global distribution of health professionals (as measured by ratios of physicians and nurses to population), and its relationship to gross national product per capita (GNP) (Wharrad & Robinson 1999). The second paper explored the relationships between the global distribution of physicians and nurses, GNP, female literacy and the health outcome indicators of infant and under five mortality rates (IMR and u5MR) (Robinson & Wharrad 2000). In the present paper, the global distribution of health professionals is explored in relation to maternal mortality rates (MMRs). The proportion of births attended by medical and nonmedical staff defined as "attendance at birth by trained personnel" (physicians, nurses, midwives or primary health care workers trained in midwifery skills), is included as an additional independent variable in the regression analyses, together with the ratio of physicians and nurses to population, female literacy and GNP. To extend our earlier analyses by considering the relationships between the global distribution of health professionals (ratios of physicians and nurses to population, and the proportion of births attended by trained health personnel), GNP, female literacy and MMR.

Analysing Twitter and web queries for flu trend prediction.

PubMed

Santos, José Carlos; Matos, Sérgio

2014-05-07

Social media platforms encourage people to share diverse aspects of their daily life. Among these, shared health related information might be used to infer health status and incidence rates for specific conditions or symptoms. In this work, we present an infodemiology study that evaluates the use of Twitter messages and search engine query logs to estimate and predict the incidence rate of influenza like illness in Portugal. Based on a manually classified dataset of 2704 tweets from Portugal, we selected a set of 650 textual features to train a Naïve Bayes classifier to identify tweets mentioning flu or flu-like illness or symptoms. We obtained a precision of 0.78 and an F-measure of 0.83, based on cross validation over the complete annotated set. Furthermore, we trained a multiple linear regression model to estimate the health-monitoring data from the Influenzanet project, using as predictors the relative frequencies obtained from the tweet classification results and from query logs, and achieved a correlation ratio of 0.89 (p<0.001). These classification and regression models were also applied to estimate the flu incidence in the following flu season, achieving a correlation of 0.72. Previous studies addressing the estimation of disease incidence based on user-generated content have mostly focused on the english language. Our results further validate those studies and show that by changing the initial steps of data preprocessing and feature extraction and selection, the proposed approaches can be adapted to other languages. Additionally, we investigated whether the predictive model created can be applied to data from the subsequent flu season. In this case, although the prediction result was good, an initial phase to adapt the regression model could be necessary to achieve more robust results.

Comparison of the Chiron Quantiplex branched DNA (bDNA) assay and the Abbott Genostics solution hybridization assay for quantification of hepatitis B viral DNA.

PubMed

Kapke, G E; Watson, G; Sheffler, S; Hunt, D; Frederick, C

1997-01-01

Several assays for quantification of DNA have been developed and are currently used in research and clinical laboratories. However, comparison of assay results has been difficult owing to the use of different standards and units of measurements as well as differences between assays in dynamic range and quantification limits. Although a few studies have compared results generated by different assays, there has been no consensus on conversion factors and thorough analysis has been precluded by small sample size and limited dynamic range studied. In this study, we have compared the Chiron branched DNA (bDNA) and Abbott liquid hybridization assays for quantification of hepatitis B virus (HBV) DNA in clinical specimens and have derived conversion factors to facilitate comparison of assay results. Additivity and variance stabilizing (AVAS) regression, a form of non-linear regression analysis, was performed on assay results for specimens from HBV clinical trials. Our results show that there is a strong linear relationship (R2 = 0.96) between log Chiron and log Abbott assay results. Conversion factors derived from regression analyses were found to be non-constant and ranged from 6-40. Analysis of paired assay results below and above each assay's limit of quantification (LOQ) indicated that a significantly (P < 0.01) larger proportion of observations were below the Abbott assay LOQ but above the Chiron assay LOQ, indicating that the Chiron assay is significantly more sensitive than the Abbott assay. Testing of replicate specimens showed that the Chiron assay consistently yielded lower per cent coefficients of variance (% CVs) than the Abbott assay, indicating that the Chiron assay provides superior precision.

Representation of limb kinematics in Purkinje cell simple spike discharge is conserved across multiple tasks

PubMed Central

Hewitt, Angela L.; Popa, Laurentiu S.; Pasalar, Siavash; Hendrix, Claudia M.

2011-01-01

Encoding of movement kinematics in Purkinje cell simple spike discharge has important implications for hypotheses of cerebellar cortical function. Several outstanding questions remain regarding representation of these kinematic signals. It is uncertain whether kinematic encoding occurs in unpredictable, feedback-dependent tasks or kinematic signals are conserved across tasks. Additionally, there is a need to understand the signals encoded in the instantaneous discharge of single cells without averaging across trials or time. To address these questions, this study recorded Purkinje cell firing in monkeys trained to perform a manual random tracking task in addition to circular tracking and center-out reach. Random tracking provides for extensive coverage of kinematic workspaces. Direction and speed errors are significantly greater during random than circular tracking. Cross-correlation analyses comparing hand and target velocity profiles show that hand velocity lags target velocity during random tracking. Correlations between simple spike firing from 120 Purkinje cells and hand position, velocity, and speed were evaluated with linear regression models including a time constant, τ, as a measure of the firing lead/lag relative to the kinematic parameters. Across the population, velocity accounts for the majority of simple spike firing variability (63 ± 30% of Radj2), followed by position (28 ± 24% of Radj2) and speed (11 ± 19% of Radj2). Simple spike firing often leads hand kinematics. Comparison of regression models based on averaged vs. nonaveraged firing and kinematics reveals lower Radj2 values for nonaveraged data; however, regression coefficients and τ values are highly similar. Finally, for most cells, model coefficients generated from random tracking accurately estimate simple spike firing in either circular tracking or center-out reach. These findings imply that the cerebellum controls movement kinematics, consistent with a forward internal model that predicts upcoming limb kinematics. PMID:21795616

Radiographic cup anteversion measurement corrected from pelvic tilt.

PubMed

Wang, Liao; Thoreson, Andrew R; Trousdale, Robert T; Morrey, Bernard F; Dai, Kerong; An, Kai-Nan

2017-11-01

The purpose of this study was to develop a novel technique to improve the accuracy of radiographic cup anteversion measurement by correcting the influence of pelvic tilt. Ninety virtual total hip arthroplasties were simulated from computed tomography data of 6 patients with 15 predetermined cup orientations. For each simulated implantation, anteroposterior (AP) virtual pelvic radiographs were generated for 11 predetermined pelvic tilts. A linear regression model was created to capture the relationship between radiographic cup anteversion angle error measured on AP pelvic radiographs and pelvic tilt. Overall, nine hundred and ninety virtual AP pelvic radiographs were measured, and 90 linear regression models were created. Pearson's correlation analyses confirmed a strong correlation between the errors of conventional radiographic cup anteversion angle measured on AP pelvic radiographs and the magnitude of pelvic tilt (P < 0.001). The mean of 90 slopes and y-intercepts of the regression lines were -0.8 and -2.5°, which were applied as the general correction parameters for the proposed tool to correct conventional cup anteversion angle from the influence of pelvic tilt. The current method proposes to measure the pelvic tilt on a lateral radiograph, and to use it as a correction for the radiographic cup anteversion measurement on an AP pelvic radiograph. Thus, both AP and lateral pelvic radiographs are required for the measurement of pelvic posture-integrated cup anteversion. Compared with conventional radiographic cup anteversion, the errors of pelvic posture-integrated radiographic cup anteversion were reduced from 10.03 (SD = 5.13) degrees to 2.53 (SD = 1.33) degrees. Pelvic posture-integrated cup anteversion measurement improves the accuracy of radiographic cup anteversion measurement, which shows the potential of further clarifying the etiology of postoperative instability based on planar radiographs. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Spiritual needs among patients with chronic pain diseases and cancer living in a secular society.

PubMed

Büssing, Arndt; Janko, Annina; Baumann, Klaus; Hvidt, Niels Christian; Kopf, Andreas

2013-09-01

Research has shown that several patients report unmet psychosocial and spiritual needs. While most studies focus on patients with advanced stages of disease, we intended to identify unmet spiritual needs in patients with chronic pain diseases and cancer living in a secular society. In an anonymous cross-sectional study, standardized questionnaires were provided to German patients with chronic pain diseases (and cancer), i.e., Spiritual Needs Questionnaire (SpNQ), Spirituality/Religiosity and Coping (SpREUK-15), Spiritual Well-being (FACIT-Sp), Brief Multidimensional Life Satisfaction Scale, Interpretation of Illness Questionnaire, and Escape from Illness (Escape). We enrolled 392 patients (67% women, mean age 56.3 ± 13.6 years; 61% Christian denomination) with chronic pain diseases (86%) and cancer (14%). Religious Needs (mean score 0.5 ± 0.8 on the scale) and Existential Needs (0.8 ± 0.8 on the scale) were low, while needs for Inner Peace (1.5 ± 0.9 on the scale) and Giving/Generativity were scored high (1.3 ± 1.0 on the scale). Regression analyses indicated that Religious Needs can be predicted best by (religious) "Trust," the illness interpretation "call for help," and living with a partner; Existential Needs can be predicted by "call for help" and to a weaker extent by (religious) "Trust." Existential Needs are influenced negatively by the illness interpretation "challenge." Needs for Inner Peace were predicted only in trend by the illness interpretation "threat," and there were no significant predictors for the Giving/Generativity needs in the respective regression model. Patients with chronic pain diseases predominantly report needs related to inner peace and generative relatedness on a personal level, whereas needs related to transcendent relatedness were of minor relevance. Nevertheless, even religious "skeptics" can express specific religious needs, and these should be recognized. Addressing patients' specific needs and also supporting them in their struggle with chronic illness remain a challenging task for the modern health care system. Wiley Periodicals, Inc.

Estimating methane emissions from landfills based on rainfall, ambient temperature, and waste composition: The CLEEN model.

PubMed

Karanjekar, Richa V; Bhatt, Arpita; Altouqui, Said; Jangikhatoonabad, Neda; Durai, Vennila; Sattler, Melanie L; Hossain, M D Sahadat; Chen, Victoria

2015-12-01

Accurately estimating landfill methane emissions is important for quantifying a landfill's greenhouse gas emissions and power generation potential. Current models, including LandGEM and IPCC, often greatly simplify treatment of factors like rainfall and ambient temperature, which can substantially impact gas production. The newly developed Capturing Landfill Emissions for Energy Needs (CLEEN) model aims to improve landfill methane generation estimates, but still require inputs that are fairly easy to obtain: waste composition, annual rainfall, and ambient temperature. To develop the model, methane generation was measured from 27 laboratory scale landfill reactors, with varying waste compositions (ranging from 0% to 100%); average rainfall rates of 2, 6, and 12 mm/day; and temperatures of 20, 30, and 37°C, according to a statistical experimental design. Refuse components considered were the major biodegradable wastes, food, paper, yard/wood, and textile, as well as inert inorganic waste. Based on the data collected, a multiple linear regression equation (R(2)=0.75) was developed to predict first-order methane generation rate constant values k as functions of waste composition, annual rainfall, and temperature. Because, laboratory methane generation rates exceed field rates, a second scale-up regression equation for k was developed using actual gas-recovery data from 11 landfills in high-income countries with conventional operation. The Capturing Landfill Emissions for Energy Needs (CLEEN) model was developed by incorporating both regression equations into the first-order decay based model for estimating methane generation rates from landfills. CLEEN model values were compared to actual field data from 6 US landfills, and to estimates from LandGEM and IPCC. For 4 of the 6 cases, CLEEN model estimates were the closest to actual. Copyright © 2015 Elsevier Ltd. All rights reserved.

Exposure to family planning messages and modern contraceptive use among men in urban Kenya, Nigeria, and Senegal: a cross-sectional study.

PubMed

Okigbo, Chinelo C; Speizer, Ilene S; Corroon, Meghan; Gueye, Abdou

2015-07-22

Family planning (FP) researchers and policy makers have often overlooked the importance of involving men in couples' fertility choices and contraception, despite the fact that male involvement is a vital factor in sexual and reproductive health programming. This study aimed to assess whether men's exposure to FP demand-generation activities is associated with their reported use of modern contraceptive methods. We used evaluation data from the Measurement, Learning & Evaluation project for the Urban Reproductive Health Initiative (URHI) in select cities of three African countries (Kenya, Nigeria, and Senegal) collected in 2012/2013. A two-stage cluster sampling design was used to select a representative sample of men in the study sites. The sample for this study includes men aged 15-59 years who had no missing data on any of the key variables: 696 men in Kenya, 2311 in Nigeria, and 1613 in Senegal. We conducted descriptive analyses and multivariate logistic regression analyses to assess the associations of interest. All analyses were weighted to account for the study design and non-response rates using Stata version 13. The proportion of men who reported use of modern contraceptive methods was 58 % in Kenya, 43 % in Nigeria, and 27 % in Senegal. About 80 % were exposed to at least one URHI demand-generation activity in each country. Certain URHI demand-generation activities were significantly associated with men's reported use of modern contraception. In Kenya, those who participated in URHI-led community events had four times higher odds of reporting use of modern methods (aOR: 3.70; p < 0.05) while in Senegal, exposure to URHI-television programs (aOR: 1.40; p < 0.05) and having heard a religious leader speak favorably about FP (aOR: 1.72; p < 0.05) were associated with modern contraceptive method use. No such associations were observed in Nigeria. Study findings are important for informing future FP program activities that seek to engage men. Program activities should be tailored by geographic context as results from this study indicate city and country-level variations. These types of gender-comprehensive and context-specific programs are likely to be the most successful at reducing unmet need for FP.

Comparative efficacy and tolerability of new-generation antidepressants for major depressive disorder in children and adolescents: protocol of an individual patient data meta-analysis.

PubMed

Zhou, Xinyu; Cipriani, Andrea; Furukawa, Toshi A; Cuijpers, Pim; Zhang, Yuqing; Hetrick, Sarah E; Pu, Juncai; Yuan, Shuai; Del Giovane, Cinzia; Xie, Peng

2018-01-05

Although previous conventional meta-analyses and network meta-analyses have provided some important findings about pharmacological treatments for children and adolescents with depressive disorders in the past decades, several questions still remain unsolved by the aggregate data from those meta-analyses. Individual participant data meta-analysis (IPD-MA) enables exploration of the impacts of individual characteristics on treatment effects, allowing matching of treatments to specific subgroups of patients. We will perform an IPD-MA to assess the efficacy and tolerability of new-generation antidepressants for major depressive disorder in children and adolescents. We will systematically search for all double-blind randomised controlled trials (RCTs) that have compared any new-generation antidepressant with placebo for the acute treatment of major depressive disorder in children and adolescents, in the following databases: PubMed, EMBASE, the Cochrane Library, PsycINFO, Web of Science, CINAHL, LILACS and ProQuest Dissertations. We will contact all corresponding authors of included RCTs and ask for their cooperation in this project by providing individual participant data from the original trials. The primary outcomes will include efficacy, measured as the mean change of depression symptoms by Children's Depression Rating Scale Revised (CDRS-R), and tolerability, measured as the proportion of patients who withdrew from the trials early due to adverse effects. The secondary outcomes will include response rates, remission rates, deterioration rate, all-cause discontinuation, suicidal-related outcomes and global functioning outcome. Using the raw de-identified study data, we will use mixed-effects logistic and linear regression models to perform the IPD-MAs. The risk of bias of included studies will be assessed using the Cochrane risk of bias tool. We will also detect the publication bias and effects of non-participation of eligible studies. Ethical approval is not required given that informed consent has already been obtained from the patients by the trial investigators before the included trials were conducted. This study may have considerable implications for practice and help improve patient care. CRD42016051657. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

The more total cognitive load is reduced by cues, the better retention and transfer of multimedia learning: A meta-analysis and two meta-regression analyses.

PubMed

Xie, Heping; Wang, Fuxing; Hao, Yanbin; Chen, Jiaxue; An, Jing; Wang, Yuxin; Liu, Huashan

2017-01-01

Cueing facilitates retention and transfer of multimedia learning. From the perspective of cognitive load theory (CLT), cueing has a positive effect on learning outcomes because of the reduction in total cognitive load and avoidance of cognitive overload. However, this has not been systematically evaluated. Moreover, what remains ambiguous is the direct relationship between the cue-related cognitive load and learning outcomes. A meta-analysis and two subsequent meta-regression analyses were conducted to explore these issues. Subjective total cognitive load (SCL) and scores on a retention test and transfer test were selected as dependent variables. Through a systematic literature search, 32 eligible articles encompassing 3,597 participants were included in the SCL-related meta-analysis. Among them, 25 articles containing 2,910 participants were included in the retention-related meta-analysis and the following retention-related meta-regression, while there were 29 articles containing 3,204 participants included in the transfer-related meta-analysis and the transfer-related meta-regression. The meta-analysis revealed a statistically significant cueing effect on subjective ratings of cognitive load (d = -0.11, 95% CI = [-0.19, -0.02], p < 0.05), retention performance (d = 0.27, 95% CI = [0.08, 0.46], p < 0.01), and transfer performance (d = 0.34, 95% CI = [0.12, 0.56], p < 0.01). The subsequent meta-regression analyses showed that dSCL for cueing significantly predicted dretention for cueing (β = -0.70, 95% CI = [-1.02, -0.38], p < 0.001), as well as dtransfer for cueing (β = -0.60, 95% CI = [-0.92, -0.28], p < 0.001). Thus in line with CLT, adding cues in multimedia materials can indeed reduce SCL and promote learning outcomes, and the more SCL is reduced by cues, the better retention and transfer of multimedia learning.

The more total cognitive load is reduced by cues, the better retention and transfer of multimedia learning: A meta-analysis and two meta-regression analyses

PubMed Central

Hao, Yanbin; Chen, Jiaxue; An, Jing; Wang, Yuxin; Liu, Huashan

2017-01-01

Cueing facilitates retention and transfer of multimedia learning. From the perspective of cognitive load theory (CLT), cueing has a positive effect on learning outcomes because of the reduction in total cognitive load and avoidance of cognitive overload. However, this has not been systematically evaluated. Moreover, what remains ambiguous is the direct relationship between the cue-related cognitive load and learning outcomes. A meta-analysis and two subsequent meta-regression analyses were conducted to explore these issues. Subjective total cognitive load (SCL) and scores on a retention test and transfer test were selected as dependent variables. Through a systematic literature search, 32 eligible articles encompassing 3,597 participants were included in the SCL-related meta-analysis. Among them, 25 articles containing 2,910 participants were included in the retention-related meta-analysis and the following retention-related meta-regression, while there were 29 articles containing 3,204 participants included in the transfer-related meta-analysis and the transfer-related meta-regression. The meta-analysis revealed a statistically significant cueing effect on subjective ratings of cognitive load (d = −0.11, 95% CI = [−0.19, −0.02], p < 0.05), retention performance (d = 0.27, 95% CI = [0.08, 0.46], p < 0.01), and transfer performance (d = 0.34, 95% CI = [0.12, 0.56], p < 0.01). The subsequent meta-regression analyses showed that dSCL for cueing significantly predicted dretention for cueing (β = −0.70, 95% CI = [−1.02, −0.38], p < 0.001), as well as dtransfer for cueing (β = −0.60, 95% CI = [−0.92, −0.28], p < 0.001). Thus in line with CLT, adding cues in multimedia materials can indeed reduce SCL and promote learning outcomes, and the more SCL is reduced by cues, the better retention and transfer of multimedia learning. PMID:28854205

Electron/proton spectrometer certification documentation analyses

NASA Technical Reports Server (NTRS)

Gleeson, P.

1972-01-01

A compilation of analyses generated during the development of the electron-proton spectrometer for the Skylab program is presented. The data documents the analyses required by the electron-proton spectrometer verification plan. The verification plan was generated to satisfy the ancillary hardware requirements of the Apollo Applications program. The certification of the spectrometer requires that various tests, inspections, and analyses be documented, approved, and accepted by reliability and quality control personnel of the spectrometer development program.

Classification and regression tree (CART) analyses of genomic signatures reveal sets of tetramers that discriminate temperature optima of archaea and bacteria

PubMed Central

Dyer, Betsey D.; Kahn, Michael J.; LeBlanc, Mark D.

2008-01-01

Classification and regression tree (CART) analysis was applied to genome-wide tetranucleotide frequencies (genomic signatures) of 195 archaea and bacteria. Although genomic signatures have typically been used to classify evolutionary divergence, in this study, convergent evolution was the focus. Temperature optima for most of the organisms examined could be distinguished by CART analyses of tetranucleotide frequencies. This suggests that pervasive (nonlinear) qualities of genomes may reflect certain environmental conditions (such as temperature) in which those genomes evolved. The predominant use of GAGA and AGGA as the discriminating tetramers in CART models suggests that purine-loading and codon biases of thermophiles may explain some of the results. PMID:19054742

The relationship between biomechanical variables and driving performance during the golf swing.

PubMed

Chu, Yungchien; Sell, Timothy C; Lephart, Scott M

2010-09-01

Swing kinematic and ground reaction force data from 308 golfers were analysed to identify the variables important to driving ball velocity. Regression models were applied at four selected events in the swing. The models accounted for 44-74% of variance in ball velocity. Based on the regression analyses, upper torso-pelvis separation (the X-Factor), delayed release (i.e. the initiation of movement) of the arms and wrists, trunk forward and lateral tilting, and weight-shifting during the swing were significantly related to ball velocity. Our results also verify several general coaching ideas that were considered important to increased ball velocity. The results of this study may serve as both skill and strength training guidelines for golfers.

Deaf college students' mathematical skills relative to morphological knowledge, reading level, and language proficiency.

PubMed

Kelly, Ronald R; Gaustad, Martha G

2007-01-01

This study of deaf college students examined specific relationships between their mathematics performance and their assessed skills in reading, language, and English morphology. Simple regression analyses showed that deaf college students' language proficiency scores, reading grade level, and morphological knowledge regarding word segmentation and meaning were all significantly correlated with both the ACT Mathematics Subtest and National Technical Institute for the Deaf (NTID) Mathematics Placement Test scores. Multiple regression analyses identified the best combination from among these potential independent predictors of students' performance on both the ACT and NTID mathematics tests. Additionally, the participating deaf students' grades in their college mathematics courses were significantly and positively associated with their reading grade level and their knowledge of morphological components of words.

Fungible Correlation Matrices: A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for Monte Carlo Research.

PubMed

Waller, Niels G

2016-01-01

For a fixed set of standardized regression coefficients and a fixed coefficient of determination (R-squared), an infinite number of predictor correlation matrices will satisfy the implied quadratic form. I call such matrices fungible correlation matrices. In this article, I describe an algorithm for generating positive definite (PD), positive semidefinite (PSD), or indefinite (ID) fungible correlation matrices that have a random or fixed smallest eigenvalue. The underlying equations of this algorithm are reviewed from both algebraic and geometric perspectives. Two simulation studies illustrate that fungible correlation matrices can be profitably used in Monte Carlo research. The first study uses PD fungible correlation matrices to compare penalized regression algorithms. The second study uses ID fungible correlation matrices to compare matrix-smoothing algorithms. R code for generating fungible correlation matrices is presented in the supplemental materials.

Exploring lifetime occupational exposure and SLE flare: a patient-focussed pilot study

PubMed Central

Squance, Marline L; Guest, Maya; Reeves, Glenn; Attia, John; Bridgman, Howard

2014-01-01

Introduction Environmental effectors, such as ultraviolet radiation exposure, infection and stress, have been established as having a role in exacerbating lupus symptoms. However, unpredictable patterns of flare events still remain a mystery. Occupational effectors have also been suggested as having a contributing role; however, they are not widely researched. In this paper we report a pilot study designed to generate focus areas for future research regarding occupational exposures and systemic lupus erythematosus (SLE). Methods The study explored potential links between exposures and the occurrence of patient-reported flare events in 80 Australian women with SLE (American College of Rheumatology (ACR) criteria classified). Specifically, the study assessed the hypothesis that occupational exposure is associated with significant changes in the likelihood of lupus flares. Lifetime employment history was analysed with the Finnish Job Exposure Matrix (FINJEM), 40 different semiquantified exposure class estimates for a wide number of occupations based on probability of exposure (p≥5%=exposed) were analysed with the construction of negative binomial regression models to test relationships between occupational agents and flare days. A backward stepwise elimination was used to generate a parsimonious model. Results Significant associations were noted for exposure classes of manual handling burden, (p=0.02, incidence rate ratio (IRR) 1.01), Iron (p=0.00, IRR 1.37), wood dust (p=0.00, IRR 3.34) and asbestos (p=0.03, IRR 2.48). Conclusion Exposure assessment results indicated that occupations, such as nursing, with a high manual handling burden, posed increased risk to patients with SLE, however, the greatest risk was associated with wood dust and iron exposure with teachers and specialist labourers. PMID:25379190

Optimizing outcome reporting after radical cystectomy for organ-confined urothelial carcinoma of the bladder using oncological trifecta and pentafecta.

PubMed

Aziz, Atiqullah; Gierth, Michael; Rink, Michael; Schmid, Marianne; Chun, Felix K; Dahlem, Roland; Roghmann, Florian; Palisaar, Rein-Jüri; Noldus, Joachim; Ellinger, Jörg; Müller, Stefan C; Pycha, Armin; Martini, Thomas; Bolenz, Christian; Moritz, Rudolf; Herrmann, Edwin; Keck, Bastian; Wullich, Bernd; Mayr, Roman; Fritsche, Hans-Martin; Burger, Maximilian; Bastian, Patrick J; Seitz, Christian; Brookman-May, Sabine; Xylinas, Evanguelos; Shariat, Shahrokh F; Fisch, Margit; May, Matthias

2015-12-01

Radical cystectomy (RC) for urothelial carcinoma of the bladder (UCB) is associated with heterogeneous functional and oncological outcomes. The aim of this study was to generate trifecta and pentafecta criteria to optimize outcome reporting after RC. We interviewed 50 experts to consider a virtual group of patients (age ≤ 75 years, ASA score ≤ 3) undergoing RC for a cT2 UCB and a final histology of ≤pT3pN0M0. A ranking was generated for the three and five criteria with the highest sum score. The criteria were applied to the Prospective Multicenter Radical Cystectomy Series 2011. Multivariable binary logistic regression analyses were used to evaluate the impact of clinical and histopathological parameters on meeting the top selected criteria. The criteria with the highest sum score were negative soft tissue surgical margin, lymph node (LN) dissection of at least 16 LNs, no complications according to Clavien-Dindo grade 3-5 within 90 days after RC, treatment-free time between TUR-BT with detection of muscle-invasive UCB and RC <3 months and the absence of local UCB-recurrence in the pelvis ≤12 months. The first three criteria formed trifecta, and all five criteria pentafecta. A total of 334 patients qualified for final analysis, whereas 35.3 and 29 % met trifecta and pentafecta criteria, respectively. Multivariable analyses showed that the relative probability of meeting trifecta and pentafecta decreases with higher age (3.2 %, p = 0.043 and 3.3 %, p = 0.042) per year, respectively. Trifecta and pentafecta incorporate essential criteria in terms of outcome reporting and might be considered for the improvement of standardized quality assessment after RC for UCB.

Pre- and Postnatal Determinants of Deciduous Molar Hypomineralisation in 6-Year-Old Children. The Generation R Study

PubMed Central

Elfrink, Marlies E. C.; Moll, Henriette A.; Kiefte-de Jong, Jessica C.; Jaddoe, Vincent W. V.; Hofman, Albert; ten Cate, Jacob M.; Veerkamp, Jaap S. J.

2014-01-01

Background Deciduous Molar Hypomineralisation (DMH) and Molar Incisor Hypomineralisation (MIH) are common developmental disturbances in pediatric dentistry. Their occurrence is related. The same determinants as suggested for MIH are expected for DMH, though somewhat earlier in life. Perinatal medical problems may influence the prevalence of DMH but this has not been studied sufficiently. Objective This study aimed to identify possible determinants of DMH in a prospective cohort study among 6-year-old children. Study Design This study was embedded in the Generation R Study, a population-based prospective cohort study from fetal life until young adulthood. The the data were used to identify the determinants of DMH. Clinical photographs of clean, moist teeth were taken with an intra-oral camera in 6690 children (mean age 6.2 years; 49.9% girls). Data on possible determinants that had occurred during pregnancy and/or the child's first year of life were on the basis of manual standardized measurements (like length and weight) and questionnaires. Multivariate analyse with backward and forward selection was performed. Results A number of factors in the pre-, peri- and postnatal phase were found to be associated with DMH. After multivariate logistic regression analyses, Dutch ethnic background, low birth weight, maternal alcohol consumption during pregnancy, and fever episodes in the first year of the child's life were found to play a role in the development of DMH in 6-year-old children. Conclusion This study shows that Dutch ethnicity, low birth weight, alcohol consumption by the mother during pregnancy and any fever in the first year of the child's life are associated with DMH. Not only childhood factors but also prenatal lifestyle factors need to be taken into account when studying determinants for DMH. PMID:24988443

Nomogram to Predict Graft Thickness in Descemet Stripping Automated Endothelial Keratoplasty: An Eye Bank Study.

PubMed

Bae, Steven S; Menninga, Isaac; Hoshino, Richard; Humphreys, Christine; Chan, Clara C

2018-06-01

The purpose of this study was to develop a nomogram to predict postcut thickness of corneal grafts prepared at an eye bank for Descemet stripping automated endothelial keratoplasty (DSAEK). Retrospective chart review was performed of DSAEK graft preparations by 3 experienced technicians from April 2012 to May 2017 at the Eye Bank of Canada-Ontario Division. Variables collected included the following: donor demographics, death-to-preservation time, death-to-processing time, precut tissue thickness, postcut tissue thickness, microkeratome head size, endothelial cell count, cut technician, and rate of perforation. Linear regression models were generated for each microkeratome head size (300 and 350 μm). A total of 780 grafts were processed during the study period. Twelve preparation attempts resulted in perforation (1.5%) and were excluded. Mean precut tissue thickness was 510 ± 49 μm (range: 363-670 μm). Mean postcut tissue thickness was 114 ± 22 μm (range: 57-193 μm). Seventy-nine percent (608/768) of grafts were ≤130 μm. The linear regression models included precut thickness and donor age, which were able to predict the thickness to within 25 μm 80% of the time. We report a nomogram to predict thickness of DSAEK corneal grafts prepared in an eye bank setting, which was accurate to within 25 μm 80% of the time. Other eye banks could consider performing similar analyses.

An Empirical Model and Ethnic Differences in Cultural Meanings Via Motives for Suicide.

PubMed

Chu, Joyce; Khoury, Oula; Ma, Johnson; Bahn, Francesca; Bongar, Bruce; Goldblum, Peter

2017-10-01

The importance of cultural meanings via motives for suicide - what is considered acceptable to motivate suicide - has been advocated as a key step in understanding and preventing development of suicidal behaviors. There have been limited systematic empirical attempts to establish different cultural motives ascribed to suicide across ethnic groups. We used a mixed methods approach and grounded theory methodology to guide the analysis of qualitative data querying for meanings via motives for suicide among 232 Caucasians, Asian Americans, and Latino/a Americans with a history of suicide attempts, ideation, intent, or plan. We used subsequent logistic regression analyses to examine ethnic differences in suicide motive themes. This inductive approach of generating theory from data yielded an empirical model of 6 cultural meanings via motives for suicide themes: intrapersonal perceptions, intrapersonal emotions, intrapersonal behavior, interpersonal, mental health/medical, and external environment. Logistic regressions showed ethnic differences in intrapersonal perceptions (low endorsement by Latino/a Americans) and external environment (high endorsement by Latino/a Americans) categories. Results advance suicide research and practice by establishing 6 empirically based cultural motives for suicide themes that may represent a key intermediary step in the pathway toward suicidal behaviors. Clinicians can use these suicide meanings via motives to guide their assessment and determination of suicide risk. Emphasis on environmental stressors rather than negative perceptions like hopelessness should be considered with Latino/a clients. © 2017 Wiley Periodicals, Inc.

Mapping health outcome measures from a stroke registry to EQ-5D weights.

PubMed

Ghatnekar, Ola; Eriksson, Marie; Glader, Eva-Lotta

2013-03-07

To map health outcome related variables from a national register, not part of any validated instrument, with EQ-5D weights among stroke patients. We used two cross-sectional data sets including patient characteristics, outcome variables and EQ-5D weights from the national Swedish stroke register. Three regression techniques were used on the estimation set (n=272): ordinary least squares (OLS), Tobit, and censored least absolute deviation (CLAD). The regression coefficients for "dressing", "toileting", "mobility", "mood", "general health" and "proxy-responders" were applied to the validation set (n=272), and the performance was analysed with mean absolute error (MAE) and mean square error (MSE). The number of statistically significant coefficients varied by model, but all models generated consistent coefficients in terms of sign. Mean utility was underestimated in all models (least in OLS) and with lower variation (least in OLS) compared to the observed. The maximum attainable EQ-5D weight ranged from 0.90 (OLS) to 1.00 (Tobit and CLAD). Health states with utility weights <0.5 had greater errors than those with weights ≥ 0.5 (P<0.01). This study indicates that it is possible to map non-validated health outcome measures from a stroke register into preference-based utilities to study the development of stroke care over time, and to compare with other conditions in terms of utility.

Voxel-based morphometry in creative writers: Gray-matter increase in a prefronto-thalamic-cerebellar network.

PubMed

Neumann, Nicola; Domin, Martin; Erhard, Katharina; Lotze, Martin

2018-05-18

Continuous practice modulates those features of brain anatomy specifically associated with requirements of the respective training task. The current study aimed to highlight brain structural changes going along with long-term experience in creative writing. To this end, we investigated the gray-matter volume of 23 expert writers with voxel-based morphometry and compared it to 28 matched non-expert controls. Expert writers had higher gray-matter volume in the right superior frontal and middle frontal gyri (BA 9,10) as well as left middle frontal gyrus (BA 9, 10, 46), the bilateral medial dorsal nuclei of the thalamus and left posterior cerebellum. A regression analysis confirmed the association of enhanced gray-matter volume in the right superior frontal gyrus (BA 10) with practice index of writing. In region-of interest based regression analyses, we found associations of gray-matter volume in the right Broca's analogue (BA 44) and right primary visual cortex (BA 17) with creativity ratings of the texts written during scanning, but not with a standardized verbal creativity test. Creative writing thus seems to be strongly connected to a prefronto-thalamic-cerebellar network that supports the continuous generation, organization and revision of ideas that is necessary to write literary texts. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Relationships between coordination, active drag and propelling efficiency in crawl.

PubMed

Seifert, Ludovic; Schnitzler, Christophe; Bideault, Gautier; Alberty, Morgan; Chollet, Didier; Toussaint, Huub Martin

2015-02-01

This study examines the relationships between the index of coordination (IdC) and active drag (D) assuming that at constant average speed, average drag equals average propulsion. The relationship between IdC and propulsive efficiency (ep) was also investigated at maximal speed. Twenty national swimmers completed two incremental speed tests swimming front crawl with arms only in free condition and using a measurement of active drag system. Each test was composed of eight 25-m bouts from 60% to 100% of maximal intensity whereby each lap was swum at constant speed. Different regression models were tested to analyse IdC-D relationship. Correlation between IdC and ep was calculated. IdC was linked to D by linear regression (IdC=0.246·D-27.06; R(2)=0.88, P<.05); swimmers switched from catch-up to superposition coordination mode at a speed of ∼1.55ms(-1) where average D is ∼110N. No correlation between IdC and ep at maximal speed was found. The intra-individual analysis revealed that coordination plays an important role in scaling propulsive forces with higher speed levels such that these are adapted to aquatic resistance. Inter-individual analysis showed that high IdC did not relate to a high ep suggesting an individual optimization of force and power generation is at play to reach high speeds. Copyright © 2014 Elsevier B.V. All rights reserved.

Associations between School-Related Factors and Depressive Symptoms among Children: A Comparative Study, Finland and Norway

ERIC Educational Resources Information Center

Minkkinen, Jaana

2014-01-01

This study compares school-related associations in depressive symptoms among children aged between 9-13 years from four schools in Finland and Norway. A total of 523 pupils participated in the cross-sectional survey. The connections between depressive symptoms and school factors were analysed using hierarchical regression analyses. School…

Air Pollution and urban climatology at Norfolk, Virginia

Treesearch

W. Maurice Pritchard; Kuldip P. Chopra

1977-01-01

The atmosphere at Norfolk is usually stable, with no strongly prevailing wind direction. Linear regression analyses of visibility data indicate a generally decreasing visibility trend between 1960 and 1972, with a possible trend reversal in later years. A 44 percent increase in the annual frequency of 0-4-mile visibility occurred in 1960-72. Similar analyses of...

Comments on `A Cautionary Note on the Interpretation of EOFs'.

NASA Astrophysics Data System (ADS)

Behera, Swadhin K.; Rao, Suryachandra A.; Saji, Hameed N.; Yamagata, Toshio

2003-04-01

The misleading aspect of the statistical analyses used in Dommenget and Latif, which raises concerns on some of the reported climate modes, is demonstrated. Adopting simple statistical techniques, the physical existence of the Indian Ocean dipole mode is shown and then the limitations of varimax and regression analyses in capturing the climate mode are discussed.

The Relationship between Retention and College Counseling for High-risk Students

ERIC Educational Resources Information Center

Bishop, Kyle K.

2016-01-01

The author used an archival study to explore the relationship between college counseling and retention. The cohort for this study was a college's 2006 class of full-time, 1st-year students (N = 429). The results of chi-square analyses and regression analyses indicated (a) a significant difference in retention between high-risk and low-risk…

Neural Network and Regression Approximations in High Speed Civil Transport Aircraft Design Optimization

NASA Technical Reports Server (NTRS)

Patniak, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.

1998-01-01

Nonlinear mathematical-programming-based design optimization can be an elegant method. However, the calculations required to generate the merit function, constraints, and their gradients, which are frequently required, can make the process computational intensive. The computational burden can be greatly reduced by using approximating analyzers derived from an original analyzer utilizing neural networks and linear regression methods. The experience gained from using both of these approximation methods in the design optimization of a high speed civil transport aircraft is the subject of this paper. The Langley Research Center's Flight Optimization System was selected for the aircraft analysis. This software was exercised to generate a set of training data with which a neural network and a regression method were trained, thereby producing the two approximating analyzers. The derived analyzers were coupled to the Lewis Research Center's CometBoards test bed to provide the optimization capability. With the combined software, both approximation methods were examined for use in aircraft design optimization, and both performed satisfactorily. The CPU time for solution of the problem, which had been measured in hours, was reduced to minutes with the neural network approximation and to seconds with the regression method. Instability encountered in the aircraft analysis software at certain design points was also eliminated. On the other hand, there were costs and difficulties associated with training the approximating analyzers. The CPU time required to generate the input-output pairs and to train the approximating analyzers was seven times that required for solution of the problem.

Optimization of miRNA-seq data preprocessing.

PubMed

Tam, Shirley; Tsao, Ming-Sound; McPherson, John D

2015-11-01

The past two decades of microRNA (miRNA) research has solidified the role of these small non-coding RNAs as key regulators of many biological processes and promising biomarkers for disease. The concurrent development in high-throughput profiling technology has further advanced our understanding of the impact of their dysregulation on a global scale. Currently, next-generation sequencing is the platform of choice for the discovery and quantification of miRNAs. Despite this, there is no clear consensus on how the data should be preprocessed before conducting downstream analyses. Often overlooked, data preprocessing is an essential step in data analysis: the presence of unreliable features and noise can affect the conclusions drawn from downstream analyses. Using a spike-in dilution study, we evaluated the effects of several general-purpose aligners (BWA, Bowtie, Bowtie 2 and Novoalign), and normalization methods (counts-per-million, total count scaling, upper quartile scaling, Trimmed Mean of M, DESeq, linear regression, cyclic loess and quantile) with respect to the final miRNA count data distribution, variance, bias and accuracy of differential expression analysis. We make practical recommendations on the optimal preprocessing methods for the extraction and interpretation of miRNA count data from small RNA-sequencing experiments. © The Author 2015. Published by Oxford University Press.

A positive take on schizophrenia negative symptom scales: Converting scores between the SANS, NSA and SDS.

PubMed

Preda, Adrian; Nguyen, Dana D; Bustillo, Juan R; Belger, Aysenil; O'Leary, Daniel S; McEwen, Sarah; Ling, Shichun; Faziola, Lawrence; Mathalon, Daniel H; Ford, Judith M; Potkin, Steven G; van Erp, Theo G M

2018-06-20

To provide quantitative conversions between commonly used scales for the assessment of negative symptoms in schizophrenia. Linear regression analyses generated conversion equations between symptom scores from the Scale for the Assessment of Negative Symptoms (SANS), the Schedule for the Deficit Syndrome (SDS), the Positive and Negative Syndrome Scale (PANSS), or the Negative Symptoms Assessment (NSA) based on a cross sectional sample of 176 individuals with schizophrenia. Intraclass correlations assessed the rating conversion accuracy based on a separate sub-sample of 29 patients who took part in the initial study as well as an independent sample of 28 additional subjects with schizophrenia. Between-scale negative symptom ratings were moderately to highly correlated (r = 0.73-0.91). Intraclass correlations between the original negative symptom rating scores and those obtained via using the conversion equations were in the range of 0.61-0.79. While there is a degree of non-overlap, several negative symptoms scores reflect measures of similar constructs and may be reliably converted between some scales. The conversion equations are provided at http://www.converteasy.org and may be used for meta- and mega-analyses that examine negative symptoms. Copyright © 2018 Elsevier B.V. All rights reserved.

Drug attitude and subjective well-being in antipsychotic treatment monotherapy in real-world settings.

PubMed

Balestrieri, Matteo; Di Sciascio, Guido; Isola, Miriam; Lomonaco, Emanuele; Maso, Elisa; Merli, Roberto; Calò, Salvatore; Bellantuono, Cesario

2009-01-01

To assess using two well-know scales (DAI-30 and SWN) the drug attitude and subjective well-being of patients treated with haloperidol or second-generation antipsychotics (SGA) in four different Italian communities. The sample included 145 patients taking five different antipsychotics (APs) in mono-therapy: haloperidol, clozapine, olanzapine, risperidone, quetiapine. A stepwise multiple regression analysis (SMRA) was used to analyse the contribution of different AP treatments and of other predictors to SWN and DAI-30 scores. Univariate analyses showed no differences in DAI-30 and SWN scores across treatments. The SMRA showed that SWN scores were negatively correlated with the severity of the psychoses (BPRS scores), while the DAI-30 scores were negatively correlated with the severity of the psychoses and positively correlated both with the length of drug treatment and with the use of olanzapine. Our study does not confirm a better drug attitude in patients treated with SGA with respect to haloperidol. The only partial exception is the better performance of olanzapine over haloperidol on DAI-30, which could be due to the lower use of anticholinergic drugs during olanzapine treatment. The differences between the SWN and DAI-30 may give good reason for the use of both instruments during AP treatments.

Investigating shared aetiology between type 2 diabetes and major depressive disorder in a population based cohort.

PubMed

Clarke, Toni-Kim; Obsteter, Jana; Hall, Lynsey S; Hayward, Caroline; Thomson, Pippa A; Smith, Blair H; Padmanabhan, Sandosh; Hocking, Lynne J; Deary, Ian J; Porteous, David J; McIntosh, Andrew M

2017-04-01

Type II diabetes (T2D) and major depressive disorder (MDD) are often co-morbid. The reasons for this co-morbidity are unclear. Some studies have highlighted the importance of environmental factors and a causal relationship between T2D and MDD has also been postulated. In the present study we set out to investigate the shared aetiology between T2D and MDD using Mendelian randomization in a population based sample, Generation Scotland: the Scottish Family Health Study (N = 21,516). Eleven SNPs found to be associated with T2D were tested for association with MDD and psychological distress (General Health Questionnaire scores). We also assessed causality and genetic overlap between T2D and MDD using polygenic risk scores (PRS) assembled from the largest available GWAS summary statistics to date. No single T2D risk SNP was associated with MDD in the MR analyses and we did not find consistent evidence of genetic overlap between MDD and T2D in the PRS analyses. Linkage disequilibrium score regression analyses supported these findings as no genetic correlation was observed between T2D and MDD (rG = 0.0278 (S.E. 0.11), P-value = 0.79). As suggested by previous studies, T2D and MDD covariance may be better explained by environmental factors. Future studies would benefit from analyses in larger cohorts where stratifying by sex and looking more closely at MDD cases demonstrating metabolic dysregulation is possible. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

Exchangeable Ions Are Responsible for the In Vitro Antibacterial Properties of Natural Clay Mixtures

PubMed Central

Otto, Caitlin C.; Haydel, Shelley E.

2013-01-01

We have identified a natural clay mixture that exhibits in vitro antibacterial activity against a broad spectrum of bacterial pathogens. We collected four samples from the same source and demonstrated through antibacterial susceptibility testing that these clay mixtures have markedly different antibacterial activity against Escherichia coli and methicillin-resistant Staphylococcus aureus (MRSA). Here, we used X-ray diffraction (XRD) and inductively coupled plasma – optical emission spectroscopy (ICP-OES) and – mass spectrometry (ICP-MS) to characterize the mineralogical and chemical features of the four clay mixture samples. XRD analyses of the clay mixtures revealed minor mineralogical differences between the four samples. However, ICP analyses demonstrated that the concentrations of many elements, Fe, Co, Cu, Ni, and Zn, in particular, vary greatly across the four clay mixture leachates. Supplementation of a non-antibacterial leachate containing lower concentrations of Fe, Co, Ni, Cu, and Zn to final ion concentrations and a pH equivalent to that of the antibacterial leachate generated antibacterial activity against E. coli and MRSA, confirming the role of these ions in the antibacterial clay mixture leachates. Speciation modeling revealed increased concentrations of soluble Cu2+ and Fe2+ in the antibacterial leachates, compared to the non-antibacterial leachates, suggesting these ionic species specifically are modulating the antibacterial activity of the leachates. Finally, linear regression analyses comparing the log10 reduction in bacterial viability to the concentration of individual ion species revealed positive correlations with Zn2+ and Cu2+ and antibacterial activity, a negative correlation with Fe3+, and no correlation with pH. Together, these analyses further indicate that the ion concentration of specific species (Fe2+, Cu2+, and Zn2+) are responsible for antibacterial activity and that killing activity is not solely attributed to pH. PMID:23691149

Deadlines at work and sleep quality. Cross-sectional and longitudinal findings among Danish knowledge workers.

PubMed

Rugulies, Reiner; Martin, Marie H T; Garde, Anne Helene; Persson, Roger; Albertsen, Karen

2012-03-01

Exposure to deadlines at work is increasing in several countries and may affect health. We aimed to investigate cross-sectional and longitudinal associations between frequency of difficult deadlines at work and sleep quality. Study participants were knowledge workers, drawn from a representative sample of Danish employees who responded to a baseline questionnaire in 2006 (n = 363) and a follow-up questionnaire in 2007 (n = 302). Frequency of difficult deadlines was measured by self-report and categorized into low, intermediate, and high. Sleep quality was measured with a Total Sleep Quality Score and two indexes (Awakening Index and Disturbed Sleep Index) derived from the Karolinska Sleep Questionnaire. Analyses on the association between frequency of deadlines and sleep quality scores were conducted with multiple linear regression models, adjusted for potential confounders. In addition, we used multiple logistic regression models to analyze whether frequency of deadlines at baseline predicted caseness of sleep problems at follow-up among participants free of sleep problems at baseline. Frequent deadlines were cross-sectionally and longitudinally associated with poorer sleep quality on all three sleep quality measures. Associations in the longitudinal analyses were greatly attenuated when we adjusted for baseline sleep quality. The logistic regression analyses showed that frequent deadlines at baseline were associated with elevated odds ratios for caseness of sleep problems at follow-up, however, confidence intervals were wide in these analyses. Frequent deadlines at work were associated with poorer sleep quality among Danish knowledge workers. We recommend investigating the relation between deadlines and health endpoints in large-scale epidemiologic studies. Copyright © 2011 Wiley Periodicals, Inc.

Reporting and methodological quality of meta-analyses in urological literature.

PubMed

Xia, Leilei; Xu, Jing; Guzzo, Thomas J

2017-01-01

To assess the overall quality of published urological meta-analyses and identify predictive factors for high quality. We systematically searched PubMed to identify meta-analyses published from January 1st, 2011 to December 31st, 2015 in 10 predetermined major paper-based urology journals. The characteristics of the included meta-analyses were collected, and their reporting and methodological qualities were assessed by the PRISMA checklist (27 items) and AMSTAR tool (11 items), respectively. Descriptive statistics were used for individual items as a measure of overall compliance, and PRISMA and AMSTAR scores were calculated as the sum of adequately reported domains. Logistic regression was used to identify predictive factors for high qualities. A total of 183 meta-analyses were included. The mean PRISMA and AMSTAR scores were 22.74 ± 2.04 and 7.57 ± 1.41, respectively. PRISMA item 5, protocol and registration, items 15 and 22, risk of bias across studies, items 16 and 23, additional analysis had less than 50% adherence. AMSTAR item 1, " a priori " design, item 5, list of studies and item 10, publication bias had less than 50% adherence. Logistic regression analyses showed that funding support and " a priori " design were associated with superior reporting quality, following PRISMA guideline and " a priori " design were associated with superior methodological quality. Reporting and methodological qualities of recently published meta-analyses in major paper-based urology journals are generally good. Further improvement could potentially be achieved by strictly adhering to PRISMA guideline and having " a priori " protocol.

An approach to checking case-crossover analyses based on equivalence with time-series methods.

PubMed

Lu, Yun; Symons, James Morel; Geyh, Alison S; Zeger, Scott L

2008-03-01

The case-crossover design has been increasingly applied to epidemiologic investigations of acute adverse health effects associated with ambient air pollution. The correspondence of the design to that of matched case-control studies makes it inferentially appealing for epidemiologic studies. Case-crossover analyses generally use conditional logistic regression modeling. This technique is equivalent to time-series log-linear regression models when there is a common exposure across individuals, as in air pollution studies. Previous methods for obtaining unbiased estimates for case-crossover analyses have assumed that time-varying risk factors are constant within reference windows. In this paper, we rely on the connection between case-crossover and time-series methods to illustrate model-checking procedures from log-linear model diagnostics for time-stratified case-crossover analyses. Additionally, we compare the relative performance of the time-stratified case-crossover approach to time-series methods under 3 simulated scenarios representing different temporal patterns of daily mortality associated with air pollution in Chicago, Illinois, during 1995 and 1996. Whenever a model-be it time-series or case-crossover-fails to account appropriately for fluctuations in time that confound the exposure, the effect estimate will be biased. It is therefore important to perform model-checking in time-stratified case-crossover analyses rather than assume the estimator is unbiased.

Analyses of non-fatal accidents in an opencast mine by logistic regression model - a case study.

PubMed

Onder, Seyhan; Mutlu, Mert

2017-09-01

Accidents cause major damage for both workers and enterprises in the mining industry. To reduce the number of occupational accidents, these incidents should be properly registered and carefully analysed. This study efficiently examines the Aegean Lignite Enterprise (ELI) of Turkish Coal Enterprises (TKI) in Soma between 2006 and 2011, and opencast coal mine occupational accident records were used for statistical analyses. A total of 231 occupational accidents were analysed for this study. The accident records were categorized into seven groups: area, reason, occupation, part of body, age, shift hour and lost days. The SPSS package program was used in this study for logistic regression analyses, which predicted the probability of accidents resulting in greater or less than 3 lost workdays for non-fatal injuries. Social facilities-area of surface installations, workshops and opencast mining areas are the areas with the highest probability for accidents with greater than 3 lost workdays for non-fatal injuries, while the reasons with the highest probability for these types of accidents are transporting and manual handling. Additionally, the model was tested for such reported accidents that occurred in 2012 for the ELI in Soma and estimated the probability of exposure to accidents with lost workdays correctly by 70%.

Requirements for Next Generation Comprehensive Analysis of Rotorcraft

NASA Technical Reports Server (NTRS)

Johnson, Wayne; Data, Anubhav

2008-01-01

The unique demands of rotorcraft aeromechanics analysis have led to the development of software tools that are described as comprehensive analyses. The next generation of rotorcraft comprehensive analyses will be driven and enabled by the tremendous capabilities of high performance computing, particularly modular and scaleable software executed on multiple cores. Development of a comprehensive analysis based on high performance computing both demands and permits a new analysis architecture. This paper describes a vision of the requirements for this next generation of comprehensive analyses of rotorcraft. The requirements are described and substantiated for what must be included and justification provided for what should be excluded. With this guide, a path to the next generation code can be found.

Second-generation PLINK: rising to the challenge of larger and richer datasets.

PubMed

Chang, Christopher C; Chow, Carson C; Tellier, Laurent Cam; Vattikuti, Shashaank; Purcell, Shaun M; Lee, James J

2015-01-01

PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, [Formula: see text]-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0). The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

Obesity Risk in Children: The Role of Acculturation in the Feeding Practices and Styles of Low-Income Hispanic Families.

PubMed

Power, Thomas G; O'Connor, Teresia M; Orlet Fisher, Jennifer; Hughes, Sheryl O

2015-12-01

Parent feeding has been associated with child overweight/obesity in low-income families. Because acculturation to the United States has been associated with increased adult obesity, our study aim was to determine whether acculturation was associated with feeding in these populations. Low-income Hispanic mothers of preschoolers were recruited to participate in a longitudinal study examining child eating behaviors. At baseline, mothers completed questionnaires on feeding styles, feeding practices, and acculturation. Regression analyses compared feeding styles and food parenting practices of first-generation, immigrant mothers born outside the United States (n = 138) and mothers born in the United States (n = 31). The correlates of acculturation with these same constructs were also examined. Immigrant mothers reported using highly directive food parenting practices more often than mothers born in the United States, including pressuring their child to consume more food, using food as a reward, and controlling child food intake by limiting less-healthy foods. First-generation mothers were more likely to show authoritarian, and less likely to show indulgent, feeding styles. Greater maternal acculturation was associated with less restriction of food for weight reasons. Although first-generation, immigrant mothers reported using highly controlling food parenting practices with their children, those born in the United States were more indulgent with their children in the feeding context. Mechanisms that promote greater indulgence in more-acculturated mothers need to be identified.

Estimating effects of limiting factors with regression quantiles

USGS Publications Warehouse

Cade, B.S.; Terrell, J.W.; Schroeder, R.L.

1999-01-01

In a recent Concepts paper in Ecology, Thomson et al. emphasized that assumptions of conventional correlation and regression analyses fundamentally conflict with the ecological concept of limiting factors, and they called for new statistical procedures to address this problem. The analytical issue is that unmeasured factors may be the active limiting constraint and may induce a pattern of unequal variation in the biological response variable through an interaction with the measured factors. Consequently, changes near the maxima, rather than at the center of response distributions, are better estimates of the effects expected when the observed factor is the active limiting constraint. Regression quantiles provide estimates for linear models fit to any part of a response distribution, including near the upper bounds, and require minimal assumptions about the form of the error distribution. Regression quantiles extend the concept of one-sample quantiles to the linear model by solving an optimization problem of minimizing an asymmetric function of absolute errors. Rank-score tests for regression quantiles provide tests of hypotheses and confidence intervals for parameters in linear models with heteroscedastic errors, conditions likely to occur in models of limiting ecological relations. We used selected regression quantiles (e.g., 5th, 10th, ..., 95th) and confidence intervals to test hypotheses that parameters equal zero for estimated changes in average annual acorn biomass due to forest canopy cover of oak (Quercus spp.) and oak species diversity. Regression quantiles also were used to estimate changes in glacier lily (Erythronium grandiflorum) seedling numbers as a function of lily flower numbers, rockiness, and pocket gopher (Thomomys talpoides fossor) activity, data that motivated the query by Thomson et al. for new statistical procedures. Both example applications showed that effects of limiting factors estimated by changes in some upper regression quantile (e.g., 90-95th) were greater than if effects were estimated by changes in the means from standard linear model procedures. Estimating a range of regression quantiles (e.g., 5-95th) provides a comprehensive description of biological response patterns for exploratory and inferential analyses in observational studies of limiting factors, especially when sampling large spatial and temporal scales.

Correlates of Spirituality among African Americans and Caribbean Blacks in the United States: Findings from the National Survey of American Life

PubMed Central

Taylor, Robert Joseph; Chatters, Linda M.; Jackson, James S.

2010-01-01

The present study examined differences in reports of spirituality among African Americans, Caribbean Blacks (Black Caribbeans), and non-Hispanic whites using data from the National Survey of American Life (NSAL). Bivariate analyses indicated that African Americans were most likely to endorse statements regarding the importance of spirituality in their lives (“How important is spirituality in your life?”) and self-assessments of spirituality (“How spiritual would you say you are?”), followed by Caribbean Blacks and non-Hispanic whites. Regression analyses indicated that African Americans and Caribbean Blacks had significantly higher levels of spirituality than did non-Hispanic whites. However, there were no significant differences in spirituality between African Americans and Caribbean Blacks. Separate regression analyses for African Americans and Caribbean Blacks indicated distinctive patterns of sociodemographic and denominational correlates of spiritual sentiments. Findings are discussed in relation to available survey and ethnographic data on self-assessments of spirituality. PMID:21031157

The Mediating Role of Emotion Dysregulation in the Relations Between Childhood Trauma History and Adult Attachment and Borderline Personality Disorder Features: A Study of Italian Nonclinical Participants.

PubMed

Fossati, Andrea; Gratz, Kim L; Somma, Antonella; Maffei, Cesare; Borroni, Serena

2016-10-01

In order to evaluate if emotion dysregulation significantly mediates the relationships between childhood abuse and adult attachment and borderline personality disorder features, 354 community Italian adults were administered the Borderline Personality Inventory (Leichsenring, 1999a), the Difficulties in Emotion Regulation Scale (Gratz & Roemer, 2004), the Child Abuse and Trauma Scale (Sanders & Becker-Lausen, 1995), and the Attachment Style Questionnaire (Feeney, Noller, & Hanrahan, 1994). Hierarchical regression analyses showed that both childhood abuse and adult attachment were positively associated with emotion dysregulation and borderline personality features; however, only emotional abuse and the attachment dimension of need for approval were common predictors of both dependent variables. No significant interaction effects were detected in regression analyses. Mediation analyses provided support for partial mediation, revealing a significant mediating role of emotion dysregulation in the relationships between both emotional abuse and need for approval and borderline personality features in this community sample.

Relationship between attachment style and posttraumatic stress symptomatology among adults who report the experience of childhood abuse.

PubMed

Muller, R T; Sicoli, L A; Lemieux, K E

2000-04-01

This study examines the relationship between adult attachment style and posttraumatic stress symptomatology in a volunteer sample of adults who reported the experience of childhood abuse. Sixty-six individuals completed measures of abuse history, attachment style, and posttraumatic stress symptomatology. Results indicated that 76% of participants endorsed one of the three insecure attachment styles (dismissing, fearful, or preoccupied). Analyses of variances revealed that those who displayed fearful and preoccupied attachment styles, which represent a negative view of the self, had the highest mean scores on posttraumatic symptoms. Correlational analyses revealed a significant positive relationship between negative view of self and posttraumatic stress symptomatology, but not between negative view of other and posttraumatic stress symptomatology. Regression analyses indicated that having a negative view of self was most highly associated with posttraumatic stress symptoms, followed by a history of physical abuse. The regression analysis further indicated that negative view of other was unrelated to posttraumatic stress symptoms.

Women's work stress and cortisol levels: a longitudinal study of the association between the psychosocial work environment and serum cortisol.

PubMed

Evolahti, Annika; Hultcrantz, Malou; Collins, Aila

2006-11-01

The aim of the present study was to investigate whether there is an association between serum cortisol and work-related stress, as defined by the demand-control model in a longitudinal design. One hundred ten women aged 47-53 years completed a health questionnaire, including the Swedish version of the Job Content Scale, and participated in a psychological interview at baseline and in a follow-up session 2 years later. Morning blood samples were drawn for analyses of cortisol. Multiple stepwise regression analyses and logistic regression analyses showed that work demands and lack of social support were significantly associated with cortisol. The results of this study showed that negative work characteristics in terms of high demands and low social support contributed significantly to the biological stress levels in middle-aged women. Participation in the study may have served as an intervention, increasing the women's awareness and thus improving their health profiles on follow-up.

Cross-sectional study on risk factors of HIV among female commercial sex workers in Cambodia.

PubMed Central

Ohshige, K.; Morio, S.; Mizushima, S.; Kitamura, K.; Tajima, K.; Ito, A.; Suyama, A.; Usuku, S.; Saphonn, V.; Heng, S.; Hor, L. B.; Tia, P.; Soda, K.

2000-01-01

To describe epidemiological features on HIV prevalence among female commercial sex workers (CSWs), a cross-sectional study on sexual behaviour and serological prevalence was carried out in Cambodia. The CSWs were interviewed on their demographic characters and behaviour and their blood samples were taken for testing on sexually transmitted diseases, including HIV, Chlamydia trachomatis, syphilis, and hepatitis B. Associations between risk factors and HIV seropositivity were analysed. High seroprevalence of HIV and Chlamydia trachomatis IgG antibody (CT-IgG-Ab) was shown among the CSWs (54 and 81.7%, respectively). Univariate logistic regression analyses showed an association between HIV seropositivity and age, duration of prostitution, the number of clients per day and CT-IgG-Ab. Especially, high-titre chlamydial seropositivity showed a strong significant association with HIV prevalence. In multiple logistic regression analyses, CT-IgG-Ab with higher titre was significantly independently related to HIV infection. These suggest that existence of Chlamydia trachomatis is highly related to HIV prevalence. PMID:10722142

Effective psychological and psychosocial approaches to reduce repetition of self-harm: a systematic review, meta-analysis and meta-regression

PubMed Central

Robinson, Jo; Spittal, Matthew J; Carter, Greg

2016-01-01

Objective To examine the efficacy of psychological and psychosocial interventions for reductions in repeated self-harm. Design We conducted a systematic review, meta-analysis and meta-regression to examine the efficacy of psychological and psychosocial interventions to reduce repeat self-harm in adults. We included a sensitivity analysis of studies with a low risk of bias for the meta-analysis. For the meta-regression, we examined whether the type, intensity (primary analyses) and other components of intervention or methodology (secondary analyses) modified the overall intervention effect. Data sources A comprehensive search of MEDLINE, PsycInfo and EMBASE (from 1999 to June 2016) was performed. Eligibility criteria for selecting studies Randomised controlled trials of psychological and psychosocial interventions for adult self-harm patients. Results Forty-five trials were included with data available from 36 (7354 participants) for the primary analysis. Meta-analysis showed a significant benefit of all psychological and psychosocial interventions combined (risk ratio 0.84; 95% CI 0.74 to 0.96; number needed to treat=33); however, sensitivity analyses showed that this benefit was non-significant when restricted to a limited number of high-quality studies. Meta-regression showed that the type of intervention did not modify the treatment effects. Conclusions Consideration of a psychological or psychosocial intervention over and above treatment as usual is worthwhile; with the public health benefits of ensuring that this practice is widely adopted potentially worth the investment. However, the specific type and nature of the intervention that should be delivered is not yet clear. Cognitive–behavioural therapy or interventions with an interpersonal focus and targeted on the precipitants to self-harm may be the best candidates on the current evidence. Further research is required. PMID:27660314

Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data.

PubMed

Rhodes, Kirsty M; Turner, Rebecca M; White, Ian R; Jackson, Dan; Spiegelhalter, David J; Higgins, Julian P T

2016-12-20

Many meta-analyses combine results from only a small number of studies, a situation in which the between-study variance is imprecisely estimated when standard methods are applied. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, providing the potential for more robust inference on the effect size of interest. We present a method for performing Bayesian meta-analysis using data augmentation, in which we represent an informative conjugate prior for between-study variance by pseudo data and use meta-regression for estimation. To assist in this, we derive predictive inverse-gamma distributions for the between-study variance expected in future meta-analyses. These may serve as priors for heterogeneity in new meta-analyses. In a simulation study, we compare approximate Bayesian methods using meta-regression and pseudo data against fully Bayesian approaches based on importance sampling techniques and Markov chain Monte Carlo (MCMC). We compare the frequentist properties of these Bayesian methods with those of the commonly used frequentist DerSimonian and Laird procedure. The method is implemented in standard statistical software and provides a less complex alternative to standard MCMC approaches. An importance sampling approach produces almost identical results to standard MCMC approaches, and results obtained through meta-regression and pseudo data are very similar. On average, data augmentation provides closer results to MCMC, if implemented using restricted maximum likelihood estimation rather than DerSimonian and Laird or maximum likelihood estimation. The methods are applied to real datasets, and an extension to network meta-analysis is described. The proposed method facilitates Bayesian meta-analysis in a way that is accessible to applied researchers. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Gene set analysis using variance component tests.

PubMed

Huang, Yen-Tsung; Lin, Xihong

2013-06-28

Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

Lung Cancer Prognosis in Elderly Solid Organ Transplant Recipients

PubMed Central

Sigel, Keith; Veluswamy, Rajwanth; Krauskopf, Katherine; Mehrotra, Anita; Mhango, Grace; Sigel, Carlie; Wisnivesky, Juan

2015-01-01

Background Treatment-related immunosuppression in organ transplant recipients has been linked to increased incidence and risk of progression for several malignancies. Using a population-based cancer cohort, we evaluated whether organ transplantation was associated with worse prognosis in elderly patients with non-small cell lung cancer (NSCLC). Methods Using the Surveillance, Epidemiology and End Results registry linked to Medicare claims we identified 597 patients age ≥65 with NSCLC who had received organ transplants (kidney, liver, heart or lung) prior to cancer diagnosis. These cases were compared to 114,410 untransplanted NSCLC patients. We compared overall survival (OS) by transplant status using Kaplan-Meier methods and Cox regression. To account for an increased risk of non-lung cancer death (competing risks) in transplant recipients, we used conditional probability function (CPF) analyses. Multiple CPF regression was used to evaluate lung cancer prognosis in organ transplant recipients while adjusting for confounders. Results Transplant recipients presented with earlier stage lung cancer (p=0.002) and were more likely to have squamous cell carcinoma (p=0.02). Cox regression analyses showed that having received a non-lung organ transplant was associated with poorer OS (p<0.05) while lung transplantation was associated with no difference in prognosis. After accounting for competing risks of death using CPF regression, no differences in cancer-specific survival were noted between non-lung transplant recipients and non-transplant patients. Conclusions Non-lung solid organ transplant recipients who developed NSCLC had worse OS than non-transplant recipients due to competing risks of death. Lung cancer-specific survival analyses suggest that NSCLC tumor behavior may be similar in these two groups. PMID:25839704

Predictors of aggression in 3.322 patients with affective disorders and schizophrenia spectrum disorders evaluated in an emergency department setting.

PubMed

Blanco, Emily A; Duque, Laura M; Rachamallu, Vivekananda; Yuen, Eunice; Kane, John M; Gallego, Juan A

2018-05-01

The aim of this study is to determine odds of aggression and associated factors in patients with schizophrenia-spectrum disorders (SSD) and affective disorders who were evaluated in an emergency department setting. A retrospective study was conducted using de-identified data from electronic medical records from 3.322 patients who were evaluated at emergency psychiatric settings. Data extracted included demographic information, variables related to aggression towards people or property in the past 6months, and other factors that could potentially impact the risk of aggression, such as comorbid diagnoses, physical abuse and sexual abuse. Bivariate analyses and multivariate regression analyses were conducted to determine the variables significantly associated with aggression. An initial multivariate regression analysis showed that SSD had 3.1 times the odds of aggression, while bipolar disorder had 2.2 times the odds of aggression compared to unipolar depression. A second regression analysis including bipolar subtypes showed, using unipolar depression as the reference group, that bipolar disorder with a recent mixed episode had an odds ratio (OR) of 4.3, schizophrenia had an OR of 2.6 and bipolar disorder with a recent manic episode had an OR of 2.2. Generalized anxiety disorder was associated with lower odds in both regression analyses. As a whole, the SSD group had higher odds of aggression than the bipolar disorder group. However, after subdividing the groups, schizophrenia had higher odds of aggression than bipolar disorder with a recent manic episode and lower odds of aggression than bipolar disorder with a recent mixed episode. Copyright © 2017 Elsevier B.V. All rights reserved.

HRCT findings of collagen vascular disease-related interstitial pneumonia (CVD-IP): a comparative study among individual underlying diseases.

PubMed

Tanaka, N; Kunihiro, Y; Kubo, M; Kawano, R; Oishi, K; Ueda, K; Gondo, T

2018-05-29

To identify characteristic high-resolution computed tomography (CT) findings for individual collagen vascular disease (CVD)-related interstitial pneumonias (IPs). The HRCT findings of 187 patients with CVD, including 55 patients with rheumatoid arthritis (RA), 50 with systemic sclerosis (SSc), 46 with polymyositis/dermatomyositis (PM/DM), 15 with mixed connective tissue disease, 11 with primary Sjögren's syndrome, and 10 with systemic lupus erythematosus, were evaluated. Lung parenchymal abnormalities were compared among CVDs using χ 2 test, Kruskal-Wallis test, and multiple logistic regression analysis. A CT-pathology correlation was performed in 23 patients. In RA-IP, honeycombing was identified as the significant indicator based on multiple logistic regression analyses. Traction bronchiectasis (81.8%) was further identified as the most frequent finding based on χ 2 test. In SSc IP, lymph node enlargement and oesophageal dilatation were identified as the indicators based on multiple logistic regression analyses, and ground-glass opacity (GGO) was the most extensive based on Kruskal-Wallis test, which reflects the higher frequency of the pathological nonspecific interstitial pneumonia (NSIP) pattern present in the CT-pathology correlation. In PM/DM IP, airspace consolidation and the absence of honeycombing were identified as the indicators based on multiple logistic regression analyses, and predominance of consolidation over GGO (32.6%) and predominant subpleural distribution of GGO/consolidation (41.3%) were further identified as the most frequent findings based on χ 2 test, which reflects the higher frequency of the pathological NSIP and/or the organising pneumonia patterns present in the CT-pathology correlation. Several characteristic high-resolution CT findings with utility for estimating underlying CVD were identified. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Should metacognition be measured by logistic regression?

PubMed

Rausch, Manuel; Zehetleitner, Michael

2017-03-01

Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.

Intake of Key Chronic Disease–Related Nutrients among Baby Boomers

PubMed Central

King, Dana E.; Xiang, Jun; Brown, Alexander

2014-01-01

Objectives The dietary habits of baby boomers (people born between 1946 and 1964) undoubtedly will have a substantial impact on their future health; however, dietary information regarding the intake of key chronic disease–related nutrients is lacking for this generation. The objective of this study was to compare the dietary intake of key chronic disease–related nutrients of the baby boomer generation with the previous generation of middle-aged adults. Methods National cross-sectional study comparison analyzing data from the National Health and Nutrition Examination Survey (NHANES) including NHANES III (1988–1994) and the NHANES for 2007–2010, focused on adult respondents ages 46 to 64 years who were not institutionalized at the time of each survey. The two cohorts were compared with regard to dietary intake of key nutritional components. The main outcome measures were intake of total calories, sodium, cholesterol, fat, fruits, vegetables, vitamin C, water, and fiber. Results The baby boomers’ average daily intake of nutrients exceeded that of the previous generation of middle-aged adults for total calories (2118/1999), total fat (82/76 g), sodium (3513/3291 mg), and cholesterol (294/262 g; all P < 0.001). The intake of vitamin C (105/89 g), water (1208/1001 g), and vegetables (199/229 g) was less than that of the previous generation (P < 0.001), and the dietary intake of fruit and fiber was unchanged. In regression analyses, dietary changes remained significant after controlling for age, race, sex, and socioeconomic status (all P < 0.01). Conclusions The study findings document higher dietary intake of key chronic disease–related nutrients along with reduced vegetable intake among baby boomers compared with the previous generation of middle-aged adults. These findings are indicative of a diet that may contribute to increased rates of chronic disease among individuals in this age group. PMID:24945165

Modulation of joint moments and work in the goat hindlimb with locomotor speed and surface grade

PubMed Central

Arnold, Allison S.; Lee, David V.; Biewener, Andrew A.

2013-01-01

SUMMARY Goats and other quadrupeds must modulate the work output of their muscles to accommodate the changing mechanical demands associated with locomotion in their natural environments. This study examined which hindlimb joint moments goats use to generate and absorb mechanical energy on level and sloped surfaces over a range of locomotor speeds. Ground reaction forces and the three-dimensional locations of joint markers were recorded as goats walked, trotted and galloped over 0, +15 and −15 deg sloped surfaces. Net joint moments, powers and work were estimated at the goats' hip, knee, ankle and metatarsophalangeal joints throughout the stance phase via inverse dynamics calculations. Differences in locomotor speed on the level, inclined and declined surfaces were characterized and accounted for by fitting regression equations to the joint moment, power and work data plotted versus non-dimensionalized speed. During level locomotion, the net work generated by moments at each of the hindlimb joints was small (less than 0.1 J kg−1 body mass) and did not vary substantially with gait or locomotor speed. During uphill running, by contrast, mechanical energy was generated at the hip, knee and ankle, and the net work at each of these joints increased dramatically with speed (P<0.05). The greatest increases in positive joint work occurred at the hip and ankle. During downhill running, mechanical energy was decreased in two main ways: goats generated larger knee extension moments in the first half of stance, absorbing energy as the knee flexed, and goats generated smaller ankle extension moments in the second half of stance, delivering less energy. The goats' hip extension moment in mid-stance was also diminished, contributing to the decrease in energy. These analyses offer new insight into quadrupedal locomotion, clarifying how the moments generated by hindlimb muscles modulate mechanical energy at different locomotor speeds and grades, as needed to accommodate the demands of variable terrain. PMID:23470662

Depression in Europe: does migrant integration have mental health payoffs? A cross-national comparison of 20 European countries.

PubMed

Levecque, Katia; Van Rossem, Ronan

2015-01-01

Objectives. Depression is a leading cause of ill health and disability. As migrants form an increasing group in Europe, already making up about 8.7% of the population in 2010, knowledge on migrant-related inequalities in depression is of main public health interest. In this study, we first assess whether migrants in Europe are at higher risk for depression compared to the native population. Second, we assess whether the association between migration and depression is dependent on different forms of migrant integration. Migrant integration is looked at both from the individual and from the national level. Design. Hierarchical linear regression analyses based on data for 20 countries in the European Social Survey 2006/2007 (N = 37,076 individuals aged 15 or more). Depression is measured using the center for Epidemiologic Depression Scale. We consider migrant integration over time (first- and second-generation migrants, differentiated according to European Union (EU) or non-EU origin), barriers to integration (low educational level, financial difficulties, being out of the labor market, ethnic minority status, discrimination), and the host country environment (national migrant integration policy). Controls are gender, age, partner relationship, social support, and welfare state regime. Results. Natives and second-generation migrants do not differ significantly in their risk profile for depression. First-generation migrants show higher levels of depression, with those born outside of Europe to be the worst off. This higher risk for depression is not attributable to ethnic minority status but is mainly due to experienced barriers to socioeconomic integration and processes of discrimination. A country's national policy on migrant integration shows not to soften the depressing effect of being a first-generation migrant nor does it have indirect beneficial health effects by reducing barriers to integration. Conclusion. In Europe, first-generation EU and non-EU migrants experience higher levels of depression. Second-generation migrants and natives show similar risk profiles.

Young children and parental physical activity levels: findings from the Canadian health measures survey.

PubMed

Adamo, Kristi B; Langlois, Kellie A; Brett, Kendra E; Colley, Rachel C

2012-08-01

Physical inactivity is a global public health concern. The relationship between dependent children in the home and parental physical activity has not been quantified using objective measures, nor has the relative association of the physical activity levels of mothers and fathers been examined. To investigate the association of children of different ages in the home on two measures of parental physical activity: daily moderate-to-vigorous physical activity (MVPA) and likelihood of meeting the guideline of 150 minutes of MVPA per week accumulated in 10-minute bouts. Data were from the 2007-2009 Canadian Health Measures Survey (n=2315), and analyses were conducted between February and December 2011. MVPA was measured directly using accelerometry. Linear (minutes of MVPA) and logistic (meeting physical activity guidelines) regression models were performed to determine if the presence, number of children, or the age of the youngest child at home was associated with parental physical activity. All models were adjusted for parental age, marital status, household income, employment, and BMI. Mothers whose youngest child was aged <6 years and fathers whose youngest was aged 6-11 years engaged in fewer minutes of daily MVPA than those without dependent children. Linear regression results identified that in comparison to those without children, women whose youngest child in the home was aged <6 years participated in 7.7 minutes less activity per day (p=0.007) whereas men engaged in 5.7 fewer minutes per day, or 54 and 40 minutes per week less, respectively. Similarly, logistic regression analyses indicated that both women and men were less likely to meet guidelines if their youngest child in the home was aged <6 years (OR=0.31, 95% CI=0.11, 0.87; OR=0.34, 95% CI=0.13, 0.93). The physical activity level of parents with young children present in the home was lower than that of those without children. Given the many physiologic, psychological, and social benefits of healthy active living, research efforts should continue to focus on strategies to encourage parents with young children to establish or re-engage in a physically active lifestyle, not only for their own health but to model healthy behavior for the next generation. Copyright © 2012 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Hybridisation is associated with increased fecundity and size in invasive taxa: meta-analytic support for the hybridisation-invasion hypothesis

PubMed Central

Hovick, Stephen M; Whitney, Kenneth D

2014-01-01

The hypothesis that interspecific hybridisation promotes invasiveness has received much recent attention, but tests of the hypothesis can suffer from important limitations. Here, we provide the first systematic review of studies experimentally testing the hybridisation-invasion (H-I) hypothesis in plants, animals and fungi. We identified 72 hybrid systems for which hybridisation has been putatively associated with invasiveness, weediness or range expansion. Within this group, 15 systems (comprising 34 studies) experimentally tested performance of hybrids vs. their parental species and met our other criteria. Both phylogenetic and non-phylogenetic meta-analyses demonstrated that wild hybrids were significantly more fecund and larger than their parental taxa, but did not differ in survival. Resynthesised hybrids (which typically represent earlier generations than do wild hybrids) did not consistently differ from parental species in fecundity, survival or size. Using meta-regression, we found that fecundity increased (but survival decreased) with generation in resynthesised hybrids, suggesting that natural selection can play an important role in shaping hybrid performance – and thus invasiveness – over time. We conclude that the available evidence supports the H-I hypothesis, with the caveat that our results are clearly driven by tests in plants, which are more numerous than tests in animals and fungi. PMID:25234578

Quality of life among Turkish immigrants in Sweden.

PubMed

Bayram, Nuran; Thorburn, Daniel; Demirhan, Haydar; Bilgel, Nazan

2007-10-01

To assess quality of life among Turkish immigrants in Sweden by using the WHOQOL-100 scale and to evaluate the domains' contribution to explain the variance in the quality of life of the immigrants. Our hypothesis was QOL among Turkish immigrants in Sweden are better than Turkish people who are living in their home country. This study was performed in the districts of Stockholm where Turkish immigrants have mostly settled. With the help and guidance of the Turkish Association, a sample of 520 participants was selected. We collected the demographic data by printed questionnaires, and to measure the quality of life, we used the WHOQOL-100 scale Turkish version. For analysis, we used the SPSS V.13.0 and R package programs, variance analyses, and Bayesian regression. The quality of life among the sample of Turkish immigrants was found to be moderate, but higher than the sample of the Turkish population. The quality of life of male immigrants was found to be higher than for females. Swedish-born Turks had better quality of life perceptions. Turkish immigrants' quality of life perceptions were better than those of the Turkish sample. The best scores were received from the third generation. The first generation and female immigrants need attention in order to receive higher quality of life perceptions.

Multivariate statistical approach to estimate mixing proportions for unknown end members

USGS Publications Warehouse

Valder, Joshua F.; Long, Andrew J.; Davis, Arden D.; Kenner, Scott J.

2012-01-01

A multivariate statistical method is presented, which includes principal components analysis (PCA) and an end-member mixing model to estimate unknown end-member hydrochemical compositions and the relative mixing proportions of those end members in mixed waters. PCA, together with the Hotelling T2 statistic and a conceptual model of groundwater flow and mixing, was used in selecting samples that best approximate end members, which then were used as initial values in optimization of the end-member mixing model. This method was tested on controlled datasets (i.e., true values of estimates were known a priori) and found effective in estimating these end members and mixing proportions. The controlled datasets included synthetically generated hydrochemical data, synthetically generated mixing proportions, and laboratory analyses of sample mixtures, which were used in an evaluation of the effectiveness of this method for potential use in actual hydrological settings. For three different scenarios tested, correlation coefficients (R2) for linear regression between the estimated and known values ranged from 0.968 to 0.993 for mixing proportions and from 0.839 to 0.998 for end-member compositions. The method also was applied to field data from a study of end-member mixing in groundwater as a field example and partial method validation.

Enhancement of multitasking performance and neural oscillations by transcranial alternating current stimulation

PubMed Central

Zanto, Theodore P.; van Schouwenburg, Martine R.; Gazzaley, Adam

2017-01-01

Multitasking is associated with the generation of stimulus-locked theta (4–7 Hz) oscillations arising from prefrontal cortex (PFC). Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that influences endogenous brain oscillations. Here, we investigate whether applying alternating current stimulation within the theta frequency band would affect multitasking performance, and explore tACS effects on neurophysiological measures. Brief runs of bilateral PFC theta-tACS were applied while participants were engaged in a multitasking paradigm accompanied by electroencephalography (EEG) data collection. Unlike an active control group, a tACS stimulation group showed enhancement of multitasking performance after a 90-minute session (F1,35 = 6.63, p = 0.01, ηp2 = 0.16; effect size = 0.96), coupled with significant modulation of posterior beta (13–30 Hz) activities (F1,32 = 7.66, p = 0.009, ηp2 = 0.19; effect size = 0.96). Across participant regression analyses indicated that those participants with greater increases in frontal theta, alpha and beta oscillations exhibited greater multitasking performance improvements. These results indicate frontal theta-tACS generates benefits on multitasking performance accompanied by widespread neuronal oscillatory changes, and suggests that future tACS studies with extended treatments are worth exploring as promising tools for cognitive enhancement. PMID:28562642

Enhancement of multitasking performance and neural oscillations by transcranial alternating current stimulation.

PubMed

Hsu, Wan-Yu; Zanto, Theodore P; van Schouwenburg, Martine R; Gazzaley, Adam

2017-01-01

Multitasking is associated with the generation of stimulus-locked theta (4-7 Hz) oscillations arising from prefrontal cortex (PFC). Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that influences endogenous brain oscillations. Here, we investigate whether applying alternating current stimulation within the theta frequency band would affect multitasking performance, and explore tACS effects on neurophysiological measures. Brief runs of bilateral PFC theta-tACS were applied while participants were engaged in a multitasking paradigm accompanied by electroencephalography (EEG) data collection. Unlike an active control group, a tACS stimulation group showed enhancement of multitasking performance after a 90-minute session (F1,35 = 6.63, p = 0.01, ηp2 = 0.16; effect size = 0.96), coupled with significant modulation of posterior beta (13-30 Hz) activities (F1,32 = 7.66, p = 0.009, ηp2 = 0.19; effect size = 0.96). Across participant regression analyses indicated that those participants with greater increases in frontal theta, alpha and beta oscillations exhibited greater multitasking performance improvements. These results indicate frontal theta-tACS generates benefits on multitasking performance accompanied by widespread neuronal oscillatory changes, and suggests that future tACS studies with extended treatments are worth exploring as promising tools for cognitive enhancement.

Narratives in Two Languages: Storytelling of Bilingual Cantonese-English Preschoolers.

PubMed

Rezzonico, Stefano; Goldberg, Ahuva; Mak, Katy Ka-Yan; Yap, Stephanie; Milburn, Trelani; Belletti, Adriana; Girolametto, Luigi

2016-06-01

The aim of this study was to compare narratives generated by 4-year-old and 5-year-old children who were bilingual in English and Cantonese. The sample included 47 children (23 who were 4 years old and 24 who were 5 years old) living in Toronto, Ontario, Canada, who spoke both Cantonese and English. The participants spoke and heard predominantly Cantonese in the home. Participants generated a story in English and Cantonese by using a wordless picture book; language order was counterbalanced. Data were transcribed and coded for story grammar, morphosyntactic quality, mean length of utterance in words, and the number of different words. Repeated measures analysis of variance revealed higher story grammar scores in English than in Cantonese, but no other significant main effects of language were observed. Analyses also revealed that older children had higher story grammar, mean length of utterance in words, and morphosyntactic quality scores than younger children in both languages. Hierarchical regressions indicated that Cantonese story grammar predicted English story grammar and Cantonese microstructure predicted English microstructure. However, no correlation was observed between Cantonese and English morphosyntactic quality. The results of this study have implications for speech-language pathologists who collect narratives in Cantonese and English from bilingual preschoolers. The results suggest that there is a possible transfer in narrative abilities between the two languages.

Relationship of saving habit determinants among undergraduate students: A case study of UiTM Negeri Sembilan, Kampus Seremban

NASA Astrophysics Data System (ADS)

Syahrom, N. S.; Nasrudin, N. S.; Mohamad Yasin, N.; Azlan, N.; Manap, N.

2017-08-01

It has been reported that students are already accumulating substantial debt from higher education fees and their spending habit are at a critical stage. These situations cause the youngsters facing bankruptcy if they cannot improve their saving habit. Many researches have acknowledged that the determinants of saving habit include financial management, parental socialization, peer influence, and self-control. However, there remains a need for investigating the relationship between these determinants in order to avoid bankruptcy among youngsters. The objectives of this study are to investigate the relationship between saving habit determinants and to generate a statistical model based on the determinants of saving habit. Data collection method used is questionnaire and its scope is undergraduate students of UiTM Negeri Sembilan, Kampus Seremban. Samples are chosen by using stratified probability sampling technique and cross-sectional method is used as the research design. The results are then analysed by using descriptive analysis, reliability test, Pearson Correlation, and Multiple Linear Regression analysis. It shows that saving habit and self-control has no relationship. It means that students with less self-control tend to save less. A statistical model has been generated that incorporated this relationship. This study is significant to help the students save more by having high self-control.

Wind tunnel test of Teledyne Geotech model 1564B cup anemometer

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

Parker, M.J.; Addis, R.P.

1991-04-04

The Department of Energy (DOE) Environment, Safety and Health Compliance Assessment (Tiger Team) of the Savannah River Site (SRS) questioned the method by which wind speed sensors (cup anemometers) are calibrated by the Environmental Technology Section (ETS). The Tiger Team member was concerned that calibration data was generated by running the wind tunnel to only 26 miles per hour (mph) when speeds exceeding 50 mph are readily obtainable. A wind tunnel experiment was conducted and confirmed the validity of the practice. Wind speeds common to SRS (6 mph) were predicted more accurately by 0--25 mph regression equations than 0--50 mphmore » regression equations. Higher wind speeds were slightly overpredicted by the 0--25 mph regression equations when compared to 0--50 mph regression equations. However, the greater benefit of more accurate lower wind speed predictions accuracy outweight the benefit of slightly better high (extreme) wind speed predictions. Therefore, it is concluded that 0--25 mph regression equations should continue to be utilized by ETS at SRS. During the Department of Energy Tiger Team audit, concerns were raised about the calibration of SRS cup anemometers. Wind speed is measured by ETS with Teledyne Geotech model 1564B cup anemometers, which are calibrated in the ETS wind tunnel. Linear regression lines are fitted to data points of tunnel speed versus anemometer output voltages up to 25 mph. The regression coefficients are then implemented into the data acquisition computer software when an instrument is installed in the field. The concern raised was that since the wind tunnel at SRS is able to generate a maximum wind speed higher than 25 mph, errors may be introduced in not using the full range of the wind tunnel.« less

Wind tunnel test of Teledyne Geotech model 1564B cup anemometer

NASA Astrophysics Data System (ADS)

Parker, M. J.; Addis, R. P.

1991-04-01

The Department of Energy (DOE) Environment, Safety, and Health Compliance Assessment (Tiger Team) of the Savannah River Site (SRS) questioned the method by which wind speed sensors (cup anemometers) are calibrated by the Environmental Technology Section (ETS). The Tiger Team member was concerned that calibration data was generated by running the wind tunnel to only 26 miles per hour (mph) when speeds exceeding 50 mph are readily obtainable. A wind tunnel experiment was conducted and confirmed the validity of the practice. Wind speeds common to SRS (6 mph) were predicted more accurately by 0-25 mph regression equations than 0-50 mph regression equations. Higher wind speeds were slightly overpredicted by the 0-25 mph regression equations when compared to 0-50 mph regression equations. However, the greater benefit of more accurate lower wind speed predictions accuracy outweigh the benefit of slightly better high (extreme) wind speed predictions. Therefore, it is concluded that 0-25 mph regression equations should continue to be utilized by ETS at SRS. During the Department of Energy Tiger Team audit, concerns were raised about the calibration of SRS cup anemometers. Wind speed is measured by ETS with Teledyne Geotech model 1564B cup anemometers, which are calibrated in the ETS wind tunnel. Linear regression lines are fitted to data points of tunnel speed versus anemometer output voltages up to 25 mph. The regression coefficients are then implemented into the data acquisition computer software when an instrument is installed in the field. The concern raised was that since the wind tunnel at SRS is able to generate a maximum wind speed higher than 25 mph, errors may be introduced in not using the full range of the wind tunnel.

Prediction by regression and intrarange data scatter in surface-process studies

USGS Publications Warehouse

Toy, T.J.; Osterkamp, W.R.; Renard, K.G.

1993-01-01

Modeling is a major component of contemporary earth science, and regression analysis occupies a central position in the parameterization, calibration, and validation of geomorphic and hydrologic models. Although this methodology can be used in many ways, we are primarily concerned with the prediction of values for one variable from another variable. Examination of the literature reveals considerable inconsistency in the presentation of the results of regression analysis and the occurrence of patterns in the scatter of data points about the regression line. Both circumstances confound utilization and evaluation of the models. Statisticians are well aware of various problems associated with the use of regression analysis and offer improved practices; often, however, their guidelines are not followed. After a review of the aforementioned circumstances and until standard criteria for model evaluation become established, we recommend, as a minimum, inclusion of scatter diagrams, the standard error of the estimate, and sample size in reporting the results of regression analyses for most surface-process studies. ?? 1993 Springer-Verlag.

Quantile regression for the statistical analysis of immunological data with many non-detects.

PubMed

Eilers, Paul H C; Röder, Esther; Savelkoul, Huub F J; van Wijk, Roy Gerth

2012-07-07

Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

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

PubMed

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

2010-01-01

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

Forecasting outbreaks of the Douglas-fir tussock moth from lower crown cocoon samples.

Treesearch

Richard R. Mason; Donald W. Scott; H. Gene Paul

1993-01-01

A predictive technique using a simple linear regression was developed to forecast the midcrown density of small tussock moth larvae from estimates of cocoon density in the previous generation. The regression estimator was derived from field samples of cocoons and larvae taken from a wide range of nonoutbreak tussock moth populations. The accuracy of the predictions was...

Estimation of the Regression Effect Using a Latent Trait Model.

ERIC Educational Resources Information Center

Quinn, Jimmy L.

A logistic model was used to generate data to serve as a proxy for an immediate retest from item responses to a fourth grade standardized reading comprehension test of 45 items. Assuming that the actual test may be considered a pretest and the proxy data may be considered a retest, the effect of regression was investigated using a percentage of…

Estimating the exceedance probability of rain rate by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

Locally-Based Kernal PLS Smoothing to Non-Parametric Regression Curve Fitting

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Trejo, Leonard J.; Wheeler, Kevin; Korsmeyer, David (Technical Monitor)

2002-01-01

We present a novel smoothing approach to non-parametric regression curve fitting. This is based on kernel partial least squares (PLS) regression in reproducing kernel Hilbert space. It is our concern to apply the methodology for smoothing experimental data where some level of knowledge about the approximate shape, local inhomogeneities or points where the desired function changes its curvature is known a priori or can be derived based on the observed noisy data. We propose locally-based kernel PLS regression that extends the previous kernel PLS methodology by incorporating this knowledge. We compare our approach with existing smoothing splines, hybrid adaptive splines and wavelet shrinkage techniques on two generated data sets.

Stolon regression

PubMed Central

Cherry Vogt, Kimberly S

2008-01-01

Many colonial organisms encrust surfaces with feeding and reproductive polyps connected by vascular stolons. Such colonies often show a dichotomy between runner-like forms, with widely spaced polyps and long stolon connections, and sheet-like forms, with closely spaced polyps and short stolon connections. Generative processes, such as rates of polyp initiation relative to rates of stolon elongation, are typically thought to underlie this dichotomy. Regressive processes, such as tissue regression and cell death, may also be relevant. In this context, we have recently characterized the process of stolon regression in a colonial cnidarian, Podocoryna carnea. Stolon regression occurs naturally in these colonies. To characterize this process in detail, high levels of stolon regression were induced in experimental colonies by treatment with reactive oxygen and reactive nitrogen species (ROS and RNS). Either treatment results in stolon regression and is accompanied by high levels of endogenous ROS and RNS as well as morphological indications of cell death in the regressing stolon. The initiating step in regression appears to be a perturbation of normal colony-wide gastrovascular flow. This suggests more general connections between stolon regression and a wide variety of environmental effects. Here we summarize our results and further discuss such connections. PMID:19704785

Information loss in approximately bayesian data assimilation: a comparison of generative and discriminative approaches to estimating agricultural yield

USDA-ARS?s Scientific Manuscript database

Data assimilation and regression are two commonly used methods for predicting agricultural yield from remote sensing observations. Data assimilation is a generative approach because it requires explicit approximations of the Bayesian prior and likelihood to compute the probability density function...

Factors Influencing the Academic Achievement of First-Generation College Students

ERIC Educational Resources Information Center

Strayhorn, Terrell L.

2006-01-01

First-generation college students face a number of unique challenges in college. These obstacles may have a disparate effect on educational outcomes such as academic achievement. This study presents findings from an analysis of the Baccalaureate & Beyond Longitudinal Study using hierarchical multiple regression techniques to measure the influence…

Poverty and Material Hardship in Grandparent-Headed Households

ERIC Educational Resources Information Center

Baker, Lindsey A.; Mutchler, Jan E.

2010-01-01

Using the 2001 Survey of Income and Program Participation, the current study examines poverty and material hardship among children living in 3-generation (n = 486), skipped-generation (n = 238), single-parent (n = 2,076), and 2-parent (n = 6,061) households. Multinomial and logistic regression models indicated that children living in…

Empirical analyses of plant-climate relationships for the western United States

Treesearch

Gerald E. Rehfeldt; Nicholas L. Crookston; Marcus V. Warwell; Jeffrey S. Evans

2006-01-01

The Random Forests multiple-regression tree was used to model climate profiles of 25 biotic communities of the western United States and nine of their constituent species. Analyses of the communities were based on a gridded sample of ca. 140,000 points, while those for the species used presence-absence data from ca. 120,000 locations. Independent variables included 35...

Factors That Contribute to the Completion of Programs of Study at Arkansas Institutions of Higher Education for African American Males

ERIC Educational Resources Information Center

Petty, Barrett Wade McCoy

2015-01-01

The study examined factors that predicted the completion of programs of study at Arkansas institutions of higher education for African American males. Astin's (1993a) Input-Environment-Output (I-E-O) Model was used as the theoretical foundation. Descriptive analyses and hierarchical logistic regression analyses were performed on the data. The…

Aggression in Primary Schools: The Predictive Power of the School and Home Environment

ERIC Educational Resources Information Center

Kozina, Ana

2015-01-01

In this study, we analyse the predictive power of home and school environment-related factors for determining pupils' aggression. The multiple regression analyses are performed for fourth- and eighth-grade pupils based on the Trends in Mathematics and Science Study (TIMSS) 2007 (N = 8394) and TIMSS 2011 (N = 9415) databases for Slovenia. At the…

Subjective Health and Mental Well-Being of Adolescents and the Health Promoting School: A Cross-Sectional Multilevel Analysis

ERIC Educational Resources Information Center

Levin, Kate; Inchley, Jo; Currie, Dorothy; Currie, Candace

2012-01-01

Purpose: The aim of this paper is to examine the impact of the health promoting school (HPS) on adolescent well-being. Design/methodology/approach: Data from the 2006 Health Behaviour in School-aged Children: WHO-collaborative Study in Scotland were analysed using multilevel linear regression analyses for outcome measures: happiness, confidence,…

Multidrug-resistant pulmonary tuberculosis in Los Altos, Selva and Norte regions, Chiapas, Mexico.

PubMed

Sánchez-Pérez, H J; Díaz-Vázquez, A; Nájera-Ortiz, J C; Balandrano, S; Martín-Mateo, M

2010-01-01

To analyse the proportion of multidrug-resistant tuberculosis (MDR-TB) in cultures performed during the period 2000-2002 in Los Altos, Selva and Norte regions, Chiapas, Mexico, and to analyse MDR-TB in terms of clinical and sociodemographic indicators. Cross-sectional study of patients with pulmonary tuberculosis (PTB) from the above regions. Drug susceptibility testing results from two research projects were analysed, as were those of routine sputum samples sent in by health personnel for processing (n = 114). MDR-TB was analysed in terms of the various variables of interest using bivariate tests of association and logistic regression. The proportion of primary MDR-TB was 4.6% (2 of 43), that of secondary MDR-TB was 29.2% (7/24), while among those whose history of treatment was unknown the proportion was 14.3% (3/21). According to the logistic regression model, the variables most highly associated with MDR-TB were as follows: having received anti-tuberculosis treatment previously, cough of >3 years' duration and not being indigenous. The high proportion of MDR cases found in the regions studied shows that it is necessary to significantly improve the control and surveillance of PTB.

The Protective Role of Resilience in Attenuating Emotional Distress and Aggression Associated with Early-life Stress in Young Enlisted Military Service Candidates.

PubMed

Kim, Joohan; Seok, Jeong-Ho; Choi, Kang; Jon, Duk-In; Hong, Hyun Ju; Hong, Narei; Lee, Eunjeong

2015-11-01

Early life stress (ELS) may induce long-lasting psychological complications in adulthood. The protective role of resilience against the development of psychopathology is also important. The purpose of this study was to investigate the relationships among ELS, resilience, depression, anxiety, and aggression in young adults. Four hundred sixty-one army inductees gave written informed consent and participated in this study. We assessed psychopathology using the Korea Military Personality Test, ELS using the Childhood Abuse Experience Scale, and resilience with the resilience scale. Analyses of variance, correlation analyses, and hierarchical multiple linear regression analyses were conducted for statistical analyses. The regression model explained 35.8%, 41.0%, and 23.3% of the total variance in the depression, anxiety, and aggression indices, respectively. We can find that even though ELS experience is positively associated with depression, anxiety, and aggression, resilience may have significant attenuating effect against the ELS effect on severity of these psychopathologies. Emotion regulation showed the most beneficial effect among resilience factors on reducing severity of psychopathologies. To improve mental health for young adults, ELS assessment and resilience enhancement program should be considered.

The Protective Role of Resilience in Attenuating Emotional Distress and Aggression Associated with Early-life Stress in Young Enlisted Military Service Candidates

PubMed Central

Kim, Joohan; Choi, Kang; Jon, Duk-In; Hong, Hyun Ju; Hong, Narei; Lee, Eunjeong

2015-01-01

Early life stress (ELS) may induce long-lasting psychological complications in adulthood. The protective role of resilience against the development of psychopathology is also important. The purpose of this study was to investigate the relationships among ELS, resilience, depression, anxiety, and aggression in young adults. Four hundred sixty-one army inductees gave written informed consent and participated in this study. We assessed psychopathology using the Korea Military Personality Test, ELS using the Childhood Abuse Experience Scale, and resilience with the resilience scale. Analyses of variance, correlation analyses, and hierarchical multiple linear regression analyses were conducted for statistical analyses. The regression model explained 35.8%, 41.0%, and 23.3% of the total variance in the depression, anxiety, and aggression indices, respectively. We can find that even though ELS experience is positively associated with depression, anxiety, and aggression, resilience may have significant attenuating effect against the ELS effect on severity of these psychopathologies. Emotion regulation showed the most beneficial effect among resilience factors on reducing severity of psychopathologies. To improve mental health for young adults, ELS assessment and resilience enhancement program should be considered. PMID:26539013

Spatial disparity in the distribution of superfund sites in South Carolina: an ecological study.

PubMed

Burwell-Naney, Kristen; Zhang, Hongmei; Samantapudi, Ashok; Jiang, Chengsheng; Dalemarre, Laura; Rice, LaShanta; Williams, Edith; Wilson, Sacoby

2013-11-06

According to the US Environmental Protection Agency (EPA), Superfund is a federal government program implemented to clean up uncontrolled hazardous waste sites. Twenty-six sites in South Carolina (SC) have been included on the National Priorities List (NPL), which has serious human health and environmental implications. The purpose of this study was to assess spatial disparities in the distribution of Superfund sites in SC. The 2000 US census tract and block level data were used to generate population characteristics, which included race/ethnicity, socioeconomic status (SES), education, home ownership, and home built before 1950. Geographic Information Systems (GIS) were used to map Superfund facilities and develop choropleth maps based on the aforementioned sociodemographic variables. Spatial methods, including mean and median distance analysis, buffer analysis, and spatial approximation were employed to characterize burden disparities. Regression analysis was performed to assess the relationship between the number of Superfund facilities and population characteristics. Spatial coincidence results showed that of the 29.5% of Blacks living in SC, 55.9% live in Superfund host census tracts. Among all populations in SC living below poverty (14.2%), 57.2% were located in Superfund host census tracts. Buffer analyses results (0.5mi, 1.0mi, 5.0mi, 0.5km, 1.0km, and 5.0km) showed a higher percentage of Whites compared to Blacks hosting a Superfund facility. Conversely, a slightly higher percentage of Blacks hosted (30.2%) a Superfund facility than those not hosting (28.8%) while their White counterparts had more equivalent values (66.7% and 67.8%, respectively). Regression analyses in the reduced model (Adj. R2 = 0.038) only explained a small percentage of the variance. In addition, the mean distance for percent of Blacks in the 90th percentile for Superfund facilities was 0.48mi. Burden disparities exist in the distribution of Superfund facilities in SC at the block and census tract levels across varying levels of demographic composition for race/ethnicity and SES.

Accuracy of PDFF estimation by magnitude-based and complex-based MRI in children with MR spectroscopy as a reference.

PubMed

Haufe, William M; Wolfson, Tanya; Hooker, Catherine A; Hooker, Jonathan C; Covarrubias, Yesenia; Schlein, Alex N; Hamilton, Gavin; Middleton, Michael S; Angeles, Jorge E; Hernando, Diego; Reeder, Scott B; Schwimmer, Jeffrey B; Sirlin, Claude B

2017-12-01

To assess and compare the accuracy of magnitude-based magnetic resonance imaging (MRI-M) and complex-based MRI (MRI-C) for estimating hepatic proton density fat fraction (PDFF) in children, using MR spectroscopy (MRS) as the reference standard. A secondary aim was to assess the agreement between MRI-M and MRI-C. This was a HIPAA-compliant, retrospective analysis of data collected in children enrolled in prospective, Institutional Review Board (IRB)-approved studies between 2012 and 2014. Informed consent was obtained from 200 children (ages 8-19 years) who subsequently underwent 3T MR exams that included MRI-M, MRI-C, and T 1 -independent, T 2 -corrected, single-voxel stimulated echo acquisition mode (STEAM) MRS. Both MRI methods acquired six echoes at low flip angles. T2*-corrected PDFF parametric maps were generated. PDFF values were recorded from regions of interest (ROIs) drawn on the maps in each of the nine Couinaud segments and three ROIs colocalized to the MRS voxel location. Regression analyses assessing agreement with MRS were performed to evaluate the accuracy of each MRI method, and Bland-Altman and intraclass correlation coefficient (ICC) analyses were performed to assess agreement between the MRI methods. MRI-M and MRI-C PDFF were accurate relative to the colocalized MRS reference standard, with regression intercepts of 0.63% and -0.07%, slopes of 0.998 and 0.975, and proportion-of-explained-variance values (R 2 ) of 0.982 and 0.979, respectively. For individual Couinaud segments and for the whole liver averages, Bland-Altman biases between MRI-M and MRI-C were small (ranging from 0.04 to 1.11%) and ICCs were high (≥0.978). Both MRI-M and MRI-C accurately estimated hepatic PDFF in children, and high intermethod agreement was observed. 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1641-1647. © 2017 International Society for Magnetic Resonance in Medicine.

Calculating the individual probability of successful ocriplasmin treatment in eyes with VMT syndrome: a multivariable prediction model from the EXPORT study.

PubMed

Paul, Christoph; Heun, Christine; Müller, Hans-Helge; Hoerauf, Hans; Feltgen, Nicolas; Wachtlin, Joachim; Kaymak, Hakan; Mennel, Stefan; Koss, Michael Janusz; Fauser, Sascha; Maier, Mathias M; Schumann, Ricarda G; Mueller, Simone; Chang, Petrus; Schmitz-Valckenberg, Steffen; Kazerounian, Sara; Szurman, Peter; Lommatzsch, Albrecht; Bertelmann, Thomas

2017-10-31

To evaluate predictive factors for the treatment success of ocriplasmin and to use these factors to generate a multivariate model to calculate the individual probability of successful treatment. Data were collected in a retrospective, multicentre cohort study. Patients with vitreomacular traction (VMT) syndrome without a full-thickness macular hole were included if they received an intravitreal injection (IVI) of ocriplasmin. Five factors (age, gender, lens status, presence of epiretinal membrane (ERM) formation and horizontal diameter of VMT) were assessed on their association with VMT resolution. A multivariable logistic regression model was employed to further analyse these factors and calculate the individual probability of successful treatment. 167 eyes of 167 patients were included. Univariate analysis revealed a significant correlation to VMT resolution for all analysed factors: age (years) (OR 0.9208; 95% CI 0.8845 to 0.9586; p<0.0001), gender (male) (OR 0.480; 95% CI 0.241 to 0.957; p=0.0371), lens status (phakic) (OR 2.042; 95% CI 1.054 to 3.958; p=0.0344), ERM formation (present) (OR 0.384; 95% CI 0.179 to 0.821; p=0.0136) and horizontal VMT diameter (µm) (OR 0.99812; 95% CI 0.99684 to 0.99941, p=0.0042). A significant multivariable logistic regression model was established with age and VMT diameter. Known predictive factors for VMT resolution after ocriplasmin IVI were confirmed in our study. We were able to combine them into a formula, ultimately allowing the calculation of an individual probability of treatment success with ocriplasmin in patients with VMT syndrome without FTHM. © 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.

Social inequalities and dental caries in six-year-old children from the Netherlands.

PubMed

van der Tas, Justin T; Kragt, Lea; Elfrink, Marlies E C; Bertens, Loes C M; Jaddoe, Vincent W V; Moll, Henriëtte A; Ongkosuwito, Edwin M; Wolvius, Eppo B

2017-07-01

The purpose of our study was to investigate the association of different socioeconomic and sociodemographic factors with dental caries in six-year-old children. Furthermore, we applied a district based approach to explore the distribution of dental caries among districts of low and high socioeconomic position (SEP). In our cross-sectional study 5189 six-year-olds were included. This study was embedded in a prospective population-based birth cohort study in Rotterdam, the Netherlands, the Generation R Study. Parental education level, parental employment status, net household income, single parenting, and teenage pregnancy were considered as indicators for SEP. Dental caries was scored on intraoral photographs by using the decayed, missing, and filled teeth (dmft) index. We compared children without caries (dmft=0) to children with mild caries (dmft=1-3) or severe caries (dmft >3). Multinomial logistic regression analyses and binary logistic regression analyses were performed to study the association between SEP and caries, and between district and caries, respectively. Only maternal education level remained significantly associated with mild caries after adjusting for all other SEP-indicators. Paternal educational level, parental employment status, and household income additionally served as independent indicators of SEP in children with severe caries. Furthermore, living in more disadvantaged districts was significantly associated with higher odds of dental caries. Dental caries is more prevalent among six-year-old children with a low SEP, which is also visible at the district level. Maternal educational level is the most important indicator of SEP in the association with caries. Our results should raise concerns about the existing social inequalities in dental caries and should encourage development of dental caries prevention strategies. New knowledge about the distribution of oral health inequalities between districts should be used to target the right audience for these strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Interferon alpha adjuvant therapy in patients with high-risk melanoma: a systematic review and meta-analysis.

PubMed

Mocellin, Simone; Pasquali, Sandro; Rossi, Carlo R; Nitti, Donato

2010-04-07

Based on previous meta-analyses of randomized controlled trials (RCTs), the use of interferon alpha (IFN-alpha) in the adjuvant setting improves disease-free survival (DFS) in patients with high-risk cutaneous melanoma. However, RCTs have yielded conflicting data on the effect of IFN-alpha on overall survival (OS). We conducted a systematic review and meta-analysis to examine the effect of IFN-alpha on DFS and OS in patients with high-risk cutaneous melanoma. The systematic review was performed by searching MEDLINE, EMBASE, Cancerlit, Cochrane, ISI Web of Science, and ASCO databases. The meta-analysis was performed using time-to-event data from which hazard ratios (HRs) and 95% confidence intervals (CIs) of DFS and OS were estimated. Subgroup and meta-regression analyses to investigate the effect of dose and treatment duration were also performed. Statistical tests were two-sided. The meta-analysis included 14 RCTs, published between 1990 and 2008, and involved 8122 patients, of which 4362 patients were allocated to the IFN-alpha arm. IFN-alpha alone was compared with observation in 12 of the 14 trials, and 17 comparisons (IFN-alpha vs comparator) were generated in total. IFN-alpha treatment was associated with a statistically significant improvement in DFS in 10 of the 17 comparisons (HR for disease recurrence = 0.82, 95% CI = 0.77 to 0.87; P < .001) and improved OS in four of the 14 comparisons (HR for death = 0.89, 95% CI = 0.83 to 0.96; P = .002). No between-study heterogeneity in either DFS or OS was observed. No optimal IFN-alpha dose and/or treatment duration or a subset of patients more responsive to adjuvant therapy was identified using subgroup analysis and meta-regression. In patients with high-risk cutaneous melanoma, IFN-alpha adjuvant treatment showed statistically significant improvement in both DFS and OS.

A Two-Phase Case-Control Study of Autism Risk Among Children Born From the Late 1990s Through the Early 2000s in the United States.

PubMed

Geier, David A; Kern, Janet K; Geier, Mark R

2016-12-29

BACKGROUND This study evaluated the hypothesis that the 1999 recommendation by the American Academy of Pediatrics (AAP) and US Public Health Service (PHS) to reduce exposure to mercury (Hg) from Thimerosal in US vaccines would be associated with a reduction in the long-term risk of being diagnosed with autism. MATERIAL AND METHODS A two-phase assessment utilizing a case (n=73) -control (n=11,783) study in the Vaccine Adverse Event Reporting System (VAERS) database (for hypothesis generating) and a more rigorous, independent matched case (n=40) -control (n=40) study (hypothesis testing) was undertaken. RESULTS Analysis of the VAERS database using logistic regression revealed that the odds ratio (OR) for being an autism case in the VAERS database significantly decreased with a more recent year of vaccination in comparison to controls (OR=0.65) from 1998 to 2003. Sex-separated analyses revealed similar significant effects for males (OR=0.62) and females (OR=0.71). Analyses of the matched case-control data revealed, using the t-test statistic, that the mean date of birth among cases diagnosed with an autism spectrum disorder (ASD) (2000.5±1.2) was significantly more in the past than in controls (2001.1±1.3). Logistic regression also revealed that the OR for being diagnosed with ASD significantly decreased with a more recent date of birth in comparison to controls (OR=0.67) from 1998-2003. CONCLUSIONS This study reveals that the risk of autism during from the late1990s to early 2000s in the US significantly decreased with reductions in Hg exposure from Thimerosal-containing childhood vaccines, but future studies should examine this phenomenon in other US populations. Vaccine programs have significantly reduced the morbidity and mortality associated with infectious disease, but Thimerosal should be removed from all vaccines.

Meta-regression analyses, meta-analyses, and trial sequential analyses of the effects of supplementation with beta-carotene, vitamin A, and vitamin E singly or in different combinations on all-cause mortality: do we have evidence for lack of harm?

PubMed

Bjelakovic, Goran; Nikolova, Dimitrinka; Gluud, Christian

2013-01-01

Evidence shows that antioxidant supplements may increase mortality. Our aims were to assess whether different doses of beta-carotene, vitamin A, and vitamin E affect mortality in primary and secondary prevention randomized clinical trials with low risk of bias. The present study is based on our 2012 Cochrane systematic review analyzing beneficial and harmful effects of antioxidant supplements in adults. Using random-effects meta-analyses, meta-regression analyses, and trial sequential analyses, we examined the association between beta-carotene, vitamin A, and vitamin E, and mortality according to their daily doses and doses below and above the recommended daily allowances (RDA). We included 53 randomized trials with low risk of bias (241,883 participants, aged 18 to 103 years, 44.6% women) assessing beta-carotene, vitamin A, and vitamin E. Meta-regression analysis showed that the dose of vitamin A was significantly positively associated with all-cause mortality. Beta-carotene in a dose above 9.6 mg significantly increased mortality (relative risk (RR) 1.06, 95% confidence interval (CI) 1.02 to 1.09, I(2) = 13%). Vitamin A in a dose above the RDA (> 800 µg) did not significantly influence mortality (RR 1.08, 95% CI 0.98 to 1.19, I(2) = 53%). Vitamin E in a dose above the RDA (> 15 mg) significantly increased mortality (RR 1.03, 95% CI 1.00 to 1.05, I(2) = 0%). Doses below the RDAs did not affect mortality, but data were sparse. Beta-carotene and vitamin E in doses higher than the RDA seem to significantly increase mortality, whereas we lack information on vitamin A. Dose of vitamin A was significantly associated with increased mortality in meta-regression. We lack information on doses below the RDA. All essential compounds to stay healthy cannot be synthesized in our body. Therefore, these compounds must be taken through our diet or obtained in other ways [1]. Oxidative stress has been suggested to cause a variety of diseases [2]. Therefore, it is speculated that antioxidant supplements could have a potential role in preventing diseases and death. Despite the fact that a normal diet in high-income countries may provide sufficient amounts of antioxidants [3,4], more than one third of adults regularly take antioxidant supplements [5,6].

Decomposition Technique for Remaining Useful Life Prediction

NASA Technical Reports Server (NTRS)

Saha, Bhaskar (Inventor); Goebel, Kai F. (Inventor); Saxena, Abhinav (Inventor); Celaya, Jose R. (Inventor)

2014-01-01

The prognostic tool disclosed here decomposes the problem of estimating the remaining useful life (RUL) of a component or sub-system into two separate regression problems: the feature-to-damage mapping and the operational conditions-to-damage-rate mapping. These maps are initially generated in off-line mode. One or more regression algorithms are used to generate each of these maps from measurements (and features derived from these), operational conditions, and ground truth information. This decomposition technique allows for the explicit quantification and management of different sources of uncertainty present in the process. Next, the maps are used in an on-line mode where run-time data (sensor measurements and operational conditions) are used in conjunction with the maps generated in off-line mode to estimate both current damage state as well as future damage accumulation. Remaining life is computed by subtracting the instance when the extrapolated damage reaches the failure threshold from the instance when the prediction is made.

Application of artificial neural network to fMRI regression analysis.

PubMed

Misaki, Masaya; Miyauchi, Satoru

2006-01-15

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

On the use of log-transformation vs. nonlinear regression for analyzing biological power laws

USGS Publications Warehouse

Xiao, X.; White, E.P.; Hooten, M.B.; Durham, S.L.

2011-01-01

Power-law relationships are among the most well-studied functional relationships in biology. Recently the common practice of fitting power laws using linear regression (LR) on log-transformed data has been criticized, calling into question the conclusions of hundreds of studies. It has been suggested that nonlinear regression (NLR) is preferable, but no rigorous comparison of these two methods has been conducted. Using Monte Carlo simulations, we demonstrate that the error distribution determines which method performs better, with NLR better characterizing data with additive, homoscedastic, normal error and LR better characterizing data with multiplicative, heteroscedastic, lognormal error. Analysis of 471 biological power laws shows that both forms of error occur in nature. While previous analyses based on log-transformation appear to be generally valid, future analyses should choose methods based on a combination of biological plausibility and analysis of the error distribution. We provide detailed guidelines and associated computer code for doing so, including a model averaging approach for cases where the error structure is uncertain. ?? 2011 by the Ecological Society of America.

Partial Least Squares Regression Models for the Analysis of Kinase Signaling.

PubMed

Bourgeois, Danielle L; Kreeger, Pamela K

2017-01-01

Partial least squares regression (PLSR) is a data-driven modeling approach that can be used to analyze multivariate relationships between kinase networks and cellular decisions or patient outcomes. In PLSR, a linear model relating an X matrix of dependent variables and a Y matrix of independent variables is generated by extracting the factors with the strongest covariation. While the identified relationship is correlative, PLSR models can be used to generate quantitative predictions for new conditions or perturbations to the network, allowing for mechanisms to be identified. This chapter will provide a brief explanation of PLSR and provide an instructive example to demonstrate the use of PLSR to analyze kinase signaling.

Neuropsychological tests for predicting cognitive decline in older adults

PubMed Central

Baerresen, Kimberly M; Miller, Karen J; Hanson, Eric R; Miller, Justin S; Dye, Richelin V; Hartman, Richard E; Vermeersch, David; Small, Gary W

2015-01-01

Summary Aim To determine neuropsychological tests likely to predict cognitive decline. Methods A sample of nonconverters (n = 106) was compared with those who declined in cognitive status (n = 24). Significant univariate logistic regression prediction models were used to create multivariate logistic regression models to predict decline based on initial neuropsychological testing. Results Rey–Osterrieth Complex Figure Test (RCFT) Retention predicted conversion to mild cognitive impairment (MCI) while baseline Buschke Delay predicted conversion to Alzheimer’s disease (AD). Due to group sample size differences, additional analyses were conducted using a subsample of demographically matched nonconverters. Analyses indicated RCFT Retention predicted conversion to MCI and AD, and Buschke Delay predicted conversion to AD. Conclusion Results suggest RCFT Retention and Buschke Delay may be useful in predicting cognitive decline. PMID:26107318

A regularization corrected score method for nonlinear regression models with covariate error.

PubMed

Zucker, David M; Gorfine, Malka; Li, Yi; Tadesse, Mahlet G; Spiegelman, Donna

2013-03-01

Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses' Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer. Copyright © 2013, The International Biometric Society.

Hepatitis B birth dose vaccination rates among children in Beijing: A comparison of local residents and first and second generation migrants

PubMed Central

Chen, Ruohan; Li, Youwei; Wangen, Knut Reidar; Nicholas, Stephen; Maitland, Elizabeth; Wang, Jian

2016-01-01

ABSTRACT Providing hepatitis B vaccine to all neonates within 24 hours of birth (Timely Birth Dose, TBD) is the key preventative measure to control perinatal hepatitis B virus infection. Previous Chinese studies of TBD only differentiated between migrant and non-migrant (local-born generation-LG) children. Our study is the first to stratify migrants in Beijing into first generation migrants (FGM) and second generation migrants (SGM). Based on a questionnaire survey of 2682 people in 3 Beijing villages, we identified 283 children aged 0–15 years, from 246 households, who were eligible for a TBD. Multinomial logistic regression and statistical analyses were used to examine factors explaining TBD rates for LG, FGM and SGM children. Surprisingly, the TBD for LG Beijing children was not significantly different from migrant children. But after stratifying migrant children into FGM and SGM, revealed significant TBD differences were revealed across LG, FGM and SGM according to domicile (p-value < 0.001, OR = 3.24), first vaccination covered by government policy (p-value < 0.05, OR = 3.24), mother's knowledge of hepatitis B (p-value < 0.05, OR = 1.01) and the government's HBV policy environment (p-value < 0.05, OR = 2.338). Birthplace (p-value = 0.002, OR = 6.21) and better policy environments (p-value = 0.01, OR = 2.80) were associated with higher TBD rate for LG and SGM children. Compared with FGM children, SGM had a significantly poorer TBD rate (Fisher exact test of chi-square = 0.013). We identified SGM as a special risk group; proposed Hukou reform to improve SGM TBD; and called for Beijing health authorities to match TBD rates in other provinces, especially by improving practices by health authorities and knowledge of parents. PMID:27043864

Witnessing images of extreme violence: a psychological study of journalists in the newsroom.

PubMed

Feinstein, Anthony; Audet, Blair; Waknine, Elizabeth

2014-08-01

User Generated Content - photos and videos submitted to newsrooms by the public - has become a prominent source of information for news organisations. Journalists working with uncensored material can frequently witness disturbing images for prolonged periods. How this might affect their psychological health is not known and it is the focus of this study. Descriptive, exploratory. The newsrooms of three international news organisations. One hundred and sixteen journalists working with User Generated Content material. Psychometric data included the re-experiencing, avoidance and autonomic arousal indices of posttraumatic stress disorder (Impact of Event Scale-revised), depression (Beck Depression Inventory-II; BDI-II), a measure of psychological distress (GHQ-28), the latter comprising four subscales measuring somatisation, anxiety, social dysfunction and depression, and mean weekly alcohol consumption divided according to gender. Regression analyses revealed that frequent (i.e. daily) exposure to violent images independently predicted higher scores on all indices of the Impact of Event Scale-revised, the BDI-II and the somatic and anxiety subscales of the GHQ-28. Exposure per shift only predicted scores on the intrusion subscale of the Impact of Event Scale-revised. The present study, the first of its kind, suggests that frequency rather than duration of exposure to images of graphic violence is more emotionally distressing to journalists working with User Generated Content material. Given that good journalism depends on healthy journalists, news organisations will need to look anew at what can be done to offset the risks inherent in viewing User Generated Content material. Our findings, in need of replication, suggest that reducing the frequency of exposure may be one way to go.

Hepatitis B birth dose vaccination rates among children in Beijing: A comparison of local residents and first and second generation migrants.

PubMed

Chen, Ruohan; Li, Youwei; Wangen, Knut Reidar; Nicholas, Stephen; Maitland, Elizabeth; Wang, Jian

2016-05-03

Providing hepatitis B vaccine to all neonates within 24 hours of birth (Timely Birth Dose, TBD) is the key preventative measure to control perinatal hepatitis B virus infection. Previous Chinese studies of TBD only differentiated between migrant and non-migrant (local-born generation-LG) children. Our study is the first to stratify migrants in Beijing into first generation migrants (FGM) and second generation migrants (SGM). Based on a questionnaire survey of 2682 people in 3 Beijing villages, we identified 283 children aged 0-15 years, from 246 households, who were eligible for a TBD. Multinomial logistic regression and statistical analyses were used to examine factors explaining TBD rates for LG, FGM and SGM children. Surprisingly, the TBD for LG Beijing children was not significantly different from migrant children. But after stratifying migrant children into FGM and SGM, revealed significant TBD differences were revealed across LG, FGM and SGM according to domicile (p-value < 0.001, OR = 3.24), first vaccination covered by government policy (p-value < 0.05, OR = 3.24), mother's knowledge of hepatitis B (p-value < 0.05, OR = 1.01) and the government's HBV policy environment (p-value < 0.05, OR = 2.338). Birthplace (p-value = 0.002, OR = 6.21) and better policy environments (p-value = 0.01, OR = 2.80) were associated with higher TBD rate for LG and SGM children. Compared with FGM children, SGM had a significantly poorer TBD rate (Fisher exact test of chi-square = 0.013). We identified SGM as a special risk group; proposed Hukou reform to improve SGM TBD; and called for Beijing health authorities to match TBD rates in other provinces, especially by improving practices by health authorities and knowledge of parents.

Higher risk of venous thrombosis associated with drospirenone-containing oral contraceptives: a population-based cohort study

PubMed Central

Gronich, Naomi; Lavi, Idit; Rennert, Gad

2011-01-01

Background: Combined oral contraceptives are a common method of contraception, but they carry a risk of venous and arterial thrombosis. We assessed whether use of drospirenone was associated with an increase in thrombotic risk relative to third-generation combined oral contraceptives. Methods: Using computerized records of the largest health care provider in Israel, we identified all women aged 12 to 50 years for whom combined oral contraceptives had been dispensed between Jan. 1, 2002, and Dec. 31, 2008. We followed the cohort until 2009. We used Poisson regression models to estimate the crude and adjusted rate ratios for risk factors for venous thrombotic events (specifically deep vein thrombosis and pulmonary embolism) and arterial thromboic events (specifically transient ischemic attack and cerebrovascular accident). We performed multivariable analyses to compare types of contraceptives, with adjustment for the various risk factors. Results: We identified a total of 1017 (0.24%) venous and arterial thrombotic events among 431 223 use episodes during 819 749 woman-years of follow-up (6.33 venous events and 6.10 arterial events per 10 000 woman-years). In a multivariable model, use of drospirenone carried an increased risk of venous thrombotic events, relative to both third-generation combined oral contraceptives (rate ratio [RR] 1.43, 95% confidence interval [CI] 1.15–1.78) and second-generation combined oral contraceptives (RR 1.65, 95% CI 1.02–2.65). There was no increase in the risk of arterial thrombosis with drospirenone. Interpretation: Use of drospirenone-containing oral contraceptives was associated with an increased risk of deep vein thrombosis and pulmonary embolism, but not transient ischemic attack or cerebrovascular attack, relative to second- and third-generation combined oral contraceptives. PMID:22065352

A Meta-Analysis of Randomized Controlled Trials and Prospective Cohort Studies of Eicosapentaenoic and Docosahexaenoic Long-Chain Omega-3 Fatty Acids and Coronary Heart Disease Risk.

PubMed

Alexander, Dominik D; Miller, Paige E; Van Elswyk, Mary E; Kuratko, Connye N; Bylsma, Lauren C

2017-01-01

To conduct meta-analyses of randomized controlled trials (RCTs) to estimate the effect of eicosapentaenoic and docosahexaenoic acid (EPA+DHA) on coronary heart disease (CHD), and to conduct meta-analyses of prospective cohort studies to estimate the association between EPA+DHA intake and CHD risk. A systematic literature search of Ovid/Medline, PubMed, Embase, and the Cochrane Library from January 1, 1947, to November 2, 2015, was conducted; 18 RCTs and 16 prospective cohort studies examining EPA+DHA from foods or supplements and CHD, including myocardial infarction, sudden cardiac death, coronary death, and angina, were identified. Random-effects meta-analysis models were used to generate summary relative risk estimates (SRREs) and 95% CIs. Heterogeneity was examined in subgroup and sensitivity analyses and by meta-regression. Dose-response was evaluated in stratified dose or intake analyses. Publication bias assessments were performed. Among RCTs, there was a nonstatistically significant reduction in CHD risk with EPA+DHA provision (SRRE=0.94; 95% CI, 0.85-1.05). Subgroup analyses of data from RCTs indicated a statistically significant CHD risk reduction with EPA+DHA provision among higher-risk populations, including participants with elevated triglyceride levels (SRRE=0.84; 95% CI, 0.72-0.98) and elevated low-density lipoprotein cholesterol (SRRE=0.86; 95% CI, 0.76-0.98). Meta-analysis of data from prospective cohort studies resulted in a statistically significant SRRE of 0.82 (95% CI, 0.74-0.92) for higher intakes of EPA+DHA and risk of any CHD event. Results indicate that EPA+DHA may be associated with reducing CHD risk, with a greater benefit observed among higher-risk populations in RCTs. Copyright © 2016 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

Correlation between Mitochondrial Reactive Oxygen and Severity of Atherosclerosis.

PubMed

Dorighello, Gabriel G; Paim, Bruno A; Kiihl, Samara F; Ferreira, Mônica S; Catharino, Rodrigo R; Vercesi, Anibal E; Oliveira, Helena C F

2016-01-01

Atherosclerosis has been associated with mitochondria dysfunction and damage. Our group demonstrated previously that hypercholesterolemic mice present increased mitochondrial reactive oxygen (mtROS) generation in several tissues and low NADPH/NADP+ ratio. Here, we investigated whether spontaneous atherosclerosis in these mice could be modulated by treatments that replenish or spare mitochondrial NADPH, named citrate supplementation, cholesterol synthesis inhibition, or both treatments simultaneously. Robust statistical analyses in pooled group data were performed in order to explain the variation of atherosclerosis lesion areas as related to the classic atherosclerosis risk factors such as plasma lipids, obesity, and oxidative stress, including liver mtROS. Using three distinct statistical tools (univariate correlation, adjusted correlation, and multiple regression) with increasing levels of stringency, we identified a novel significant association and a model that reliably predicts the extent of atherosclerosis due to variations in mtROS. Thus, results show that atherosclerosis lesion area is positively and independently correlated with liver mtROS production rates. Based on these findings, we propose that modulation of mitochondrial redox state influences the atherosclerosis extent.

Religious Attendance, Health-Promoting Lifestyle Behaviors, and Depressive Symptoms Among Koreans in the United Arab Emirates (UAE).

PubMed

Kim, Hee Jun; Pearce, Michelle; Choi-Kwon, Smi

2015-08-01

Migrants, who comprise 80 % of the population in the United Arab Emirates, are at risk of developing mental health disorders. To test associations among religious attendance, health-promoting lifestyle behaviors (HPLB), and depressive symptoms, we conducted a cross-sectional survey in Dubai. Measures included frequency of religious attendance, the Health-Promoting Lifestyle Profile, and the Depression, Anxiety, and Stress Scale. Multiple regression analyses were used to explore relationships among religious attendance, HPLB, and depressive symptoms. Religious attendance was significantly associated with self-actualization, stress management, and depressive symptoms. Self-actualization and stress management mediated the relationships between religious attendance and depressive symptoms for both males and females, and interpersonal support mediated the relationship for females, controlling for age and education. The facilitation of positive internal and external resources generated by participating in religious activities may have increased the likelihood that the Korean migrants would engage in psychosocial healthy lifestyle behaviors, and may have decreased depressive symptoms.

Analysis of the lateral push-off in the freestyle flip turn.

PubMed

Araujo, Luciana; Pereira, Suzana; Gatti, Roberta; Freitas, Elinai; Jacomel, Gabriel; Roesler, Helio; Villas-Boas, Joao

2010-09-01

The aim of this study was to examine the contact phase during the lateral push-off in the turn of front crawl swimming to determine which biomechanical variables (maximum normalized peak force, contact time, impulse, angle of knee flexion, and total turn time within 15 m) contribute to the performance of this turn technique. Thirty-four swimmers of state, national, and international competitive standard participated in the study. For data collection, the following equipment was used: an underwater force platform, a 30-Hz VHS video camera, and a MiniDv digital camera within an underwater box. Data are expressed as descriptive statistics. Inferential analyses were performed using Pearson's correlation and multiple linear regressions. All variables studied had a significant relationship with turn performance. We conclude that a turn executed with a knee flexion angle of between 100° and 120° provides optimum peak forces to generate impulses that allow the swimmer to lose less time in the turn without the need for an excessive force application and with less energy lost.

A national analysis of the relationship between hospital factors and post-cardiac arrest mortality.

PubMed

Carr, Brendan G; Goyal, Munish; Band, Roger A; Gaieski, David F; Abella, Benjamin S; Merchant, Raina M; Branas, Charles C; Becker, Lance B; Neumar, Robert W

2009-03-01

We sought to generate national estimates for post-cardiac arrest mortality, to assess trends, and to identify hospital factors associated with survival. We used a national sample of US hospitals to identify patients resuscitated after cardiac arrest from 2000 to 2004 to describe the association between hospital factors (teaching status, location, size) and mortality, length of stay, and hospital charges. Analyses were performed using logistic regression. A total of 109,739 patients were identified. In-hospital mortality was 70.6%. A 2% decrease in unadjusted mortality from 71.6% in 2000 to 69.6% in 2004 (OR 0.96, P < 0.001) was observed. Mortality was lower at teaching hospitals (OR 0.58, P = 0.001), urban hospitals (OR 0.63, P = 0.004), and large hospitals (OR 0.55, P < 0.001). Mortality after in-hospital cardiac arrest decreased over 5 years. Mortality was lower at urban, teaching, and large hospitals. There are implications for dissemination of best practices or regionalization of post-cardiac arrest care.

Comparison of different modelling approaches of drive train temperature for the purposes of wind turbine failure detection

NASA Astrophysics Data System (ADS)

Tautz-Weinert, J.; Watson, S. J.

2016-09-01

Effective condition monitoring techniques for wind turbines are needed to improve maintenance processes and reduce operational costs. Normal behaviour modelling of temperatures with information from other sensors can help to detect wear processes in drive trains. In a case study, modelling of bearing and generator temperatures is investigated with operational data from the SCADA systems of more than 100 turbines. The focus is here on automated training and testing on a farm level to enable an on-line system, which will detect failures without human interpretation. Modelling based on linear combinations, artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines and Gaussian process regression is compared. The selection of suitable modelling inputs is discussed with cross-correlation analyses and a sensitivity study, which reveals that the investigated modelling techniques react in different ways to an increased number of inputs. The case study highlights advantages of modelling with linear combinations and artificial neural networks in a feedforward configuration.

Parental representations in drug-dependent patients and their parents.

PubMed

Torresani, S; Favaretto, E; Zimmermann, C

2000-01-01

The Parental Bonding Instrument (PBI), a measure of perceived parental care and protection, was administered to drug-dependent patients and their parents with the aim to assess the reliability of the instrument in such samples and to compare the parental representations across generations. Ninety drug-dependent patients and 44 mothers and 35 fathers participated. Reliability indices were calculated, and parental representations of parents and their offspring were compared. Linear regression analyses were performed with the patient's PBI score as the dependent variable and the mother's and father's PBI scores as predictor variables. The reliability indices were highly satisfactory and varied between 0.61 and 0.91. The parental bonding of patients, fathers, and mothers was similar. All three groups reported high maternal and paternal control and low maternal care, a pattern characteristic of an "affectionless control" rearing style. Maternal care received by the fathers and paternal protection received by the mothers predicted the care and protection they themselves gave to their drug-dependent offspring.

Bagging Voronoi classifiers for clustering spatial functional data

NASA Astrophysics Data System (ADS)

Secchi, Piercesare; Vantini, Simone; Vitelli, Valeria

2013-06-01

We propose a bagging strategy based on random Voronoi tessellations for the exploration of geo-referenced functional data, suitable for different purposes (e.g., classification, regression, dimensional reduction, …). Urged by an application to environmental data contained in the Surface Solar Energy database, we focus in particular on the problem of clustering functional data indexed by the sites of a spatial finite lattice. We thus illustrate our strategy by implementing a specific algorithm whose rationale is to (i) replace the original data set with a reduced one, composed by local representatives of neighborhoods covering the entire investigated area; (ii) analyze the local representatives; (iii) repeat the previous analysis many times for different reduced data sets associated to randomly generated different sets of neighborhoods, thus obtaining many different weak formulations of the analysis; (iv) finally, bag together the weak analyses to obtain a conclusive strong analysis. Through an extensive simulation study, we show that this new procedure - which does not require an explicit model for spatial dependence - is statistically and computationally efficient.

Associations between social support network characteristics and receipt of emotional and material support among a sample of male sexual minority youth

PubMed Central

Kapadia, Farzana; Halkitis, Perry; Barton, Staci; Siconolfi, Daniel; Figueroa, Rafael Perez

2014-01-01

Few studies have examined how social support network characteristics are related to perceived receipt of social support among male sexual minority youth. Using egocentric network data collected from a study of male sexual minority youth (n=592), multivariable logistic regression analyses examined distinct associations between individual and social network characteristics with receipt of (1) emotional and (2) material support. In multivariable models, frequent communication and having friends in one’s network yielded a two-fold increase in the likelihood of receiving emotional support whereas frequent communication was associated with an almost three-fold higher likelihood of perceived material support. Finally, greater internalized homophobia and personal experiences of gay-related stigma were inversely associated with perceived receipt of emotional and material support, respectively. Understanding the evolving social context and social interactions of this new generation of male sexual minority youth is warranted in order to understand the broader, contextual factors associated with their overall health and well-being. PMID:25214756

Assessing corporate social responsibility in China's sports lottery administration and its influence on consumption behavior.

PubMed

Li, Hai; Zhang, James J; Mao, Luke Lunhua; Min, Sophia D

2012-09-01

The purpose of this study was to identify and examine consumer perception of corporate social responsibility (CSR) in China's sports lottery industry, and the effect of perceived CSR initiatives on sports lottery consumption behavior. Research participants (N = 4,980), selected based on a computer-generated, randomly stratified multistage sampling process, comprised Chinese residents who had purchased sports lottery tickets in the past 12 months. They completed a questionnaire that was derived from a qualitative research process. A factor analysis extracted two factors associated with perceptions of CSR in China's sports lottery administration: Regulatory and Prevention Responsibilities and Product Development Responsibility. Logistic regression analyses revealed that these two factors were influential of consumer behavior (i.e., relative and absolute expenditure, purchasing frequency, and time commitment). This study represents an initial effort to understand the dimensions of perceived CSR associated with Chinese sports lottery. The findings signify the importance of enforcing CSR in sports lottery administration.

Optimization and comparison of three spatial interpolation methods for electromagnetic levels in the AM band within an urban area.

PubMed

Rufo, Montaña; Antolín, Alicia; Paniagua, Jesús M; Jiménez, Antonio

2018-04-01

A comparative study was made of three methods of interpolation - inverse distance weighting (IDW), spline and ordinary kriging - after optimization of their characteristic parameters. These interpolation methods were used to represent the electric field levels for three emission frequencies (774kHz, 900kHz, and 1107kHz) and for the electrical stimulation quotient, Q E , characteristic of complex electromagnetic environments. Measurements were made with a spectrum analyser in a village in the vicinity of medium-wave radio broadcasting antennas. The accuracy of the models was quantified by comparing their predictions with levels measured at the control points not used to generate the models. The results showed that optimizing the characteristic parameters of each interpolation method allows any of them to be used. However, the best results in terms of the regression coefficient between each model's predictions and the actual control point field measurements were for the IDW method. Copyright © 2018 Elsevier Inc. All rights reserved.

Peroxiredoxin 2 regulates PGF2α-induced corpus luteum regression in mice by inhibiting ROS-dependent JNK activation.

PubMed

Park, Sun-Ji; Kim, Jung-Hak; Kim, Tae-Shin; Lee, Sang-Rae; Park, Jeen-Woo; Lee, Seunghoon; Kim, Jin-Man; Lee, Dong-Seok

2017-07-01

Luteal regression is a natural and necessary event to regulate the reproductive process in all mammals. Prostaglandin F2α (PGF2α) is the main factor that causes functional and structural regression of the corpus luteum (CL). It is well known that PGF2α-mediated ROS generation is closely involved in luteal regression. Peroxiredoxin 2 (Prx2) as an antioxidant enzyme plays a protective role against oxidative stress-induced cell death. However, the effect of Prx2 on PGF2α-induced luteal regression has not been reported. Here, we investigated the role of Prx2 in functional and structural CL regression induced by PGF2α-mediated ROS using Prx2-deficient (-/-) mice. We found that PGF2α-induced ROS generation was significantly higher in Prx2-/- MEF cells compared with that in wild-type (WT) cells, which induced apoptosis by activating JNK-mediated apoptotic signaling pathway. Also, PGF2α treatment in the CL derived from Prx2-/- mice promoted the reduction of steroidogenic enzyme expression and the activation of JNK and caspase3. Compared to WT mice, serum progesterone levels and luteal expression of steroidogenic enzymes decreased more rapidly whereas JNK and caspase3 activations were significantly increased in Prx2-/- mice injected with PGF2α. However, the impaired steroidogenesis and PGF2α-induced JNK-dependent apoptosis were rescued by the addition of the antioxidant N-acetyl-L-cysteine (NAC). This is the first study to demonstrate that Prx2 deficiency ultimately accelerated the PGF2α-induced luteal regression through activation of the ROS-dependent JNK pathway. These findings suggest that Prx2 plays a crucial role in preventing accelerated luteal regression via inhibition of the ROS/JNK pathway. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing.

PubMed

Stamate, Mirela Cristina; Todor, Nicolae; Cosgarea, Marcel

2015-01-01

The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing

PubMed Central

STAMATE, MIRELA CRISTINA; TODOR, NICOLAE; COSGAREA, MARCEL

2015-01-01

Background and aim The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. Methods The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. Results We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Conclusion Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies. PMID:26733749

Early Preterm Birth Across Generations Among Whites and African-Americans: A Population-Based Study.

PubMed

Dorner, Rebecca A; Rankin, Kristin M; Collins, James W

2017-11-01

Objectives To determine the extent to which non-Latina White and African-American mother's gestational age is associated with extremely early (<30 weeks), modestly early (30-33 weeks), and late (34-36 weeks) infant preterm birth (PTB) rates. Methods Race-specific stratified and multivariable logistic regression analyses were performed on the Illinois Transgenerational Birth File of non-Latino White and African-American infants (born 1989-1991) and their mothers (born 1956-1976). Results White mothers (n = 184) born at <30 weeks had a greater extremely early infant PTB rate than White mothers (n = 131,980) born at term: 1.6 versus 0.5%, respectively; RR = 3.6 (1.2, 11.0). African-American mothers (n = 269) born at <30 weeks had a greater extremely early infant PTB rate than African-American mothers (n = 34,885) born at term: 4.1 versus 2.1%, respectively; RR = 2.0 (1.1, 3.6). In logistic regression models the adjusted (controlling for maternal age, education, parity, prenatal care, marital status, and cigarette smoking) OR of extremely early PTB for White and African-American mothers born <30 (compared to ≥37) weeks equaled 4.0 (1.2, 12.6) and 2.3 (1.2, 4.3), respectively. The adjusted OR of modestly early PTB for White and African-American mothers born 30-33 (compared to ≥37) weeks equaled 1.6 (1.0, 2.5) and 1.3 (0.9, 1.7), respectively. The adjusted OR of late PTB for White and African-American mothers born 34-36 (compared to ≥37) weeks equaled 1.2 (1.0, 1.3) and 1.1 (1.0, 1.2), respectively. Conclusions A generational association of extremely early, but not modestly early or late, PTB exists among non-Latino Whites and African-Americans.

Performance of an Axisymmetric Rocket Based Combined Cycle Engine During Rocket Only Operation Using Linear Regression Analysis

NASA Technical Reports Server (NTRS)

Smith, Timothy D.; Steffen, Christopher J., Jr.; Yungster, Shaye; Keller, Dennis J.

1998-01-01

The all rocket mode of operation is shown to be a critical factor in the overall performance of a rocket based combined cycle (RBCC) vehicle. An axisymmetric RBCC engine was used to determine specific impulse efficiency values based upon both full flow and gas generator configurations. Design of experiments methodology was used to construct a test matrix and multiple linear regression analysis was used to build parametric models. The main parameters investigated in this study were: rocket chamber pressure, rocket exit area ratio, injected secondary flow, mixer-ejector inlet area, mixer-ejector area ratio, and mixer-ejector length-to-inlet diameter ratio. A perfect gas computational fluid dynamics analysis, using both the Spalart-Allmaras and k-omega turbulence models, was performed with the NPARC code to obtain values of vacuum specific impulse. Results from the multiple linear regression analysis showed that for both the full flow and gas generator configurations increasing mixer-ejector area ratio and rocket area ratio increase performance, while increasing mixer-ejector inlet area ratio and mixer-ejector length-to-diameter ratio decrease performance. Increasing injected secondary flow increased performance for the gas generator analysis, but was not statistically significant for the full flow analysis. Chamber pressure was found to be not statistically significant.