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.

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.

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…

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…

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.

A Profile of Latino School-Based Extracurricular Activity Involvement

ERIC Educational Resources Information Center

Peguero, Anthony A.

2010-01-01

Participation in school-based extracurricular activities influences educational success. Thus, it is important to depict a profile of school-based extracurricular activity involvement for a Latino student population that is marginalized in schools. This research uses the Educational Longitudinal Study of 2002 and logistic regression analyses to…

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.

Mechanisms behind the estimation of photosynthesis traits from leaf reflectance observations

NASA Astrophysics Data System (ADS)

Dechant, Benjamin; Cuntz, Matthias; Doktor, Daniel; Vohland, Michael

2016-04-01

Many studies have investigated the reflectance-based estimation of leaf chlorophyll, water and dry matter contents of plants. Only few studies focused on photosynthesis traits, however. The maximum potential uptake of carbon dioxide under given environmental conditions is determined mainly by RuBisCO activity, limiting carboxylation, or the speed of photosynthetic electron transport. These two main limitations are represented by the maximum carboxylation capacity, V cmax,25, and the maximum electron transport rate, Jmax,25. These traits were estimated from leaf reflectance before but the mechanisms underlying the estimation remain rather speculative. The aim of this study was therefore to reveal the mechanisms behind reflectance-based estimation of V cmax,25 and Jmax,25. Leaf reflectance, photosynthetic response curves as well as nitrogen content per area, Narea, and leaf mass per area, LMA, were measured on 37 deciduous tree species. V cmax,25 and Jmax,25 were determined from the response curves. Partial Least Squares (PLS) regression models for the two photosynthesis traits V cmax,25 and Jmax,25 as well as Narea and LMA were studied using a cross-validation approach. Analyses of linear regression models based on Narea and other leaf traits estimated via PROSPECT inversion, PLS regression coefficients and model residuals were conducted in order to reveal the mechanisms behind the reflectance-based estimation. We found that V cmax,25 and Jmax,25 can be estimated from leaf reflectance with good to moderate accuracy for a large number of species and different light conditions. The dominant mechanism behind the estimations was the strong relationship between photosynthesis traits and leaf nitrogen content. This was concluded from very strong relationships between PLS regression coefficients, the model residuals as well as the prediction performance of Narea- based linear regression models compared to PLS regression models. While the PLS regression model for V cmax,25 was fully based on the correlation to Narea, the PLS regression model for Jmax,25 was not entirely based on it. Analyses of the contributions of different parts of the reflectance spectrum revealed that the information contributing to the Jmax,25 PLS regression model in addition to the main source of information, Narea, was mainly located in the visible part of the spectrum (500-900 nm). Estimated chlorophyll content could be excluded as potential source of this extra information. The PLS regression coefficients of the Jmax,25 model indicated possible contributions from chlorophyll fluorescence and cytochrome f content. In summary, we found that the main mechanism behind the estimation of V cmax,25 and Jmax,25 from leaf reflectance observations is the correlation to Narea but that there is additional information related to Jmax,25 mainly in the visible part of the spectrum.

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.

Selected Streamflow Statistics and Regression Equations for Predicting Statistics at Stream Locations in Monroe County, Pennsylvania

USGS Publications Warehouse

Thompson, Ronald E.; Hoffman, Scott A.

2006-01-01

A suite of 28 streamflow statistics, ranging from extreme low to high flows, was computed for 17 continuous-record streamflow-gaging stations and predicted for 20 partial-record stations in Monroe County and contiguous counties in north-eastern Pennsylvania. The predicted statistics for the partial-record stations were based on regression analyses relating inter-mittent flow measurements made at the partial-record stations indexed to concurrent daily mean flows at continuous-record stations during base-flow conditions. The same statistics also were predicted for 134 ungaged stream locations in Monroe County on the basis of regression analyses relating the statistics to GIS-determined basin characteristics for the continuous-record station drainage areas. The prediction methodology for developing the regression equations used to estimate statistics was developed for estimating low-flow frequencies. This study and a companion study found that the methodology also has application potential for predicting intermediate- and high-flow statistics. The statistics included mean monthly flows, mean annual flow, 7-day low flows for three recurrence intervals, nine flow durations, mean annual base flow, and annual mean base flows for two recurrence intervals. Low standard errors of prediction and high coefficients of determination (R2) indicated good results in using the regression equations to predict the statistics. Regression equations for the larger flow statistics tended to have lower standard errors of prediction and higher coefficients of determination (R2) than equations for the smaller flow statistics. The report discusses the methodologies used in determining the statistics and the limitations of the statistics and the equations used to predict the statistics. Caution is indicated in using the predicted statistics for small drainage area situations. Study results constitute input needed by water-resource managers in Monroe County for planning purposes and evaluation of water-resources availability.

Unobtrusive measurement of indoor energy expenditure using an infrared sensor-based activity monitoring system.

PubMed

Hwang, Bosun; Han, Jonghee; Choi, Jong Min; Park, Kwang Suk

2008-11-01

The purpose of this study was to develop an unobtrusive energy expenditure (EE) measurement system using an infrared (IR) sensor-based activity monitoring system to measure indoor activities and to estimate individual quantitative EE. IR-sensor activation counts were measured with a Bluetooth-based monitoring system and the standard EE was calculated using an established regression equation. Ten male subjects participated in the experiment and three different EE measurement systems (gas analyzer, accelerometer, IR sensor) were used simultaneously in order to determine the regression equation and evaluate the performance. As a standard measurement, oxygen consumption was simultaneously measured by a portable metabolic system (Metamax 3X, Cortex, Germany). A single room experiment was performed to develop a regression model of the standard EE measurement from the proposed IR sensor-based measurement system. In addition, correlation and regression analyses were done to compare the performance of the IR system with that of the Actigraph system. We determined that our proposed IR-based EE measurement system shows a similar correlation to the Actigraph system with the standard measurement system.

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...

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…

An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling.

PubMed

Estelles-Lopez, Lucia; Ropodi, Athina; Pavlidis, Dimitris; Fotopoulou, Jenny; Gkousari, Christina; Peyrodie, Audrey; Panagou, Efstathios; Nychas, George-John; Mohareb, Fady

2017-09-01

Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, "MeatReg", a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg" was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC-MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: www.sorfml.com. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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.

Application of classification tree and logistic regression for the management and health intervention plans in a community-based study.

PubMed

Teng, Ju-Hsi; Lin, Kuan-Chia; Ho, Bin-Shenq

2007-10-01

A community-based aboriginal study was conducted and analysed to explore the application of classification tree and logistic regression. A total of 1066 aboriginal residents in Yilan County were screened during 2003-2004. The independent variables include demographic characteristics, physical examinations, geographic location, health behaviours, dietary habits and family hereditary diseases history. Risk factors of cardiovascular diseases were selected as the dependent variables in further analysis. The completion rate for heath interview is 88.9%. The classification tree results find that if body mass index is higher than 25.72 kg m(-2) and the age is above 51 years, the predicted probability for number of cardiovascular risk factors > or =3 is 73.6% and the population is 322. If body mass index is higher than 26.35 kg m(-2) and geographical latitude of the village is lower than 24 degrees 22.8', the predicted probability for number of cardiovascular risk factors > or =4 is 60.8% and the population is 74. As the logistic regression results indicate that body mass index, drinking habit and menopause are the top three significant independent variables. The classification tree model specifically shows the discrimination paths and interactions between the risk groups. The logistic regression model presents and analyses the statistical independent factors of cardiovascular risks. Applying both models to specific situations will provide a different angle for the design and management of future health intervention plans after community-based study.

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

The Impact of Youth and Family Risk Factors on Service Recommendations and Delivery in a School-Based System of Care

PubMed Central

Whitson, Melissa L.; Connell, Christian M.; Bernard, Stanley; Kaufman, Joy S.

2010-01-01

The present study examines the impact of child and family risk factors on service access for youth and families in a school-based system of care. Regression analyses examined the relationships between risk factors and services recommended, services received, and dosage of services received. Logistic regression analyses examined the relationship between risk factors and whether or not youth received specific types of services within the system of care. Results revealed that youth with a personal or family history of substance use had more services recommended than youth without these risk factors, while youth with a family history of substance use received more services. Youth with a history of substance use received a significantly higher dosage of services overall. Finally, history of family mental illness was associated with receiving mental health and operational services (e.g., family advocacy, emergency funds). Implications and limitations are discussed. PMID:20165927

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.

A Retrospective Population-Based Data Analyses of Inpatient Care Use and Medical Expenditure in People with Intellectual Disability Co-Occurring Schizophrenia

ERIC Educational Resources Information Center

Lai, Chia-Im; Hung, Wen-Jiu; Lin, Lan-Ping; Chien, Wu-Chien; Lin, Jin-Ding

2011-01-01

The paper aims to analyze the hospital inpatient care use and medical fee of people with ID co-occurring with schizophrenia in Taiwan. A nationwide data were collected concerning hospital admission and medical expenditure of people with ID (n = 2565) among national health insurance beneficiaries in Taiwan. Multiple regression analyses were…

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.

Measuring the Impact of Inquiry-Based Learning on Outcomes and Student Satisfaction

ERIC Educational Resources Information Center

Zafra-Gómez, José Luis; Román-Martínez, Isabel; Gómez-Miranda, María Elena

2015-01-01

The aim of this study is to determine the impact of inquiry-based learning (IBL) on students' academic performance and to assess their satisfaction with the process. Linear and logistic regression analyses show that examination grades are positively related to attendance at classes and tutorials; moreover, there is a positive significant…

Reading Cooperatively or Independently? Study on ELL Student Reading Development

ERIC Educational Resources Information Center

Liu, Siping; Wang, Jian

2015-01-01

This study examines the effectiveness of cooperative reading teaching activities and independent reading activities for English language learner (ELL) students at 4th grade level. Based on simple linear regression and correlational analyses of data collected from two large data bases, PIRLS and NAEP, the study found that cooperative reading…

Predictors of Word-Reading Ability in 7-Year-Olds: Analysis of Data from a U.K. Cohort Study

ERIC Educational Resources Information Center

Russell, Ginny; Ukoumunne, Obioha C.; Ryder, Denise; Golding, Jean; Norwich, Brahm

2018-01-01

Previous U.K. population-based studies have found associations amongst early speech and language difficulties, socioeconomic disadvantage and children's word-reading ability later on. We examine the strength of these associations in a recent U.K. population-based birth cohort. Analyses were based on 13,680 participants. Linear regression models…

Regression Analysis of Optical Coherence Tomography Disc Variables for Glaucoma Diagnosis.

PubMed

Richter, Grace M; Zhang, Xinbo; Tan, Ou; Francis, Brian A; Chopra, Vikas; Greenfield, David S; Varma, Rohit; Schuman, Joel S; Huang, David

2016-08-01

To report diagnostic accuracy of optical coherence tomography (OCT) disc variables using both time-domain (TD) and Fourier-domain (FD) OCT, and to improve the use of OCT disc variable measurements for glaucoma diagnosis through regression analyses that adjust for optic disc size and axial length-based magnification error. Observational, cross-sectional. In total, 180 normal eyes of 112 participants and 180 eyes of 138 participants with perimetric glaucoma from the Advanced Imaging for Glaucoma Study. Diagnostic variables evaluated from TD-OCT and FD-OCT were: disc area, rim area, rim volume, optic nerve head volume, vertical cup-to-disc ratio (CDR), and horizontal CDR. These were compared with overall retinal nerve fiber layer thickness and ganglion cell complex. Regression analyses were performed that corrected for optic disc size and axial length. Area-under-receiver-operating curves (AUROC) were used to assess diagnostic accuracy before and after the adjustments. An index based on multiple logistic regression that combined optic disc variables with axial length was also explored with the aim of improving diagnostic accuracy of disc variables. Comparison of diagnostic accuracy of disc variables, as measured by AUROC. The unadjusted disc variables with the highest diagnostic accuracies were: rim volume for TD-OCT (AUROC=0.864) and vertical CDR (AUROC=0.874) for FD-OCT. Magnification correction significantly worsened diagnostic accuracy for rim variables, and while optic disc size adjustments partially restored diagnostic accuracy, the adjusted AUROCs were still lower. Axial length adjustments to disc variables in the form of multiple logistic regression indices led to a slight but insignificant improvement in diagnostic accuracy. Our various regression approaches were not able to significantly improve disc-based OCT glaucoma diagnosis. However, disc rim area and vertical CDR had very high diagnostic accuracy, and these disc variables can serve to complement additional OCT measurements for diagnosis of glaucoma.

Reliable change indices and regression-based measures for the Rey-Osterreith Complex Figure test in partial epilepsy patients.

PubMed

Nakhutina, L; Pramataris, P; Morrison, C; Devinsky, O; Barr, W B

2010-01-01

The Rey-Osterreith Complex Figure (ROCF) is commonly used in evaluations of patients undergoing epilepsy surgery. We assessed test-retest performance on ROCF in 30 partial epilepsy patients (mean interval = 33.7 months) to derive reliable change indices (RCIs) and regression-based measures for change. ROCF reproductions were rescored by three raters (IRR Copy: 0.963; Delayed Recall: 0.986). The derived adjusted RC (90% CI) cutoff values for the ROCF Copy were (or=8.4) and were (or=10.0) for the Delayed Recall. Results from regression-based analyses were negative, using age, education, seizure duration, and age of onset, whereas a baseline score was a significant predictor of a follow-up score. The results provide a means to evaluate long-term outcome in epilepsy patients using the ROCF.

High correlations between MRI brain volume measurements based on NeuroQuant® and FreeSurfer.

PubMed

Ross, David E; Ochs, Alfred L; Tate, David F; Tokac, Umit; Seabaugh, John; Abildskov, Tracy J; Bigler, Erin D

2018-05-30

NeuroQuant ® (NQ) and FreeSurfer (FS) are commonly used computer-automated programs for measuring MRI brain volume. Previously they were reported to have high intermethod reliabilities but often large intermethod effect size differences. We hypothesized that linear transformations could be used to reduce the large effect sizes. This study was an extension of our previously reported study. We performed NQ and FS brain volume measurements on 60 subjects (including normal controls, patients with traumatic brain injury, and patients with Alzheimer's disease). We used two statistical approaches in parallel to develop methods for transforming FS volumes into NQ volumes: traditional linear regression, and Bayesian linear regression. For both methods, we used regression analyses to develop linear transformations of the FS volumes to make them more similar to the NQ volumes. The FS-to-NQ transformations based on traditional linear regression resulted in effect sizes which were small to moderate. The transformations based on Bayesian linear regression resulted in all effect sizes being trivially small. To our knowledge, this is the first report describing a method for transforming FS to NQ data so as to achieve high reliability and low effect size differences. Machine learning methods like Bayesian regression may be more useful than traditional methods. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

Poisson regression models outperform the geometrical model in estimating the peak-to-trough ratio of seasonal variation: a simulation study.

PubMed

Christensen, A L; Lundbye-Christensen, S; Dethlefsen, C

2011-12-01

Several statistical methods of assessing seasonal variation are available. Brookhart and Rothman [3] proposed a second-order moment-based estimator based on the geometrical model derived by Edwards [1], and reported that this estimator is superior in estimating the peak-to-trough ratio of seasonal variation compared with Edwards' estimator with respect to bias and mean squared error. Alternatively, seasonal variation may be modelled using a Poisson regression model, which provides flexibility in modelling the pattern of seasonal variation and adjustments for covariates. Based on a Monte Carlo simulation study three estimators, one based on the geometrical model, and two based on log-linear Poisson regression models, were evaluated in regards to bias and standard deviation (SD). We evaluated the estimators on data simulated according to schemes varying in seasonal variation and presence of a secular trend. All methods and analyses in this paper are available in the R package Peak2Trough[13]. Applying a Poisson regression model resulted in lower absolute bias and SD for data simulated according to the corresponding model assumptions. Poisson regression models had lower bias and SD for data simulated to deviate from the corresponding model assumptions than the geometrical model. This simulation study encourages the use of Poisson regression models in estimating the peak-to-trough ratio of seasonal variation as opposed to the geometrical model. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Prosthesis rejection in acquired major upper-limb amputees: a population-based survey.

PubMed

Østlie, Kristin; Lesjø, Ingrid Marie; Franklin, Rosemary Joy; Garfelt, Beate; Skjeldal, Ola Hunsbeth; Magnus, Per

2012-07-01

To estimate the rates of primary and secondary prosthesis rejection in acquired major upper-limb amputees (ULAs), to describe the most frequently reported reasons for rejection and to estimate the influence of background factors on the risk of rejection. Cross-sectional study analysing population-based questionnaire data (n = 224). Effects were analysed by logistic regression analyses and Cox regression analyses. Primary prosthesis rejection was found in 4.5% whereas 13.4% had discontinued prosthesis use. The main reasons reported for primary non-wear were a perceived lack of need and discrepancies between perceived need and the prostheses available. The main reasons reported for secondary prosthesis rejection were dissatisfaction with prosthetic comfort, function and control. Primary prosthesis rejection was more likely in ULAs amputated at high age and in ULAs with proximal amputations. Secondary prosthesis rejection was more likely in proximal ULAs and in women. Clinicians should be aware of the increased risk of rejection in proximal ULAs, elderly ULAs and in women. Emphasising individual needs will probably facilitate successful prosthetic fitting. Improved prosthesis quality and individualised prosthetic training may increase long-term prosthesis use. Further studies of the effect of prosthetic training and of the reasons for rejection of different prosthetic types are suggested.

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space.

PubMed

Jacob, Benjamin J; Krapp, Fiorella; Ponce, Mario; Gottuzzo, Eduardo; Griffith, Daniel A; Novak, Robert J

2010-05-01

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multi-drug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDRTB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e., the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird 0.61 m data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centers, using a 10 m2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health center were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS(R) module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centers and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

Development of Super-Ensemble techniques for ocean analyses: the Mediterranean Sea case

NASA Astrophysics Data System (ADS)

Pistoia, Jenny; Pinardi, Nadia; Oddo, Paolo; Collins, Matthew; Korres, Gerasimos; Drillet, Yann

2017-04-01

Short-term ocean analyses for Sea Surface Temperature SST in the Mediterranean Sea can be improved by a statistical post-processing technique, called super-ensemble. This technique consists in a multi-linear regression algorithm applied to a Multi-Physics Multi-Model Super-Ensemble (MMSE) dataset, a collection of different operational forecasting analyses together with ad-hoc simulations produced by modifying selected numerical model parameterizations. A new linear regression algorithm based on Empirical Orthogonal Function filtering techniques is capable to prevent overfitting problems, even if best performances are achieved when we add correlation to the super-ensemble structure using a simple spatial filter applied after the linear regression. Our outcomes show that super-ensemble performances depend on the selection of an unbiased operator and the length of the learning period, but the quality of the generating MMSE dataset has the largest impact on the MMSE analysis Root Mean Square Error (RMSE) evaluated with respect to observed satellite SST. Lower RMSE analysis estimates result from the following choices: 15 days training period, an overconfident MMSE dataset (a subset with the higher quality ensemble members), and the least square algorithm being filtered a posteriori.

Reporting and methodological quality of meta-analyses in urological literature

PubMed Central

Xu, Jing

2017-01-01

Purpose To assess the overall quality of published urological meta-analyses and identify predictive factors for high quality. Materials and Methods 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. Results 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. Conclusions 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. PMID:28439452

Are low wages risk factors for hypertension?

PubMed Central

Du, Juan

2012-01-01

Objective: Socio-economic status (SES) is strongly correlated with hypertension. But SES has several components, including income and correlations in cross-sectional data need not imply SES is a risk factor. This study investigates whether wages—the largest category within income—are risk factors. Methods: We analysed longitudinal, nationally representative US data from four waves (1999, 2001, 2003 and 2005) of the Panel Study of Income Dynamics. The overall sample was restricted to employed persons age 25–65 years, n = 17 295. Separate subsamples were constructed of persons within two age groups (25–44 and 45–65 years) and genders. Hypertension incidence was self-reported based on physician diagnosis. Our study was prospective since data from three base years (1999, 2001, 2003) were used to predict newly diagnosed hypertension for three subsequent years (2001, 2003, 2005). In separate analyses, data from the first base year were used to predict time-to-reporting hypertension. Logistic regressions with random effects and Cox proportional hazards regressions were run. Results: Negative and strongly statistically significant correlations between wages and hypertension were found both in logistic and Cox regressions, especially for subsamples containing the younger age group (25–44 years) and women. Correlations were stronger when three health variables—obesity, subjective measures of health and number of co-morbidities—were excluded from regressions. Doubling the wage was associated with 25–30% lower chances of hypertension for persons aged 25–44 years. Conclusions: The strongest evidence for low wages being risk factors for hypertension among working people were for women and persons aged 25–44 years. PMID:22262559

Are low wages risk factors for hypertension?

PubMed

Leigh, J Paul; Du, Juan

2012-12-01

Socio-economic status (SES) is strongly correlated with hypertension. But SES has several components, including income and correlations in cross-sectional data need not imply SES is a risk factor. This study investigates whether wages-the largest category within income-are risk factors. We analysed longitudinal, nationally representative US data from four waves (1999, 2001, 2003 and 2005) of the Panel Study of Income Dynamics. The overall sample was restricted to employed persons age 25-65 years, n = 17 295. Separate subsamples were constructed of persons within two age groups (25-44 and 45-65 years) and genders. Hypertension incidence was self-reported based on physician diagnosis. Our study was prospective since data from three base years (1999, 2001, 2003) were used to predict newly diagnosed hypertension for three subsequent years (2001, 2003, 2005). In separate analyses, data from the first base year were used to predict time-to-reporting hypertension. Logistic regressions with random effects and Cox proportional hazards regressions were run. Negative and strongly statistically significant correlations between wages and hypertension were found both in logistic and Cox regressions, especially for subsamples containing the younger age group (25-44 years) and women. Correlations were stronger when three health variables-obesity, subjective measures of health and number of co-morbidities-were excluded from regressions. Doubling the wage was associated with 25-30% lower chances of hypertension for persons aged 25-44 years. The strongest evidence for low wages being risk factors for hypertension among working people were for women and persons aged 25-44 years.

The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching.

PubMed

Szekér, Szabolcs; Vathy-Fogarassy, Ágnes

2018-01-01

Logistic regression based propensity score matching is a widely used method in case-control studies to select the individuals of the control group. This method creates a suitable control group if all factors affecting the output variable are known. However, if relevant latent variables exist as well, which are not taken into account during the calculations, the quality of the control group is uncertain. In this paper, we present a statistics-based research in which we try to determine the relationship between the accuracy of the logistic regression model and the uncertainty of the dependent variable of the control group defined by propensity score matching. Our analyses show that there is a linear correlation between the fit of the logistic regression model and the uncertainty of the output variable. In certain cases, a latent binary explanatory variable can result in a relative error of up to 70% in the prediction of the outcome variable. The observed phenomenon calls the attention of analysts to an important point, which must be taken into account when deducting conclusions.

The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features.

PubMed

Cui, Zaixu; Gong, Gaolang

2018-06-02

Individualized behavioral/cognitive prediction using machine learning (ML) regression approaches is becoming increasingly applied. The specific ML regression algorithm and sample size are two key factors that non-trivially influence prediction accuracies. However, the effects of the ML regression algorithm and sample size on individualized behavioral/cognitive prediction performance have not been comprehensively assessed. To address this issue, the present study included six commonly used ML regression algorithms: ordinary least squares (OLS) regression, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic-net regression, linear support vector regression (LSVR), and relevance vector regression (RVR), to perform specific behavioral/cognitive predictions based on different sample sizes. Specifically, the publicly available resting-state functional MRI (rs-fMRI) dataset from the Human Connectome Project (HCP) was used, and whole-brain resting-state functional connectivity (rsFC) or rsFC strength (rsFCS) were extracted as prediction features. Twenty-five sample sizes (ranged from 20 to 700) were studied by sub-sampling from the entire HCP cohort. The analyses showed that rsFC-based LASSO regression performed remarkably worse than the other algorithms, and rsFCS-based OLS regression performed markedly worse than the other algorithms. Regardless of the algorithm and feature type, both the prediction accuracy and its stability exponentially increased with increasing sample size. The specific patterns of the observed algorithm and sample size effects were well replicated in the prediction using re-testing fMRI data, data processed by different imaging preprocessing schemes, and different behavioral/cognitive scores, thus indicating excellent robustness/generalization of the effects. The current findings provide critical insight into how the selected ML regression algorithm and sample size influence individualized predictions of behavior/cognition and offer important guidance for choosing the ML regression algorithm or sample size in relevant investigations. Copyright © 2018 Elsevier Inc. All rights reserved.

Alcohol Control Policies and Alcohol Consumption by Youth: A Multi-National Study

PubMed Central

Paschall, Mallie J.; Grube, Joel W.; Kypri, Kypros

2009-01-01

Aims The study examined relationships between alcohol control policies and adolescent alcohol use in 26 countries. Design Cross-sectional analyses of alcohol policy ratings based on the Alcohol Policy Index (API), per capita consumption, and national adolescent survey data. Setting Data are from 26 countries. Participants Adolescents (15-17 years old) who participated in the 2003 ESPAD (European countries) or national secondary school surveys in Spain, Canada, Australia, New Zealand and the USA. Measurements Alcohol control policy ratings based on the API; prevalence of alcohol use, heavy drinking, and first drink by age 13 based on national secondary school surveys; per capita alcohol consumption for each country in 2003. Analysis Correlational and linear regression analyses were conducted to examine relationships between alcohol control policy ratings and past-30-day prevalence of adolescent alcohol use, heavy drinking, and having first drink by age 13. Per capita consumption of alcohol was included as a covariate in regression analyses. Findings More comprehensive API ratings and alcohol availability and advertising control ratings were inversely related to the past-30-day prevalence of alcohol use and prevalence rates for drinking 3-5 times and 6 or more times in the past 30 days. Alcohol advertising control was also inversely related to the prevalence of past-30-day heavy drinking and having first drink by age 13. Most of the relationships between API, alcohol availability and advertising control and drinking prevalence rates were attenuated and no longer statistically significant when controlling for per capita consumption in regression analyses, suggesting that alcohol use in the general population may confound or mediate observed relationships between alcohol control policies and youth alcohol consumption. Several of the inverse relationships remained statistically significant when controlling for per capita consumption. Conclusions More comprehensive and stringent alcohol control policies, particularly policies affecting alcohol availability and marketing, are associated with lower prevalence and frequency of adolescent alcohol consumption and age of first alcohol use. PMID:19832785

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.

An Exploratory Study of Religion and Trust in Ghana

ERIC Educational Resources Information Center

Addai, Isaac; Opoku-Agyeman, Chris; Ghartey, Helen Tekyiwa

2013-01-01

Based on individual-level data from 2008 Afro-barometer survey, this study explores the relationship between religion (religious affiliation and religious importance) and trust (interpersonal and institutional) among Ghanaians. Employing hierarchical multiple regression technique, our analyses reveal a positive relationship between religious…

Escherichia coli bacteria density in relation to turbidity, streamflow characteristics, and season in the Chattahoochee River near Atlanta, Georgia, October 2000 through September 2008—Description, statistical analysis, and predictive modeling

USGS Publications Warehouse

Lawrence, Stephen J.

2012-01-01

Regression analyses show that E. coli density in samples was strongly related to turbidity, streamflow characteristics, and season at both sites. The regression equation chosen for the Norcross data showed that 78 percent of the variability in E. coli density (in log base 10 units) was explained by the variability in turbidity values (in log base 10 units), streamflow event (dry-weather flow or stormflow), season (cool or warm), and an interaction term that is the cross product of streamflow event and turbidity. The regression equation chosen for the Atlanta data showed that 76 percent of the variability in E. coli density (in log base 10 units) was explained by the variability in turbidity values (in log base 10 units), water temperature, streamflow event, and an interaction term that is the cross product of streamflow event and turbidity. Residual analysis and model confirmation using new data indicated the regression equations selected at both sites predicted E. coli density within the 90 percent prediction intervals of the equations and could be used to predict E. coli density in real time at both sites.

Development of a mobbing short scale in the Gutenberg Health Study.

PubMed

Garthus-Niegel, Susan; Nübling, Matthias; Letzel, Stephan; Hegewald, Janice; Wagner, Mandy; Wild, Philipp S; Blettner, Maria; Zwiener, Isabella; Latza, Ute; Jankowiak, Sylvia; Liebers, Falk; Seidler, Andreas

2016-01-01

Despite its highly detrimental potential, most standard questionnaires assessing psychosocial stress at work do not include mobbing as a risk factor. In the German standard version of COPSOQ, mobbing is assessed with a single item. In the Gutenberg Health Study, this version was used together with a newly developed short scale based on the Leymann Inventory of Psychological Terror. The purpose of the present study was to evaluate the psychometric properties of these two measures, to compare them and to test their differential impact on relevant outcome parameters. This analysis is based on a population-based sample of 1441 employees participating in the Gutenberg Health Study. Exploratory and confirmatory factor analyses and reliability analyses were used to assess the mobbing scale. To determine their predictive validities, multiple linear regression analyses with six outcome parameters and log-binomial regression models for two of the outcome aspects were run. Factor analyses of the five-item scale confirmed a one-factor solution, reliability was α = 0.65. Both the single-item and the five-item scales were associated with all six outcome scales. Effect sizes were similar for both mobbing measures. Mobbing is an important risk factor for health-related outcomes. For the purpose of psychosocial risk assessment in the workplace, both the single-item and the five-item constructs were psychometrically appropriate. Associations with outcomes were about equivalent. However, the single item has the advantage of parsimony, whereas the five-item construct depicts several distinct forms of mobbing.

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.

Body Mass Index (BMI) Is Associated with Microalbuminuria in Chinese Hypertensive Patients

PubMed Central

Liu, Xinyu; Liu, Yu; Chen, Youming; Li, Yongqiang; Shao, Xiaofei; Liang, Yan; Li, Bin; Holthöfer, Harry; Zhang, Guanjing; Zou, Hequn

2015-01-01

There is no general consensus on possible factors associated with microalbuminuria in hypertensive patients nor any reported study about this issue in Chinese patients. To examine this issues, 944 hypertensive patients were enrolled in a study based on a cross-sectional survey conducted in Southern China. Multivariate regression analyses were performed to identify the factors related with the presence of microalbuminuria and urinary excretion of albumin. The prevalence of microalbuminuria in hypertensive and non-diabetic hypertensive patients were 17.16% and 15.25%, respectively. Body mass index (BMI), but not waist circumference (WC), were independently associated with microalbuminuria and the values of urinary albumin to creatinine ratio (ACR) based on multiple regression analyses, even after excluding diabetic patients and patients taking inhibitors of the renin-angiotensin system from the analyses. Furthermore, patients with obesity (BMI ≥28) had higher levels of ACR, compared with those with normal weight (BMI <24 kg/m2) and overweight (24 kg/m2≤ BMI < 28). In conclusion, BMI, as a modifiable factor, is closely associated with microalbuminuria among Chinese hypertensive patients, which may provide a basis for future development of intervention approaches for these patients. PMID:25674785

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.

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

Taguchi Based Regression Analysis of End-Wall Film Cooling in a Gas Turbine Cascade with Single Row of Holes

NASA Astrophysics Data System (ADS)

Ravi, D.; Parammasivam, K. M.

2016-09-01

Numerical investigations were conducted on a turbine cascade, with end-wall cooling by a single row of cylindrical holes, inclined at 30°. The mainstream fluid was hot air and the coolant was CO2 gas. Based on the Reynolds number, the flow was turbulent at the inlet. The film hole row position, its pitch and blowing ratio was varied with five different values. Taguchi approach was used in designing a L25 orthogonal array (OA) for these parameters. The end-wall averaged film cooling effectiveness (bar η) was chosen as the quality characteristic. CFD analyses were carried out using Ansys Fluent on computational domains designed with inputs from OA. Experiments were conducted for one chosen OA configuration and the computational results were found to correlate well with experimental measurements. The responses from the CFD analyses were fed to the statistical tool to develop a correlation for bar η using regression analysis.

Prediction models for Arabica coffee beverage quality based on aroma analyses and chemometrics.

PubMed

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

2012-11-15

In this work, soft modeling based on chemometric analyses of coffee beverage sensory data and the chromatographic profiles of volatile roasted coffee compounds is proposed to predict the scores of acidity, bitterness, flavor, cleanliness, body, and overall quality of the coffee beverage. A partial least squares (PLS) regression method was used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the compounds for the regression model of each sensory attribute in order to take only significant chromatographic peaks into account. The prediction errors of these models, using 4 or 5 latent variables, were equal to 0.28, 0.33, 0.35, 0.33, 0.34 and 0.41, for each of the attributes and compatible with the errors of the mean scores of the experts. Thus, the results proved the feasibility of using a similar methodology in on-line or routine applications to predict the sensory quality of Brazilian Arabica coffee. Copyright © 2012 Elsevier B.V. All rights reserved.

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...

Predictors of the number of under-five malnourished children in Bangladesh: application of the generalized poisson regression model

PubMed Central

2013-01-01

Background Malnutrition is one of the principal causes of child mortality in developing countries including Bangladesh. According to our knowledge, most of the available studies, that addressed the issue of malnutrition among under-five children, considered the categorical (dichotomous/polychotomous) outcome variables and applied logistic regression (binary/multinomial) to find their predictors. In this study malnutrition variable (i.e. outcome) is defined as the number of under-five malnourished children in a family, which is a non-negative count variable. The purposes of the study are (i) to demonstrate the applicability of the generalized Poisson regression (GPR) model as an alternative of other statistical methods and (ii) to find some predictors of this outcome variable. Methods The data is extracted from the Bangladesh Demographic and Health Survey (BDHS) 2007. Briefly, this survey employs a nationally representative sample which is based on a two-stage stratified sample of households. A total of 4,460 under-five children is analysed using various statistical techniques namely Chi-square test and GPR model. Results The GPR model (as compared to the standard Poisson regression and negative Binomial regression) is found to be justified to study the above-mentioned outcome variable because of its under-dispersion (variance < mean) property. Our study also identify several significant predictors of the outcome variable namely mother’s education, father’s education, wealth index, sanitation status, source of drinking water, and total number of children ever born to a woman. Conclusions Consistencies of our findings in light of many other studies suggest that the GPR model is an ideal alternative of other statistical models to analyse the number of under-five malnourished children in a family. Strategies based on significant predictors may improve the nutritional status of children in Bangladesh. PMID:23297699

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

Reporting quality of statistical methods in surgical observational studies: protocol for systematic review.

PubMed

Wu, Robert; Glen, Peter; Ramsay, Tim; Martel, Guillaume

2014-06-28

Observational studies dominate the surgical literature. Statistical adjustment is an important strategy to account for confounders in observational studies. Research has shown that published articles are often poor in statistical quality, which may jeopardize their conclusions. The Statistical Analyses and Methods in the Published Literature (SAMPL) guidelines have been published to help establish standards for statistical reporting.This study will seek to determine whether the quality of statistical adjustment and the reporting of these methods are adequate in surgical observational studies. We hypothesize that incomplete reporting will be found in all surgical observational studies, and that the quality and reporting of these methods will be of lower quality in surgical journals when compared with medical journals. Finally, this work will seek to identify predictors of high-quality reporting. This work will examine the top five general surgical and medical journals, based on a 5-year impact factor (2007-2012). All observational studies investigating an intervention related to an essential component area of general surgery (defined by the American Board of Surgery), with an exposure, outcome, and comparator, will be included in this systematic review. Essential elements related to statistical reporting and quality were extracted from the SAMPL guidelines and include domains such as intent of analysis, primary analysis, multiple comparisons, numbers and descriptive statistics, association and correlation analyses, linear regression, logistic regression, Cox proportional hazard analysis, analysis of variance, survival analysis, propensity analysis, and independent and correlated analyses. Each article will be scored as a proportion based on fulfilling criteria in relevant analyses used in the study. A logistic regression model will be built to identify variables associated with high-quality reporting. A comparison will be made between the scores of surgical observational studies published in medical versus surgical journals. Secondary outcomes will pertain to individual domains of analysis. Sensitivity analyses will be conducted. This study will explore the reporting and quality of statistical analyses in surgical observational studies published in the most referenced surgical and medical journals in 2013 and examine whether variables (including the type of journal) can predict high-quality reporting.

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP.

PubMed

Winkler, Irene; Debener, Stefan; Müller, Klaus-Robert; Tangermann, Michael

2015-01-01

Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.

A multi-criteria evaluation system for marine litter pollution based on statistical analyses of OSPAR beach litter monitoring time series.

PubMed

Schulz, Marcus; Neumann, Daniel; Fleet, David M; Matthies, Michael

2013-12-01

During the last decades, marine pollution with anthropogenic litter has become a worldwide major environmental concern. Standardized monitoring of litter since 2001 on 78 beaches selected within the framework of the Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) has been used to identify temporal trends of marine litter. Based on statistical analyses of this dataset a two-part multi-criteria evaluation system for beach litter pollution of the North-East Atlantic and the North Sea is proposed. Canonical correlation analyses, linear regression analyses, and non-parametric analyses of variance were used to identify different temporal trends. A classification of beaches was derived from cluster analyses and served to define different states of beach quality according to abundances of 17 input variables. The evaluation system is easily applicable and relies on the above-mentioned classification and on significant temporal trends implied by significant rank correlations. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

Probability of Corporal Punishment: Lack of Resources and Vulnerable Students

ERIC Educational Resources Information Center

Han, Seunghee

2011-01-01

The author examined corporal punishment practices in the United States based on data from 362 public school principals where corporal punishment is available. Results from multiple regression analyses show that schools with multiple student violence prevention programs and teacher training programs had fewer possibilities of use corporal…

Are your covariates under control? How normalization can re-introduce covariate effects.

PubMed

Pain, Oliver; Dudbridge, Frank; Ronald, Angelica

2018-04-30

Many statistical tests rely on the assumption that the residuals of a model are normally distributed. Rank-based inverse normal transformation (INT) of the dependent variable is one of the most popular approaches to satisfy the normality assumption. When covariates are included in the analysis, a common approach is to first adjust for the covariates and then normalize the residuals. This study investigated the effect of regressing covariates against the dependent variable and then applying rank-based INT to the residuals. The correlation between the dependent variable and covariates at each stage of processing was assessed. An alternative approach was tested in which rank-based INT was applied to the dependent variable before regressing covariates. Analyses based on both simulated and real data examples demonstrated that applying rank-based INT to the dependent variable residuals after regressing out covariates re-introduces a linear correlation between the dependent variable and covariates, increasing type-I errors and reducing power. On the other hand, when rank-based INT was applied prior to controlling for covariate effects, residuals were normally distributed and linearly uncorrelated with covariates. This latter approach is therefore recommended in situations were normality of the dependent variable is required.

Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses

PubMed Central

Nickerson, Lisa D.; Smith, Stephen M.; Öngür, Döst; Beckmann, Christian F.

2017-01-01

Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or “shape”) as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia. PMID:28348512

Habitat features and predictive habitat modeling for the Colorado chipmunk in southern New Mexico

USGS Publications Warehouse

Rivieccio, M.; Thompson, B.C.; Gould, W.R.; Boykin, K.G.

2003-01-01

Two subspecies of Colorado chipmunk (state threatened and federal species of concern) occur in southern New Mexico: Tamias quadrivittatus australis in the Organ Mountains and T. q. oscuraensis in the Oscura Mountains. We developed a GIS model of potentially suitable habitat based on vegetation and elevation features, evaluated site classifications of the GIS model, and determined vegetation and terrain features associated with chipmunk occurrence. We compared GIS model classifications with actual vegetation and elevation features measured at 37 sites. At 60 sites we measured 18 habitat variables regarding slope, aspect, tree species, shrub species, and ground cover. We used logistic regression to analyze habitat variables associated with chipmunk presence/absence. All (100%) 37 sample sites (28 predicted suitable, 9 predicted unsuitable) were classified correctly by the GIS model regarding elevation and vegetation. For 28 sites predicted suitable by the GIS model, 18 sites (64%) appeared visually suitable based on habitat variables selected from logistic regression analyses, of which 10 sites (36%) were specifically predicted as suitable habitat via logistic regression. We detected chipmunks at 70% of sites deemed suitable via the logistic regression models. Shrub cover, tree density, plant proximity, presence of logs, and presence of rock outcrop were retained in the logistic model for the Oscura Mountains; litter, shrub cover, and grass cover were retained in the logistic model for the Organ Mountains. Evaluation of predictive models illustrates the need for multi-stage analyses to best judge performance. Microhabitat analyses indicate prospective needs for different management strategies between the subspecies. Sensitivities of each population of the Colorado chipmunk to natural and prescribed fire suggest that partial burnings of areas inhabited by Colorado chipmunks in southern New Mexico may be beneficial. These partial burnings may later help avoid a fire that could substantially reduce habitat of chipmunks over a mountain range.

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.

Quantification and regionalization of groundwater recharge in South-Central Kansas: Integrating field characterization, statistical analysis, and GIS

USGS Publications Warehouse

Sophocleous, M.

2000-01-01

A practical methodology for recharge characterization was developed based on several years of field-oriented research at 10 sites in the Great Bend Prairie of south-central Kansas. This methodology combines the soil-water budget on a storm-by-storm year-round basis with the resulting watertable rises. The estimated 1985-1992 average annual recharge was less than 50mm/year with a range from 15 mm/year (during the 1998 drought) to 178 mm/year (during the 1993 flood year). Most of this recharge occurs during the spring months. To regionalize these site-specific estimates, an additional methodology based on multiple (forward) regression analysis combined with classification and GIS overlay analyses was developed and implemented. The multiple regression analysis showed that the most influential variables were, in order of decreasing importance, total annual precipitation, average maximum springtime soil-profile water storage, average shallowest springtime depth to watertable, and average springtime precipitation rate. Therefore, four GIS (ARC/INFO) data "layers" or coverages were constructed for the study region based on these four variables, and each such coverage was classified into the same number of data classes to avoid biasing the results. The normalized regression coefficients were employed to weigh the class rankings of each recharge-affecting variable. This approach resulted in recharge zonations that agreed well with the site recharge estimates. During the "Great Flood of 1993," when rainfall totals exceeded normal levels by -200% in the northern portion of the study region, the developed regionalization methodology was tested against such extreme conditions, and proved to be both practical, based on readily available or easily measurable data, and robust. It was concluded that the combination of multiple regression and GIS overlay analyses is a powerful and practical approach to regionalizing small samples of recharge estimates.

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.

Predictors of Long-Term School-Based Behavioral Outcomes in the Multimodal Treatment Study of Children with Attention-Deficit/Hyperactivity Disorder

PubMed Central

Reed, Margot O.; Jakubovski, Ewgeni; Johnson, Jessica A.

2017-01-01

Abstract Objective: To explore predictors of 8-year school-based behavioral outcomes in attention-deficit/hyperactivity disorder (ADHD). Methods: We examined potential baseline predictors of school-based behavioral outcomes in children who completed the 8-year follow-up in the multimodal treatment study of children with ADHD. Stepwise logistic regression and receiver operating characteristic (ROC) analysis identified baseline predictors that were associated with a higher risk of truancy, school discipline, and in-school fights. Results: Stepwise regression analysis explained between 8.1% (in-school fights) and 12.0% (school discipline) of the total variance in school-based behavioral outcomes. Logistic regression identified several baseline characteristics that were associated with school-based behavioral difficulties 8 years later, including being male (associated with truancy and school discipline), African American (school discipline, in-school fights), increased conduct disorder (CD) symptoms (truancy), decreased affection from parents (school discipline), ADHD severity (in-school fights), and study site (truancy and school discipline). ROC analyses identified the most discriminative predictors of truancy, school discipline, and in-school fights, which were Aggression and Conduct Problem Scale Total score, family income, and race, respectively. Conclusions: A modest, but nontrivial portion of school-based behavioral outcomes, was predicted by baseline childhood characteristics. Exploratory analyses identified modifiable (lack of paternal involvement, lower parental knowledge of behavioral principles, and parental use of physical punishment), somewhat modifiable (income and having comorbid CD), and nonmodifiable (African American and male) factors that were associated with school-based behavioral difficulties. Future research should confirm that the associations between earlier specific parenting behaviors and poor subsequent school-based behavioral outcomes are, indeed, causally related and independent cooccurring childhood psychopathology. Future research might target increasing paternal involvement and parental knowledge of behavioral principles and reducing use of physical punishment to improve school-based behavioral outcomes in children with ADHD. PMID:28253029

Predictors of Long-Term School-Based Behavioral Outcomes in the Multimodal Treatment Study of Children with Attention-Deficit/Hyperactivity Disorder.

PubMed

Reed, Margot O; Jakubovski, Ewgeni; Johnson, Jessica A; Bloch, Michael H

2017-05-01

To explore predictors of 8-year school-based behavioral outcomes in attention-deficit/hyperactivity disorder (ADHD). We examined potential baseline predictors of school-based behavioral outcomes in children who completed the 8-year follow-up in the multimodal treatment study of children with ADHD. Stepwise logistic regression and receiver operating characteristic (ROC) analysis identified baseline predictors that were associated with a higher risk of truancy, school discipline, and in-school fights. Stepwise regression analysis explained between 8.1% (in-school fights) and 12.0% (school discipline) of the total variance in school-based behavioral outcomes. Logistic regression identified several baseline characteristics that were associated with school-based behavioral difficulties 8 years later, including being male (associated with truancy and school discipline), African American (school discipline, in-school fights), increased conduct disorder (CD) symptoms (truancy), decreased affection from parents (school discipline), ADHD severity (in-school fights), and study site (truancy and school discipline). ROC analyses identified the most discriminative predictors of truancy, school discipline, and in-school fights, which were Aggression and Conduct Problem Scale Total score, family income, and race, respectively. A modest, but nontrivial portion of school-based behavioral outcomes, was predicted by baseline childhood characteristics. Exploratory analyses identified modifiable (lack of paternal involvement, lower parental knowledge of behavioral principles, and parental use of physical punishment), somewhat modifiable (income and having comorbid CD), and nonmodifiable (African American and male) factors that were associated with school-based behavioral difficulties. Future research should confirm that the associations between earlier specific parenting behaviors and poor subsequent school-based behavioral outcomes are, indeed, causally related and independent cooccurring childhood psychopathology. Future research might target increasing paternal involvement and parental knowledge of behavioral principles and reducing use of physical punishment to improve school-based behavioral outcomes in children with ADHD.

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.

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,…

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

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.

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.

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.

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.

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.

Relationship between Organizational Mobbing and Silence Behavior among Teachers

ERIC Educational Resources Information Center

Hüsrevsahi, Selda Polat

2015-01-01

This study mainly aims to investigate the correlation between teachers' exposure to mobbing in their workplaces and their display of the act of silence. This study is based on a survey design where data from 312 teachers were collected and analyzed using correlation and regression analyses. Specifically, "The Structure and Dimensions of…

Prosocial Motivation, Stress and Burnout among Direct Support Workers

ERIC Educational Resources Information Center

Hickey, Robert

2014-01-01

Aim: This study explores whether the desire to engage in work that is beneficial to others moderates the effects of stress on burnout. Method: Based on a survey of 1570 direct support professionals in Ontario, this study conducted linear regression analyses and tested for the interaction effects of prosocial motivation on occupational stress and…

The Utilization of Community Mental Health Services by the Hispanic Elderly.

ERIC Educational Resources Information Center

Starrett,Richard A.; And Others

Multiple regression and path analyses of 29 demographic, social, and psychological variables were carried out to determine those variables that influenced the use of community-based mental health services by the Hispanic elderly. The variables were classified using the Andersen and Newman framework which conceptualizes the individual's demand for…

Narratives Boost Entrepreneurial Attitudes: Making an Entrepreneurial Career Attractive?

ERIC Educational Resources Information Center

Fellnhofer, Katharina

2018-01-01

This article analyses the impact of narratives on entrepreneurial attitudes and intentions. To this end, a quasi-experiment was conducted to evaluate web-based entrepreneurial narratives. The paired-sample tests and regression analysis use a sample of 466 people from Austria, Finland, and Greece and indicate that individuals' perceptions of the…

The Association Between Perceived Discrimination and Obesity in a Population-Based Multiracial and Multiethnic Adult Sample

PubMed Central

Williams, David R.

2009-01-01

Objectives. We examined whether perceived chronic discrimination was related to excess body fat accumulation in a random, multiethnic, population-based sample of US adults. Methods. We used multivariate multinomial logistic regression and logistic regression analyses to examine the relationship between interpersonal experiences of perceived chronic discrimination and body mass index and high-risk waist circumference. Results. Consistent with other studies, our analyses showed that perceived unfair treatment was associated with increased abdominal obesity. Compared with Irish, Jewish, Polish, and Italian Whites who did not experience perceived chronic discrimination, Irish, Jewish, Polish, and Italian Whites who perceived chronic discrimination were 2 to 6 times more likely to have a high-risk waist circumference. No significant relationship between perceived discrimination and the obesity measures was found among the other Whites, Blacks, or Hispanics. Conclusions. These findings are not completely unsupported. White ethnic groups including Polish, Italians, Jews, and Irish have historically been discriminated against in the United States, and other recent research suggests that they experience higher levels of perceived discrimination than do other Whites and that these experiences adversely affect their health. PMID:18923119

Income or living standard and health in Germany: different ways of measurement of relative poverty with regard to self-rated health.

PubMed

Pfoertner, Timo-Kolja; Andress, Hans-Juergen; Janssen, Christian

2011-08-01

Current study introduces the living standard concept as an alternative approach of measuring poverty and compares its explanatory power to an income-based poverty measure with regard to subjective health status of the German population. Analyses are based on the German Socio-Economic Panel (2001, 2003 and 2005) and refer to binary logistic regressions of poor subjective health status with regard to each poverty condition, their duration and their causal influence from a previous time point. To calculate the discriminate power of both poverty indicators, initially the indicators were considered separately in regression models and subsequently, both were included simultaneously. The analyses reveal a stronger poverty-health relationship for the living standard indicator. An inadequate living standard in 2005, longer spells of an inadequate living standard between 2001, 2003 and 2005 as well as an inadequate living standard at a previous time point is significantly strongly associated with subjective health than income poverty. Our results challenge conventional measurements of the relationship between poverty and health that probably has been underestimated by income measures so far.

Triglyceride and glucose (TyG) index as a predictor of incident hypertension: a 9-year longitudinal population-based study.

PubMed

Zheng, Rongjiong; Mao, Yushan

2017-09-13

Hypertension and the triglyceride and glucose index both have been associated with insulin resistance; however, the longitudinal association remains unclear. This study was designed to investigate the longitudinal association between the triglyceride and glucose index and incident hypertension among the Chinese population. We studied 4686 subjects (3177 males and 1509 females) and followed up for 9 years. The subjects were divided into four groups based on the triglyceride and glucose index. Univariate and multivariate Cox regression models were used to analyse the risk factors of hypertension. After 9 years of follow-up, 2047 subjects developed hypertension. The overall 9-year cumulative incidence of hypertension was 43.7%, ranging from 28.5% in quartile 1 to 36.9% in quartile 2, 49.2% in quartile 3 and 59.8% in quartile 4 (p for trend < 0.001). Cox regression analyses indicated that higher triglyceride and glucose index was associated with an increased risk of subsequent incident hypertension. The triglyceride and glucose index can predict the incident hypertension among the Chinese population.

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

PubMed

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

Cesarean delivery rates among family physicians versus obstetricians: a population-based cohort study using instrumental variable methods

PubMed Central

Dawe, Russell Eric; Bishop, Jessica; Pendergast, Amanda; Avery, Susan; Monaghan, Kelly; Duggan, Norah; Aubrey-Bassler, Kris

2017-01-01

Background: Previous research suggests that family physicians have rates of cesarean delivery that are lower than or equivalent to those for obstetricians, but adjustments for risk differences in these analyses may have been inadequate. We used an econometric method to adjust for observed and unobserved factors affecting the risk of cesarean delivery among women attended by family physicians versus obstetricians. Methods: This retrospective population-based cohort study included all Canadian (except Quebec) hospital deliveries by family physicians and obstetricians between Apr. 1, 2006, and Mar. 31, 2009. We excluded women with multiple gestations, and newborns with a birth weight less than 500 g or gestational age less than 20 weeks. We estimated the relative risk of cesarean delivery using instrumental-variable-adjusted and logistic regression. Results: The final cohort included 776 299 women who gave birth in 390 hospitals. The risk of cesarean delivery was 27.3%, and the mean proportion of deliveries by family physicians was 26.9% (standard deviation 23.8%). The relative risk of cesarean delivery for family physicians versus obstetricians was 0.48 (95% confidence interval [CI] 0.41-0.56) with logistic regression and 1.27 (95% CI 1.02-1.57) with instrumental-variable-adjusted regression. Interpretation: Our conventional analyses suggest that family physicians have a lower rate of cesarean delivery than obstetricians, but instrumental variable analyses suggest the opposite. Because instrumental variable methods adjust for unmeasured factors and traditional methods do not, the large discrepancy between these estimates of risk suggests that clinical and/or sociocultural factors affecting the decision to perform cesarean delivery may not be accounted for in our database. PMID:29233843

Beyond logistic regression: structural equations modelling for binary variables and its application to investigating unobserved confounders.

PubMed

Kupek, Emil

2006-03-15

Structural equation modelling (SEM) has been increasingly used in medical statistics for solving a system of related regression equations. However, a great obstacle for its wider use has been its difficulty in handling categorical variables within the framework of generalised linear models. A large data set with a known structure among two related outcomes and three independent variables was generated to investigate the use of Yule's transformation of odds ratio (OR) into Q-metric by (OR-1)/(OR+1) to approximate Pearson's correlation coefficients between binary variables whose covariance structure can be further analysed by SEM. Percent of correctly classified events and non-events was compared with the classification obtained by logistic regression. The performance of SEM based on Q-metric was also checked on a small (N = 100) random sample of the data generated and on a real data set. SEM successfully recovered the generated model structure. SEM of real data suggested a significant influence of a latent confounding variable which would have not been detectable by standard logistic regression. SEM classification performance was broadly similar to that of the logistic regression. The analysis of binary data can be greatly enhanced by Yule's transformation of odds ratios into estimated correlation matrix that can be further analysed by SEM. The interpretation of results is aided by expressing them as odds ratios which are the most frequently used measure of effect in medical statistics.

Personality and attention: Levels of neuroticism and extraversion can predict attentional performance during a change detection task.

PubMed

Hahn, Sowon; Buttaccio, Daniel R; Hahn, Jungwon; Lee, Taehun

2015-01-01

The present study demonstrates that levels of extraversion and neuroticism can predict attentional performance during a change detection task. After completing a change detection task built on the flicker paradigm, participants were assessed for personality traits using the Revised Eysenck Personality Questionnaire (EPQ-R). Multiple regression analyses revealed that higher levels of extraversion predict increased change detection accuracies, while higher levels of neuroticism predict decreased change detection accuracies. In addition, neurotic individuals exhibited decreased sensitivity A' and increased fixation dwell times. Hierarchical regression analyses further revealed that eye movement measures mediate the relationship between neuroticism and change detection accuracies. Based on the current results, we propose that neuroticism is associated with decreased attentional control over the visual field, presumably due to decreased attentional disengagement. Extraversion can predict increased attentional performance, but the effect is smaller than the relationship between neuroticism and attention.

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

What is new about covered interest parity condition in the European Union? Evidence from fractal cross-correlation regressions

NASA Astrophysics Data System (ADS)

Ferreira, Paulo; Kristoufek, Ladislav

2017-11-01

We analyse the covered interest parity (CIP) using two novel regression frameworks based on cross-correlation analysis (detrended cross-correlation analysis and detrending moving-average cross-correlation analysis), which allow for studying the relationships at different scales and work well under non-stationarity and heavy tails. CIP is a measure of capital mobility commonly used to analyse financial integration, which remains an interesting feature of study in the context of the European Union. The importance of this features is related to the fact that the adoption of a common currency is associated with some benefits for countries, but also involves some risks such as the loss of economic instruments to face possible asymmetric shocks. While studying the Eurozone members could explain some problems in the common currency, studying the non-Euro countries is important to analyse if they are fit to take the possible benefits. Our results point to the CIP verification mainly in the Central European countries while in the remaining countries, the verification of the parity is only residual.

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.

The Association Between Immigration Status and Office-based Medical Provider Visits for Cancer Patients in the United States.

PubMed

Wang, Yang; Wilson, Fernando A; Chen, Li-Wu

2017-06-01

We examined differences in cancer-related office-based provider visits associated with immigration status in the United States. Data from the 2007-2012 Medical Expenditure Panel Survey and National Health Interview Survey included adult patients diagnosed with cancer. Univariate analyses described distributions of cancer-related office-based provider visits received, expenditures, visit characteristics, as well as demographic, socioeconomic, and health covariates, across immigration groups. We measured the relationships of immigrant status to number of visits and associated expenditure within the past 12 months, adjusting for age, sex, educational attainment, race/ethnicity, self-reported health status, time since cancer diagnosis, cancer remission status, marital status, poverty status, insurance status, and usual source of care. We finally performed sensitivity analyses for regression results by using the propensity score matching method to adjust for potential selection bias. Noncitizens had about 2 fewer visits in a 12-month period in comparison to US-born citizens (4.0 vs. 5.9). Total expenditure per patient was higher for US-born citizens than immigrants (not statistically significant). Noncitizens (88.3%) were more likely than US-born citizens (76.6%) to be seen by a medical doctor during a visit. Multivariate regression results showed that noncitizens had 42% lower number of visiting medical providers at office-based settings for cancer care than US-born citizens, after adjusting for all the other covariates. There were no significant differences in expenditures across immigration groups. The propensity score matching results were largely consistent with those in multivariate-adjusted regressions. Results suggest targeted interventions are needed to reduce disparities in utilization between immigrants and US-born citizen cancer patients.

The effect of different exercise protocols and regression-based algorithms on the assessment of the anaerobic threshold.

PubMed

Zuniga, Jorge M; Housh, Terry J; Camic, Clayton L; Bergstrom, Haley C; Schmidt, Richard J; Johnson, Glen O

2014-09-01

The purpose of this study was to examine the effect of ramp and step incremental cycle ergometer tests on the assessment of the anaerobic threshold (AT) using 3 different computerized regression-based algorithms. Thirteen healthy adults (mean age and body mass [SD] = 23.4 [3.3] years and body mass = 71.7 [11.1] kg) visited the laboratory on separate occasions. Two-way repeated measures analyses of variance with appropriate follow-up procedures were used to analyze the data. The step protocol resulted in greater mean values across algorithms than the ramp protocol for the V[Combining Dot Above]O2 (step = 1.7 [0.6] L·min and ramp = 1.5 [0.4] L·min) and heart rate (HR) (step = 133 [21] b·min and ramp = 124 [15] b·min) at the AT. There were no significant mean differences, however, in power outputs at the AT between the step (115.2 [44.3] W) and the ramp (112.2 [31.2] W) protocols. Furthermore, there were no significant mean differences for V[Combining Dot Above]O2, HR, or power output across protocols among the 3 computerized regression-based algorithms used to estimate the AT. The current findings suggested that the protocol selection, but not the regression-based algorithms can affect the assessment of the V[Combining Dot Above]O2 and HR at the AT.

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

Use of probabilistic weights to enhance linear regression myoelectric control

NASA Astrophysics Data System (ADS)

Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

2015-12-01

Objective. Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts’ law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main results. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

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.

Evaluation of the comprehensive palatability of Japanese sake paired with dishes by multiple regression analysis based on subdomains.

PubMed

Nakamura, Ryo; Nakano, Kumiko; Tamura, Hiroyasu; Mizunuma, Masaki; Fushiki, Tohru; Hirata, Dai

2017-08-01

Many factors contribute to palatability. In order to evaluate the palatability of Japanese alcohol sake paired with certain dishes by integrating multiple factors, here we applied an evaluation method previously reported for palatability of cheese by multiple regression analysis based on 3 subdomain factors (rewarding, cultural, and informational). We asked 94 Japanese participants/subjects to evaluate the palatability of sake (1st evaluation/E1 for the first cup, 2nd/E2 and 3rd/E3 for the palatability with aftertaste/afterglow of certain dishes) and to respond to a questionnaire related to 3 subdomains. In E1, 3 factors were extracted by a factor analysis, and the subsequent multiple regression analyses indicated that the palatability of sake was interpreted by mainly the rewarding. Further, the results of attribution-dissections in E1 indicated that 2 factors (rewarding and informational) contributed to the palatability. Finally, our results indicated that the palatability of sake was influenced by the dish eaten just before drinking.

SOCR Analyses - an Instructional Java Web-based Statistical Analysis Toolkit.

PubMed

Chu, Annie; Cui, Jenny; Dinov, Ivo D

2009-03-01

The Statistical Online Computational Resource (SOCR) designs web-based tools for educational use in a variety of undergraduate courses (Dinov 2006). Several studies have demonstrated that these resources significantly improve students' motivation and learning experiences (Dinov et al. 2008). SOCR Analyses is a new component that concentrates on data modeling and analysis using parametric and non-parametric techniques supported with graphical model diagnostics. Currently implemented analyses include commonly used models in undergraduate statistics courses like linear models (Simple Linear Regression, Multiple Linear Regression, One-Way and Two-Way ANOVA). In addition, we implemented tests for sample comparisons, such as t-test in the parametric category; and Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, in the non-parametric category. SOCR Analyses also include several hypothesis test models, such as Contingency tables, Friedman's test and Fisher's exact test.The code itself is open source (http://socr.googlecode.com/), hoping to contribute to the efforts of the statistical computing community. The code includes functionality for each specific analysis model and it has general utilities that can be applied in various statistical computing tasks. For example, concrete methods with API (Application Programming Interface) have been implemented in statistical summary, least square solutions of general linear models, rank calculations, etc. HTML interfaces, tutorials, source code, activities, and data are freely available via the web (www.SOCR.ucla.edu). Code examples for developers and demos for educators are provided on the SOCR Wiki website.In this article, the pedagogical utilization of the SOCR Analyses is discussed, as well as the underlying design framework. As the SOCR project is on-going and more functions and tools are being added to it, these resources are constantly improved. The reader is strongly encouraged to check the SOCR site for most updated information and newly added models.

Exploring Person Fit with an Approach Based on Multilevel Logistic Regression

ERIC Educational Resources Information Center

Walker, A. Adrienne; Engelhard, George, Jr.

2015-01-01

The idea that test scores may not be valid representations of what students know, can do, and should learn next is well known. Person fit provides an important aspect of validity evidence. Person fit analyses at the individual student level are not typically conducted and person fit information is not communicated to educational stakeholders. In…

Body Image Concerns of Gay Men: The Roles of Minority Stress and Conformity to Masculine Norms

ERIC Educational Resources Information Center

Kimmel, Sara B.; Mahalik, James R.

2005-01-01

The authors hypothesized that gay men's experiences of minority stress and their conformity to masculine norms would be associated with increased body image dissatisfaction and masculine body ideal distress. For this cross-sectional study, 357 gay males completed a Web-based survey, and 2 multiple regression analyses indicated that minority stress…

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.

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

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.

Collapse susceptibility mapping in karstified gypsum terrain (Sivas basin - Turkey) by conditional probability, logistic regression, artificial neural network models

NASA Astrophysics Data System (ADS)

Yilmaz, Isik; Keskin, Inan; Marschalko, Marian; Bednarik, Martin

2010-05-01

This study compares the GIS based collapse susceptibility mapping methods such as; conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) applied in gypsum rock masses in Sivas basin (Turkey). Digital Elevation Model (DEM) was first constructed using GIS software. Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index- TWI, stream power index- SPI, Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from CP, LR and ANN models, and they were then compared by means of their validations. Area Under Curve (AUC) values obtained from all three methodologies showed that the map obtained from ANN model looks like more accurate than the other models, and the results also showed that the artificial neural networks is a usefull tool in preparation of collapse susceptibility map and highly compatible with GIS operating features. Key words: Collapse; doline; susceptibility map; gypsum; GIS; conditional probability; logistic regression; artificial neural networks.

Spatially Explicit Estimates of Suspended Sediment and Bedload Transport Rates for Western Oregon and Northwestern California

NASA Astrophysics Data System (ADS)

O'Connor, J. E.; Wise, D. R.; Mangano, J.; Jones, K.

2015-12-01

Empirical analyses of suspended sediment and bedload transport gives estimates of sediment flux for western Oregon and northwestern California. The estimates of both bedload and suspended load are from regression models relating measured annual sediment yield to geologic, physiographic, and climatic properties of contributing basins. The best models include generalized geology and either slope or precipitation. The best-fit suspended-sediment model is based on basin geology, precipitation, and area of recent wildfire. It explains 65% of the variance for 68 suspended sediment measurement sites within the model area. Predicted suspended sediment yields range from no yield from the High Cascades geologic province to 200 tonnes/ km2-yr in the northern Oregon Coast Range and 1000 tonnes/km2-yr in recently burned areas of the northern Klamath terrain. Bed-material yield is similarly estimated from a regression model based on 22 sites of measured bed-material transport, mostly from reservoir accumulation analyses but also from several bedload measurement programs. The resulting best-fit regression is based on basin slope and the presence/absence of the Klamath geologic terrane. For the Klamath terrane, bed-material yield is twice that of the other geologic provinces. This model explains more than 80% of the variance of the better-quality measurements. Predicted bed-material yields range up to 350 tonnes/ km2-yr in steep areas of the Klamath terrane. Applying these regressions to small individual watersheds (mean size; 66 km2 for bed-material; 3 km2 for suspended sediment) and cumulating totals down the hydrologic network (but also decreasing the bed-material flux by experimentally determined attrition rates) gives spatially explicit estimates of both bed-material and suspended sediment flux. This enables assessment of several management issues, including the effects of dams on bedload transport, instream gravel mining, habitat formation processes, and water-quality. The combined fluxes can also be compared to long-term rock uplift and cosmogenically determined landscape erosion rates.

Total and domain-specific sitting time among employees in desk-based work settings in Australia.

PubMed

Bennie, Jason A; Pedisic, Zeljko; Timperio, Anna; Crawford, David; Dunstan, David; Bauman, Adrian; van Uffelen, Jannique; Salmon, Jo

2015-06-01

To describe the total and domain-specific daily sitting time among a sample of Australian office-based employees. In April 2010, paper-based surveys were provided to desk-based employees (n=801) in Victoria, Australia. Total daily and domain-specific (work, leisure-time and transport-related) sitting time (minutes/day) were assessed by validated questionnaires. Differences in sitting time were examined across socio-demographic (age, sex, occupational status) and lifestyle characteristics (physical activity levels, body mass index [BMI]) using multiple linear regression analyses. The median (95% confidence interval [CI]) of total daily sitting time was 540 (531-557) minutes/day. Insufficiently active adults (median=578 minutes/day, [95%CI: 564-602]), younger adults aged 18-29 years (median=561 minutes/day, [95%CI: 540-577]) reported the highest total daily sitting times. Occupational sitting time accounted for almost 60% of total daily sitting time. In multivariate analyses, total daily sitting time was negatively associated with age (unstandardised regression coefficient [B]=-1.58, p<0.001) and overall physical activity (minutes/week) (B=-0.03, p<0.001) and positively associated with BMI (B=1.53, p=0.038). Desk-based employees reported that more than half of their total daily sitting time was accrued in the work setting. Given the high contribution of occupational sitting to total daily sitting time among desk-based employees, interventions should focus on the work setting. © 2014 Public Health Association of Australia.

Personal discrimination and satisfaction with life: Exploring perceived functional effects of Asian American race/ethnicity as a moderator.

PubMed

Tran, Alisia G T T; Sangalang, Cindy C

2016-01-01

This study aims to understand the relations between experiences of racial/ethnic discrimination, perceptions of the harmful or helpful effects of one's Asian American race/ethnicity within educational and occupational contexts (perceived functional effects), and well-being (i.e., satisfaction with life). A primary focus was to evaluate whether the association between racial/ethnic discrimination and satisfaction with life varied based on the degree to which Asian Americans believe that their race or ethnicity is helpful or harmful to educational and occupational functioning. This study draws on nationally representative data from ethnically diverse Asian American adults (N = 3,335) and utilizes weighted descriptive, correlational, and logistic regression moderation analyses. Ethnic variations emerged across analyses. Logistic regression analyses revealed a significant moderation effect for Chinese and Filipino Americans. Follow-up analyses revealed a protective effect of perceiving more positive or helpful functional effects in nullifying the link between discrimination and dissatisfaction with life for Chinese Americans. By contrast, viewing more harmful functional effects had a buffering effect for Filipino Americans. Results have implications for conceptualizing the potential impact of perspectives that imply Asian American advantage or disadvantage. Opportunities to apply and extend these initial findings are discussed. (c) 2016 APA, all rights reserved).

[Sanitation and racial inequality conditions in urban Brazil: an analysis focused on the indigenous population based on the 2010 Population Census].

PubMed

Raupp, Ludimila; Fávaro, Thatiana Regina; Cunha, Geraldo Marcelo; Santos, Ricardo Ventura

2017-01-01

The aims of this study were to analyze and describe the presence and infrastructure of basic sanitation in the urban areas of Brazil, contrasting indigenous with non-indigenous households. Methods: A cross-sectional study based on microdata from the 2010 Census was conducted. The analyses were based on descriptive statistics (prevalence) and the construction of multiple logistic regression models (adjusted by socioeconomic and demographic covariates). The odds ratios were estimated for the association between the explanatory variables (covariates) and the outcome variables (water supply, sewage, garbage collection, and adequate sanitation). The statistical significance level established was 5%. Among the analyzed services, sewage proved to be the most precarious. Regarding race or color, indigenous households presented the lowest rate of sanitary infrastructure in Urban Brazil. The adjusted regression showed that, in general, indigenous households were at a disadvantage when compared to other categories of race or color, especially in terms of the presence of garbage collection services. These inequalities were much more pronounced in the South and Southeastern regions. The analyses of this study not only confirm the profile of poor conditions and infrastructure of the basic sanitation of indigenous households in urban areas, but also demonstrate the persistence of inequalities associated with race or color in the country.

New 1,6-heptadienes with pyrimidine bases attached: Syntheses and spectroscopic analyses

NASA Astrophysics Data System (ADS)

Hammud, Hassan H.; Ghannoum, Amer M.; Fares, Fares A.; Abramian, Lara K.; Bouhadir, Kamal H.

2008-06-01

A simple, high yielding synthesis leading to the functionalization of some pyrimidine bases with a 1,6-heptadienyl moiety spaced from the N - 1 position by a methylene group is described. A key step in this synthesis involves a Mitsunobu reaction by coupling 3N-benzoyluracil and 3N-benzoylthymine to 2-allyl-pent-4-en-1-ol followed by alkaline hydrolysis of the 3N-benzoyl protecting groups. This protocol should eventually lend itself to the synthesis of a host of N-alkylated nucleoside analogs. The absorption and emission properties of these pyrimidine derivatives ( 3- 6) were studied in solvents of different physical properties. Computerized analysis and multiple regression techniques were applied to calculate the regression and correlation coefficients based on the equation that relates peak position λmax to the solvent parameters that depend on the H-bonding ability, refractive index, and dielectric constant of solvents.

Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation

PubMed Central

Nilssen, Ingunn; Eide, Ingvar; de Oliveira Figueiredo, Marcia Abreu; de Souza Tâmega, Frederico Tapajós; Nattkemper, Tim W.

2016-01-01

This paper presents a machine learning based approach for analyses of photos collected from laboratory experiments conducted to assess the potential impact of water-based drill cuttings on deep-water rhodolith-forming calcareous algae. This pilot study uses imaging technology to quantify and monitor the stress levels of the calcareous algae Mesophyllum engelhartii (Foslie) Adey caused by various degrees of light exposure, flow intensity and amount of sediment. A machine learning based algorithm was applied to assess the temporal variation of the calcareous algae size (∼ mass) and color automatically. Measured size and color were correlated to the photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, ΦPSIImax) and degree of sediment coverage using multivariate regression. The multivariate regression showed correlations between time and calcareous algae sizes, as well as correlations between fluorescence and calcareous algae colors. PMID:27285611

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

Association between month of birth and melanoma risk: fact or fiction?

PubMed

Fiessler, Cornelia; Pfahlberg, Annette B; Keller, Andrea K; Radespiel-Tröger, Martin; Uter, Wolfgang; Gefeller, Olaf

2017-04-01

Evidence on the effect of ultraviolet radiation (UVR) exposure in infancy on melanoma risk in later life is scarce. Three recent studies suggest that people born in spring carry a higher melanoma risk. Our study aimed at verifying whether such a seasonal pattern of melanoma risk actually exists. Data from the population-based Cancer Registry Bavaria (CRB) on the birth months of 28 374 incident melanoma cases between 2002 and 2012 were analysed and compared with data from the Bavarian State Office for Statistics and Data Processing on the birth month distribution in the Bavarian population. Crude and adjusted analyses using negative binomial regression models were performed in the total study group and supplemented by several subgroup analyses. In the crude analysis, the birth months March-May were over-represented among melanoma cases. Negative binomial regression models adjusted only for sex and birth year revealed a seasonal association between melanoma risk and birth month with 13-21% higher relative incidence rates for March, April and May compared with the reference December. However, after additionally adjusting for the birth month distribution of the Bavarian population, these risk estimates decreased markedly and no association with the birth month was observed any more. Similar results emerged in all subgroup analyses. Our large registry-based study provides no evidence that people born in spring carry a higher risk for developing melanoma in later life and thus lends no support to the hypothesis of higher UVR susceptibility during the first months of life. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association

Association between birthweight and later body mass index: an individual-based pooled analysis of 27 twin cohorts participating in the CODATwins project

PubMed Central

Jelenkovic, Aline; Yokoyama, Yoshie; Sund, Reijo; Pietiläinen, Kirsi H; Hur, Yoon-Mi; Willemsen, Gonneke; Bartels, Meike; van Beijsterveldt, Toos CEM; Ooki, Syuichi; Saudino, Kimberly J; Stazi, Maria A; Fagnani, Corrado; D’Ippolito, Cristina; Nelson, Tracy L; Whitfield, Keith E; Knafo-Noam, Ariel; Mankuta, David; Abramson, Lior; Heikkilä, Kauko; Cutler, Tessa L; Hopper, John L; Wardle, Jane; Llewellyn, Clare H; Fisher, Abigail; Corley, Robin P; Huibregtse, Brooke M; Derom, Catherine A; Vlietinck, Robert F; Loos, Ruth JF; Bjerregaard-Andersen, Morten; Beck-Nielsen, Henning; Sodemann, Morten; Tarnoki, Adam D; Tarnoki, David L; Burt, S Alexandra; Klump, Kelly L; Ordoñana, Juan R; Sánchez-Romera, Juan F; Colodro-Conde, Lucia; Dubois, Lise; Boivin, Michel; Brendgen, Mara; Dionne, Ginette; Vitaro, Frank; Harris, Jennifer R; Brandt, Ingunn; Nilsen, Thomas Sevenius; Craig, Jeffrey M; Saffery, Richard; Rasmussen, Finn; Tynelius, Per; Bayasgalan, Gombojav; Narandalai, Danshiitsoodol; Haworth, Claire MA; Plomin, Robert; Ji, Fuling; Ning, Feng; Pang, Zengchang; Rebato, Esther; Krueger, Robert F; McGue, Matt; Pahlen, Shandell; Boomsma, Dorret I; Sørensen, Thorkild IA; Kaprio, Jaakko; Silventoinen, Karri

2017-01-01

Abstract Background There is evidence that birthweight is positively associated with body mass index (BMI) in later life, but it remains unclear whether this is explained by genetic factors or the intrauterine environment. We analysed the association between birthweight and BMI from infancy to adulthood within twin pairs, which provides insights into the role of genetic and environmental individual-specific factors. Methods This study is based on the data from 27 twin cohorts in 17 countries. The pooled data included 78 642 twin individuals (20 635 monozygotic and 18 686 same-sex dizygotic twin pairs) with information on birthweight and a total of 214 930 BMI measurements at ages ranging from 1 to 49 years. The association between birthweight and BMI was analysed at both the individual and within-pair levels using linear regression analyses. Results At the individual level, a 1-kg increase in birthweight was linearly associated with up to 0.9 kg/m2 higher BMI (P < 0.001). Within twin pairs, regression coefficients were generally greater (up to 1.2 kg/m2 per kg birthweight, P < 0.001) than those from the individual-level analyses. Intra-pair associations between birthweight and later BMI were similar in both zygosity groups and sexes and were lower in adulthood. Conclusions These findings indicate that environmental factors unique to each individual have an important role in the positive association between birthweight and later BMI, at least until young adulthood. PMID:28369451

Factors predicting work outcome in Japanese patients with schizophrenia: role of multiple functioning levels.

PubMed

Sumiyoshi, Chika; Harvey, Philip D; Takaki, Manabu; Okahisa, Yuko; Sato, Taku; Sora, Ichiro; Nuechterlein, Keith H; Subotnik, Kenneth L; Sumiyoshi, Tomiki

2015-09-01

Functional outcomes in individuals with schizophrenia suggest recovery of cognitive, everyday, and social functioning. Specifically improvement of work status is considered to be most important for their independent living and self-efficacy. The main purposes of the present study were 1) to identify which outcome factors predict occupational functioning, quantified as work hours, and 2) to provide cut-offs on the scales for those factors to attain better work status. Forty-five Japanese patients with schizophrenia and 111 healthy controls entered the study. Cognition, capacity for everyday activities, and social functioning were assessed by the Japanese versions of the MATRICS Cognitive Consensus Battery (MCCB), the UCSD Performance-based Skills Assessment-Brief (UPSA-B), and the Social Functioning Scale Individuals' version modified for the MATRICS-PASS (Modified SFS for PASS), respectively. Potential factors for work outcome were estimated by multiple linear regression analyses (predicting work hours directly) and a multiple logistic regression analyses (predicting dichotomized work status based on work hours). ROC curve analyses were performed to determine cut-off points for differentiating between the better- and poor work status. The results showed that a cognitive component, comprising visual/verbal learning and emotional management, and a social functioning component, comprising independent living and vocational functioning, were potential factors for predicting work hours/status. Cut-off points obtained in ROC analyses indicated that 60-70% achievements on the measures of those factors were expected to maintain the better work status. Our findings suggest that improvement on specific aspects of cognitive and social functioning are important for work outcome in patients with schizophrenia.

Race-based job discrimination, disparities in job control, and their joint effects on health.

PubMed

Meyer, John D

2014-05-01

To examine disparities between job control scores in Black and White subjects and attempt to discern whether self-rated low job control in Blacks may arise from structural segregation into different jobs, or represents individual responses to race-based discrimination in hiring or promotion. Data from the National Survey of Midlife in the United States (MIDUS) were analyzed by mixed-effects linear regression and variance regression to determine the effects of grouping by occupation, and racial discrimination in hiring or promotion, on control scores from the Job Content Questionnaire in Black and White subjects. Path analyses were constructed to determine the mediating effect of discrimination on pathways from education and job control to self-rated health. Black subjects exhibited lower mean job control scores compared to Whites (mean score difference 2.26, P < 0.001) adjusted for age, sex, education, and income. This difference narrowed to 1.86 when adjusted for clustering by occupation, and was greatly reduced by conditioning on race-based discrimination (score difference 1.03, P = 0.12). Path analyses showed greater reported discrimination in Blacks with increasing education, and a stronger effect of job control on health in Black subjects. Individual racially-based discrimination appears a stronger determinant than structural segregation in reduced job control in Black workers, and may contribute to health disparities consequent on work. © 2013 Wiley Periodicals, Inc.

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.

A critical review of the protracted domestication model for Near-Eastern founder crops: linear regression, long-distance gene flow, archaeological, and archaeobotanical evidence.

PubMed

Heun, Manfred; Abbo, Shahal; Lev-Yadun, Simcha; Gopher, Avi

2012-07-01

The recent review by Fuller et al. (2012a) in this journal is part of a series of papers maintaining that plant domestication in the Near East was a slow process lasting circa 4000 years and occurring independently in different locations across the Fertile Crescent. Their protracted domestication scenario is based entirely on linear regression derived from the percentage of domesticated plant remains at specific archaeological sites and the age of these sites themselves. This paper discusses why estimates like haldanes and darwins cannot be applied to the seven founder crops in the Near East (einkorn and emmer wheat, barley, peas, chickpeas, lentils, and bitter vetch). All of these crops are self-fertilizing plants and for this reason they do not fulfil the requirements for performing calculations of this kind. In addition, the percentage of domesticates at any site may be the result of factors other than those that affect the selection for domesticates growing in the surrounding area. These factors are unlikely to have been similar across prehistoric sites of habitation, societies, and millennia. The conclusion here is that single crop analyses are necessary rather than general reviews drawing on regression analyses based on erroneous assumptions. The fact that all seven of these founder crops are self-fertilizers should be incorporated into a comprehensive domestication scenario for the Near East, as self-fertilization naturally isolates domesticates from their wild progenitors.

Predicted effect size of lisdexamfetamine treatment of attention deficit/hyperactivity disorder (ADHD) in European adults: Estimates based on indirect analysis using a systematic review and meta-regression analysis.

PubMed

Fridman, M; Hodgkins, P S; Kahle, J S; Erder, M H

2015-06-01

There are few approved therapies for adults with attention-deficit/hyperactivity disorder (ADHD) in Europe. Lisdexamfetamine (LDX) is an effective treatment for ADHD; however, no clinical trials examining the efficacy of LDX specifically in European adults have been conducted. Therefore, to estimate the efficacy of LDX in European adults we performed a meta-regression of existing clinical data. A systematic review identified US- and Europe-based randomized efficacy trials of LDX, atomoxetine (ATX), or osmotic-release oral system methylphenidate (OROS-MPH) in children/adolescents and adults. A meta-regression model was then fitted to the published/calculated effect sizes (Cohen's d) using medication, geographical location, and age group as predictors. The LDX effect size in European adults was extrapolated from the fitted model. Sensitivity analyses performed included using adult-only studies and adding studies with placebo designs other than a standard pill-placebo design. Twenty-two of 2832 identified articles met inclusion criteria. The model-estimated effect size of LDX for European adults was 1.070 (95% confidence interval: 0.738, 1.401), larger than the 0.8 threshold for large effect sizes. The overall model fit was adequate (80%) and stable in the sensitivity analyses. This model predicts that LDX may have a large treatment effect size in European adults with ADHD. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

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.

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.

Partial Least Squares Regression Can Aid in Detecting Differential Abundance of Multiple Features in Sets of Metagenomic Samples

PubMed Central

Libiger, Ondrej; Schork, Nicholas J.

2015-01-01

It is now feasible to examine the composition and diversity of microbial communities (i.e., “microbiomes”) that populate different human organs and orifices using DNA sequencing and related technologies. To explore the potential links between changes in microbial communities and various diseases in the human body, it is essential to test associations involving different species within and across microbiomes, environmental settings and disease states. Although a number of statistical techniques exist for carrying out relevant analyses, it is unclear which of these techniques exhibit the greatest statistical power to detect associations given the complexity of most microbiome datasets. We compared the statistical power of principal component regression, partial least squares regression, regularized regression, distance-based regression, Hill's diversity measures, and a modified test implemented in the popular and widely used microbiome analysis methodology “Metastats” across a wide range of simulated scenarios involving changes in feature abundance between two sets of metagenomic samples. For this purpose, simulation studies were used to change the abundance of microbial species in a real dataset from a published study examining human hands. Each technique was applied to the same data, and its ability to detect the simulated change in abundance was assessed. We hypothesized that a small subset of methods would outperform the rest in terms of the statistical power. Indeed, we found that the Metastats technique modified to accommodate multivariate analysis and partial least squares regression yielded high power under the models and data sets we studied. The statistical power of diversity measure-based tests, distance-based regression and regularized regression was significantly lower. Our results provide insight into powerful analysis strategies that utilize information on species counts from large microbiome data sets exhibiting skewed frequency distributions obtained on a small to moderate number of samples. PMID:26734061

High school science enrollment of black students

NASA Astrophysics Data System (ADS)

Goggins, Ellen O.; Lindbeck, Joy S.

How can the high school science enrollment of black students be increased? School and home counseling and classroom procedures could benefit from variables identified as predictors of science enrollment. The problem in this study was to identify a set of variables which characterize science course enrollment by black secondary students. The population consisted of a subsample of 3963 black high school seniors from The High School and Beyond 1980 Base-Year Survey. Using multiple linear regression, backward regression, and correlation analyses, the US Census regions and grades mostly As and Bs in English were found to be significant predictors of the number of science courses scheduled by black seniors.

Marine aerosol source regions to Prince of Wales Icefield, Ellesmere Island, and influence from the tropical Pacific, 1979-2001

NASA Astrophysics Data System (ADS)

Criscitiello, Alison S.; Marshall, Shawn J.; Evans, Matthew J.; Kinnard, Christophe; Norman, Ann-Lise; Sharp, Martin J.

2016-08-01

Using a coastal ice core collected from Prince of Wales (POW) Icefield on Ellesmere Island, we investigate source regions of sea ice-modulated chemical species (methanesulfonic acid (MSA) and chloride (Cl-)) to POW Icefield and the influence of large-scale atmospheric variability on the transport of these marine aerosols (1979-2001). Our key findings are (1) MSA in the POW Icefield core is derived primarily from productivity in the sea ice zone of Baffin Bay and the Labrador Sea, with influence from waters within the North Water (NOW) polynya, (2) sea ice formation processes within the NOW polynya may be a significant source of sea-salt aerosols to the POW core site, in addition to offshore open water source regions primarily in Hudson Bay, and (3) the tropical Pacific influences the source and transport of marine aerosols to POW Icefield through its remote control on regional winds and sea ice variability. Regression analyses during times of MSA deposition reveal sea level pressure (SLP) anomalies favorable for opening of the NOW polynya and subsequent oceanic dimethyl sulfide production. Regression analyses during times of Cl- deposition reveal SLP anomalies that indicate a broader oceanic region of sea-salt sources to the core site. These results are supported by Scanning Multichannel Microwave Radiometer- and Special Sensor Microwave/Imager-based sea ice reconstructions and air mass transport density analyses and suggest that the marine biogenic record may capture local polynya variability, while sea-salt transport to the site from larger offshore source regions in Baffin Bay is likely. Regression analyses show a link to tropical dynamics via an atmospheric Rossby wave.

Beliefs about meditating among university students, faculty, and staff: a theory-based salient belief elicitation.

PubMed

Lederer, Alyssa M; Middlestadt, Susan E

2014-01-01

Stress impacts college students, faculty, and staff alike. Although meditation has been found to decrease stress, it is an underutilized strategy. This study used the Reasoned Action Approach (RAA) to identify beliefs underlying university constituents' decision to meditate. N=96 students, faculty, and staff at a large midwestern university during spring 2012. A survey measured the RAA global constructs and elicited the beliefs underlying intention to meditate. Thematic and frequency analyses and multiple regression were performed. Quantitative analyses showed that intention to meditate was significantly predicted (R2=.632) by attitude, perceived norm, and perceived behavioral control. Qualitative analyses revealed advantages (eg, reduced stress; feeling calmer), disadvantages (eg, takes time; will not work), and facilitating circumstances (eg, having more time; having quiet space) of meditating. Results of this theory-based research suggest how college health professionals can encourage meditation practice through individual, interpersonal, and environmental interventions.

"Assessing the methodological quality of systematic reviews in radiation oncology: A systematic review".

PubMed

Hasan, Haroon; Muhammed, Taaha; Yu, Jennifer; Taguchi, Kelsi; Samargandi, Osama A; Howard, A Fuchsia; Lo, Andrea C; Olson, Robert; Goddard, Karen

2017-10-01

The objective of our study was to evaluate the methodological quality of systematic reviews and meta-analyses in Radiation Oncology. A systematic literature search was conducted for all eligible systematic reviews and meta-analyses in Radiation Oncology from 1966 to 2015. Methodological characteristics were abstracted from all works that satisfied the inclusion criteria and quality was assessed using the critical appraisal tool, AMSTAR. Regression analyses were performed to determine factors associated with a higher score of quality. Following exclusion based on a priori criteria, 410 studies (157 systematic reviews and 253 meta-analyses) satisfied the inclusion criteria. Meta-analyses were found to be of fair to good quality while systematic reviews were found to be of less than fair quality. Factors associated with higher scores of quality in the multivariable analysis were including primary studies consisting of randomized control trials, performing a meta-analysis, and applying a recommended guideline related to establishing a systematic review protocol and/or reporting. Systematic reviews and meta-analyses may introduce a high risk of bias if applied to inform decision-making based on AMSTAR. We recommend that decision-makers in Radiation Oncology scrutinize the methodological quality of systematic reviews and meta-analyses prior to assessing their utility to inform evidence-based medicine and researchers adhere to methodological standards outlined in validated guidelines when embarking on a systematic review. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Multicomponent analysis of a digital Trail Making Test.

PubMed

Fellows, Robert P; Dahmen, Jessamyn; Cook, Diane; Schmitter-Edgecombe, Maureen

2017-01-01

The purpose of the current study was to use a newly developed digital tablet-based variant of the TMT to isolate component cognitive processes underlying TMT performance. Similar to the paper-based trail making test, this digital variant consists of two conditions, Part A and Part B. However, this digital version automatically collects additional data to create component subtest scores to isolate cognitive abilities. Specifically, in addition to the total time to completion and number of errors, the digital Trail Making Test (dTMT) records several unique components including the number of pauses, pause duration, lifts, lift duration, time inside each circle, and time between circles. Participants were community-dwelling older adults who completed a neuropsychological evaluation including measures of processing speed, inhibitory control, visual working memory/sequencing, and set-switching. The abilities underlying TMT performance were assessed through regression analyses of component scores from the dTMT with traditional neuropsychological measures. Results revealed significant correlations between paper and digital variants of Part A (r s  = .541, p < .001) and paper and digital versions of Part B (r s  = .799, p < .001). Regression analyses with traditional neuropsychological measures revealed that Part A components were best predicted by speeded processing, while inhibitory control and visual/spatial sequencing were predictors of specific components of Part B. Exploratory analyses revealed that specific dTMT-B components were associated with a performance-based medication management task. Taken together, these results elucidate specific cognitive abilities underlying TMT performance, as well as the utility of isolating digital components.

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,…

Genome-wide regression and prediction with the BGLR statistical package.

PubMed

Pérez, Paulino; de los Campos, Gustavo

2014-10-01

Many modern genomic data analyses require implementing regressions where the number of parameters (p, e.g., the number of marker effects) exceeds sample size (n). Implementing these large-p-with-small-n regressions poses several statistical and computational challenges, some of which can be confronted using Bayesian methods. This approach allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner. The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures (Bayesian reproducing kernel Hilbert spaces regressions, RKHS). The software was originally developed for genomic applications; however, the methods implemented are useful for many nongenomic applications as well. The response can be continuous (censored or not) or categorical (either binary or ordinal). The algorithm is based on a Gibbs sampler with scalar updates and the implementation takes advantage of efficient compiled C and Fortran routines. In this article we describe the methods implemented in BGLR, present examples of the use of the package, and discuss practical issues emerging in real-data analysis. Copyright © 2014 by the Genetics Society of America.

Partial Least Squares with Structured Output for Modelling the Metabolomics Data Obtained from Complex Experimental Designs: A Study into the Y-Block Coding.

PubMed

Xu, Yun; Muhamadali, Howbeer; Sayqal, Ali; Dixon, Neil; Goodacre, Royston

2016-10-28

Partial least squares (PLS) is one of the most commonly used supervised modelling approaches for analysing multivariate metabolomics data. PLS is typically employed as either a regression model (PLS-R) or a classification model (PLS-DA). However, in metabolomics studies it is common to investigate multiple, potentially interacting, factors simultaneously following a specific experimental design. Such data often cannot be considered as a "pure" regression or a classification problem. Nevertheless, these data have often still been treated as a regression or classification problem and this could lead to ambiguous results. In this study, we investigated the feasibility of designing a hybrid target matrix Y that better reflects the experimental design than simple regression or binary class membership coding commonly used in PLS modelling. The new design of Y coding was based on the same principle used by structural modelling in machine learning techniques. Two real metabolomics datasets were used as examples to illustrate how the new Y coding can improve the interpretability of the PLS model compared to classic regression/classification coding.

Modeling Antimicrobial Activity of Clorox(R) Using an Agar-Diffusion Test: A New Twist On an Old Experiment.

ERIC Educational Resources Information Center

Mitchell, James K.; Carter, William E.

2000-01-01

Describes using a computer statistical software package called Minitab to model the sensitivity of several microbes to the disinfectant NaOCl (Clorox') using the Kirby-Bauer technique. Each group of students collects data from one microbe, conducts regression analyses, then chooses the best-fit model based on the highest r-values obtained.…

Impact of School Violence on Youth Alcohol Abuse: Differences Based on Gender and Grade Level

ERIC Educational Resources Information Center

Vidourek, Rebecca A.; King, Keith A.; Merianos, Ashley L.

2016-01-01

The purpose of this study was to examine the impact of school violence on recent alcohol use and episodic heavy drinking among seventh- through 12th-grade students. A total of 54,631 students completed a survey assessing substance use and other risky behaviors. Logistic regression analyses were conducted to examine the research questions. Results…

Multiple Regression as a Flexible Alternative to ANOVA in L2 Research

ERIC Educational Resources Information Center

Plonsky, Luke; Oswald, Frederick L.

2017-01-01

Second language (L2) research relies heavily and increasingly on ANOVA (analysis of variance)-based results as a means to advance theory and practice. This fact alone should merit some reflection on the utility and value of ANOVA. It is possible that we could use this procedure more appropriately and, as argued here, other analyses such as…

Differential Third-Grade Outcomes Associated with Attending Publicly Funded Preschool Programs for Low-Income Latino Children

ERIC Educational Resources Information Center

Ansari, Arya; L?pez, Michael; Manfra, Louis; Bleiker, Charles; Dinehart, Laura H. B.; Hartman, Suzanne C.; Winsler, Adam

2017-01-01

This study examined the third-grade outcomes of 11,902 low-income Latino children who experienced public school pre-K or child care via subsidies (center-based care) at age 4 in Miami-Dade County, Florida. Regression and propensity score analyses revealed that children who experienced public school pre-K earned higher scores on standardized…

The Costs of Today's Jobs: Job Characteristics and Organizational Supports as Antecedents of Negative Spillover

ERIC Educational Resources Information Center

Grotto, Angela R.; Lyness, Karen S.

2010-01-01

This study examined job characteristics and organizational supports as antecedents of negative work-to-nonwork spillover for 1178 U.S. employees. Based on hierarchical regression analyses of 2002 National Study of the Changing Workforce data and O*NET data, job demands (requirements to work at home beyond scheduled hours, job complexity, time and…

Effects of Age and Schooling on Intellectual Performance: Estimates Obtained from Analysis of Continuous Variation in Age and Length of Schooling

ERIC Educational Resources Information Center

Cliffordson, Christina; Gustafsson, Jan-Eric

2008-01-01

The effects of age and schooling on different aspects of intellectual performance, taking track of study into account, are investigated. The analyses were based on military enlistment test scores, obtained by 48,269 males, measuring Fluid ability (Gf), Crystallized intelligence (Gc), and General visualization (Gv) ability. A regression method,…

Effects of personality traits on collaborative performance in problem-based learning tutorials

PubMed Central

Jang, Hye Won; Park, Seung Won

2016-01-01

Objectives To examine the relationship between students’ collaborative performance in a problem-based learning (PBL) environment and their personality traits. Methods This retrospective, cross-sectional study was conducted using student data of a PBL program between 2013 and 2014 at Sungkyunkwan University School of Medicine, Seoul, South Korea. Eighty students were included in the study. Student data from the Temperament and Character Inventory were used as a measure of their personality traits. Peer evaluation scores during PBL were used as a measure of students’ collaborative performance. Results Simple regression analyses indicated that participation was negatively related to harm avoidance and positively related to persistence, whereas preparedness for the group work was negatively related to reward dependence. On multiple regression analyses, low reward dependence remained a significant predictor of preparedness. Grade-point average (GPA) was negatively associated with novelty seeking and cooperativeness and was positively associated with persistence. Conclusion Medical students who are less dependent on social reward are more likely to complete assigned independent work to prepare for the PBL tutorials. The findings of this study can help educators better understand and support medical students who are at risk of struggling in collaborative learning environments. PMID:27874153

Effects of personality traits on collaborative performance in problem-based learning tutorials.

PubMed

Jang, Hye Won; Park, Seung Won

2016-12-01

To examine the relationship between students' collaborative performance in a problem-based learning (PBL) environment and their personality traits. Methods:This retrospective, cross-sectional study was conducted using student data of a PBL program between 2013 and 2014 at Sungkyunkwan University School of Medicine, Seoul, South Korea. Eighty students were included in the study. Student data from the Temperament and Character Inventory were used as a measure of their personality traits. Peer evaluation scores during PBL were used as a measure of students' collaborative performance. Results: Simple regression analyses indicated that participation was negatively related to harm avoidance and positively related to persistence, whereas preparedness for the group work was negatively related to reward dependence. On multiple regression analyses, low reward dependence remained a significant predictor of preparedness. Grade-point average (GPA)  was negatively associated with novelty seeking and cooperativeness and was positively associated with persistence.  Conclusion: Medical students who are less dependent on social reward are more likely to complete assigned independent work to prepare for the PBL tutorials. The findings of this study can help educators better understand and support medical students who are at risk of struggling in collaborative learning environments.

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

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.

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.

Clinical evaluation of a novel population-based regression analysis for detecting glaucomatous visual field progression.

PubMed

Kovalska, M P; Bürki, E; Schoetzau, A; Orguel, S F; Orguel, S; Grieshaber, M C

2011-04-01

The distinction of real progression from test variability in visual field (VF) series may be based on clinical judgment, on trend analysis based on follow-up of test parameters over time, or on identification of a significant change related to the mean of baseline exams (event analysis). The aim of this study was to compare a new population-based method (Octopus field analysis, OFA) with classic regression analyses and clinical judgment for detecting glaucomatous VF changes. 240 VF series of 240 patients with at least 9 consecutive examinations available were included into this study. They were independently classified by two experienced investigators. The results of such a classification served as a reference for comparison for the following statistical tests: (a) t-test global, (b) r-test global, (c) regression analysis of 10 VF clusters and (d) point-wise linear regression analysis. 32.5 % of the VF series were classified as progressive by the investigators. The sensitivity and specificity were 89.7 % and 92.0 % for r-test, and 73.1 % and 93.8 % for the t-test, respectively. In the point-wise linear regression analysis, the specificity was comparable (89.5 % versus 92 %), but the sensitivity was clearly lower than in the r-test (22.4 % versus 89.7 %) at a significance level of p = 0.01. A regression analysis for the 10 VF clusters showed a markedly higher sensitivity for the r-test (37.7 %) than the t-test (14.1 %) at a similar specificity (88.3 % versus 93.8 %) for a significant trend (p = 0.005). In regard to the cluster distribution, the paracentral clusters and the superior nasal hemifield progressed most frequently. The population-based regression analysis seems to be superior to the trend analysis in detecting VF progression in glaucoma, and may eliminate the drawbacks of the event analysis. Further, it may assist the clinician in the evaluation of VF series and may allow better visualization of the correlation between function and structure owing to VF clusters. © Georg Thieme Verlag KG Stuttgart · New York.

Age-, sex-, and education-specific norms for an extended CERAD Neuropsychological Assessment Battery-Results from the population-based LIFE-Adult-Study.

PubMed

Luck, Tobias; Pabst, Alexander; Rodriguez, Francisca S; Schroeter, Matthias L; Witte, Veronica; Hinz, Andreas; Mehnert, Anja; Engel, Christoph; Loeffler, Markus; Thiery, Joachim; Villringer, Arno; Riedel-Heller, Steffi G

2018-05-01

To provide new age-, sex-, and education-specific reference values for an extended version of the well-established Consortium to Establish a Registry for Alzheimer's Disease Neuropsychological Assessment Battery (CERAD-NAB) that additionally includes the Trail Making Test and the Verbal Fluency Test-S-Words. Norms were calculated based on the cognitive performances of n = 1,888 dementia-free participants (60-79 years) from the population-based German LIFE-Adult-Study. Multiple regressions were used to examine the association of the CERAD-NAB scores with age, sex, and education. In order to calculate the norms, quantile and censored quantile regression analyses were performed estimating marginal means of the test scores at 2.28, 6.68, 10, 15.87, 25, 50, 75, and 90 percentiles for age-, sex-, and education-specific subgroups. Multiple regression analyses revealed that younger age was significantly associated with better cognitive performance in 15 CERAD-NAB measures and higher education with better cognitive performance in all 17 measures. Women performed significantly better than men in 12 measures and men than women in four measures. The determined norms indicate ceiling effects for the cognitive performances in the Boston Naming, Word List Recognition, Constructional Praxis Copying, and Constructional Praxis Recall tests. The new norms for the extended CERAD-NAB will be useful for evaluating dementia-free German-speaking adults in a broad variety of relevant cognitive domains. The extended CERAD-NAB follows more closely the criteria for the new DSM-5 Mild and Major Neurocognitive Disorder. Additionally, it could be further developed to include a test for social cognition. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Global Food Demand Scenarios for the 21st Century

PubMed Central

Biewald, Anne; Weindl, Isabelle; Popp, Alexander; Lotze-Campen, Hermann

2015-01-01

Long-term food demand scenarios are an important tool for studying global food security and for analysing the environmental impacts of agriculture. We provide a simple and transparent method to create scenarios for future plant-based and animal-based calorie demand, using time-dependent regression models between calorie demand and income. The scenarios can be customized to a specific storyline by using different input data for gross domestic product (GDP) and population projections and by assuming different functional forms of the regressions. Our results confirm that total calorie demand increases with income, but we also found a non-income related positive time-trend. The share of animal-based calories is estimated to rise strongly with income for low-income groups. For high income groups, two ambiguous relations between income and the share of animal-based products are consistent with historical data: First, a positive relation with a strong negative time-trend and second a negative relation with a slight negative time-trend. The fits of our regressions are highly significant and our results compare well to other food demand estimates. The method is exemplarily used to construct four food demand scenarios until the year 2100 based on the storylines of the IPCC Special Report on Emissions Scenarios (SRES). We find in all scenarios a strong increase of global food demand until 2050 with an increasing share of animal-based products, especially in developing countries. PMID:26536124

Global Food Demand Scenarios for the 21st Century.

PubMed

Bodirsky, Benjamin Leon; Rolinski, Susanne; Biewald, Anne; Weindl, Isabelle; Popp, Alexander; Lotze-Campen, Hermann

2015-01-01

Long-term food demand scenarios are an important tool for studying global food security and for analysing the environmental impacts of agriculture. We provide a simple and transparent method to create scenarios for future plant-based and animal-based calorie demand, using time-dependent regression models between calorie demand and income. The scenarios can be customized to a specific storyline by using different input data for gross domestic product (GDP) and population projections and by assuming different functional forms of the regressions. Our results confirm that total calorie demand increases with income, but we also found a non-income related positive time-trend. The share of animal-based calories is estimated to rise strongly with income for low-income groups. For high income groups, two ambiguous relations between income and the share of animal-based products are consistent with historical data: First, a positive relation with a strong negative time-trend and second a negative relation with a slight negative time-trend. The fits of our regressions are highly significant and our results compare well to other food demand estimates. The method is exemplarily used to construct four food demand scenarios until the year 2100 based on the storylines of the IPCC Special Report on Emissions Scenarios (SRES). We find in all scenarios a strong increase of global food demand until 2050 with an increasing share of animal-based products, especially in developing countries.

Optimizing methods for linking cinematic features to fMRI data.

PubMed

Kauttonen, Janne; Hlushchuk, Yevhen; Tikka, Pia

2015-04-15

One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed brain activations in relation to the multiplicity of time-locked stimulus features. As previous studies have shown less inter-subject synchronization across viewers of random video footage than story-driven films, new methods need to be developed for analysis of less story-driven contents. To optimize the linkage between our fMRI data collected during viewing of a deliberately non-narrative silent film 'At Land' by Maya Deren (1944) and its annotated content, we combined the method of elastic-net regularization with the model-driven linear regression and the well-established data-driven independent component analysis (ICA) and inter-subject correlation (ISC) methods. In the linear regression analysis, both IC and region-of-interest (ROI) time-series were fitted with time-series of a total of 36 binary-valued and one real-valued tactile annotation of film features. The elastic-net regularization and cross-validation were applied in the ordinary least-squares linear regression in order to avoid over-fitting due to the multicollinearity of regressors, the results were compared against both the partial least-squares (PLS) regression and the un-regularized full-model regression. Non-parametric permutation testing scheme was applied to evaluate the statistical significance of regression. We found statistically significant correlation between the annotation model and 9 ICs out of 40 ICs. Regression analysis was also repeated for a large set of cubic ROIs covering the grey matter. Both IC- and ROI-based regression analyses revealed activations in parietal and occipital regions, with additional smaller clusters in the frontal lobe. Furthermore, we found elastic-net based regression more sensitive than PLS and un-regularized regression since it detected a larger number of significant ICs and ROIs. Along with the ISC ranking methods, our regression analysis proved a feasible method for ordering the ICs based on their functional relevance to the annotated cinematic features. The novelty of our method is - in comparison to the hypothesis-driven manual pre-selection and observation of some individual regressors biased by choice - in applying data-driven approach to all content features simultaneously. We found especially the combination of regularized regression and ICA useful when analyzing fMRI data obtained using non-narrative movie stimulus with a large set of complex and correlated features. Copyright © 2015. Published by Elsevier Inc.

The influence of the design matrix on treatment effect estimates in the quantitative analyses of single-subject experimental design research.

PubMed

Moeyaert, Mariola; Ugille, Maaike; Ferron, John M; Beretvas, S Natasha; Van den Noortgate, Wim

2014-09-01

The quantitative methods for analyzing single-subject experimental data have expanded during the last decade, including the use of regression models to statistically analyze the data, but still a lot of questions remain. One question is how to specify predictors in a regression model to account for the specifics of the design and estimate the effect size of interest. These quantitative effect sizes are used in retrospective analyses and allow synthesis of single-subject experimental study results which is informative for evidence-based decision making, research and theory building, and policy discussions. We discuss different design matrices that can be used for the most common single-subject experimental designs (SSEDs), namely, the multiple-baseline designs, reversal designs, and alternating treatment designs, and provide empirical illustrations. The purpose of this article is to guide single-subject experimental data analysts interested in analyzing and meta-analyzing SSED data. © The Author(s) 2014.

Sleep, Internalizing Problems, and Social Withdrawal: Unique Associations in Clinic-Referred Youth With Elevated Sluggish Cognitive Tempo Symptoms.

PubMed

Rondon, Ana T; Hilton, Dane C; Jarrett, Matthew A; Ollendick, Thomas H

2018-02-01

We compared clinic-referred youth with ADHD + sluggish cognitive tempo (SCT; n = 34), ADHD Only ( n = 108), and SCT Only ( n = 22) on demographics, co-occurring symptomatology, comorbid diagnoses, and social functioning. In total, 164 youth (age = 6-17 years, M = 9.97) and their parent(s) presented to an outpatient clinic for a psychoeducational assessment. Between-group analyses and regressions were used to examine study variables. SCT groups were older and exhibited more parent-reported internalizing problems, externalizing problems, sleep problems, and social withdrawal on the Child Behavior Checklist. No significant differences emerged between groups on the Teacher Report Form. Regression analyses involving multiple covariates revealed that SCT symptoms were uniquely related to social withdrawal but not general social problems. Based on parent report, SCT symptoms have a unique relationship with internalizing problems, sleep problems, and social withdrawal. Future research should explore correlates of SCT in youth using multiple informants.

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,…

[New method of mixed gas infrared spectrum analysis based on SVM].

PubMed

Bai, Peng; Xie, Wen-Jun; Liu, Jun-Hua

2007-07-01

A new method of infrared spectrum analysis based on support vector machine (SVM) for mixture gas was proposed. The kernel function in SVM was used to map the seriously overlapping absorption spectrum into high-dimensional space, and after transformation, the high-dimensional data could be processed in the original space, so the regression calibration model was established, then the regression calibration model with was applied to analyze the concentration of component gas. Meanwhile it was proved that the regression calibration model with SVM also could be used for component recognition of mixture gas. The method was applied to the analysis of different data samples. Some factors such as scan interval, range of the wavelength, kernel function and penalty coefficient C that affect the model were discussed. Experimental results show that the component concentration maximal Mean AE is 0.132%, and the component recognition accuracy is higher than 94%. The problems of overlapping absorption spectrum, using the same method for qualitative and quantitative analysis, and limit number of training sample, were solved. The method could be used in other mixture gas infrared spectrum analyses, promising theoretic and application values.

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.

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.

Testing of a simplified LED based vis/NIR system for rapid ripeness evaluation of white grape (Vitis vinifera L.) for Franciacorta wine.

PubMed

Giovenzana, Valentina; Civelli, Raffaele; Beghi, Roberto; Oberti, Roberto; Guidetti, Riccardo

2015-11-01

The aim of this work was to test a simplified optical prototype for a rapid estimation of the ripening parameters of white grape for Franciacorta wine directly in field. Spectral acquisition based on reflectance at four wavelengths (630, 690, 750 and 850 nm) was proposed. The integration of a simple processing algorithm in the microcontroller software would allow to visualize real time values of spectral reflectance. Non-destructive analyses were carried out on 95 grape bunches for a total of 475 berries. Samplings were performed weekly during the last ripening stages. Optical measurements were carried out both using the simplified system and a portable commercial vis/NIR spectrophotometer, as reference instrument for performance comparison. Chemometric analyses were performed in order to extract the maximum useful information from optical data. Principal component analysis (PCA) was performed for a preliminary evaluation of the data. Correlations between the optical data matrix and ripening parameters (total soluble solids content, SSC; titratable acidity, TA) were carried out using partial least square (PLS) regression for spectra and using multiple linear regression (MLR) for data from the simplified device. Classification analysis were also performed with the aim of discriminate ripe and unripe samples. PCA, MLR and classification analyses show the effectiveness of the simplified system in separating samples among different sampling dates and in discriminating ripe from unripe samples. Finally, simple equations for SSC and TA prediction were calculated. Copyright © 2015 Elsevier B.V. All rights reserved.

Quantitative genetic analysis of the body composition and blood pressure association in two ethnically diverse populations.

PubMed

Ghosh, Sudipta; Dosaev, Tasbulat; Prakash, Jai; Livshits, Gregory

2017-04-01

The major aim of this study was to conduct comparative quantitative-genetic analysis of the body composition (BCP) and somatotype (STP) variation, as well as their correlations with blood pressure (BP) in two ethnically, culturally and geographically different populations: Santhal, indigenous ethnic group from India and Chuvash, indigenous population from Russia. Correspondently two pedigree-based samples were collected from 1,262 Santhal and1,558 Chuvash individuals, respectively. At the first stage of the study, descriptive statistics and a series of univariate regression analyses were calculated. Finally, multiple and multivariate regression (MMR) analyses, with BP measurements as dependent variables and age, sex, BCP and STP as independent variables were carried out in each sample separately. The significant and independent covariates of BP were identified and used for re-examination in pedigree-based variance decomposition analysis. Despite clear and significant differences between the populations in BCP/STP, both Santhal and Chuvash were found to be predominantly mesomorphic irrespective of their sex. According to MMR analyses variation of BP significantly depended on age and mesomorphic component in both samples, and in addition on sex, ectomorphy and fat mass index in Santhal and on fat free mass index in Chuvash samples, respectively. Additive genetic component contributes to a substantial proportion of blood pressure and body composition variance. Variance component analysis in addition to above mentioned results suggests that additive genetic factors influence BP and BCP/STP associations significantly. © 2017 Wiley Periodicals, Inc.

What Makes a Word Easy to Acquire? The Effects of Word Class, Frequency, Imageability and Phonological Neighbourhood Density on Lexical Development

ERIC Educational Resources Information Center

Hansen, Pernille

2017-01-01

This article analyses how a set of psycholinguistic factors may account for children's lexical development. Age of acquisition is compared to a measure of lexical development based on vocabulary size rather than age, and robust regression models are used to assess the individual and joint effects of word class, frequency, imageability and…

Associations of circulating plasma microRNAs with age, body mass index and sex in a population-based study.

PubMed

Ameling, Sabine; Kacprowski, Tim; Chilukoti, Ravi Kumar; Malsch, Carolin; Liebscher, Volkmar; Suhre, Karsten; Pietzner, Maik; Friedrich, Nele; Homuth, Georg; Hammer, Elke; Völker, Uwe

2015-10-14

Non-cellular blood circulating microRNAs (plasma miRNAs) represent a promising source for the development of prognostic and diagnostic tools owing to their minimally invasive sampling, high stability, and simple quantification by standard techniques such as RT-qPCR. So far, the majority of association studies involving plasma miRNAs were disease-specific case-control analyses. In contrast, in the present study, plasma miRNAs were analysed in a sample of 372 individuals from a population-based cohort study, the Study of Health in Pomerania (SHIP). Quantification of miRNA levels was performed by RT-qPCR using the Exiqon Serum/Plasma Focus microRNA PCR Panel V3.M covering 179 different miRNAs. Of these, 155 were included in our analyses after quality-control. Associations between plasma miRNAs and the phenotypes age, body mass index (BMI), and sex were assessed via a two-step linear regression approach per miRNA. The first step regressed out the technical parameters and the second step determined the remaining associations between the respective plasma miRNA and the phenotypes of interest. After regressing out technical parameters and adjusting for the respective other two phenotypes, 7, 15, and 35 plasma miRNAs were significantly (q < 0.05) associated with age, BMI, and sex, respectively. Additional adjustment for the blood cell parameters identified 12 and 19 miRNAs to be significantly associated with age and BMI, respectively. Most of the BMI-associated miRNAs likely originate from liver. Sex-associated differences in miRNA levels were largely determined by differences in blood cell parameters. Thus, only 7 as compared to originally 35 sex-associated miRNAs displayed sex-specific differences after adjustment for blood cell parameters. These findings emphasize that circulating miRNAs are strongly impacted by age, BMI, and sex. Hence, these parameters should be considered as covariates in association studies based on plasma miRNA levels. The established experimental and computational workflow can now be used in future screening studies to determine associations of plasma miRNAs with defined disease phenotypes.

Social capital, political trust, and health locus of control: a population-based study.

PubMed

Lindström, Martin

2011-02-01

To investigate the association between political trust in the Riksdag and lack of belief in the possibility to influence one's own health (external locus of control), taking horizontal trust into account. The 2008 public health survey in Skåne is a cross-sectional postal questionnaire study with a 55% participation rate. A random sample of 28,198 persons aged 18-80 years participated. Logistic regression models were used to investigate the associations between political trust in the Riksdag (an aspect of vertical trust) and lack of belief in the possibility to influence one's own health (external locus of control). The multiple regression analyses included age, country of birth, education, and horizontal trust in other people. A 33.7% of all men and 31.8% of all women lack internal locus of control. Low (external) health locus of control is more common in higher age groups, among people born outside Sweden, with lower education, low horizontal trust, low political trust, and no opinion concerning political trust. Respondents with not particularly strong political trust, no political trust at all and no opinion have significantly higher odds ratios of external locus of control throughout the multiple regression analyses. Low political trust in the Riksdag seems to be independently associated with external health locus of control.

Statistical relations among earthquake magnitude, surface rupture length, and surface fault displacement

USGS Publications Warehouse

Bonilla, M.G.; Mark, R.K.; Lienkaemper, J.J.

1984-01-01

In order to refine correlations of surface-wave magnitude, fault rupture length at the ground surface, and fault displacement at the surface by including the uncertainties in these variables, the existing data were critically reviewed and a new data base was compiled. Earthquake magnitudes were redetermined as necessary to make them as consistent as possible with the Gutenberg methods and results, which necessarily make up much of the data base. Measurement errors were estimated for the three variables for 58 moderate to large shallow-focus earthquakes. Regression analyses were then made utilizing the estimated measurement errors. The regression analysis demonstrates that the relations among the variables magnitude, length, and displacement are stochastic in nature. The stochastic variance, introduced in part by incomplete surface expression of seismogenic faulting, variation in shear modulus, and regional factors, dominates the estimated measurement errors. Thus, it is appropriate to use ordinary least squares for the regression models, rather than regression models based upon an underlying deterministic relation with the variance resulting from measurement errors. Significant differences exist in correlations of certain combinations of length, displacement, and magnitude when events are qrouped by fault type or by region, including attenuation regions delineated by Evernden and others. Subdivision of the data results in too few data for some fault types and regions, and for these only regressions using all of the data as a group are reported. Estimates of the magnitude and the standard deviation of the magnitude of a prehistoric or future earthquake associated with a fault can be made by correlating M with the logarithms of rupture length, fault displacement, or the product of length and displacement. Fault rupture area could be reliably estimated for about 20 of the events in the data set. Regression of MS on rupture area did not result in a marked improvement over regressions that did not involve rupture area. Because no subduction-zone earthquakes are included in this study, the reported results do not apply to such zones.

Learning climate and feedback as predictors of dental students' self-determined motivation: The mediating role of basic psychological needs satisfaction.

PubMed

Orsini, C; Binnie, V; Wilson, S; Villegas, M J

2018-05-01

The aim of this study was to test the mediating role of the satisfaction of dental students' basic psychological needs of autonomy, competence and relatedness on the association between learning climate, feedback and student motivation. The latter was based on the self-determination theory's concepts of differentiation of autonomous motivation, controlled motivation and amotivation. A cross-sectional correlational study was conducted where 924 students completed self-reported questionnaires measuring motivation, perception of the learning climate, feedback and basic psychological needs satisfaction. Descriptive statistics, Cronbach's alpha scores and bivariate correlations were computed. Mediation of basic needs on each predictor-outcome association was tested based on a series of regression analyses. Finally, all variables were integrated into one structural equation model, controlling for the effects of age, gender and year of study. Cronbach's alpha scores were acceptable (.655 to .905). Correlation analyses showed positive and significant associations between both an autonomy-supportive learning climate and the quantity and quality of feedback received, and students' autonomous motivation, which decreased and became negative when correlated with controlled motivation and amotivation, respectively. Regression analyses revealed that these associations were indirect and mediated by how these predictors satisfied students' basic psychological needs. These results were corroborated by the structural equation analysis, in which data fit the model well and regression paths were in the expected direction. An autonomy-supportive learning climate and the quantity and quality of feedback were positive predictors of students' autonomous motivation and negative predictors of amotivation. However, this was an indirect association mediated by the satisfaction of students' basic psychological needs. Consequently, supporting students' needs of autonomy, competence and relatedness might lead to optimal types of motivation, which has an important influence on dental education. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Visual field progression in glaucoma: estimating the overall significance of deterioration with permutation analyses of pointwise linear regression (PoPLR).

PubMed

O'Leary, Neil; Chauhan, Balwantray C; Artes, Paul H

2012-10-01

To establish a method for estimating the overall statistical significance of visual field deterioration from an individual patient's data, and to compare its performance to pointwise linear regression. The Truncated Product Method was used to calculate a statistic S that combines evidence of deterioration from individual test locations in the visual field. The overall statistical significance (P value) of visual field deterioration was inferred by comparing S with its permutation distribution, derived from repeated reordering of the visual field series. Permutation of pointwise linear regression (PoPLR) and pointwise linear regression were evaluated in data from patients with glaucoma (944 eyes, median mean deviation -2.9 dB, interquartile range: -6.3, -1.2 dB) followed for more than 4 years (median 10 examinations over 8 years). False-positive rates were estimated from randomly reordered series of this dataset, and hit rates (proportion of eyes with significant deterioration) were estimated from the original series. The false-positive rates of PoPLR were indistinguishable from the corresponding nominal significance levels and were independent of baseline visual field damage and length of follow-up. At P < 0.05, the hit rates of PoPLR were 12, 29, and 42%, at the fifth, eighth, and final examinations, respectively, and at matching specificities they were consistently higher than those of pointwise linear regression. In contrast to population-based progression analyses, PoPLR provides a continuous estimate of statistical significance for visual field deterioration individualized to a particular patient's data. This allows close control over specificity, essential for monitoring patients in clinical practice and in clinical trials.

The in vitro wear behavior of experimental resin-based composites derived from a commercial formulation.

PubMed

Finlay, Nessa; Hahnel, Sebastian; Dowling, Adam H; Fleming, Garry J P

2013-04-01

To investigate the short- and long-term in vitro wear resistance of experimental resin-based composites (RBCs) derived from a commercial formulation. Six experimental RBCs were manufactured by manipulating the monomeric resin composition and the filler characteristics of Grandio (Voco GmbH, Cuxhaven, Germany). The Oregon Health Sciences University (OHSU) oral wear simulator was used in the presence of a food-like slurry to simulate three-body abrasion and attrition wear for 50,000, 150,000 and 300,000 cycles. A three-dimensional image of each wear facet was created and the total volumetric wear (mm(3)) and maximum wear depth (μm) were quantified for the RBC and antagonist. Statistical analyses of the total volumetric wear and maximum wear depth data (two- and one-way analyses of variance (ANOVA), with Tukey's post hoc tests where required) and regression analyses, were conducted at p=0.05. Two-way ANOVAs identified a significant effect of RBC material×wear cycles, RBC material and wear cycles (all p<0.0001). Regression analyses showed significant increases in the total volumetric wear (p≤0.001) and maximum wear depth data (p≤0.004) for all RBCs with increasing wear cycles. Differences between all RBC materials were evident after ≥150,000 wear cycles and antagonist wear provided valuable information to support the experimental findings. Wear simulating machines can provide an indication of the clinical performance but clinical performance is multi-factorial and wear is only a single facet. Employing experimental RBCs provided by a dental manufacturer rather than using self-manufactured RBCs or dental products provides increased experimental control by limiting the variables involved. Copyright © 2013 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Association between birthweight and later body mass index: an individual-based pooled analysis of 27 twin cohorts participating in the CODATwins project.

PubMed

Jelenkovic, Aline; Yokoyama, Yoshie; Sund, Reijo; Pietiläinen, Kirsi H; Hur, Yoon-Mi; Willemsen, Gonneke; Bartels, Meike; van Beijsterveldt, Toos C E M; Ooki, Syuichi; Saudino, Kimberly J; Stazi, Maria A; Fagnani, Corrado; D'Ippolito, Cristina; Nelson, Tracy L; Whitfield, Keith E; Knafo-Noam, Ariel; Mankuta, David; Abramson, Lior; Heikkilä, Kauko; Cutler, Tessa L; Hopper, John L; Wardle, Jane; Llewellyn, Clare H; Fisher, Abigail; Corley, Robin P; Huibregtse, Brooke M; Derom, Catherine A; Vlietinck, Robert F; Loos, Ruth J F; Bjerregaard-Andersen, Morten; Beck-Nielsen, Henning; Sodemann, Morten; Tarnoki, Adam D; Tarnoki, David L; Burt, S Alexandra; Klump, Kelly L; Ordoñana, Juan R; Sánchez-Romera, Juan F; Colodro-Conde, Lucia; Dubois, Lise; Boivin, Michel; Brendgen, Mara; Dionne, Ginette; Vitaro, Frank; Harris, Jennifer R; Brandt, Ingunn; Nilsen, Thomas Sevenius; Craig, Jeffrey M; Saffery, Richard; Rasmussen, Finn; Tynelius, Per; Bayasgalan, Gombojav; Narandalai, Danshiitsoodol; Haworth, Claire M A; Plomin, Robert; Ji, Fuling; Ning, Feng; Pang, Zengchang; Rebato, Esther; Krueger, Robert F; McGue, Matt; Pahlen, Shandell; Boomsma, Dorret I; Sørensen, Thorkild I A; Kaprio, Jaakko; Silventoinen, Karri

2017-10-01

There is evidence that birthweight is positively associated with body mass index (BMI) in later life, but it remains unclear whether this is explained by genetic factors or the intrauterine environment. We analysed the association between birthweight and BMI from infancy to adulthood within twin pairs, which provides insights into the role of genetic and environmental individual-specific factors. This study is based on the data from 27 twin cohorts in 17 countries. The pooled data included 78 642 twin individuals (20 635 monozygotic and 18 686 same-sex dizygotic twin pairs) with information on birthweight and a total of 214 930 BMI measurements at ages ranging from 1 to 49 years. The association between birthweight and BMI was analysed at both the individual and within-pair levels using linear regression analyses. At the individual level, a 1-kg increase in birthweight was linearly associated with up to 0.9 kg/m2 higher BMI (P < 0.001). Within twin pairs, regression coefficients were generally greater (up to 1.2 kg/m2 per kg birthweight, P < 0.001) than those from the individual-level analyses. Intra-pair associations between birthweight and later BMI were similar in both zygosity groups and sexes and were lower in adulthood. These findings indicate that environmental factors unique to each individual have an important role in the positive association between birthweight and later BMI, at least until young adulthood. © The Author 2017. Published by Oxford University Press on behalf of the International Epidemiological Association

iHWG-μNIR: a miniaturised near-infrared gas sensor based on substrate-integrated hollow waveguides coupled to a micro-NIR-spectrophotometer.

PubMed

Rohwedder, J J R; Pasquini, C; Fortes, P R; Raimundo, I M; Wilk, A; Mizaikoff, B

2014-07-21

A miniaturised gas analyser is described and evaluated based on the use of a substrate-integrated hollow waveguide (iHWG) coupled to a microsized near-infrared spectrophotometer comprising a linear variable filter and an array of InGaAs detectors. This gas sensing system was applied to analyse surrogate samples of natural fuel gas containing methane, ethane, propane and butane, quantified by using multivariate regression models based on partial least square (PLS) algorithms and Savitzky-Golay 1(st) derivative data preprocessing. The external validation of the obtained models reveals root mean square errors of prediction of 0.37, 0.36, 0.67 and 0.37% (v/v), for methane, ethane, propane and butane, respectively. The developed sensing system provides particularly rapid response times upon composition changes of the gaseous sample (approximately 2 s) due the minute volume of the iHWG-based measurement cell. The sensing system developed in this study is fully portable with a hand-held sized analyser footprint, and thus ideally suited for field analysis. Last but not least, the obtained results corroborate the potential of NIR-iHWG analysers for monitoring the quality of natural gas and petrochemical gaseous products.

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…

Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data

NASA Astrophysics Data System (ADS)

Sampson, Paul D.; Szpiro, Adam A.; Sheppard, Lianne; Lindström, Johan; Kaufman, Joel D.

2011-11-01

Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in "land use" regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM 2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation.

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.

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.

A Regression Framework for Effect Size Assessments in Longitudinal Modeling of Group Differences

PubMed Central

Feingold, Alan

2013-01-01

The use of growth modeling analysis (GMA)--particularly multilevel analysis and latent growth modeling--to test the significance of intervention effects has increased exponentially in prevention science, clinical psychology, and psychiatry over the past 15 years. Model-based effect sizes for differences in means between two independent groups in GMA can be expressed in the same metric (Cohen’s d) commonly used in classical analysis and meta-analysis. This article first reviews conceptual issues regarding calculation of d for findings from GMA and then introduces an integrative framework for effect size assessments that subsumes GMA. The new approach uses the structure of the linear regression model, from which effect sizes for findings from diverse cross-sectional and longitudinal analyses can be calculated with familiar statistics, such as the regression coefficient, the standard deviation of the dependent measure, and study duration. PMID:23956615

[Turnover of Non-medical Staff in Outpatient Oncology Practices: Is Building Social Capital a Solution?].

PubMed

Gloede, T D; Ernstmann, N; Baumann, W; Groß, S E; Ansmann, L; Nitzsche, A; Neumann, M; Wirtz, M; Schmitz, S; Schulz-Nieswandt, F; Pfaff, H

2015-11-01

While a lot is known about potential and actual turnover of non-medical hospital staff, only few data exist for the outpatient setting. In addition, little is known about actual instruments which leaders can use to influence staff turnover in physician practices. In the literature, the social capital of an organisation, which means the amount of trust, common values and reciprocal behaviour in the organisation, has been discussed as a possible field of action. In the present study, staff turnover as perceived by outpatient haematologists and oncologists is presented and analysed as to whether social capital is associated with that staff turnover. In conclusion, measures to increase the social capital of a practice are presented. The present study is based on data gathered in a questionnaire-based survey with members of the Professional Organisation of -Office-Based Haematologists and Oncologists (N=551). The social capital of the practice was captured from the haematologists and oncologists using an existing and validated scale. To analyse the impact of the practice's social capital on staff turnover, as perceived by the physicians, bivariate correlations and linear regression analyses were calculated. In total, 152 haematologists and oncologists participated in the study which represents a response rate of 28%. In the regression analyses, social capital appears as a significant and strong predictor of staff turnover (beta=-0.34; p<0.001). Building social capital within the practice may be an important contribution to reducing staff turnover although the underlying study design does not allow for drawing causal conclusions regarding this relationship. To create social capital in their practice, outpatient physicians may apply measures that facilitate social interaction among staff, foster trust and facilitate cooperation. Such measures may already be applied when hiring and training new staff, but also continuously when leading employees and when organising work tasks, e.g., by establishing regular team meetings. © Georg Thieme Verlag KG Stuttgart · New York.

Kidney function changes with aging in adults: comparison between cross-sectional and longitudinal data analyses in renal function assessment.

PubMed

Chung, Sang M; Lee, David J; Hand, Austin; Young, Philip; Vaidyanathan, Jayabharathi; Sahajwalla, Chandrahas

2015-12-01

The study evaluated whether the renal function decline rate per year with age in adults varies based on two primary statistical analyses: cross-section (CS), using one observation per subject, and longitudinal (LT), using multiple observations per subject over time. A total of 16628 records (3946 subjects; age range 30-92 years) of creatinine clearance and relevant demographic data were used. On average, four samples per subject were collected for up to 2364 days (mean: 793 days). A simple linear regression and random coefficient models were selected for CS and LT analyses, respectively. The renal function decline rates per year were 1.33 and 0.95 ml/min/year for CS and LT analyses, respectively, and were slower when the repeated individual measurements were considered. The study confirms that rates are different based on statistical analyses, and that a statistically robust longitudinal model with a proper sampling design provides reliable individual as well as population estimates of the renal function decline rates per year with age in adults. In conclusion, our findings indicated that one should be cautious in interpreting the renal function decline rate with aging information because its estimation was highly dependent on the statistical analyses. From our analyses, a population longitudinal analysis (e.g. random coefficient model) is recommended if individualization is critical, such as a dose adjustment based on renal function during a chronic therapy. Copyright © 2015 John Wiley & Sons, Ltd.

Traffic-related air pollution and spectacles use in schoolchildren

PubMed Central

Nieuwenhuijsen, Mark J.; Basagaña, Xavier; Alvarez-Pedrerol, Mar; Dalmau-Bueno, Albert; Cirach, Marta; Rivas, Ioar; Brunekreef, Bert; Querol, Xavier; Morgan, Ian G.; Sunyer, Jordi

2017-01-01

Purpose To investigate the association between exposure to traffic-related air pollution and use of spectacles (as a surrogate measure for myopia) in schoolchildren. Methods We analyzed the impact of exposure to NO2 and PM2.5 light absorbance at home (predicted by land-use regression models) and exposure to NO2 and black carbon (BC) at school (measured by monitoring campaigns) on the use of spectacles in a cohort of 2727 schoolchildren (7–10 years old) in Barcelona (2012–2015). We conducted cross-sectional analyses based on lifelong exposure to air pollution and prevalent cases of spectacles at baseline data collection campaign as well as longitudinal analyses based on incident cases of spectacles use and exposure to air pollution during the three-year period between the baseline and last data collection campaigns. Logistic regression models were developed to quantify the association between spectacles use and each of air pollutants adjusted for relevant covariates. Results An interquartile range increase in exposure to NO2 and PM2.5 absorbance at home was respectively associated with odds ratios (95% confidence intervals (CIs)) for spectacles use of 1.16 (1.03, 1.29) and 1.13 (0.99, 1.28) in cross-sectional analyses and 1.15 (1.00, 1.33) and 1.23 (1.03, 1.46) in longitudinal analyses. Similarly, odds ratio (95% CIs) of spectacles use associated with an interquartile range increase in exposures to NO2 and black carbon at school was respectively 1.32 (1.09, 1.59) and 1.13 (0.97, 1.32) in cross-sectional analyses and 1.12 (0.84, 1.50) and 1.27 (1.03, 1.56) in longitudinal analyses. These findings were robust to a range of sensitivity analyses that we conducted. Conclusion We observed increased risk of spectacles use associated with exposure to traffic-related air pollution. These findings require further confirmation by future studies applying more refined outcome measures such as quantified visual acuity and separating different types of refractive errors. PMID:28369072

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].

A reliable and cost effective approach for radiographic monitoring in nutritional rickets.

PubMed

Chatterjee, D; Gupta, V; Sharma, V; Sinha, B; Samanta, S

2014-04-01

Radiological scoring is particularly useful in rickets, where pre-treatment radiographical findings can reflect the disease severity and can be used to monitor the improvement. However, there is only a single radiographic scoring system for rickets developed by Thacher and, to the best of our knowledge, no study has evaluated radiographic changes in rickets based on this scoring system apart from the one done by Thacher himself. The main objective of this study is to compare and analyse the pre-treatment and post-treatment radiographic parameters in nutritional rickets with the help of Thacher's scoring technique. 176 patients with nutritional rickets were given a single intramuscular injection of vitamin D (600 000 IU) along with oral calcium (50 mg kg(-1)) and vitamin D (400 IU per day) until radiological resolution and followed for 1 year. Pre- and post-treatment radiological parameters were compared and analysed statistically based on Thacher's scoring system. Radiological resolution was complete by 6 months. Time for radiological resolution and initial radiological score were linearly associated on regression analysis. The distal ulna was the last to heal in most cases except when the initial score was 10, when distal femur was the last to heal. Thacher's scoring system can effectively monitor nutritional rickets. The formula derived through linear regression has prognostic significance. The distal femur is a better indicator in radiologically severe rickets and when resolution is delayed. Thacher's scoring is very useful for monitoring of rickets. The formula derived through linear regression can predict the expected time for radiological resolution.

The base rates and factors associated with reported access to firearms in psychiatric inpatients.

PubMed

Kolla, Bhanu Prakash; O'Connor, Stephen S; Lineberry, Timothy W

2011-01-01

The aim of this study was to define whether specific patient demographic groups, diagnoses or other factors are associated with psychiatric inpatients reporting firearms access. A retrospective medical records review study was conducted using information on access to firearms from electronic medical records for all patients 16 years and older admitted between July 2007 and May 2008 at the Mayo Clinic Psychiatric Hospital in Rochester, MN. Data were obtained only on patients providing authorization for record review. Data were analyzed using univariate and multivariate logistic regression analyses accounting for gender, diagnostic groups, comorbid substance use, history of suicide attempts and family history of suicide/suicide attempts. Seventy-four percent (1169/1580) of patients provided research authorization. The ratio of men to women was identical in both research and nonresearch authorization groups. There were 14.6% of inpatients who reported firearms access. In univariate analysis, men were more likely (P<.0001) to report access than women, and a history of previous suicide attempt(s) was associated with decreased access (P=.02). Multiple logistic regression analyses controlling for other factors found females and patients with history of previous suicide attempt(s) less likely to report access, while patients with a family history of suicide or suicide attempts reported increased firearms access. Diagnostic groups were not associated with access on univariate or multiple logistic regression analyses. Men and inpatients with a family history of suicide/suicide attempts were more likely to report firearms access. Clinicians should develop standardized systems of identification of firearms access and provide guidance on removal. Copyright © 2011 Elsevier Inc. All rights reserved.

Survival Regression Modeling Strategies in CVD Prediction.

PubMed

Barkhordari, Mahnaz; Padyab, Mojgan; Sardarinia, Mahsa; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-04-01

A fundamental part of prevention is prediction. Potential predictors are the sine qua non of prediction models. However, whether incorporating novel predictors to prediction models could be directly translated to added predictive value remains an area of dispute. The difference between the predictive power of a predictive model with (enhanced model) and without (baseline model) a certain predictor is generally regarded as an indicator of the predictive value added by that predictor. Indices such as discrimination and calibration have long been used in this regard. Recently, the use of added predictive value has been suggested while comparing the predictive performances of the predictive models with and without novel biomarkers. User-friendly statistical software capable of implementing novel statistical procedures is conspicuously lacking. This shortcoming has restricted implementation of such novel model assessment methods. We aimed to construct Stata commands to help researchers obtain the aforementioned statistical indices. We have written Stata commands that are intended to help researchers obtain the following. 1, Nam-D'Agostino X 2 goodness of fit test; 2, Cut point-free and cut point-based net reclassification improvement index (NRI), relative absolute integrated discriminatory improvement index (IDI), and survival-based regression analyses. We applied the commands to real data on women participating in the Tehran lipid and glucose study (TLGS) to examine if information relating to a family history of premature cardiovascular disease (CVD), waist circumference, and fasting plasma glucose can improve predictive performance of Framingham's general CVD risk algorithm. The command is adpredsurv for survival models. Herein we have described the Stata package "adpredsurv" for calculation of the Nam-D'Agostino X 2 goodness of fit test as well as cut point-free and cut point-based NRI, relative and absolute IDI, and survival-based regression analyses. We hope this work encourages the use of novel methods in examining predictive capacity of the emerging plethora of novel biomarkers.

Exposure-Response Analyses for Tafenoquine after Administration to Patients with Plasmodium vivax Malaria.

PubMed

Tenero, David; Green, Justin A; Goyal, Navin

2015-10-01

Tafenoquine (TQ), a new 8-aminoquinoline with activity against all stages of the Plasmodium vivax life cycle, is being developed for the radical cure of acute P. vivax malaria in combination with chloroquine. The efficacy and exposure data from a pivotal phase 2b dose-ranging study were used to conduct exposure-response analyses for TQ after administration to subjects with P. vivax malaria. TQ exposure (i.e., area under the concentration-time curve [AUC]) and region (Thailand compared to Peru and Brazil) were found to be statistically significant predictors of clinical response based on multivariate logistic regression analyses. After accounting for region/country, the odds of being relapse free at 6 months increased by approximately 51% (95% confidence intervals [CI], 25%, 82%) for each 25-U increase in AUC above the median value of 54.5 μg · h/ml. TQ exposure was also a significant predictor of the time to relapse of the infection. The final parametric, time-to-event model for the time to relapse, included a Weibull distribution hazard function, AUC, and country as covariates. Based on the model, the risk of relapse decreased by 30% (95% CI, 17% to 42%) for every 25-U increase in AUC. Monte Carlo simulations indicated that the 300-mg dose of TQ would provide an AUC greater than the clinically relevant breakpoint obtained in a classification and regression tree (CART) analysis (56.4 μg · h/ml) in more than 90% of subjects and consequently result in a high probability of being relapse free at 6 months. This model-based approach was critical in selecting an appropriate phase 3 dose. (This study has been registered at ClinicalTrials.gov under registration no. NCT01376167.). Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Exposure-Response Analyses for Tafenoquine after Administration to Patients with Plasmodium vivax Malaria

PubMed Central

Green, Justin A.; Goyal, Navin

2015-01-01

Tafenoquine (TQ), a new 8-aminoquinoline with activity against all stages of the Plasmodium vivax life cycle, is being developed for the radical cure of acute P. vivax malaria in combination with chloroquine. The efficacy and exposure data from a pivotal phase 2b dose-ranging study were used to conduct exposure-response analyses for TQ after administration to subjects with P. vivax malaria. TQ exposure (i.e., area under the concentration-time curve [AUC]) and region (Thailand compared to Peru and Brazil) were found to be statistically significant predictors of clinical response based on multivariate logistic regression analyses. After accounting for region/country, the odds of being relapse free at 6 months increased by approximately 51% (95% confidence intervals [CI], 25%, 82%) for each 25-U increase in AUC above the median value of 54.5 μg · h/ml. TQ exposure was also a significant predictor of the time to relapse of the infection. The final parametric, time-to-event model for the time to relapse, included a Weibull distribution hazard function, AUC, and country as covariates. Based on the model, the risk of relapse decreased by 30% (95% CI, 17% to 42%) for every 25-U increase in AUC. Monte Carlo simulations indicated that the 300-mg dose of TQ would provide an AUC greater than the clinically relevant breakpoint obtained in a classification and regression tree (CART) analysis (56.4 μg · h/ml) in more than 90% of subjects and consequently result in a high probability of being relapse free at 6 months. This model-based approach was critical in selecting an appropriate phase 3 dose. (This study has been registered at ClinicalTrials.gov under registration no. NCT01376167.) PMID:26248362

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

NASA Technical Reports Server (NTRS)

Duda, David P.; Minnis, Patrick

2009-01-01

Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data.

Median and Low-Flow Characteristics for Streams under Natural and Diverted Conditions, Northeast Maui, Hawaii

USGS Publications Warehouse

Gingerich, Stephen B.

2005-01-01

Flow-duration statistics under natural (undiverted) and diverted flow conditions were estimated for gaged and ungaged sites on 21 streams in northeast Maui, Hawaii. The estimates were made using the optimal combination of continuous-record gaging-station data, low-flow measurements, and values determined from regression equations developed as part of this study. Estimated 50- and 95-percent flow duration statistics for streams are presented and the analyses done to develop and evaluate the methods used in estimating the statistics are described. Estimated streamflow statistics are presented for sites where various amounts of streamflow data are available as well as for locations where no data are available. Daily mean flows were used to determine flow-duration statistics for continuous-record stream-gaging stations in the study area following U.S. Geological Survey established standard methods. Duration discharges of 50- and 95-percent were determined from total flow and base flow for each continuous-record station. The index-station method was used to adjust all of the streamflow records to a common, long-term period. The gaging station on West Wailuaiki Stream (16518000) was chosen as the index station because of its record length (1914-2003) and favorable geographic location. Adjustments based on the index-station method resulted in decreases to the 50-percent duration total flow, 50-percent duration base flow, 95-percent duration total flow, and 95-percent duration base flow computed on the basis of short-term records that averaged 7, 3, 4, and 1 percent, respectively. For the drainage basin of each continuous-record gaged site and selected ungaged sites, morphometric, geologic, soil, and rainfall characteristics were quantified using Geographic Information System techniques. Regression equations relating the non-diverted streamflow statistics to basin characteristics of the gaged basins were developed using ordinary-least-squares regression analyses. Rainfall rate, maximum basin elevation, and the elongation ratio of the basin were the basin characteristics used in the final regression equations for 50-percent duration total flow and base flow. Rainfall rate and maximum basin elevation were used in the final regression equations for the 95-percent duration total flow and base flow. The relative errors between observed and estimated flows ranged from 10 to 20 percent for the 50-percent duration total flow and base flow, and from 29 to 56 percent for the 95-percent duration total flow and base flow. The regression equations developed for this study were used to determine the 50-percent duration total flow, 50-percent duration base flow, 95-percent duration total flow, and 95-percent duration base flow at selected ungaged diverted and undiverted sites. Estimated streamflow, prediction intervals, and standard errors were determined for 48 ungaged sites in the study area and for three gaged sites west of the study area. Relative errors were determined for sites where measured values of 95-percent duration discharge of total flow were available. East of Keanae Valley, the 95-percent duration discharge equation generally underestimated flow, and within and west of Keanae Valley, the equation generally overestimated flow. Reduction in 50- and 95-percent flow-duration values in stream reaches affected by diversions throughout the study area average 58 to 60 percent.

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.

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 ...

Associations Between Perceived Race-based Discrimination and Contraceptive Use Among Women Veterans in the ECUUN Study.

PubMed

MacDonald, Serena; Hausmann, Leslie R M; Sileanu, Florentina E; Zhao, Xinhua; Mor, Maria K; Borrero, Sonya

2017-09-01

To describe perceived race-based discrimination in Veterans Affairs (VA) health care settings and assess its associations with contraceptive use among a sample of women Veterans. This study used data from a national telephone survey of women Veterans aged 18-44 receiving health care in VA who were at risk of unintended pregnancy. Participants were asked about their perceptions of race-based discrimination while seeking VA health care and about their contraceptive use at last heterosexual intercourse. Logistic and multinomial regression analyses were used to examine associations between perceived race-based discrimination with use of prescription contraception. In our sample of 1341 women Veterans, 7.9% report perceived race-based discrimination when receiving VA care, with blacks and Hispanics reporting higher levels of perceived discrimination than white women (11.3% and 11.2% vs. 4.4%; P<0.001). In logistic and multinomial regression analyses adjusting for race/ethnicity, age, income, marital status, parity, and insurance, women who perceived race-based discrimination were less likely to use any prescription birth control than women who did not (odds ratio, 0.65; 95% confidence interval, 0.42-1.00), with the largest difference seen in rates of intrauterine device or implant use (odds ratio, 0.40; 95% confidence interval, 0.20-0.79). In this national sample of women Veterans, over 10% of racial/ethnic minority women perceived race-based discrimination when receiving care in VA settings, and perceived racial/ethnic discrimination was associated with lower likelihood of prescription contraception use, especially intrauterine devices and implants. VA efforts to enhance respectful interactions may not only improve patient health care experiences, but also represent an opportunity to improve reproductive health outcomes for women Veterans.

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,…

SOCR Analyses – an Instructional Java Web-based Statistical Analysis Toolkit

PubMed Central

Chu, Annie; Cui, Jenny; Dinov, Ivo D.

2011-01-01

The Statistical Online Computational Resource (SOCR) designs web-based tools for educational use in a variety of undergraduate courses (Dinov 2006). Several studies have demonstrated that these resources significantly improve students' motivation and learning experiences (Dinov et al. 2008). SOCR Analyses is a new component that concentrates on data modeling and analysis using parametric and non-parametric techniques supported with graphical model diagnostics. Currently implemented analyses include commonly used models in undergraduate statistics courses like linear models (Simple Linear Regression, Multiple Linear Regression, One-Way and Two-Way ANOVA). In addition, we implemented tests for sample comparisons, such as t-test in the parametric category; and Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, in the non-parametric category. SOCR Analyses also include several hypothesis test models, such as Contingency tables, Friedman's test and Fisher's exact test. The code itself is open source (http://socr.googlecode.com/), hoping to contribute to the efforts of the statistical computing community. The code includes functionality for each specific analysis model and it has general utilities that can be applied in various statistical computing tasks. For example, concrete methods with API (Application Programming Interface) have been implemented in statistical summary, least square solutions of general linear models, rank calculations, etc. HTML interfaces, tutorials, source code, activities, and data are freely available via the web (www.SOCR.ucla.edu). Code examples for developers and demos for educators are provided on the SOCR Wiki website. In this article, the pedagogical utilization of the SOCR Analyses is discussed, as well as the underlying design framework. As the SOCR project is on-going and more functions and tools are being added to it, these resources are constantly improved. The reader is strongly encouraged to check the SOCR site for most updated information and newly added models. PMID:21546994

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.

Estimating population prevalence of psychiatric conditions by small area with applications to analysing outcome and referral variations.

PubMed

Congdon, Peter

2006-12-01

This paper considers the development of estimates of mental illness prevalence for small areas and applications in explaining psychiatric outcomes and in assessing service provision. Estimates of prevalence are based on a logistic regression analysis of two national studies that provides model based estimates of relative morbidity risk by demographic, socio-economic and ethnic group for major psychiatric conditions; household/marital and area status also figure in the regression. Relative risk estimates are used, along with suitably disaggregated census populations, to make prevalence estimates for 354 English local authorities (LAs). Two applications are considered: the first involves analysis of variations in schizophrenia referrals and suicide mortality over English LAs that takes account of prevalence differences, and the second involves assessing hospital referral and bed use in relation to prevalence (for ages 16-74) for a case study area, Waltham Forest in NE London.

Prediction of cold and heat patterns using anthropometric measures based on machine learning.

PubMed

Lee, Bum Ju; Lee, Jae Chul; Nam, Jiho; Kim, Jong Yeol

2018-01-01

To examine the association of body shape with cold and heat patterns, to determine which anthropometric measure is the best indicator for discriminating between the two patterns, and to investigate whether using a combination of measures can improve the predictive power to diagnose these patterns. Based on a total of 4,859 subjects (3,000 women and 1,859 men), statistical analyses using binary logistic regression were performed to assess the significance of the difference and the predictive power of each anthropometric measure, and binary logistic regression and Naive Bayes with the variable selection technique were used to assess the improvement in the predictive power of the patterns using the combined measures. In women, the strongest indicators for determining the cold and heat patterns among anthropometric measures were body mass index (BMI) and rib circumference; in men, the best indicator was BMI. In experiments using a combination of measures, the values of the area under the receiver operating characteristic curve in women were 0.776 by Naive Bayes and 0.772 by logistic regression, and the values in men were 0.788 by Naive Bayes and 0.779 by logistic regression. Individuals with a higher BMI have a tendency toward a heat pattern in both women and men. The use of a combination of anthropometric measures can slightly improve the diagnostic accuracy. Our findings can provide fundamental information for the diagnosis of cold and heat patterns based on body shape for personalized medicine.

The Impact of Additional Weekdays of Active Commuting to School on Children Achieving a Criterion of 300+ Minutes of Moderate-to-Vigorous Physical Activity

ERIC Educational Resources Information Center

Daly-Smith, Andy J. W.; McKenna, Jim; Radley, Duncan; Long, Jonathan

2011-01-01

Objective: To investigate the value of additional days of active commuting for meeting a criterion of 300+ minutes of moderate-to-vigorous physical activity (MVPA; 60+ mins/day x 5) during the school week. Methods: Based on seven-day diaries supported by teachers, binary logistic regression analyses were used to predict achievement of MVPA…

Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

PubMed Central

Zeng, Fangfang; Li, Zhongtao; Yu, Xiaoling; Zhou, Linuo

2013-01-01

Background This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. Methods and Materials We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. Conclusion The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset. PMID:23940593

Saturated fat consumption and the Theory of Planned Behaviour: exploring additive and interactive effects of habit strength.

PubMed

de Bruijn, Gert-Jan; Kroeze, Willemieke; Oenema, Anke; Brug, Johannes

2008-09-01

The additive and interactive effects of habit strength in the explanation of saturated fat intake were explored within the framework of the Theory of Planned Behaviour (TPB). Cross-sectional data were gathered in a Dutch adult sample (n=764) using self-administered questionnaires and analyzed using hierarchical regression analyses and simple slope analyses. Results showed that habit strength was a significant correlate of fat intake (beta=-0.11) and significantly increased the amount of explained variance in fat intake (R(2-change)=0.01). Furthermore, based on a significant interaction effect (beta=0.11), simple slope analyses revealed that intention was a significant correlate of fat intake for low levels (beta=-0.29) and medium levels (beta=-0.19) of habit strength, but a weaker and non-significant correlate for high levels (beta=-0.07) of habit strength. Higher habit strength may thus make limiting fat intake a non-intentional behaviour. Implications for information and motivation-based interventions are discussed.

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…

Flexible Meta-Regression to Assess the Shape of the Benzene–Leukemia Exposure–Response Curve

PubMed Central

Vlaanderen, Jelle; Portengen, Lützen; Rothman, Nathaniel; Lan, Qing; Kromhout, Hans; Vermeulen, Roel

2010-01-01

Background Previous evaluations of the shape of the benzene–leukemia exposure–response curve (ERC) were based on a single set or on small sets of human occupational studies. Integrating evidence from all available studies that are of sufficient quality combined with flexible meta-regression models is likely to provide better insight into the functional relation between benzene exposure and risk of leukemia. Objectives We used natural splines in a flexible meta-regression method to assess the shape of the benzene–leukemia ERC. Methods We fitted meta-regression models to 30 aggregated risk estimates extracted from nine human observational studies and performed sensitivity analyses to assess the impact of a priori assessed study characteristics on the predicted ERC. Results The natural spline showed a supralinear shape at cumulative exposures less than 100 ppm-years, although this model fitted the data only marginally better than a linear model (p = 0.06). Stratification based on study design and jackknifing indicated that the cohort studies had a considerable impact on the shape of the ERC at high exposure levels (> 100 ppm-years) but that predicted risks for the low exposure range (< 50 ppm-years) were robust. Conclusions Although limited by the small number of studies and the large heterogeneity between studies, the inclusion of all studies of sufficient quality combined with a flexible meta-regression method provides the most comprehensive evaluation of the benzene–leukemia ERC to date. The natural spline based on all data indicates a significantly increased risk of leukemia [relative risk (RR) = 1.14; 95% confidence interval (CI), 1.04–1.26] at an exposure level as low as 10 ppm-years. PMID:20064779

Application of seemingly unrelated regression in medical data with intermittently observed time-dependent covariates.

PubMed

Keshavarzi, Sareh; Ayatollahi, Seyyed Mohammad Taghi; Zare, Najaf; Pakfetrat, Maryam

2012-01-01

BACKGROUND. In many studies with longitudinal data, time-dependent covariates can only be measured intermittently (not at all observation times), and this presents difficulties for standard statistical analyses. This situation is common in medical studies, and methods that deal with this challenge would be useful. METHODS. In this study, we performed the seemingly unrelated regression (SUR) based models, with respect to each observation time in longitudinal data with intermittently observed time-dependent covariates and further compared these models with mixed-effect regression models (MRMs) under three classic imputation procedures. Simulation studies were performed to compare the sample size properties of the estimated coefficients for different modeling choices. RESULTS. In general, the proposed models in the presence of intermittently observed time-dependent covariates showed a good performance. However, when we considered only the observed values of the covariate without any imputations, the resulted biases were greater. The performances of the proposed SUR-based models in comparison with MRM using classic imputation methods were nearly similar with approximately equal amounts of bias and MSE. CONCLUSION. The simulation study suggests that the SUR-based models work as efficiently as MRM in the case of intermittently observed time-dependent covariates. Thus, it can be used as an alternative to MRM.

Long-term sickness absence during pregnancy and the gender balance of workplaces.

PubMed

Melsom, Anne M

2014-11-01

This study addresses how the gender composition of workplaces affects pregnant women's sickness absence. It also assesses whether an observed association may be explaine by differential selection to female- or male-dominated workplaces. The analyses are based on Norwegian registry data from 2003-2011. Using Poisson regressions with detailed control for occupational categories, I examine whether the number of absence days are associated with the proportion of females at the workplace. I address possible selection effects by Poisson regressions with fixed individual effects using only within-individual variation on women with two or more pregnancies during the time window. The analyses indicate a positive and significant relationship between the female proportion in workplaces and sickness absence rates during pregnancy. Analyses limited to within-individual variation also show positive and significant effects of similar strength, indicating that the observed relationship is not due to differential selection of absence-prone pregnant workers to female-dominated workplaces. The proportion of female individuals at workplaces is positively associated with sickness absence rates during pregnancy this association is not likely explained by occupational nor individual characteristics the results are consistent with absence culture theory and more lenient norms concerning sickness absence during pregnancy at female-dominated workplaces. © 2014 the Nordic Societies of Public Health.

Reflected appraisals and perceived importance of significant others' appraisals as predictors of college athletes' self-perceptions of competence.

PubMed

Amorose, Anthony J

2003-03-01

This study examined the reflected appraisal process with college athletes (N = 325). Specifically, the study tested (a) the relative influence of the reflected appraisals of mothers, fathers, coaches, and teammates (i.e., how athletes perceive these others view their ability) on athletes' self-perceptions of competence, and (b) whether the importance placed on these significant others as sources of competence information moderated the relationship. Based on a factor analysis, composite variables were formed representing the reflected appraisals of the athletes' parents (i.e., father, mother) and the reflected appraisals of sport-others (i.e., coach, teammates). Regression analyses revealed that the reflected appraisals of parents (beta = .21) and sport-others (beta = .55) predicted self-perceptions of competence (p < .05, R2 = .45). Follow-up analyses determined that the reflected appraisal of sport-others was a significantly stronger predictor. Hierarchical regression analyses revealed that the interaction of reflected appraisals and the importance of significant others did not significantly add to the prediction of self-perceptions of competence (p > .05, deltaR2 = .01) beyond the independent effects of these constructs. Results are discussed in terms of the reflected appraisal process and the influence of significant others on athletes' self-perceptions.

Physical and Sexual Violence and Incident Sexually Transmitted Infections

PubMed Central

Anand, Mallika; Redding, Colleen A.; Peipert, Jeffrey F.

2009-01-01

Abstract Objective To investigate whether women aged 13–35 who were victims of interpersonal violence were more likely than nonvictims to experience incident sexually transmitted infections (STIs). Methods We examined 542 women aged 13–35 enrolled in Project PROTECT, a randomized clinical trial that compared two different methods of computer-based intervention to promote the use of dual methods of contraception. Participants completed a baseline questionnaire that included questions about their history of interpersonal violence and were followed for incident STIs over the 2-year study period. We compared the incidence of STIs in women with and without a history of interpersonal violence using bivariate analyses and multiple logistic regression. Results In the bivariate analyses, STI incidence was found to be significantly associated with African American race/ethnicity, a higher number of sexual partners in the past month, and a lower likelihood of avoidance of sexual partners who pressure to have sex without a condom. In both crude and adjusted regression analyses, time to STI incidence was faster among women who reported physical or sexual abuse in the year before study enrollment (HRRadj = 1.68, 95% CI 1.06, 2.65). Conclusions Women with a recent history of abuse are at significantly increased risk of STI incidence than are nonvictims. PMID:19245303

Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression.

PubMed

Chen, Yanguang

2016-01-01

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson's statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran's index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China's regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test.

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

NASA Astrophysics Data System (ADS)

Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno

2017-03-01

This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

Recent trends in counts of migrant hawks from northeastern North America

USGS Publications Warehouse

Titus, K.; Fuller, M.R.

1990-01-01

Using simple regression, pooled-sites route-regression, and nonparametric rank-trend analyses, we evaluated trends in counts of hawks migrating past 6 eastern hawk lookouts from 1972 to 1987. The indexing variable was the total count for a season. Bald eagle (Haliaeetus leucocephalus), peregrine falcon (Falco peregrinus), merlin (F. columbarius), osprey (Pandion haliaetus), and Cooper's hawk (Accipiter cooperii) counts increased using route-regression and nonparametric methods (P 0.10). We found no consistent trends (P > 0.10) in counts of sharp-shinned hawks (A. striatus), northern goshawks (A. gentilis) red-shouldered hawks (Buteo lineatus), red-tailed hawks (B. jamaicensis), rough-legged hawsk (B. lagopus), and American kestrels (F. sparverius). Broad-winged hawk (B. platypterus) counts declined (P < 0.05) based on the route-regression method. Empirical comparisons of our results with those for well-studied species such as the peregrine falcon, bald eagle, and osprey indicated agreement with nesting surveys. We suggest that counts of migrant hawks are a useful and economical method for detecting long-term trends in species across regions, particularly for species that otherwise cannot be easily surveyed.

Hybrid fuel formulation and technology development

NASA Technical Reports Server (NTRS)

Dean, D. L.

1995-01-01

The objective was to develop an improved hybrid fuel with higher regression rate, a regression rate expression exponent close to 0.5, lower cost, and higher density. The approach was to formulate candidate fuels based on promising concepts, perform thermomechanical analyses to select the most promising candidates, develop laboratory processes to fabricate fuel grains as needed, fabricate fuel grains and test in a small lab-scale motor, select the best candidate, and then scale up and validate performance in a 2500 lbf scale, 11-inch diameter motor. The characteristics of a high performance fuel have been verified in 11-inch motor testing. The advanced fuel exhibits a 15% increase in density over an all hydrocarbon formulation accompanied by a 50% increase in regression rate, which when multiplied by the increase in density yields a 70% increase in fuel mass flow rate; has a significantly lower oxidizer-to-fuel (O/F) ratio requirement at 1.5; has a significantly decreased axial regression rate variation making for more uniform propellant flow throughout motor operation; is very clean burning; extinguishes cleanly and quickly; and burns with a high combustion efficiency.

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.

Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland

USGS Publications Warehouse

Olexa, Edward M.; Lawrence, Rick L

2014-01-01

Federal land management agencies provide stewardship over much of the rangelands in the arid andsemi-arid western United States, but they often lack data of the proper spatiotemporal resolution andextent needed to assess range conditions and monitor trends. Recent advances in the blending of com-plementary, remotely sensed data could provide public lands managers with the needed information.We applied the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to five Landsat TMand concurrent Terra MODIS scenes, and used pixel-based regression and difference image analyses toevaluate the quality of synthetic reflectance and NDVI products associated with semi-arid rangeland. Pre-dicted red reflectance data consistently demonstrated higher accuracy, less bias, and stronger correlationwith observed data than did analogous near-infrared (NIR) data. The accuracy of both bands tended todecline as the lag between base and prediction dates increased; however, mean absolute errors (MAE)were typically ≤10%. The quality of area-wide NDVI estimates was less consistent than either spectra lband, although the MAE of estimates predicted using early season base pairs were ≤10% throughout the growing season. Correlation between known and predicted NDVI values and agreement with the 1:1regression line tended to decline as the prediction lag increased. Further analyses of NDVI predictions,based on a 22 June base pair and stratified by land cover/land use (LCLU), revealed accurate estimates through the growing season; however, inter-class performance varied. This work demonstrates the successful application of the STARFM algorithm to semi-arid rangeland; however, we encourage evaluation of STARFM’s performance on a per product basis, stratified by LCLU, with attention given to the influence of base pair selection and the impact of the time lag.

Identification of indicator congeners and evaluation of emission pattern of polychlorinated naphthalenes in industrial stack gas emissions by statistical analyses.

PubMed

Liu, Guorui; Cai, Zongwei; Zheng, Minghui; Jiang, Xiaoxu; Nie, Zhiqiang; Wang, Mei

2015-01-01

Identifying marker congeners of unintentionally produced polychlorinated naphthalenes (PCNs) from industrial thermal sources might be useful for predicting total PCN (∑2-8PCN) emissions by the determination of only indicator congeners. In this study, potential indicator congeners were identified based on the PCN data in 122 stack gas samples from over 60 plants involved in more than ten industrial thermal sources reported in our previous case studies. Linear regression analyses identified that the concentrations of CN27/30, CN52/60, and CN66/67 correlated significantly with ∑2-8PCN (R(2)=0.77, 0.80, and 0.58, respectively; n=122, p<0.05), which might be good candidates for indicator congeners. Equations describing relationships between indicators and ∑2-8PCN were established. The linear regression analyses involving 122 samples showed that the relationships between the indicator congeners and ∑2-8PCN were not significantly affected by factors such as industry types, raw materials used, or operating conditions. Hierarchical cluster analysis and similarity calculations for the 122 stack gas samples were adopted to group those samples and evaluating their similarity and difference based on the PCN homolog distributions from different industrial thermal sources. Generally, the fractions of less chlorinated homologs comprised of di-, tri-, and tetra-homologs were much higher than that of more chlorinated homologs for up to 111 stack gas samples contained in group 1 and 2, which indicating the dominance of lower chlorinated homologs in stack gas from industrial thermal sources. Copyright © 2014 Elsevier Ltd. All rights reserved.

Teacher psychological needs, locus of control and engagement.

PubMed

Betoret, Fernando Doménech

2013-01-01

This study examines the relationships among psychological needs, locus of control and engagement in a sample of 282 Spanish secondary school teachers. Nine teacher needs were identified based on the study of Bess (1977) and on the Self-Determination Theory (Deci & Ryan, 1985, 2000, 2002). Self-report questionnaires were used to measure the construct selected for this study and their interrelationships were examined by conducting hierarchical regression analyses. An analysis of teacher responses using hierarchical regression reveals that psychological needs have significant positive effects on the three engagement dimensions (vigor, dedication and absorption). Furthermore, the results show the moderator role played by locus of control in the relationship between teacher psychological needs and the so-called core of engagement (vigor and dedication). Finally, practical implications are discussed.

Non-proportional odds multivariate logistic regression of ordinal family data.

PubMed

Zaloumis, Sophie G; Scurrah, Katrina J; Harrap, Stephen B; Ellis, Justine A; Gurrin, Lyle C

2015-03-01

Methods to examine whether genetic and/or environmental sources can account for the residual variation in ordinal family data usually assume proportional odds. However, standard software to fit the non-proportional odds model to ordinal family data is limited because the correlation structure of family data is more complex than for other types of clustered data. To perform these analyses we propose the non-proportional odds multivariate logistic regression model and take a simulation-based approach to model fitting using Markov chain Monte Carlo methods, such as partially collapsed Gibbs sampling and the Metropolis algorithm. We applied the proposed methodology to male pattern baldness data from the Victorian Family Heart Study. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Longer-Term Impact of High and Low Temperature on Mortality: An International Study to Clarify Length of Mortality Displacement

PubMed Central

Bell, Michelle L.; de Sousa Zanotti Stagliorio Coelho, Micheline; Leon Guo, Yue-Liang; Guo, Yuming; Goodman, Patrick; Hashizume, Masahiro; Honda, Yasushi; Kim, Ho; Lavigne, Eric; Michelozzi, Paola; Hilario Nascimento Saldiva, Paulo; Schwartz, Joel; Scortichini, Matteo; Sera, Francesco; Tobias, Aurelio; Tong, Shilu; Wu, Chang-fu; Zanobetti, Antonella; Zeka, Ariana; Gasparrini, Antonio

2017-01-01

Background: In many places, daily mortality has been shown to increase after days with particularly high or low temperatures, but such daily time-series studies cannot identify whether such increases reflect substantial life shortening or short-term displacement of deaths (harvesting). Objectives: To clarify this issue, we estimated the association between annual mortality and annual summaries of heat and cold in 278 locations from 12 countries. Methods: Indices of annual heat and cold were used as predictors in regressions of annual mortality in each location, allowing for trends over time and clustering of annual count anomalies by country and pooling estimates using meta-regression. We used two indices of annual heat and cold based on preliminary standard daily analyses: a) mean annual degrees above/below minimum mortality temperature (MMT), and b) estimated fractions of deaths attributed to heat and cold. The first index was simpler and matched previous related research; the second was added because it allowed the interpretation that coefficients equal to 0 and 1 are consistent with none (0) or all (1) of the deaths attributable in daily analyses being displaced by at least 1 y. Results: On average, regression coefficients of annual mortality on heat and cold mean degrees were 1.7% [95% confidence interval (CI): 0.3, 3.1] and 1.1% (95% CI: 0.6, 1.6) per degree, respectively, and daily attributable fractions were 0.8 (95% CI: 0.2, 1.3) and 1.1 (95% CI: 0.9, 1.4). The proximity of the latter coefficients to 1.0 provides evidence that most deaths found attributable to heat and cold in daily analyses were brought forward by at least 1 y. Estimates were broadly robust to alternative model assumptions. Conclusions: These results provide strong evidence that most deaths associated in daily analyses with heat and cold are displaced by at least 1 y. https://doi.org/10.1289/EHP1756 PMID:29084393

Statistical relations among earthquake magnitude, surface rupture length, and surface fault displacement

USGS Publications Warehouse

Bonilla, Manuel G.; Mark, Robert K.; Lienkaemper, James J.

1984-01-01

In order to refine correlations of surface-wave magnitude, fault rupture length at the ground surface, and fault displacement at the surface by including the uncertainties in these variables, the existing data were critically reviewed and a new data base was compiled. Earthquake magnitudes were redetermined as necessary to make them as consistent as possible with the Gutenberg methods and results, which make up much of the data base. Measurement errors were estimated for the three variables for 58 moderate to large shallow-focus earthquakes. Regression analyses were then made utilizing the estimated measurement errors.The regression analysis demonstrates that the relations among the variables magnitude, length, and displacement are stochastic in nature. The stochastic variance, introduced in part by incomplete surface expression of seismogenic faulting, variation in shear modulus, and regional factors, dominates the estimated measurement errors. Thus, it is appropriate to use ordinary least squares for the regression models, rather than regression models based upon an underlying deterministic relation in which the variance results primarily from measurement errors.Significant differences exist in correlations of certain combinations of length, displacement, and magnitude when events are grouped by fault type or by region, including attenuation regions delineated by Evernden and others.Estimates of the magnitude and the standard deviation of the magnitude of a prehistoric or future earthquake associated with a fault can be made by correlating Ms with the logarithms of rupture length, fault displacement, or the product of length and displacement.Fault rupture area could be reliably estimated for about 20 of the events in the data set. Regression of Ms on rupture area did not result in a marked improvement over regressions that did not involve rupture area. Because no subduction-zone earthquakes are included in this study, the reported results do not apply to such zones.

Methods for estimating confidence intervals in interrupted time series analyses of health interventions.

PubMed

Zhang, Fang; Wagner, Anita K; Soumerai, Stephen B; Ross-Degnan, Dennis

2009-02-01

Interrupted time series (ITS) is a strong quasi-experimental research design, which is increasingly applied to estimate the effects of health services and policy interventions. We describe and illustrate two methods for estimating confidence intervals (CIs) around absolute and relative changes in outcomes calculated from segmented regression parameter estimates. We used multivariate delta and bootstrapping methods (BMs) to construct CIs around relative changes in level and trend, and around absolute changes in outcome based on segmented linear regression analyses of time series data corrected for autocorrelated errors. Using previously published time series data, we estimated CIs around the effect of prescription alerts for interacting medications with warfarin on the rate of prescriptions per 10,000 warfarin users per month. Both the multivariate delta method (MDM) and the BM produced similar results. BM is preferred for calculating CIs of relative changes in outcomes of time series studies, because it does not require large sample sizes when parameter estimates are obtained correctly from the model. Caution is needed when sample size is small.

Estimated prevalence of halitosis: a systematic review and meta-regression analysis.

PubMed

Silva, Manuela F; Leite, Fábio R M; Ferreira, Larissa B; Pola, Natália M; Scannapieco, Frank A; Demarco, Flávio F; Nascimento, Gustavo G

2018-01-01

This study aims to conduct a systematic review to determine the prevalence of halitosis in adolescents and adults. Electronic searches were performed using four different databases without restrictions: PubMed, Scopus, Web of Science, and SciELO. Population-based observational studies that provided data about the prevalence of halitosis in adolescents and adults were included. Additionally, meta-analyses, meta-regression, and sensitivity analyses were conducted to synthesize the evidence. A total of 584 articles were initially found and considered for title and abstract evaluation. Thirteen articles met inclusion criteria. The combined prevalence of halitosis was found to be 31.8% (95% CI 24.6-39.0%). Methodological aspects such as the year of publication and the socioeconomic status of the country where the study was conducted seemed to influence the prevalence of halitosis. Our results demonstrated that the estimated prevalence of halitosis was 31.8%, with high heterogeneity between studies. The results suggest a worldwide trend towards a rise in halitosis prevalence. Given the high prevalence of halitosis and its complex etiology, dental professionals should be aware of their roles in halitosis prevention and treatment.

A Head-to-Head Comparison of the Personality Inventory for DSM-5 (PID-5) With the Personality Diagnostic Questionnaire-4 (PDQ-4) in Predicting the General Level of Personality Pathology Among Community Dwelling Subjects.

PubMed

Fossati, Andrea; Somma, Antonella; Borroni, Serena; Maffei, Cesare; Markon, Kristian E; Krueger, Robert F

2016-02-01

In order to evaluate if measures of DSM-5 Alternative PD Model domains predicted interview-based scores of general personality pathology when compared to self-report measures of DSM-IV Axis II/DSM-5 Section II PD criteria, 300 Italian community adults were administered the Iowa Personality Disorder Screen (IPDS) interview, the Personality Inventory for DSM-5 (PID-5), and the Personality Diagnostic Questionnaire-4+ (PDQ-4+). Multiple regression analyses showed that the five PID-5 domain scales collectively explained an adequate rate of the variance of the IPDS interview total score. This result was slightly lower than the amount of variance in the IPDS total score explained by the 10 PDQ-4+ scales. The PID-5 traits scales performed better than the PDQ-4+, although the difference was marginal. Hierarchical regression analyses revealed that the PID-5 domain and trait scales provided a moderate, but significant increase in the prediction of the general level of personality pathology above and beyond the PDQ-4+ scales.

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…

The influence of training characteristics on the effect of exercise training in patients with coronary artery disease: Systematic review and meta-regression analysis.

PubMed

Kraal, Jos J; Vromen, Tom; Spee, Ruud; Kemps, Hareld M C; Peek, Niels

2017-10-15

Although exercise-based cardiac rehabilitation improves exercise capacity of coronary artery disease patients, it is unclear which training characteristic determines this improvement. Total energy expenditure and its constituent training characteristics (training intensity, session frequency, session duration and programme length) vary considerably among clinical trials, making it hard to compare studies directly. Therefore, we performed a systematic review and meta-regression analysis to assess the effect of total energy expenditure and its constituent training characteristics on exercise capacity. We identified randomised controlled trials comparing continuous aerobic exercise training with usual care for patients with coronary artery disease. Studies were included when training intensity, session frequency, session duration and programme length was described, and exercise capacity was reported in peakVO 2 . Energy expenditure was calculated from the four training characteristics. The effect of training characteristics on exercise capacity was determined using mixed effects linear regression analyses. The analyses were performed with and without total energy expenditure as covariate. Twenty studies were included in the analyses. The mean difference in peakVO 2 between the intervention group and control group was 3.97ml·min -1 ·kg -1 (p<0.01, 95% CI 2.86 to 5.07). Total energy expenditure was significantly related to improvement of exercise capacity (effect size 0.91ml·min -1 ·kg -1 per 100J·kg, p<0.01, 95% CI 0.77 to 1.06), no effect was found for its constituent training characteristics after adjustment for total energy expenditure. We conclude that the design of an exercise programme should primarily be aimed at optimising total energy expenditure rather than on one specific training characteristic. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Converting positive and negative symptom scores between PANSS and SAPS/SANS.

PubMed

van Erp, Theo G M; Preda, Adrian; Nguyen, Dana; Faziola, Lawrence; Turner, Jessica; Bustillo, Juan; Belger, Aysenil; Lim, Kelvin O; McEwen, Sarah; Voyvodic, James; Mathalon, Daniel H; Ford, Judith; Potkin, Steven G; Fbirn

2014-01-01

The Scale for the Assessment of Positive Symptoms (SAPS), the Scale for the Assessment of Negative Symptoms (SANS), and the Positive and Negative Syndrome Scale for Schizophrenia (PANSS) are the most widely used schizophrenia symptom rating scales, but despite their co-existence for 25 years no easily usable between-scale conversion mechanism exists. The aim of this study was to provide equations for between-scale symptom rating conversions. Two-hundred-and-five schizophrenia patients [mean age±SD=39.5±11.6, 156 males] were assessed with the SANS, SAPS, and PANSS. Pearson's correlations between symptom scores from each of the scales were computed. Linear regression analyses, on data from 176 randomly selected patients, were performed to derive equations for converting ratings between the scales. Intraclass correlations, on data from the remaining 29 patients, not part of the regression analyses, were performed to determine rating conversion accuracy. Between-scale positive and negative symptom ratings were highly correlated. Intraclass correlations between the original positive and negative symptom ratings and those obtained via conversion of alternative ratings using the conversion equations were moderate to high (ICCs=0.65 to 0.91). Regression-based equations may be useful for conversion between schizophrenia symptom severity as measured by the SANS/SAPS and PANSS, though additional validation is warranted. This study's conversion equations, implemented at http:/converteasy.org, may aid in the comparison of medication efficacy studies, in meta- and mega-analyses examining symptoms as moderator variables, and in retrospective combination of symptom data in multi-center data sharing projects that need to pool symptom rating data when such data are obtained using different scales. Copyright © 2013 Elsevier B.V. All rights reserved.

Serum 25-hydroxyvitamin D level is associated with myopia in the Korea national health and nutrition examination survey.

PubMed

Kwon, Jin-Woo; Choi, Jin A; La, Tae Yoon

2016-11-01

The aim of this article was to assess the associations of serum 25-hydroxyvitamin D [25(OH)D] and daily sun exposure time with myopia in Korean adults.This study is based on the Korea National Health and Nutrition Examination Survey (KNHANES) of Korean adults in 2010-2012; multiple logistic regression analyses were performed to examine the associations of serum 25(OH)D levels and daily sun exposure time with myopia, defined as spherical equivalent ≤-0.5D, after adjustment for age, sex, household income, body mass index (BMI), exercise, intraocular pressure (IOP), and education level. Also, multiple linear regression analyses were performed to examine the relationship between serum 25(OH)D levels with spherical equivalent after adjustment for daily sun exposure time in addition to the confounding factors above.Between the nonmyopic and myopic groups, spherical equivalent, age, IOP, BMI, waist circumference, education level, household income, and area of residence differed significantly (all P < 0.05). Compared with subjects with daily sun exposure time <2 hour, subjects with sun exposure time ≥2 to <5 hour, and those with sun exposure time ≥5 hour had significantly less myopia (P < 0.001). In addition, compared with subjects were categorized into quartiles of serum 25(OH)D, the higher quartiles had gradually lower prevalences of myopia after adjustment for confounding factors (P < 0.001). In multiple linear regression analyses, spherical equivalent was significantly associated with serum 25(OH)D concentration after adjustment for confounding factors (P = 0.002).Low serum 25(OH)D levels and shorter daily sun exposure time may be independently associated with a high prevalence of myopia in Korean adults. These data suggest a direct role for vitamin D in the development of myopia.

Drivers of wetland conversion: a global meta-analysis.

PubMed

van Asselen, Sanneke; Verburg, Peter H; Vermaat, Jan E; Janse, Jan H

2013-01-01

Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions.

Drivers of Wetland Conversion: a Global Meta-Analysis

PubMed Central

van Asselen, Sanneke; Verburg, Peter H.; Vermaat, Jan E.; Janse, Jan H.

2013-01-01

Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions. PMID:24282580

A reliable and cost effective approach for radiographic monitoring in nutritional rickets

PubMed Central

Gupta, V; Sharma, V; Sinha, B; Samanta, S

2014-01-01

Objective: Radiological scoring is particularly useful in rickets, where pre-treatment radiographical findings can reflect the disease severity and can be used to monitor the improvement. However, there is only a single radiographic scoring system for rickets developed by Thacher and, to the best of our knowledge, no study has evaluated radiographic changes in rickets based on this scoring system apart from the one done by Thacher himself. The main objective of this study is to compare and analyse the pre-treatment and post-treatment radiographic parameters in nutritional rickets with the help of Thacher's scoring technique. Methods: 176 patients with nutritional rickets were given a single intramuscular injection of vitamin D (600 000 IU) along with oral calcium (50 mg kg−1) and vitamin D (400 IU per day) until radiological resolution and followed for 1 year. Pre- and post-treatment radiological parameters were compared and analysed statistically based on Thacher's scoring system. Results: Radiological resolution was complete by 6 months. Time for radiological resolution and initial radiological score were linearly associated on regression analysis. The distal ulna was the last to heal in most cases except when the initial score was 10, when distal femur was the last to heal. Conclusion: Thacher's scoring system can effectively monitor nutritional rickets. The formula derived through linear regression has prognostic significance. Advances in knowledge: The distal femur is a better indicator in radiologically severe rickets and when resolution is delayed. Thacher's scoring is very useful for monitoring of rickets. The formula derived through linear regression can predict the expected time for radiological resolution. PMID:24593231

Spatial analysis and land use regression of VOCs and NO(2) from school-based urban air monitoring in Detroit/Dearborn, USA.

PubMed

Mukerjee, Shaibal; Smith, Luther A; Johnson, Mary M; Neas, Lucas M; Stallings, Casson A

2009-08-01

Passive ambient air sampling for nitrogen dioxide (NO(2)) and volatile organic compounds (VOCs) was conducted at 25 school and two compliance sites in Detroit and Dearborn, Michigan, USA during the summer of 2005. Geographic Information System (GIS) data were calculated at each of 116 schools. The 25 selected schools were monitored to assess and model intra-urban gradients of air pollutants to evaluate impact of traffic and urban emissions on pollutant levels. Schools were chosen to be statistically representative of urban land use variables such as distance to major roadways, traffic intensity around the schools, distance to nearest point sources, population density, and distance to nearest border crossing. Two approaches were used to investigate spatial variability. First, Kruskal-Wallis analyses and pairwise comparisons on data from the schools examined coarse spatial differences based on city section and distance from heavily trafficked roads. Secondly, spatial variation on a finer scale and as a response to multiple factors was evaluated through land use regression (LUR) models via multiple linear regression. For weeklong exposures, VOCs did not exhibit spatial variability by city section or distance from major roads; NO(2) was significantly elevated in a section dominated by traffic and industrial influence versus a residential section. Somewhat in contrast to coarse spatial analyses, LUR results revealed spatial gradients in NO(2) and selected VOCs across the area. The process used to select spatially representative sites for air sampling and the results of coarse and fine spatial variability of air pollutants provide insights that may guide future air quality studies in assessing intra-urban gradients.

Predictors of prison-based treatment outcomes: a comparison of men and women participants.

PubMed

Messina, Nena; Burdon, William; Hagopian, Garo; Prendergast, Michael

2006-01-01

The purpose of this study was to examine differences between men and women entering prison-based therapeutic community (TC) treatment and to explore the relationship of those differences to posttreatment outcomes (i.e., aftercare participation and reincarceration rates). Extensive treatment-intake interview data for 4,386 women and 4,164 men from 16 prison-based TCs in California were compared using chi-square analyses and t-tests. Logistic regression analyses were then conducted separately for men and women to identify gender-specific factors associated with post-treatment outcomes. Prison intake data and treatment participation data come from a 5-year process and outcome evaluation of the California Department of Corrections' (CDC) Prison Treatment Expansion Initiative. The return-to-custody data came from the CDC's Offender Based Information System. Bivariate results showed that women were at a substantial disadvantage compared with their male counterparts with regard to histories of employment, substance abuse, psychological functioning, and sexual and physical abuse prior to incarceration. In contrast, men had more serious criminal justice involvement than women prior to incarceration. After controlling for these and other factors related to outcomes, regression findings showed that there were both similarities and differences with regard to gender-specific predictors of posttreatment outcomes. Time in treatment and motivation for treatment were similar predictors of aftercare participation for men and women. Psychological impairment was the strongest predictor of recidivism for both men and women. Substantial differences in background characteristics and the limited number of predictors related to posttreatment outcomes for women suggests the plausibility of gender-specific paths in the recovery process.

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.

An inferential and descriptive statistical examination of the relationship between cumulative work metrics and injury in Major League Baseball pitchers.

PubMed

Karakolis, Thomas; Bhan, Shivam; Crotin, Ryan L

2013-08-01

In Major League Baseball (MLB), games pitched, total innings pitched, total pitches thrown, innings pitched per game, and pitches thrown per game are used to measure cumulative work. Often, pitchers are allocated limits, based on pitches thrown per game and total innings pitched in a season, in an attempt to prevent future injuries. To date, the efficacy in predicting injuries from these cumulative work metrics remains in question. It was hypothesized that the cumulative work metrics would be a significant predictor for future injury in MLB pitchers. Correlations between cumulative work for pitchers during 2002-07 and injury days in the following seasons were examined using regression analyses to test this hypothesis. Each metric was then "binned" into smaller cohorts to examine trends in the associated risk of injury for each cohort. During the study time period, 27% of pitchers were injured after a season in which they pitched. Although some interesting trends were noticed during the binning process, based on the regression analyses, it was found that no cumulative work metric was a significant predictor for future injury. It was concluded that management of a pitcher's playing schedule based on these cumulative work metrics alone could not be an effective means of preventing injury. These findings indicate that an integrated approach to injury prevention is required. This approach will likely involve advanced cumulative work metrics and biomechanical assessment.

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.

Visual field progression with frequency-doubling matrix perimetry and standard automated perimetry in patients with glaucoma and in healthy controls.

PubMed

Redmond, Tony; O'Leary, Neil; Hutchison, Donna M; Nicolela, Marcelo T; Artes, Paul H; Chauhan, Balwantray C

2013-12-01

A new analysis method called permutation of pointwise linear regression measures the significance of deterioration over time at each visual field location, combines the significance values into an overall statistic, and then determines the likelihood of change in the visual field. Because the outcome is a single P value, individualized to that specific visual field and independent of the scale of the original measurement, the method is well suited for comparing techniques with different stimuli and scales. To test the hypothesis that frequency-doubling matrix perimetry (FDT2) is more sensitive than standard automated perimetry (SAP) in identifying visual field progression in glaucoma. Patients with open-angle glaucoma and healthy controls were examined by FDT2 and SAP, both with the 24-2 test pattern, on the same day at 6-month intervals in a longitudinal prospective study conducted in a hospital-based setting. Only participants with at least 5 examinations were included. Data were analyzed with permutation of pointwise linear regression. Permutation of pointwise linear regression is individualized to each participant, in contrast to current analyses in which the statistical significance is inferred from population-based approaches. Analyses were performed with both total deviation and pattern deviation. Sixty-four patients and 36 controls were included in the study. The median age, SAP mean deviation, and follow-up period were 65 years, -2.6 dB, and 5.4 years, respectively, in patients and 62 years, +0.4 dB, and 5.2 years, respectively, in controls. Using total deviation analyses, statistically significant deterioration was identified in 17% of patients with FDT2, in 34% of patients with SAP, and in 14% of patients with both techniques; in controls these percentages were 8% with FDT2, 31% with SAP, and 8% with both. Using pattern deviation analyses, statistically significant deterioration was identified in 16% of patients with FDT2, in 17% of patients with SAP, and in 3% of patients with both techniques; in controls these values were 3% with FDT2 and none with SAP. No evidence was found that FDT2 is more sensitive than SAP in identifying visual field deterioration. In about one-third of healthy controls, age-related deterioration with SAP reached statistical significance.

Numeric promoter description - A comparative view on concepts and general application.

PubMed

Beier, Rico; Labudde, Dirk

2016-01-01

Nucleic acid molecules play a key role in a variety of biological processes. Starting from storage and transfer tasks, this also comprises the triggering of biological processes, regulatory effects and the active influence gained by target binding. Based on the experimental output (in this case promoter sequences), further in silico analyses aid in gaining new insights into these processes and interactions. The numerical description of nucleic acids thereby constitutes a bridge between the concrete biological issues and the analytical methods. Hence, this study compares 26 descriptor sets obtained by applying well-known numerical description concepts to an established dataset of 38 DNA promoter sequences. The suitability of the description sets was evaluated by computing partial least squares regression models and assessing the model accuracy. We conclude that the major importance regarding the descriptive power is attached to positional information rather than to explicitly incorporated physico-chemical information, since a sufficient amount of implicit physico-chemical information is already encoded in the nucleobase classification. The regression models especially benefited from employing the information that is encoded in the sequential and structural neighborhood of the nucleobases. Thus, the analyses of n-grams (short fragments of length n) suggested that they are valuable descriptors for DNA target interactions. A mixed n-gram descriptor set thereby yielded the best description of the promoter sequences. The corresponding regression model was checked and found to be plausible as it was able to reproduce the characteristic binding motifs of promoter sequences in a reasonable degree. As most functional nucleic acids are based on the principle of molecular recognition, the findings are not restricted to promoter sequences, but can rather be transferred to other kinds of functional nucleic acids. Thus, the concepts presented in this study could provide advantages for future nucleic acid-based technologies, like biosensoring, therapeutics and molecular imaging. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

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.

Assessing the dependence of sensitivity and specificity on prevalence in meta-analysis

PubMed Central

Li, Jialiang; Fine, Jason P.

2011-01-01

We consider modeling the dependence of sensitivity and specificity on the disease prevalence in diagnostic accuracy studies. Many meta-analyses compare test accuracy across studies and fail to incorporate the possible connection between the accuracy measures and the prevalence. We propose a Pearson type correlation coefficient and an estimating equation–based regression framework to help understand such a practical dependence. The results we derive may then be used to better interpret the results from meta-analyses. In the biomedical examples analyzed in this paper, the diagnostic accuracy of biomarkers are shown to be associated with prevalence, providing insights into the utility of these biomarkers in low- and high-prevalence populations. PMID:21525421

Childhood Leukemia and 50 Hz Magnetic Fields: Findings from the Italian SETIL Case-Control Study

PubMed Central

Salvan, Alberto; Ranucci, Alessandra; Lagorio, Susanna; Magnani, Corrado

2015-01-01

We report on an Italian case-control study on childhood leukemia and exposure to extremely low frequency magnetic fields (ELF-MF). Eligible for inclusion were 745 leukemia cases, aged 0–10 years at diagnosis in 1998–2001, and 1475 sex- and age-matched population controls. Parents of 683 cases and 1044 controls (92% vs. 71%) were interviewed. ELF-MF measurements (24–48 h), in the child’s bedroom of the dwelling inhabited one year before diagnosis, were available for 412 cases and 587 controls included in the main conditional regression analyses. The magnetic field induction was 0.04 μT on average (geometric mean), with 0.6% of cases and 1.6% of controls exposed to >0.3 μT. The impact of changes in the statistical model, exposure metric, and data-set restriction criteria was explored via sensitivity analyses. No exposure-disease association was observed in analyses based on continuous exposure, while analyses based on categorical variables were characterized by incoherent exposure-outcome relationships. In conclusion, our results may be affected by several sources of bias and they are noninformative at exposure levels >0.3 μT. Nonetheless, the study may contribute to future meta- or pooled analyses. Furthermore, exposure levels among population controls are useful to estimate attributable risk. PMID:25689995

Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis

PubMed Central

Hill, Andrew; Loh, Po-Ru; Bharadwaj, Ragu B.; Pons, Pascal; Shang, Jingbo; Guinan, Eva; Lakhani, Karim; Kilty, Iain

2017-01-01

Abstract Background: The association of differing genotypes with disease-related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low-cost genotyping and sequencing has made collecting large-scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies is being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets. Results: Using open innovation (OI) and contest-based crowdsourcing, the logistic regression analysis in a leading, community-standard genetics software package (PLINK 1.07) was substantially accelerated. OI allowed us to do this in <6 months by providing rapid access to highly skilled programmers with specialized, difficult-to-find skill sets. Through a crowd-based contest a combination of computational, numeric, and algorithmic approaches was identified that accelerated the logistic regression in PLINK 1.07 by 18- to 45-fold. Combining contest-derived logistic regression code with coarse-grained parallelization, multithreading, and associated changes to data initialization code further developed through distributed innovation, we achieved an end-to-end speedup of 591-fold for a data set size of 6678 subjects by 645 863 variants, compared to PLINK 1.07's logistic regression. This represents a reduction in run time from 4.8 hours to 29 seconds. Accelerated logistic regression code developed in this project has been incorporated into the PLINK2 project. Conclusions: Using iterative competition-based OI, we have developed a new, faster implementation of logistic regression for genome-wide association studies analysis. We present lessons learned and recommendations on running a successful OI process for bioinformatics. PMID:28327993

Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis.

PubMed

Hill, Andrew; Loh, Po-Ru; Bharadwaj, Ragu B; Pons, Pascal; Shang, Jingbo; Guinan, Eva; Lakhani, Karim; Kilty, Iain; Jelinsky, Scott A

2017-05-01

The association of differing genotypes with disease-related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low-cost genotyping and sequencing has made collecting large-scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies is being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets. Using open innovation (OI) and contest-based crowdsourcing, the logistic regression analysis in a leading, community-standard genetics software package (PLINK 1.07) was substantially accelerated. OI allowed us to do this in <6 months by providing rapid access to highly skilled programmers with specialized, difficult-to-find skill sets. Through a crowd-based contest a combination of computational, numeric, and algorithmic approaches was identified that accelerated the logistic regression in PLINK 1.07 by 18- to 45-fold. Combining contest-derived logistic regression code with coarse-grained parallelization, multithreading, and associated changes to data initialization code further developed through distributed innovation, we achieved an end-to-end speedup of 591-fold for a data set size of 6678 subjects by 645 863 variants, compared to PLINK 1.07's logistic regression. This represents a reduction in run time from 4.8 hours to 29 seconds. Accelerated logistic regression code developed in this project has been incorporated into the PLINK2 project. Using iterative competition-based OI, we have developed a new, faster implementation of logistic regression for genome-wide association studies analysis. We present lessons learned and recommendations on running a successful OI process for bioinformatics. © The Author 2017. Published by Oxford University Press.

Air quality and ventilation fan control based on aerosol measurement in the bi-directional undersea Bømlafjord tunnel.

PubMed

Indrehus, Oddny; Aralt, Tor Tybring

2005-04-01

Aerosol, NO and CO concentration, temperature, air humidity, air flow and number of running ventilation fans were measured by continuous analysers every minute for a whole week for six different one-week periods spread over ten months in 2001 and 2002 at measuring stations in the 7860 m long tunnel. The ventilation control system was mainly based on aerosol measurements taken by optical scatter sensors. The ventilation turned out to be satisfactory according to Norwegian air quality standards for road tunnels; however, there was some uncertainty concerning the NO2 levels. The air humidity and temperature inside the tunnel were highly influenced by the outside metrological conditions. Statistical models for NO concentration were developed and tested; correlations between predicted and measured NO were 0.81 for a partial least squares regression (PLS1) model based on CO and aerosol, and 0.77 for a linear regression model based only on aerosol. Hence, the ventilation control system should not solely be based on aerosol measurements. Since NO2 is the hazardous polluter, modelling NO2 concentration rather than NO should be preferred in any further optimising of the ventilation control.

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…

Genomic investigation of porcine periweaning failure to thrive syndrome (PFTS).

PubMed

Bertolini, Francesca; Yang, Tianfu; Huang, Yanyun; Harding, John C S; Plastow, Graham S; Rothschild, Max F

2018-04-25

Porcine periweaning failure to thrive syndrome (PFTS) can be defined by anorexia, lethargy, progressive debilitation and compulsive behaviours that occur in seemingly healthy pigs within two to threeweeks of weaning in the absence of any known infectious, nutritional, management or environmental factors. A genetic component has been hypothesised for this syndrome. In the present study, 119 commercial pigs (80 cases and 39 controls) were genotyped with the porcine 80K single nucleotide polymorphism-chip and were analysed with logistic regression and two Fixation Index-based approaches. The analyses revealed several regions on chromosomes 1, 3, 6 and 11 with moderate divergence between cases and controls, particularly three haplotypes on SSC3 and 11. The gene-based analyses of the candidate regions revealed the presence of genes that have been reported to be associated with phenotypes like PFST including depression ( PDE10A ) and intestinal villous atrophy ( CUL4A ). It is important to increase the effort of collecting more samples to improve the power of these analyses. © British Veterinary Association (unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Regression Trees Identify Relevant Interactions: Can This Improve the Predictive Performance of Risk Adjustment?

PubMed

Buchner, Florian; Wasem, Jürgen; Schillo, Sonja

2017-01-01

Risk equalization formulas have been refined since their introduction about two decades ago. Because of the complexity and the abundance of possible interactions between the variables used, hardly any interactions are considered. A regression tree is used to systematically search for interactions, a methodologically new approach in risk equalization. Analyses are based on a data set of nearly 2.9 million individuals from a major German social health insurer. A two-step approach is applied: In the first step a regression tree is built on the basis of the learning data set. Terminal nodes characterized by more than one morbidity-group-split represent interaction effects of different morbidity groups. In the second step the 'traditional' weighted least squares regression equation is expanded by adding interaction terms for all interactions detected by the tree, and regression coefficients are recalculated. The resulting risk adjustment formula shows an improvement in the adjusted R 2 from 25.43% to 25.81% on the evaluation data set. Predictive ratios are calculated for subgroups affected by the interactions. The R 2 improvement detected is only marginal. According to the sample level performance measures used, not involving a considerable number of morbidity interactions forms no relevant loss in accuracy. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Computational tools for exact conditional logistic regression.

PubMed

Corcoran, C; Mehta, C; Patel, N; Senchaudhuri, P

Logistic regression analyses are often challenged by the inability of unconditional likelihood-based approximations to yield consistent, valid estimates and p-values for model parameters. This can be due to sparseness or separability in the data. Conditional logistic regression, though useful in such situations, can also be computationally unfeasible when the sample size or number of explanatory covariates is large. We review recent developments that allow efficient approximate conditional inference, including Monte Carlo sampling and saddlepoint approximations. We demonstrate through real examples that these methods enable the analysis of significantly larger and more complex data sets. We find in this investigation that for these moderately large data sets Monte Carlo seems a better alternative, as it provides unbiased estimates of the exact results and can be executed in less CPU time than can the single saddlepoint approximation. Moreover, the double saddlepoint approximation, while computationally the easiest to obtain, offers little practical advantage. It produces unreliable results and cannot be computed when a maximum likelihood solution does not exist. Copyright 2001 John Wiley & Sons, Ltd.

The Association of Sitting Time With Sarcopenia Status and Physical Performance at Baseline and 18-Month Follow-Up in the Residential Aged Care Setting.

PubMed

Reid, Natasha; Keogh, Justin W; Swinton, Paul; Gardiner, Paul A; Henwood, Timothy R

2018-06-18

This study investigated the association of sitting time with sarcopenia and physical performance in residential aged care residents at baseline and 18-month follow-up. Measures included the International Physical Activity Questionnaire (sitting time), European Working Group definition of sarcopenia, and the short physical performance battery (physical performance). Logistic regression and linear regression analyses were used to investigate associations. For each hour of sitting, the unadjusted odds ratio of sarcopenia was 1.16 (95% confidence interval [0.98, 1.37]). Linear regression showed that each hour of sitting was significantly associated with a 0.2-unit lower score for performance. Associations of baseline sitting with follow-up sarcopenia status and performance were nonsignificant. Cross-sectionally, increased sitting time in residential aged care may be detrimentally associated with sarcopenia and physical performance. Based on current reablement models of care, future studies should investigate if reducing sedentary time improves performance among adults in end of life care.

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.

Meaning profiles of dwellings, pathways, and metaphors in design: implications for education

NASA Astrophysics Data System (ADS)

Casakin, Hernan; Kreitler, Shulamith

2017-11-01

The study deals with the roles and interrelations of the meaning-based assessments of dwellings, pathways and metaphors in design performance. It is grounded in the Meaning Theory [Kreitler, S., and H. Kreitler. 1990. The Cognitive Foundations of Personality Traits. New York: Plenum], which enables identifying the cognitive contents and processes underlying cognitive performance in different domains, thus rendering them more accessible to educational training. The objectives were to identify the components of the meaning profiles of dwellings, pathways, and metaphors as perceived by design students; to analyse their interrelations; and to examine which of the identified components of these constructs serve as best predictors of design performance aided by the use of metaphors. Participants were administered a design task and questionnaires about the Dimensional Profiles of Dwellings, Pathways, and Metaphors, based on the meaning system. Factors based on the factor analyses of the responses to the three questionnaires were used in regression analyses as predictors of the performance score in a design task. The following three factors of the dimensional meaning profiles of metaphors were significant predictors of design performance: sensory, functional, and structural evaluations. Implications for design education are discussed, primarily concerning the important role of metaphor in design problem-solving.

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.

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.

Plant selection for ethnobotanical uses on the Amalfi Coast (Southern Italy).

PubMed

Savo, V; Joy, R; Caneva, G; McClatchey, W C

2015-07-15

Many ethnobotanical studies have investigated selection criteria for medicinal and non-medicinal plants. In this paper we test several statistical methods using different ethnobotanical datasets in order to 1) define to which extent the nature of the datasets can affect the interpretation of results; 2) determine if the selection for different plant uses is based on phylogeny, or other selection criteria. We considered three different ethnobotanical datasets: two datasets of medicinal plants and a dataset of non-medicinal plants (handicraft production, domestic and agro-pastoral practices) and two floras of the Amalfi Coast. We performed residual analysis from linear regression, the binomial test and the Bayesian approach for calculating under-used and over-used plant families within ethnobotanical datasets. Percentages of agreement were calculated to compare the results of the analyses. We also analyzed the relationship between plant selection and phylogeny, chorology, life form and habitat using the chi-square test. Pearson's residuals for each of the significant chi-square analyses were examined for investigating alternative hypotheses of plant selection criteria. The three statistical analysis methods differed within the same dataset, and between different datasets and floras, but with some similarities. In the two medicinal datasets, only Lamiaceae was identified in both floras as an over-used family by all three statistical methods. All statistical methods in one flora agreed that Malvaceae was over-used and Poaceae under-used, but this was not found to be consistent with results of the second flora in which one statistical result was non-significant. All other families had some discrepancy in significance across methods, or floras. Significant over- or under-use was observed in only a minority of cases. The chi-square analyses were significant for phylogeny, life form and habitat. Pearson's residuals indicated a non-random selection of woody species for non-medicinal uses and an under-use of plants of temperate forests for medicinal uses. Our study showed that selection criteria for plant uses (including medicinal) are not always based on phylogeny. The comparison of different statistical methods (regression, binomial and Bayesian) under different conditions led to the conclusion that the most conservative results are obtained using regression 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

Cone-beam computed tomography for lung cancer - validation with CT and monitoring tumour response during chemo-radiation therapy.

PubMed

Michienzi, Alissa; Kron, Tomas; Callahan, Jason; Plumridge, Nikki; Ball, David; Everitt, Sarah

2017-04-01

Cone-beam computed tomography (CBCT) is a valuable image-guidance tool in radiation therapy (RT). This study was initiated to assess the accuracy of CBCT for quantifying non-small cell lung cancer (NSCLC) tumour volumes compared to the anatomical 'gold standard', CT. Tumour regression or progression on CBCT was also analysed. Patients with Stage I-III NSCLC, prescribed 60 Gy in 30 fractions RT with concurrent platinum-based chemotherapy, routine CBCT and enrolled in a prospective study of serial PET/CT (baseline, weeks two and four) were eligible. Time-matched CBCT and CT gross tumour volumes (GTVs) were manually delineated by a single observer on MIM software, and were analysed descriptively and using Pearson's correlation coefficient (r) and linear regression (R 2 ). Of 94 CT/CBCT pairs, 30 patients were eligible for inclusion. The mean (± SD) CT GTV vs CBCT GTV on the four time-matched pairs were 95 (±182) vs 98.8 (±160.3), 73.6 (±132.4) vs 70.7 (±96.6), 54.7 (±92.9) vs 61.0 (±98.8) and 61.3 (±53.3) vs 62.1 (±47.9) respectively. Pearson's correlation coefficient (r) was 0.98 (95% CI 0.97-0.99, ρ < 0.001). The mean (±SD) CT/CBCT Dice's similarity coefficient was 0.66 (±0.16). Of 289 CBCT scans, tumours in 27 (90%) patients regressed by a mean (±SD) rate of 1.5% (±0.75) per fraction. The mean (±SD) GTV regression was 43.1% (±23.1) from the first to final CBCT. Primary lung tumour volumes observed on CBCT and time-matched CT are highly correlated (although not identical), thereby validating observations of GTV regression on CBCT in NSCLC. © 2016 The Royal Australian and New Zealand College of Radiologists.

Quantification of the impact of a confounding variable on functional connectivity confirms anti-correlated networks in the resting-state.

PubMed

Carbonell, F; Bellec, P; Shmuel, A

2014-02-01

The effect of regressing out the global average signal (GAS) in resting state fMRI data has become a concern for interpreting functional connectivity analyses. It is not clear whether the reported anti-correlations between the Default Mode and the Dorsal Attention Networks are intrinsic to the brain, or are artificially created by regressing out the GAS. Here we introduce a concept, Impact of the Global Average on Functional Connectivity (IGAFC), for quantifying the sensitivity of seed-based correlation analyses to the regression of the GAS. This voxel-wise IGAFC index is defined as the product of two correlation coefficients: the correlation between the GAS and the fMRI time course of a voxel, times the correlation between the GAS and the seed time course. This definition enables the calculation of a threshold at which the impact of regressing-out the GAS would be large enough to introduce spurious negative correlations. It also yields a post-hoc impact correction procedure via thresholding, which eliminates spurious correlations introduced by regressing out the GAS. In addition, we introduce an Artificial Negative Correlation Index (ANCI), defined as the absolute difference between the IGAFC index and the impact threshold. The ANCI allows a graded confidence scale for ranking voxels according to their likelihood of showing artificial correlations. By applying this method, we observed regions in the Default Mode and Dorsal Attention Networks that were anti-correlated. These findings confirm that the previously reported negative correlations between the Dorsal Attention and Default Mode Networks are intrinsic to the brain and not the result of statistical manipulations. Our proposed quantification of the impact that a confound may have on functional connectivity can be generalized to global effect estimators other than the GAS. It can be readily applied to other confounds, such as systemic physiological or head movement interferences, in order to quantify their impact on functional connectivity in the resting state. © 2013.

Distinct Patterns of Desynchronized Limb Regression in Malagasy Scincine Lizards (Squamata, Scincidae)

PubMed Central

Miralles, Aurélien; Hipsley, Christy A.; Erens, Jesse; Gehara, Marcelo; Rakotoarison, Andolalao; Glaw, Frank; Müller, Johannes; Vences, Miguel

2015-01-01

Scincine lizards in Madagascar form an endemic clade of about 60 species exhibiting a variety of ecomorphological adaptations. Several subclades have adapted to burrowing and convergently regressed their limbs and eyes, resulting in a variety of partial and completely limbless morphologies among extant taxa. However, patterns of limb regression in these taxa have not been studied in detail. Here we fill this gap in knowledge by providing a phylogenetic analysis of DNA sequences of three mitochondrial and four nuclear gene fragments in an extended sampling of Malagasy skinks, and microtomographic analyses of osteology of various burrowing taxa adapted to sand substrate. Based on our data we propose to (i) consider Sirenoscincus Sakata & Hikida, 2003, as junior synonym of Voeltzkowia Boettger, 1893; (ii) resurrect the genus name Grandidierina Mocquard, 1894, for four species previously included in Voeltzkowia; and (iii) consider Androngo Brygoo, 1982, as junior synonym of Pygomeles Grandidier, 1867. By supporting the clade consisting of the limbless Voeltzkowia mira and the forelimb-only taxa V. mobydick and V. yamagishii, our data indicate that full regression of limbs and eyes occurred in parallel twice in the genus Voeltzkowia (as hitherto defined) that we consider as a sand-swimming ecomorph: in the Voeltzkowia clade sensu stricto the regression first affected the hindlimbs and subsequently the forelimbs, whereas the Grandidierina clade first regressed the forelimbs and subsequently the hindlimbs following the pattern prevalent in squamates. Timetree reconstructions for the Malagasy Scincidae contain a substantial amount of uncertainty due to the absence of suitable primary fossil calibrations. However, our preliminary reconstructions suggest rapid limb regression in Malagasy scincids with an estimated maximal duration of 6 MYr for a complete regression in Paracontias, and 4 and 8 MYr respectively for complete regression of forelimbs in Grandidierina and hindlimbs in Voeltzkowia. PMID:26042667

Distinct patterns of desynchronized limb regression in malagasy scincine lizards (squamata, scincidae).

PubMed

Miralles, Aurélien; Hipsley, Christy A; Erens, Jesse; Gehara, Marcelo; Rakotoarison, Andolalao; Glaw, Frank; Müller, Johannes; Vences, Miguel

2015-01-01

Scincine lizards in Madagascar form an endemic clade of about 60 species exhibiting a variety of ecomorphological adaptations. Several subclades have adapted to burrowing and convergently regressed their limbs and eyes, resulting in a variety of partial and completely limbless morphologies among extant taxa. However, patterns of limb regression in these taxa have not been studied in detail. Here we fill this gap in knowledge by providing a phylogenetic analysis of DNA sequences of three mitochondrial and four nuclear gene fragments in an extended sampling of Malagasy skinks, and microtomographic analyses of osteology of various burrowing taxa adapted to sand substrate. Based on our data we propose to (i) consider Sirenoscincus Sakata & Hikida, 2003, as junior synonym of Voeltzkowia Boettger, 1893; (ii) resurrect the genus name Grandidierina Mocquard, 1894, for four species previously included in Voeltzkowia; and (iii) consider Androngo Brygoo, 1982, as junior synonym of Pygomeles Grandidier, 1867. By supporting the clade consisting of the limbless Voeltzkowia mira and the forelimb-only taxa V. mobydick and V. yamagishii, our data indicate that full regression of limbs and eyes occurred in parallel twice in the genus Voeltzkowia (as hitherto defined) that we consider as a sand-swimming ecomorph: in the Voeltzkowia clade sensu stricto the regression first affected the hindlimbs and subsequently the forelimbs, whereas the Grandidierina clade first regressed the forelimbs and subsequently the hindlimbs following the pattern prevalent in squamates. Timetree reconstructions for the Malagasy Scincidae contain a substantial amount of uncertainty due to the absence of suitable primary fossil calibrations. However, our preliminary reconstructions suggest rapid limb regression in Malagasy scincids with an estimated maximal duration of 6 MYr for a complete regression in Paracontias, and 4 and 8 MYr respectively for complete regression of forelimbs in Grandidierina and hindlimbs in Voeltzkowia.

Interhemispheric comparison of atmospheric circulation features as evaluated from NIMBUS satellite data

NASA Technical Reports Server (NTRS)

Reiter, E. R.; Vonderhaar, T. H.; Lovill, J. E.; Adler, R.; Srivatsangam, S.; Abbey, R.

1971-01-01

Findings are presented for IRIS data from NIMBUS 3 in mapping the global ozone distribution. The seasonal and regional variations of ozone, especially in the Southern Hemisphere, reveal features that were not evident from the sparse ground-based ozone observation network in this hemisphere. A regression analysis was undertaken for temperature and height fields on radiance data. Spectrum analyses of upper wind data from the North American section and Australia were completed.

Childhood antecedents of adolescent personality disorders.

PubMed

Bernstein, D P; Cohen, P; Skodol, A; Bezirganian, S; Brook, J S

1996-07-01

The purpose of this study was to investigate the childhood antecedents of personality disorders that are diagnosed in adolescence. A randomly selected community sample of 641 youths was assessed initially in childhood and followed longitudinally over 10 years. Childhood behavior ratings were based on maternal report; diagnoses of adolescent personality disorders were based on data obtained from both maternal and youth informants. Four composite measures of childhood behavior problems were used: conduct problems, depressive symptoms, anxiety/fear, and immaturity. Adolescent personality disorders were considered present only if the disorders persisted over a 2-year period. For all analyses, personality disorders were grouped into the three clusters (A, B, and C) of DSM-III-R. Logistic regression analyses indicated that all four of the putative childhood antecedents were associated with greater odds of an adolescent personality disorder 10 years later. Childhood conduct problems remained an independent predictor of personality disorders in all three clusters, even when other childhood problems were included in the same regression model. Additionally, depressive symptoms emerged as an independent predictor of cluster A personality disorders in boys, while immaturity was an independent predictor of cluster B personality disorders in girls. No moderating effects of age at time of childhood assessment were found. These results support the view that personality disorders can be traced to childhood emotional and behavioral disturbances and suggest that these problems have both general and specific relationships to adolescent personality functioning.

Relationship between alcohol intake, body fat, and physical activity – a population-based study

PubMed Central

Liangpunsakul, Suthat; Crabb, David W.; Qi, Rong

2010-01-01

Objectives Aside from fat, ethanol is the macronutrient with the highest energy density. Whether the energy derived from ethanol affects the body composition and fat mass is debatable. We investigated the relationship between alcohol intake, body composition, and physical activity in the US population using the third National Health and Nutrition Examination Survey (NHANES III). Methods Ten thousand five hundred and fifty subjects met eligible criteria and constituted our study cohort. Estimated percent body fat and resting metabolic rate were calculated based on the sum of the skinfolds. Multivariate regression analyses were performed accounting for the study sampling weight. Results In both genders, moderate and hazardous alcohol drinkers were younger (p<0.05), had significantly lower BMI (P<0.01) and body weight (p<0.01) than controls, non drinkers. Those with hazardous alcohol consumption had significantly less physical activity compared to those with no alcohol use and moderate drinkers in both genders. Female had significantly higher percent body fat than males. In the multivariate linear regression analyses, the levels of alcohol consumption were found to be an independent predictor associated with lower percent body fat only in male subjects. Conclusions Our results showed that alcoholics are habitually less active and that alcohol drinking is an independent predictor of lower percent body fat especially in male alcoholics. PMID:20696406

Prioritization of Evidence-Based Preventive Health Services During Periodic Health Examinations

PubMed Central

Shires, Deirdre A.; Stange, Kurt C.; Divine, George; Ratliff, Scott; Vashi, Ronak; Tai-Seale, Ming; Lafata, Jennifer Elston

2011-01-01

Background Delivery of preventive services sometimes falls short of guideline recommendations. Purpose To evaluate the multilevel factors associated with evidence-based preventive service delivery during periodic health examinations (PHE). Methods Primary care physicians were recruited from an integrated delivery system in southeast Michigan. Office visit audio-recordings of PHE visits conducted from 2007–2009 were used to ascertain physician recommendation for or delivery of 19 guideline-recommended preventive services. Alternating logistic regression was used to evaluate factors associated with service delivery. Data analyses were completed in 2011. Results Among 484 PHE visits to 64 general internal medicine and family physicians by insured patients aged 50–80 years, there were 2662 services for which patients were due; 54% were recommended or delivered. Regression analyses indicated that the likelihood of service delivery decreased with patient age and with each concern the patient raised, and increased with increasing BMI and with each additional minute after scheduled appointment time the physician first presented. The likelihood was greater with patient/physician gender concordance and less if the physician used the electronic medical record in the exam room and had seen the patient in the past 12 months. Conclusions A combination of patient, physician, visit and contextual factors are associated with preventive service delivery. Additional studies are warranted to understand the complex interplay of factors that support and compromise preventive service delivery. PMID:22261213

A Clinical Decision Support System for Breast Cancer Patients

NASA Astrophysics Data System (ADS)

Fernandes, Ana S.; Alves, Pedro; Jarman, Ian H.; Etchells, Terence A.; Fonseca, José M.; Lisboa, Paulo J. G.

This paper proposes a Web clinical decision support system for clinical oncologists and for breast cancer patients making prognostic assessments, using the particular characteristics of the individual patient. This system comprises three different prognostic modelling methodologies: the clinically widely used Nottingham prognostic index (NPI); the Cox regression modelling and a partial logistic artificial neural network with automatic relevance determination (PLANN-ARD). All three models yield a different prognostic index that can be analysed together in order to obtain a more accurate prognostic assessment of the patient. Missing data is incorporated in the mentioned models, a common issue in medical data that was overcome using multiple imputation techniques. Risk group assignments are also provided through a methodology based on regression trees, where Boolean rules can be obtained expressed with patient characteristics.

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

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.

Exploring the relationship between parental worry about their children's health and usage of an internet intervention for pediatric encopresis.

PubMed

Magee, Joshua C; Ritterband, Lee M; Thorndike, Frances P; Cox, Daniel J; Borowitz, Stephen M

2009-06-01

To investigate whether parental worry about their children's health predicts usage of a pediatric Internet intervention for encopresis. Thirty-nine families with a child diagnosed with encopresis completed a national clinical trial of an Internet-based intervention for encopresis (www.ucanpooptoo.com). Parents rated worry about their children's health, encopresis severity, current parent treatment for depression, and parent comfort with the Internet. Usage indicators were collected while participants utilized the intervention. Regression analyses showed that parents who reported higher baseline levels of worry about their children's health showed greater subsequent intervention use (beta =.52, p =.002), even after accounting for other plausible predictors. Exploratory analyses indicated that this effect may be stronger for families with younger children. Characteristics of individuals using Internet-based treatment programs, such as parental worry about their children's health, can influence intervention usage, and should be considered by developers of Internet interventions.

The Effect of Exposure to Ultraviolet Radiation in Infancy on Melanoma Risk.

PubMed

Gefeller, Olaf; Fiessler, Cornelia; Radespiel-Tröger, Martin; Uter, Wolfgang; Pfahlberg, Annette B

2016-01-01

Evidence on the effect of ultraviolet radiation (UVR) exposure in infancy on melanoma risk in later life is scarce. Three recent studies suffering from methodological shortcomings suggested that people born in spring carry a higher melanoma risk. Data from the Bavarian population-based cancer registry on 28374 incident melanoma cases between 2002 and 2012 were analyzed to reexamine this finding. Crude and adjusted analyses - using negative binomial regression models - were performed addressing the relationship. In the crude analysis, the birth months March - May were significantly overrepresented among melanoma cases. However, after additionally adjusting for the birth month distribution of the Bavarian population, the ostensible seasonal effect disappeared. Similar results emerged in all subgroup analyses. Our large registry-based study provides no evidence that people born in spring carry a higher risk for developing melanoma in later life and thus lends no support to the hypothesis of higher UVR-susceptibility during the first months of life.

The impact of prison-based treatment on sex offender recidivism: evidence from Minnesota.

PubMed

Duwe, Grant; Goldman, Robin A

2009-09-01

Using a retrospective quasi-experimental design, this study evaluates the effectiveness of prison-based treatment by examining recidivism outcomes among 2,040 sex offenders released from Minnesota prisons between 1990 and 2003 (average follow-up period of 9.3 years). To reduce observed selection bias, the authors used propensity score matching to create a comparison group of 1,020 untreated sex offenders who were not significantly different from the 1,020 treated offenders. In addition, intent-to-treat analyses and the Rosenbaum bounds method were used to test the sensitivity of the findings to treatment refuser and unobserved selection bias. Results from the Cox regression analyses revealed that participating in treatment significantly reduced the hazard ratio for rearrest by 27% for sexual recidivism, 18% for violent recidivism, and 12% for general recidivism. These findings are consistent with the growing body of research supporting the effectiveness of cognitive-behavioral treatment for sex offenders.

The effect of marital status on stage and survival of prostate cancer patients treated with radical prostatectomy: a population-based study.

PubMed

Abdollah, Firas; Sun, Maxine; Thuret, Rodolphe; Abdo, Al'a; Morgan, Monica; Jeldres, Claudio; Shariat, Shahrokh F; Perrotte, Paul; Montorsi, Francesco; Karakiewicz, Pierre I

2011-08-01

The detrimental effect of unmarried marital status on stage and survival has been confirmed in several malignancies. We set to test whether this applied to patients diagnosed with prostate cancer (PCa) treated with radical prostatectomy (RP). We identified 163,697 non-metastatic PCa patients treated with RP, within 17 Surveillance, Epidemiology, and End Results registries. Logistic regression analyses focused on the rate of locally advanced stage (pT3-4/pN1) at RP. Cox regression analyses tested the relationship between marital status and cancer-specific (CSM), as well as all-cause mortality (ACM). Respectively, 9.1 and 7.8% of individuals were separated/divorced/widowed (SDW) and never married. SDW men had more advanced stage at surgery (odds ratio: 1.1; p < 0.001), higher CSM and ACM (both hazard ratio [HR]: 1.3; p < 0.001) than married men. Similarly, never married marital status portended to a higher ACM rate (HR:1.2, p = 0.001). These findings were consistent when analyses were stratified according to organ confined vs. locally advanced stages. Being SDW significantly increased the risk of more advanced stage at RP. Following surgery, SDW men portended to a higher CSM and ACM rate than married men. Consequently, these individuals may benefit from a more focused health care throughout the natural history of their disease.

Agreement between methods of measurement of mean aortic wall thickness by MRI.

PubMed

Rosero, Eric B; Peshock, Ronald M; Khera, Amit; Clagett, G Patrick; Lo, Hao; Timaran, Carlos

2009-03-01

To assess the agreement between three methods of calculation of mean aortic wall thickness (MAWT) using magnetic resonance imaging (MRI). High-resolution MRI of the infrarenal abdominal aorta was performed on 70 subjects with a history of coronary artery disease who were part of a multi-ethnic population-based sample. MAWT was calculated as the mean distance between the adventitial and luminal aortic boundaries using three different methods: average distance at four standard positions (AWT-4P), average distance at 100 automated positions (AWT-100P), and using a mathematical computation derived from the total vessel and luminal areas (AWT-VA). Bland-Altman plots and Passing-Bablok regression analyses were used to assess agreement between methods. Bland-Altman analyses demonstrated a positive bias of 3.02+/-7.31% between the AWT-VA and the AWT-4P methods, and of 1.76+/-6.82% between the AWT-100P and the AWT-4P methods. Passing-Bablok regression analyses demonstrated constant bias between the AWT-4P method and the other two methods. Proportional bias was, however, not evident among the three methods. MRI methods of measurement of MAWT using a limited number of positions of the aortic wall systematically underestimate the MAWT value compared with the method that calculates MAWT from the vessel areas. Copyright (c) 2009 Wiley-Liss, Inc.

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...

Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression

PubMed Central

Chen, Yanguang

2016-01-01

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson’s statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran’s index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China’s regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test. PMID:26800271

Forecast and analysis of the ratio of electric energy to terminal energy consumption for global energy internet

NASA Astrophysics Data System (ADS)

Wang, Wei; Zhong, Ming; Cheng, Ling; Jin, Lu; Shen, Si

2018-02-01

In the background of building global energy internet, it has both theoretical and realistic significance for forecasting and analysing the ratio of electric energy to terminal energy consumption. This paper firstly analysed the influencing factors of the ratio of electric energy to terminal energy and then used combination method to forecast and analyse the global proportion of electric energy. And then, construct the cointegration model for the proportion of electric energy by using influence factor such as electricity price index, GDP, economic structure, energy use efficiency and total population level. At last, this paper got prediction map of the proportion of electric energy by using the combination-forecasting model based on multiple linear regression method, trend analysis method, and variance-covariance method. This map describes the development trend of the proportion of electric energy in 2017-2050 and the proportion of electric energy in 2050 was analysed in detail using scenario analysis.

Finite Element Creep Damage Analyses and Life Prediction of P91 Pipe Containing Local Wall Thinning Defect

NASA Astrophysics Data System (ADS)

Xue, Jilin; Zhou, Changyu

2016-03-01

Creep continuum damage finite element (FE) analyses were performed for P91 steel pipe containing local wall thinning (LWT) defect subjected to monotonic internal pressure, monotonic bending moment and combined internal pressure and bending moment by orthogonal experimental design method. The creep damage lives of pipe containing LWT defect under different load conditions were obtained. Then, the creep damage life formulas were regressed based on the creep damage life results from FE method. At the same time a skeletal point rupture stress was found and used for life prediction which was compared with creep damage lives obtained by continuum damage analyses. From the results, the failure lives of pipe containing LWT defect can be obtained accurately by using skeletal point rupture stress method. Finally, the influence of LWT defect geometry was analysed, which indicated that relative defect depth was the most significant factor for creep damage lives of pipe containing LWT defect.

Body Mass Index Class Is Independently Associated With Health-Related Quality of Life After Primary Total Hip Arthroplasty: An Institutional Registry-Based Study.

PubMed

McLawhorn, Alexander S; Steinhaus, Michael E; Southren, Daniel L; Lee, Yuo-Yu; Dodwell, Emily R; Figgie, Mark P

2017-01-01

The purpose of this study was to compare the health-related quality of life (HRQoL) of patients across World Health Organization (WHO) body mass index (BMI) classes before and after total hip arthroplasty (THA). Patients with end-stage hip osteoarthritis who received elective primary unilateral THA were identified through an institutional registry and categorized based on the World Health Organization BMI classification. Age, sex, laterality, year of surgery, and Charlson-Deyo comorbidity index were recorded. The primary outcome was the EQ-5D-3L index and visual analog scale (EQ-VAS) scores at 2 years postoperatively. Inferential statistics and regression analyses were performed to determine associations between BMI classes and HRQoL. EQ-5D-3L scores at baseline and at 2 years were statistically different across BMI classes, with higher EQ-VAS and index scores in patients with lower BMI. There was no difference observed for the 2-year change in EQ-VAS scores, but there was a statistically greater increase in index scores for more obese patients. In the regression analyses, there were statistically significant negative effect estimates for EQ-VAS and index scores associated with increasing BMI class. BMI class is independently associated with lower HRQoL scores 2 years after primary THA. While absolute scores in obese patients were lower than in nonobese patients, obese patients enjoyed more positive changes in EQ-5D index scores after THA. These results may provide the most detailed information on how BMI influences HRQoL before and after THA, and they are relevant to future economic decision analyses on the topic. Copyright © 2016 Elsevier Inc. All rights reserved.

Sub-hubs of baseline functional brain networks are related to early improvement following two-week pharmacological therapy for major depressive disorder.

PubMed

Shen, Yuedi; Yao, Jiashu; Jiang, Xueyan; Zhang, Lei; Xu, Luoyi; Feng, Rui; Cai, Liqiang; Liu, Jing; Wang, Jinhui; Chen, Wei

2015-08-01

Accumulating evidence suggests that early improvement after two-week antidepressant treatment is predictive of later outcomes of patients with major depressive disorder (MDD); however, whether this early improvement is associated with baseline neural architecture remains largely unknown. Utilizing resting-state functional MRI data and graph-based network approaches, this study calculated voxel-wise degree centrality maps for 24 MDD patients at baseline and linked them with changes in the Hamilton Rating Scale for Depression (HAMD) scores after two weeks of medication. Six clusters exhibited significant correlations of their baseline degree centrality with treatment-induced HAMD changes for the patients, which were mainly categorized into the posterior default-mode network (i.e., the left precuneus, supramarginal gyrus, middle temporal gyrus, and right angular gyrus) and frontal regions. Receiver operating characteristic curve and logistic regression analyses convergently revealed excellent performance of these regions in discriminating the early improvement status for the patients, especially the angular gyrus (sensitivity and specificity of 100%). Moreover, the angular gyrus was identified as the optimal regressor as determined by stepwise regression. Interestingly, these regions possessed higher centrality than others in the brain (P < 10(-3)) although they were not the most highly connected hubs. Finally, we demonstrate a high reproducibility of our findings across several factors (e.g., threshold choice, anatomical distance, and temporal cutting) in our analyses. Together, these preliminary exploratory analyses demonstrate the potential of neuroimaging-based network analysis in predicting the early therapeutic improvement of MDD patients and have important implications in guiding earlier personalized therapeutic regimens for possible treatment-refractory depression. © 2015 Wiley Periodicals, Inc.

Childhood adversity is linked to differential brain volumes in adolescents with alcohol use disorder: a voxel-based morphometry study.

PubMed

Brooks, Samantha J; Dalvie, Shareefa; Cuzen, Natalie L; Cardenas, Valerie; Fein, George; Stein, Dan J

2014-06-01

Previous neuroimaging studies link both alcohol use disorder (AUD) and early adversity to neurobiological differences in the adult brain. However, the association between AUD and childhood adversity and effects on the developing adolescent brain are less clear, due in part to the confound of psychiatric comorbidity. Here we examine early life adversity and its association with brain volume in a unique sample of 116 South African adolescents (aged 12-16) with AUD but without psychiatric comorbidity. Participants were 58 adolescents with DSM-IV alcohol dependence and with no other psychiatric comorbidities, and 58 age-, gender- and protocol-matched light/non-drinking controls (HC). Assessments included the Childhood Trauma Questionnaire (CTQ). MR images were acquired on a 3T Siemens Magnetom Allegra scanner. Volumes of global and regional structures were estimated using SPM8 Voxel Based Morphometry (VBM), with analysis of covariance (ANCOVA) and regression analyses. In whole brain ANCOVA analyses, a main effect of group when examining the AUD effect after covarying out CTQ was observed on brain volume in bilateral superior temporal gyrus. Subsequent regression analyses to examine how childhood trauma scores are linked to brain volumes in the total cohort revealed a negative correlation in the left hippocampus and right precentral gyrus. Furthermore, bilateral (but most significantly left) hippocampal volume was negatively associated with sub-scores on the CTQ in the total cohort. These findings support our view that some alterations found in brain volumes in studies of adolescent AUD may reflect the impact of confounding factors such as psychiatric comorbidity rather than the effects of alcohol per se. In particular, early life adversity may influence the developing adolescent brain in specific brain regions, such as the hippocampus.

Biological integrity in mid-atlantic coastal plains headwater streams.

PubMed

Megan, Mehaffey H; Nash, Maliha S; Neale, Anne C; Pitchford, Ann M

2007-01-01

The objective of this study was to assess the applicability of using landscape variables in conjunction with water quality and benthic data to efficiently estimate stream condition of select headwater streams in the Mid-Atlantic Coastal Plains. Eighty-two streams with riffle sites were selected from eight-two independent watersheds across the region for sampling and analyses. Clustering of the watersheds by landscape resulted in three distinct groups (forest, crop, and urban) which coincided with watersheds dominant land cover or use. We used non-parametric analyses to test differences in benthos and water chemistry between groups, and used regression analyses to evaluate responses of benthic communities to water chemistry within each of the landscape groups. We found that typical water chemistry measures associated with urban runoff such as specific conductance and dissolved chloride were significantly higher in the urban group. In the crop group, we found variables commonly associated with farming such as nutrients and pesticides significantly greater than in the other two groups. Regression analyses demonstrated that the numbers of tolerant and facultative macroinvertebrates increased significantly in forested watersheds with small shifts in pollutants, while in human use dominated watersheds the intolerant macroinvertebrates were more sensitive to shifts in chemicals present at lower concentrations. The results from this study suggest that landscape based clustering can be used to link upstream landscape characteristics, water chemistry and biotic integrity in order to assess stream condition and likely cause of degradation without the use of reference sites. Notice: Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.

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

Comparative Assessment of Physical and Social Determinants of Water Quantity and Water Quality Concerns

NASA Astrophysics Data System (ADS)

Gunda, T.; Hornberger, G. M.

2017-12-01

Concerns over water resources have evolved over time, from physical availability to economic access and recently, to a more comprehensive study of "water security," which is inherently interdisciplinary because a secure water system is influenced by and affects both physical and social components. The concept of water security carries connotations of both an adequate supply of water as well as water that meets certain quality standards. Although the term "water security" has many interpretations in the literature, the research field has not yet developed a synthetic analysis of water security as both a quantity (availability) and quality (contamination) issue. Using qualitative comparative and multi-regression analyses, we evaluate the primary physical and social factors influencing U.S. states' water security from a quantity perspective and from a quality perspective. Water system characteristics are collated from academic and government sources and include access/use, governance, and sociodemographic, and ecosystem metrics. Our analysis indicates differences in variables driving availability and contamination concerns; for example, climate is a more significant determinant in water quantity-based security analyses than in water quality-based security analyses. We will also discuss coevolution of system traits and the merits of constructing a robust water security index based on the relative importance of metrics from our analyses. These insights will improve understanding of the complex interactions between quantity and quality aspects and thus, overall security of water systems.

To Control False Positives in Gene-Gene Interaction Analysis: Two Novel Conditional Entropy-Based Approaches

PubMed Central

Lin, Meihua; Li, Haoli; Zhao, Xiaolei; Qin, Jiheng

2013-01-01

Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects. PMID:24339984

Health behaviours associated with indoor tanning based on the 2012/13 Manitoba Youth Health Survey

PubMed Central

Harland, E.; Griffith, J.; Lu, H.; Erickson, T.; Magsino, K.

2016-01-01

Abstract Introduction: Although indoor tanning causes cancer, it remains relatively common among adolescents. Little is known about indoor tanning prevalence and habits in Canada, and even less about associated behaviours. This study explores the prevalence of adolescent indoor tanning in Manitoba and its association with other demographic characteristics and health behaviours. Methods: We conducted secondary analyses of the 2012/13 Manitoba Youth Health Survey data collected from Grade 7 to 12 students (n = 64 174) and examined associations between indoor tanning (whether participants had ever used artificial tanning equipment) and 25 variables. Variables with statistically significant associations to indoor tanning were tested for collinearity and grouped based on strong associations. For each group of highly associated variables, the variable with the greatest effect upon indoor tanning was placed into the final logistic regression model. Separate analyses were conducted for males and females to better understand sex-based differences, and analyses were adjusted for age. Results: Overall, 4% of male and 9% of female students reported indoor tanning, and prevalence increased with age. Relationships between indoor tanning and other variables were similar for male and female students. Binary logistic regression models indicated that several variables significantly predicted indoor tanning, including having part-time work, being physically active, engaging in various risk behaviours such as driving after drinking for males and unplanned sex after alcohol/drugs for females, experiencing someone say something bad about one’s body shape/size/appearance, identifying as trans or with another gender, consuming creatine/other supplements and, for females only, never/rarely using sun protection. Conclusion: Indoor tanning among adolescents was associated with age, part-time work, physical activity and many consumption behaviours and lifestyle risk factors. Though legislation prohibiting adolescent indoor tanning is critical, health promotion to discourage indoor tanning may be most beneficial if it also addresses these associated factors. PMID:27556919

Health behaviours associated with indoor tanning based on the 2012/13 Manitoba Youth Health Survey.

PubMed

Harland, E; Griffith, J; Lu, H; Erickson, T; Magsino, K

2016-08-01

Although indoor tanning causes cancer, it remains relatively common among adolescents. Little is known about indoor tanning prevalence and habits in Canada, and even less about associated behaviours. This study explores the prevalence of adolescent indoor tanning in Manitoba and its association with other demographic characteristics and health behaviours. We conducted secondary analyses of the 2012/13 Manitoba Youth Health Survey data collected from Grade 7 to 12 students (n = 64 174) and examined associations between indoor tanning (whether participants had ever used artificial tanning equipment) and 25 variables. Variables with statistically significant associations to indoor tanning were tested for collinearity and grouped based on strong associations. For each group of highly associated variables, the variable with the greatest effect upon indoor tanning was placed into the final logistic regression model. Separate analyses were conducted for males and females to better understand sex-based differences, and analyses were adjusted for age. Overall, 4% of male and 9% of female students reported indoor tanning, and prevalence increased with age. Relationships between indoor tanning and other variables were similar for male and female students. Binary logistic regression models indicated that several variables significantly predicted indoor tanning, including having part-time work, being physically active, engaging in various risk behaviours such as driving after drinking for males and unplanned sex after alcohol/drugs for females, experiencing someone say something bad about one's body shape/size/appearance, identifying as trans or with another gender, consuming creatine/other supplements and, for females only, never/rarely using sun protection. Indoor tanning among adolescents was associated with age, part-time work, physical activity and many consumption behaviours and lifestyle risk factors. Though legislation prohibiting adolescent indoor tanning is critical, health promotion to discourage indoor tanning may be most beneficial if it also addresses these associated factors.

Surface dust wipes are the best predictors of blood leads in young children with elevated blood lead levels

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

Gulson, Brian, E-mail: brian.gulson@mq.edu.au; CSIRO Earth Science and Resource Engineering, North Ryde NSW 2113; Anderson, Phil

Background: As part of the only national survey of lead in Australian children, which was undertaken in 1996, lead isotopic and lead concentration measurements were obtained from children from 24 dwellings whose blood lead levels were ≥15 µg/dL in an attempt to determine the source(s) of their elevated blood lead. Comparisons were made with data for six children with lower blood lead levels (<10 µg/dL). Methods: Thermal ionisation and isotope dilution mass spectrometry were used to determine high precision lead isotopic ratios ({sup 208}Pb/{sup 206}Pb, {sup 207}Pb/{sup 206}Pb and {sup 206}Pb/{sup 204}Pb) and lead concentrations in blood, dust from floormore » wipes, soil, drinking water and paint (where available). Evaluation of associations between blood and the environmental samples was based on the analysis of individual cases, and Pearson correlations and multiple regression analyses based on the whole dataset. Results and discussion: The correlations showed an association for isotopic ratios in blood and wipes (r=0.52, 95% CI 0.19–0.74), blood and soil (r=0.33, 95% CI −0.05–0.62), and blood and paint (r=0.56, 95% CI 0.09–0.83). The regression analyses indicated that the only statistically significant relationship for blood isotopic ratios was with dust wipes (B=0.65, 95% CI 0.35–0.95); there were no significant associations for lead concentrations in blood and environmental samples. There is a strong isotopic correlation of soils and house dust (r=0.53, 95% CI 0.20–0.75) indicative of a common source(s) for lead in soil and house dust. In contrast, as with the regression analyses, no such association is present for bulk lead concentrations (r=−0.003, 95% CI −0.37–0.36), the most common approach employed in source investigations. In evaluation of the isotopic results on a case by case basis, the strongest associations were for dust wipes and blood. -- Highlights: • Children with elevated blood lead ≥15 µg/dL compared with a group with <10 µg/dL. • High precision lead isotopic ratios in blood, house dust wipes, soil, water, paint. • Associations for isotopic measures of blood and dust, blood and soil, blood and paint. • Regressions gave significance for isotopic measures of blood/dust and dust/soil.« less

Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models.

PubMed

Yilmaz, Banu; Aras, Egemen; Nacar, Sinan; Kankal, Murat

2018-05-23

The functional life of a dam is often determined by the rate of sediment delivery to its reservoir. Therefore, an accurate estimate of the sediment load in rivers with dams is essential for designing and predicting a dam's useful lifespan. The most credible method is direct measurements of sediment input, but this can be very costly and it cannot always be implemented at all gauging stations. In this study, we tested various regression models to estimate suspended sediment load (SSL) at two gauging stations on the Çoruh River in Turkey, including artificial bee colony (ABC), teaching-learning-based optimization algorithm (TLBO), and multivariate adaptive regression splines (MARS). These models were also compared with one another and with classical regression analyses (CRA). Streamflow values and previously collected data of SSL were used as model inputs with predicted SSL data as output. Two different training and testing dataset configurations were used to reinforce the model accuracy. For the MARS method, the root mean square error value was found to range between 35% and 39% for the test two gauging stations, which was lower than errors for other models. Error values were even lower (7% to 15%) using another dataset. Our results indicate that simultaneous measurements of streamflow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL. Copyright © 2017 Elsevier B.V. All rights reserved.

Characterizing nonconstant instrumental variance in emerging miniaturized analytical techniques.

PubMed

Noblitt, Scott D; Berg, Kathleen E; Cate, David M; Henry, Charles S

2016-04-07

Measurement variance is a crucial aspect of quantitative chemical analysis. Variance directly affects important analytical figures of merit, including detection limit, quantitation limit, and confidence intervals. Most reported analyses for emerging analytical techniques implicitly assume constant variance (homoskedasticity) by using unweighted regression calibrations. Despite the assumption of constant variance, it is known that most instruments exhibit heteroskedasticity, where variance changes with signal intensity. Ignoring nonconstant variance results in suboptimal calibrations, invalid uncertainty estimates, and incorrect detection limits. Three techniques where homoskedasticity is often assumed were covered in this work to evaluate if heteroskedasticity had a significant quantitative impact-naked-eye, distance-based detection using paper-based analytical devices (PADs), cathodic stripping voltammetry (CSV) with disposable carbon-ink electrode devices, and microchip electrophoresis (MCE) with conductivity detection. Despite these techniques representing a wide range of chemistries and precision, heteroskedastic behavior was confirmed for each. The general variance forms were analyzed, and recommendations for accounting for nonconstant variance discussed. Monte Carlo simulations of instrument responses were performed to quantify the benefits of weighted regression, and the sensitivity to uncertainty in the variance function was tested. Results show that heteroskedasticity should be considered during development of new techniques; even moderate uncertainty (30%) in the variance function still results in weighted regression outperforming unweighted regressions. We recommend utilizing the power model of variance because it is easy to apply, requires little additional experimentation, and produces higher-precision results and more reliable uncertainty estimates than assuming homoskedasticity. Copyright © 2016 Elsevier B.V. All rights reserved.

Parametric and Nonparametric Statistical Methods for Genomic Selection of Traits with Additive and Epistatic Genetic Architectures

PubMed Central

Howard, Réka; Carriquiry, Alicia L.; Beavis, William D.

2014-01-01

Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cπ. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, and neural networks. We assess the relative merits of these 14 methods in terms of accuracy and mean squared error (MSE) using simulated genetic architectures consisting of completely additive or two-way epistatic interactions in an F2 population derived from crosses of inbred lines. Each simulated genetic architecture explained either 30% or 70% of the phenotypic variability. The greatest impact on estimates of accuracy and MSE was due to genetic architecture. Parametric methods were unable to predict phenotypic values when the underlying genetic architecture was based entirely on epistasis. Parametric methods were slightly better than nonparametric methods for additive genetic architectures. Distinctions among parametric methods for additive genetic architectures were incremental. Heritability, i.e., proportion of phenotypic variability, had the second greatest impact on estimates of accuracy and MSE. PMID:24727289

A cross-sectional study to estimate associations between education level and osteoporosis in a Chinese postmenopausal women sample.

PubMed

Piao, Hui-Hong; He, Jiajia; Zhang, Keqin; Tang, Zihui

2015-01-01

Our research aims to investigate the associations between education level and osteoporosis (OP) in Chinese postmenopausal women. A large-scale, community-based, cross-sectional study was conducted to examine the associations between education level and OP. A self-reported questionnaire was used to access the demographical information and medical history of the participants. A total of 1905 postmenopausal women were available for data analysis in this study. Multiple regression models controlling for confounding factors to include education level were performed to investigate the relationship with OP. The prevalence of OP was 28.29% in our study sample. Multivariate linear regression analyses adjusted for relevant potential confounding factors detected significant associations between education level and T-score (β = 0.025, P-value = 0.095, 95% CI: -0.004-0.055 for model 1; and β = 0.092, P-value = 0.032, 95% CI: 0.008-0.175 for model 2). Multivariate logistic regression analyses detected significant associations between education level and OP in model 1 (P-value = 0.070 for model 1, Table 5), while no significant associations was reported in model 2 (P value = 0.131). In participants with high education levels, the OR for OP was 0.914 (95% CI: 0.830-1.007). The findings indicated that education level was independently and significantly associated with OP. The prevalence of OP was more frequent in Chinese postmenopausal women with low educational status.

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

Improved Dietary Guidelines for Vitamin D: Application of Individual Participant Data (IPD)-Level Meta-Regression Analyses

PubMed Central

Cashman, Kevin D.; Ritz, Christian; Kiely, Mairead

2017-01-01

Dietary Reference Values (DRVs) for vitamin D have a key role in the prevention of vitamin D deficiency. However, despite adopting similar risk assessment protocols, estimates from authoritative agencies over the last 6 years have been diverse. This may have arisen from diverse approaches to data analysis. Modelling strategies for pooling of individual subject data from cognate vitamin D randomized controlled trials (RCTs) are likely to provide the most appropriate DRV estimates. Thus, the objective of the present work was to undertake the first-ever individual participant data (IPD)-level meta-regression, which is increasingly recognized as best practice, from seven winter-based RCTs (with 882 participants ranging in age from 4 to 90 years) of the vitamin D intake–serum 25-hydroxyvitamin D (25(OH)D) dose-response. Our IPD-derived estimates of vitamin D intakes required to maintain 97.5% of 25(OH)D concentrations >25, 30, and 50 nmol/L across the population are 10, 13, and 26 µg/day, respectively. In contrast, standard meta-regression analyses with aggregate data (as used by several agencies in recent years) from the same RCTs estimated that a vitamin D intake requirement of 14 µg/day would maintain 97.5% of 25(OH)D >50 nmol/L. These first IPD-derived estimates offer improved dietary recommendations for vitamin D because the underpinning modeling captures the between-person variability in response of serum 25(OH)D to vitamin D intake. PMID:28481259

Relationship Between Column-Density and Surface Mixing Ratio: Statistical Analysis of O3 and NO2 Data from the July 2011 Maryland DISCOVER-AQ Mission

NASA Technical Reports Server (NTRS)

Flynn, Clare; Pickering, Kenneth E.; Crawford, James H.; Lamsol, Lok; Krotkov, Nickolay; Herman, Jay; Weinheimer, Andrew; Chen, Gao; Liu, Xiong; Szykman, James;

Methods for estimating flood frequency in Montana based on data through water year 1998

USGS Publications Warehouse

Parrett, Charles; Johnson, Dave R.

2004-01-01

Annual peak discharges having recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years (T-year floods) were determined for 660 gaged sites in Montana and in adjacent areas of Idaho, Wyoming, and Canada, based on data through water year 1998. The updated flood-frequency information was subsequently used in regression analyses, either ordinary or generalized least squares, to develop equations relating T-year floods to various basin and climatic characteristics, equations relating T-year floods to active-channel width, and equations relating T-year floods to bankfull width. The equations can be used to estimate flood frequency at ungaged sites. Montana was divided into eight regions, within which flood characteristics were considered to be reasonably homogeneous, and the three sets of regression equations were developed for each region. A measure of the overall reliability of the regression equations is the average standard error of prediction. The average standard errors of prediction for the equations based on basin and climatic characteristics ranged from 37.4 percent to 134.1 percent. Average standard errors of prediction for the equations based on active-channel width ranged from 57.2 percent to 141.3 percent. Average standard errors of prediction for the equations based on bankfull width ranged from 63.1 percent to 155.5 percent. In most regions, the equations based on basin and climatic characteristics generally had smaller average standard errors of prediction than equations based on active-channel or bankfull width. An exception was the Southeast Plains Region, where all equations based on active-channel width had smaller average standard errors of prediction than equations based on basin and climatic characteristics or bankfull width. Methods for weighting estimates derived from the basin- and climatic-characteristic equations and the channel-width equations also were developed. The weights were based on the cross correlation of residuals from the different methods and the average standard errors of prediction. When all three methods were combined, the average standard errors of prediction ranged from 37.4 percent to 120.2 percent. Weighting of estimates reduced the standard errors of prediction for all T-year flood estimates in four regions, reduced the standard errors of prediction for some T-year flood estimates in two regions, and provided no reduction in average standard error of prediction in two regions. A computer program for solving the regression equations, weighting estimates, and determining reliability of individual estimates was developed and placed on the USGS Montana District World Wide Web page. A new regression method, termed Region of Influence regression, also was tested. Test results indicated that the Region of Influence method was not as reliable as the regional equations based on generalized least squares regression. Two additional methods for estimating flood frequency at ungaged sites located on the same streams as gaged sites also are described. The first method, based on a drainage-area-ratio adjustment, is intended for use on streams where the ungaged site of interest is located near a gaged site. The second method, based on interpolation between gaged sites, is intended for use on streams that have two or more streamflow-gaging stations.

Associations of insulin-like growth factor-I and insulin-like growth factor binding protein-3 with bone quality in the general adult population.

PubMed

Böker, J; Völzke, H; Nauck, M; Hannemann, A; Friedrich, N

2018-03-01

Growth hormone (GH) and its main mediator, insulin-like growth factor-I (IGF-I), play a significant role in bone metabolism. The relations between IGF-I and bone mineral density (BMD) or osteoporosis have been assessed in previous studies but whether the associations are sex-specific remains uncertain. Moreover, only a few studies examined bone quality assessed by quantitative ultrasound (QUS). We aimed to investigate these associations in the general population of north-east Germany. Data from 1759 men and 1784 women who participated in the baseline examination of the Study of Health in Pomerania (SHIP)-Trend were used. IGF-I and IGF-binding protein-3 (IGFBP-3) concentrations were measured on the IDS-iSYS multidiscipline automated analyser (Immunodiagnostic Systems Limited). QUS measurements were performed at the heel (Achilles InSight, GE Healthcare). Sex-specific linear and multinomial logistic regression models adjusted for potential confounders were calculated. Linear regression analyses revealed significant positive associations between IGF-I and IGF-I/IGFBP-3 ratio, a marker for free IGF-I, with all QUS parameters in men. Among women, we found an inverse association between IGF-I and the QUS-based fracture risk but no association with any other QUS parameter. There was no association between IGFBP-3 and the QUS-based fracture risk. Our data suggest an important role of IGF-I on bone quality in men. The observed association of IGF-I with the QUS-based stiffness index and QUS-based fracture risk in this study might animate clinicians to refer patients with low IGF-I levels, particularly men, to a further evaluation of risk factors for osteoporosis and a detailed examination of the skeletal system. © 2018 John Wiley & Sons Ltd.

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

The costs of heparin-induced thrombocytopenia: a patient-based cost of illness analysis.

PubMed

Wilke, T; Tesch, S; Scholz, A; Kohlmann, T; Greinacher, A

2009-05-01

SUMMARY BACKGROUND AND OBJECTIVES: Due to the complexity of heparin-induced thrombocytopenia (HIT), currently available cost analyses are rough estimates. The objectives of this study were quantification of costs involved in HIT and identification of main cost drivers based on a patient-oriented approach. Patients diagnosed with HIT (1995-2004, University-hospital Greifswald, Germany) based on a positive functional assay (HIPA test) were retrieved from the laboratory records and scored (4T-score) by two medical experts using the patient file. For cost of illness analysis, predefined HIT-relevant cost parameters (medication costs, prolonged in-hospital stay, diagnostic and therapeutic interventions, laboratory tests, blood transfusions) were retrieved from the patient files. The data were analysed by linear regression estimates with the log of costs and a gamma regression model. Mean length of stay data of non-HIT patients were obtained from the German Federal Statistical Office, adjusted for patient characteristics, comorbidities and year of treatment. Hospital costs were provided by the controlling department. One hundred and thirty HIT cases with a 4T-score >or=4 and a positive HIPA test were analyzed. Mean additional costs of a HIT case were 9008 euro. The main cost drivers were prolonged in-hospital stay (70.3%) and costs of alternative anticoagulants (19.7%). HIT was more costly in surgical patients compared with medical patients and in patients with thrombosis. Early start of alternative anticoagulation did not increase HIT costs despite the high medication costs indicating prevention of costly complications. An HIT cost calculator is provided, allowing online calculation of HIT costs based on local cost structures and different currencies.

Whole exome sequence-based association analyses of plasma amyloid-β in African and European Americans; the Atherosclerosis Risk in Communities-Neurocognitive Study.

PubMed

Simino, Jeannette; Wang, Zhiying; Bressler, Jan; Chouraki, Vincent; Yang, Qiong; Younkin, Steven G; Seshadri, Sudha; Fornage, Myriam; Boerwinkle, Eric; Mosley, Thomas H

2017-01-01

We performed single-variant and gene-based association analyses of plasma amyloid-β (aβ) concentrations using whole exome sequence from 1,414 African and European Americans. Our goal was to identify genes that influence plasma aβ42 concentrations and aβ42:aβ40 ratios in late middle age (mean = 59 years), old age (mean = 77 years), or change over time (mean = 18 years). Plasma aβ measures were linearly regressed onto age, gender, APOE ε4 carrier status, and time elapsed between visits (fold-changes only) separately by race. Following inverse normal transformation of the residuals, seqMeta was used to conduct race-specific single-variant and gene-based association tests while adjusting for population structure. Linear regression models were fit on autosomal variants with minor allele frequencies (MAF)≥1%. T5 burden and Sequence Kernel Association (SKAT) gene-based tests assessed functional variants with MAF≤5%. Cross-race fixed effects meta-analyses were Bonferroni-corrected for the number of variants or genes tested. Seven genes were associated with aβ in late middle age or change over time; no associations were identified in old age. Single variants in KLKB1 (rs3733402; p = 4.33x10-10) and F12 (rs1801020; p = 3.89x10-8) were significantly associated with midlife aβ42 levels through cross-race meta-analysis; the KLKB1 variant replicated internally using 1,014 additional participants with exome chip. ITPRIP, PLIN2, and TSPAN18 were associated with the midlife aβ42:aβ40 ratio via the T5 test; TSPAN18 was significant via the cross-race meta-analysis, whereas ITPRIP and PLIN2 were European American-specific. NCOA1 and NT5C3B were associated with the midlife aβ42:aβ40 ratio and the fold-change in aβ42, respectively, via SKAT in African Americans. No associations replicated externally (N = 725). We discovered age-dependent genetic effects, established associations between vascular-related genes (KLKB1, F12, PLIN2) and midlife plasma aβ levels, and identified a plausible Alzheimer's Disease candidate gene (ITPRIP) influencing cell death. Plasma aβ concentrations may have dynamic biological determinants across the lifespan; plasma aβ study designs or analyses must consider age.

Evaluating the Effectiveness of Physlet-Based Materials in Supporting Conceptual Learning About Electricity

NASA Astrophysics Data System (ADS)

Ülen, Simon; Gerlič, Ivan; Slavinec, Mitja; Repnik, Robert

2017-04-01

To provide a good understanding of many abstract concepts in the field of electricity above that of their students is often a major challenge for secondary school teachers. Many educational researchers promote conceptual learning as a teaching approach that can help teachers to achieve this goal. In this paper, we present Physlet-based materials for supporting conceptual learning about electricity. To conduct research into the effectiveness of these materials, we designed two different physics courses: one group of students, the experimental group, was taught using Physlet-based materials and the second group of students, the control group, was taught using expository instruction without using Physlets. After completion of the teaching, we assessed students' thinking skills and analysed the materials with an independent t test, multiple regression analyses and one-way analysis of covariance. The test scores were significantly higher in the experimental group than in the control group ( p < 0.05). The results of this study confirmed the effectiveness of conceptual learning about electricity with the help of Physlet-based materials.

Variations in management of common inpatient pediatric illnesses: hospitalists and community pediatricians.

PubMed

Conway, Patrick H; Edwards, Sarah; Stucky, Erin R; Chiang, Vincent W; Ottolini, Mary C; Landrigan, Christopher P

2006-08-01

The goal was to test the hypothesis that pediatric hospitalists use evidence-based therapies and tests more consistently in the care of inpatients and use therapies and tests of unproven benefit less often, compared with community pediatricians. A national survey was administered to hospitalists and a random sample of community pediatricians. Hospitalists and community pediatricians reported their frequency of use of diagnostic tests and therapies, on 5-point Likert scales (ranging from never to almost always), for common inpatient pediatric illnesses. Responses were compared in univariate and multivariable logistic regression analyses controlling for gender, race, years out of residency, days spent attending per year, hospital practice type, and completion of fellowship/postgraduate training. Two hundred thirteen pediatric hospitalists and 352 community pediatricians responded. In multivariable regression analyses, hospitalists were significantly more likely to report often or almost always using the following evidence-based therapies for asthma: albuterol and ipratropium in the first 24 hours of hospitalization. After the first urinary tract infection, hospitalists were more likely to report obtaining the recommended renal ultrasound and voiding cystourethrogram. Hospitalists were significantly more likely than community pediatricians to report rarely or never using the following therapies of unproven benefit: levalbuterol, inhaled steroid therapy, and oral steroid therapy for bronchiolitis; stool culture and rotavirus testing for gastroenteritis; and ipratropium after 24 hours of hospitalization for asthma. Overall, in comparison with community pediatricians, hospitalists reported greater adherence to evidence-based therapies and tests in the care of hospitalized patients and less use of therapies and tests of unproven benefit.

Trends and risk factors of stillbirths and neonatal deaths in Eastern Uganda (1982-2011): a cross-sectional, population-based study.

PubMed

Kujala, Sanni; Waiswa, Peter; Kadobera, Daniel; Akuze, Joseph; Pariyo, George; Hanson, Claudia

2017-01-01

To identify mortality trends and risk factors associated with stillbirths and neonatal deaths 1982-2011. Population-based cross-sectional study based on reported pregnancy history in Iganga-Mayuge Health and Demographic Surveillance Site (HDSS) in Uganda. A pregnancy history survey was conducted among women aged 15-49 years living in the HDSS during May-July 2011 (n = 10 540). Time trends were analysed with cubic splines and linear regression. Potential risk factors were examined with multilevel logistic regression with adjusted odds ratios (AOR) and 95% confidence intervals (CI). 34 073 births from 1982 to 2011 were analysed. The annual rate of decrease was 0.9% for stillbirths and 1.8% for neonatal mortality. Stillbirths were associated with several risk factors: multiple births (AOR 2.57, CI 1.66-3.99), previous adverse outcome (AOR 6.16, CI 4.26-8.88) and grand multiparity among 35- to 49-year-olds (AOR 1.97, CI 1.32-2.89). Neonatal deaths were associated with multiple births (AOR 6.16, CI 4.80-7.92) and advanced maternal age linked with parity of 1-4 (AOR 2.34, CI 1.28-4.25) and grand multiparity (AOR 1.44, CI 1.09-1.90). Education, marital status and household wealth were not associated with the outcomes. The slow decline in mortality rates and easily identifiable risk factors calls for improving quality of care at birth and a rethinking of how to address obstetric risks, potentially a revival of the risk approach in antenatal care. © 2016 John Wiley & Sons Ltd.

Normative data for the Digit Symbol Substitution Test in a population-based sample aged 65-79 years: Results from the German Health Interview and Examination Survey for Adults (DEGS1).

PubMed

Gaertner, Beate; Wagner, Michael; Luck, Tobias; Buttery, Amanda K; Fuchs, Judith; Busch, Markus A

2018-06-17

To provide normative data for the Digit Symbol Substitution Test (DSST) of the Wechsler Adult Intelligence Scale, 3rd edition (WAIS-III) in a population-based sample of community-dwelling older adults in Germany according to age, sex, and level of education. The sample comprised 1385 participants aged 65-79 years from the nationwide representative 'German Health Interview and Examination Survey for Adults' (DEGS1, 2008-2011). Participants with known cognitive impairment or dementia, other medical conditions affecting cognition, or currently using psychotropic drugs were excluded. Educational level was categorized as low, medium, and high according to the Comparative Analyses of Social Mobility in Industrial Nations (CASMIN) scale. Normative values for the DSST according to age, sex, and level of education were estimated by multiple linear regression using population weights. Mean age was 71.1 years, 48.6% were men and low, medium, and high education levels were 62.8, 24.6, and 12.6%, respectively. Younger age, female sex, and higher level of education were significantly associated with higher DSST scores. Regression-based normative data for the DSST is provided according to age, sex, and level of education. In addition, a normative score calculator is provided. These are the first age-, sex-, and education-specific normative data for older individuals for the DSST of the WAIS-III in Germany. These normative data will enable future population-level analyses on impaired cognitive function according to DSST.

Hyperspectral analysis of soil organic matter in coal mining regions using wavelets, correlations, and partial least squares regression.

PubMed

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Wang, Xuchen

2016-02-01

Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation--partial least squares regression (PLSR) method effectively solves the information loss problem of correlation--multiple linear stepwise regression, but results of the correlation analysis must be optimized to improve precision. This study considers the relationship between spectral reflectance and SOM based on spectral reflectance curves of soil samples collected from coal mining regions. Based on the major absorption troughs in the 400-1006 nm spectral range, PLSR analysis was performed using 289 independent bands of the second derivative (SDR) with three levels and measured SOM values. A wavelet-correlation-PLSR (W-C-PLSR) model was then constructed. By amplifying useful information that was previously obscured by noise, the W-C-PLSR model was optimal for estimating SOM content, with smaller prediction errors in both calibration (R(2) = 0.970, root mean square error (RMSEC) = 3.10, and mean relative error (MREC) = 8.75) and validation (RMSEV = 5.85 and MREV = 14.32) analyses, as compared with other models. Results indicate that W-C-PLSR has great potential to estimate SOM in coal mining regions.

Estimating parasitic sea lamprey abundance in Lake Huron from heterogenous data sources

USGS Publications Warehouse

Young, Robert J.; Jones, Michael L.; Bence, James R.; McDonald, Rodney B.; Mullett, Katherine M.; Bergstedt, Roger A.

2003-01-01

The Great Lakes Fishery Commission uses time series of transformer, parasitic, and spawning population estimates to evaluate the effectiveness of its sea lamprey (Petromyzon marinus) control program. This study used an inverse variance weighting method to integrate Lake Huron sea lamprey population estimates derived from two estimation procedures: 1) prediction of the lake-wide spawning population from a regression model based on stream size and, 2) whole-lake mark and recapture estimates. In addition, we used a re-sampling procedure to evaluate the effect of trading off sampling effort between the regression and mark-recapture models. Population estimates derived from the regression model ranged from 132,000 to 377,000 while mark-recapture estimates of marked recently metamorphosed juveniles and parasitic sea lampreys ranged from 536,000 to 634,000 and 484,000 to 1,608,000, respectively. The precision of the estimates varied greatly among estimation procedures and years. The integrated estimate of the mark-recapture and spawner regression procedures ranged from 252,000 to 702,000 transformers. The re-sampling procedure indicated that the regression model is more sensitive to reduction in sampling effort than the mark-recapture model. Reliance on either the regression or mark-recapture model alone could produce misleading estimates of abundance of sea lampreys and the effect of the control program on sea lamprey abundance. These analyses indicate that the precision of the lakewide population estimate can be maximized by re-allocating sampling effort from marking sea lampreys to trapping additional streams.

New strategy for determination of anthocyanins, polyphenols and antioxidant capacity of Brassica oleracea liquid extract using infrared spectroscopies and multivariate regression

NASA Astrophysics Data System (ADS)

de Oliveira, Isadora R. N.; Roque, Jussara V.; Maia, Mariza P.; Stringheta, Paulo C.; Teófilo, Reinaldo F.

2018-04-01

A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry.

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

PubMed

Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

Phylogenetically diverse macrophyte community promotes species diversity of mobile epi-benthic invertebrates

NASA Astrophysics Data System (ADS)

Nakamoto, Kenta; Hayakawa, Jun; Kawamura, Tomohiko; Kodama, Masafumi; Yamada, Hideaki; Kitagawa, Takashi; Watanabe, Yoshiro

2018-07-01

Various aspects of plant diversity such as species diversity and phylogenetic diversity enhance the species diversity of associated animals in terrestrial systems. In marine systems, however, the effects of macrophyte diversity on the species diversity of associated animals have received little attention. Here, we sampled in a subtropical seagrass-seaweed mixed bed to elucidate the effect of the macrophyte phylogenetic diversity based on the taxonomic relatedness as well as the macrophyte species diversity on species diversity of mobile epi-benthic invertebrates. Using regression analyses for each macrophyte parameter as well as multiple regression analyses, we found that the macrophyte phylogenetic diversity (taxonomic diversity index: Delta) positively influenced the invertebrate species richness and diversity index (H‧). Although the macrophyte species richness and H‧ also positively influenced the invertebrate species richness, the best fit model for invertebrate species richness did not include them, suggesting that the macrophyte species diversity indirectly influenced invertebrate species diversity. Possible explanations of the effects of macrophyte Delta on the invertebrate species diversity were the niche complementarity effect and the selection effect. This is the first study which demonstrates that macrophyte phylogenetic diversity has a strong effect on the species diversity of mobile epi-benthic invertebrates.

Predictors affecting personal health information management skills.

PubMed

Kim, Sujin; Abner, Erin

2016-01-01

This study investigated major factors affecting personal health records (PHRs) management skills associated with survey respondents' health information management related activities. A self-report survey was used to assess individuals' personal characteristics, health knowledge, PHR skills, and activities. Factors underlying respondents' current PHR-related activities were derived using principal component analysis (PCA). Scale scores were calculated based on the results of the PCA, and hierarchical linear regression analyses were used to identify respondent characteristics associated with the scale scores. Internal consistency of the derived scale scores was assessed with Cronbach's α. Among personal health information activities surveyed (N = 578 respondents), the four extracted factors were subsequently grouped and labeled as: collecting skills (Cronbach's α = 0.906), searching skills (Cronbach's α = 0.837), sharing skills (Cronbach's α = 0.763), and implementing skills (Cronbach's α = 0.908). In the hierarchical regression analyses, education and computer knowledge significantly increased the explanatory power of the models. Health knowledge (β = 0.25, p < 0.001) emerged as a positive predictor of PHR collecting skills. This study confirmed that PHR training and learning should consider a full spectrum of information management skills including collection, utilization and distribution to support patients' care and prevention continua.

Dietary phytoestrogens and plasma lipids in Dutch postmenopausal women; a cross-sectional study.

PubMed

Kreijkamp-Kaspers, Sanne; Kok, Linda; Bots, Michiel L; Grobbee, Diederick E; van der Schouw, Yvonne T

2005-01-01

Isoflavone supplementation in high doses is associated with plasma lipid, glucose and insulin levels. Little is known about the effects of intake within the range of western diets on these endpoints. We conducted a population-based cross-sectional study in 301 women aged 60-75 years. Dietary isoflavone and lignan intake was assessed with a food frequency questionnaire covering habitual diet during the year preceding enrollment. The outcome measures were total cholesterol, LDL-cholesterol, HDL-cholesterol, triglycerides, Lp(a), fasting glucose and insulin levels. Data were analysed using linear regression and logistic regression models. In the analyses we adjusted for a wide range of potential confounders. High intake of isoflavones was associated with lower Lp(a) levels (tertile three versus tertile one: odds ratio 0.36, 95% CI 0.16; 0.80). No relation was found between blood levels and the other plasma lipids, glucose or insulin was found. The results of this study suggest that an effect of dietary phytoestrogen intake at low levels on plasma lipid levels is of limited magnitude. It is premature to advise postmenopausal women with low phytoestrogen intake to change their diet towards a phytoestrogen rich diet with the sole aim to prevent cardiovascular disease.

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.

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.

Designing for deeper learning in a blended computer science course for middle school students

NASA Astrophysics Data System (ADS)

Grover, Shuchi; Pea, Roy; Cooper, Stephen

2015-04-01

The focus of this research was to create and test an introductory computer science course for middle school. Titled "Foundations for Advancing Computational Thinking" (FACT), the course aims to prepare and motivate middle school learners for future engagement with algorithmic problem solving. FACT was also piloted as a seven-week course on Stanford's OpenEdX MOOC platform for blended in-class learning. Unique aspects of FACT include balanced pedagogical designs that address the cognitive, interpersonal, and intrapersonal aspects of "deeper learning"; a focus on pedagogical strategies for mediating and assessing for transfer from block-based to text-based programming; curricular materials for remedying misperceptions of computing; and "systems of assessments" (including formative and summative quizzes and tests, directed as well as open-ended programming assignments, and a transfer test) to get a comprehensive picture of students' deeper computational learning. Empirical investigations, accomplished over two iterations of a design-based research effort with students (aged 11-14 years) in a public school, sought to examine student understanding of algorithmic constructs, and how well students transferred this learning from Scratch to text-based languages. Changes in student perceptions of computing as a discipline were measured. Results and mixed-method analyses revealed that students in both studies (1) achieved substantial learning gains in algorithmic thinking skills, (2) were able to transfer their learning from Scratch to a text-based programming context, and (3) achieved significant growth toward a more mature understanding of computing as a discipline. Factor analyses of prior computing experience, multivariate regression analyses, and qualitative analyses of student projects and artifact-based interviews were conducted to better understand the factors affecting learning outcomes. Prior computing experiences (as measured by a pretest) and math ability were found to be strong predictors of learning outcomes.

Network structure and travel time perception.

PubMed

Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

2013-01-01

The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time.

Key factors contributing to accident severity rate in construction industry in Iran: a regression modelling approach.

PubMed

Soltanzadeh, Ahmad; Mohammadfam, Iraj; Moghimbeigi, Abbas; Ghiasvand, Reza

2016-03-01

Construction industry involves the highest risk of occupational accidents and bodily injuries, which range from mild to very severe. The aim of this cross-sectional study was to identify the factors associated with accident severity rate (ASR) in the largest Iranian construction companies based on data about 500 occupational accidents recorded from 2009 to 2013. We also gathered data on safety and health risk management and training systems. Data were analysed using Pearson's chi-squared coefficient and multiple regression analysis. Median ASR (and the interquartile range) was 107.50 (57.24- 381.25). Fourteen of the 24 studied factors stood out as most affecting construction accident severity (p<0.05). These findings can be applied in the design and implementation of a comprehensive safety and health risk management system to reduce ASR.

Assessment of wetland productive capacity from a remote-sensing-based model - A NASA/NMFS joint research project

NASA Technical Reports Server (NTRS)

Butera, M. K.; Frick, A. L.; Browder, J.

1983-01-01

NASA and the U.S. National Marine Fisheries Service have undertaken the development of Landsat Thematic Mapper (TM) technology for the evaluation of the usefulness of wetlands to estuarine fish and shellfish production. Toward this end, a remote sensing-based Productive Capacity model has been developed which characterizes the biological and hydrographic features of a Gulf Coast Marsh to predict detrital export. Regression analyses of TM simulator data for wetland plant production estimation are noted to more accurately estimate the percent of total vegetative cover than biomass, indicating that a nonlinear relationship may be involved.

Interannual drought index variations in Central Europe related to the large-scale atmospheric circulation—application and evaluation of statistical downscaling approaches based on circulation type classifications

NASA Astrophysics Data System (ADS)

Beck, Christoph; Philipp, Andreas; Jacobeit, Jucundus

2015-08-01

This contribution investigates the relationship between the large-scale atmospheric circulation and interannual variations of the standardized precipitation index (SPI) in Central Europe. To this end, circulation types (CT) have been derived from a variety of circulation type classifications (CTC) applied to daily sea level pressure (SLP) data and mean circulation indices of vorticity ( V), zonality ( Z) and meridionality ( M) have been calculated. Occurrence frequencies of CTs and circulation indices have been utilized as predictors within multiple regression models (MRM) for the estimation of gridded 3-month SPI values over Central Europe, for the period 1950 to 2010. CTC-based MRMs used in the analyses comprise variants concerning the basic method for CT classification, the number of CTs, the size and location of the spatial domain used for CTCs and the exclusive use of CT frequencies or the combined use of CT frequencies and mean circulation indices as predictors. Adequate MRM predictor combinations have been identified by applying stepwise multiple regression analyses within a resampling framework. The performance (robustness) of the resulting MRMs has been quantified based on a leave-one-out cross-validation procedure applying several skill scores. Furthermore, the relative importance of individual predictors has been estimated for each MRM. From these analyses, it can be stated that model skill is improved by (i) the consideration of vorticity characteristics within CTCs, (ii) a relatively small size of the spatial domain to which CTCs are applied and (iii) the inclusion of mean circulation indices. However, model skill exhibits distinct variations between seasons and regions. Whereas promising skill can be stated for the western and northwestern parts of the Central European domain, only unsatisfactory skill is reached in the more continental regions and particularly during summer. Thus, it can be concluded that the presented approaches feature the potential for the downscaling of Central European drought index variations from the large-scale circulation, at least for some regions. Further improvements of CTC-based approaches may be expected from the optimization of CTCs for explaining the SPI, e.g. via the inclusion of additional variables in the classification procedure.

Techniques for estimating flood-peak discharges of rural, unregulated streams in Ohio

USGS Publications Warehouse

Koltun, G.F.

2003-01-01

Regional equations for estimating 2-, 5-, 10-, 25-, 50-, 100-, and 500-year flood-peak discharges at ungaged sites on rural, unregulated streams in Ohio were developed by means of ordinary and generalized least-squares (GLS) regression techniques. One-variable, simple equations and three-variable, full-model equations were developed on the basis of selected basin characteristics and flood-frequency estimates determined for 305 streamflow-gaging stations in Ohio and adjacent states. The average standard errors of prediction ranged from about 39 to 49 percent for the simple equations, and from about 34 to 41 percent for the full-model equations. Flood-frequency estimates determined by means of log-Pearson Type III analyses are reported along with weighted flood-frequency estimates, computed as a function of the log-Pearson Type III estimates and the regression estimates. Values of explanatory variables used in the regression models were determined from digital spatial data sets by means of a geographic information system (GIS), with the exception of drainage area, which was determined by digitizing the area within basin boundaries manually delineated on topographic maps. Use of GIS-based explanatory variables represents a major departure in methodology from that described in previous reports on estimating flood-frequency characteristics of Ohio streams. Examples are presented illustrating application of the regression equations to ungaged sites on ungaged and gaged streams. A method is provided to adjust regression estimates for ungaged sites by use of weighted and regression estimates for a gaged site on the same stream. A region-of-influence method, which employs a computer program to estimate flood-frequency characteristics for ungaged sites based on data from gaged sites with similar characteristics, was also tested and compared to the GLS full-model equations. For all recurrence intervals, the GLS full-model equations had superior prediction accuracy relative to the simple equations and therefore are recommended for use.

Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods

NASA Astrophysics Data System (ADS)

Erener, Arzu; Sivas, A. Abdullah; Selcuk-Kestel, A. Sevtap; Düzgün, H. Sebnem

2017-07-01

All of the quantitative landslide susceptibility mapping (QLSM) methods requires two basic data types, namely, landslide inventory and factors that influence landslide occurrence (landslide influencing factors, LIF). Depending on type of landslides, nature of triggers and LIF, accuracy of the QLSM methods differs. Moreover, how to balance the number of 0 (nonoccurrence) and 1 (occurrence) in the training set obtained from the landslide inventory and how to select which one of the 1's and 0's to be included in QLSM models play critical role in the accuracy of the QLSM. Although performance of various QLSM methods is largely investigated in the literature, the challenge of training set construction is not adequately investigated for the QLSM methods. In order to tackle this challenge, in this study three different training set selection strategies along with the original data set is used for testing the performance of three different regression methods namely Logistic Regression (LR), Bayesian Logistic Regression (BLR) and Fuzzy Logistic Regression (FLR). The first sampling strategy is proportional random sampling (PRS), which takes into account a weighted selection of landslide occurrences in the sample set. The second method, namely non-selective nearby sampling (NNS), includes randomly selected sites and their surrounding neighboring points at certain preselected distances to include the impact of clustering. Selective nearby sampling (SNS) is the third method, which concentrates on the group of 1's and their surrounding neighborhood. A randomly selected group of landslide sites and their neighborhood are considered in the analyses similar to NNS parameters. It is found that LR-PRS, FLR-PRS and BLR-Whole Data set-ups, with order, yield the best fits among the other alternatives. The results indicate that in QLSM based on regression models, avoidance of spatial correlation in the data set is critical for the model's performance.

Identification of Key Beliefs Explaining Male Circumcision Motivation Among Adolescent Boys in Zimbabwe: Targets for Behavior Change Communication.

PubMed

Kasprzyk, Danuta; Tshimanga, Mufuta; Hamilton, Deven T; Gorn, Gerald J; Montaño, Daniel E

2018-02-01

Male circumcision (MC) significantly reduces HIV acquisition among men, leading WHO/UNAIDS to recommend high HIV and low MC prevalence countries circumcise 80% of adolescents and men age 15-49. Despite significant investment to increase MC capacity only 27% of the goal has been achieved in Zimbabwe. To increase adoption, research to create evidence-based messages is greatly needed. The Integrated Behavioral Model (IBM) was used to investigate factors affecting MC motivation among adolescents. Based on qualitative elicitation study results a survey was designed and administered to a representative sample of 802 adolescent boys aged 13-17 in two urban and two rural areas in Zimbabwe. Multiple regression analysis found all six IBM constructs (2 attitude, 2 social influence, 2 personal agency) significantly explained MC intention (R 2  = 0.55). Stepwise regression analysis of beliefs underlying each IBM belief-based construct found 9 behavioral, 6 injunctive norm, 2 descriptive norm, 5 efficacy, and 8 control beliefs significantly explained MC intention. A final stepwise regression of all the significant IBM construct beliefs identified 12 key beliefs best explaining intention. Similar analyses were carried out with subgroups of adolescents by urban-rural and age. Different sets of behavioral, normative, efficacy, and control beliefs were significant for each sub-group. This study demonstrates the application of theory-driven research to identify evidence-based targets for the design of effective MC messages for interventions to increase adolescents' motivation. Incorporating these findings into communication campaigns is likely to improve demand for MC.

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.

Retooling CalEnviroScreen: Cumulative Pollution Burden and Race-Based Environmental Health Vulnerabilities in California.

PubMed

Liévanos, Raoul S

2018-04-16

The California Community Environmental Health Screening Tool (CalEnviroScreen) advances research and policy pertaining to environmental health vulnerability. However, CalEnviroScreen departs from its historical foundations and comparable screening tools by no longer considering racial status as an indicator of environmental health vulnerability and predictor of cumulative pollution burden. This study used conceptual frameworks and analytical techniques from environmental health and inequality literature to address the limitations of CalEnviroScreen, especially its inattention to race-based environmental health vulnerabilities. It developed an adjusted measure of cumulative pollution burden from the CalEnviroScreen 2.0 data that facilitates multivariate analyses of the effect of neighborhood racial composition on cumulative pollution burden, net of other indicators of population vulnerability, traffic density, industrial zoning, and local and regional clustering of pollution burden. Principal component analyses produced three new measures of population vulnerability, including Latina/o cumulative disadvantage that represents the spatial concentration of Latinas/os, economic disadvantage, limited English-speaking ability, and health vulnerability. Spatial error regression analyses demonstrated that concentrations of Latinas/os, followed by Latina/o cumulative disadvantage, are the strongest demographic determinants of adjusted cumulative pollution burden. Findings have implications for research and policy pertaining to cumulative impacts and race-based environmental health vulnerabilities within and beyond California.

Retooling CalEnviroScreen: Cumulative Pollution Burden and Race-Based Environmental Health Vulnerabilities in California

PubMed Central

2018-01-01

The California Community Environmental Health Screening Tool (CalEnviroScreen) advances research and policy pertaining to environmental health vulnerability. However, CalEnviroScreen departs from its historical foundations and comparable screening tools by no longer considering racial status as an indicator of environmental health vulnerability and predictor of cumulative pollution burden. This study used conceptual frameworks and analytical techniques from environmental health and inequality literature to address the limitations of CalEnviroScreen, especially its inattention to race-based environmental health vulnerabilities. It developed an adjusted measure of cumulative pollution burden from the CalEnviroScreen 2.0 data that facilitates multivariate analyses of the effect of neighborhood racial composition on cumulative pollution burden, net of other indicators of population vulnerability, traffic density, industrial zoning, and local and regional clustering of pollution burden. Principal component analyses produced three new measures of population vulnerability, including Latina/o cumulative disadvantage that represents the spatial concentration of Latinas/os, economic disadvantage, limited English-speaking ability, and health vulnerability. Spatial error regression analyses demonstrated that concentrations of Latinas/os, followed by Latina/o cumulative disadvantage, are the strongest demographic determinants of adjusted cumulative pollution burden. Findings have implications for research and policy pertaining to cumulative impacts and race-based environmental health vulnerabilities within and beyond California. PMID:29659481

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...

A new predictive indicator for development of pressure ulcers in bedridden patients based on common laboratory tests results.

PubMed

Hatanaka, N; Yamamoto, Y; Ichihara, K; Mastuo, S; Nakamura, Y; Watanabe, M; Iwatani, Y

2008-04-01

Various scales have been devised to predict development of pressure ulcers on the basis of clinical and laboratory data, such as the Braden Scale (Braden score), which is used to monitor activity and skin conditions of bedridden patients. However, none of these scales facilitates clinically reliable prediction. To develop a clinical laboratory data-based predictive equation for the development of pressure ulcers. Subjects were 149 hospitalised patients with respiratory disorders who were monitored for the development of pressure ulcers over a 3-month period. The proportional hazards model (Cox regression) was used to analyse the results of 12 basic laboratory tests on the day of hospitalisation in comparison with Braden score. Pressure ulcers developed in 38 patients within the study period. A Cox regression model consisting solely of Braden scale items showed that none of these items contributed to significantly predicting pressure ulcers. Rather, a combination of haemoglobin (Hb), C-reactive protein (CRP), albumin (Alb), age, and gender produced the best model for prediction. Using the set of explanatory variables, we created a new indicator based on a multiple logistic regression equation. The new indicator showed high sensitivity (0.73) and specificity (0.70), and its diagnostic power was higher than that of Alb, Hb, CRP, or the Braden score alone. The new indicator may become a more useful clinical tool for predicting presser ulcers than Braden score. The new indicator warrants verification studies to facilitate its clinical implementation in the future.

A robust and efficient stepwise regression method for building sparse polynomial chaos expansions

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

Abraham, Simon, E-mail: Simon.Abraham@ulb.ac.be; Raisee, Mehrdad; Ghorbaniasl, Ghader

2017-03-01

Polynomial Chaos (PC) expansions are widely used in various engineering fields for quantifying uncertainties arising from uncertain parameters. The computational cost of classical PC solution schemes is unaffordable as the number of deterministic simulations to be calculated grows dramatically with the number of stochastic dimension. This considerably restricts the practical use of PC at the industrial level. A common approach to address such problems is to make use of sparse PC expansions. This paper presents a non-intrusive regression-based method for building sparse PC expansions. The most important PC contributions are detected sequentially through an automatic search procedure. The variable selectionmore » criterion is based on efficient tools relevant to probabilistic method. Two benchmark analytical functions are used to validate the proposed algorithm. The computational efficiency of the method is then illustrated by a more realistic CFD application, consisting of the non-deterministic flow around a transonic airfoil subject to geometrical uncertainties. To assess the performance of the developed methodology, a detailed comparison is made with the well established LAR-based selection technique. The results show that the developed sparse regression technique is able to identify the most significant PC contributions describing the problem. Moreover, the most important stochastic features are captured at a reduced computational cost compared to the LAR method. The results also demonstrate the superior robustness of the method by repeating the analyses using random experimental designs.« less

Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models.

PubMed

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models' with and without novel biomarkers. Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham's "general CVD risk" algorithm. The command is addpred for logistic regression models. The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers.

Environmental, Spatial, and Sociodemographic Factors Associated with Nonfatal Injuries in Indonesia.

PubMed

Irianti, Sri; Prasetyoputra, Puguh

2017-01-01

Background . The determinants of injuries and their reoccurrence in Indonesia are not well understood, despite their importance in the prevention of injuries. Therefore, this study seeks to investigate the environmental, spatial, and sociodemographic factors associated with the reoccurrence of injuries among Indonesian people. Methods . Data from the 2013 round of the Indonesia Baseline Health Research (IBHR 2013) were analysed using a two-part hurdle regression model. A logit regression model was chosen for the zero-hurdle part , while a zero-truncated negative binomial regression model was selected for the counts part . Odds ratio (OR) and incidence rate ratio (IRR) were the measures of association, respectively. Results . The results suggest that living in a household with distant drinking water source, residing in slum areas, residing in Eastern Indonesia, having low educational attainment, being men, and being poorer are positively related to the likelihood of experiencing injury. Moreover, being a farmer or fishermen, having low educational attainment, and being men are positively associated with the frequency of injuries. Conclusion . This study would be useful to prioritise injury prevention programs in Indonesia based on the environmental, spatial, and sociodemographic characteristics.

Short- and long-term theory-based predictors of physical activity in women who participated in a weight-management program.

PubMed

Wasserkampf, A; Silva, M N; Santos, I C; Carraça, E V; Meis, J J M; Kremers, S P J; Teixeira, P J

2014-12-01

This study analyzed psychosocial predictors of the Theory of Planned Behavior (TPB) and Self-Determination Theory (SDT) and evaluated their associations with short- and long-term moderate plus vigorous physical activity (MVPA) and lifestyle physical activity (PA) outcomes in women who underwent a weight-management program. 221 participants (age 37.6 ± 7.02 years) completed a 12-month SDT-based lifestyle intervention and were followed-up for 24 months. Multiple linear regression analyses tested associations between psychosocial variables and self-reported short- and long-term PA outcomes. Regression analyses showed that control constructs of both theories were significant determinants of short- and long-term MVPA, whereas affective and self-determination variables were strong predictors of short- and long-term lifestyle PA. Regarding short-term prediction models, TPB constructs were stronger in predicting MVPA, whereas SDT was more effective in predicting lifestyle PA. For long-term models, both forms of PA were better predicted by SDT in comparison to TPB. These results highlight the importance of comparing health behavior theories to identify the mechanisms involved in the behavior change process. Control and competence constructs are crucial during early adoption of structured PA behaviors, whereas affective and intrinsic sources of motivation are more involved in incidental types of PA, particularly in relation to behavioral maintenance. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Risk of Peripheral Artery Occlusive Disease in Patients with Vertigo, Tinnitus, or Sudden Deafness: A Secondary Case-Control Analysis of a Nationwide, Population-Based Health Claims Database.

PubMed

Koo, Malcolm; Chen, Jin-Cherng; Hwang, Juen-Haur

2016-01-01

Cochleovestibular symptoms, such as vertigo, tinnitus, and sudden deafness, are common manifestations of microvascular diseases. However, it is unclear whether these symptoms occurred preceding the diagnosis of peripheral artery occlusive disease (PAOD). Therefore, the aim of this case-control study was to investigate the risk of PAOD among patients with vertigo, tinnitus, and sudden deafness using a nationwide, population-based health claim database in Taiwan. We identified 5,340 adult patients with PAOD diagnosed between January 1, 2006 and December 31, 2010 and 16,020 controls, frequency matched on age interval, sex, and year of index date, from the Taiwan National Health Insurance Research Database. Risks of PAOD in patients with vertigo, tinnitus, or sudden deafness were separately evaluated with multivariate logistic regression analyses. Of the 5,340 patients with PAOD, 12.7%, 6.7%, and 0.3% were diagnosed with vertigo, tinnitus, and sudden deafness, respectively. In the controls, 10.6%, 6.1%, and 0.3% were diagnosed with vertigo (P < 0.001), tinnitus (P = 0.161), and sudden deafness (P = 0.774), respectively. Results from the multivariate logistic regression analyses showed that the risk of PAOD was significantly increased in patients with vertigo (adjusted odds ratio = 1.12, P = 0.027) but not in those with tinnitus or sudden deafness. A modest increase in the risk of PAOD was observed among Taiwanese patients with vertigo, after adjustment for comorbidities.

Probabilistic modeling of percutaneous absorption for risk-based exposure assessments and transdermal drug delivery.

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

Ho, Clifford Kuofei

Chemical transport through human skin can play a significant role in human exposure to toxic chemicals in the workplace, as well as to chemical/biological warfare agents in the battlefield. The viability of transdermal drug delivery also relies on chemical transport processes through the skin. Models of percutaneous absorption are needed for risk-based exposure assessments and drug-delivery analyses, but previous mechanistic models have been largely deterministic. A probabilistic, transient, three-phase model of percutaneous absorption of chemicals has been developed to assess the relative importance of uncertain parameters and processes that may be important to risk-based assessments. Penetration routes through the skinmore » that were modeled include the following: (1) intercellular diffusion through the multiphase stratum corneum; (2) aqueous-phase diffusion through sweat ducts; and (3) oil-phase diffusion through hair follicles. Uncertainty distributions were developed for the model parameters, and a Monte Carlo analysis was performed to simulate probability distributions of mass fluxes through each of the routes. Sensitivity analyses using stepwise linear regression were also performed to identify model parameters that were most important to the simulated mass fluxes at different times. This probabilistic analysis of percutaneous absorption (PAPA) method has been developed to improve risk-based exposure assessments and transdermal drug-delivery analyses, where parameters and processes can be highly uncertain.« less

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.

Is complex allometry in field metabolic rates of mammals a statistical artifact?

PubMed

Packard, Gary C

2017-01-01

Recent reports indicate that field metabolic rates (FMRs) of mammals conform to a pattern of complex allometry in which the exponent in a simple, two-parameter power equation increases steadily as a dependent function of body mass. The reports were based, however, on indirect analyses performed on logarithmic transformations of the original data. I re-examined values for FMR and body mass for 114 species of mammal by the conventional approach to allometric analysis (to illustrate why the approach is unreliable) and by linear and nonlinear regression on untransformed variables (to illustrate the power and versatility of newer analytical methods). The best of the regression models fitted directly to untransformed observations is a three-parameter power equation with multiplicative, lognormal, heteroscedastic error and an allometric exponent of 0.82. The mean function is a good fit to data in graphical display. The significant intercept in the model may simply have gone undetected in prior analyses because conventional allometry assumes implicitly that the intercept is zero; or the intercept may be a spurious finding resulting from bias introduced by the haphazard sampling that underlies "exploratory" analyses like the one reported here. The aforementioned issues can be resolved only by gathering new data specifically intended to address the question of scaling of FMR with body mass in mammals. However, there is no support for the concept of complex allometry in the relationship between FMR and body size in mammals. Copyright © 2016 Elsevier Inc. All rights reserved.

Evaluating exposures to complex mixtures of chemicals during a new production process in the plastics industry.

PubMed

Meijster, Tim; Burstyn, Igor; Van Wendel De Joode, Berna; Posthumus, Maarten A; Kromhout, Hans

2004-08-01

The goal of this study was to monitor emission of chemicals at a factory where plastics products were fabricated by a new robotic (impregnated tape winding) production process. Stationary and personal air measurements were taken to determine which chemicals were released and at what concentrations. Principal component analyses (PCA) and linear regression were used to determine the emission sources of different chemicals found in the air samples. We showed that complex mixtures of chemicals were released, but most concentrations were below Dutch exposure limits. Based on the results of the principal component analyses, the chemicals found were divided into three groups. The first group consisted of short chain aliphatic hydrocarbons (C2-C6). The second group included larger hydrocarbons (C9-C11) and some cyclic hydrocarbons. The third group contained all aromatic and two aliphatic hydrocarbons. Regression analyses showed that emission of the first group of chemicals was associated with cleaning activities and the use of epoxy resins. The second and third group showed strong association with the type of tape used in the new tape winding process. High levels of CO and HCN (above exposure limits) were measured on one occasion when a different brand of impregnated polypropylene sulphide tape was used in the tape winding process. Plans exist to drastically increase production with the new tape winding process. This will cause exposure levels to rise and therefore further control measures should be installed to reduce release of these chemicals.

The influence of endometriosis-related symptoms on work life and work ability: a study of Danish endometriosis patients in employment.

PubMed

Hansen, Karina E; Kesmodel, Ulrik S; Baldursson, Einar B; Schultz, Rikke; Forman, Axel

2013-07-01

Little is known about the implications of endometriosis on women's work life. This study aimed at examining the relation between endometriosis-related symptoms and work ability in employed women with endometriosis. In a cohort study, 610 patients with diagnosed endometriosis and 751 reference women completed an electronic survey based on the Endometriosis Health Profile 30-questionnaire and the Work Ability Index (short form). Percentages were reported for all data. Binary and multivariate logistic regression analyses were used to assess risk factors for low work ability. The level of statistical significance was set at p<0.025 in all analyses. In binary analyses a diagnosis of endometriosis was associated with more sick days, work disturbances due to symptoms, lower work ability and a wide number of other implications on work life in employed women. Moreover, a higher pain level and degree of symptoms were associated with low work ability. Full regression analysis indicated that tiredness, frequent pain, a higher daily pain level, a higher number of sick days and feeling depressed at work were associated with low work ability. A long delay from symptom onset to diagnosis was associated with low work ability. These data indicate a severe impact of endometriosis on the work ability of employed women with endometriosis and add to the evidence that this disease represents a significant socio-economic burden. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Regional Regression Equations to Estimate Flow-Duration Statistics at Ungaged Stream Sites in Connecticut

USGS Publications Warehouse

Ahearn, Elizabeth A.

2010-01-01

Multiple linear regression equations for determining flow-duration statistics were developed to estimate select flow exceedances ranging from 25- to 99-percent for six 'bioperiods'-Salmonid Spawning (November), Overwinter (December-February), Habitat Forming (March-April), Clupeid Spawning (May), Resident Spawning (June), and Rearing and Growth (July-October)-in Connecticut. Regression equations also were developed to estimate the 25- and 99-percent flow exceedances without reference to a bioperiod. In total, 32 equations were developed. The predictive equations were based on regression analyses relating flow statistics from streamgages to GIS-determined basin and climatic characteristics for the drainage areas of those streamgages. Thirty-nine streamgages (and an additional 6 short-term streamgages and 28 partial-record sites for the non-bioperiod 99-percent exceedance) in Connecticut and adjacent areas of neighboring States were used in the regression analysis. Weighted least squares regression analysis was used to determine the predictive equations; weights were assigned based on record length. The basin characteristics-drainage area, percentage of area with coarse-grained stratified deposits, percentage of area with wetlands, mean monthly precipitation (November), mean seasonal precipitation (December, January, and February), and mean basin elevation-are used as explanatory variables in the equations. Standard errors of estimate of the 32 equations ranged from 10.7 to 156 percent with medians of 19.2 and 55.4 percent to predict the 25- and 99-percent exceedances, respectively. Regression equations to estimate high and median flows (25- to 75-percent exceedances) are better predictors (smaller variability of the residual values around the regression line) than the equations to estimate low flows (less than 75-percent exceedance). The Habitat Forming (March-April) bioperiod had the smallest standard errors of estimate, ranging from 10.7 to 20.9 percent. In contrast, the Rearing and Growth (July-October) bioperiod had the largest standard errors, ranging from 30.9 to 156 percent. The adjusted coefficient of determination of the equations ranged from 77.5 to 99.4 percent with medians of 98.5 and 90.6 percent to predict the 25- and 99-percent exceedances, respectively. Descriptive information on the streamgages used in the regression, measured basin and climatic characteristics, and estimated flow-duration statistics are provided in this report. Flow-duration statistics and the 32 regression equations for estimating flow-duration statistics in Connecticut are stored on the U.S. Geological Survey World Wide Web application ?StreamStats? (http://water.usgs.gov/osw/streamstats/index.html). The regression equations developed in this report can be used to produce unbiased estimates of select flow exceedances statewide.

Associations Between Night Work and Anxiety, Depression, Insomnia, Sleepiness and Fatigue in a Sample of Norwegian Nurses

PubMed Central

Øyane, Nicolas M. F.; Pallesen, Ståle; Moen, Bente Elisabeth; Åkerstedt, Torbjörn; Bjorvatn, Bjørn

2013-01-01

Background Night work has been reported to be associated with various mental disorders and complaints. We investigated relationships between night work and anxiety, depression, insomnia, sleepiness and fatigue among Norwegian nurses. Methods The study design was cross-sectional, based on validated self-assessment questionnaires. A total of 5400 nurses were invited to participate in a health survey through the Norwegian Nurses' Organization, whereof 2059 agreed to participate (response rate 38.1%). Nurses completed a questionnaire containing items on demographic variables (gender, age, years of experience as a nurse, marital status and children living at home), work schedule, anxiety/depression (Hospital Anxiety and Depression Scale), insomnia (Bergen Insomnia Scale), sleepiness (Epworth Sleepiness Scale) and fatigue (Fatigue Questionnaire). They were also asked to report number of night shifts in the last 12 months (NNL). First, the parameters were compared between nurses i) never working nights, ii) currently working nights, and iii) previously working nights, using binary logistic regression analyses. Subsequently, a cumulative approach was used investigating associations between NNL with the continuous scores on the same dependent variables in hierarchical multiple regression analyses. Results Nurses with current night work were more often categorized with insomnia (OR = 1.48, 95% CI = 1.10–1.99) and chronic fatigue (OR = 1.78, 95% CI = 1.02–3.11) than nurses with no night work experience. Previous night work experience was also associated with insomnia (OR = 1.45, 95% CI = 1.04–2.02). NNL was not associated with any parameters in the regression analyses. Conclusion Nurses with current or previous night work reported more insomnia than nurses without any night work experience, and current night work was also associated with chronic fatigue. Anxiety, depression and sleepiness were not associated with night work, and no cumulative effect of night shifts during the last 12 months was found on any parameters. PMID:23950914

The Campus Demotechnic Index: A comparison of technological energy consumption at U.S. colleges and universities

NASA Astrophysics Data System (ADS)

Vance, Leisha Ann

The Campus Demotechnic Index (CDI) is a normalized metric developed to provide universities with a method for tracking progress toward or retreat from sustainability in their energy consumption. The CDI is modified after the Demotechnic Index of Mata et al. (1994). CDI values assess the total campus energy consumed against the total energy required to meet the campus population's basal metabolism. Like the D-Index, the CDI is thus a measure of the scalar quantity of energy consumed in excess of the quantity of energy required for simple survival on a per capita basis. For this research, data were collected from an on-line survey designed for U.S. colleges and universities, which requested information related to campus demographics and campus built and mobile environmental energy consumption. Data were requested for the years of 2000 to 2005. Wilcoxon signed rank test analyses were conducted to determine if CDI values significantly increased over time. ANOVAs, GLMs, correlations and regressions were conducted to determine if climate and campus size significantly influenced CDI. ANOVAs, correlations and regressions were conducted to determine the effect of acreage on mobile fuel consumption and to ascertain whether differing proportions between the built and mobile environments significantly influenced CDI values. Correlations and regressions were carried out to which variables best predicted CDI, and cluster analyses were conducted to find out if any significant groups existed based on CDI values, fossil fuel consumption and population per square foot. The knowledge gained from results of these analyses not only provides a depiction of campus energy consumption, but also puts campus energy consumption into context in that CDI scores allow peer institutional comparisons. Awareness of factors that contribute to campus energy use (and CDI ranks) could also facilitate prioritization of sustainability-related issues, as well as the design and establishment of sustainable management systems.

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.

Allometry of sexual size dimorphism in turtles: a comparison of mass and length data.

PubMed

Regis, Koy W; Meik, Jesse M

2017-01-01

The macroevolutionary pattern of Rensch's Rule (positive allometry of sexual size dimorphism) has had mixed support in turtles. Using the largest carapace length dataset and only large-scale body mass dataset assembled for this group, we determine (a) whether turtles conform to Rensch's Rule at the order, suborder, and family levels, and (b) whether inferences regarding allometry of sexual size dimorphism differ based on choice of body size metric used for analyses. We compiled databases of mean body mass and carapace length for males and females for as many populations and species of turtles as possible. We then determined scaling relationships between males and females for average body mass and straight carapace length using traditional and phylogenetic comparative methods. We also used regression analyses to evalutate sex-specific differences in the variance explained by carapace length on body mass. Using traditional (non-phylogenetic) analyses, body mass supports Rensch's Rule, whereas straight carapace length supports isometry. Using phylogenetic independent contrasts, both body mass and straight carapace length support Rensch's Rule with strong congruence between metrics. At the family level, support for Rensch's Rule is more frequent when mass is used and in phylogenetic comparative analyses. Turtles do not differ in slopes of sex-specific mass-to-length regressions and more variance in body size within each sex is explained by mass than by carapace length. Turtles display Rensch's Rule overall and within families of Cryptodires, but not within Pleurodire families. Mass and length are strongly congruent with respect to Rensch's Rule across turtles, and discrepancies are observed mostly at the family level (the level where Rensch's Rule is most often evaluated). At macroevolutionary scales, the purported advantages of length measurements over weight are not supported in turtles.

The Association of Albuminuria With Tubular Reabsorption of Uric Acid: Results From a General Population Cohort

PubMed Central

Scheven, Lieneke; Joosten, Michel M.; de Jong, Paul E.; Bakker, Stephan J. L.; Gansevoort, Ron T.

2014-01-01

Background Elevated albuminuria as well as an increased serum uric acid concentration is associated with poor cardiovascular outcome. We questioned whether these 2 variables (albuminuria and serum uric concentration) may be interrelated via tubular uric acid reabsorption. Methods and Results Included were 7688 participants of the PREVEND Study, an observational, general population‐based cohort study. Linear regression analyses were used to test associations of baseline albuminuria with baseline serum uric acid concentration and tubular uric acid reabsorption (calculated as [100−fractional uric acid excretion]%). Cox regression analyses were used to study the association of baseline serum uric acid and albuminuria with incident cardiovascular morbidity and mortality. In cross‐sectional analyses, albuminuria was associated positively with serum uric acid concentration, both crude and after adjustment for potential confounders (both P<0.001). Albuminuria was found to be associated positively with tubular uric acid reabsorption, again both crude and after adjustment for potential confounders (both P<0.001). In longitudinal analyses during a median follow‐up of 10.5 years, 702 cardiovascular events occurred. After adjusting for cardiovascular risk factors, both albuminuria and serum uric acid were associated with incident cardiovascular events (Hazard Ratios 1.09 [1.03 to 1.17], P=0.01 and 1.19 [1.09 to 1.30], P<0.001, respectively). A significant interaction between these variables was present (P<0.001), consistent with high serum uric acid being less predictive for cardiovascular morbidity and mortality in the presence of high albuminuria and vice versa. Conclusions Albuminuria is strongly associated with tubular uric acid reabsorption, and consequently with serum uric acid concentration. This phenomenon may explain in part why albuminuria is associated with cardiovascular outcome. PMID:24772520

Factors associated with the practice of nursing staff sharing information about patients' nutritional status with their colleagues in hospitals.

PubMed

Kawasaki, Y; Tamaura, Y; Akamatsu, R; Sakai, M; Fujiwara, K

2018-01-01

Nursing staff have an important role in patients' nutritional care. The aim of this study was to demonstrate how the practice of sharing a patient's nutritional status with colleagues was affected by the nursing staff's attitude, knowledge and their priority to provide nutritional care. The participants were 492 nursing staff. We obtained participants' demographic data, the practice of sharing patients' nutritional information and information about participants' knowledge, attitude and priority of providing nutritional care by the questionnaire. We performed partial correlation analyses and linear regression analyses to describe the relationship between the total scores of the practice of sharing patients' nutritional information based on their knowledge, attitude and priority to provide nutritional care. Among the 492 participants, 396 nursing staff (80.5%) completed the questionnaire and were included in analyses. Mean±s.d. of total score of the 396 participants was 8.4±3.1. Nursing staff shared information when they had a high nutritional knowledge (r=0.36, P<0.01) and attitude (r=0.13, P<0.05); however, their correlation coefficients were low. In the linear regression analyses, job categories (β=-0.28, P<0.01), knowledge (β=0.33, P<0.01) and attitude (β=0.10, P<0.05) were independently associated with the practice of sharing information. Nursing staff's priority to provide nutritional care practice was not significantly associated with the practice of sharing information. Knowledge and attitude were independently associated with the practice of sharing patients' nutrition information with colleagues, regardless of their priority to provide nutritional care. An effective approach should be taken to improve the practice of providing nutritional care practice.

Effectiveness of a worksite mindfulness-based multi-component intervention on lifestyle behaviors

PubMed Central

2014-01-01

Introduction Overweight and obesity are associated with an increased risk of morbidity. Mindfulness training could be an effective strategy to optimize lifestyle behaviors related to body weight gain. The aim of this study was to evaluate the effectiveness of a worksite mindfulness-based multi-component intervention on vigorous physical activity in leisure time, sedentary behavior at work, fruit intake and determinants of these behaviors. The control group received information on existing lifestyle behavior- related facilities that were already available at the worksite. Methods In a randomized controlled trial design (n = 257), 129 workers received a mindfulness training, followed by e-coaching, lunch walking routes and fruit. Outcome measures were assessed at baseline and after 6 and 12 months using questionnaires. Physical activity was also measured using accelerometers. Effects were analyzed using linear mixed effect models according to the intention-to-treat principle. Linear regression models (complete case analyses) were used as sensitivity analyses. Results There were no significant differences in lifestyle behaviors and determinants of these behaviors between the intervention and control group after 6 or 12 months. The sensitivity analyses showed effect modification for gender in sedentary behavior at work at 6-month follow-up, although the main analyses did not. Conclusions This study did not show an effect of a worksite mindfulness-based multi-component intervention on lifestyle behaviors and behavioral determinants after 6 and 12 months. The effectiveness of a worksite mindfulness-based multi-component intervention as a health promotion intervention for all workers could not be established. PMID:24467802

Modelling nitrate pollution pressure using a multivariate statistical approach: the case of Kinshasa groundwater body, Democratic Republic of Congo

NASA Astrophysics Data System (ADS)

Mfumu Kihumba, Antoine; Ndembo Longo, Jean; Vanclooster, Marnik

2016-03-01

A multivariate statistical modelling approach was applied to explain the anthropogenic pressure of nitrate pollution on the Kinshasa groundwater body (Democratic Republic of Congo). Multiple regression and regression tree models were compared and used to identify major environmental factors that control the groundwater nitrate concentration in this region. The analyses were made in terms of physical attributes related to the topography, land use, geology and hydrogeology in the capture zone of different groundwater sampling stations. For the nitrate data, groundwater datasets from two different surveys were used. The statistical models identified the topography, the residential area, the service land (cemetery), and the surface-water land-use classes as major factors explaining nitrate occurrence in the groundwater. Also, groundwater nitrate pollution depends not on one single factor but on the combined influence of factors representing nitrogen loading sources and aquifer susceptibility characteristics. The groundwater nitrate pressure was better predicted with the regression tree model than with the multiple regression model. Furthermore, the results elucidated the sensitivity of the model performance towards the method of delineation of the capture zones. For pollution modelling at the monitoring points, therefore, it is better to identify capture-zone shapes based on a conceptual hydrogeological model rather than to adopt arbitrary circular capture zones.

Performance on the adult rheumatology in-training examination and relationship to outcomes on the rheumatology certification examination.

PubMed

Lohr, Kristine M; Clauser, Amanda; Hess, Brian J; Gelber, Allan C; Valeriano-Marcet, Joanne; Lipner, Rebecca S; Haist, Steven A; Hawley, Janine L; Zirkle, Sarah; Bolster, Marcy B

2015-11-01

The American College of Rheumatology (ACR) Adult Rheumatology In-Training Examination (ITE) is a feedback tool designed to identify strengths and weaknesses in the content knowledge of individual fellows-in-training and the training program curricula. We determined whether scores on the ACR ITE, as well as scores on other major standardized medical examinations and competency-based ratings, could be used to predict performance on the American Board of Internal Medicine (ABIM) Rheumatology Certification Examination. Between 2008 and 2012, 629 second-year fellows took the ACR ITE. Bivariate correlation analyses of assessment scores and multiple linear regression analyses were used to determine whether ABIM Rheumatology Certification Examination scores could be predicted on the basis of ACR ITE scores, United States Medical Licensing Examination scores, ABIM Internal Medicine Certification Examination scores, fellowship directors' ratings of overall clinical competency, and demographic variables. Logistic regression was used to evaluate whether these assessments were predictive of a passing outcome on the Rheumatology Certification Examination. In the initial linear model, the strongest predictors of the Rheumatology Certification Examination score were the second-year fellows' ACR ITE scores (β = 0.438) and ABIM Internal Medicine Certification Examination scores (β = 0.273). Using a stepwise model, the strongest predictors of higher scores on the Rheumatology Certification Examination were second-year fellows' ACR ITE scores (β = 0.449) and ABIM Internal Medicine Certification Examination scores (β = 0.276). Based on the findings of logistic regression analysis, ACR ITE performance was predictive of a pass/fail outcome on the Rheumatology Certification Examination (odds ratio 1.016 [95% confidence interval 1.011-1.021]). The predictive value of the ACR ITE score with regard to predicting performance on the Rheumatology Certification Examination supports use of the Adult Rheumatology ITE as a valid feedback tool during fellowship training. © 2015, American College of Rheumatology.

Preliminary Survey on TRY Forest Traits and Growth Index Relations - New Challenges

NASA Astrophysics Data System (ADS)

Lyubenova, Mariyana; Kattge, Jens; van Bodegom, Peter; Chikalanov, Alexandre; Popova, Silvia; Zlateva, Plamena; Peteva, Simona

2016-04-01

Forest ecosystems provide critical ecosystem goods and services, including food, fodder, water, shelter, nutrient cycling, and cultural and recreational value. Forests also store carbon, provide habitat for a wide range of species and help alleviate land degradation and desertification. Thus they have a potentially significant role to play in climate change adaptation planning through maintaining ecosystem services and providing livelihood options. Therefore the study of forest traits is such an important issue not just for individual countries but for the planet as a whole. We need to know what functional relations between forest traits exactly can express TRY data base and haw it will be significant for the global modeling and IPBES. The study of the biodiversity characteristics at all levels and functional links between them is extremely important for the selection of key indicators for assessing biodiversity and ecosystem services for sustainable natural capital control. By comparing the available information in tree data bases: TRY, ITR (International Tree Ring) and SP-PAM the 42 tree species are selected for the traits analyses. The dependence between location characteristics (latitude, longitude, altitude, annual precipitation, annual temperature and soil type) and forest traits (specific leaf area, leaf weight ratio, wood density and growth index) is studied by by multiply regression analyses (RDA) using the statistical software package Canoco 4.5. The Pearson correlation coefficient (measure of linear correlation), Kendal rank correlation coefficient (non parametric measure of statistical dependence) and Spearman correlation coefficient (monotonic function relationship between two variables) are calculated for each pair of variables (indexes) and species. After analysis of above mentioned correlation coefficients the dimensional linear regression models, multidimensional linear and nonlinear regression models and multidimensional neural networks models are built. The strongest dependence between It and WD was obtained. The research will support the work on: Strategic Plan for Biodiversity 2011-2020, modelling and implementation of ecosystem-based approaches to climate change adaptation and disaster risk reduction. Key words: Specific leaf area (SLA), Leaf weight ratio (LWR), Wood density (WD), Growth index (It)

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...

Regional regression equations for the estimation of selected monthly low-flow duration and frequency statistics at ungaged sites on streams in New Jersey

USGS Publications Warehouse

Watson, Kara M.; McHugh, Amy R.

2014-01-01

Regional regression equations were developed for estimating monthly flow-duration and monthly low-flow frequency statistics for ungaged streams in Coastal Plain and non-coastal regions of New Jersey for baseline and current land- and water-use conditions. The equations were developed to estimate 87 different streamflow statistics, which include the monthly 99-, 90-, 85-, 75-, 50-, and 25-percentile flow-durations of the minimum 1-day daily flow; the August–September 99-, 90-, and 75-percentile minimum 1-day daily flow; and the monthly 7-day, 10-year (M7D10Y) low-flow frequency. These 87 streamflow statistics were computed for 41 continuous-record streamflow-gaging stations (streamgages) with 20 or more years of record and 167 low-flow partial-record stations in New Jersey with 10 or more streamflow measurements. The regression analyses used to develop equations to estimate selected streamflow statistics were performed by testing the relation between flow-duration statistics and low-flow frequency statistics for 32 basin characteristics (physical characteristics, land use, surficial geology, and climate) at the 41 streamgages and 167 low-flow partial-record stations. The regression analyses determined drainage area, soil permeability, average April precipitation, average June precipitation, and percent storage (water bodies and wetlands) were the significant explanatory variables for estimating the selected flow-duration and low-flow frequency statistics. Streamflow estimates were computed for two land- and water-use conditions in New Jersey—land- and water-use during the baseline period of record (defined as the years a streamgage had little to no change in development and water use) and current land- and water-use conditions (1989–2008)—for each selected station using data collected through water year 2008. The baseline period of record is representative of a period when the basin was unaffected by change in development. The current period is representative of the increased development of the last 20 years (1989–2008). The two different land- and water-use conditions were used as surrogates for development to determine whether there have been changes in low-flow statistics as a result of changes in development over time. The State was divided into two low-flow regression regions, the Coastal Plain and the non-coastal region, in order to improve the accuracy of the regression equations. The left-censored parametric survival regression method was used for the analyses to account for streamgages and partial-record stations that had zero flow values for some of the statistics. The average standard error of estimate for the 348 regression equations ranged from 16 to 340 percent. These regression equations and basin characteristics are presented in the U.S. Geological Survey (USGS) StreamStats Web-based geographic information system application. This tool allows users to click on an ungaged site on a stream in New Jersey and get the estimated flow-duration and low-flow frequency statistics. Additionally, the user can click on a streamgage or partial-record station and get the “at-site” streamflow statistics. The low-flow characteristics of a stream ultimately affect the use of the stream by humans. Specific information on the low-flow characteristics of streams is essential to water managers who deal with problems related to municipal and industrial water supply, fish and wildlife conservation, and dilution of wastewater.

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'.

A Network Method of Measuring Affiliation-based Peer Influence: Assessing the Influences of Teammates’ Smoking on Adolescent Smoking

PubMed Central

Fujimoto, Kayo; Unger, Jennifer B.; Valente, Thomas W.

2011-01-01

Using a network analytic framework, this study introduces a new method to measure peer influence based on adolescents’ affiliations or two-mode social network data. Exposure based on affiliations is referred to as the “affiliation exposure model.” This study demonstrates the methodology using data on young adolescent smoking being influenced by joint participation in school-based organized sports activities with smokers. The analytic sample consisted of 1260 American adolescents from age 10 to 13 in middle schools, and the results of the longitudinal regression analyses showed that adolescents were more likely to smoke as they were increasingly exposed to teammates who smoke. This study illustrates the importance of peer influence via affiliation through team sports. PMID:22313152

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

Quantifying and analysing food waste generated by Indonesian undergraduate students

NASA Astrophysics Data System (ADS)

Mandasari, P.

2018-03-01

Despite the fact that environmental consequences derived from food waste have been widely known, studies on the amount of food waste and its influencing factors have relatively been paid little attention. Addressing this shortage, this paper aimed to quantify monthly avoidable food waste generated by Indonesian undergraduate students and analyse factors influencing the occurrence of avoidable food waste. Based on data from 106 undergraduate students, descriptive statistics and logistic regression were applied in this study. The results indicated that 4,987.5 g of food waste was generated in a month (equal to 59,850 g yearly); or 47.05 g per person monthly (equal to 564.62 g per person per a year). Meanwhile, eating out frequency and gender were found to be significant predictors of food waste occurrence.

The Relationship Between Gratitude and Loneliness: The Potential Benefits of Gratitude for Promoting Social Bonds.

PubMed

Caputo, Andrea

2015-05-01

This paper explores the potential role of gratitude on the reduction of loneliness feelings, even controlling for several variables related to social desirability, well-being (subjective happiness and life satisfaction) and socio-demographic characteristics. Through a web-based survey a convenience sample of 197 participants completed an online questionnaire including these measures. Correlation analyses and four-step hierarchical multiple regression analyses were conducted. The results show a negative correlation between gratitude and loneliness; specifically, gratitude succeeds in accounting for up to almost one-fifth of the total variability of loneliness even controlling for further variables. Being female, not having a stable and consolidated relationship and not participating in the labor force represent some risk factors affecting loneliness which should be taken into account in further research.

The Relationship Between Gratitude and Loneliness: The Potential Benefits of Gratitude for Promoting Social Bonds

PubMed Central

Caputo, Andrea

2015-01-01

This paper explores the potential role of gratitude on the reduction of loneliness feelings, even controlling for several variables related to social desirability, well-being (subjective happiness and life satisfaction) and socio-demographic characteristics. Through a web-based survey a convenience sample of 197 participants completed an online questionnaire including these measures. Correlation analyses and four-step hierarchical multiple regression analyses were conducted. The results show a negative correlation between gratitude and loneliness; specifically, gratitude succeeds in accounting for up to almost one-fifth of the total variability of loneliness even controlling for further variables. Being female, not having a stable and consolidated relationship and not participating in the labor force represent some risk factors affecting loneliness which should be taken into account in further research. PMID:27247660

Stem cell-associated genes are extremely poor prognostic factors for soft-tissue sarcoma patients.

PubMed

Taubert, H; Würl, P; Greither, T; Kappler, M; Bache, M; Bartel, F; Kehlen, A; Lautenschläger, C; Harris, L C; Kaushal, D; Füssel, S; Meye, A; Böhnke, A; Schmidt, H; Holzhausen, H-J; Hauptmann, S

2007-11-01

Cancer stem cells can play an important role in tumorigenesis and tumor progression. However, it is still difficult to detect and isolate cancer stem cells. An alternative approach is to analyse stem cell-associated gene expression. We investigated the coexpression of three stem cell-associated genes, Hiwi, hTERT and survivin, by quantitative real-time-PCR in 104 primary soft-tissue sarcomas (STS). Multivariate Cox's proportional hazards regression analyses allowed correlating gene expression with overall survival for STS patients. Coexpression of all three stem cell-associated genes resulted in a significantly increased risk of tumor-related death. Importantly, tumors of patients with the poorest prognosis were of all four tumor stages, suggesting that their risk is based upon coexpression of stem cell-associated genes rather than on tumor stage.

Predictors of VO2Peak in children age 6- to 7-years-old.

PubMed

Dencker, Magnus; Hermansen, Bianca; Bugge, Anna; Froberg, Karsten; Andersen, Lars B

2011-02-01

This study investigated the predictors of aerobic fitness (VO2PEAK) in young children on a population-base. Participants were 436 children (229 boys and 207 girls) aged 6.7 ± 0.4 yrs. VO2PEAK was measured during a maximal treadmill exercise test. Physical activity was assessed by accelerometers. Total body fat and total fat free mass were estimated from skinfold measurements. Regression analyses indicated that significant predictors for VO2PEAK per kilogram body mass were total body fat, maximal heart rate, sex, and age. Physical activity explained an additional 4-7%. Further analyses showed the main contributing factors for absolute values of VO2PEAK were fat free mass, maximal heart rate, sex, and age. Physical activity explained an additional 3-6%.

The Association Between Health Program Participation and Employee Retention.

PubMed

Mitchell, Rebecca J; Ozminkowski, Ronald J; Hartley, Stephen K

2016-09-01

Using health plan membership as a proxy for employee retention, the objective of this study was to examine whether use of health promotion programs was associated with employee retention. Propensity score weighted generalized linear regression models were used to estimate the association between telephonic programs or health risk surveys and retention. Analyses were conducted with six study samples based on type of program participation. Retention rates were highest for employees with either telephonic program activity or health risk surveys and lowest for employees who did not participate in any interventions. Participants ranged from 71% more likely to 5% less likely to remain with their employers compared with nonparticipants, depending on the sample used in analyses. Using health promotion programs in combination with health risk surveys may lead to improvements in employee retention.

Analysis on the Climate Change Characteristics of Dianchi Lake Basin under the Background of Global Warming

NASA Astrophysics Data System (ADS)

Zhenyu, Yu; Luo, Yi; Yang, Kun; Qiongfei, Deng

2017-05-01

Based on the data published by the State Statistical Bureau and the weather station data, the annual mean temperature, wind speed, humidity, light duration and precipitation of Dianchi Lake in 1990 ~ 2014 were analysed. Combined with the population The results show that the climatic changes in Dianchi Lake basin are related to the climatic change in the past 25 years, and the correlation between these factors and the main climatic factors are analysed by linear regression, Mann-Kendall test, cumulative anomaly, R/S and Morlet wavelet analysis. Population, housing construction area growth and other aspects of the correlation trends and changes in the process, revealing the population expansion and housing construction area growth on the climate of the main factors of the cycle tendency of significant impact.

Effect of socio-cultural factors on spontaneous abortion in Burdur, Turkey: A population based case-control study.

PubMed

Catak, Binali; Oner, Can; Sutlu, Sevinc; Kilinc, Selcuk

2016-01-01

To determine the sociocultural factors that have effect on spontaneous abortion in Burdur, Turkey. Study was designed as case-control study. The case group consist of 257 women whose pregnancies ended with spontaneous abortion. The control group consisted of 514 women whose pregnancy continued since 22 weeks and more during the study. Chi-square, and backward LR logistic regression were utilized in analyses. In multifactorial-analyses it was determined that four factors (educational status of women, employment status of women, exposure to physical violence and non-receipt of ANC) created independent risk on spontaneous abortions. Pregnant women with these risk factors should be followed up more frequently and in a more qualified way in primary and secondary and tertiary health institutions.

Incidence and survival trends of lip, intra-oral cavity and tongue base cancers in south-east England

PubMed Central

Ekrikpo, U; Lyne, O; Wiseberg, J

2015-01-01

Background Oral cavity cancers are on the increase in the UK. Understanding site-specific epidemiological trends is important for cancer control measures. This study demonstrates the changing epidemiological trends in lip, intra-oral cavity and tongue base cancers in south-east England from 1987 to 2006. Aim: Methods This was a retrospective study using anonymised data obtained from the Thames Cancer Registry (TCR) London. Data were analysed using SPSS v.17 and survival analyses with Kaplan-Meier and Cox regression. Age standardisation of the incidence rates was performed. It was conducted in south-east England, which has an average population of 12 million. The study analysed 9,318 cases (ICD-10 code C00–C06, C14). Kent Research Ethics Committee UK granted ethical approval. Results Oral cancers were more common in men, with male: female ratio of 1.6:1. Tongue cancers had the highest frequency at 3,088 (33.1%). Incidence varied with each cancer type. Mean incidence (per 1,000,000) ranged from 2.3 (lip cancer) to 13.8 (tongue cancer). There has been a statistically significant increase in incidence for cancers of the tongue base, other parts of tongue, gum and palate (p<0.001). Median survival time varied by sub-site, with lip cancer having the best median survival time (11.09 years) compared with tongue base cancer (2.42 years). Survival analyses showed worse prognosis for men, older age at diagnosis, and presence of synchronous tumours (p<0.001). Conclusion There is a rising incidence of tongue and tongue base, gum and palate cancers in south-east England with wide variability in survival. Oral cancer awareness and screening programmes should be encouraged. PMID:26263810

Incidence and survival trends of lip, intra-oral cavity and tongue base cancers in south-east England.

PubMed

Olaleye, O; Ekrikpo, U; Lyne, O; Wiseberg, J

2015-04-01

Oral cavity cancers are on the increase in the UK. Understanding site-specific epidemiological trends is important for cancer control measures. This study demonstrates the changing epidemiological trends in lip, intra-oral cavity and tongue base cancers in south-east England from 1987 to 2006. This was a retrospective study using anonymised data obtained from the Thames Cancer Registry (TCR) London. Data were analysed using SPSS v.17 and survival analyses with Kaplan-Meier and Cox regression. Age standardisation of the incidence rates was performed. It was conducted in south-east England, which has an average population of 12 million. The study analysed 9,318 cases (ICD-10 code C00-C06, C14). Kent Research Ethics Committee UK granted ethical approval. Oral cancers were more common in men, with male: female ratio of 1.6:1. Tongue cancers had the highest frequency at 3,088 (33.1%). Incidence varied with each cancer type. Mean incidence (per 1,000,000) ranged from 2.3 (lip cancer) to 13.8 (tongue cancer). There has been a statistically significant increase in incidence for cancers of the tongue base, other parts of tongue, gum and palate (p<0.001). Median survival time varied by sub-site, with lip cancer having the best median survival time (11.09 years) compared with tongue base cancer (2.42 years). Survival analyses showed worse prognosis for men, older age at diagnosis, and presence of synchronous tumours (p<0.001). There is a rising incidence of tongue and tongue base, gum and palate cancers in south-east England with wide variability in survival. Oral cancer awareness and screening programmes should be encouraged.

Coordinate based random effect size meta-analysis of neuroimaging studies.

PubMed

Tench, C R; Tanasescu, Radu; Constantinescu, C S; Auer, D P; Cottam, W J

2017-06-01

Low power in neuroimaging studies can make them difficult to interpret, and Coordinate based meta-analysis (CBMA) may go some way to mitigating this issue. CBMA has been used in many analyses to detect where published functional MRI or voxel-based morphometry studies testing similar hypotheses report significant summary results (coordinates) consistently. Only the reported coordinates and possibly t statistics are analysed, and statistical significance of clusters is determined by coordinate density. Here a method of performing coordinate based random effect size meta-analysis and meta-regression is introduced. The algorithm (ClusterZ) analyses both coordinates and reported t statistic or Z score, standardised by the number of subjects. Statistical significance is determined not by coordinate density, but by a random effects meta-analyses of reported effects performed cluster-wise using standard statistical methods and taking account of censoring inherent in the published summary results. Type 1 error control is achieved using the false cluster discovery rate (FCDR), which is based on the false discovery rate. This controls both the family wise error rate under the null hypothesis that coordinates are randomly drawn from a standard stereotaxic space, and the proportion of significant clusters that are expected under the null. Such control is necessary to avoid propagating and even amplifying the very issues motivating the meta-analysis in the first place. ClusterZ is demonstrated on both numerically simulated data and on real data from reports of grey matter loss in multiple sclerosis (MS) and syndromes suggestive of MS, and of painful stimulus in healthy controls. The software implementation is available to download and use freely. Copyright © 2017 Elsevier Inc. All rights reserved.

A web-based peer-modeling intervention aimed at lifestyle changes in patients with coronary heart disease and chronic back pain: sequential controlled trial.

PubMed

Schweier, Rebecca; Romppel, Matthias; Richter, Cynthia; Hoberg, Eike; Hahmann, Harry; Scherwinski, Inge; Kosmützky, Gregor; Grande, Gesine

2014-07-23

Traditional secondary prevention programs often fail to produce sustainable behavioral changes in everyday life. Peer-modeling interventions and integration of peer experiences in health education are a promising way to improve long-term effects in behavior modification. However, effects of peer support modeling on behavioral change have not been evaluated yet. Therefore, we implemented and evaluated a website featuring patient narratives about successful lifestyle changes. Our aim is to examine the effects of using Web-based patient narratives about successful lifestyle change on improvements in physical activity and eating behavior for patients with coronary heart disease and chronic back pain 3 months after participation in a rehabilitation program. The lebensstil-aendern ("lifestyle-change") website is a nonrestricted, no-cost, German language website that provides more than 1000 video, audio, and text clips from interviews with people with coronary heart disease and chronic back pain. To test efficacy, we conducted a sequential controlled trial and recruited patients with coronary heart disease and chronic back pain from 7 inpatient rehabilitation centers in Germany. The intervention group attended a presentation on the website; the control group did not. Physical activity and eating behavior were assessed by questionnaire during the rehabilitation program and 12 weeks later. Analyses were conducted based on an intention-to-treat and an as-treated protocol. A total of 699 patients were enrolled and 571 cases were included in the analyses (control: n=313, intervention: n=258; female: 51.1%, 292/571; age: mean 53.2, SD 8.6 years; chronic back pain: 62.5%, 357/571). Website usage in the intervention group was 46.1% (119/258). In total, 141 trial participants used the website. Independent t tests based on the intention-to-treat protocol only demonstrated nonsignificant trends in behavioral change related to physical activity and eating behavior. Multivariate regression analyses confirmed belonging to the intervention group was an independent predictor of self-reported improvements in physical activity regularity (β=.09, P=.03) and using less fat for cooking (β=.09, P=.04). In independent t tests based on the as-treated protocol, website use was associated with higher self-reported improvements in integrating physical activity into daily routine (d=0.22, P=.02), in physical activity regularity (d=0.23, P=.02), and in using less fat for cooking (d=0.21, P=.03). Multivariate regression analyses revealed that using the website at least 3 times was the only factor associated with improved lifestyle behaviors. Usage of the lebensstil-aendern website corresponds to more positive lifestyle changes. However, as-treated analyses do not allow for differentiating between causal effects and selection bias. Despite these limitations, the trial indicates that more than occasional website usage is necessary to reach dose-response efficacy. Therefore, future studies should concentrate on strategies to improve adherence to Web-based interventions and to encourage more frequent usage of these programs.

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.

An analysis of first-time blood donors return behaviour using regression models.

PubMed

Kheiri, S; Alibeigi, Z

2015-08-01

Blood products have a vital role in saving many patients' lives. The aim of this study was to analyse blood donor return behaviour. Using a cross-sectional follow-up design of 5-year duration, 864 first-time donors who had donated blood were selected using a systematic sampling. The behaviours of donors via three response variables, return to donation, frequency of return to donation and the time interval between donations, were analysed based on logistic regression, negative binomial regression and Cox's shared frailty model for recurrent events respectively. Successful return to donation rated at 49·1% and the deferral rate was 13·3%. There was a significant reverse relationship between the frequency of return to donation and the time interval between donations. Sex, body weight and job had an effect on return to donation; weight and frequency of donation during the first year had a direct effect on the total frequency of donations. Age, weight and job had a significant effect on the time intervals between donations. Aging decreases the chances of return to donation and increases the time interval between donations. Body weight affects the three response variables, i.e. the higher the weight, the more the chances of return to donation and the shorter the time interval between donations. There is a positive correlation between the frequency of donations in the first year and the total number of return to donations. Also, the shorter the time interval between donations is, the higher the frequency of donations. © 2015 British Blood Transfusion Society.

Interrelations between orthostatic postural deviations and subjects' age, sex, malocclusion, and specific signs and symptoms of functional pathologies of the temporomandibular system: a preliminary correlation and regression study.

PubMed

Munhoz, Wagner Cesar; Hsing, Wu Tu

2014-07-01

Studies on the relationships between postural deviations and the temporomandibular system (TS) functional health are controversial and inconclusive. This study stems from the hypothesis that such inconclusiveness is due to authors considering functional pathologies of the TS (FPTS) as a whole, without taking into account subjects' specific FPTS signs and symptoms. Based on the author and collaborators' previous studies, the present study analyzed data on body posture from a sample of 50 subjects with (30) and without (20) FPTS. Correlation analyses were applied, taking as independent variables age, sex, Helkimo anamnestic, occlusal, and dysfunction indices, as well as FPTS specific signs and symptoms. Postural assessments of the head, cervical spine, shoulders, lumbar spine, and hips were the dependent variables. Linear regression equations were built that proved to partially predict the presence and magnitude of body posture deviations by drawing on subjects' characteristics and specific FPTS symptoms. Determination coefficients for these equations ranged from 0.082 to 0.199 in the univariate, and from 0.121 to 0.502 in the multivariate regression analyses. Results show that factors intrinsic to the subjects or the TS may potentially interfere in results of studies that analyze relationships between FPTS and body posture. Furthermore, a trend to specificity was found, e.g. the degree of cervical lordosis was found to correlate to age and FPTS degree of severity, suggesting that some TS pathological features, or malocclusion, age or sex, may be more strongly correlated than others with specific posture patterns.

Utility of the comprehensive marijuana motives questionnaire among medical cannabis patients.

PubMed

Bohnert, Kipling M; Bonar, Erin E; Arnedt, J Todd; Conroy, Deirdre A; Walton, Maureen A; Ilgen, Mark A

2018-01-01

Little is known about motives for cannabis use among the population of adults using cannabis medically. Therefore, we evaluated the performance of the 12 factor, 36-item Comprehensive Marijuana Motives Questionnaire (CMMQ) among a sample of medical cannabis patients. Study participants were adults ages 21years or older with scheduled appointments to obtain new or renewed medical cannabis certification from clinics in one Midwestern state (n=1116). Confirmatory factor analysis was used to evaluate properties of the CMMQ. Multiple regressions were used to estimate associations between motives and cannabis use, physical health functioning, and mental health functioning. Fit indices were acceptable, and factor loadings ranged from 0.57 to 0.94. Based on regression analyses, motives accounted for 7% of the variance in recent cannabis use, and independent of cannabis use, accounted for 5% and 19% of physical and mental health functioning, respectively. Regression analyses also revealed that distinct motives were associated with cannabis use and physical and mental health functioning. Among adults seeking medical cannabis certification, the factor structure of the CMMQ was supported, and consistent with prior studies of adolescents and young adults using cannabis recreationally. Thus, individuals who use cannabis medically may have diverse reasons for use that extend beyond the management of medical symptoms. In addition, coping and sleep-related motives may be particularly salient for this population. Findings support the utility of the CMMQ in future research on medical cannabis use; however, expansion of the scale may be needed to address medical motives for use. Published by Elsevier Ltd.

Quantitative X-ray Map Analyser (Q-XRMA): A new GIS-based statistical approach to Mineral Image Analysis

NASA Astrophysics Data System (ADS)

Ortolano, Gaetano; Visalli, Roberto; Godard, Gaston; Cirrincione, Rosolino

2018-06-01

We present a new ArcGIS®-based tool developed in the Python programming language for calibrating EDS/WDS X-ray element maps, with the aim of acquiring quantitative information of petrological interest. The calibration procedure is based on a multiple linear regression technique that takes into account interdependence among elements and is constrained by the stoichiometry of minerals. The procedure requires an appropriate number of spot analyses for use as internal standards and provides several test indexes for a rapid check of calibration accuracy. The code is based on an earlier image-processing tool designed primarily for classifying minerals in X-ray element maps; the original Python code has now been enhanced to yield calibrated maps of mineral end-members or the chemical parameters of each classified mineral. The semi-automated procedure can be used to extract a dataset that is automatically stored within queryable tables. As a case study, the software was applied to an amphibolite-facies garnet-bearing micaschist. The calibrated images obtained for both anhydrous (i.e., garnet and plagioclase) and hydrous (i.e., biotite) phases show a good fit with corresponding electron microprobe analyses. This new GIS-based tool package can thus find useful application in petrology and materials science research. Moreover, the huge quantity of data extracted opens new opportunities for the development of a thin-section microchemical database that, using a GIS platform, can be linked with other major global geoscience databases.

Understanding heterogeneity in grey matter research of adults with childhood maltreatment-A meta-analysis and review.

PubMed

Paquola, Casey; Bennett, Maxwell R; Lagopoulos, Jim

2016-10-01

Childhood trauma has been associated with long term effects on prefrontal-limbic grey matter. A literature search was conducted to identify structural magnetic resonance imaging studies of adults with a history of childhood trauma. We performed three meta-analyses. Hedges' g effect sizes were calculated for each study providing hippocampal or amygdala volumes of trauma and non-trauma groups. Seed based differential mapping was utilised to synthesise whole brain voxel based morphometry (VBM) studies. A total of 38 articles (17 hippocampus, 13 amygdala, 19 whole brain VBM) were included in the meta-analyses. Trauma cohorts exhibited smaller hippocampus and amygdala volumes bilaterally. The most robust findings of the whole brain VBM meta-analysis were reduced grey matter in the right dorsolateral prefrontal cortex and right hippocampus amongst adults with a history of childhood trauma. Subgroup analyses and meta-regressions showed results were moderated by age, gender, the cohort's psychiatric health and the study's definition of childhood trauma. We provide evidence of abnormal grey matter in prefrontal-limbic brain regions of adults with a history of childhood maltreatment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Effects of a Web-Based Computer-Tailored Game to Reduce Binge Drinking Among Dutch Adolescents: A Cluster Randomized Controlled Trial

PubMed Central

Crutzen, Rik; Mercken, Liesbeth; Candel, Math; de Vries, Hein

2016-01-01

Background Binge drinking among Dutch adolescents is among the highest in Europe. Few interventions so far have focused on adolescents aged 15 to 19 years. Because binge drinking increases significantly during those years, it is important to develop binge drinking prevention programs for this group. Web-based computer-tailored interventions can be an effective tool for reducing this behavior in adolescents. Embedding the computer-tailored intervention in a serious game may make it more attractive to adolescents. Objective The aim was to assess whether a Web-based computer-tailored intervention is effective in reducing binge drinking in Dutch adolescents aged 15 to 19 years. Secondary outcomes were reduction in excessive drinking and overall consumption during the previous week. Personal characteristics associated with program adherence were also investigated. Methods A cluster randomized controlled trial was conducted among 34 Dutch schools. Each school was randomized into either an experimental (n=1622) or a control (n=1027) condition. Baseline assessment took place in January and February 2014. At baseline, demographic variables and alcohol use were assessed. Follow-up assessment of alcohol use took place 4 months later (May and June 2014). After the baseline assessment, participants in the experimental condition started with the intervention consisting of a game about alcohol in which computer-tailored feedback regarding motivational characteristics was embedded. Participants in the control condition only received the baseline questionnaire. Both groups received the 4-month follow-up questionnaire. Effects of the intervention were assessed using logistic regression mixed models analyses for binge and excessive drinking and linear regression mixed models analyses for weekly consumption. Factors associated with intervention adherence in the experimental condition were explored by means of a linear regression model. Results In total, 2649 adolescents participated in the baseline assessment. At follow-up, 824 (31.11%) adolescents returned. The intervention was effective in reducing binge drinking among adolescents aged 15 years (P=.03) and those aged 16 years when they participated in at least 2 intervention sessions (P=.04). Interaction effects between excessive drinking and educational level (P=.08) and between weekly consumption and age (P=.09) were found; however, in-depth analyses revealed no significant subgroup effects for both interaction effects. Additional analyses revealed that prolonged use of the intervention was associated with stronger effects for binge drinking. Yet, overall adherence to the intervention was low. Analyses revealed that being Protestant, female, younger, a nonbinge drinker, and having a higher educational background were associated with adherence. Conclusions The intervention was effective for adolescents aged 15 and 16 years concerning binge drinking. Prevention messages may be more effective for those at the start of their drinking career, whereas other methods may be needed for those with a longer history of alcohol consumption. Despite using game elements, intervention completion was low. Trial Registration Dutch Trial Register: NTR4048; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4048 (Archived by WebCite® at http://www.webcitation.org/6eSJD3FiY) PMID:26842694

Effects of a Web-Based Computer-Tailored Game to Reduce Binge Drinking Among Dutch Adolescents: A Cluster Randomized Controlled Trial.

PubMed

Jander, Astrid; Crutzen, Rik; Mercken, Liesbeth; Candel, Math; de Vries, Hein

2016-02-03

Binge drinking among Dutch adolescents is among the highest in Europe. Few interventions so far have focused on adolescents aged 15 to 19 years. Because binge drinking increases significantly during those years, it is important to develop binge drinking prevention programs for this group. Web-based computer-tailored interventions can be an effective tool for reducing this behavior in adolescents. Embedding the computer-tailored intervention in a serious game may make it more attractive to adolescents. The aim was to assess whether a Web-based computer-tailored intervention is effective in reducing binge drinking in Dutch adolescents aged 15 to 19 years. Secondary outcomes were reduction in excessive drinking and overall consumption during the previous week. Personal characteristics associated with program adherence were also investigated. A cluster randomized controlled trial was conducted among 34 Dutch schools. Each school was randomized into either an experimental (n=1622) or a control (n=1027) condition. Baseline assessment took place in January and February 2014. At baseline, demographic variables and alcohol use were assessed. Follow-up assessment of alcohol use took place 4 months later (May and June 2014). After the baseline assessment, participants in the experimental condition started with the intervention consisting of a game about alcohol in which computer-tailored feedback regarding motivational characteristics was embedded. Participants in the control condition only received the baseline questionnaire. Both groups received the 4-month follow-up questionnaire. Effects of the intervention were assessed using logistic regression mixed models analyses for binge and excessive drinking and linear regression mixed models analyses for weekly consumption. Factors associated with intervention adherence in the experimental condition were explored by means of a linear regression model. In total, 2649 adolescents participated in the baseline assessment. At follow-up, 824 (31.11%) adolescents returned. The intervention was effective in reducing binge drinking among adolescents aged 15 years (P=.03) and those aged 16 years when they participated in at least 2 intervention sessions (P=.04). Interaction effects between excessive drinking and educational level (P=.08) and between weekly consumption and age (P=.09) were found; however, in-depth analyses revealed no significant subgroup effects for both interaction effects. Additional analyses revealed that prolonged use of the intervention was associated with stronger effects for binge drinking. Yet, overall adherence to the intervention was low. Analyses revealed that being Protestant, female, younger, a nonbinge drinker, and having a higher educational background were associated with adherence. The intervention was effective for adolescents aged 15 and 16 years concerning binge drinking. Prevention messages may be more effective for those at the start of their drinking career, whereas other methods may be needed for those with a longer history of alcohol consumption. Despite using game elements, intervention completion was low. Dutch Trial Register: NTR4048; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4048 (Archived by WebCite® at http://www.webcitation.org/6eSJD3FiY).

Clinical Phenotype Classifications Based on Static Varus Alignment and Varus Thrust in Japanese Patients With Medial Knee Osteoarthritis

PubMed Central

Iijima, Hirotaka; Fukutani, Naoto; Fukumoto, Takahiko; Uritani, Daisuke; Kaneda, Eishi; Ota, Kazuo; Kuroki, Hiroshi; Matsuda, Shuichi

2015-01-01

Objective To investigate the association between knee pain during gait and 4 clinical phenotypes based on static varus alignment and varus thrust in patients with medial knee osteoarthritis (OA). Methods Patients in an orthopedic clinic (n = 266) diagnosed as having knee OA (Kellgren/Lawrence [K/L] grade ≥1) were divided into 4 phenotype groups according to the presence or absence of static varus alignment and varus thrust (dynamic varus): no varus (n = 173), dynamic varus (n = 17), static varus (n = 50), and static varus + dynamic varus (n = 26). The knee range of motion, spatiotemporal gait parameters, visual analog scale scores for knee pain, and scores on the Japanese Knee Osteoarthritis Measure were used to assess clinical outcomes. Multiple logistic regression analyses identified the relationship between knee pain during gait and the 4 phenotypes, adjusted for possible risk factors, including age, sex, body mass index, K/L grade, and gait velocity. Results Multiple logistic regression analysis showed that varus thrust without varus alignment was associated with knee pain during gait (odds ratio [OR] 3.30, 95% confidence interval [95% CI] 1.08–12.4), and that varus thrust combined with varus alignment was strongly associated with knee pain during gait (OR 17.1, 95% CI 3.19–320.0). Sensitivity analyses applying alternative cutoff values for defining static varus alignment showed comparable results. Conclusion Varus thrust with or without static varus alignment was associated with the occurrence of knee pain during gait. Tailored interventions based on individual malalignment phenotypes may improve clinical outcomes in patients with knee OA. PMID:26017348

Comorbidity and its relevance on general hospital based mortality in major depressive disorder: a naturalistic 12-year follow-up in general hospital admissions.

PubMed

Schoepf, Dieter; Uppal, Hardeep; Potluri, Rahul; Chandran, Suresh; Heun, Reinhard

2014-05-01

Major depressive disorder (MDD) is associated with physical comorbidity, but the risk factors of general hospital-based mortality are unclear. Consequently, we investigated whether the burden of comorbidity and its relevance on in-hospital death differs between patients with and without MDD in a 12-year follow-up in general hospital admissions. During 1 January 2000 and 30 June 2012, 9604 MDD patients were admitted to three General Manchester Hospitals. All comorbidities with a prevalence ≥1% were compared with those of 96,040 age-gender matched hospital controls. Risk factors of in-hospital death were identified using multivariate logistic regression analyses. Crude hospital-based mortality rates within the period under observation were 997/9604 (10.4%) in MDD patients and 8495/96,040 (8.8%) in controls. MDD patients compared to controls had a substantial higher burden of comorbidity. The highest comorbidities included hypertension, asthma, and anxiety disorders. Subsequently, twenty-six other diseases were disproportionally increased, many of them linked to chronic lung diseases and to diabetes. In deceased MDD patients, chronic obstructive pulmonary disease and type-2 diabetes mellitus were the most common comorbidities, contributing to 18.6% and 17.1% of deaths. Furthermore, fifteen physical diseases contributed to in-hospital death in the MDD population. However, there were no significant differences in their impact on mortality compared to controls in multivariate logistic regression analyses. Thus in one of the largest samples of MDD patients in general hospitals, MDD patients have a substantial higher burden of comorbidity compared to controls, but they succumb to the same physical diseases as their age-gender matched peers without MDD. Copyright © 2014 Elsevier Ltd. All rights reserved.

Risk of colorectal cancer in patients with ulcerative colitis: a meta-analysis of population-based cohort studies.

PubMed

Jess, Tine; Rungoe, Christine; Peyrin-Biroulet, Laurent

2012-06-01

Patients with ulcerative colitis (UC) have an increased risk of developing colorectal cancer (CRC). Studies examining the magnitude of this association have yielded conflicting results. We performed a meta-analysis of population-based cohort studies to determine the risk of CRC in patients with UC. We used MEDLINE, EMBASE, Cochrane, and CINAHL to perform a systematic literature search. We included 8 studies in the meta-analysis on the basis of strict inclusion and exclusion criteria. We calculated pooled standardized incidence ratios (SIRs) with 95% confidence intervals (CIs) for risk of CRC in patients with UC and performed meta-regression analyses of the effect of cohort size, calendar period, observation time, percentage with proctitis, and rates of colectomy on the risk of CRC. An average of 1.6% of patients with UC was diagnosed with CRC during 14 years of follow-up. SIRs ranged from 1.05 to 3.1, with a pooled SIR of 2.4 (95% CI, 2.1-2.7). Men with UC had a greater risk of CRC (SIR, 2.6; 95% CI, 2.2-3.0) than women (SIR, 1.9; 95% CI, 1.5-2.3). Young age was a risk factor for CRC (SIR, 8.6; 95% CI, 3.8-19.5; although this might have resulted from small numbers), as was extensive colitis (SIR, 4.8; 95% CI, 3.9-5.9). In meta-regression analyses, only cohort size was associated with risk of CRC. In population-based cohorts, UC increases the risk of CRC 2.4-fold. Male sex, young age at diagnosis with UC, and extensive colitis increase the risk. Copyright © 2012 AGA Institute. Published by Elsevier Inc. All rights reserved.

Bootstrap-based procedures for inference in nonparametric receiver-operating characteristic curve regression analysis.

PubMed

Rodríguez-Álvarez, María Xosé; Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Tahoces, Pablo G

2018-03-01

Prior to using a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is essential. The receiver-operating characteristic curve is the measure of accuracy most widely used for continuous diagnostic tests. However, the possible impact of extra information about the patient (or even the environment) on diagnostic accuracy also needs to be assessed. In this paper, we focus on an estimator for the covariate-specific receiver-operating characteristic curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalised additive models for the receiver-operating characteristic curve. The main aim of the paper is to offer new inferential procedures for testing the effect of covariates on the conditional receiver-operating characteristic curve within the above-mentioned class. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of continuous covariates on the receiver-operating characteristic curve and (b) the presence of factor-by-curve interaction terms. The validity of the proposed bootstrap-based procedures is supported by simulations. To facilitate the application of these new procedures in practice, an R-package, known as npROCRegression, is provided and briefly described. Finally, data derived from a computer-aided diagnostic system for the automatic detection of tumour masses in breast cancer is analysed.

Exploring the Relationship between Parental Worry about their Children's Health and Usage of an Internet Intervention for Pediatric Encopresis

PubMed Central

Magee, Joshua C.; Thorndike, Frances P.; Cox, Daniel J.; Borowitz, Stephen M.

2009-01-01

Objective To investigate whether parental worry about their children's health predicts usage of a pediatric Internet intervention for encopresis. Methods Thirty-nine families with a child diagnosed with encopresis completed a national clinical trial of an Internet-based intervention for encopresis (www.ucanpooptoo.com). Parents rated worry about their children's health, encopresis severity, current parent treatment for depression, and parent comfort with the Internet. Usage indicators were collected while participants utilized the intervention. Results Regression analyses showed that parents who reported higher baseline levels of worry about their children's health showed greater subsequent intervention use (β =.52, p =.002), even after accounting for other plausible predictors. Exploratory analyses indicated that this effect may be stronger for families with younger children. Conclusions Characteristics of individuals using Internet-based treatment programs, such as parental worry about their children's health, can influence intervention usage, and should be considered by developers of Internet interventions. PMID:18772228

Travel abroad increases sexual health risk-taking among Swedish youth: a population-based study using a case-crossover strategy.

PubMed

Sundbeck, Mats; Agardh, Anette; Östergren, Per-Olof

2017-01-01

The fact that youth take sexual risks when they are abroad have been shown in previous studies. However, it is not known if they increased their sexual risk-taking when travelling abroad, compared to the stay in their homeland. To assess whether Swedish youth increased their individual sexual risk behaviour, defined as having a casual sex partner, when travelling abroad and to examine possible factors that may be associated with increased risk-taking abroad. In 2013, a population-based sample of 2189 Swedes, 18-29 years, was assessed by a questionnaire (45% response rate). Sexuality, duration of travel, parents' country of origin, mental health, heavy episodic drinking (HED), use of illicit drugs, and socio-demographic background were assessed. Increased risk of casual sex in relation to time spent abroad vs. time spent in Sweden was analysed by a variant of case-crossover design. Factors that could be associated with increased risk of casual sex in Sweden and abroad, separately, were analysed by logistic regression.

Sex differences in the effect of aging on dry eye disease.

PubMed

Ahn, Jong Ho; Choi, Yoon-Hyeong; Paik, Hae Jung; Kim, Mee Kum; Wee, Won Ryang; Kim, Dong Hyun

2017-01-01

Aging is a major risk factor in dry eye disease (DED), and understanding sexual differences is very important in biomedical research. However, there is little information about sex differences in the effect of aging on DED. We investigated sex differences in the effect of aging and other risk factors for DED. This study included data of 16,824 adults from the Korea National Health and Nutrition Examination Survey (2010-2012), which is a population-based cross-sectional survey. DED was defined as the presence of frequent ocular dryness or a previous diagnosis by an ophthalmologist. Basic sociodemographic factors and previously known risk factors for DED were included in the analyses. Linear regression modeling and multivariate logistic regression modeling were used to compare the sex differences in the effect of risk factors for DED; we additionally performed tests for interactions between sex and other risk factors for DED in logistic regression models. In our linear regression models, the prevalence of DED symptoms in men increased with age ( R =0.311, P =0.012); however, there was no association between aging and DED in women ( P >0.05). Multivariate logistic regression analyses showed that aging in men was not associated with DED (DED symptoms/diagnosis: odds ratio [OR] =1.01/1.04, each P >0.05), while aging in women was protectively associated with DED (DED symptoms/diagnosis: OR =0.94/0.91, P =0.011/0.003). Previous ocular surgery was significantly associated with DED in both men and women (men/women: OR =2.45/1.77 [DED symptoms] and 3.17/2.05 [DED diagnosis], each P <0.001). Tests for interactions of sex revealed significantly different aging × sex and previous ocular surgery × sex interactions ( P for interaction of sex: DED symptoms/diagnosis - 0.044/0.011 [age] and 0.012/0.006 [previous ocular surgery]). There were distinct sex differences in the effect of aging on DED in the Korean population. DED following ocular surgery also showed sexually different patterns. Age matching and sex matching are strongly recommended in further studies about DED, especially DED following ocular surgery.

Soil Particle Size Analysis by Laser Diffractometry: Result Comparison with Pipette Method

NASA Astrophysics Data System (ADS)

Šinkovičová, Miroslava; Igaz, Dušan; Kondrlová, Elena; Jarošová, Miriam

2017-10-01

Soil texture as the basic soil physical property provides a basic information on the soil grain size distribution as well as grain size fraction representation. Currently, there are several methods of particle dimension measurement available that are based on different physical principles. Pipette method based on the different sedimentation velocity of particles with different diameter is considered to be one of the standard methods of individual grain size fraction distribution determination. Following the technical advancement, optical methods such as laser diffraction can be also used nowadays for grain size distribution determination in the soil. According to the literature review of domestic as well as international sources related to this topic, it is obvious that the results obtained by laser diffractometry do not correspond with the results obtained by pipette method. The main aim of this paper was to analyse 132 samples of medium fine soil, taken from the Nitra River catchment in Slovakia, from depths of 15-20 cm and 40-45 cm, respectively, using laser analysers: ANALYSETTE 22 MicroTec plus (Fritsch GmbH) and Mastersizer 2000 (Malvern Instruments Ltd). The results obtained by laser diffractometry were compared with pipette method and the regression relationships using linear, exponential, power and polynomial trend were derived. Regressions with the three highest regression coefficients (R2) were further investigated. The fit with the highest tightness was observed for the polynomial regression. In view of the results obtained, we recommend using the estimate of the representation of the clay fraction (<0.01 mm) polynomial regression, to achieve a highest confidence value R2 at the depths of 15-20 cm 0.72 (Analysette 22 MicroTec plus) and 0.95 (Mastersizer 2000), from a depth of 40-45 cm 0.90 (Analysette 22 MicroTec plus) and 0.96 (Mastersizer 2000). Since the percentage representation of clayey particles (2nd fraction according to the methodology of Complex Soil Survey done in Slovakia) in soil is the determinant for soil type specification, we recommend using the derived relationships in soil science when the soil texture analysis is done according to laser diffractometry. The advantages of laser diffraction method comprise the short analysis time, usage of small sample amount, application for the various grain size fraction and soil type classification systems, and a wide range of determined fractions. Therefore, it is necessary to focus on this issue further to address the needs of soil science research and attempt to replace the standard pipette method with more progressive laser diffraction method.

Serum Uric Acid Levels and Risk of Incident Hypertriglyceridemia: A Longitudinal Population-based Epidemiological Study.

PubMed

Zheng, Rongjiong; Ren, Ping; Chen, Qingmei; Yang, Tianmeng; Chen, Changxi; Mao, Yushan

2017-09-01

Hypertriglyceridemia is one of lipid metabolism abnormalities; however, it is still debatable whether serum uric acid is a cause or a consequence of hypertriglyceridemia. We performed the study to investigate the longitudinal association between serum uric acid levels and hypertriglyceridemia. The study included 4190 subjects without hypertriglyceridemia. The subjects had annual health examinations for 8 years to assess incident hyperglyceridemia, and the subjects were divided into groups based on the serum uric acid quartile. Cox regression models were used to analyze the risk factors of development hypertriglyceridemia. During follow-up, 1461 (34.9%) subjects developed hypertriglyceridemia over 8 years of follow-up. The cumulative incidence of hypertriglyceridemia was 28.2%, 29.1%, 36.9%, and 45.6% in quartile 1,2,3 and 4, respectively ( P for trend <0.001). Cox regression analyses indicated that serum uric acid levels were independently and positively associated with the risk of incident hypertriglyceridemia. Hypertriglyceridemia has become a serious public health problem. This longitudinal study demonstrates that high serum uric acid levels increase the risk of hypertriglyceridemia. © 2017 by the Association of Clinical Scientists, Inc.

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.

Results of the 2009 AORN salary survey.

PubMed

Bacon, Donald

2009-12-01

AORN conducted its seventh annual compensation survey for perioperative nurses in August of 2009. A multiple regression model was used to examine how a variety of variables including job title, education level, certification, experience, and geographic region affect nursing compensation. Comparisons between the 2009 data and previous years' data are presented. The effects of other forms of compensation, such as on-call compensation, overtime, bonuses, and shift differentials on average base compensation rates also are examined. Additional analyses explore the effect of the current economic downturn on the perioperative work environment. (c) AORN, Inc, 2009.

Psychological inflexibility and depressive symptoms among Asian English speakers: A study on Indian, Philippine, and Singaporean samples.

PubMed

Kato, Tsukasa

2016-04-30

Psychological inflexibility is a core concept in Acceptance and Commitment Therapy. The primary aim of this study was to examine psychological inflexibility and depressive symptoms among Asian English speakers. A total of 900 adults in India, the Philippines, and Singapore completed some measures related to psychological inflexibility and depressive symptoms through a Web-based survey. Multiple regression analyses revealed that higher psychological inflexibility was significantly associated with higher levels of depressive symptoms in all the samples, after controlling for the effects of gender, marital status, and interpersonal stress. In addition, the effect sizes of the changes in the R(2) values when only psychological flexibility scores were entered in the regression model were large for all the samples. Moreover, overall, the beta-weight of the psychological flexibility scores obtained by the Philippine sample was the lowest of all three samples. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Network Structure and Travel Time Perception

PubMed Central

Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

2013-01-01

The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time. PMID:24204932

Role of social support, hardiness, and acculturation as predictors of mental health among international students of Asian Indian origin.

PubMed

Atri, Ashutosh; Sharma, Manoj; Cottrell, Randall

This study determined the role of social support, hardiness, and acculturation as predictors of mental health among international Asian Indian students enrolled at two large public universities in Ohio. A sample of 185 students completed a 75-item online instrument assessing their social support levels, acculturation, hardiness, and their mental health. Regression analyses were conducted to test for variance in mental health attributable to each of the three independent variables. The final regression model revealed that the belonging aspect of social support, acculturation and prejudice of acculturation scale, and commitment and control of hardiness were all predictive of mental health (R2 = 0.523). Recommendations have been offered to develop interventions that will help strengthen the social support, hardiness, and acculturation of international students and help improve their mental health. Recommendations for development of future Web-based studies also are offered.

Compulsive buying: Earlier illicit drug use, impulse buying, depression, and adult ADHD symptoms.

PubMed

Brook, Judith S; Zhang, Chenshu; Brook, David W; Leukefeld, Carl G

2015-08-30

This longitudinal study examined the association between psychosocial antecedents, including illicit drug use, and adult compulsive buying (CB) across a 29-year time period from mean age 14 to mean age 43. Participants originally came from a community-based random sample of residents in two upstate New York counties. Multivariate linear regression analysis was used to study the relationship between the participant's earlier psychosocial antecedents and adult CB in the fifth decade of life. The results of the multivariate linear regression analyses showed that gender (female), earlier adult impulse buying (IB), depressive mood, illicit drug use, and concurrent ADHD symptoms were all significantly associated with adult CB at mean age 43. It is important that clinicians treating CB in adults should consider the role of drug use, symptoms of ADHD, IB, depression, and family factors in CB. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Compulsive Buying: Earlier Illicit Drug Use, Impulse Buying, Depression, and Adult ADHD Symptoms

PubMed Central

Brook, Judith S.; Zhang, Chenshu; Brook, David W.; Leukefeld, Carl G.

2015-01-01

This longitudinal study examined the association between psychosocial antecedents, including illicit drug use, and adult compulsive buying (CB) across a 29-year time period from mean age 14 to mean age 43. Participants originally came from a community-based random sample of residents in two upstate New York counties. Multivariate linear regression analysis was used to study the relationship between the participant’s earlier psychosocial antecedents and adult CB in the fifth decade of life. The results of the multivariate linear regression analyses showed that gender (female), earlier adult impulse buying (IB), depressive mood, illicit drug use, and concurrent ADHD symptoms were all significantly associated with adult CB at mean age 43. It is important that clinicians treating CB in adults should consider the role of drug use, symptoms of ADHD, IB, depression, and family factors in CB. PMID:26165963

Use of antidementia drugs in frontotemporal lobar degeneration.

PubMed

López-Pousa, Secundino; Calvó-Perxas, Laia; Lejarreta, Saioa; Cullell, Marta; Meléndez, Rosa; Hernández, Erélido; Bisbe, Josep; Perkal, Héctor; Manzano, Anna; Roig, Anna Maria; Turró-Garriga, Oriol; Vilalta-Franch, Joan; Garre-Olmo, Josep

2012-06-01

Clinical evidence indicates that acetylcholinesterase inhibitors (AChEIs) are not efficacious to treat frontotemporal lobar degeneration (FTLD). The British Association for Psychopharmacology recommends avoiding the use of AChEI and memantine in patients with FTLD. Cross-sectional design using 1092 cases with Alzheimer's disease (AD) and 64 cases with FTLD registered by the Registry of Dementias of Girona. Bivariate analyses were performed, and binary logistic regressions were used to detect variables associated with antidementia drugs consumption. The AChEIs were consumed by 57.6% and 42.2% of the patients with AD and FTLD, respectively. Memantine was used by 17.2% and 10.9% of patients with AD and FTLD, respectively. Binary logistic regressions yielded no associations with antidementia drugs consumption. There is a discrepancy regarding clinical practice and the recommendations based upon clinical evidence. The increased central nervous system drug use detected in FTLD requires multicentric studies aiming at finding the best means to treat these patients.

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.

Vesicular stomatitis forecasting based on Google Trends

PubMed Central

Lu, Yi; Zhou, GuangYa; Chen, Qin

2018-01-01

Background Vesicular stomatitis (VS) is an important viral disease of livestock. The main feature of VS is irregular blisters that occur on the lips, tongue, oral mucosa, hoof crown and nipple. Humans can also be infected with vesicular stomatitis and develop meningitis. This study analyses 2014 American VS outbreaks in order to accurately predict vesicular stomatitis outbreak trends. Methods American VS outbreaks data were collected from OIE. The data for VS keywords were obtained by inputting 24 disease-related keywords into Google Trends. After calculating the Pearson and Spearman correlation coefficients, it was found that there was a relationship between outbreaks and keywords derived from Google Trends. Finally, the predicted model was constructed based on qualitative classification and quantitative regression. Results For the regression model, the Pearson correlation coefficients between the predicted outbreaks and actual outbreaks are 0.953 and 0.948, respectively. For the qualitative classification model, we constructed five classification predictive models and chose the best classification predictive model as the result. The results showed, SN (sensitivity), SP (specificity) and ACC (prediction accuracy) values of the best classification predictive model are 78.52%,72.5% and 77.14%, respectively. Conclusion This study applied Google search data to construct a qualitative classification model and a quantitative regression model. The results show that the method is effective and that these two models obtain more accurate forecast. PMID:29385198

Production of Selected Key Ductile Iron Castings Used in Large-Scale Windmills

NASA Astrophysics Data System (ADS)

Pan, Yung-Ning; Lin, Hsuan-Te; Lin, Chi-Chia; Chang, Re-Mo

Both the optimal alloy design and microstructures that conform to the mechanical properties requirements of selected key components used in large-scale windmills have been established in this study. The target specifications in this study are EN-GJS-350-22U-LT, EN-GJS-350-22U-LT and EN-GJS-700-2U. In order to meet the impact requirement of spec. EN-GJS-350-22U-LT, the Si content should be kept below 1.97%, and also the maximum pearlite content shouldn't exceed 7.8%. On the other hand, Si content below 2.15% and pearlite content below 12.5% were registered for specification EN-GJS-400-18U-LT. On the other hand, the optimal alloy designs that can comply with specification EN-GJS-700-2U include 0.25%Mn+0.6%Cu+0.05%Sn, 0.25%Mn+0.8%Cu+0.01%Sn and 0.45%Mn+0.6%Cu+0.01%Sn. Furthermore, based upon the experimental results, multiple regression analyses have been performed to correlate the mechanical properties with chemical compositions and microstructures. The derived regression equations can be used to attain the optimal alloy design for castings with target specifications. Furthermore, by employing these regression equations, the mechanical properties can be predicted based upon the chemical compositions and microstructures of cast irons.

On the Period-Amplitude and Amplitude-Period Relationships

NASA Technical Reports Server (NTRS)

Wilson, Robert M.; Hathaway, David H.

2008-01-01

Examined are Period-Amplitude and Amplitude-Period relationships based on the cyclic behavior of the 12-month moving averages of monthly mean sunspot numbers for cycles 0.23, both in terms of Fisher's exact tests for 2x2 contingency tables and linear regression analyses. Concerning the Period-Amplitude relationship (same cycle), because cycle 23's maximum amplitude is known to be 120.8, the inferred regressions (90-percent prediction intervals) suggest that its period will be 131 +/- 24 months (using all cycles) or 131 +/- 18 months (ignoring cycles 2 and 4, which have the extremes of period, 108 and 164 months, respectively). Because cycle 23 has already persisted for 142 months (May 1996 through February 2008), based on the latter prediction, it should end before September 2008. Concerning the Amplitude-Period relationship (following cycle maximum amplitude versus preceding cycle period), because cycle 23's period is known to be at least 142 months, the inferred regressions (90-percent prediction intervals) suggest that cycle 24's maximum amplitude will be about less than or equal to 96.1 +/- 55.0 (using all cycle pairs) or less than or equal to 91.0 +/- 36.7 (ignoring statistical outlier cycle pairs). Hence, cycle 24's maximum amplitude is expected to be less than 151, perhaps even less than 128, unless cycle pair 23/24 proves to be a statistical outlier.

Regional abundance of on-premise outlets and drinking patterns among Swiss young men: district level analyses and geographic adjustments.

PubMed

Astudillo, Mariana; Kuendig, Hervé; Centeno-Gil, Adriana; Wicki, Matthias; Gmel, Gerhard

2014-09-01

This study investigated the associations of alcohol outlet density with specific alcohol outcomes (consumption and consequences) among young men in Switzerland and assessed the possible geographically related variations. Alcohol consumption and drinking consequences were measured in a 2010-2011 study assessing substance use risk factors (Cohort Study on Substance Use Risk Factors) among 5519 young Swiss men. Outlet density was based on the number of on- and off-premise outlets in the district of residence. Linear regression models were run separately for drinking level, heavy episodic drinking (HED) and drinking consequences. Geographically weighted regression models were estimated when variations were recorded at the district level. No consistent association was found between outlet density and drinking consequences. A positive association between drinking level and HED with on-premise outlet density was found. Geographically weighted regressions were run for drinking level and HED. The predicted values for HED were higher in the southwest part of Switzerland (French-speaking part). Among Swiss young men, the density of outlets and, in particular, the abundance of bars, clubs and other on-premise outlets was associated with drinking level and HED, even when drinking consequences were not significantly affected. These findings support the idea that outlet density needs to be considered when developing and implementing regional-based prevention initiatives. © 2014 Australasian Professional Society on Alcohol and other Drugs.

Development of a partial least squares-artificial neural network (PLS-ANN) hybrid model for the prediction of consumer liking scores of ready-to-drink green tea beverages.

PubMed

Yu, Peigen; Low, Mei Yin; Zhou, Weibiao

2018-01-01

In order to develop products that would be preferred by consumers, the effects of the chemical compositions of ready-to-drink green tea beverages on consumer liking were studied through regression analyses. Green tea model systems were prepared by dosing solutions of 0.1% green tea extract with differing concentrations of eight flavour keys deemed to be important for green tea aroma and taste, based on a D-optimal experimental design, before undergoing commercial sterilisation. Sensory evaluation of the green tea model system was carried out using an untrained consumer panel to obtain hedonic liking scores of the samples. Regression models were subsequently trained to objectively predict the consumer liking scores of the green tea model systems. A linear partial least squares (PLS) regression model was developed to describe the effects of the eight flavour keys on consumer liking, with a coefficient of determination (R 2 ) of 0.733, and a root-mean-square error (RMSE) of 3.53%. The PLS model was further augmented with an artificial neural network (ANN) to establish a PLS-ANN hybrid model. The established hybrid model was found to give a better prediction of consumer liking scores, based on its R 2 (0.875) and RMSE (2.41%). Copyright © 2017 Elsevier Ltd. All rights reserved.

How distributed processing produces false negatives in voxel-based lesion-deficit analyses.

PubMed

Gajardo-Vidal, Andrea; Lorca-Puls, Diego L; Crinion, Jennifer T; White, Jitrachote; Seghier, Mohamed L; Leff, Alex P; Hope, Thomas M H; Ludersdorfer, Philipp; Green, David W; Bowman, Howard; Price, Cathy J

2018-07-01

In this study, we hypothesized that if the same deficit can be caused by damage to one or another part of a distributed neural system, then voxel-based analyses might miss critical lesion sites because preservation of each site will not be consistently associated with preserved function. The first part of our investigation used voxel-based multiple regression analyses of data from 359 right-handed stroke survivors to identify brain regions where lesion load is associated with picture naming abilities after factoring out variance related to object recognition, semantics and speech articulation so as to focus on deficits arising at the word retrieval level. A highly significant lesion-deficit relationship was identified in left temporal and frontal/premotor regions. Post-hoc analyses showed that damage to either of these sites caused the deficit of interest in less than half the affected patients (76/162 = 47%). After excluding all patients with damage to one or both of the identified regions, our second analysis revealed a new region, in the anterior part of the left putamen, which had not been previously detected because many patients had the deficit of interest after temporal or frontal damage that preserved the left putamen. The results illustrate how (i) false negative results arise when the same deficit can be caused by different lesion sites; (ii) some of the missed effects can be unveiled by adopting an iterative approach that systematically excludes patients with lesions to the areas identified in previous analyses, (iii) statistically significant voxel-based lesion-deficit mappings can be driven by a subset of patients; (iv) focal lesions to the identified regions are needed to determine whether the deficit of interest is the consequence of focal damage or much more extensive damage that includes the identified region; and, finally, (v) univariate voxel-based lesion-deficit mappings cannot, in isolation, be used to predict outcome in other patients. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

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.

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.

A reanalysis of cluster randomized trials showed interrupted time-series studies were valuable in health system evaluation.

PubMed

Fretheim, Atle; Zhang, Fang; Ross-Degnan, Dennis; Oxman, Andrew D; Cheyne, Helen; Foy, Robbie; Goodacre, Steve; Herrin, Jeph; Kerse, Ngaire; McKinlay, R James; Wright, Adam; Soumerai, Stephen B

2015-03-01

There is often substantial uncertainty about the impacts of health system and policy interventions. Despite that, randomized controlled trials (RCTs) are uncommon in this field, partly because experiments can be difficult to carry out. An alternative method for impact evaluation is the interrupted time-series (ITS) design. Little is known, however, about how results from the two methods compare. Our aim was to explore whether ITS studies yield results that differ from those of randomized trials. We conducted single-arm ITS analyses (segmented regression) based on data from the intervention arm of cluster randomized trials (C-RCTs), that is, discarding control arm data. Secondarily, we included the control group data in the analyses, by subtracting control group data points from intervention group data points, thereby constructing a time series representing the difference between the intervention and control groups. We compared the results from the single-arm and controlled ITS analyses with results based on conventional aggregated analyses of trial data. The findings were largely concordant, yielding effect estimates with overlapping 95% confidence intervals (CI) across different analytical methods. However, our analyses revealed the importance of a concurrent control group and of taking baseline and follow-up trends into account in the analysis of C-RCTs. The ITS design is valuable for evaluation of health systems interventions, both when RCTs are not feasible and in the analysis and interpretation of data from C-RCTs. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Nurses' Internet self-efficacy and attitudes toward web-based continuing learning.

PubMed

Liang, Jyh-Chong; Wu, Szu-Hsien; Tsai, Chin-Chung

2011-11-01

There are increasing opportunities for nurses to engage in continuing learning via the Internet; hence, it is important to explore nurses' attitudes toward web-based continuing learning. This paper explores 267 Taiwanese clinical nurses' attitudes toward web-based continuing learning. In addition, the role of the nurses' Internet self-efficacy in their attitudes is investigated. This study utilizes two questionnaires to respectively survey the nurses' Internet self-efficacy and their attitudes toward web-based continuing learning. In particular, the Internet Self-efficacy Survey includes two scales: 'Basic self-efficacy' (the perceived confidence of using basic Internet functions, such as the confidence in using a web browser or searching for online information) and 'Advanced self-efficacy' (the perceived confidence of using advanced Internet functions, such as the confidence in online discussion or making online payments). Exploratory factor analyses indicated adequate reliability and validity of the two questionnaires. The regression analyses revealed that both nurses' basic and advanced Internet self-efficacy can positively explain the perceived usefulness, ease of use and friendly feeling when using web-based continuing learning environments, whereas nurses' advanced Internet self-efficacy was the only predictor to explain how they intend to use web-based continuing learning environments more. Copyright © 2010 Elsevier Ltd. All rights reserved.

Vitamin D is associated with testosterone and hypogonadism in Chinese men: Results from a cross-sectional SPECT-China study.

PubMed

Wang, Ningjian; Han, Bing; Li, Qin; Chen, Yi; Chen, Yingchao; Xia, Fangzhen; Lin, Dongping; Jensen, Michael D; Lu, Yingli

2015-07-16

To date, no study has explored the association between androgen levels and 25-hydroxyvitamin D (25(OH)D) levels in Chinese men. We aimed to investigate the relationship between 25(OH)D levels and total and free testosterone (T), sex hormone binding globulin (SHBG), estradiol, and hypogonadism in Chinese men. Our data, which were based on the population, were collected from 16 sites in East China. There were 2,854 men enrolled in the study, with a mean (SD) age of 53.0 (13.5) years. Hypogonadism was defined as total T <11.3 nmol/L or free T <22.56 pmol/L. The 25(OH)D, follicle-stimulating hormone, luteinizing hormone, total T, estradiol and SHBG were measured using chemiluminescence and free T by enzyme-linked immune-sorbent assay. The associations between 25(OH)D and reproductive hormones and hypogonadism were analyzed using linear regression and binary logistic regression analyses, respectively. A total of 713 (25.0 %) men had hypogonadism with significantly lower 25(OH)D levels but greater BMI and HOMA-IR. Using linear regression, after fully adjusting for age, residence area, economic status, smoking, BMI, HOMA-IR, diabetes and systolic pressure, 25(OH)D was associated with total T and estradiol (P < 0.05). In the logistic regression analyses, increased quartiles of 25(OH)D were associated with significantly decreased odds ratios of hypogonadism (P for trend <0.01). This association, which was considerably attenuated by BMI and HOMA-IR, persisted in the fully adjusted model (P for trend <0.01) in which for the lowest compared with the highest quartile of 25(OH)D, the odds ratio of hypogonadism was 1.50 (95 % CI, 1.14, 1.97). A lower vitamin D level was associated with a higher prevalence of hypogonadism in Chinese men. This association might, in part, be explained by adiposity and insulin resistance and warrants additional investigation.

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…

Inverse associations between perceived racism and coronary artery calcification.

PubMed

Everage, Nicholas J; Gjelsvik, Annie; McGarvey, Stephen T; Linkletter, Crystal D; Loucks, Eric B

2012-03-01

To evaluate whether racial discrimination is associated with coronary artery calcification (CAC) in African-American participants of the Coronary Artery Risk Development in Young Adults (CARDIA) study. The study included American Black men (n = 571) and women (n = 791) aged 33 to 45 years in the CARDIA study. Perceived racial discrimination was assessed based on the Experiences of Discrimination scale (range, 1-35). CAC was evaluated using computed tomography. Primary analyses assessed associations between perceived racial discrimination and presence of CAC using multivariable-adjusted logistic regression analysis, adjusted for age, gender, socioeconomic position (SEP), psychosocial variables, and coronary heart disease (CHD) risk factors. In age- and gender-adjusted logistic regression models, odds of CAC decreased as the perceived racial discrimination score increased (odds ratio [OR], 0.94; 95% confidence interval [CI], 0.90-0.98 per 1-unit increase in Experiences of Discrimination scale). The relationship did not markedly change after further adjustment for SEP, psychosocial variables, or CHD risk factors (OR, 0.93; 95% CI, 0.87-0.99). Perceived racial discrimination was negatively associated with CAC in this study. Estimation of more forms of racial discrimination as well as replication of analyses in other samples will help to confirm or refute these findings. Copyright © 2012 Elsevier Inc. All rights reserved.

Relations Between Trait Impulsivity, Behavioral Impulsivity, Physiological Arousal, and Risky Sexual Behavior among Young Men

PubMed Central

Derefinko, Karen J.; Peters, Jessica R.; Eisenlohr-Moul, Tory A.; Walsh, Erin C.; Adams, Zachary W.; Lynam, Donald R.

2014-01-01

The current study examined how impulsivity-related traits (negative urgency, sensation seeking, and positive urgency), behavioral measures of risk taking and reward seeking, and physiological reactivity related to three different risky sexual behaviors in sexually active undergraduate men (N = 135). Regression analyses indicated that sensation seeking and behavioral risk-taking predicted unique variance in number of sexual partners. These findings suggest that, for young men, acquisition of new partners is associated with need for excitement and reward and willingness to take risks to meet those needs. Sensation seeking, behavioral risk-taking, and skin conductance reactivity to arousing stimuli was related to ever having engaged in sex with a stranger, indicating that, for men, willingness to have sex with a stranger is related not only to the need for excitement and risk-taking but also with innate responsiveness to arousing environmental triggers. In contrast, regression analyses indicated that young men who were impulsive in the context of negative emotions were less likely to use condoms, suggesting that emotion-based impulsivity may be an important factor in negligent prophylactic use. This study adds to the current understanding of the divergence between the correlates of risky sexual behaviors and may lend utility to the development of individualized HIV prevention programming. PMID:24958252

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.

Ecdysis period and rate deviations of dengue mosquito vector, Aedes aegypti reared in different artificial water-holding containers.

PubMed

Almanzor, Beatriz Louise J; Ho, Howell T; Carvajal, Thaddeus M

2016-03-01

Artificial water-holding containers (AWHCs) have been well-documented in many Aedes aegypti studies for dengue surveillance and developmental research. Hence, we investigated the role of different AHWCs on the development and ecdysis period of Ae. aegypti dengue vector, a container breeding mosquito. Nine types of AWHCs, namely glass, polystyrene foam, rubber, steel, porcelain, plastic, aluminum, clay and concrete, were chosen for the study. All AWHCs were subjected to the developmental assay for an observation period of 10 days. Regression and hazard analyses were employed to the developmental stages and the characteristics of the AWHCs. The observations revealed that Ae. aegypti development is fastest in glass and polystyrene containers while slowest in concrete containers. Moreover, pupal ecdysis appears to be the most affected by the characteristics of the AWHCs based on regression and hazard analyses. Characteristics of the container that can regulate water temperature seem to be the driving force with regards to the slow or fast development of Ae. aegypti, more notably in pupal ecdysis. The results of the study further strengthen our understanding on the dynamics of Ae. aegypti's developmental biology to different characteristics of artificial water containers. This, in turn, would aid in devising vector control strategies against dengue especially in endemic areas.

Trends in the incidence of dementia: design and methods in the Alzheimer Cohorts Consortium.

PubMed

Chibnik, Lori B; Wolters, Frank J; Bäckman, Kristoffer; Beiser, Alexa; Berr, Claudine; Bis, Joshua C; Boerwinkle, Eric; Bos, Daniel; Brayne, Carol; Dartigues, Jean-Francois; Darweesh, Sirwan K L; Debette, Stephanie; Davis-Plourde, Kendra L; Dufouil, Carole; Fornage, Myriam; Grasset, Leslie; Gudnason, Vilmundur; Hadjichrysanthou, Christoforos; Helmer, Catherine; Ikram, M Arfan; Ikram, M Kamran; Kern, Silke; Kuller, Lewis H; Launer, Lenore; Lopez, Oscar L; Matthews, Fiona; Meirelles, Osorio; Mosley, Thomas; Ower, Alison; Psaty, Bruce M; Satizabal, Claudia L; Seshadri, Sudha; Skoog, Ingmar; Stephan, Blossom C M; Tzourio, Christophe; Waziry, Reem; Wong, Mei Mei; Zettergren, Anna; Hofman, Albert

2017-10-01

Several studies have reported a decline in incidence of dementia which may have large implications for the projected burden of disease, and provide important guidance to preventive efforts. However, reports are conflicting or inconclusive with regard to the impact of gender and education with underlying causes of a presumed declining trend remaining largely unidentified. The Alzheimer Cohorts Consortium aggregates data from nine international population-based cohorts to determine changes in the incidence of dementia since 1990. We will employ Poisson regression models to calculate incidence rates in each cohort and Cox proportional hazard regression to compare 5-year cumulative hazards across study-specific epochs. Finally, we will meta-analyse changes per decade across cohorts, and repeat all analysis stratified by sex, education and APOE genotype. In all cohorts combined, there are data on almost 69,000 people at risk of dementia with the range of follow-up years between 2 and 27. The average age at baseline is similar across cohorts ranging between 72 and 77. Uniting a wide range of disease-specific and methodological expertise in research teams, the first analyses within the Alzheimer Cohorts Consortium are underway to tackle outstanding challenges in the assessment of time-trends in dementia occurrence.

Geo-Chip analysis reveals reduced functional diversity of the bacterial community at a dumping site for dredged Elbe sediment.

PubMed

Störmer, Rebecca; Wichels, Antje; Gerdts, Gunnar

2013-12-15

The dumping of dredged sediments represents a major stressor for coastal ecosystems. The impact on the ecosystem function is determined by its complexity not easy to assess. In the present study, we evaluated the potential of bacterial community analyses to act as ecological indicators in environmental monitoring programmes. We investigated the functional structure of bacterial communities, applying functional gene arrays (GeoChip4.2). The relationship between functional genes and environmental factors was analysed using distance-based multivariate multiple regression. Apparently, both the function and structure of the bacterial communities are impacted by dumping activities. The bacterial community at the dumping centre displayed a significant reduction of its entire functional diversity compared with that found at a reference site. DDX compounds separated bacterial communities of the dumping site from those of un-impacted sites. Thus, bacterial community analyses show great potential as ecological indicators in environmental monitoring. Copyright © 2013 Elsevier Ltd. All rights reserved.

The relative effect of noise at different times of day: An analysis of existing survey data

NASA Technical Reports Server (NTRS)

Fields, J. M.

1986-01-01

This report examines survey evidence on the relative impact of noise at different times of day and assesses the survey methodology which produces that evidence. Analyses of the regression of overall (24-hour) annoyance on noise levels in different time periods can provide direct estimates of the value of the parameters in human reaction models which are used in environmental noise indices such as LDN and CNEL. In this report these analyses are based on the original computer tapes containing the responses of 22,000 respondents from ten studies of response to noise in residential areas. The estimates derived from these analyses are found to be so inaccurate that they do not provide useful information for policy or scientific purposes. The possibility that the type of questionnaire item could be biasing the estimates of the time-of-day weightings is considered but not supported by the data. Two alternatives to the conventional noise reaction model (adjusted energy model) are considered but not supported by the data.

The relative effect of noise at different times of day: An analysis of existing survey data

NASA Astrophysics Data System (ADS)

Fields, J. M.

1986-04-01

This report examines survey evidence on the relative impact of noise at different times of day and assesses the survey methodology which produces that evidence. Analyses of the regression of overall (24-hour) annoyance on noise levels in different time periods can provide direct estimates of the value of the parameters in human reaction models which are used in environmental noise indices such as LDN and CNEL. In this report these analyses are based on the original computer tapes containing the responses of 22,000 respondents from ten studies of response to noise in residential areas. The estimates derived from these analyses are found to be so inaccurate that they do not provide useful information for policy or scientific purposes. The possibility that the type of questionnaire item could be biasing the estimates of the time-of-day weightings is considered but not supported by the data. Two alternatives to the conventional noise reaction model (adjusted energy model) are considered but not supported by the data.

The interactive effects of proactive personality and work-family interference on well-being.

PubMed

Cunningham, Christopher J L; De La Rosa, Gabriel M

2008-07-01

Proactive personality was expected to moderate the relationship between controllable work and nonwork stressors (e.g., time-based work-family interference) and job/life satisfaction. Moderated multiple regression analyses of survey data from a sample of professionals (N=133) revealed a significant interaction between time-based family interfering-with work and proactive personality predicting life satisfaction and several main effects offering partial support for the hypothesized relationships (alpha<.05). No other interactions between proactive personality and other forms of work-family interference were observed. The benefits of proactive personality may only emerge when personal control over occupational stressors can be exercised. Copyright (c) 2008 APA, all rights reserved.

Ambivalent Sexism in Close Relationships: (Hostile) Power and (Benevolent) Romance Shape Relationship Ideals

PubMed Central

Lee, Tiane L.; Fiske, Susan T.; Glick, Peter; Chen, Zhixia

2013-01-01

Gender-based structural power and heterosexual dependency produce ambivalent gender ideologies, with hostility and benevolence separately shaping close-relationship ideals. The relative importance of romanticized benevolent versus more overtly power-based hostile sexism, however, may be culturally dependent. Testing this, northeast US (N=311) and central Chinese (N=290) undergraduates rated prescriptions and proscriptions (ideals) for partners and completed Ambivalent Sexism and Ambivalence toward Men Inventories (ideologies). Multiple regressions analyses conducted on group-specific relationship ideals revealed that benevolent ideologies predicted partner ideals, in both countries, especially for US culture’s romance-oriented relationships. Hostile attitudes predicted men’s ideals, both American and Chinese, suggesting both societies’ dominant-partner advantage. PMID:23914004

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.

MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.

PubMed

Van der Sluis, Sophie; Dolan, Conor V; Li, Jiang; Song, Youqiang; Sham, Pak; Posthuma, Danielle; Li, Miao-Xin

2015-04-01

Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models. MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

The interplay of frequency of volunteering and prosocial motivation on purpose in life in emerging adults.

PubMed

Okun, Morris A; Kim, Ga Young

2016-01-01

One developmental task in emerging adulthood is finding meaning and purpose in life. Volunteering has been touted as one role that fosters purpose in life. We examined whether the association between frequency of volunteering and purpose in life varies with pleasure-based prosocial motivation and pressure-based prosocial motivation in a sample of 576 undergraduates, ages 18-22 years old. In a regression analysis predicting purpose in life, the frequency of volunteering by pleasure-based prosocial motivation by pressure-based prosocial motivation interaction effect was significant (p = .042). Simple slopes analyses revealed that frequency of volunteering was not significantly (p = .478) related to purpose in life among college students who were low in both pleasure-based and pressure-based prosocial motivation. The findings of the present study highlight the importance of prosocial motivation for understanding whether emerging adults' purpose in life will be enhanced by volunteering.

Effects of the X:IT smoking intervention: a school-based cluster randomized trial.

PubMed

Andersen, Anette; Krølner, Rikker; Bast, Lotus Sofie; Thygesen, Lau Caspar; Due, Pernille

2015-12-01

Uptake of smoking in adolescence is still of major public health concern. Evaluations of school-based programmes for smoking prevention show mixed results. The aim of this study was to examine the effect of X:IT, a multi-component school-based programme to prevent adolescent smoking. Data from a Danish cluster randomized trial included 4041 year-7 students (mean age: 12.5) from 51 intervention and 43 control schools. Outcome measure 'current smoking' was dichotomized into smoking daily, weekly, monthly or more seldom vs do not smoke. Analyses were adjusted for baseline covariates: sex, family socioeconomic position (SEP), best friend's smoking and parental smoking. We performed multilevel, logistic regression analyses of available cases and intention-to-treat (ITT) analyses, replacing missing outcome values by multiple imputation. At baseline, 4.7% and 6.8% of the students at the intervention and the control schools smoked, respectively. After 1 year of the intervention, the prevalence was 7.9% and 10.7%, respectively. At follow-up, 553 students (13.7%) did not answer the question on smoking. Available case analyses: crude odds ratios (OR) for smoking at intervention schools compared with control schools: 0.65 (0.48-0.88) and adjusted: 0.70 (0.47-1.04). ITT analyses: crude OR for smoking at intervention schools compared with control schools: 0.67 (0.50-0.89) and adjusted: 0.61 (0.45-0.82). Students at intervention schools had a lower risk of smoking after a year of intervention in year 7. This multi-component intervention involving educational, parental and context-related intervention components seems to be efficient in lowering or postponing smoking uptake in Danish adolescents. © The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

Methods for estimating selected spring and fall low-flow frequency statistics for ungaged stream sites in Iowa, based on data through June 2014

USGS Publications Warehouse

Eash, David A.; Barnes, Kimberlee K.; O'Shea, Padraic S.

2016-09-19

A statewide study was led to develop regression equations for estimating three selected spring and three selected fall low-flow frequency statistics for ungaged stream sites in Iowa. The estimation equations developed for the six low-flow frequency statistics include spring (April through June) 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years and fall (October through December) 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years. Estimates of the three selected spring statistics are provided for 241 U.S. Geological Survey continuous-record streamgages, and estimates of the three selected fall statistics are provided for 238 of these streamgages, using data through June 2014. Because only 9 years of fall streamflow record were available, three streamgages included in the development of the spring regression equations were not included in the development of the fall regression equations. Because of regulation, diversion, or urbanization, 30 of the 241 streamgages were not included in the development of the regression equations. The study area includes Iowa and adjacent areas within 50 miles of the Iowa border. Because trend analyses indicated statistically significant positive trends when considering the period of record for most of the streamgages, the longest, most recent period of record without a significant trend was determined for each streamgage for use in the study. Geographic information system software was used to measure 63 selected basin characteristics for each of the 211streamgages used to develop the regional regression equations. The study area was divided into three low-flow regions that were defined in a previous study for the development of regional regression equations.Because several streamgages included in the development of regional regression equations have estimates of zero flow calculated from observed streamflow for selected spring and fall low-flow frequency statistics, the final equations for the three low-flow regions were developed using two types of regression analyses—left-censored and generalized-least-squares regression analyses. A total of 211 streamgages were included in the development of nine spring regression equations—three equations for each of the three low-flow regions. A total of 208 streamgages were included in the development of nine fall regression equations—three equations for each of the three low-flow regions. A censoring threshold was used to develop 15 left-censored regression equations to estimate the three fall low-flow frequency statistics for each of the three low-flow regions and to estimate the three spring low-flow frequency statistics for the southern and northwest regions. For the northeast region, generalized-least-squares regression was used to develop three equations to estimate the three spring low-flow frequency statistics. For the northeast region, average standard errors of prediction range from 32.4 to 48.4 percent for the spring equations and average standard errors of estimate range from 56.4 to 73.8 percent for the fall equations. For the northwest region, average standard errors of estimate range from 58.9 to 62.1 percent for the spring equations and from 83.2 to 109.4 percent for the fall equations. For the southern region, average standard errors of estimate range from 43.2 to 64.0 percent for the spring equations and from 78.1 to 78.7 percent for the fall equations.The regression equations are applicable only to stream sites in Iowa with low flows not substantially affected by regulation, diversion, or urbanization and with basin characteristics within the range of those used to develop the equations. The regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system application. StreamStats allows users to click on any ungaged stream site and compute estimates of the six selected spring and fall low-flow statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged site are provided. StreamStats also allows users to click on any Iowa streamgage to obtain computed estimates for the six selected spring and fall low-flow statistics.

Evidence-based identification of key beliefs explaining adult male circumcision motivation in Zimbabwe: targets for behavior change messaging.

PubMed

Montaño, Daniel E; Kasprzyk, Danuta; Hamilton, Deven T; Tshimanga, Mufuta; Gorn, Gerald

2014-05-01

Male circumcision (MC) reduces HIV acquisition among men, leading WHO/UNAIDS to recommend a goal to circumcise 80 % of men in high HIV prevalence countries. Significant investment to increase MC capacity in priority countries was made, yet only 5 % of the goal has been achieved in Zimbabwe. The integrated behavioral model (IBM) was used as a framework to investigate the factors affecting MC motivation among men in Zimbabwe. A survey instrument was designed based on elicitation study results, and administered to a representative household-based sample of 1,201 men aged 18-30 from two urban and two rural areas in Zimbabwe. Multiple regression analysis found all five IBM constructs significantly explained MC Intention. Nearly all beliefs underlying the IBM constructs were significantly correlated with MC Intention. Stepwise regression analysis of beliefs underlying each construct respectively found that 13 behavioral beliefs, 5 normative beliefs, 4 descriptive norm beliefs, 6 efficacy beliefs, and 10 control beliefs were significant in explaining MC Intention. A final stepwise regression of the five sets of significant IBM construct beliefs identified 14 key beliefs that best explain Intention. Similar analyses were carried out with subgroups of men by urban-rural and age. Different sets of behavioral, normative, efficacy, and control beliefs were significant for each sub-group, suggesting communication messages need to be targeted to be most effective for sub-groups. Implications for the design of effective MC demand creation messages are discussed. This study demonstrates the application of theory-driven research to identify evidence-based targets for intervention messages to increase men's motivation to get circumcised and thereby improve demand for male circumcision.

Comparing the index-flood and multiple-regression methods using L-moments

NASA Astrophysics Data System (ADS)

Malekinezhad, H.; Nachtnebel, H. P.; Klik, A.

In arid and semi-arid regions, the length of records is usually too short to ensure reliable quantile estimates. Comparing index-flood and multiple-regression analyses based on L-moments was the main objective of this study. Factor analysis was applied to determine main influencing variables on flood magnitude. Ward’s cluster and L-moments approaches were applied to several sites in the Namak-Lake basin in central Iran to delineate homogeneous regions based on site characteristics. Homogeneity test was done using L-moments-based measures. Several distributions were fitted to the regional flood data and index-flood and multiple-regression methods as two regional flood frequency methods were compared. The results of factor analysis showed that length of main waterway, compactness coefficient, mean annual precipitation, and mean annual temperature were the main variables affecting flood magnitude. The study area was divided into three regions based on the Ward’s method of clustering approach. The homogeneity test based on L-moments showed that all three regions were acceptably homogeneous. Five distributions were fitted to the annual peak flood data of three homogeneous regions. Using the L-moment ratios and the Z-statistic criteria, GEV distribution was identified as the most robust distribution among five candidate distributions for all the proposed sub-regions of the study area, and in general, it was concluded that the generalised extreme value distribution was the best-fit distribution for every three regions. The relative root mean square error (RRMSE) measure was applied for evaluating the performance of the index-flood and multiple-regression methods in comparison with the curve fitting (plotting position) method. In general, index-flood method gives more reliable estimations for various flood magnitudes of different recurrence intervals. Therefore, this method should be adopted as regional flood frequency method for the study area and the Namak-Lake basin in central Iran. To estimate floods of various return periods for gauged catchments in the study area, the mean annual peak flood of the catchments may be multiplied by corresponding values of the growth factors, and computed using the GEV distribution.

University Students' Knowledge Structures and Informal Reasoning on the Use of Genetically Modified Foods: Multidimensional Analyses

NASA Astrophysics Data System (ADS)

Wu, Ying-Tien

2013-10-01

This study aims to provide insights into the role of learners' knowledge structures about a socio-scientific issue (SSI) in their informal reasoning on the issue. A total of 42 non-science major university students' knowledge structures and informal reasoning were assessed with multidimensional analyses. With both qualitative and quantitative analyses, this study revealed that those students with more extended and better-organized knowledge structures, as well as those who more frequently used higher-order information processing modes, were more oriented towards achieving a higher-level informal reasoning quality. The regression analyses further showed that the "richness" of the students' knowledge structures explained 25 % of the variation in their rebuttal construction, an important indicator of reasoning quality, indicating the significance of the role of students' sophisticated knowledge structure in SSI reasoning. Besides, this study also provides some initial evidence for the significant role of the "core" concept within one's knowledge structure in one's SSI reasoning. The findings in this study suggest that, in SSI-based instruction, science instructors should try to identify students' core concepts within their prior knowledge regarding the SSI, and then they should try to guide students to construct and structure relevant concepts or ideas regarding the SSI based on their core concepts. Thus, students could obtain extended and well-organized knowledge structures, which would then help them achieve better learning transfer in dealing with SSIs.

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.

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

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.

Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models

PubMed Central

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Background Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models’ with and without novel biomarkers. Objectives Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. Materials and Methods We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham’s “general CVD risk” algorithm. Results The command is addpred for logistic regression models. Conclusions The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers. PMID:27279830

Improving validation methods for molecular diagnostics: application of Bland-Altman, Deming and simple linear regression analyses in assay comparison and evaluation for next-generation sequencing

PubMed Central

Misyura, Maksym; Sukhai, Mahadeo A; Kulasignam, Vathany; Zhang, Tong; Kamel-Reid, Suzanne; Stockley, Tracy L

2018-01-01

Aims A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R2), using R2 as the primary metric of assay agreement. However, the use of R2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. Methods We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Results Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. Conclusions The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. PMID:28747393

Forest Type and Above Ground Biomass Estimation Based on Sentinel-2A and WorldView-2 Data Evaluation of Predictor nd Data Suitability

NASA Astrophysics Data System (ADS)

Fritz, Andreas; Enßle, Fabian; Zhang, Xiaoli; Koch, Barbara

2016-08-01

The present study analyses the two earth observation sensors regarding their capability of modelling forest above ground biomass and forest density. Our research is carried out at two different demonstration sites. The first is located in south-western Germany (region Karlsruhe) and the second is located in southern China in Jiangle County (Province Fujian). A set of spectral and spatial predictors are computed from both, Sentinel-2A and WorldView-2 data. Window sizes in the range of 3*3 pixels to 21*21 pixels are computed in order to cover the full range of the canopy sizes of mature forest stands. Textural predictors of first and second order (grey-level-co-occurrence matrix) are calculated and are further used within a feature selection procedure. Additionally common spectral predictors from WorldView-2 and Sentinel-2A data such as all relevant spectral bands and NDVI are integrated in the analyses. To examine the most important predictors, a predictor selection algorithm is applied to the data, whereas the entire predictor set of more than 1000 predictors is used to find most important ones. Out of the original set only the most important predictors are then further analysed. Predictor selection is done with the Boruta package in R (Kursa and Rudnicki (2010)), whereas regression is computed with random forest. Prior the classification and regression a tuning of parameters is done by a repetitive model selection (100 runs), based on the .632 bootstrapping. Both are implemented in the caret R pack- age (Kuhn et al. (2016)). To account for the variability in the data set 100 independent runs are performed. Within each run 80 percent of the data is used for training and the 20 percent are used for an independent validation. With the subset of original predictors mapping of above ground biomass is performed.

Prognosis Related to Metastatic Burden Measured by 18F-Fluorocholine PET/CT in Castrate Resistant Prostate Cancer

PubMed Central

Kwee, Sandi A.; Lim, John; Watanabe, Alex; Kromer-Baker, Kathleen; Coel, Marc N.

2015-01-01

This study investigates the prognostic significance of metabolically active tumor volume (MATV) measurements applied to fluorine-18 fluorocholine (FC) PET/CT in castrate-resistant prostate cancer (CRPC). Methods FC PET/CT imaging was performed in 30 patients with CRPC. Metastatic disease was quantified on the basis of maximum standardized uptake value (SUVmax), MATV, and total lesion activity (TLA = MATV × mean SUV). Tumor burden indices derived from whole-body summation of PET tumor volume measurements (ie. net MATV and net TLA) were evaluated as variables in Cox regression and Kaplan-Meier survival analyses. Results Net MATV ranged from 0.12 cm3 to 1543.9 cm3 (median 52.6 cm3). Net TLA ranged from 0.40g to 6688.7g (median 225.1g). PSA level at the time of PET correlated significantly with net MATV (Pearson r = 0.65, p = 0.0001) and net TLA (r = 0.60, p = 0.0005) but not highest lesional SUVmax of each scan. Survivors were followed for a median 23 months (range 6 – 38 months). On Cox regression analyses, overall survival was significantly associated with net MATV (p = 0.0068), net TLA (p = 0.0072), and highest lesion SUVmax (p = 0.0173), and borderline associated with PSA level (p = 0.0458). Only net MATV and net TLA remained significant in univariate-adjusted survival analyses. Kaplan-Meier analysis demonstrated significant differences in survival between groups stratified by median net MATV (log-rank P = 0.0371), net TLA (log-rank P = 0.0371), and highest lesion SUVmax (log-rank P = 0.0223). Conclusions Metastatic prostate cancer detected by FC PET/CT can be quantified based on volumetric measurements of tumor metabolic activity. The prognostic value of FC PET/CT may stem from this capacity to assess whole-body tumor burden. With further clinical validation, FC PET-based indices of global disease activity and mortality risk could prove useful in patient-individualized treatment of CRPC. PMID:24676753

Development of a macrophyte-based index of biotic integrity for Minnesota lakes

USGS Publications Warehouse

Beck, M.W.; Hatch, L.K.; Vondracek, B.; Valley, R.D.

2010-01-01

Traditional approaches for managing aquatic resources have often failed to account for effects of anthropogenic disturbances on biota that are not directly reflected by chemical and physical proxies of environmental condition. The index of biotic integrity (IBI) is a potentially effective assessment method to integrate ecological, functional, and structural aspects of aquatic systems. A macrophyte-based IBI was developed for Minnesota lakes to assess the ability of aquatic plant communities to indicate environmental condition. The index was developed using quantitative point intercept vegetation surveys for 97 lakes that represent a range of limnological and watershed characteristics. We followed an approach similar to that used in Wisconsin to develop the aquatic macrophyte community index (AMCI). Regional adaptation of the AMCI required the identification of species representative of macrophyte communities in Minnesota. Metrics and scaling methods were also substantially modified to produce a more empirically robust index. Regression analyses indicated that IBI scores reflected statewide differences in lake trophic state (R2 = 0.57, F = 130.3, df = 1, 95, p < 0.005), agricultural (R2 = 0.51, F = 83.0, df = 1, 79, p < 0.005), urban (R2 = 0.22, F = 23.0, df = 1, 79, p < 0.005), and forested land uses (R2 = 0.51, F = 84.7, df = 1, 79, p < 0.005), and county population density (R2 = 0.14, F = 16.6, df = 1, 95, p < 0.005). Variance partitioning analyses using multiple regression models indicated a unique response of the IBI to human-induced stress separate from a response to natural lake characteristics. The IBI was minimally affected by differences in sample point density as indicated by Monte Carlo analyses of reduced sampling effort. Our analysis indicates that a macrophyte IBI calibrated for Minnesota lakes could be useful for identifying differences in environmental condition attributed to human-induced stress gradients. ?? 2010 Elsevier Ltd.

Influence of systemic bone mineral density on atlantoaxial subluxation in patients with rheumatoid arthritis.

PubMed

Han, M H; Ryu, J I; Kim, C H; Kim, J M; Cheong, J H; Bak, K H; Chun, H J

2017-06-01

Osteopenia and osteoporosis were independent predictive factors for higher atlantoaxial subluxation occurrence in patients with lower body mass index. Our findings suggest that patients with rheumatoid arthritis with osteopenia or osteoporosis, particularly those with lower body mass index (BMI), should be screened regularly to determine the status of their cervical spines. Cervical spine involvement in rheumatoid arthritis (RA) patients may cause serious adverse effects on quality of life and overall health. This study aimed to evaluate the association between atlantodental interval (ADI), atlantoaxial subluxation (AAS), and systemic bone mineral density (BMD) based on BMI variations among established patients with RA. The ADI was transformed to the natural log scale to normalize distributions for all analyses. Multivariable linear regression analyses were used to identify independent predictive factors for ADI based on each BMD classification. Multivariate Cox regression analyses were also performed to identify independent predictive factors for the risk of AAS, which were classified by tertile groups of BMI. A total of 1220 patients with RA who had undergone at least one or more cervical radiography and BMD assessments were identified and enrolled. We found that the association between BMD and ADI (β, -0.029; 95% CI, -0.059 to 0.002; p = 0.070) fell short of achieving statistical significance. However, the ADI showed a 3.6% decrease per 1 BMI increase in the osteoporosis group (β, -0.036; 95% CI, -0.061 to -0.011; p = 0.004). The osteopenia and osteoporosis groups showed about a 1.5-fold and a 1.8-fold increased risk of AAS occurrence among the first tertile of the BMI group. Our study showed a possible association between lower BMD and AAS occurrence in patients with RA with lower BMI. Further studies are needed to confirm our findings.

Mixed oxidizer hybrid propulsion system optimization under uncertainty using applied response surface methodology and Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Whitehead, James Joshua

The analysis documented herein provides an integrated approach for the conduct of optimization under uncertainty (OUU) using Monte Carlo Simulation (MCS) techniques coupled with response surface-based methods for characterization of mixture-dependent variables. This novel methodology provides an innovative means of conducting optimization studies under uncertainty in propulsion system design. Analytic inputs are based upon empirical regression rate information obtained from design of experiments (DOE) mixture studies utilizing a mixed oxidizer hybrid rocket concept. Hybrid fuel regression rate was selected as the target response variable for optimization under uncertainty, with maximization of regression rate chosen as the driving objective. Characteristic operational conditions and propellant mixture compositions from experimental efforts conducted during previous foundational work were combined with elemental uncertainty estimates as input variables. Response surfaces for mixture-dependent variables and their associated uncertainty levels were developed using quadratic response equations incorporating single and two-factor interactions. These analysis inputs, response surface equations and associated uncertainty contributions were applied to a probabilistic MCS to develop dispersed regression rates as a function of operational and mixture input conditions within design space. Illustrative case scenarios were developed and assessed using this analytic approach including fully and partially constrained operational condition sets over all of design mixture space. In addition, optimization sets were performed across an operationally representative region in operational space and across all investigated mixture combinations. These scenarios were selected as representative examples relevant to propulsion system optimization, particularly for hybrid and solid rocket platforms. Ternary diagrams, including contour and surface plots, were developed and utilized to aid in visualization. The concept of Expanded-Durov diagrams was also adopted and adapted to this study to aid in visualization of uncertainty bounds. Regions of maximum regression rate and associated uncertainties were determined for each set of case scenarios. Application of response surface methodology coupled with probabilistic-based MCS allowed for flexible and comprehensive interrogation of mixture and operating design space during optimization cases. Analyses were also conducted to assess sensitivity of uncertainty to variations in key elemental uncertainty estimates. The methodology developed during this research provides an innovative optimization tool for future propulsion design efforts.

Interannual drought index variations in Central Europe related to large-scale atmospheric circulation

NASA Astrophysics Data System (ADS)

Beck, Christoph; Philipp, Andreas; Jacobeit, Jucundus

2014-05-01

This contribution investigates the relationship between large-scale atmospheric circulation and interannual variations of the standardized precipitation index (SPI) in central Europe. To this end occurrence frequencies of circulation types (CT) derived from a variety of circulation type classifications (CTC) applied to daily sea level pressure (SLP) data and mean circulation indices of vorticity (V), zonality (Z) and meridionality (M) have been utilized as predictors within multiple regression models (MRM) for the estimation of gridded 3-month SPI values over central Europe for the period 1950 to 2010. CTC based MRMs used in the analyses comprise variants concerning the basic method for CT classification, the number of CTs, the size and location of the spatial domain used for CTCs and the exclusive use of CT frequencies or the combined use of CT frequencies and mean circulation indices as predictors. Adequate MRM predictor combinations have been identified by applying stepwise multiple regression analyses within a resampling framework. The performance (robustness) of the resulting MRMs has been quantified based on a leave-one out cross-validation procedure applying several skill scores. Furthermore the relative importance of individual predictors has been estimated for each MRM. From these analyses it can be stated that i.) the consideration of vorticity characteristics within CTCs, ii.) a relatively small size of the spatial domain to which CTCs are applied and iii.) the inclusion of mean circulation indices appear to improve model skill. However model skill exhibits distinct variations between seasons and regions. Whereas promising skill can be stated for the western and northwestern parts of the central European domain only unsatisfactorily skill is reached in the more continental regions and particularly during summer. Thus it can be concluded that the here presented approaches feature the potential for the downscaling of central European drought index variations from large-scale circulation at least for some regions. Further improvements of CTC based approaches may be expected from the optimization of CTCs for explaining the SPI e.g. via the inclusion of additional variables into the classification procedure.

Stability and change in screen-based sedentary behaviours and associated factors among Norwegian children in the transition between childhood and adolescence.

PubMed

Gebremariam, Mekdes K; Totland, Torunn H; Andersen, Lene F; Bergh, Ingunn H; Bjelland, Mona; Grydeland, May; Ommundsen, Yngvar; Lien, Nanna

2012-02-06

In order to inform interventions to prevent sedentariness, more longitudinal studies are needed focusing on stability and change over time in multiple sedentary behaviours. This paper investigates patterns of stability and change in TV/DVD use, computer/electronic game use and total screen time (TST) and factors associated with these patterns among Norwegian children in the transition between childhood and adolescence. The baseline of this longitudinal study took place in September 2007 and included 975 students from 25 control schools of an intervention study, the HEalth In Adolescents (HEIA) study. The first follow-up took place in May 2008 and the second follow-up in May 2009, with 885 students participating at all time points (average age at baseline = 11.2, standard deviation ± 0.3). Time used for/spent on TV/DVD and computer/electronic games was self-reported, and a TST variable (hours/week) was computed. Tracking analyses based on absolute and rank measures, as well as regression analyses to assess factors associated with change in TST and with tracking high TST were conducted. Time spent on all sedentary behaviours investigated increased in both genders. Findings based on absolute and rank measures revealed a fair to moderate level of tracking over the 2 year period. High parental education was inversely related to an increase in TST among females. In males, self-efficacy related to barriers to physical activity and living with married or cohabitating parents were inversely related to an increase in TST. Factors associated with tracking high vs. low TST in the multinomial regression analyses were low self-efficacy and being of an ethnic minority background among females, and low self-efficacy, being overweight/obese and not living with married or cohabitating parents among males. Use of TV/DVD and computer/electronic games increased with age and tracked over time in this group of 11-13 year old Norwegian children. Interventions targeting these sedentary behaviours should thus be introduced early. The identified modifiable and non-modifiable factors associated with change in TST and tracking of high TST should be taken into consideration when planning such interventions.

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

Responding to nonwords in the lexical decision task: Insights from the English Lexicon Project.

PubMed

Yap, Melvin J; Sibley, Daragh E; Balota, David A; Ratcliff, Roger; Rueckl, Jay

2015-05-01

Researchers have extensively documented how various statistical properties of words (e.g., word frequency) influence lexical processing. However, the impact of lexical variables on nonword decision-making performance is less clear. This gap is surprising, because a better specification of the mechanisms driving nonword responses may provide valuable insights into early lexical processes. In the present study, item-level and participant-level analyses were conducted on the trial-level lexical decision data for almost 37,000 nonwords in the English Lexicon Project in order to identify the influence of different psycholinguistic variables on nonword lexical decision performance and to explore individual differences in how participants respond to nonwords. Item-level regression analyses reveal that nonword response time was positively correlated with number of letters, number of orthographic neighbors, number of affixes, and base-word number of syllables, and negatively correlated with Levenshtein orthographic distance and base-word frequency. Participant-level analyses also point to within- and between-session stability in nonword responses across distinct sets of items, and intriguingly reveal that higher vocabulary knowledge is associated with less sensitivity to some dimensions (e.g., number of letters) but more sensitivity to others (e.g., base-word frequency). The present findings provide well-specified and interesting new constraints for informing models of word recognition and lexical decision. (c) 2015 APA, all rights reserved).

Comparison of detection methods for HPV status as a prognostic marker for loco-regional control after radiochemotherapy in patients with HNSCC.

PubMed

Linge, Annett; Schötz, Ulrike; Löck, Steffen; Lohaus, Fabian; von Neubeck, Cläre; Gudziol, Volker; Nowak, Alexander; Tinhofer, Inge; Budach, Volker; Sak, Ali; Stuschke, Martin; Balermpas, Panagiotis; Rödel, Claus; Bunea, Hatice; Grosu, Anca-Ligia; Abdollahi, Amir; Debus, Jürgen; Ganswindt, Ute; Lauber, Kirsten; Pigorsch, Steffi; Combs, Stephanie E; Mönnich, David; Zips, Daniel; Baretton, Gustavo B; Buchholz, Frank; Krause, Mechthild; Belka, Claus; Baumann, Michael

2018-04-01

To compare six HPV detection methods in pre-treatment FFPE tumour samples from patients with locally advanced head and neck squamous cell carcinoma (HNSCC) who received postoperative (N = 175) or primary (N = 90) radiochemotherapy. HPV analyses included detection of (i) HPV16 E6/E7 RNA, (ii) HPV16 DNA (PCR-based arrays, A-PCR), (iii) HPV DNA (GP5+/GP6+ qPCR, (GP-PCR)), (iv) p16 (immunohistochemistry, p16 IHC), (v) combining p16 IHC and the A-PCR result and (vi) combining p16 IHC and the GP-PCR result. Differences between HPV positive and negative subgroups were evaluated for the primary endpoint loco-regional control (LRC) using Cox regression. Correlation between the HPV detection methods was high (chi-squared test, p < 0.001). While p16 IHC analysis resulted in several false positive classifications, A-PCR, GP-PCR and the combination of p16 IHC and A-PCR or GP-PCR led to results comparable to RNA analysis. In both cohorts, Cox regression analyses revealed significantly prolonged LRC for patients with HPV positive tumours irrespective of the detection method. The most stringent classification was obtained by detection of HPV16 RNA, or combining p16 IHC with A-PCR or GP-PCR. This approach revealed the lowest rate of recurrence in patients with tumours classified as HPV positive and therefore appears most suited for patient stratification in HPV-based clinical studies. Copyright © 2017 Elsevier B.V. All rights reserved.

Left frontal cortex connectivity underlies cognitive reserve in prodromal Alzheimer disease

PubMed Central

Franzmeier, Nicolai; Duering, Marco; Weiner, Michael; Dichgans, Martin

2017-01-01

Objective: To test whether higher global functional connectivity of the left frontal cortex (LFC) in Alzheimer disease (AD) is associated with more years of education (a proxy of cognitive reserve [CR]) and mitigates the association between AD-related fluorodeoxyglucose (FDG)-PET hypometabolism and episodic memory. Methods: Forty-four amyloid-PET–positive patients with amnestic mild cognitive impairment (MCI-Aβ+) and 24 amyloid-PET–negative healthy controls (HC) were included. Voxel-based linear regression analyses were used to test the association between years of education and FDG-PET in MCI-Aβ+, controlled for episodic memory performance. Global LFC (gLFC) connectivity was computed through seed-based resting-state fMRI correlations between the LFC (seed) and each voxel in the gray matter. In linear regression analyses, education as a predictor of gLFC connectivity and the interaction of gLFC connectivity × FDG-PET hypometabolism on episodic memory were tested. Results: FDG-PET metabolism in the precuneus was reduced in MCI-Aβ+ compared to HC (p = 0.028), with stronger reductions observed in MCI-Aβ+ with more years of education (p = 0.006). In MCI-Aβ+, higher gLFC connectivity was associated with more years of education (p = 0.021). At higher levels of gLFC connectivity, the association between precuneus FDG-PET hypometabolism and lower memory performance was attenuated (p = 0.027). Conclusions: Higher gLFC connectivity is a functional substrate of CR that helps to maintain episodic memory relatively well in the face of emerging FDG-PET hypometabolism in early-stage AD. PMID:28188306

Physical activity behaviour in men with inflammatory joint disease: a cross-sectional register-based study.

PubMed

Hammer, Nanna Maria; Midtgaard, Julie; Hetland, Merete Lund; Krogh, Niels Steen; Esbensen, Bente Appel

2018-05-01

Physical activity is recommended as an essential part of the non-pharmacological management of inflammatory joint disease, but previous research in this area has predominantly included women. The aim of this study was to examine physical activity behaviour in men with inflammatory joint disease. The study was conducted as a cross-sectional register-based study. Data on physical activity behaviour in men with RA, PsA and AS were matched with sociodemographic and clinical variables extracted from the DANBIO registry. Logistic regression analyses using multiple imputations were performed to investigate demographic and clinical variables associated with regular engagement in physical activity (moderate-vigorous ⩾2 h/week). Descriptive statistics were applied to explore motivation, barriers and preferences for physical activity. A total of 325 men were included of whom 129 (40%) engaged in regular physical activity. In univariate analyses, higher age, visual analogue scale (VAS) for pain, VAS fatigue, VAS patient's global, CRP level, disease activity, functional disability and current smoking were negatively associated with regular engagement in physical activity. In the final multivariable regression model only a high VAS fatigue score (⩾61 mm) (OR = 0.228; CI: 0.119, 0.436) remained significantly independently associated with regular physical activity. A majority of men with inflammatory joint disease do not meet the recommendations of regular physical activity. Both sociodemographic and clinical parameters were associated with engagement in physical activity, and fatigue especially seems to play a pivotal role in explaining suboptimal physical activity behaviour in this patient group.

Left frontal cortex connectivity underlies cognitive reserve in prodromal Alzheimer disease.

PubMed

Franzmeier, Nicolai; Duering, Marco; Weiner, Michael; Dichgans, Martin; Ewers, Michael

2017-03-14

To test whether higher global functional connectivity of the left frontal cortex (LFC) in Alzheimer disease (AD) is associated with more years of education (a proxy of cognitive reserve [CR]) and mitigates the association between AD-related fluorodeoxyglucose (FDG)-PET hypometabolism and episodic memory. Forty-four amyloid-PET-positive patients with amnestic mild cognitive impairment (MCI-Aβ+) and 24 amyloid-PET-negative healthy controls (HC) were included. Voxel-based linear regression analyses were used to test the association between years of education and FDG-PET in MCI-Aβ+, controlled for episodic memory performance. Global LFC (gLFC) connectivity was computed through seed-based resting-state fMRI correlations between the LFC (seed) and each voxel in the gray matter. In linear regression analyses, education as a predictor of gLFC connectivity and the interaction of gLFC connectivity × FDG-PET hypometabolism on episodic memory were tested. FDG-PET metabolism in the precuneus was reduced in MCI-Aβ+ compared to HC ( p = 0.028), with stronger reductions observed in MCI-Aβ+ with more years of education ( p = 0.006). In MCI-Aβ+, higher gLFC connectivity was associated with more years of education ( p = 0.021). At higher levels of gLFC connectivity, the association between precuneus FDG-PET hypometabolism and lower memory performance was attenuated ( p = 0.027). Higher gLFC connectivity is a functional substrate of CR that helps to maintain episodic memory relatively well in the face of emerging FDG-PET hypometabolism in early-stage AD. © 2017 American Academy of Neurology.

Are tuition-free primary education policies associated with lower infant and neonatal mortality in low- and middle-income countries?

PubMed

Quamruzzaman, Amm; Mendoza Rodríguez, José M; Heymann, Jody; Kaufman, Jay S; Nandi, Arijit

2014-11-01

Robust evidence from low- and middle-income countries (LMICs) suggests that maternal education is associated with better child health outcomes. However, whether or not policies aimed at increasing access to education, including tuition-free education policies, contribute to lower infant and neonatal mortality has not been empirically tested. We joined country-level data on national education policies for 37 LMICs to information on live births to young mothers aged 15-21 years, who were surveyed as part of the population-based Demographic and Health Surveys. We used propensity scores to match births to mothers who were exposed to a tuition-free primary education policy with births to mothers who were not, based on individual-level, household, and country-level characteristics, including GDP per capita, urbanization, and health expenditures per capita. Multilevel logistic regression models, fitted using generalized estimating equations, were used to estimate the effect of exposure to tuition-free primary education policies on the risk of infant and neonatal mortality. We also tested whether this effect was modified by household socioeconomic status. The propensity score matched samples for analyses of infant and neonatal mortality comprised 24,396 and 36,030 births, respectively, from 23 countries. Multilevel regression analyses showed that, on average, exposure to a tuition-free education policy was associated with 15 (95% CI=-32, 1) fewer infant and 5 (95% CI=-13, 4) fewer neonatal deaths per 1000 live births. We found no strong evidence of heterogeneity of this effect by socioeconomic level. Copyright © 2014. Published by Elsevier Ltd.

Comparison of the Risk of Gastrointestinal Bleeding among Different Statin Exposures with Concomitant Administration of Warfarin: Electronic Health Record-Based Retrospective Cohort Study.

PubMed

Shin, Dahye; Yoon, Dukyong; Lim, Sun Gyo; Hong, Ji Man; Park, Rae Woong; Lee, Jin Soo

2016-01-01

Patients who should be treated with both warfarin and a statin are frequently seen in vascular clinics. The risk for bleeding and potential drug interactions should be considered when prescribing both medications together. This study aimed to compare the risk for gastrointestinal bleeding among different statin exposures with concomitant administration of warfarin. This is a single-hospital retrospective cohort study. We included patients who were concomitantly exposed to one of four statins (pravastatin, simvastatin, atorvastatin, and rosuvastatin) and warfarin for up to 2 years (730 days). The observation period ended when a gastrointestinal bleeding event occurred or the observation was censored. Within-class comparisons were used, and 1:1 matching using a propensity score was performed for comparisons between each statin and all of the other statins. Kaplan-Meier analyses with log-rank tests and Cox proportional hazard regression analyses were conducted to determine associations with the risk of gastrointestinal bleeding. Data were analyzed for 1,686 patients who were concomitantly administered a statin and warfarin. Log-rank tests for the gastrointestinal bleeding-free survival rate showed that the risk for gastrointestinal bleeding was significantly lower in the pravastatin group (p = 0.0499) and higher in the rosuvastatin group (p = 0.009). In the Cox proportional hazard regression analysis, the hazard ratio of 5.394 for gastrointestinal bleeding based on statin exposure in the rosuvastatin group was significant (95% confidence interval, 1.168-24.916). There was a relatively high risk of gastrointestinal bleeding with rosuvastatin when administered concomitantly with warfarin.

Risk of Peripheral Artery Occlusive Disease in Patients with Vertigo, Tinnitus, or Sudden Deafness: A Secondary Case-Control Analysis of a Nationwide, Population-Based Health Claims Database

PubMed Central

Hwang, Juen-Haur

2016-01-01

Background Cochleovestibular symptoms, such as vertigo, tinnitus, and sudden deafness, are common manifestations of microvascular diseases. However, it is unclear whether these symptoms occurred preceding the diagnosis of peripheral artery occlusive disease (PAOD). Therefore, the aim of this case-control study was to investigate the risk of PAOD among patients with vertigo, tinnitus, and sudden deafness using a nationwide, population-based health claim database in Taiwan. Methods We identified 5,340 adult patients with PAOD diagnosed between January 1, 2006 and December 31, 2010 and 16,020 controls, frequency matched on age interval, sex, and year of index date, from the Taiwan National Health Insurance Research Database. Risks of PAOD in patients with vertigo, tinnitus, or sudden deafness were separately evaluated with multivariate logistic regression analyses. Results Of the 5,340 patients with PAOD, 12.7%, 6.7%, and 0.3% were diagnosed with vertigo, tinnitus, and sudden deafness, respectively. In the controls, 10.6%, 6.1%, and 0.3% were diagnosed with vertigo (P < 0.001), tinnitus (P = 0.161), and sudden deafness (P = 0.774), respectively. Results from the multivariate logistic regression analyses showed that the risk of PAOD was significantly increased in patients with vertigo (adjusted odds ratio = 1.12, P = 0.027) but not in those with tinnitus or sudden deafness. Conclusions A modest increase in the risk of PAOD was observed among Taiwanese patients with vertigo, after adjustment for comorbidities. PMID:27631630

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 ...

Associations between Grawe's general mechanisms of change and Young's early maladaptive schemas in psychotherapy research: a comparative study of change processes.

PubMed

Mander, Johannes V; Jacob, Gitta A; Götz, Lea; Sammet, Isa; Zipfel, Stephan; Teufel, Martin

2015-01-01

The study aimed at analyzing associations between Grawe's general mechanisms of change and Young's early maladaptive schemas (EMS). Therefore, 98 patients completed the Scale for the Multiperspective Assessment of General Change Mechanisms in Psychotherapy (SACiP), the Young Shema Questionnaire-Short Form Revised (YSQ S3R), and diverse outcome measures at the beginning and end of treatment. Our results are important for clinical applications, as we demonstrated strong predictive effects of change mechanisms on schema domains using regression analyses and cross-lagged panel models. Resource activation experiences seem to be especially crucial in fostering alterations in EMS, as this change mechanism demonstrated significant associations with several schema domains. Future research should investigate these aspects in more detail using observer-based micro-process analyses.

Associations between belief in conspiracy theories and the maladaptive personality traits of the personality inventory for DSM-5.

PubMed

Swami, Viren; Weis, Laura; Lay, Alixe; Barron, David; Furnham, Adrian

2016-02-28

Conspiracy theories can be treated as both rational narratives of the world as well as outcomes of underlying maladaptive traits. Here, we examined associations between belief in conspiracy theories and individual differences in personality disorders. An Internet-based sample (N=259) completed measures of belief in conspiracy theories and the 25 facets of the Personality Inventory for DSM-5 (PID-5). Preliminary analyses showed no significant differences in belief in conspiracy theories across participant sex, ethnicity, and education. Regression analyses showed that the PID-5 facets of Unusual Beliefs and Experiences and, to a lesser extent, Suspiciousness, significantly predicted belief in conspiracy theories. These findings highlight a role for maladaptive personality traits in understanding belief in conspiracy theories, but require further investigation. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Factors influencing quit attempts among male daily smokers in China.

PubMed

Zhao, Luhua; Song, Yang; Xiao, Lin; Palipudi, Krishna; Asma, Samira

2015-12-01

China has the largest population of smokers in the world, yet the quit rate is low. We used data from the 2010 Global Adult Tobacco Survey China to identify factors influencing quit attempts among male Chinese daily smokers. The study sample included 3303 male daily smokers. To determine the factors that were significantly associated with making a quit attempt, we conducted logistic regression analyses. In addition, mediation analyses were carried out to investigate how the intermediate association among demographics (age, education, urbanicity) and smoking-related variables affected making a quit attempt. An estimated 11.0% of male daily smokers tried to quit smoking in the 12 months prior to the survey. Logistic regression analysis indicated that younger age (15-24 years), being advised to quit by a health care provider (HCP) in the past 12 months, lower cigarette cost per pack, monthly or less frequent exposure to smoking at home, and awareness of the harms of tobacco use were significantly associated with making a quit attempt. Additional mediation analyses showed that having knowledge of the harm of tobacco, exposure to smoking at home, and having been advised to quit by an HCP were mediators of making a quit attempt for other independent variables. Evidence-based tobacco control measures such as conducting educational campaigns on the harms of tobacco use, establishing smoke-free policies at home, and integrating tobacco cessation advice into primary health care services can increase quit attempts and reduce smoking among male Chinese daily smokers. Copyright © 2015 Elsevier Inc. All rights reserved.

Fluoride exposure and indicators of thyroid functioning in the Canadian population: implications for community water fluoridation

PubMed Central

Barberio, Amanda M; Hosein, F Shaun; Quiñonez, Carlos; McLaren, Lindsay

2017-01-01

Background There are concerns that altered thyroid functioning could be the result of ingesting too much fluoride. Community water fluoridation (CWF) is an important source of fluoride exposure. Our objectives were to examine the association between fluoride exposure and (1) diagnosis of a thyroid condition and (2) indicators of thyroid functioning among a national population-based sample of Canadians. Methods We analysed data from Cycles 2 and 3 of the Canadian Health Measures Survey (CHMS). Logistic regression was used to assess associations between fluoride from urine and tap water samples and the diagnosis of a thyroid condition. Multinomial logistic regression was used to examine the relationship between fluoride exposure and thyroid-stimulating hormone (TSH) level (low/normal/high). Other available variables permitted additional exploratory analyses among the subset of participants for whom we could discern some fluoride exposure from drinking water and/or dental products. Results There was no evidence of a relationship between fluoride exposure (from urine and tap water) and the diagnosis of a thyroid condition. There was no statistically significant association between fluoride exposure and abnormal (low or high) TSH levels relative to normal TSH levels. Rerunning the models with the sample constrained to the subset of participants for whom we could discern some source(s) of fluoride exposure from drinking water and/or dental products revealed no significant associations. Conclusion These analyses suggest that, at the population level, fluoride exposure is not associated with impaired thyroid functioning in a time and place where multiple sources of fluoride exposure, including CWF, exist. PMID:28839078

Morbidity study of extruder personnel with potential exposure to brominated dioxins and furans. II. Results of clinical laboratory studies.

PubMed

Ott, M G; Zober, A

1996-12-01

To test whether dioxins affect liver and thyroid function, lipid metabolism and glucose or immunological variables, in workers exposed to brominated dioxins and furans. 34 male production employees (29 were extruder operators) and eight technical support personnel were studied, all of whom were potentially exposed to polybrominated dibenzo-p-dioxins (PBDDs) and furans (PBDFs) during production of resins containing polybrominated diphenyl ethers (PBDEs). Controls were from a similar resin producing plant that did not use PBDEs. Blood samples were analysed for tetra, penta, and hexabrominated congeners, but 2,3,7,8-TBDD was the only exposure measure used in the regression analyses. Seven liver function indicators, five measures of blood lipids and glucose, four haematology and blood coagulation measures, and three measures of thyroid function were examined. None of the variables was statistically related to concentration of 2,3,7,8-TBDD in the regression analyses. Cigarette smoking was related to several outcomes at the 0.05 level: aspartate aminotransferase, alanine aminotransferase, glutamate dehydrogenase (GLDH), erythrocyte sedimentation rate, and white blood cell count. Body mass index was also related to alanine aminotransferase, gamma-glutamyltranspeptidase, cholinesterase, GLDH, cholesterol, triglycerides, high density lipoprotein, low density lipoprotein, and glucose concentrations. No definitive associations between liver, blood lipid, thyroid, or immunological variables and exposure to brominated dioxins or blood lipid concentration of 2,3,7,8-TBDD were found. The study population was small and hence the findings must be interpreted with caution. Nevertheless, these results provide a base for interpreting the results of clinical studies in similarly exposed populations.

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,…

Techniques for Estimating the Magnitude and Frequency of Peak Flows on Small Streams in Minnesota Based on Data through Water Year 2005

USGS Publications Warehouse

Lorenz, David L.; Sanocki, Chris A.; Kocian, Matthew J.

2010-01-01

Knowledge of the peak flow of floods of a given recurrence interval is essential for regulation and planning of water resources and for design of bridges, culverts, and dams along Minnesota's rivers and streams. Statistical techniques are needed to estimate peak flow at ungaged sites because long-term streamflow records are available at relatively few places. Because of the need to have up-to-date peak-flow frequency information in order to estimate peak flows at ungaged sites, the U.S. Geological Survey (USGS) conducted a peak-flow frequency study in cooperation with the Minnesota Department of Transportation and the Minnesota Pollution Control Agency. Estimates of peak-flow magnitudes for 1.5-, 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence intervals are presented for 330 streamflow-gaging stations in Minnesota and adjacent areas in Iowa and South Dakota based on data through water year 2005. The peak-flow frequency information was subsequently used in regression analyses to develop equations relating peak flows for selected recurrence intervals to various basin and climatic characteristics. Two statistically derived techniques-regional regression equation and region of influence regression-can be used to estimate peak flow on ungaged streams smaller than 3,000 square miles in Minnesota. Regional regression equations were developed for selected recurrence intervals in each of six regions in Minnesota: A (northwestern), B (north central and east central), C (northeastern), D (west central and south central), E (southwestern), and F (southeastern). The regression equations can be used to estimate peak flows at ungaged sites. The region of influence regression technique dynamically selects streamflow-gaging stations with characteristics similar to a site of interest. Thus, the region of influence regression technique allows use of a potentially unique set of gaging stations for estimating peak flow at each site of interest. Two methods of selecting streamflow-gaging stations, similarity and proximity, can be used for the region of influence regression technique. The regional regression equation technique is the preferred technique as an estimate of peak flow in all six regions for ungaged sites. The region of influence regression technique is not appropriate for regions C, E, and F because the interrelations of some characteristics of those regions do not agree with the interrelations throughout the rest of the State. Both the similarity and proximity methods for the region of influence technique can be used in the other regions (A, B, and D) to provide additional estimates of peak flow. The peak-flow-frequency estimates and basin characteristics for selected streamflow-gaging stations and regional peak-flow regression equations are included in this report.

Plant leaf chlorophyll content retrieval based on a field imaging spectroscopy system.

PubMed

Liu, Bo; Yue, Yue-Min; Li, Ru; Shen, Wen-Jing; Wang, Ke-Lin

2014-10-23

A field imaging spectrometer system (FISS; 380-870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%-35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector.

Plant Leaf Chlorophyll Content Retrieval Based on a Field Imaging Spectroscopy System

PubMed Central

Liu, Bo; Yue, Yue-Min; Li, Ru; Shen, Wen-Jing; Wang, Ke-Lin

2014-01-01

A field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%–35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector. PMID:25341439

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.

A Systematic Review and Meta-Regression Analysis of Lung Cancer Risk and Inorganic Arsenic in Drinking Water.

PubMed

Lamm, Steven H; Ferdosi, Hamid; Dissen, Elisabeth K; Li, Ji; Ahn, Jaeil

2015-12-07

High levels (> 200 µg/L) of inorganic arsenic in drinking water are known to be a cause of human lung cancer, but the evidence at lower levels is uncertain. We have sought the epidemiological studies that have examined the dose-response relationship between arsenic levels in drinking water and the risk of lung cancer over a range that includes both high and low levels of arsenic. Regression analysis, based on six studies identified from an electronic search, examined the relationship between the log of the relative risk and the log of the arsenic exposure over a range of 1-1000 µg/L. The best-fitting continuous meta-regression model was sought and found to be a no-constant linear-quadratic analysis where both the risk and the exposure had been logarithmically transformed. This yielded both a statistically significant positive coefficient for the quadratic term and a statistically significant negative coefficient for the linear term. Sub-analyses by study design yielded results that were similar for both ecological studies and non-ecological studies. Statistically significant X-intercepts consistently found no increased level of risk at approximately 100-150 µg/L arsenic.

A Systematic Review and Meta-Regression Analysis of Lung Cancer Risk and Inorganic Arsenic in Drinking Water

PubMed Central

Lamm, Steven H.; Ferdosi, Hamid; Dissen, Elisabeth K.; Li, Ji; Ahn, Jaeil

2015-01-01

High levels (> 200 µg/L) of inorganic arsenic in drinking water are known to be a cause of human lung cancer, but the evidence at lower levels is uncertain. We have sought the epidemiological studies that have examined the dose-response relationship between arsenic levels in drinking water and the risk of lung cancer over a range that includes both high and low levels of arsenic. Regression analysis, based on six studies identified from an electronic search, examined the relationship between the log of the relative risk and the log of the arsenic exposure over a range of 1–1000 µg/L. The best-fitting continuous meta-regression model was sought and found to be a no-constant linear-quadratic analysis where both the risk and the exposure had been logarithmically transformed. This yielded both a statistically significant positive coefficient for the quadratic term and a statistically significant negative coefficient for the linear term. Sub-analyses by study design yielded results that were similar for both ecological studies and non-ecological studies. Statistically significant X-intercepts consistently found no increased level of risk at approximately 100–150 µg/L arsenic. PMID:26690190

Bias-motivated bullying and psychosocial problems: implications for HIV risk behaviors among young men who have sex with men.

PubMed

Li, Michael Jonathan; Distefano, Anthony; Mouttapa, Michele; Gill, Jasmeet K

2014-02-01

The present study aimed to determine whether the experience of bias-motivated bullying was associated with behaviors known to increase the risk of HIV infection among young men who have sex with men (YMSM) aged 18-29, and to assess whether the psychosocial problems moderated this relationship. Using an Internet-based direct marketing approach in sampling, we recruited 545 YMSM residing in the USA to complete an online questionnaire. Multiple linear regression analyses tested three regression models where we controlled for sociodemographics. The first model indicated that bullying during high school was associated with unprotected receptive anal intercourse within the past 12 months, while the second model indicated that bullying after high school was associated with engaging in anal intercourse while under the influence of drugs or alcohol in the past 12 months. In the final regression model, our composite measure of HIV risk behavior was found to be associated with lifetime verbal harassment. None of the psychosocial problems measured in this study - depression, low self-esteem, and internalized homonegativity - moderated any of the associations between bias-motivated bullying victimization and HIV risk behaviors in our regression models. Still, these findings provide novel evidence that bullying prevention programs in schools and communities should be included in comprehensive approaches to HIV prevention among YMSM.

Numerical analysis of combustion characteristics of hybrid rocket motor with multi-section swirl injection

NASA Astrophysics Data System (ADS)

Li, Chengen; Cai, Guobiao; Tian, Hui

2016-06-01

This paper is aimed to analyse the combustion characteristics of hybrid rocket motor with multi-section swirl injection by simulating the combustion flow field. Numerical combustion flow field and combustion performance parameters are obtained through three-dimensional numerical simulations based on a steady numerical model proposed in this paper. The hybrid rocket motor adopts 98% hydrogen peroxide and polyethylene as the propellants. Multiple injection sections are set along the axis of the solid fuel grain, and the oxidizer enters the combustion chamber by means of tangential injection via the injector ports in the injection sections. Simulation results indicate that the combustion flow field structure of the hybrid rocket motor could be improved by multi-section swirl injection method. The transformation of the combustion flow field can greatly increase the fuel regression rate and the combustion efficiency. The average fuel regression rate of the motor with multi-section swirl injection is improved by 8.37 times compared with that of the motor with conventional head-end irrotational injection. The combustion efficiency is increased to 95.73%. Besides, the simulation results also indicate that (1) the additional injection sections can increase the fuel regression rate and the combustion efficiency; (2) the upstream offset of the injection sections reduces the combustion efficiency; and (3) the fuel regression rate and the combustion efficiency decrease with the reduction of the number of injector ports in each injection section.

Studying Gene and Gene-Environment Effects of Uncommon and Common Variants on Continuous Traits: A Marker-Set Approach Using Gene-Trait Similarity Regression

PubMed Central

Tzeng, Jung-Ying; Zhang, Daowen; Pongpanich, Monnat; Smith, Chris; McCarthy, Mark I.; Sale, Michèle M.; Worrall, Bradford B.; Hsu, Fang-Chi; Thomas, Duncan C.; Sullivan, Patrick F.

2011-01-01

Genomic association analyses of complex traits demand statistical tools that are capable of detecting small effects of common and rare variants and modeling complex interaction effects and yet are computationally feasible. In this work, we introduce a similarity-based regression method for assessing the main genetic and interaction effects of a group of markers on quantitative traits. The method uses genetic similarity to aggregate information from multiple polymorphic sites and integrates adaptive weights that depend on allele frequencies to accomodate common and uncommon variants. Collapsing information at the similarity level instead of the genotype level avoids canceling signals that have the opposite etiological effects and is applicable to any class of genetic variants without the need for dichotomizing the allele types. To assess gene-trait associations, we regress trait similarities for pairs of unrelated individuals on their genetic similarities and assess association by using a score test whose limiting distribution is derived in this work. The proposed regression framework allows for covariates, has the capacity to model both main and interaction effects, can be applied to a mixture of different polymorphism types, and is computationally efficient. These features make it an ideal tool for evaluating associations between phenotype and marker sets defined by linkage disequilibrium (LD) blocks, genes, or pathways in whole-genome analysis. PMID:21835306

Correction of body-mass index using body-shape perception and socioeconomic status in adolescent self-report surveys.

PubMed

Legleye, Stéphane; Beck, François; Spilka, Stanislas; Chau, Nearkasen

2014-01-01

To propose a simple correction of body-mass index (BMI) based on self-reported weight and height (reported BMI) using gender, body shape perception and socioeconomic status in an adolescent population. 341 boys and girls aged 17-18 years were randomly selected from a representative sample of 2165 French adolescents living in Paris surveyed in 2010. After an anonymous self-administered pen-and-paper questionnaire asking for height, weight, body shape perception (feeling too thin, about the right weight or too fat) and socioeconomic status, subjects were measured and weighed. BMI categories were computed according to Cole's cut-offs. Reported BMIs were corrected using linear regressions and ROC analyses and checked with cross-validation and multiple imputations to handle missing values. Agreement between actual and corrected BMI values was estimated with Kappa indexes and Intraclass correlation coefficients (ICC). On average, BMIs were underreported, especially among girls. Kappa indexes between actual and reported BMI were low, especially for girls: 0.56 95%CI = [0.42-0.70] for boys and 0.45 95%CI = [0.30-0.60] for girls. The regression of reported BMI by gender and body shape perception gave the most balanced results for both genders: the Kappa and ICC obtained were 0.63 95%CI = [0.50-0.76] and 0.67, 95%CI = [0.58-0.74] for boys; 0.65 95%CI = [0.52-0.78] and 0.74, 95%CI = [0.66-0.81] for girls. The regression of reported BMI by gender and socioeconomic status led to similar corrections while the ROC analyses were inaccurate. Using body shape perception, or socioeconomic status and gender is a promising way of correcting BMI in self-administered questionnaires, especially for girls.

Quantification of trace metals in infant formula premixes using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Cama-Moncunill, Raquel; Casado-Gavalda, Maria P.; Cama-Moncunill, Xavier; Markiewicz-Keszycka, Maria; Dixit, Yash; Cullen, Patrick J.; Sullivan, Carl

2017-09-01

Infant formula is a human milk substitute generally based upon fortified cow milk components. In order to mimic the composition of breast milk, trace elements such as copper, iron and zinc are usually added in a single operation using a premix. The correct addition of premixes must be verified to ensure that the target levels in infant formulae are achieved. In this study, a laser-induced breakdown spectroscopy (LIBS) system was assessed as a fast validation tool for trace element premixes. LIBS is a promising emission spectroscopic technique for elemental analysis, which offers real-time analyses, little to no sample preparation and ease of use. LIBS was employed for copper and iron determinations of premix samples ranging approximately from 0 to 120 mg/kg Cu/1640 mg/kg Fe. LIBS spectra are affected by several parameters, hindering subsequent quantitative analyses. This work aimed at testing three matrix-matched calibration approaches (simple-linear regression, multi-linear regression and partial least squares regression (PLS)) as means for precision and accuracy enhancement of LIBS quantitative analysis. All calibration models were first developed using a training set and then validated with an independent test set. PLS yielded the best results. For instance, the PLS model for copper provided a coefficient of determination (R2) of 0.995 and a root mean square error of prediction (RMSEP) of 14 mg/kg. Furthermore, LIBS was employed to penetrate through the samples by repetitively measuring the same spot. Consequently, LIBS spectra can be obtained as a function of sample layers. This information was used to explore whether measuring deeper into the sample could reduce possible surface-contaminant effects and provide better quantifications.

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…

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.

Lessons from trial-based cost-effectiveness analyses of mental health interventions: why uncertainty about the outcome, estimate and willingness to pay matters.

PubMed

Hoch, Jeffrey S; Dewa, Carolyn S

2007-01-01

The principal aim of this article is to share lessons learned by the authors while conducting economic evaluations, using clinical trial data, of mental health interventions. These lessons are quite general and have clear relevance for pharmacoeconomic studies. In addition, we explore how net benefit regression can be used to enhance consideration of key issues when conducting an economic evaluation based on clinical trial data. The first study we discuss found that cost-effectiveness results varied markedly based on the choice of both the patient outcome and the willingness to pay for more of that outcome. The importance of willingness to pay was also highlighted in the results from the second study. Even with a set willingness-to-pay value, most of the time the probability that the new treatment was cost effective was not 100%. In the third study, the cost effectiveness of the new treatment varied by patient characteristics. These observations have important implications for pharmacoeconomic studies. Namely, analysts must carefully consider choice of patient outcome, willingness to pay, patient heterogeneity and the statistical uncertainty inherent in the data. Net benefit regression is a useful technique for exploring these crucial issues when undertaking an economic evaluation using patient-level data on both costs and effects.

Assessment and prediction of inter-joint upper limb movement correlations based on kinematic analysis and statistical regression

NASA Astrophysics Data System (ADS)

Toth-Tascau, Mirela; Balanean, Flavia; Krepelka, Mircea

2013-10-01

Musculoskeletal impairment of the upper limb can cause difficulties in performing basic daily activities. Three dimensional motion analyses can provide valuable data of arm movement in order to precisely determine arm movement and inter-joint coordination. The purpose of this study was to develop a method to evaluate the degree of impairment based on the influence of shoulder movements in the amplitude of elbow flexion and extension based on the assumption that a lack of motion of the elbow joint will be compensated by an increased shoulder activity. In order to develop and validate a statistical model, one healthy young volunteer has been involved in the study. The activity of choice simulated blowing the nose, starting from a slight flexion of the elbow and raising the hand until the middle finger touches the tip of the nose and return to the start position. Inter-joint coordination between the elbow and shoulder movements showed significant correlation. Statistical regression was used to fit an equation model describing the influence of shoulder movements on the elbow mobility. The study provides a brief description of the kinematic analysis protocol and statistical models that may be useful in describing the relation between inter-joint movements of daily activities.

Global Prevalence of Elder Abuse: A Meta-analysis and Meta-regression.

PubMed

Ho, C Sh; Wong, S Y; Chiu, M M; Ho, R Cm

2017-06-01

Elder abuse is increasingly recognised as a global public health and social problem. There has been limited inter-study comparison of the prevalence and risk factors for elder abuse. This study aimed to estimate the pooled and subtype prevalence of elder abuse worldwide and identify significant associated risk factors. We conducted a meta-analysis and meta-regression of 34 population-based and 17 non-population-based studies. The pooled prevalences of elder abuse were 10.0% (95% confidence interval, 5.2%-18.6%) and 34.3% (95% confidence interval, 22.9%-47.8%) in population-based studies and third party- or caregiver-reported studies, respectively. Being in a marital relationship was found to be a significant moderator using random-effects model. This meta-analysis revealed that third parties or caregivers were more likely to report abuse than older abused adults. Subgroup analyses showed that females and those resident in non-western countries were more likely to be abused. Emotional abuse was the most prevalent elder abuse subtype and financial abuse was less commonly reported by third parties or caregivers. Heterogeneity in the prevalence was due to the high proportion of married older adults in the sample. Subgroup analysis showed that cultural factors, subtypes of abuse, and gender also contributed to heterogeneity in the pooled prevalence of elder abuse.

Computed statistics at streamgages, and methods for estimating low-flow frequency statistics and development of regional regression equations for estimating low-flow frequency statistics at ungaged locations in Missouri

USGS Publications Warehouse

Southard, Rodney E.

2013-01-01

The weather and precipitation patterns in Missouri vary considerably from year to year. In 2008, the statewide average rainfall was 57.34 inches and in 2012, the statewide average rainfall was 30.64 inches. This variability in precipitation and resulting streamflow in Missouri underlies the necessity for water managers and users to have reliable streamflow statistics and a means to compute select statistics at ungaged locations for a better understanding of water availability. Knowledge of surface-water availability is dependent on the streamflow data that have been collected and analyzed by the U.S. Geological Survey for more than 100 years at approximately 350 streamgages throughout Missouri. The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, computed streamflow statistics at streamgages through the 2010 water year, defined periods of drought and defined methods to estimate streamflow statistics at ungaged locations, and developed regional regression equations to compute selected streamflow statistics at ungaged locations. Streamflow statistics and flow durations were computed for 532 streamgages in Missouri and in neighboring States of Missouri. For streamgages with more than 10 years of record, Kendall’s tau was computed to evaluate for trends in streamflow data. If trends were detected, the variable length method was used to define the period of no trend. Water years were removed from the dataset from the beginning of the record for a streamgage until no trend was detected. Low-flow frequency statistics were then computed for the entire period of record and for the period of no trend if 10 or more years of record were available for each analysis. Three methods are presented for computing selected streamflow statistics at ungaged locations. The first method uses power curve equations developed for 28 selected streams in Missouri and neighboring States that have multiple streamgages on the same streams. Statistical estimates on one of these streams can be calculated at an ungaged location that has a drainage area that is between 40 percent of the drainage area of the farthest upstream streamgage and within 150 percent of the drainage area of the farthest downstream streamgage along the stream of interest. The second method may be used on any stream with a streamgage that has operated for 10 years or longer and for which anthropogenic effects have not changed the low-flow characteristics at the ungaged location since collection of the streamflow data. A ratio of drainage area of the stream at the ungaged location to the drainage area of the stream at the streamgage was computed to estimate the statistic at the ungaged location. The range of applicability is between 40- and 150-percent of the drainage area of the streamgage, and the ungaged location must be located on the same stream as the streamgage. The third method uses regional regression equations to estimate selected low-flow frequency statistics for unregulated streams in Missouri. This report presents regression equations to estimate frequency statistics for the 10-year recurrence interval and for the N-day durations of 1, 2, 3, 7, 10, 30, and 60 days. Basin and climatic characteristics were computed using geographic information system software and digital geospatial data. A total of 35 characteristics were computed for use in preliminary statewide and regional regression analyses based on existing digital geospatial data and previous studies. Spatial analyses for geographical bias in the predictive accuracy of the regional regression equations defined three low-flow regions with the State representing the three major physiographic provinces in Missouri. Region 1 includes the Central Lowlands, Region 2 includes the Ozark Plateaus, and Region 3 includes the Mississippi Alluvial Plain. A total of 207 streamgages were used in the regression analyses for the regional equations. Of the 207 U.S. Geological Survey streamgages, 77 were located in Region 1, 120 were located in Region 2, and 10 were located in Region 3. Streamgages located outside of Missouri were selected to extend the range of data used for the independent variables in the regression analyses. Streamgages included in the regression analyses had 10 or more years of record and were considered to be affected minimally by anthropogenic activities or trends. Regional regression analyses identified three characteristics as statistically significant for the development of regional equations. For Region 1, drainage area, longest flow path, and streamflow-variability index were statistically significant. The range in the standard error of estimate for Region 1 is 79.6 to 94.2 percent. For Region 2, drainage area and streamflow variability index were statistically significant, and the range in the standard error of estimate is 48.2 to 72.1 percent. For Region 3, drainage area and streamflow-variability index also were statistically significant with a range in the standard error of estimate of 48.1 to 96.2 percent. Limitations on the use of estimating low-flow frequency statistics at ungaged locations are dependent on the method used. The first method outlined for use in Missouri, power curve equations, were developed to estimate the selected statistics for ungaged locations on 28 selected streams with multiple streamgages located on the same stream. A second method uses a drainage-area ratio to compute statistics at an ungaged location using data from a single streamgage on the same stream with 10 or more years of record. Ungaged locations on these streams may use the ratio of the drainage area at an ungaged location to the drainage area at a streamgage location to scale the selected statistic value from the streamgage location to the ungaged location. This method can be used if the drainage area of the ungaged location is within 40 to 150 percent of the streamgage drainage area. The third method is the use of the regional regression equations. The limits for the use of these equations are based on the ranges of the characteristics used as independent variables and that streams must be affected minimally by anthropogenic activities.

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...

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...

Does shared family background influence the impact of educational differences on early mortality?

PubMed

Søndergaard, Grethe; Mortensen, Laust H; Nybo Andersen, Anne-Marie; Andersen, Per Kragh; Dalton, Susanne Oksbjerg; Madsen, Mia; Osler, Merete

2012-10-15

The mechanisms behind social differences in mortality rates have been debated. The authors examined the extent to which shared family background and health in early life could explain the association between educational status and all-cause mortality rates using a sibling design. The study was register-based and included all individuals born in Denmark between 1950 and 1979 who had at least 1 full sibling born in the same time period (n = 1,381,436). All individuals were followed from 28 years of age until death, emigration, or December 2009. The authors used Cox regression analyses to estimate hazard ratios for mortality according to educational level. Conventional cohort and intersibling analyses were carried out and conducted separately for deaths occurring before and after the age of 45 years, respectively. The cohort analyses showed an inverse association between educational status and all-cause mortality that was strongest for males, increased with younger birth cohorts, and tended to be strongest in the analyses of death before 45 years of age. The associations were attenuated slightly in the intersibling analyses and after adjustment for serious health conditions in early life. Hence, health selection and confounding by factors shared by siblings explained only a minor part of the association between educational level and all-cause mortality.

Crimes against the elderly in Italy, 2007-2014.

PubMed

Terranova, Claudio; Bevilacqua, Greta; Zen, Margherita; Montisci, Massimo

2017-08-01

Crimes against the elderly have physical, psychological, and economic consequences. Approaches for mitigating them must be based on comprehensive knowledge of the phenomenon. This study analyses crimes against the elderly in Italy during the period 2007-2014 from an epidemiological viewpoint. Data on violent and non-violent crimes derived from the Italian Institute of Statistics were analysed in relation to trends, gender and age by linear regression, T-test, and calculation of the odds ratio with a 95% confidence interval. Results show that the elderly are at higher risk of being victimized in two types of crime, violent (residential robbery) and non-violent (pick-pocketing and purse-snatching) compared with other age groups during the period considered. A statistically significant increase in residential robbery and pick-pocketing was also observed. The rate of homicide against the elderly was stable during the study period, in contrast with reduced rates in other age groups. These results may be explained by risk factors increasing the profiles of elderly individuals as potential victims, such as frailty, cognitive impairment, and social isolation. Further studies analysing the characteristics of victims are required. Based on the results presented here, appropriate preventive strategies should be planned to reduce crimes against the elderly. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Cross-sectional and longitudinal evaluation of liver volume and total liver fat burden in adults with nonalcoholic steatohepatitis

PubMed Central

Tang, An; Chen, Joshua; Le, Thuy-Anh; Changchien, Christopher; Hamilton, Gavin; Middleton, Michael S.; Loomba, Rohit; Sirlin, Claude B.

2014-01-01

Purpose To explore the cross-sectional and longitudinal relationships between fractional liver fat content, liver volume, and total liver fat burden. Methods In 43 adults with non-alcoholic steatohepatitis participating in a clinical trial, liver volume was estimated by segmentation of magnitude-based low-flip-angle multiecho GRE images. The liver mean proton density fat fraction (PDFF) was calculated. The total liver fat index (TLFI) was estimated as the product of liver mean PDFF and liver volume. Linear regression analyses were performed. Results Cross-sectional analyses revealed statistically significant relationships between TLFI and liver mean PDFF (R2 = 0.740 baseline/0.791 follow-up, P < 0.001 baseline/P < 0.001 follow-up), and between TLFI and liver volume (R2 = 0.352/0.452, P < 0.001/< 0.001). Longitudinal analyses revealed statistically significant relationships between liver volume change and liver mean PDFF change (R2 = 0.556, P < 0.001), between TLFI change and liver mean PDFF change (R2 = 0.920, P < 0.001), and between TLFI change and liver volume change (R2 = 0.735, P < 0.001). Conclusion Liver segmentation in combination with MRI-based PDFF estimation may be used to monitor liver volume, liver mean PDFF, and TLFI in a clinical trial. PMID:25015398

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

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.

A population-based study on the association between rheumatoid arthritis and voice problems.

PubMed

Hah, J Hun; An, Soo-Youn; Sim, Songyong; Kim, So Young; Oh, Dong Jun; Park, Bumjung; Kim, Sung-Gyun; Choi, Hyo Geun

2016-07-01

The objective of this study was to investigate whether rheumatoid arthritis increases the frequency of organic laryngeal lesions and the subjective voice complaint rate in those with no organic laryngeal lesion. We performed a cross-sectional study using the data from 19,368 participants (418 rheumatoid arthritis patients and 18,950 controls) of the 2008-2011 Korea National Health and Nutrition Examination Survey. The associations between rheumatoid arthritis and organic laryngeal lesions/subjective voice complaints were analyzed using simple/multiple logistic regression analysis with complex sample adjusting for confounding factors, including age, sex, smoking status, stress level, and body mass index, which could provoke voice problems. Vocal nodules, vocal polyp, and vocal palsy were not associated with rheumatoid arthritis in a multiple regression analysis, and only laryngitis showed a positive association (adjusted odds ratio, 1.59; 95 % confidence interval, 1.01-2.52; P = 0.047). Rheumatoid arthritis was associated with subjective voice discomfort in a simple regression analysis, but not in a multiple regression analysis. Participants with rheumatoid arthritis were older, more often female, and had higher stress levels than those without rheumatoid arthritis. These factors were associated with subjective voice complaints in both simple and multiple regression analyses. Rheumatoid arthritis was not associated with organic laryngeal diseases except laryngitis. Rheumatoid arthritis did not increase the odds ratio for subjective voice complaints. Voice problems in participants with rheumatoid arthritis originated from the characteristics of the rheumatoid arthritis group (higher mean age, female sex, and stress level) rather than rheumatoid arthritis itself.

Juvenile entry into prostitution: the role of emotional abuse.

PubMed

Roe-Sepowitz, Dominique E

2012-05-01

This study seeks to assess the nature and extent of childhood emotional abuse among adult women in a residential prostitution-exiting program. Regression analyses were conducted to assess the unique role of childhood emotional abuse in the prediction of age of entry into prostitution. Childhood emotional abuse, a history of running away during childhood, and participating in survival-based exchanges of sex were significantly associated with the commercial sexual exploitation of girls younger than age 18, while childhood emotional abuse contributed to predicting a younger age of entry. Results are discussed regarding policy, prevention, and future research.