Analysis of the influence of quantile regression model on mainland tourists' service satisfaction performance.

PubMed

Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen

2014-01-01

It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.

Interior car noise created by textured pavement surfaces : final report.

DOT National Transportation Integrated Search

1975-01-01

Because of widespread concern about the effect of textured pavement surfaces on interior car noise, sound pressure levels (SPL) were measured inside a test vehicle as it traversed 21 pavements with various textures. A linear regression analysis run o...

A framework for longitudinal data analysis via shape regression

NASA Astrophysics Data System (ADS)

Fishbaugh, James; Durrleman, Stanley; Piven, Joseph; Gerig, Guido

2012-02-01

Traditional longitudinal analysis begins by extracting desired clinical measurements, such as volume or head circumference, from discrete imaging data. Typically, the continuous evolution of a scalar measurement is estimated by choosing a 1D regression model, such as kernel regression or fitting a polynomial of fixed degree. This type of analysis not only leads to separate models for each measurement, but there is no clear anatomical or biological interpretation to aid in the selection of the appropriate paradigm. In this paper, we propose a consistent framework for the analysis of longitudinal data by estimating the continuous evolution of shape over time as twice differentiable flows of deformations. In contrast to 1D regression models, one model is chosen to realistically capture the growth of anatomical structures. From the continuous evolution of shape, we can simply extract any clinical measurements of interest. We demonstrate on real anatomical surfaces that volume extracted from a continuous shape evolution is consistent with a 1D regression performed on the discrete measurements. We further show how the visualization of shape progression can aid in the search for significant measurements. Finally, we present an example on a shape complex of the brain (left hemisphere, right hemisphere, cerebellum) that demonstrates a potential clinical application for our framework.

Latent profile analysis of regression-based norms demonstrates relationship of compounding MS symptom burden and negative work events.

PubMed

Frndak, Seth E; Smerbeck, Audrey M; Irwin, Lauren N; Drake, Allison S; Kordovski, Victoria M; Kunker, Katrina A; Khan, Anjum L; Benedict, Ralph H B

2016-10-01

We endeavored to clarify how distinct co-occurring symptoms relate to the presence of negative work events in employed multiple sclerosis (MS) patients. Latent profile analysis (LPA) was utilized to elucidate common disability patterns by isolating patient subpopulations. Samples of 272 employed MS patients and 209 healthy controls (HC) were administered neuroperformance tests of ambulation, hand dexterity, processing speed, and memory. Regression-based norms were created from the HC sample. LPA identified latent profiles using the regression-based z-scores. Finally, multinomial logistic regression tested for negative work event differences among the latent profiles. Four profiles were identified via LPA: a common profile (55%) characterized by slightly below average performance in all domains, a broadly low-performing profile (18%), a poor motor abilities profile with average cognition (17%), and a generally high-functioning profile (9%). Multinomial regression analysis revealed that the uniformly low-performing profile demonstrated a higher likelihood of reported negative work events. Employed MS patients with co-occurring motor, memory and processing speed impairments were most likely to report a negative work event, classifying them as uniquely at risk for job loss.

Determination of variability in leaf biomass densities of conifers and mixed conifers under different environmental conditions in the San Joaquin Valley air basin. Final report

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

Temple, P.J.; Mutters, R.J.; Adams, C.

1995-06-01

Biomass sampling plots were established at 29 locations within the dominant vegetation zones of the study area. Estimates of foliar biomass were made for each plot by three independent methods: regression analysis on the basis of tree diameter, calculation of the amount of light intercepted by the leaf canopy, and extrapolation from branch leaf area. Multivariate regression analysis was used to relate these foliar biomass estimates for oak plots and conifer plots to several independent predictor variables, including elevation, slope, aspect, temperature, precipitation, and soil chemical characteristics.

Interrupted time series regression for the evaluation of public health interventions: a tutorial.

PubMed

Bernal, James Lopez; Cummins, Steven; Gasparrini, Antonio

2017-02-01

Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.

Interrupted time series regression for the evaluation of public health interventions: a tutorial

PubMed Central

Bernal, James Lopez; Cummins, Steven; Gasparrini, Antonio

2017-01-01

Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design. PMID:27283160

Regression analysis for LED color detection of visual-MIMO system

NASA Astrophysics Data System (ADS)

Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo

2018-04-01

Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.

Analysis of Market Opportunities for Chinese Private Express Delivery Industry

NASA Astrophysics Data System (ADS)

Jiang, Changbing; Bai, Lijun; Tong, Xiaoqing

China's express delivery market has become the arena in which each express enterprise struggles to chase due to the huge potential demand and high profitable prospects. So certain qualitative and quantitative forecast for the future changes of China's express delivery market will help enterprises understand various types of market conditions and social changes in demand and adjust business activities to enhance their competitiveness timely. The development of China's express delivery industry is first introduced in this chapter. Then the theoretical basis of the regression model is overviewed. We also predict the demand trends of China's express delivery market by using Pearson correlation analysis and regression analysis from qualitative and quantitative aspects, respectively. Finally, we draw some conclusions and recommendations for China's express delivery industry.

Hypomagnesemia predicts postoperative biochemical hypocalcemia after thyroidectomy.

PubMed

Luo, Han; Yang, Hongliu; Zhao, Wanjun; Wei, Tao; Su, Anping; Wang, Bin; Zhu, Jingqiang

2017-05-25

To investigate the role of magnesium in biochemical and symptomatic hypocalcemia, a retrospective study was conducted. Less-than-total thyroidectomy patients were excluded from the final analysis. Identified the risk factors of biochemical and symptomatic hypocalcemia, and investigated the correlation by logistic regression and correlation test respectively. A total of 304 patients were included in the final analysis. General incidence of hypomagnesemia was 23.36%. Logistic regression showed that gender (female) (OR = 2.238, p = 0.015) and postoperative hypomagnesemia (OR = 2.010, p = 0.017) were independent risk factors for biochemical hypocalcemia. Both Pearson and partial correlation tests indicated there was indeed significant relation between calcium and magnesium. However, relative decreasing of iPTH (>70%) (6.691, p < 0.001) and hypocalcemia (2.222, p = 0.046) were identified as risk factors of symptomatic hypocalcemia. The difference remained significant even in normoparathyroidism patients. Postoperative hypomagnesemia was independent risk factor of biochemical hypocalcemia. Relative decline of iPTH was predominating in predicting symptomatic hypocalcemia.

A hybrid sales forecasting scheme by combining independent component analysis with K-means clustering and support vector regression.

PubMed

Lu, Chi-Jie; Chang, Chi-Chang

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.

A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression

PubMed Central

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting. PMID:25045738

Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

PubMed

Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L

2017-02-06

Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.

Logistic regression for risk factor modelling in stuttering research.

PubMed

Reed, Phil; Wu, Yaqionq

2013-06-01

To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.

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.

Adaptive surrogate modeling by ANOVA and sparse polynomial dimensional decomposition for global sensitivity analysis in fluid simulation

NASA Astrophysics Data System (ADS)

Tang, Kunkun; Congedo, Pietro M.; Abgrall, Rémi

2016-06-01

The Polynomial Dimensional Decomposition (PDD) is employed in this work for the global sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to a moderate to large number of input random variables. Due to the intimate connection between the PDD and the Analysis of Variance (ANOVA) approaches, PDD is able to provide a simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to the Polynomial Chaos expansion (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of standard methods unaffordable for real engineering applications. In order to address the problem of the curse of dimensionality, this work proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model (i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed by regression. Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionality for ANOVA component functions, 2) the active dimension technique especially for second- and higher-order parameter interactions, and 3) the stepwise regression approach designed to retain only the most influential polynomials in the PDD expansion. During this adaptive procedure featuring stepwise regressions, the surrogate model representation keeps containing few terms, so that the cost to resolve repeatedly the linear systems of the least-squares regression problem is negligible. The size of the finally obtained sparse PDD representation is much smaller than the one of the full expansion, since only significant terms are eventually retained. Consequently, a much smaller number of calls to the deterministic model is required to compute the final PDD coefficients.

Magnitude and Frequency of Floods for Urban and Small Rural Streams in Georgia, 2008

USGS Publications Warehouse

Gotvald, Anthony J.; Knaak, Andrew E.

2011-01-01

A study was conducted that updated methods for estimating the magnitude and frequency of floods in ungaged urban basins in Georgia that are not substantially affected by regulation or tidal fluctuations. Annual peak-flow data for urban streams from September 2008 were analyzed for 50 streamgaging stations (streamgages) in Georgia and 6 streamgages on adjacent urban streams in Florida and South Carolina having 10 or more years of data. Flood-frequency estimates were computed for the 56 urban streamgages by fitting logarithms of annual peak flows for each streamgage to a Pearson Type III distribution. Additionally, basin characteristics for the streamgages were computed by using a geographical information system and computer algorithms. Regional regression analysis, using generalized least-squares regression, was used to develop a set of equations for estimating flows with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for ungaged urban basins in Georgia. In addition to the 56 urban streamgages, 171 rural streamgages were included in the regression analysis to maintain continuity between flood estimates for urban and rural basins as the basin characteristics pertaining to urbanization approach zero. Because 21 of the rural streamgages have drainage areas less than 1 square mile, the set of equations developed for this study can also be used for estimating small ungaged rural streams in Georgia. Flood-frequency estimates and basin characteristics for 227 streamgages were combined to form the final database used in the regional regression analysis. Four hydrologic regions were developed for Georgia. The final equations are functions of drainage area and percentage of impervious area for three of the regions and drainage area, percentage of developed land, and mean basin slope for the fourth region. Average standard errors of prediction for these regression equations range from 20.0 to 74.5 percent.

Is math anxiety in the secondary classroom limiting physics mastery? A study of math anxiety and physics performance

NASA Astrophysics Data System (ADS)

Mercer, Gary J.

This quantitative study examined the relationship between secondary students with math anxiety and physics performance in an inquiry-based constructivist classroom. The Revised Math Anxiety Rating Scale was used to evaluate math anxiety levels. The results were then compared to the performance on a physics standardized final examination. A simple correlation was performed, followed by a multivariate regression analysis to examine effects based on gender and prior math background. The correlation showed statistical significance between math anxiety and physics performance. The regression analysis showed statistical significance for math anxiety, physics performance, and prior math background, but did not show statistical significance for math anxiety, physics performance, and gender.

Perceptions about Homeless Elders and Community Responsibility

ERIC Educational Resources Information Center

Kane, Michael N.; Green, Diane; Jacobs, Robin

2013-01-01

Human service students were surveyed ("N" = 207) to determine their perceptions about homeless elders and communal responsibility for their well-being. Using a backward regression analysis, a final model ("F" = 15.617, "df" = 7, "p" < 0.001) for Perceptions about Homeless Persons and Community…

Utility of an Abbreviated Dizziness Questionnaire to Differentiate between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study

PubMed Central

Roland, Lauren T.; Kallogjeri, Dorina; Sinks, Belinda C.; Rauch, Steven D.; Shepard, Neil T.; White, Judith A.; Goebel, Joel A.

2015-01-01

Objective Test performance of a focused dizziness questionnaire’s ability to discriminate between peripheral and non-peripheral causes of vertigo. Study Design Prospective multi-center Setting Four academic centers with experienced balance specialists Patients New dizzy patients Interventions A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Main outcomes Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and non-peripheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. Results 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and non-peripheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central and other causes were considered good as measured by c-indices of 0.75, 0.7 and 0.78, respectively. Conclusions This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from non-peripheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed. PMID:26485598

Utility of an Abbreviated Dizziness Questionnaire to Differentiate Between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study.

PubMed

Roland, Lauren T; Kallogjeri, Dorina; Sinks, Belinda C; Rauch, Steven D; Shepard, Neil T; White, Judith A; Goebel, Joel A

2015-12-01

Test performance of a focused dizziness questionnaire's ability to discriminate between peripheral and nonperipheral causes of vertigo. Prospective multicenter. Four academic centers with experienced balance specialists. New dizzy patients. A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and nonperipheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. In total, 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and nonperipheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central, and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central, and other causes was considered good as measured by c-indices of 0.75, 0.7, and 0.78, respectively. This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from nonperipheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed.

Application of principal component regression and partial least squares regression in ultraviolet spectrum water quality detection

NASA Astrophysics Data System (ADS)

Li, Jiangtong; Luo, Yongdao; Dai, Honglin

2018-01-01

Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.

Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks.

PubMed

Duda, Piotr; Jaworski, Maciej; Rutkowski, Leszek

2018-03-01

One of the greatest challenges in data mining is related to processing and analysis of massive data streams. Contrary to traditional static data mining problems, data streams require that each element is processed only once, the amount of allocated memory is constant and the models incorporate changes of investigated streams. A vast majority of available methods have been developed for data stream classification and only a few of them attempted to solve regression problems, using various heuristic approaches. In this paper, we develop mathematically justified regression models working in a time-varying environment. More specifically, we study incremental versions of generalized regression neural networks, called IGRNNs, and we prove their tracking properties - weak (in probability) and strong (with probability one) convergence assuming various concept drift scenarios. First, we present the IGRNNs, based on the Parzen kernels, for modeling stationary systems under nonstationary noise. Next, we extend our approach to modeling time-varying systems under nonstationary noise. We present several types of concept drifts to be handled by our approach in such a way that weak and strong convergence holds under certain conditions. Finally, in the series of simulations, we compare our method with commonly used heuristic approaches, based on forgetting mechanism or sliding windows, to deal with concept drift. Finally, we apply our concept in a real life scenario solving the problem of currency exchange rates prediction.

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

PubMed Central

Dipnall, Joanna F.

2016-01-01

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

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

PubMed

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

2016-01-01

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

Methods for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in North Georgia

USGS Publications Warehouse

Gotvald, Anthony J.

2017-01-13

The U.S. Geological Survey, in cooperation with the Georgia Department of Natural Resources, Environmental Protection Division, developed regional regression equations for estimating selected low-flow frequency and mean annual flow statistics for ungaged streams in north Georgia that are not substantially affected by regulation, diversions, or urbanization. Selected low-flow frequency statistics and basin characteristics for 56 streamgage locations within north Georgia and 75 miles beyond the State’s borders in Alabama, Tennessee, North Carolina, and South Carolina were combined to form the final dataset used in the regional regression analysis. Because some of the streamgages in the study recorded zero flow, the final regression equations were developed using weighted left-censored regression analysis to analyze the flow data in an unbiased manner, with weights based on the number of years of record. The set of equations includes the annual minimum 1- and 7-day average streamflow with the 10-year recurrence interval (referred to as 1Q10 and 7Q10), monthly 7Q10, and mean annual flow. The final regional regression equations are functions of drainage area, mean annual precipitation, and relief ratio for the selected low-flow frequency statistics and drainage area and mean annual precipitation for mean annual flow. The average standard error of estimate was 13.7 percent for the mean annual flow regression equation and ranged from 26.1 to 91.6 percent for the selected low-flow frequency equations.The equations, which are based on data from streams with little to no flow alterations, can be used to provide estimates of the natural flows for selected ungaged stream locations in the area of Georgia north of the Fall Line. The regression equations are not to be used to estimate flows for streams that have been altered by the effects of major dams, surface-water withdrawals, groundwater withdrawals (pumping wells), diversions, or wastewater discharges. The regression equations should be used only for ungaged sites with drainage areas between 1.67 and 576 square miles, mean annual precipitation between 47.6 and 81.6 inches, and relief ratios between 0.146 and 0.607; these are the ranges of the explanatory variables used to develop the equations. An attempt was made to develop regional regression equations for the area of Georgia south of the Fall Line by using the same approach used during this study for north Georgia; however, the equations resulted with high average standard errors of estimates and poorly predicted flows below 0.5 cubic foot per second, which may be attributed to the karst topography common in that area.The final regression equations developed from this study are planned to be incorporated into the U.S. Geological Survey StreamStats program. StreamStats is a Web-based geographic information system that provides users with access to an assortment of analytical tools useful for water-resources planning and management, and for engineering design applications, such as the design of bridges. The StreamStats program provides streamflow statistics and basin characteristics for U.S. Geological Survey streamgage locations and ungaged sites of interest. StreamStats also can compute basin characteristics and provide estimates of streamflow statistics for ungaged sites when users select the location of a site along any stream in Georgia.

Computer-Assisted, Programmed Text, and Lecture Modes of Instruction in Three Medical Training Courses: Comparative Evaluation. Final Report.

ERIC Educational Resources Information Center

Deignan, Gerard M.; And Others

This report contains a comparative analysis of the differential effectiveness of computer-assisted instruction (CAI), programmed instructional text (PIT), and lecture methods of instruction in three medical courses--Medical Laboratory, Radiology, and Dental. The summative evaluation includes (1) multiple regression analyses conducted to predict…

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

Tang, Kunkun, E-mail: ktg@illinois.edu; Inria Bordeaux – Sud-Ouest, Team Cardamom, 200 avenue de la Vieille Tour, 33405 Talence; Congedo, Pietro M.

The Polynomial Dimensional Decomposition (PDD) is employed in this work for the global sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to a moderate to large number of input random variables. Due to the intimate connection between the PDD and the Analysis of Variance (ANOVA) approaches, PDD is able to provide a simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to the Polynomial Chaos expansion (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of standard methods unaffordable formore » real engineering applications. In order to address the problem of the curse of dimensionality, this work proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model (i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed by regression. Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionality for ANOVA component functions, 2) the active dimension technique especially for second- and higher-order parameter interactions, and 3) the stepwise regression approach designed to retain only the most influential polynomials in the PDD expansion. During this adaptive procedure featuring stepwise regressions, the surrogate model representation keeps containing few terms, so that the cost to resolve repeatedly the linear systems of the least-squares regression problem is negligible. The size of the finally obtained sparse PDD representation is much smaller than the one of the full expansion, since only significant terms are eventually retained. Consequently, a much smaller number of calls to the deterministic model is required to compute the final PDD coefficients.« less

Evaluation of Regression Models of Balance Calibration Data Using an Empirical Criterion

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert; Volden, Thomas R.

2012-01-01

An empirical criterion for assessing the significance of individual terms of regression models of wind tunnel strain gage balance outputs is evaluated. The criterion is based on the percent contribution of a regression model term. It considers a term to be significant if its percent contribution exceeds the empirical threshold of 0.05%. The criterion has the advantage that it can easily be computed using the regression coefficients of the gage outputs and the load capacities of the balance. First, a definition of the empirical criterion is provided. Then, it is compared with an alternate statistical criterion that is widely used in regression analysis. Finally, calibration data sets from a variety of balances are used to illustrate the connection between the empirical and the statistical criterion. A review of these results indicated that the empirical criterion seems to be suitable for a crude assessment of the significance of a regression model term as the boundary between a significant and an insignificant term cannot be defined very well. Therefore, regression model term reduction should only be performed by using the more universally applicable statistical criterion.

A logistic regression analysis of factors related to the treatment compliance of infertile patients with polycystic ovary syndrome.

PubMed

Li, Saijiao; He, Aiyan; Yang, Jing; Yin, TaiLang; Xu, Wangming

2011-01-01

To investigate factors that can affect compliance with treatment of polycystic ovary syndrome (PCOS) in infertile patients and to provide a basis for clinical treatment, specialist consultation and health education. Patient compliance was assessed via a questionnaire based on the Morisky-Green test and the treatment principles of PCOS. Then interviews were conducted with 99 infertile patients diagnosed with PCOS at Renmin Hospital of Wuhan University in China, from March to September 2009. Finally, these data were analyzed using logistic regression analysis. Logistic regression analysis revealed that a total of 23 (25.6%) of the participants showed good compliance. Factors that significantly (p < 0.05) affected compliance with treatment were the patient's body mass index, convenience of medical treatment and concerns about adverse drug reactions. Patients who are obese, experience inconvenient medical treatment or are concerned about adverse drug reactions are more likely to exhibit noncompliance. Treatment education and intervention aimed at these patients should be strengthened in the clinic to improve treatment compliance. Further research is needed to better elucidate the compliance behavior of patients with PCOS.

Prognostic factors in Acanthamoeba keratitis.

PubMed

Kaiserman, Igor; Bahar, Irit; McAllum, Penny; Srinivasan, Sathish; Elbaz, Uri; Slomovic, Allan R; Rootman, David S

2012-06-01

To assess the prognostic factors influencing visual prognosis and length of treatment after acanthamoeba keratitis (AK). Forty-two AK eyes of 41 patients treated between 1999 and 2006 were included. A diagnosis of AK was made on the basis of culture results with a corresponding clinical presentation. We calculated the prognostic effect of the various factors on final visual acuity and the length of treatment. Multivariate regression analysis was used to adjust for the simultaneous effects of the various prognostic factors. Mean follow-up was 19.7 ± 21.0 months. Sixty-four percent of cases had > 1 identified risk factor for AK, the most common risk factor being contact lens wear (92.9% of eyes). At presentation, median best spectacle corrected visual acuity (BCVA) was 20/200 (20/30 to Hand Motion [HM]) that improved after treatment to 20/50 (20/20 to Counting Fingers [CF]). Infection acquired by swimming or related to contact lenses had significantly better final BCVA (p = 0.03 and p = 0.007, respectively). Neuritis and pseudodendrites were also associated with better final BCVA (p = 0.04 and p = 0.05, respectively). Having had an epithelial defect on presentation and having been treated with topical steroid were associated with worse final best spectacle corrected visual acuity (BSCVA) (p = 0.0006 and p = 0.04). Multivariate regression analysis found a good initial visual acuity (p = 0.002), infections related to swimming (p = 0.01), the absence of an epithelial defect (p = 0.03), having been treated with chlorhexidine (p = 0.05), and not having receive steroids (p = 0.003) to significantly forecast a good final BCVA. We identified several prognostic factors that can help clinicians evaluate the expected visual damage of the AK infection and thus tailor treatment accordingly. Copyright © 2012 Canadian Ophthalmological Society. All rights reserved.

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

USGS Publications Warehouse

Anderson, Ryan; Clegg, Samuel M.; Frydenvang, Jens; Wiens, Roger C.; McLennan, Scott M.; Morris, Richard V.; Ehlmann, Bethany L.; Dyar, M. Darby

2017-01-01

Accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response of an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “sub-model” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. The sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.

Fatigue design of a cellular phone folder using regression model-based multi-objective optimization

NASA Astrophysics Data System (ADS)

Kim, Young Gyun; Lee, Jongsoo

2016-08-01

In a folding cellular phone, the folding device is repeatedly opened and closed by the user, which eventually results in fatigue damage, particularly to the front of the folder. Hence, it is important to improve the safety and endurance of the folder while also reducing its weight. This article presents an optimal design for the folder front that maximizes its fatigue endurance while minimizing its thickness. Design data for analysis and optimization were obtained experimentally using a test jig. Multi-objective optimization was carried out using a nonlinear regression model. Three regression methods were employed: back-propagation neural networks, logistic regression and support vector machines. The AdaBoost ensemble technique was also used to improve the approximation. Two-objective Pareto-optimal solutions were identified using the non-dominated sorting genetic algorithm (NSGA-II). Finally, a numerically optimized solution was validated against experimental product data, in terms of both fatigue endurance and thickness index.

Students' Engagement with a Collaborative Wiki Tool Predicts Enhanced Written Exam Performance

ERIC Educational Resources Information Center

Stafford, Tom; Elgueta, Herman; Cameron, Harriet

2014-01-01

We introduced voluntary wiki-based exercises to a long-running cognitive psychology course, part of the core curriculum for an undergraduate degree in psychology. Over 2 yearly cohorts, students who used the wiki more also scored higher on the final written exam. Using regression analysis, it is possible to account for students' tendency to score…

The Effect of Attending Tutoring on Course Grades in Calculus I

ERIC Educational Resources Information Center

Rickard, Brian; Mills, Melissa

2018-01-01

Tutoring centres are common in universities in the United States, but there are few published studies that statistically examine the effects of tutoring on student success. This study utilizes multiple regression analysis to model the effect of tutoring attendance on final course grades in Calculus I. Our model predicted that every three visits to…

Early Change in Stroke Size Performs Best in Predicting Response to Therapy.

PubMed

Simpkins, Alexis Nétis; Dias, Christian; Norato, Gina; Kim, Eunhee; Leigh, Richard

2017-01-01

Reliable imaging biomarkers of response to therapy in acute stroke are needed. The final infarct volume and percent of early reperfusion have been used for this purpose. Early fluctuation in stroke size is a recognized phenomenon, but its utility as a biomarker for response to therapy has not been established. This study examined the clinical relevance of early change in stroke volume and compared it with the final infarct volume and percent of early reperfusion in identifying early neurologic improvement (ENI). Acute stroke patients, enrolled between 2013 and 2014 with serial magnetic resonance imaging (MRI) scans (pretreatment baseline, 2 h post, and 24 h post), who received thrombolysis were included in the analysis. Early change in stroke volume, infarct volume at 24 h on diffusion, and percent of early reperfusion were calculated from the baseline and 2 h MRI scans were compared. ENI was defined as ≥4 point decrease in National Institutes of Health Stroke Scales within 24 h. Logistic regression models and receiver operator characteristics analysis were used to compare the efficacy of 3 imaging biomarkers. Serial MRIs of 58 acute stroke patients were analyzed. Early change in stroke volume was significantly associated with ENI by logistic regression analysis (OR 0.93, p = 0.048) and remained significant after controlling for stroke size and severity (OR 0.90, p = 0.032). Thus, for every 1 mL increase in stroke volume, there was a 10% decrease in the odds of ENI, while for every 1 mL decrease in stroke volume, there was a 10% increase in the odds of ENI. Neither infarct volume at 24 h nor percent of early reperfusion were significantly associated with ENI by logistic regression. Receiver-operator characteristic analysis identified early change in stroke volume as the only biomarker of the 3 that performed significantly different than chance (p = 0.03). Early fluctuations in stroke size may represent a more reliable biomarker for response to therapy than the more traditional measures of final infarct volume and percent of early reperfusion. © 2017 S. Karger AG, Basel.

Simulation of parametric model towards the fixed covariate of right censored lung cancer data

NASA Astrophysics Data System (ADS)

Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Ridwan Olaniran, Oyebayo; Enera Amran, Syahila

2017-09-01

In this study, simulation procedure was applied to measure the fixed covariate of right censored data by using parametric survival model. The scale and shape parameter were modified to differentiate the analysis of parametric regression survival model. Statistically, the biases, mean biases and the coverage probability were used in this analysis. Consequently, different sample sizes were employed to distinguish the impact of parametric regression model towards right censored data with 50, 100, 150 and 200 number of sample. R-statistical software was utilised to develop the coding simulation with right censored data. Besides, the final model of right censored simulation was compared with the right censored lung cancer data in Malaysia. It was found that different values of shape and scale parameter with different sample size, help to improve the simulation strategy for right censored data and Weibull regression survival model is suitable fit towards the simulation of survival of lung cancer patients data in Malaysia.

Design of an optimum computer vision-based automatic abalone (Haliotis discus hannai) grading algorithm.

PubMed

Lee, Donggil; Lee, Kyounghoon; Kim, Seonghun; Yang, Yongsu

2015-04-01

An automatic abalone grading algorithm that estimates abalone weights on the basis of computer vision using 2D images is developed and tested. The algorithm overcomes the problems experienced by conventional abalone grading methods that utilize manual sorting and mechanical automatic grading. To design an optimal algorithm, a regression formula and R(2) value were investigated by performing a regression analysis for each of total length, body width, thickness, view area, and actual volume against abalone weights. The R(2) value between the actual volume and abalone weight was 0.999, showing a relatively high correlation. As a result, to easily estimate the actual volumes of abalones based on computer vision, the volumes were calculated under the assumption that abalone shapes are half-oblate ellipsoids, and a regression formula was derived to estimate the volumes of abalones through linear regression analysis between the calculated and actual volumes. The final automatic abalone grading algorithm is designed using the abalone volume estimation regression formula derived from test results, and the actual volumes and abalone weights regression formula. In the range of abalones weighting from 16.51 to 128.01 g, the results of evaluation of the performance of algorithm via cross-validation indicate root mean square and worst-case prediction errors of are 2.8 and ±8 g, respectively. © 2015 Institute of Food Technologists®

Methods for estimating annual exceedance probability discharges for streams in Arkansas, based on data through water year 2013

USGS Publications Warehouse

Wagner, Daniel M.; Krieger, Joshua D.; Veilleux, Andrea G.

2016-08-04

In 2013, the U.S. Geological Survey initiated a study to update regional skew, annual exceedance probability discharges, and regional regression equations used to estimate annual exceedance probability discharges for ungaged locations on streams in the study area with the use of recent geospatial data, new analytical methods, and available annual peak-discharge data through the 2013 water year. An analysis of regional skew using Bayesian weighted least-squares/Bayesian generalized-least squares regression was performed for Arkansas, Louisiana, and parts of Missouri and Oklahoma. The newly developed constant regional skew of -0.17 was used in the computation of annual exceedance probability discharges for 281 streamgages used in the regional regression analysis. Based on analysis of covariance, four flood regions were identified for use in the generation of regional regression models. Thirty-nine basin characteristics were considered as potential explanatory variables, and ordinary least-squares regression techniques were used to determine the optimum combinations of basin characteristics for each of the four regions. Basin characteristics in candidate models were evaluated based on multicollinearity with other basin characteristics (variance inflation factor < 2.5) and statistical significance at the 95-percent confidence level (p ≤ 0.05). Generalized least-squares regression was used to develop the final regression models for each flood region. Average standard errors of prediction of the generalized least-squares models ranged from 32.76 to 59.53 percent, with the largest range in flood region D. Pseudo coefficients of determination of the generalized least-squares models ranged from 90.29 to 97.28 percent, with the largest range also in flood region D. The regional regression equations apply only to locations on streams in Arkansas where annual peak discharges are not substantially affected by regulation, diversion, channelization, backwater, or urbanization. The applicability and accuracy of the regional regression equations depend on the basin characteristics measured for an ungaged location on a stream being within range of those used to develop the equations.

Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment.

PubMed

Rosswog, Carolina; Schmidt, Rene; Oberthuer, André; Juraeva, Dilafruz; Brors, Benedikt; Engesser, Anne; Kahlert, Yvonne; Volland, Ruth; Bartenhagen, Christoph; Simon, Thorsten; Berthold, Frank; Hero, Barbara; Faldum, Andreas; Fischer, Matthias

2017-12-01

Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consecutive Cox regression models, resulting in a risk score that integrates hazard ratios of prognostic variables. A cohort of 695 neuroblastoma patients was divided into a discovery set (n=75) for multigene predictor generation, a training set (n=411) for risk score development, and a validation set (n=209). Relevant prognostic variables were identified by stepwise multivariable L1-penalized least absolute shrinkage and selection operator (LASSO) Cox regression, followed by backward selection in multivariable Cox regression, and then integrated into a novel risk score. The variables stage, age, MYCN status, and two multigene predictors, NB-th24 and NB-th44, were selected as independent prognostic markers by LASSO Cox regression analysis. Following backward selection, only the multigene predictors were retained in the final model. Integration of these classifiers in a risk scoring system distinguished three patient subgroups that differed substantially in their outcome. The scoring system discriminated patients with diverging outcome in the validation cohort (5-year event-free survival, 84.9±3.4 vs 63.6±14.5 vs 31.0±5.4; P<.001), and its prognostic value was validated by multivariable analysis. We here propose a translational strategy for developing risk assessment systems based on hazard ratios of relevant prognostic variables. Our final neuroblastoma risk score comprised two multigene predictors only, supporting the notion that molecular properties of the tumor cells strongly impact clinical courses of neuroblastoma patients. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Cross-country Analysis of ICT and Education Indicators: An Exploratory Study

NASA Astrophysics Data System (ADS)

Pratama, Ahmad R.

2017-03-01

This paper explores the relationship between world ICT and education indicators by using the latest available data from World Bank and UNESCO in range of 2011-2014 with the help of different exploratory methods such as principal component analysis (PCA), factor analysis (FA), cluster analysis, and ordinary least square (OLS) regression. After dealing with all missing values, 119 countries were included in the final dataset. The findings show that most ICT and education indicators are highly associated with income of the respective country and therefore confirm the existence of digital divide in ICT utilization and participation gap in education between rich and poor countries. It also indicates that digital divide and participation gap is highly associated with each other. Finally, the findings also confirm reverse causality in ICT and education; higher participation rate in education increases technology utilization, which in turn helps promote better outcomes of education.

Determining association constants from titration experiments in supramolecular chemistry.

PubMed

Thordarson, Pall

2011-03-01

The most common approach for quantifying interactions in supramolecular chemistry is a titration of the guest to solution of the host, noting the changes in some physical property through NMR, UV-Vis, fluorescence or other techniques. Despite the apparent simplicity of this approach, there are several issues that need to be carefully addressed to ensure that the final results are reliable. This includes the use of non-linear rather than linear regression methods, careful choice of stoichiometric binding model, the choice of method (e.g., NMR vs. UV-Vis) and concentration of host, the application of advanced data analysis methods such as global analysis and finally the estimation of uncertainties and confidence intervals for the results obtained. This tutorial review will give a systematic overview of all these issues-highlighting some of the key messages herein with simulated data analysis examples.

An investigation on fatality of drivers in vehicle-fixed object accidents on expressways in China: Using multinomial logistic regression model.

PubMed

Peng, Yong; Peng, Shuangling; Wang, Xinghua; Tan, Shiyang

2018-06-01

This study aims to identify the effects of characteristics of vehicle, roadway, driver, and environment on fatality of drivers in vehicle-fixed object accidents on expressways in Changsha-Zhuzhou-Xiangtan district of Hunan province in China by developing multinomial logistic regression models. For this purpose, 121 vehicle-fixed object accidents from 2011-2017 are included in the modeling process. First, descriptive statistical analysis is made to understand the main characteristics of the vehicle-fixed object crashes. Then, 19 explanatory variables are selected, and correlation analysis of each two variables is conducted to choose the variables to be concluded. Finally, five multinomial logistic regression models including different independent variables are compared, and the model with best fitting and prediction capability is chosen as the final model. The results showed that the turning direction in avoiding fixed objects raised the possibility that drivers would die. About 64% of drivers died in the accident were found being ejected out of the car, of which 50% did not use a seatbelt before the fatal accidents. Drivers are likely to die when they encounter bad weather on the expressway. Drivers with less than 10 years of driving experience are more likely to die in these accidents. Fatigue or distracted driving is also a significant factor in fatality of drivers. Findings from this research provide an insight into reducing fatality of drivers in vehicle-fixed object accidents.

The Effect of Alcohol Abuse and Dependence and School Experiences on Depression: A National Study of Adolescents

ERIC Educational Resources Information Center

Merianos, Ashley L.; King, Keith A.; Vidourek, Rebecca A.; Hardee, Angelica M.

2016-01-01

The study purpose was to examine the effect alcohol abuse/dependence and school experiences have on depression among a nationwide sample of adolescents. A secondary analysis of the 2013 National Survey on Drug Use and Health was conducted. The results of the final multivariable logistic regression model revealed that adolescents who reported…

The effect of attending tutoring on course grades in Calculus I

NASA Astrophysics Data System (ADS)

Rickard, Brian; Mills, Melissa

2018-04-01

Tutoring centres are common in universities in the United States, but there are few published studies that statistically examine the effects of tutoring on student success. This study utilizes multiple regression analysis to model the effect of tutoring attendance on final course grades in Calculus I. Our model predicted that every three visits to the tutoring centre is correlated with an increase of a students' course grade by one per cent, after controlling for prior academic ability. We also found that for lower-achieving students, attending tutoring had a greater impact on final grades.

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

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

Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens

We report that accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response ofmore » an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “submodel” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. Lastly, the sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.« less

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

DOE PAGES

Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens; ...

2016-12-15

We report that accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response ofmore » an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “submodel” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. Lastly, the sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.« less

Independent contrasts and PGLS regression estimators are equivalent.

PubMed

Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary

2012-05-01

We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.

Regression Analysis of Mixed Panel Count Data with Dependent Terminal Events

PubMed Central

Yu, Guanglei; Zhu, Liang; Li, Yang; Sun, Jianguo; Robison, Leslie L.

2017-01-01

Event history studies are commonly conducted in many fields and a great deal of literature has been established for the analysis of the two types of data commonly arising from these studies: recurrent event data and panel count data. The former arises if all study subjects are followed continuously, while the latter means that each study subject is observed only at discrete time points. In reality, a third type of data, a mixture of the two types of the data above, may occur and furthermore, as with the first two types of the data, there may exist a dependent terminal event, which may preclude the occurrences of recurrent events of interest. This paper discusses regression analysis of mixed recurrent event and panel count data in the presence of a terminal event and an estimating equation-based approach is proposed for estimation of regression parameters of interest. In addition, the asymptotic properties of the proposed estimator are established and a simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well in practical situations. Finally the methodology is applied to a childhood cancer study that motivated this study. PMID:28098397

Quantifying components of the hydrologic cycle in Virginia using chemical hydrograph separation and multiple regression analysis

USGS Publications Warehouse

Sanford, Ward E.; Nelms, David L.; Pope, Jason P.; Selnick, David L.

2012-01-01

This study by the U.S. Geological Survey, prepared in cooperation with the Virginia Department of Environmental Quality, quantifies the components of the hydrologic cycle across the Commonwealth of Virginia. Long-term, mean fluxes were calculated for precipitation, surface runoff, infiltration, total evapotranspiration (ET), riparian ET, recharge, base flow (or groundwater discharge) and net total outflow. Fluxes of these components were first estimated on a number of real-time-gaged watersheds across Virginia. Specific conductance was used to distinguish and separate surface runoff from base flow. Specific-conductance data were collected every 15 minutes at 75 real-time gages for approximately 18 months between March 2007 and August 2008. Precipitation was estimated for 1971–2000 using PRISM climate data. Precipitation and temperature from the PRISM data were used to develop a regression-based relation to estimate total ET. The proportion of watershed precipitation that becomes surface runoff was related to physiographic province and rock type in a runoff regression equation. Component flux estimates from the watersheds were transferred to flux estimates for counties and independent cities using the ET and runoff regression equations. Only 48 of the 75 watersheds yielded sufficient data, and data from these 48 were used in the final runoff regression equation. The base-flow proportion for the 48 watersheds averaged 72 percent using specific conductance, a value that was substantially higher than the 61 percent average calculated using a graphical-separation technique (the USGS program PART). Final results for the study are presented as component flux estimates for all counties and independent cities in Virginia.

Prevalence of treponema species detected in endodontic infections: systematic review and meta-regression analysis.

PubMed

Leite, Fábio R M; Nascimento, Gustavo G; Demarco, Flávio F; Gomes, Brenda P F A; Pucci, Cesar R; Martinho, Frederico C

2015-05-01

This systematic review and meta-regression analysis aimed to calculate a combined prevalence estimate and evaluate the prevalence of different Treponema species in primary and secondary endodontic infections, including symptomatic and asymptomatic cases. The MEDLINE/PubMed, Embase, Scielo, Web of Knowledge, and Scopus databases were searched without starting date restriction up to and including March 2014. Only reports in English were included. The selected literature was reviewed by 2 authors and classified as suitable or not to be included in this review. Lists were compared, and, in case of disagreements, decisions were made after a discussion based on inclusion and exclusion criteria. A pooled prevalence of Treponema species in endodontic infections was estimated. Additionally, a meta-regression analysis was performed. Among the 265 articles identified in the initial search, only 51 were included in the final analysis. The studies were classified into 2 different groups according to the type of endodontic infection and whether it was an exclusively primary/secondary study (n = 36) or a primary/secondary comparison (n = 15). The pooled prevalence of Treponema species was 41.5% (95% confidence interval, 35.9-47.0). In the multivariate model of meta-regression analysis, primary endodontic infections (P < .001), acute apical abscess, symptomatic apical periodontitis (P < .001), and concomitant presence of 2 or more species (P = .028) explained the heterogeneity regarding the prevalence rates of Treponema species. Our findings suggest that Treponema species are important pathogens involved in endodontic infections, particularly in cases of primary and acute infections. Copyright © 2015 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Hypothesis testing in functional linear regression models with Neyman's truncation and wavelet thresholding for longitudinal data.

PubMed

Yang, Xiaowei; Nie, Kun

2008-03-15

Longitudinal data sets in biomedical research often consist of large numbers of repeated measures. In many cases, the trajectories do not look globally linear or polynomial, making it difficult to summarize the data or test hypotheses using standard longitudinal data analysis based on various linear models. An alternative approach is to apply the approaches of functional data analysis, which directly target the continuous nonlinear curves underlying discretely sampled repeated measures. For the purposes of data exploration, many functional data analysis strategies have been developed based on various schemes of smoothing, but fewer options are available for making causal inferences regarding predictor-outcome relationships, a common task seen in hypothesis-driven medical studies. To compare groups of curves, two testing strategies with good power have been proposed for high-dimensional analysis of variance: the Fourier-based adaptive Neyman test and the wavelet-based thresholding test. Using a smoking cessation clinical trial data set, this paper demonstrates how to extend the strategies for hypothesis testing into the framework of functional linear regression models (FLRMs) with continuous functional responses and categorical or continuous scalar predictors. The analysis procedure consists of three steps: first, apply the Fourier or wavelet transform to the original repeated measures; then fit a multivariate linear model in the transformed domain; and finally, test the regression coefficients using either adaptive Neyman or thresholding statistics. Since a FLRM can be viewed as a natural extension of the traditional multiple linear regression model, the development of this model and computational tools should enhance the capacity of medical statistics for longitudinal data.

High and low frequency unfolded partial least squares regression based on empirical mode decomposition for quantitative analysis of fuel oil samples.

PubMed

Bian, Xihui; Li, Shujuan; Lin, Ligang; Tan, Xiaoyao; Fan, Qingjie; Li, Ming

2016-06-21

Accurate prediction of the model is fundamental to the successful analysis of complex samples. To utilize abundant information embedded over frequency and time domains, a novel regression model is presented for quantitative analysis of hydrocarbon contents in the fuel oil samples. The proposed method named as high and low frequency unfolded PLSR (HLUPLSR), which integrates empirical mode decomposition (EMD) and unfolded strategy with partial least squares regression (PLSR). In the proposed method, the original signals are firstly decomposed into a finite number of intrinsic mode functions (IMFs) and a residue by EMD. Secondly, the former high frequency IMFs are summed as a high frequency matrix and the latter IMFs and residue are summed as a low frequency matrix. Finally, the two matrices are unfolded to an extended matrix in variable dimension, and then the PLSR model is built between the extended matrix and the target values. Coupled with Ultraviolet (UV) spectroscopy, HLUPLSR has been applied to determine hydrocarbon contents of light gas oil and diesel fuels samples. Comparing with single PLSR and other signal processing techniques, the proposed method shows superiority in prediction ability and better model interpretation. Therefore, HLUPLSR method provides a promising tool for quantitative analysis of complex samples. Copyright © 2016 Elsevier B.V. All rights reserved.

New robust statistical procedures for the polytomous logistic regression models.

PubMed

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

Applications of Some Artificial Intelligence Methods to Satellite Soundings

NASA Technical Reports Server (NTRS)

Munteanu, M. J.; Jakubowicz, O.

1985-01-01

Hard clustering of temperature profiles and regression temperature retrievals were used to refine the method using the probabilities of membership of each pattern vector in each of the clusters derived with discriminant analysis. In hard clustering the maximum probability is taken and the corresponding cluster as the correct cluster are considered discarding the rest of the probabilities. In fuzzy partitioned clustering these probabilities are kept and the final regression retrieval is a weighted regression retrieval of several clusters. This method was used in the clustering of brightness temperatures where the purpose was to predict tropopause height. A further refinement is the division of temperature profiles into three major regions for classification purposes. The results are summarized in the tables total r.m.s. errors are displayed. An approach based on fuzzy logic which is intimately related to artificial intelligence methods is recommended.

A Retrospective Medical Records Review of Risk Factors for the Development of Respiratory Tract Secretions (Death Rattle) in the Dying Patient.

PubMed

Kolb, Hildegard; Snowden, Austyn; Stevens, Elaine; Atherton, Iain

2018-05-09

Identification of risk factors predicting the development of death rattle. Respiratory tract secretions, often called death rattle, are among the most common symptoms in dying patients around the world. It is unknown whether death rattle causes distress in patients, but it has been globally reported that distress levels can be high in family members. Although there is a poor evidence base, treatment with antimuscarinic medication is standard practice worldwide and prompt intervention is recognised as crucial for effectiveness. The identification of risk factors for the development of death rattle would allow for targeted interventions. A case ̶ control study was designed to retrospectively review two hundred consecutive medical records of mainly cancer patients who died in a hospice inpatient setting between 2009 - 2011. Fifteen potential risk factors including the original factors weight, smoking, final opioid dose and final Midazolam dose were investigated. Binary logistic regression to identify risk factors for death rattle development. Univariate analysis showed death rattle was significantly associated with final Midazolam doses and final opioid doses, length of dying phase and anticholinergic drug load in the pre-terminal phase. In the final logistic regression model only Midazolam was statistically significant and only at final doses of 20 mg/24hrs or over (OR 3.81 CI 1.41-10.34). Dying patients with a requirement for a high dose of Midazolam have an increased likelihood of developing death rattle. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.

PubMed

Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul

2015-11-04

Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.

Factor Analysis of Linear Type Traits and Their Relation with Longevity in Brazilian Holstein Cattle

PubMed Central

Kern, Elisandra Lurdes; Cobuci, Jaime Araújo; Costa, Cláudio Napolis; Pimentel, Concepta Margaret McManus

2014-01-01

In this study we aimed to evaluate the reduction in dimensionality of 20 linear type traits and more final score in 14,943 Holstein cows in Brazil using factor analysis, and indicate their relationship with longevity and 305 d first lactation milk production. Low partial correlations (−0.19 to 0.38), the medium to high Kaiser sampling mean (0.79) and the significance of the Bartlett sphericity test (p<0.001), indicated correlations between type traits and the suitability of these data for a factor analysis, after the elimination of seven traits. Two factors had autovalues greater than one. The first included width and height of posterior udder, udder texture, udder cleft, loin strength, bone quality and final score. The second included stature, top line, chest width, body depth, fore udder attachment, angularity and final score. The linear regression of the factors on several measures of longevity and 305 d milk production showed that selection considering only the first factor should lead to improvements in longevity and 305 milk production. PMID:25050015

Random forest models to predict aqueous solubility.

PubMed

Palmer, David S; O'Boyle, Noel M; Glen, Robert C; Mitchell, John B O

2007-01-01

Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules. The Random Forest regression model predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offered methods for automatic descriptor selection, an assessment of descriptor importance, and an in-parallel measure of predictive ability, all of which serve to recommend its use. The prediction of log molar solubility for an external test set of 330 molecules that are solid at 25 degrees C gave an r2 = 0.89 and RMSE = 0.69 log S units. For a standard data set selected from the literature, the model performed well with respect to other documented methods. Finally, the diversity of the training and test sets are compared to the chemical space occupied by molecules in the MDL drug data report, on the basis of molecular descriptors selected by the regression analysis.

Calibration and Data Analysis of the MC-130 Air Balance

NASA Technical Reports Server (NTRS)

Booth, Dennis; Ulbrich, N.

2012-01-01

Design, calibration, calibration analysis, and intended use of the MC-130 air balance are discussed. The MC-130 balance is an 8.0 inch diameter force balance that has two separate internal air flow systems and one external bellows system. The manual calibration of the balance consisted of a total of 1854 data points with both unpressurized and pressurized air flowing through the balance. A subset of 1160 data points was chosen for the calibration data analysis. The regression analysis of the subset was performed using two fundamentally different analysis approaches. First, the data analysis was performed using a recently developed extension of the Iterative Method. This approach fits gage outputs as a function of both applied balance loads and bellows pressures while still allowing the application of the iteration scheme that is used with the Iterative Method. Then, for comparison, the axial force was also analyzed using the Non-Iterative Method. This alternate approach directly fits loads as a function of measured gage outputs and bellows pressures and does not require a load iteration. The regression models used by both the extended Iterative and Non-Iterative Method were constructed such that they met a set of widely accepted statistical quality requirements. These requirements lead to reliable regression models and prevent overfitting of data because they ensure that no hidden near-linear dependencies between regression model terms exist and that only statistically significant terms are included. Finally, a comparison of the axial force residuals was performed. Overall, axial force estimates obtained from both methods show excellent agreement as the differences of the standard deviation of the axial force residuals are on the order of 0.001 % of the axial force capacity.

Quantitative characterization of the regressive ecological succession by fractal analysis of plant spatial patterns

USGS Publications Warehouse

Alados, C.L.; Pueyo, Y.; Giner, M.L.; Navarro, T.; Escos, J.; Barroso, F.; Cabezudo, B.; Emlen, J.M.

2003-01-01

We studied the effect of grazing on the degree of regression of successional vegetation dynamic in a semi-arid Mediterranean matorral. We quantified the spatial distribution patterns of the vegetation by fractal analyses, using the fractal information dimension and spatial autocorrelation measured by detrended fluctuation analyses (DFA). It is the first time that fractal analysis of plant spatial patterns has been used to characterize the regressive ecological succession. Plant spatial patterns were compared over a long-term grazing gradient (low, medium and heavy grazing pressure) and on ungrazed sites for two different plant communities: A middle dense matorral of Chamaerops and Periploca at Sabinar-Romeral and a middle dense matorral of Chamaerops, Rhamnus and Ulex at Requena-Montano. The two communities differed also in the microclimatic characteristics (sea oriented at the Sabinar-Romeral site and inland oriented at the Requena-Montano site). The information fractal dimension increased as we moved from a middle dense matorral to discontinuous and scattered matorral and, finally to the late regressive succession, at Stipa steppe stage. At this stage a drastic change in the fractal dimension revealed a change in the vegetation structure, accurately indicating end successional vegetation stages. Long-term correlation analysis (DFA) revealed that an increase in grazing pressure leads to unpredictability (randomness) in species distributions, a reduction in diversity, and an increase in cover of the regressive successional species, e.g. Stipa tenacissima L. These comparisons provide a quantitative characterization of the successional dynamic of plant spatial patterns in response to grazing perturbation gradient. ?? 2002 Elsevier Science B.V. All rights reserved.

Regression Analysis of Stage Variability for West-Central Florida Lakes

USGS Publications Warehouse

Sacks, Laura A.; Ellison, Donald L.; Swancar, Amy

2008-01-01

The variability in a lake's stage depends upon many factors, including surface-water flows, meteorological conditions, and hydrogeologic characteristics near the lake. An understanding of the factors controlling lake-stage variability for a population of lakes may be helpful to water managers who set regulatory levels for lakes. The goal of this study is to determine whether lake-stage variability can be predicted using multiple linear regression and readily available lake and basin characteristics defined for each lake. Regressions were evaluated for a recent 10-year period (1996-2005) and for a historical 10-year period (1954-63). Ground-water pumping is considered to have affected stage at many of the 98 lakes included in the recent period analysis, and not to have affected stage at the 20 lakes included in the historical period analysis. For the recent period, regression models had coefficients of determination (R2) values ranging from 0.60 to 0.74, and up to five explanatory variables. Standard errors ranged from 21 to 37 percent of the average stage variability. Net leakage was the most important explanatory variable in regressions describing the full range and low range in stage variability for the recent period. The most important explanatory variable in the model predicting the high range in stage variability was the height over median lake stage at which surface-water outflow would occur. Other explanatory variables in final regression models for the recent period included the range in annual rainfall for the period and several variables related to local and regional hydrogeology: (1) ground-water pumping within 1 mile of each lake, (2) the amount of ground-water inflow (by category), (3) the head gradient between the lake and the Upper Floridan aquifer, and (4) the thickness of the intermediate confining unit. Many of the variables in final regression models are related to hydrogeologic characteristics, underscoring the importance of ground-water exchange in controlling the stage of karst lakes in Florida. Regression equations were used to predict lake-stage variability for the recent period for 12 additional lakes, and the median difference between predicted and observed values ranged from 11 to 23 percent. Coefficients of determination for the historical period were considerably lower (maximum R2 of 0.28) than for the recent period. Reasons for these low R2 values are probably related to the small number of lakes (20) with stage data for an equivalent time period that were unaffected by ground-water pumping, the similarity of many of the lake types (large surface-water drainage lakes), and the greater uncertainty in defining historical basin characteristics. The lack of lake-stage data unaffected by ground-water pumping and the poor regression results obtained for that group of lakes limit the ability to predict natural lake-stage variability using this method in west-central Florida.

Regression analysis of case K interval-censored failure time data in the presence of informative censoring.

PubMed

Wang, Peijie; Zhao, Hui; Sun, Jianguo

2016-12-01

Interval-censored failure time data occur in many fields such as demography, economics, medical research, and reliability and many inference procedures on them have been developed (Sun, 2006; Chen, Sun, and Peace, 2012). However, most of the existing approaches assume that the mechanism that yields interval censoring is independent of the failure time of interest and it is clear that this may not be true in practice (Zhang et al., 2007; Ma, Hu, and Sun, 2015). In this article, we consider regression analysis of case K interval-censored failure time data when the censoring mechanism may be related to the failure time of interest. For the problem, an estimated sieve maximum-likelihood approach is proposed for the data arising from the proportional hazards frailty model and for estimation, a two-step procedure is presented. In the addition, the asymptotic properties of the proposed estimators of regression parameters are established and an extensive simulation study suggests that the method works well. Finally, we apply the method to a set of real interval-censored data that motivated this study. © 2016, The International Biometric Society.

Family and school environmental predictors of sleep bruxism in children.

PubMed

Rossi, Debora; Manfredini, Daniele

2013-01-01

To identify potential predictors of self-reported sleep bruxism (SB) within children's family and school environments. A total of 65 primary school children (55.4% males, mean age 9.3 ± 1.9 years) were administered a 10-item questionnaire investigating the prevalence of self-reported SB as well as nine family and school-related potential bruxism predictors. Regression analyses were performed to assess the correlation between the potential predictors and SB. A positive answer to the self-reported SB item was endorsed by 18.8% of subjects, with no sex differences. Multiple variable regression analysis identified a final model showing that having divorced parents and not falling asleep easily were the only two weak predictors of self-reported SB. The percentage of explained variance for SB by the final multiple regression model was 13.3% (Nagelkerke's R² = 0.133). While having a high specificity and a good negative predictive value, the model showed unacceptable sensitivity and positive predictive values. The resulting accuracy to predict the presence of self-reported SB was 73.8%. The present investigation suggested that, among family and school-related matters, having divorced parents and not falling asleep easily were two predictors, even if weak, of a child's self-report of SB.

Characterizing mammographic images by using generic texture features

PubMed Central

2012-01-01

Introduction Although mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design. Methods A case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model. Results Of the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model. Conclusions Using texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy. PMID:22490545

The N-shaped environmental Kuznets curve: an empirical evaluation using a panel quantile regression approach.

PubMed

Allard, Alexandra; Takman, Johanna; Uddin, Gazi Salah; Ahmed, Ali

2018-02-01

We evaluate the N-shaped environmental Kuznets curve (EKC) using panel quantile regression analysis. We investigate the relationship between CO 2 emissions and GDP per capita for 74 countries over the period of 1994-2012. We include additional explanatory variables, such as renewable energy consumption, technological development, trade, and institutional quality. We find evidence for the N-shaped EKC in all income groups, except for the upper-middle-income countries. Heterogeneous characteristics are, however, observed over the N-shaped EKC. Finally, we find a negative relationship between renewable energy consumption and CO 2 emissions, which highlights the importance of promoting greener energy in order to combat global warming.

Parametric optimization of multiple quality characteristics in laser cutting of Inconel-718 by using hybrid approach of multiple regression analysis and genetic algorithm

NASA Astrophysics Data System (ADS)

Shrivastava, Prashant Kumar; Pandey, Arun Kumar

2018-06-01

Inconel-718 has found high demand in different industries due to their superior mechanical properties. The traditional cutting methods are facing difficulties for cutting these alloys due to their low thermal potential, lower elasticity and high chemical compatibility at inflated temperature. The challenges of machining and/or finishing of unusual shapes and/or sizes in these materials have also faced by traditional machining. Laser beam cutting may be applied for the miniaturization and ultra-precision cutting and/or finishing by appropriate control of different process parameter. This paper present multi-objective optimization the kerf deviation, kerf width and kerf taper in the laser cutting of Incone-718 sheet. The second order regression models have been developed for different quality characteristics by using the experimental data obtained through experimentation. The regression models have been used as objective function for multi-objective optimization based on the hybrid approach of multiple regression analysis and genetic algorithm. The comparison of optimization results to experimental results shows an improvement of 88%, 10.63% and 42.15% in kerf deviation, kerf width and kerf taper, respectively. Finally, the effects of different process parameters on quality characteristics have also been discussed.

Modelling the effect of the physical and chemical characteristics of the materials used as casing layers on the production parameters of Agaricus bisporus.

PubMed

Pardo, Arturo; Emilio Pardo, J; de Juan, J Arturo; Zied, Diego Cunha

2010-12-01

The aim of this research was to show the mathematical data obtained through the correlations found between the physical and chemical characteristics of casing layers and the final mushrooms' properties. For this purpose, 8 casing layers were used: soil, soil + peat moss, soil + black peat, soil + composted pine bark, soil + coconut fibre pith, soil + wood fibre, soil + composted vine shoots and, finally, the casing of La Rioja subjected to the ruffling practice. The conclusion that interplays in the fructification process with only the physical and chemical characteristics of casing are complicated was drawn. The mathematical data obtained in earliness could be explained in non-ruffled cultivation. The variability observed for the mushroom weight and the mushroom diameter variables could be explained in both ruffled and non-ruffled cultivations. Finally, the properties of the final quality of mushrooms were established by regression analysis.

Iterative Strain-Gage Balance Calibration Data Analysis for Extended Independent Variable Sets

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred

2011-01-01

A new method was developed that makes it possible to use an extended set of independent calibration variables for an iterative analysis of wind tunnel strain gage balance calibration data. The new method permits the application of the iterative analysis method whenever the total number of balance loads and other independent calibration variables is greater than the total number of measured strain gage outputs. Iteration equations used by the iterative analysis method have the limitation that the number of independent and dependent variables must match. The new method circumvents this limitation. It simply adds a missing dependent variable to the original data set by using an additional independent variable also as an additional dependent variable. Then, the desired solution of the regression analysis problem can be obtained that fits each gage output as a function of both the original and additional independent calibration variables. The final regression coefficients can be converted to data reduction matrix coefficients because the missing dependent variables were added to the data set without changing the regression analysis result for each gage output. Therefore, the new method still supports the application of the two load iteration equation choices that the iterative method traditionally uses for the prediction of balance loads during a wind tunnel test. An example is discussed in the paper that illustrates the application of the new method to a realistic simulation of temperature dependent calibration data set of a six component balance.

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Identification and quantification of ciprofloxacin in urine through excitation-emission fluorescence and three-way PARAFAC calibration.

PubMed

Ortiz, M C; Sarabia, L A; Sánchez, M S; Giménez, D

2009-05-29

Due to the second-order advantage, calibration models based on parallel factor analysis (PARAFAC) decomposition of three-way data are becoming important in routine analysis. This work studies the possibility of fitting PARAFAC models with excitation-emission fluorescence data for the determination of ciprofloxacin in human urine. The finally chosen PARAFAC decomposition is built with calibration samples spiked with ciprofloxacin, and with other series of urine samples that were also spiked. One of the series of samples has also another drug because the patient was taking mesalazine. The mesalazine is a fluorescent substance that interferes with the ciprofloxacin. Finally, the procedure is applied to samples of a patient who was being treated with ciprofloxacin. The trueness has been established by the regression "predicted concentration versus added concentration". The recovery factor is 88.3% for ciprofloxacin in urine, and the mean of the absolute value of the relative errors is 4.2% for 46 test samples. The multivariate sensitivity of the fit calibration model is evaluated by a regression between the loadings of PARAFAC linked to ciprofloxacin versus the true concentration in spiked samples. The multivariate capability of discrimination is near 8 microg L(-1) when the probabilities of false non-compliance and false compliance are fixed at 5%.

Research on Influence and Prediction Model of Urban Traffic Link Tunnel curvature on Fire Temperature Based on Pyrosim--SPSS Multiple Regression Analysis

NASA Astrophysics Data System (ADS)

Li, Xiao Ju; Yao, Kun; Dai, Jun Yu; Song, Yun Long

2018-05-01

The underground space, also known as the “fourth dimension” of the city, reflects the efficient use of urban development intensive. Urban traffic link tunnel is a typical underground limited-length space. Due to the geographical location, the special structure of space and the curvature of the tunnel, high-temperature smoke can easily form the phenomenon of “smoke turning” and the fire risk is extremely high. This paper takes an urban traffic link tunnel as an example to focus on the relationship between curvature and the temperature near the fire source, and use the pyrosim built different curvature fire model to analyze the influence of curvature on the temperature of the fire, then using SPSS Multivariate regression analysis simulate curvature of the tunnel and fire temperature data. Finally, a prediction model of urban traffic link tunnel curvature on fire temperature was proposed. The regression model analysis and test show that the curvature is negatively correlated with the tunnel temperature. This model is feasible and can provide a theoretical reference for the urban traffic link tunnel fire protection design and the preparation of the evacuation plan. And also, it provides some reference for other related curved tunnel curvature design and smoke control measures.

Regression analysis of mixed panel count data with dependent terminal events.

PubMed

Yu, Guanglei; Zhu, Liang; Li, Yang; Sun, Jianguo; Robison, Leslie L

2017-05-10

Event history studies are commonly conducted in many fields, and a great deal of literature has been established for the analysis of the two types of data commonly arising from these studies: recurrent event data and panel count data. The former arises if all study subjects are followed continuously, while the latter means that each study subject is observed only at discrete time points. In reality, a third type of data, a mixture of the two types of the data earlier, may occur and furthermore, as with the first two types of the data, there may exist a dependent terminal event, which may preclude the occurrences of recurrent events of interest. This paper discusses regression analysis of mixed recurrent event and panel count data in the presence of a terminal event and an estimating equation-based approach is proposed for estimation of regression parameters of interest. In addition, the asymptotic properties of the proposed estimator are established, and a simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well in practical situations. Finally, the methodology is applied to a childhood cancer study that motivated this study. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Cardiovascular risk from water arsenic exposure in Vietnam: Application of systematic review and meta-regression analysis in chemical health risk assessment.

PubMed

Phung, Dung; Connell, Des; Rutherford, Shannon; Chu, Cordia

2017-06-01

A systematic review (SR) and meta-analysis cannot provide the endpoint answer for a chemical risk assessment (CRA). The objective of this study was to apply SR and meta-regression (MR) analysis to address this limitation using a case study in cardiovascular risk from arsenic exposure in Vietnam. Published studies were searched from PubMed using the keywords of arsenic exposure and cardiovascular diseases (CVD). Random-effects meta-regression was applied to model the linear relationship between arsenic concentration in water and risk of CVD, and then the no-observable-adverse-effect level (NOAEL) were identified from the regression function. The probabilistic risk assessment (PRA) technique was applied to characterize risk of CVD due to arsenic exposure by estimating the overlapping coefficient between dose-response and exposure distribution curves. The risks were evaluated for groundwater, treated and drinking water. A total of 8 high quality studies for dose-response and 12 studies for exposure data were included for final analyses. The results of MR suggested a NOAEL of 50 μg/L and a guideline of 5 μg/L for arsenic in water which valued as a half of NOAEL and guidelines recommended from previous studies and authorities. The results of PRA indicated that the observed exposure level with exceeding CVD risk was 52% for groundwater, 24% for treated water, and 10% for drinking water in Vietnam, respectively. The study found that systematic review and meta-regression can be considered as an ideal method to chemical risk assessment due to its advantages to bring the answer for the endpoint question of a CRA. Copyright © 2017 Elsevier Ltd. All rights reserved.

A meta-analysis investigating factors underlying attrition rates in infant ERP studies.

PubMed

Stets, Manuela; Stahl, Daniel; Reid, Vincent M

2012-01-01

In this meta-analysis, we examined interrelationships between characteristics of infant event-related potential (ERP) studies and their attrition rates. One-hundred and forty-nine published studies provided information on 314 experimental groups of which 181 provided data on attrition. A random effects meta-analysis revealed a high average attrition rate of 49.2%. Additionally, we used meta-regression for 178 groups with attrition data to analyze which variables best explained attrition variance. Our main findings were that the nature of the stimuli-visual, auditory, or combined as well as if stimuli were animated-influenced exclusion rates from the final analysis and that infant age did not alter attrition rates.

Regression Analysis of Top of Descent Location for Idle-thrust Descents

NASA Technical Reports Server (NTRS)

Stell, Laurel; Bronsvoort, Jesper; McDonald, Greg

2013-01-01

In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. The independent variables cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also recorded or computed post-operations. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajec- tory parameters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowl- edge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace. In particular, a model for TOD location that is linear in the independent variables would enable decision support tool human-machine interfaces for which a kinetic approach would be computationally too slow.

Socioeconomic and Demographic Disparities in Knowledge of Reproductive Healthcare among Female University Students in Bangladesh

PubMed Central

Islam Mondal, Md. Nazrul; Nasir Ullah, Md. Monzur Morshad; Khan, Md. Nuruzzaman; Islam, Mohammad Zamirul; Islam, Md. Nurul; Moni, Sabiha Yasmin; Hoque, Md. Nazrul; Rahman, Md. Mashiur

2015-01-01

Background: Reproductive health (RH) is a critical component of women’s health and overall well-being around the world, especially in developing countries. We examine the factors that determine knowledge of RH care among female university students in Bangladesh. Methods: Data on 300 female students were collected from Rajshahi University, Bangladesh through a structured questionnaire using purposive sampling technique. The data were used for univariate analysis, to carry out the description of the variables; bivariate analysis was used to examine the associations between the variables; and finally, multivariate analysis (binary logistic regression model) was used to examine and fit the model and interpret the parameter estimates, especially in terms of odds ratios. Results: The results revealed that more than one-third (34.3%) respondents do not have sufficient knowledge of RH care. The χ2-test identified the significant (p < 0.05) associations between respondents’ knowledge of RH care with respondents’ age, education, family type, watching television; and knowledge about pregnancy, family planning, and contraceptive use. Finally, the binary logistic regression model identified respondents’ age, education, family type; and knowledge about family planning, and contraceptive use as the significant (p < 0.05) predictors of RH care. Conclusions and Global Health Implications: Knowledge of RH care among female university students was found unsatisfactory. Government and concerned organizations should promote and strengthen various health education programs to focus on RH care especially for the female university students in Bangladesh. PMID:27622005

Magnitude and Frequency of Rural Floods in the Southeastern United States, through 2006: Volume 2, North Carolina

USGS Publications Warehouse

Weaver, J. Curtis; Feaster, Toby D.; Gotvald, Anthony J.

2009-01-01

Reliable estimates of the magnitude and frequency of floods are required for the economical and safe design of transportation and water-conveyance structures. A multistate approach was used to update methods for estimating the magnitude and frequency of floods in rural, ungaged basins in North Carolina, South Carolina, and Georgia that are not substantially affected by regulation, tidal fluctuations, or urban development. In North Carolina, annual peak-flow data available through September 2006 were available for 584 sites; 402 of these sites had a total of 10 or more years of systematic record that is required for at-site, flood-frequency analysis. Following data reviews and the computation of 20 physical and climatic basin characteristics for each station as well as at-site flood-frequency statistics, annual peak-flow data were identified for 363 sites in North Carolina suitable for use in this analysis. Among these 363 sites, 19 sites had records that could be divided into unregulated and regulated/ channelized annual peak discharges, which means peak-flow records were identified for a total of 382 cases in North Carolina. Considering the 382 cases, at-site flood-frequency statistics are provided for 333 unregulated cases (also used for the regression database) and 49 regulated/channelized cases. The flood-frequency statistics for the 333 unregulated sites were combined with data for sites from South Carolina, Georgia, and adjacent parts of Alabama, Florida, Tennessee, and Virginia to create a database of 943 sites considered for use in the regional regression analysis. Flood-frequency statistics were computed by fitting logarithms (base 10) of the annual peak flows to a log-Pearson Type III distribution. As part of the computation process, a new generalized skew coefficient was developed by using a Bayesian generalized least-squares regression model. Exploratory regression analyses using ordinary least-squares regression completed on the initial database of 943 sites resulted in defining five hydrologic regions for North Carolina, South Carolina, and Georgia. Stations with drainage areas less than 1 square mile were removed from the database, and a procedure to examine for basin redundancy (based on drainage area and periods of record) also resulted in the removal of some stations from the regression database. Flood-frequency estimates and basin characteristics for 828 gaged stations were combined to form the final database that was used in the regional regression analysis. Regional regression analysis, using generalized least-squares regression, was used to develop a set of predictive equations that can be used for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent chance exceedance flows for rural ungaged, basins in North Carolina, South Carolina, and Georgia. The final predictive equations are all functions of drainage area and the percentage of drainage basin within each of the five hydrologic regions. Average errors of prediction for these regression equations range from 34.0 to 47.7 percent. Discharge estimates determined from the systematic records for the current study are, on average, larger in magnitude than those from a previous study for the highest percent chance exceedances (50 and 20 percent) and tend to be smaller than those from the previous study for the lower percent chance exceedances when all sites are considered as a group. For example, mean differences for sites in the Piedmont hydrologic region range from positive 0.5 percent for the 50-percent chance exceedance flow to negative 4.6 percent for the 0.2-percent chance exceedance flow when stations are grouped by hydrologic region. Similarly for the same hydrologic region, median differences range from positive 0.9 percent for the 50-percent chance exceedance flow to negative 7.1 percent for the 0.2-percent chance exceedance flow. However, mean and median percentage differences between the estimates from the previous and curre

Blood Based Biomarkers of Early Onset Breast Cancer

DTIC Science & Technology

2016-12-01

discretizes the data, and also using logistic elastic net – a form of linear regression - we were unable to build a classifier that could accurately...classifier for differentiating cases from controls off discretized data. The first pass analysis demonstrated a 35 gene signature that differentiated...to the discretized data for mRNA gene signature, the samples used to “train” were also included in the final samples used to “test” the algorithm

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.

The impact of young drivers' lifestyle on their road traffic accident risk in greater Athens area.

PubMed

Chliaoutakis, J E; Darviri, C; Demakakos, P T

1999-11-01

Young drivers (18-24) both in Greece and elsewhere appear to have high rates of road traffic accidents. Many factors contribute to the creation of these high road traffic accidents rates. It has been suggested that lifestyle is an important one. The main objective of this study is to find out and clarify the (potential) relationship between young drivers' lifestyle and the road traffic accident risk they face. Moreover, to examine if all the youngsters have the same elevated risk on the road or not. The sample consisted of 241 young Greek drivers of both sexes. The statistical analysis included factor analysis and logistic regression analysis. Through the principal component analysis a ten factor scale was created which included the basic lifestyle traits of young Greek drivers. The logistic regression analysis showed that the young drivers whose dominant lifestyle trait is alcohol consumption or drive without destination have high accident risk, while these whose dominant lifestyle trait is culture, face low accident risk. Furthermore, young drivers who are religious in one way or another seem to have low accident risk. Finally, some preliminary observations on how health promotion should be put into practice are discussed.

Asymmetry of perceived key movement in chorale sequences: converging evidence from a probe-tone analysis.

PubMed

Cuddy, L L; Thompson, W F

1992-01-01

In a probe-tone experiment, two groups of listeners--one trained, the other untrained, in traditional music theory--rated the goodness of fit of each of the 12 notes of the chromatic scale to four-voice harmonic sequences. Sequences were 12 simplified excerpts from Bach chorales, 4 nonmodulating, and 8 modulating. Modulations occurred either one or two steps in either the clockwise or the counterclockwise direction on the cycle of fifths. A consistent pattern of probe-tone ratings was obtained for each sequence, with no significant differences between listener groups. Two methods of analysis (Fourier analysis and regression analysis) revealed a directional asymmetry in the perceived key movement conveyed by modulating sequences. For a given modulation distance, modulations in the counterclockwise direction effected a clearer shift in tonal organization toward the final key than did clockwise modulations. The nature of the directional asymmetry was consistent with results reported for identification and rating of key change in the sequences (Thompson & Cuddy, 1989a). Further, according to the multiple-regression analysis, probe-tone ratings did not merely reflect the distribution of tones in the sequence. Rather, ratings were sensitive to the temporal structure of the tonal organization in the sequence.

Biostatistics Series Module 6: Correlation and Linear Regression.

PubMed

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient ( r ). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r 2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation ( y = a + bx ), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous.

Biostatistics Series Module 6: Correlation and Linear Regression

PubMed Central

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient (r). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation (y = a + bx), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous. PMID:27904175

The Relationship Between Serum Endocan Levels With the Presence of Slow Coronary Flow: A Cross-Sectional Study.

PubMed

Kundi, Harun; Gok, Murat; Kiziltunc, Emrullah; Topcuoglu, Canan; Cetin, Mustafa; Cicekcioglu, Hulya; Ugurlu, Burcu; Ulusoy, Feridun Vasfi

2017-07-01

The aim of this study was to investigate the relationship between endocan levels with the presence of slow coronary flow (SCF). In this cross-sectional study, a total of 88 patients, who admitted to our hospital, were included in this study. Of these, 53 patients with SCF and 35 patients with normal coronary flow were included in the final analysis. Coronary flow rates of all patients were determined by the Timi Frame Count (TFC) method. In correlation analysis, endocan levels revealed a significantly positive correlation with high sensitive C-reactive protein and corrected TFC. In multivariate logistic regression analysis, the endocan levels were found as independently associated with the presence of SCF. Finally, using a cutoff level of 2.3, endocan level predicted the presence of SCF with a sensitivity of 77.2% and specificity of 75.2%. In conclusion, our study showed that higher endocan levels were significantly and independently related to the presence of SCF.

The moderation of resilience on the negative effect of pain on depression and post-traumatic growth in individuals with spinal cord injury.

PubMed

Min, Jung-Ah; Lee, Chang-Uk; Hwang, Sung-Il; Shin, Jung-In; Lee, Bum-Suk; Han, Sang-Hoon; Ju, Hye-In; Lee, Cha-Yeon; Lee, Chul; Chae, Jeong-Ho

2014-01-01

To determine the moderating effect of resilience on the negative effects of chronic pain on depression and post-traumatic growth. Community-dwelling individuals with SCI (n = 37) were recruited at short-term admission for yearly regular health examination. Participants completed self-rating standardized questionnaires measuring pain, resilience, depression and post-traumatic growth. Hierarchical linear regression analysis was performed to identify the moderating effect of resilience on the relationships of pain with depression and post-traumatic growth after controlling for relevant covariates. In the regression model of depression, the effect of pain severity on depression was decreased (β was changed from 0.47 to 0.33) after entering resilience into the model. In the final model, both pain and resilience were significant independent predictors for depression (β = 0.33, p = 0.038 and β = -0.47, p = 0.012, respectively). In the regression model of post-traumatic growth, the effect of pain severity became insignificant after entering resilience into the model. In the final model, resilience was a significant predictor (β = 0.51, p = 0.016). Resilience potentially mitigated the negative effects of pain. Moreover, it independently contributed to reduced depression and greater post-traumatic growth. Our findings suggest that resilience might provide a potential target for intervention in SCI individuals.

Serum Folate Shows an Inverse Association with Blood Pressure in a Cohort of Chinese Women of Childbearing Age: A Cross-Sectional Study

PubMed Central

Shen, Minxue; Tan, Hongzhuan; Zhou, Shujin; Retnakaran, Ravi; Smith, Graeme N.; Davidge, Sandra T.; Trasler, Jacquetta; Walker, Mark C.; Wen, Shi Wu

2016-01-01

Background It has been reported that higher folate intake from food and supplementation is associated with decreased blood pressure (BP). The association between serum folate concentration and BP has been examined in few studies. We aim to examine the association between serum folate and BP levels in a cohort of young Chinese women. Methods We used the baseline data from a pre-conception cohort of women of childbearing age in Liuyang, China, for this study. Demographic data were collected by structured interview. Serum folate concentration was measured by immunoassay, and homocysteine, blood glucose, triglyceride and total cholesterol were measured through standardized clinical procedures. Multiple linear regression and principal component regression model were applied in the analysis. Results A total of 1,532 healthy normotensive non-pregnant women were included in the final analysis. The mean concentration of serum folate was 7.5 ± 5.4 nmol/L and 55% of the women presented with folate deficiency (< 6.8 nmol/L). Multiple linear regression and principal component regression showed that serum folate levels were inversely associated with systolic and diastolic BP, after adjusting for demographic, anthropometric, and biochemical factors. Conclusions Serum folate is inversely associated with BP in non-pregnant women of childbearing age with high prevalence of folate deficiency. PMID:27182603

Animal models of maternal high fat diet exposure and effects on metabolism in offspring: a meta-regression analysis.

PubMed

Ribaroff, G A; Wastnedge, E; Drake, A J; Sharpe, R M; Chambers, T J G

2017-06-01

Animal models of maternal high fat diet (HFD) demonstrate perturbed offspring metabolism although the effects differ markedly between models. We assessed studies investigating metabolic parameters in the offspring of HFD fed mothers to identify factors explaining these inter-study differences. A total of 171 papers were identified, which provided data from 6047 offspring. Data were extracted regarding body weight, adiposity, glucose homeostasis and lipidaemia. Information regarding the macronutrient content of diet, species, time point of exposure and gestational weight gain were collected and utilized in meta-regression models to explore predictive factors. Publication bias was assessed using Egger's regression test. Maternal HFD exposure did not affect offspring birthweight but increased weaning weight, final bodyweight, adiposity, triglyceridaemia, cholesterolaemia and insulinaemia in both female and male offspring. Hyperglycaemia was found in female offspring only. Meta-regression analysis identified lactational HFD exposure as a key moderator. The fat content of the diet did not correlate with any outcomes. There was evidence of significant publication bias for all outcomes except birthweight. Maternal HFD exposure was associated with perturbed metabolism in offspring but between studies was not accounted for by dietary constituents, species, strain or maternal gestational weight gain. Specific weaknesses in experimental design predispose many of the results to bias. © 2017 The Authors. Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation.

[State Recognition of Solid Fermentation Process Based on Near Infrared Spectroscopy with Adaboost and Spectral Regression Discriminant Analysis].

PubMed

Yu, Shuang; Liu, Guo-hai; Xia, Rong-sheng; Jiang, Hui

2016-01-01

In order to achieve the rapid monitoring of process state of solid state fermentation (SSF), this study attempted to qualitative identification of process state of SSF of feed protein by use of Fourier transform near infrared (FT-NIR) spectroscopy analysis technique. Even more specifically, the FT-NIR spectroscopy combined with Adaboost-SRDA-NN integrated learning algorithm as an ideal analysis tool was used to accurately and rapidly monitor chemical and physical changes in SSF of feed protein without the need for chemical analysis. Firstly, the raw spectra of all the 140 fermentation samples obtained were collected by use of Fourier transform near infrared spectrometer (Antaris II), and the raw spectra obtained were preprocessed by use of standard normal variate transformation (SNV) spectral preprocessing algorithm. Thereafter, the characteristic information of the preprocessed spectra was extracted by use of spectral regression discriminant analysis (SRDA). Finally, nearest neighbors (NN) algorithm as a basic classifier was selected and building state recognition model to identify different fermentation samples in the validation set. Experimental results showed as follows: the SRDA-NN model revealed its superior performance by compared with other two different NN models, which were developed by use of the feature information form principal component analysis (PCA) and linear discriminant analysis (LDA), and the correct recognition rate of SRDA-NN model achieved 94.28% in the validation set. In this work, in order to further improve the recognition accuracy of the final model, Adaboost-SRDA-NN ensemble learning algorithm was proposed by integrated the Adaboost and SRDA-NN methods, and the presented algorithm was used to construct the online monitoring model of process state of SSF of feed protein. Experimental results showed as follows: the prediction performance of SRDA-NN model has been further enhanced by use of Adaboost lifting algorithm, and the correct recognition rate of the Adaboost-SRDA-NN model achieved 100% in the validation set. The overall results demonstrate that SRDA algorithm can effectively achieve the spectral feature information extraction to the spectral dimension reduction in model calibration process of qualitative analysis of NIR spectroscopy. In addition, the Adaboost lifting algorithm can improve the classification accuracy of the final model. The results obtained in this work can provide research foundation for developing online monitoring instruments for the monitoring of SSF process.

The effects of practice on movement distance and final position reproduction: implications for the equilibrium-point control of movements.

PubMed

Jaric, S; Corcos, D M; Gottlieb, G L; Ilic, D B; Latash, M L

1994-01-01

Predictions of two views on single-joint motor control, namely programming of muscle force patterns and equilibrium-point control, were compared with the results of experiments with reproduction of movement distance and final location during fast unidirectional elbow flexions. Two groups of subjects were tested. The first group practiced movements over a fixed distance (36 degrees), starting from seven different initial positions (distance group, DG). The second group practiced movements from the same seven initial positions to a fixed final location (location group, LG). Later, all the subjects were tested at the practiced task with their eyes closed, and then, unexpectedly for the subjects, they were tested at the other, unpracticed task. In both groups, the task to reproduce final position had lower indices of final position variability than the task to reproduce movement distance. Analysis of the linear regression lines between initial position and final position (or movement distance) also demonstrated a better (more accurate) performance during final position reproduction than during distance reproduction. The data are in a good correspondence with the predictions of the equilibrium-point hypothesis, but not with the predictions of the force-pattern control approach.

The emerging dental workforce: why dentistry? A quantitative study of final year dental students' views on their professional career

PubMed Central

Gallagher, Jennifer E; Patel, Resmi; Donaldson, Nora; Wilson, Nairn HF

2007-01-01

Background Dental graduates are joining a profession experiencing changes in systems of care, funding and skill mix. Research into the motivation and expectations of the emerging workforce is vital to inform professional and policy decisions. The objective of this research was to investigate final year dental students' perceived motivation for their choice of career in relation to sex, ethnicity and mode of entry. Methods Self-administered questionnaire survey of all final year dental students at King's College London. Data were entered into SPSS; statistical analysis included Chi Squared tests for linear association, multiple regression, factor analysis and logistic regression. Results A response of 90% (n = 126) was achieved. The majority were aged 23 years (59%), female (58%) and Asian (70%). One in 10 were mature students. Eighty per cent identified 11 or more 'important' or 'very important' influences, the most common of which were related to features of the job: 'regular working hours' (91%), 'degree leading to recognised job' (90%) and 'job security' (90%). There were significant differences in important influences by sex (males > females: 'able to run own business'; females > males: 'a desire to work with people'), ethnic group (Asians > white: 'wish to provide public service', 'influence of friends', 'desire to work in healthcare', having 'tried an alternative career/course' and 'work experience') and mode of entry (mature > early entry: 'a desire to work with people'). Multivariate analysis suggested 61% of the variation in influences is explained by five factors: the 'professional job' (31%), 'healthcare-people' (11%), 'academic-scientific' (8%), 'careers-advising' (6%), and 'family/friends' (6%). The single major influence on choice of career was a 'desire to work with people'; Indian students were twice as likely to report this as white or other ethnic groups. Conclusion Final year dental students report a wide range of important influences on their choice of dentistry, with variation by sex, ethnicity and mode of entry in relation to individual influences. Features of the 'professional job', followed by 'healthcare and people' were the most important underlying factors influencing choice of career. PMID:17573967

Increasing URM Undergraduate Student Success through Assessment-Driven Interventions: A Multiyear Study Using Freshman-Level General Biology as a Model System

PubMed Central

Carmichael, Mary C.; St. Clair, Candace; Edwards, Andrea M.; Barrett, Peter; McFerrin, Harris; Davenport, Ian; Awad, Mohamed; Kundu, Anup; Ireland, Shubha Kale

2016-01-01

Xavier University of Louisiana leads the nation in awarding BS degrees in the biological sciences to African-American students. In this multiyear study with ∼5500 participants, data-driven interventions were adopted to improve student academic performance in a freshman-level general biology course. The three hour-long exams were common and administered concurrently to all students. New exam questions were developed using Bloom’s taxonomy, and exam results were analyzed statistically with validated assessment tools. All but the comprehensive final exam were returned to students for self-evaluation and remediation. Among other approaches, course rigor was monitored by using an identical set of 60 questions on the final exam across 10 semesters. Analysis of the identical sets of 60 final exam questions revealed that overall averages increased from 72.9% (2010) to 83.5% (2015). Regression analysis demonstrated a statistically significant correlation between high-risk students and their averages on the 60 questions. Additional analysis demonstrated statistically significant improvements for at least one letter grade from midterm to final and a 20% increase in the course pass rates over time, also for the high-risk population. These results support the hypothesis that our data-driven interventions and assessment techniques are successful in improving student retention, particularly for our academically at-risk students. PMID:27543637

Using social cognitive theory to explain discretionary, "leisure-time" physical exercise among high school students.

PubMed

Winters, Eric R; Petosa, Rick L; Charlton, Thomas E

2003-06-01

To examine whether knowledge of high school students' actions of self-regulation, and perceptions of self-efficacy to overcome exercise barriers, social situation, and outcome expectation will predict non-school related moderate and vigorous physical exercise. High school students enrolled in introductory Physical Education courses completed questionnaires that targeted selected Social Cognitive Theory variables. They also self-reported their typical "leisure-time" exercise participation using a standardized questionnaire. Bivariate correlation statistic and hierarchical regression were conducted on reports of moderate and vigorous exercise frequency. Each predictor variable was significantly associated with measures of moderate and vigorous exercise frequency. All predictor variables were significant in the final regression model used to explain vigorous exercise. After controlling for the effects of gender, the psychosocial variables explained 29% of variance in vigorous exercise frequency. Three of four predictor variables were significant in the final regression equation used to explain moderate exercise. The final regression equation accounted for 11% of variance in moderate exercise frequency. Professionals who attempt to increase the prevalence of physical exercise through educational methods should focus on the psychosocial variables utilized in this study.

Emotional intelligence, perceived stress and academic performance of Sri Lankan medical undergraduates.

PubMed

Ranasinghe, P; Wathurapatha, W S; Mathangasinghe, Y; Ponnamperuma, G

2017-02-20

Previous research has shown that higher Emotional Intelligence (EI) is associated with better academic and work performance. The present study intended to explore the relationship between EI, perceived stress and academic performance and associated factors among medical undergraduates. This descriptive cross-sectional research study was conducted among 471 medical undergraduates of 2nd, 4th and final years of University of Colombo, Sri Lanka. Students were rated on self administered Perceived Stress Scale (PSS) and Schutte Self-Report Emotional Intelligence Test (SEIT). Examination results were used as the dichotomous outcome variable in a logistic regression analysis. Females had higher mean EI scores (p = 0.014). A positive correlation was found between the EI score and the number of extracurricular activities (r = 0.121, p = 0.008). Those who were satisfied regarding their choice to study medicine, and who were planning to do postgraduate studies had significantly higher EI scores and lower PSS scores (p <0.001). Among final year undergraduates, those who passed the Clinical Sciences examination in the first attempt had a higher EI score (p <0.001) and a lower PSS score (p <0.05). Results of the binary logistic-regression analysis in the entire study population indicated that female gender (OR:1.98) and being satisfied regarding their choice of the medical undergraduate programme (OR:3.69) were significantly associated with passing the examinations. However, PSS Score and engagement in extracurricular activities were not associated with 'Examination Results'. Higher EI was associated with better academic performance amongst final year medical students. In addition a higher EI was observed in those who had a higher level of self satisfaction. Self-perceived stress was lower in those with a higher EI. Enhancing EI might help to improve academic performance among final year medical student and also help to reduce the stress levels and cultivate better coping during professional life in the future.

Household water treatment in developing countries: comparing different intervention types using meta-regression.

PubMed

Hunter, Paul R

2009-12-01

Household water treatment (HWT) is being widely promoted as an appropriate intervention for reducing the burden of waterborne disease in poor communities in developing countries. A recent study has raised concerns about the effectiveness of HWT, in part because of concerns over the lack of blinding and in part because of considerable heterogeneity in the reported effectiveness of randomized controlled trials. This study set out to attempt to investigate the causes of this heterogeneity and so identify factors associated with good health gains. Studies identified in an earlier systematic review and meta-analysis were supplemented with more recently published randomized controlled trials. A total of 28 separate studies of randomized controlled trials of HWT with 39 intervention arms were included in the analysis. Heterogeneity was studied using the "metareg" command in Stata. Initial analyses with single candidate predictors were undertaken and all variables significant at the P < 0.2 level were included in a final regression model. Further analyses were done to estimate the effect of the interventions over time by MonteCarlo modeling using @Risk and the parameter estimates from the final regression model. The overall effect size of all unblinded studies was relative risk = 0.56 (95% confidence intervals 0.51-0.63), but after adjusting for bias due to lack of blinding the effect size was much lower (RR = 0.85, 95% CI = 0.76-0.97). Four main variables were significant predictors of effectiveness of intervention in a multipredictor meta regression model: Log duration of study follow-up (regression coefficient of log effect size = 0.186, standard error (SE) = 0.072), whether or not the study was blinded (coefficient 0.251, SE 0.066) and being conducted in an emergency setting (coefficient -0.351, SE 0.076) were all significant predictors of effect size in the final model. Compared to the ceramic filter all other interventions were much less effective (Biosand 0.247, 0.073; chlorine and safe waste storage 0.295, 0.061; combined coagulant-chlorine 0.2349, 0.067; SODIS 0.302, 0.068). A Monte Carlo model predicted that over 12 months ceramic filters were likely to be still effective at reducing disease, whereas SODIS, chlorination, and coagulation-chlorination had little if any benefit. Indeed these three interventions are predicted to have the same or less effect than what may be expected due purely to reporting bias in unblinded studies With the currently available evidence ceramic filters are the most effective form of HWT in the longterm, disinfection-only interventions including SODIS appear to have poor if any longterm public health benefit.

Error analysis of leaf area estimates made from allometric regression models

NASA Technical Reports Server (NTRS)

Feiveson, A. H.; Chhikara, R. S.

1986-01-01

Biological net productivity, measured in terms of the change in biomass with time, affects global productivity and the quality of life through biochemical and hydrological cycles and by its effect on the overall energy balance. Estimating leaf area for large ecosystems is one of the more important means of monitoring this productivity. For a particular forest plot, the leaf area is often estimated by a two-stage process. In the first stage, known as dimension analysis, a small number of trees are felled so that their areas can be measured as accurately as possible. These leaf areas are then related to non-destructive, easily-measured features such as bole diameter and tree height, by using a regression model. In the second stage, the non-destructive features are measured for all or for a sample of trees in the plots and then used as input into the regression model to estimate the total leaf area. Because both stages of the estimation process are subject to error, it is difficult to evaluate the accuracy of the final plot leaf area estimates. This paper illustrates how a complete error analysis can be made, using an example from a study made on aspen trees in northern Minnesota. The study was a joint effort by NASA and the University of California at Santa Barbara known as COVER (Characterization of Vegetation with Remote Sensing).

Using Spatial Multiple Regression to Identify Intrinsic Connectivity Networks Involved in Working Memory Performance

PubMed Central

Gordon, Evan M.; Stollstorff, Melanie; Vaidya, Chandan J.

2012-01-01

Many researchers have noted that the functional architecture of the human brain is relatively invariant during task performance and the resting state. Indeed, intrinsic connectivity networks (ICNs) revealed by resting-state functional connectivity analyses are spatially similar to regions activated during cognitive tasks. This suggests that patterns of task-related activation in individual subjects may result from the engagement of one or more of these ICNs; however, this has not been tested. We used a novel analysis, spatial multiple regression, to test whether the patterns of activation during an N-back working memory task could be well described by a linear combination of ICNs delineated using Independent Components Analysis at rest. We found that across subjects, the cingulo-opercular Set Maintenance ICN, as well as right and left Frontoparietal Control ICNs, were reliably activated during working memory, while Default Mode and Visual ICNs were reliably deactivated. Further, involvement of Set Maintenance, Frontoparietal Control, and Dorsal Attention ICNs was sensitive to varying working memory load. Finally, the degree of left Frontoparietal Control network activation predicted response speed, while activation in both left Frontoparietal Control and Dorsal Attention networks predicted task accuracy. These results suggest that a close relationship between resting-state networks and task-evoked activation is functionally relevant for behavior, and that spatial multiple regression analysis is a suitable method for revealing that relationship. PMID:21761505

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.

Total pubertal growth in patients with juvenile idiopathic arthritis treated with growth hormone: analysis of a single center.

PubMed

Bechtold, S; Beyerlein, A; Ripperger, P; Roeb, J; Dalla Pozza, R; Häfner, R; Haas, J P; Schmidt, H

2012-10-01

Growth failure is a permanent sequelae in juvenile idiopathic arthritis (JIA). The aim of the study was to compare pubertal growth in control and growth hormone (GH) treated JIA subjects. 64 children with JIA at a mean age of 10.38 ± 2.80 years were enrolled and followed until final height (measured in standard deviation (SD) scores). 39 children (20 m) received GH therapy and 24 (9 m) served as controls. GH dose was 0.33 mg/kg/week. Linear regression analysis was performed to identify factors influencing total pubertal growth. Mean total pubertal growth was 21.1 ± 1.3 cm (mean ± SD) in GH treated JIA patients and 13.8 ± 1.5 cm in controls. Final height was significantly higher with GH treatment (-1.67 ± 1.20 SD) compared to controls (-3.20 ± 1.84 SD). Linear regression model identified age at onset of puberty (ß=-4.2,CI: -5.9, -2.6 in controls and ß=-2.3,CI: -3.6, -1.1 in GH treated) as the main factor for total pubertal growth. Final height SDS was determined by the difference to target height at onset of puberty (ß=-0.59;CI: -0.80, -0.37 in controls and ß=-0.30,CI: -0.52, -0.08 in GH treated), age at onset of puberty (ß=0.47;CI:0.02,0.93 in controls and 0.23;CI: -0.00,0.46 in GH treated) and height gain during puberty (ß=0.13;CI:0.05,0.21 in controls and ß=0.11;CI:0.07,0.16 in GH treated). Total pubertal growth in JIA patients treated with GH was increased by a factor of 1.5 greater in comparison to controls leading to a significantly better final height. To maximize final height GH treatment should be initiated early to reduce the height deficit at onset of puberty. Copyright © 2012 Elsevier Ltd. All rights reserved.

Growth status of Korean orphans raised in the affluent West: anthropometric trend, multivariate determinants, and descriptive comparison with their North and South Korean peers.

PubMed

Schwekendiek, Daniel J

2017-04-01

This paper investigates the trend in height among adult Korean orphans who were adopted in early life into affluent Western nations. Final heights of 148 females were analyzed based on a Korean government survey conducted in 2008. Height of the orphans was descriptively compared against final heights of South and North Koreans. Furthermore, statistical determinants of orphan height were investigated in multivariate regressions. Mean height of Korean orphans was 160.44 cm (SD 5.89), which was higher than that of South Koreans at 158.83 cm (SD 5.01). Both Korean orphans and South Koreans were taller than North Koreans at 155.30 cm (SD 4.94). However, height of Korean orphans stagnated at around 160-161 cm while those of North and South Koreans improved over time. In the regression analysis, the socioeconomic status of the adoptive family was statistically significant in all models, while dummies for the adoptive nations and age at adoption were insignificant. This study shows that the mean final height of women experiencing extreme environmental improvements in early-life is capped at 160-161 cm, tentatively suggesting that social stress factors in the host nation or early-life factors in the birth nation might have offset some of the environmental enrichment effects achieved through intercountry adoption.

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

Combined analysis of magnetic and gravity anomalies using normalized source strength (NSS)

NASA Astrophysics Data System (ADS)

Li, L.; Wu, Y.

2017-12-01

Gravity field and magnetic field belong to potential fields which lead inherent multi-solution. Combined analysis of magnetic and gravity anomalies based on Poisson's relation is used to determinate homology gravity and magnetic anomalies and decrease the ambiguity. The traditional combined analysis uses the linear regression of the reduction to pole (RTP) magnetic anomaly to the first order vertical derivative of the gravity anomaly, and provides the quantitative or semi-quantitative interpretation by calculating the correlation coefficient, slope and intercept. In the calculation process, due to the effect of remanent magnetization, the RTP anomaly still contains the effect of oblique magnetization. In this case the homology gravity and magnetic anomalies display irrelevant results in the linear regression calculation. The normalized source strength (NSS) can be transformed from the magnetic tensor matrix, which is insensitive to the remanence. Here we present a new combined analysis using NSS. Based on the Poisson's relation, the gravity tensor matrix can be transformed into the pseudomagnetic tensor matrix of the direction of geomagnetic field magnetization under the homologous condition. The NSS of pseudomagnetic tensor matrix and original magnetic tensor matrix are calculated and linear regression analysis is carried out. The calculated correlation coefficient, slope and intercept indicate the homology level, Poisson's ratio and the distribution of remanent respectively. We test the approach using synthetic model under complex magnetization, the results show that it can still distinguish the same source under the condition of strong remanence, and establish the Poisson's ratio. Finally, this approach is applied in China. The results demonstrated that our approach is feasible.

Is Early Prescribing of Opioid and Psychotropic Medications Associated With Delayed Return to Work and Increased Final Workers' Compensation Cost?

PubMed

Tao, Xuguang Grant; Lavin, Robert A; Yuspeh, Larry; Weaver, Virginia M; Bernacki, Edward J

2015-12-01

To explore the association between the initial 60 days of prescriptions for psychotropic medications and final workers' compensation claim outcomes. A cohort of 11,394 claimants involved in lost time injuries between 1999 and 2002 were followed through December 31, 2009. Logistic regressions and Cox Proportional Hazard Models were used in the analysis. The initial 60 days of prescriptions for psychotropic medications were significantly associated with a final claim cost at least $100,000. Odds ratios were 1.88 for short-acting opioids, 2.14 for hypnotics, antianxiety agents, or antidepressants, and 3.91 for long-acting opioids, respectively. Significant associations were also found between decreased time lost from work and decreased claim closures during the study period. Early prescription of opioids and other psychotropic drugs may be useful predictors of high claim costs and time lost from work.

Aroma profile and sensory characteristics of a sulfur dioxide-free mulberry (Morus nigra) wine subjected to non-thermal accelerating aging techniques.

PubMed

Tchabo, William; Ma, Yongkun; Kwaw, Emmanuel; Zhang, Haining; Xiao, Lulu; Tahir, Haroon Elrasheid

2017-10-01

The present study was undertaken to assess accelerating aging effects of high pressure, ultrasound and manosonication on the aromatic profile and sensorial attributes of aged mulberry wines (AMW). A total of 166 volatile compounds were found amongst the AMW. The outcomes of the investigation were presented by means of geometric mean (GM), cluster analysis (CA), principal component analysis (PCA), partial least squares regressions (PLSR) and principal component regression (PCR). GM highlighted 24 organoleptic attributes responsible for the sensorial profile of the AMW. Moreover, CA revealed that the volatile composition of the non-thermal accelerated aged wines differs from that of the conventional aged wines. Besides, PCA discriminated the AMW on the basis of their main sensorial characteristics. Furthermore, PLSR identified 75 aroma compounds which were mainly responsible for the olfactory notes of the AMW. Finally, the overall quality of the AMW was noted to be better predicted by PLSR than PCR. Copyright © 2017 Elsevier Ltd. All rights reserved.

PCI fuel failure analysis: a report on a cooperative program undertaken by Pacific Northwest Laboratory and Chalk River Nuclear Laboratories.

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

Mohr, C.L.; Pankaskie, P.J.; Heasler, P.G.

Reactor fuel failure data sets in the form of initial power (P/sub i/), final power (P/sub f/), transient increase in power (..delta..P), and burnup (Bu) were obtained for pressurized heavy water reactors (PHWRs), boiling water reactors (BWRs), and pressurized water reactors (PWRs). These data sets were evaluated and used as the basis for developing two predictive fuel failure models, a graphical concept called the PCI-OGRAM, and a nonlinear regression based model called PROFIT. The PCI-OGRAM is an extension of the FUELOGRAM developed by AECL. It is based on a critical threshold concept for stress dependent stress corrosion cracking. The PROFITmore » model, developed at Pacific Northwest Laboratory, is the result of applying standard statistical regression methods to the available PCI fuel failure data and an analysis of the environmental and strain rate dependent stress-strain properties of the Zircaloy cladding.« less

Genotype-phenotype association study via new multi-task learning model

PubMed Central

Huo, Zhouyuan; Shen, Dinggang

2018-01-01

Research on the associations between genetic variations and imaging phenotypes is developing with the advance in high-throughput genotype and brain image techniques. Regression analysis of single nucleotide polymorphisms (SNPs) and imaging measures as quantitative traits (QTs) has been proposed to identify the quantitative trait loci (QTL) via multi-task learning models. Recent studies consider the interlinked structures within SNPs and imaging QTs through group lasso, e.g. ℓ2,1-norm, leading to better predictive results and insights of SNPs. However, group sparsity is not enough for representing the correlation between multiple tasks and ℓ2,1-norm regularization is not robust either. In this paper, we propose a new multi-task learning model to analyze the associations between SNPs and QTs. We suppose that low-rank structure is also beneficial to uncover the correlation between genetic variations and imaging phenotypes. Finally, we conduct regression analysis of SNPs and QTs. Experimental results show that our model is more accurate in prediction than compared methods and presents new insights of SNPs. PMID:29218896

A canonical correlation neural network for multicollinearity and functional data.

PubMed

Gou, Zhenkun; Fyfe, Colin

2004-03-01

We review a recent neural implementation of Canonical Correlation Analysis and show, using ideas suggested by Ridge Regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity. We develop a second model which not only performs as well on multicollinear data but also on general data sets. This model allows us to vary a single parameter so that the network is capable of performing Partial Least Squares regression (at one extreme) to Canonical Correlation Analysis (at the other)and every intermediate operation between the two. On multicollinear data, the parameter setting is shown to be important but on more general data no particular parameter setting is required. Finally, we develop a second penalty term which acts on such data as a smoother in that the resulting weight vectors are much smoother and more interpretable than the weights without the robustification term. We illustrate our algorithms on both artificial and real data.

Using Gamma and Quantile Regressions to Explore the Association between Job Strain and Adiposity in the ELSA-Brasil Study: Does Gender Matter?

PubMed

Fonseca, Maria de Jesus Mendes da; Juvanhol, Leidjaira Lopes; Rotenberg, Lúcia; Nobre, Aline Araújo; Griep, Rosane Härter; Alves, Márcia Guimarães de Mello; Cardoso, Letícia de Oliveira; Giatti, Luana; Nunes, Maria Angélica; Aquino, Estela M L; Chor, Dóra

2017-11-17

This paper explores the association between job strain and adiposity, using two statistical analysis approaches and considering the role of gender. The research evaluated 11,960 active baseline participants (2008-2010) in the ELSA-Brasil study. Job strain was evaluated through a demand-control questionnaire, while body mass index (BMI) and waist circumference (WC) were evaluated in continuous form. The associations were estimated using gamma regression models with an identity link function. Quantile regression models were also estimated from the final set of co-variables established by gamma regression. The relationship that was found varied by analytical approach and gender. Among the women, no association was observed between job strain and adiposity in the fitted gamma models. In the quantile models, a pattern of increasing effects of high strain was observed at higher BMI and WC distribution quantiles. Among the men, high strain was associated with adiposity in the gamma regression models. However, when quantile regression was used, that association was found not to be homogeneous across outcome distributions. In addition, in the quantile models an association was observed between active jobs and BMI. Our results point to an association between job strain and adiposity, which follows a heterogeneous pattern. Modelling strategies can produce different results and should, accordingly, be used to complement one another.

Cephalometric landmark detection in dental x-ray images using convolutional neural networks

NASA Astrophysics Data System (ADS)

Lee, Hansang; Park, Minseok; Kim, Junmo

2017-03-01

In dental X-ray images, an accurate detection of cephalometric landmarks plays an important role in clinical diagnosis, treatment and surgical decisions for dental problems. In this work, we propose an end-to-end deep learning system for cephalometric landmark detection in dental X-ray images, using convolutional neural networks (CNN). For detecting 19 cephalometric landmarks in dental X-ray images, we develop a detection system using CNN-based coordinate-wise regression systems. By viewing x- and y-coordinates of all landmarks as 38 independent variables, multiple CNN-based regression systems are constructed to predict the coordinate variables from input X-ray images. First, each coordinate variable is normalized by the length of either height or width of an image. For each normalized coordinate variable, a CNN-based regression system is trained on training images and corresponding coordinate variable, which is a variable to be regressed. We train 38 regression systems with the same CNN structure on coordinate variables, respectively. Finally, we compute 38 coordinate variables with these trained systems from unseen images and extract 19 landmarks by pairing the regressed coordinates. In experiments, the public database from the Grand Challenges in Dental X-ray Image Analysis in ISBI 2015 was used and the proposed system showed promising performance by successfully locating the cephalometric landmarks within considerable margins from the ground truths.

Methods for estimating the magnitude and frequency of floods for urban and small, rural streams in Georgia, South Carolina, and North Carolina, 2011

USGS Publications Warehouse

Feaster, Toby D.; Gotvald, Anthony J.; Weaver, J. Curtis

2014-01-01

Reliable estimates of the magnitude and frequency of floods are essential for the design of transportation and water-conveyance structures, flood-insurance studies, and flood-plain management. Such estimates are particularly important in densely populated urban areas. In order to increase the number of streamflow-gaging stations (streamgages) available for analysis, expand the geographical coverage that would allow for application of regional regression equations across State boundaries, and build on a previous flood-frequency investigation of rural U.S Geological Survey streamgages in the Southeast United States, a multistate approach was used to update methods for determining the magnitude and frequency of floods in urban and small, rural streams that are not substantially affected by regulation or tidal fluctuations in Georgia, South Carolina, and North Carolina. The at-site flood-frequency analysis of annual peak-flow data for urban and small, rural streams (through September 30, 2011) included 116 urban streamgages and 32 small, rural streamgages, defined in this report as basins draining less than 1 square mile. The regional regression analysis included annual peak-flow data from an additional 338 rural streamgages previously included in U.S. Geological Survey flood-frequency reports and 2 additional rural streamgages in North Carolina that were not included in the previous Southeast rural flood-frequency investigation for a total of 488 streamgages included in the urban and small, rural regression analysis. The at-site flood-frequency analyses for the urban and small, rural streamgages included the expected moments algorithm, which is a modification of the Bulletin 17B log-Pearson type III method for fitting the statistical distribution to the logarithms of the annual peak flows. Where applicable, the flood-frequency analysis also included low-outlier and historic information. Additionally, the application of a generalized Grubbs-Becks test allowed for the detection of multiple potentially influential low outliers. Streamgage basin characteristics were determined using geographical information system techniques. Initial ordinary least squares regression simulations reduced the number of basin characteristics on the basis of such factors as statistical significance, coefficient of determination, Mallow’s Cp statistic, and ease of measurement of the explanatory variable. Application of generalized least squares regression techniques produced final predictive (regression) equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability flows for urban and small, rural ungaged basins for three hydrologic regions (HR1, Piedmont–Ridge and Valley; HR3, Sand Hills; and HR4, Coastal Plain), which previously had been defined from exploratory regression analysis in the Southeast rural flood-frequency investigation. Because of the limited availability of urban streamgages in the Coastal Plain of Georgia, South Carolina, and North Carolina, additional urban streamgages in Florida and New Jersey were used in the regression analysis for this region. Including the urban streamgages in New Jersey allowed for the expansion of the applicability of the predictive equations in the Coastal Plain from 3.5 to 53.5 square miles. Average standard error of prediction for the predictive equations, which is a measure of the average accuracy of the regression equations when predicting flood estimates for ungaged sites, range from 25.0 percent for the 10-percent annual exceedance probability regression equation for the Piedmont–Ridge and Valley region to 73.3 percent for the 0.2-percent annual exceedance probability regression equation for the Sand Hills region.

Machine learning action parameters in lattice quantum chromodynamics

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

Shanahan, Phiala; Trewartha, Daneil; Detmold, William

Numerical lattice quantum chromodynamics studies of the strong interaction underpin theoretical understanding of many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. Finally, the high information contentmore » and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.« less

Machine learning action parameters in lattice quantum chromodynamics

DOE PAGES

Shanahan, Phiala; Trewartha, Daneil; Detmold, William

2018-05-16

Numerical lattice quantum chromodynamics studies of the strong interaction underpin theoretical understanding of many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. Finally, the high information contentmore » and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.« less

Workers' compensation costs among construction workers: a robust regression analysis.

PubMed

Friedman, Lee S; Forst, Linda S

2009-11-01

Workers' compensation data are an important source for evaluating costs associated with construction injuries. We describe the characteristics of injured construction workers filing claims in Illinois between 2000 and 2005 and the factors associated with compensation costs using a robust regression model. In the final multivariable model, the cumulative percent temporary and permanent disability-measures of severity of injury-explained 38.7% of the variance of cost. Attorney costs explained only 0.3% of the variance of the dependent variable. The model used in this study clearly indicated that percent disability was the most important determinant of cost, although the method and uniformity of percent impairment allocation could be better elucidated. There is a need to integrate analytical methods that are suitable for skewed data when analyzing claim costs.

Multi-criteria manufacturability indices for ranking high-concentration monoclonal antibody formulations.

PubMed

Yang, Yang; Velayudhan, Ajoy; Thornhill, Nina F; Farid, Suzanne S

2017-09-01

The need for high-concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high-concentration formulations. This work presents novel frameworks for deriving a set of manufacturability indices related to viscosity and thermostability to rank high-concentration mAb formulation conditions in terms of their ease of manufacture. This is illustrated by analyzing published high-throughput biophysical screening data that explores the influence of different formulation conditions (pH, ions, and excipients) on the solution viscosity and product thermostability. A decision tree classification method, CART (Classification and Regression Tree) is used to identify the critical formulation conditions that influence the viscosity and thermostability. In this work, three different multi-criteria data analysis frameworks were investigated to derive manufacturability indices from analysis of the stress maps and the process conditions experienced in the final UF/DF step. Polynomial regression techniques were used to transform the experimental data into a set of stress maps that show viscosity and thermostability as functions of the formulation conditions. A mathematical filtrate flux model was used to capture the time profiles of protein concentration and flux decay behavior during UF/DF. Multi-criteria decision-making analysis was used to identify the optimal formulation conditions that minimize the potential for both viscosity and aggregation issues during UF/DF. Biotechnol. Bioeng. 2017;114: 2043-2056. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc.

Inflated responsibility in obsessive compulsive disorder: validation of an operational definition.

PubMed

Rhéaume, J; Ladouceur, R; Freeston, M H; Letarte, H

1995-02-01

An excessive sense of responsibility has been identified in obsessive-compulsive disorder (OCD) where patients evaluate their thoughts in terms of the harm they could cause to themselves or others. In a new definition, responsibility was defined as the belief that one possesses pivotal power to provoke or prevent subjective crucial negative outcomes. In order to empirically test the validity of this definition, two studies used a semi-idiographic design to evaluate responsibility across ambiguous situations related to major OCD themes like contamination, verification, somatic concerns, loss of control, making errors, sexuality and magical thinking. In the first study, 397 volunteer adults participated in the experiment. For each situation, subjects briefly described a possible negative outcome and then rated this outcome on four dimensions: (1) probability; (2) severity; (3) influence; and (4) pivotal influence, using a 9-point Likert scale. Finally Ss rated perceived responsibility and personal relevance. Highly relevant situations were retained for the final analysis. Regression analysis suggested that influence and pivotal influence were better predictors of responsibility ratings than probability and severity. The second study examined the effect of the order of the questions on the responsibility ratings. A first group of Ss (n = 85) answered the Responsibility Questionnaire (RQ) in the original order, while a second group (n = 53) rated responsibility before the other ratings. Regression analysis showed that although proportion of variance explained diminished when the order was reversed, pivotal influence was still the best predictor of responsibility. Results are discussed in terms of current models of OCD and implications for future research and cognitive treatment are identified.

Reliability and validity of tongue color analysis in the prediction of symptom patterns in terms of East Asian Medicine.

PubMed

Park, Young-Jae; Lee, Jin-Moo; Yoo, Seung-Yeon; Park, Young-Bae

2016-04-01

To examine whether color parameters of tongue inspection (TI) using a digital camera was reliable and valid, and to examine which color parameters serve as predictors of symptom patterns in terms of East Asian medicine (EAM). Two hundred female subjects' tongue substances were photographed by a mega-pixel digital camera. Together with the photographs, the subjects were asked to complete Yin deficiency, Phlegm pattern, and Cold-Heat pattern questionnaires. Using three sets of digital imaging software, each digital image was exposure- and white balance-corrected, and finally L* (luminance), a* (red-green balance), and b* (yellow-blue balance) values of the tongues were calculated. To examine intra- and inter-rater reliabilities and criterion validity of the color analysis method, three raters were asked to calculate color parameters for 20 digital image samples. Finally, four hierarchical regression models were formed. Color parameters showed good or excellent reliability (0.627-0.887 for intra-class correlation coefficients) and significant criterion validity (0.523-0.718 for Spearman's correlation). In the hierarchical regression models, age was a significant predictor of Yin deficiency (β = 0.192), and b* value of the tip of the tongue was a determinant predictor of Yin deficiency, Phlegm, and Heat patterns (β = - 0.212, - 0.172, and - 0.163). Luminance (L*) was predictive of Yin deficiency (β = -0.172) and Cold (β = 0.173) pattern. Our results suggest that color analysis of the tongue using the L*a*b* system is reliable and valid, and that color parameters partially serve as symptom pattern predictors in EAM practice.

Multi‐criteria manufacturability indices for ranking high‐concentration monoclonal antibody formulations

PubMed Central

Velayudhan, Ajoy; Thornhill, Nina F.

2017-01-01

ABSTRACT The need for high‐concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high‐concentration formulations. This work presents novel frameworks for deriving a set of manufacturability indices related to viscosity and thermostability to rank high‐concentration mAb formulation conditions in terms of their ease of manufacture. This is illustrated by analyzing published high‐throughput biophysical screening data that explores the influence of different formulation conditions (pH, ions, and excipients) on the solution viscosity and product thermostability. A decision tree classification method, CART (Classification and Regression Tree) is used to identify the critical formulation conditions that influence the viscosity and thermostability. In this work, three different multi‐criteria data analysis frameworks were investigated to derive manufacturability indices from analysis of the stress maps and the process conditions experienced in the final UF/DF step. Polynomial regression techniques were used to transform the experimental data into a set of stress maps that show viscosity and thermostability as functions of the formulation conditions. A mathematical filtrate flux model was used to capture the time profiles of protein concentration and flux decay behavior during UF/DF. Multi‐criteria decision‐making analysis was used to identify the optimal formulation conditions that minimize the potential for both viscosity and aggregation issues during UF/DF. Biotechnol. Bioeng. 2017;114: 2043–2056. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc. PMID:28464235

Continuing or Temporarily Stopping Prestroke Antihypertensive Medication in Acute Stroke: An Individual Patient Data Meta-Analysis.

PubMed

Woodhouse, Lisa J; Manning, Lisa; Potter, John F; Berge, Eivind; Sprigg, Nikola; Wardlaw, Joanna; Lees, Kennedy R; Bath, Philip M; Robinson, Thompson G

2017-05-01

Over 50% of patients are already taking blood pressure-lowering therapy on hospital admission for acute stroke. An individual patient data meta-analysis from randomized controlled trials was undertaken to determine the effect of continuation versus temporarily stopping preexisting antihypertensive medication in acute stroke. Key databases were searched for trials against the following inclusion criteria: randomized design; stroke onset ≤48 hours; investigating the effect of continuation versus stopping prestroke antihypertensive medication; and follow-up of ≥2 weeks. Two randomized controlled trials were identified and included in this meta-analysis of individual patient data from 2860 patients with ≤48 hours of acute stroke. Risk of bias in each study was low. In adjusted logistic regression and multiple regression analyses (using random effects), we found no significant association between continuation of prestroke antihypertensive therapy (versus stopping) and risk of death or dependency at final follow-up: odds ratio 0.96 (95% confidence interval, 0.80-1.14). No significant associations were found between continuation (versus stopping) of therapy and secondary outcomes at final follow-up. Analyses for death and dependency in prespecified subgroups revealed no significant associations with continuation versus temporarily stopping therapy, with the exception of patients randomized ≤12 hours, in whom a difference favoring stopping treatment met statistical significance. We found no significant benefit with continuation of antihypertensive treatment in the acute stroke period. Therefore, there is no urgency to administer preexisting antihypertensive therapy in the first few hours or days after stroke, unless indicated for other comorbid conditions. © 2017 American Heart Association, Inc.

Perception of difficulty and glucose control: Effects on academic performance in youth with type I diabetes.

PubMed

Potts, Tiffany M; Nguyen, Jacqueline L; Ghai, Kanika; Li, Kathy; Perlmuter, Lawrence

2015-04-15

To investigate whether perceptions of task difficulty on neuropsychological tests predicted academic achievement after controlling for glucose levels and depression. Participants were type 1 diabetic adolescents, with a mean age = 12.5 years (23 females and 16 males), seen at a northwest suburban Chicago hospital. The sample population was free of co-morbid clinical health conditions. Subjects completed a three-part neuropsychological battery including the Digit Symbol Task, Trail Making Test, and Controlled Oral Word Association test. Following each task, individuals rated task difficulty and then completed a depression inventory. Performance on these three tests is reflective of neuropsychological status in relation to glucose control. Blood glucose levels were measured immediately prior to and after completing the neuropsychological battery using a glucose meter. HbA1c levels were obtained from medical records. Academic performance was based on self-reported grades in Math, Science, and English. Data was analyzed using multiple regression models to evaluate the associations between academic performance, perception of task difficulty, and glucose control. Perceptions of difficulty on a neuropsychological battery significantly predicted academic performance after accounting for glucose control and depression. Perceptions of difficulty on the neuropsychological tests were inversely correlated with academic performance (r = -0.48), while acute (blood glucose) and long-term glucose levels increased along with perceptions of task difficulty (r = 0.47). Additionally, higher depression scores were associated with poorer academic performance (r = -0.43). With the first regression analysis, perception of difficulty on the neuropsychological tasks contributed to 8% of the variance in academic performance after controlling for peripheral blood glucose and depression. In the second regression analysis, perception of difficulty accounted for 11% of the variance after accounting for academic performance and depression. The final regression analysis indicated that perception of difficulty increased with peripheral blood glucose, contributing to 22% of the variance. Most importantly, after controlling for perceptions of task difficulty, academic performance no longer predicted glucose levels. Finally, subjects who found the cognitive battery difficult were likely to have poor academic grades. Perceptions of difficulty on neurological tests exhibited a significant association with academic achievement, indicating that deficits in this skill may lead to academic disadvantage in diabetic patients.

Dietary consumption patterns and laryngeal cancer risk.

PubMed

Vlastarakos, Petros V; Vassileiou, Andrianna; Delicha, Evie; Kikidis, Dimitrios; Protopapas, Dimosthenis; Nikolopoulos, Thomas P

2016-06-01

We conducted a case-control study to investigate the effect of diet on laryngeal carcinogenesis. Our study population was made up of 140 participants-70 patients with laryngeal cancer (LC) and 70 controls with a non-neoplastic condition that was unrelated to diet, smoking, or alcohol. A food-frequency questionnaire determined the mean consumption of 113 different items during the 3 years prior to symptom onset. Total energy intake and cooking mode were also noted. The relative risk, odds ratio (OR), and 95% confidence interval (CI) were estimated by multiple logistic regression analysis. We found that the total energy intake was significantly higher in the LC group (p < 0.001), and that the difference remained statistically significant after logistic regression analysis (p < 0.001; OR: 118.70). Notably, meat consumption was higher in the LC group (p < 0.001), and the difference remained significant after logistic regression analysis (p = 0.029; OR: 1.16). LC patients also consumed significantly more fried food (p = 0.036); this difference also remained significant in the logistic regression model (p = 0.026; OR: 5.45). The LC group also consumed significantly more seafood (p = 0.012); the difference persisted after logistic regression analysis (p = 0.009; OR: 2.48), with the consumption of shrimp proving detrimental (p = 0.049; OR: 2.18). Finally, the intake of zinc was significantly higher in the LC group before and after logistic regression analysis (p = 0.034 and p = 0.011; OR: 30.15, respectively). Cereal consumption (including pastas) was also higher among the LC patients (p = 0.043), with logistic regression analysis showing that their negative effect was possibly associated with the sauces and dressings that traditionally accompany pasta dishes (p = 0.006; OR: 4.78). Conversely, a higher consumption of dairy products was found in controls (p < 0.05); logistic regression analysis showed that calcium appeared to be protective at the micronutrient level (p < 0.001; OR: 0.27). We found no difference in the overall consumption of fruits and vegetables between the LC patients and controls; however, the LC patients did have a greater consumption of cooked tomatoes and cooked root vegetables (p = 0.039 for both), and the controls had more consumption of leeks (p = 0.042) and, among controls younger than 65 years, cooked beans (p = 0.037). Lemon (p = 0.037), squeezed fruit juice (p = 0.032), and watermelon (p = 0.018) were also more frequently consumed by the controls. Other differences at the micronutrient level included greater consumption by the LC patients of retinol (p = 0.044), polyunsaturated fats (p = 0.041), and linoleic acid (p = 0.008); LC patients younger than 65 years also had greater intake of riboflavin (p = 0.045). We conclude that the differences in dietary consumption patterns between LC patients and controls indicate a possible role for lifestyle modifications involving nutritional factors as a means of decreasing the risk of laryngeal cancer.

Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries.

PubMed

Liu, Quan; Ma, Li; Fan, Shou-Zen; Abbod, Maysam F; Shieh, Jiann-Shing

2018-01-01

Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the proposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels. Furthermore, the Random Forest output was extensively linear in relation to BIS, thus with better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients' anaesthetic level during surgeries.

Comparing Methods for Assessing Reliability Uncertainty Based on Pass/Fail Data Collected Over Time

DOE PAGES

Abes, Jeff I.; Hamada, Michael S.; Hills, Charles R.

2017-12-20

In this paper, we compare statistical methods for analyzing pass/fail data collected over time; some methods are traditional and one (the RADAR or Rationale for Assessing Degradation Arriving at Random) was recently developed. These methods are used to provide uncertainty bounds on reliability. We make observations about the methods' assumptions and properties. Finally, we illustrate the differences between two traditional methods, logistic regression and Weibull failure time analysis, and the RADAR method using a numerical example.

Comparing Methods for Assessing Reliability Uncertainty Based on Pass/Fail Data Collected Over Time

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

Abes, Jeff I.; Hamada, Michael S.; Hills, Charles R.

In this paper, we compare statistical methods for analyzing pass/fail data collected over time; some methods are traditional and one (the RADAR or Rationale for Assessing Degradation Arriving at Random) was recently developed. These methods are used to provide uncertainty bounds on reliability. We make observations about the methods' assumptions and properties. Finally, we illustrate the differences between two traditional methods, logistic regression and Weibull failure time analysis, and the RADAR method using a numerical example.

Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA

USGS Publications Warehouse

Ohlmacher, G.C.; Davis, J.C.

2003-01-01

Landslides in the hilly terrain along the Kansas and Missouri rivers in northeastern Kansas have caused millions of dollars in property damage during the last decade. To address this problem, a statistical method called multiple logistic regression has been used to create a landslide-hazard map for Atchison, Kansas, and surrounding areas. Data included digitized geology, slopes, and landslides, manipulated using ArcView GIS. Logistic regression relates predictor variables to the occurrence or nonoccurrence of landslides within geographic cells and uses the relationship to produce a map showing the probability of future landslides, given local slopes and geologic units. Results indicated that slope is the most important variable for estimating landslide hazard in the study area. Geologic units consisting mostly of shale, siltstone, and sandstone were most susceptible to landslides. Soil type and aspect ratio were considered but excluded from the final analysis because these variables did not significantly add to the predictive power of the logistic regression. Soil types were highly correlated with the geologic units, and no significant relationships existed between landslides and slope aspect. ?? 2003 Elsevier Science B.V. All rights reserved.

An Empirical Analysis of the Default Rate of Informal Lending—Evidence from Yiwu, China

NASA Astrophysics Data System (ADS)

Lu, Wei; Yu, Xiaobo; Du, Juan; Ji, Feng

This study empirically analyzes the underlying factors contributing to the default rate of informal lending. This paper adopts snowball sampling interview to collect data and uses the logistic regression model to explore the specific factors. The results of these analyses validate the explanation of how the informal lending differs from the commercial loan. Factors that contribute to the default rate have particular attributes, while sharing some similarities with commercial bank or FICO credit scoring Index. Finally, our concluding remarks draw some inferences from empirical analysis and speculate as to what this may imply for the role of formal and informal financial sectors.

The relationship between white matter brain metabolites and cognition in normal aging: the GENIE study.

PubMed

Charlton, R A; McIntyre, D J O; Howe, F A; Morris, R G; Markus, H S

2007-08-20

Magnetic resonance spectroscopy (MRS) has demonstrated age-related changes in brain metabolites that may underlie micro-structural brain changes, but few studies have examined their relationship with cognitive decline. We performed a cross-sectional study of brain metabolism and cognitive function in 82 healthy adults (aged 50-90) participating in the GENIE (St GEorge's Neuropsychology and Imaging in the Elderly) study. Absolute metabolite concentrations were measured by proton chemical shift imaging within voxels placed in the centrum semiovale white matter. Cognitive abilities assessed were executive function, working memory, information processing speed, long-term memory and fluid intelligence. Correlations showed that all cognitive domains declined with age. Total creatine (tCr) concentration increased with age (r=0.495, p<0.001). Regression analyses were performed for each cognitive variable, including estimated intelligence and the metabolites, with age then added as a final step. A significant relationship was observed between tCr and executive function, long-term memory, and fluid intelligence, although these relationships did not remain significant after age was added as a final step in the regression. The regression analysis also demonstrated a significant relationship between N-acetylaspartate (NAA) and executive function. As there was no age-related decline in NAA, this argues against axonal loss with age; however the relationship between NAA and executive function independent of age and estimated intelligence is consistent with white matter axonal integrity having an important role in executive function in normal individuals.

Water Quality Variable Estimation using Partial Least Squares Regression and Multi-Scale Remote Sensing.

NASA Astrophysics Data System (ADS)

Peterson, K. T.; Wulamu, A.

2017-12-01

Water, essential to all living organisms, is one of the Earth's most precious resources. Remote sensing offers an ideal approach to monitor water quality over traditional in-situ techniques that are highly time and resource consuming. Utilizing a multi-scale approach, incorporating data from handheld spectroscopy, UAS based hyperspectal, and satellite multispectral images were collected in coordination with in-situ water quality samples for the two midwestern watersheds. The remote sensing data was modeled and correlated to the in-situ water quality variables including chlorophyll content (Chl), turbidity, and total dissolved solids (TDS) using Normalized Difference Spectral Indices (NDSI) and Partial Least Squares Regression (PLSR). The results of the study supported the original hypothesis that correlating water quality variables with remotely sensed data benefits greatly from the use of more complex modeling and regression techniques such as PLSR. The final results generated from the PLSR analysis resulted in much higher R2 values for all variables when compared to NDSI. The combination of NDSI and PLSR analysis also identified key wavelengths for identification that aligned with previous study's findings. This research displays the advantages and future for complex modeling and machine learning techniques to improve water quality variable estimation from spectral data.

Germplasm-regression-combined (GRC) marker-trait association identification in plant breeding: a challenge for plant biotechnological breeding under soil water deficit conditions.

PubMed

Ruan, Cheng-Jiang; Xu, Xue-Xuan; Shao, Hong-Bo; Jaleel, Cheruth Abdul

2010-09-01

In the past 20 years, the major effort in plant breeding has changed from quantitative to molecular genetics with emphasis on quantitative trait loci (QTL) identification and marker assisted selection (MAS). However, results have been modest. This has been due to several factors including absence of tight linkage QTL, non-availability of mapping populations, and substantial time needed to develop such populations. To overcome these limitations, and as an alternative to planned populations, molecular marker-trait associations have been identified by the combination between germplasm and the regression technique. In the present preview, the authors (1) survey the successful applications of germplasm-regression-combined (GRC) molecular marker-trait association identification in plants; (2) describe how to do the GRC analysis and its differences from mapping QTL based on a linkage map reconstructed from the planned populations; (3) consider the factors that affect the GRC association identification, including selections of optimal germplasm and molecular markers and testing of identification efficiency of markers associated with traits; and (4) finally discuss the future prospects of GRC marker-trait association analysis used in plant MAS/QTL breeding programs, especially in long-juvenile woody plants when no other genetic information such as linkage maps and QTL are available.

Sensitivity Analysis of Mechanical Parameters of Different Rock Layers to the Stability of Coal Roadway in Soft Rock Strata

PubMed Central

Zhao, Zeng-hui; Wang, Wei-ming; Gao, Xin; Yan, Ji-xing

2013-01-01

According to the geological characteristics of Xinjiang Ili mine in western area of China, a physical model of interstratified strata composed of soft rock and hard coal seam was established. Selecting the tunnel position, deformation modulus, and strength parameters of each layer as influencing factors, the sensitivity coefficient of roadway deformation to each parameter was firstly analyzed based on a Mohr-Columb strain softening model and nonlinear elastic-plastic finite element analysis. Then the effect laws of influencing factors which showed high sensitivity were further discussed. Finally, a regression model for the relationship between roadway displacements and multifactors was obtained by equivalent linear regression under multiple factors. The results show that the roadway deformation is highly sensitive to the depth of coal seam under the floor which should be considered in the layout of coal roadway; deformation modulus and strength of coal seam and floor have a great influence on the global stability of tunnel; on the contrary, roadway deformation is not sensitive to the mechanical parameters of soft roof; roadway deformation under random combinations of multi-factors can be deduced by the regression model. These conclusions provide theoretical significance to the arrangement and stability maintenance of coal roadway. PMID:24459447

Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models.

PubMed

Bilgili, Mehmet; Sahin, Besir; Sangun, Levent

2013-01-01

The aim of this study is to estimate the soil temperatures of a target station using only the soil temperatures of neighboring stations without any consideration of the other variables or parameters related to soil properties. For this aim, the soil temperatures were measured at depths of 5, 10, 20, 50, and 100 cm below the earth surface at eight measuring stations in Turkey. Firstly, the multiple nonlinear regression analysis was performed with the "Enter" method to determine the relationship between the values of target station and neighboring stations. Then, the stepwise regression analysis was applied to determine the best independent variables. Finally, an artificial neural network (ANN) model was developed to estimate the soil temperature of a target station. According to the derived results for the training data set, the mean absolute percentage error and correlation coefficient ranged from 1.45% to 3.11% and from 0.9979 to 0.9986, respectively, while corresponding ranges of 1.685-3.65% and 0.9988-0.9991, respectively, were obtained based on the testing data set. The obtained results show that the developed ANN model provides a simple and accurate prediction to determine the soil temperature. In addition, the missing data at the target station could be determined within a high degree of accuracy.

Lidocaine dose-response effect on postoperative cognitive deficit: meta-analysis and meta-regression.

PubMed

Habibi, Mohammad Reza; Habibi, Valiollah; Habibi, Ali; Soleimani, Aria

2018-04-01

The true influence of the perioperative intravenous lidocaine on the development of postoperative cognitive deficit (POCD) in coronary artery bypass grafting (CABG) remains controversial. The principal aim is to undertake a meta-regression to determine whether moderator variables mediate the relationship between lidocaine and POCD. Areas covered: We searched the Web of Science, PubMed database, Scopus and the Cochrane Library database (up to June 2017) and systematically reviewed a list of retrieved articles. Our final review includes only randomized controlled trials (RCTs) that compared infusion of lidocaine and placebo during cardiopulmonary bypass (CPB). Mantel-Haenszel risk ratio (MH RR) and corresponding 95% confidence interval (CI) was used to report the overall effect and meta-regression analysis. A total of 688 patients in five RCTs were included. POCD occurred in 34% of all cases. Perioperative lidocaine reduces POCD (MH RR 0.702 (95% CI: 0.541-0.909). Younger age, male gender, longer CPB and higher concentration of lidocaine significantly mediate the relationship between lidocaine and POCD in favour of the neuroprotective effect of lidocaine. Expert commentary: The neuroprotective effect of lidocaine on POCD is consistent in spite of longer CPB time. A higher concentration of lidocaine strengthened the neuroprotective effect of lidocaine.

An Exploratory Analysis of Personality, Attitudes, and Study Skills on the Learning Curve within a Team-based Learning Environment

PubMed Central

Henry, Teague; Campbell, Ashley

2015-01-01

Objective. To examine factors that determine the interindividual variability of learning within a team-based learning environment. Methods. Students in a pharmacokinetics course were given 4 interim, low-stakes cumulative assessments throughout the semester and a cumulative final examination. Students’ Myers-Briggs personality type was assessed, as well as their study skills, motivations, and attitudes towards team-learning. A latent curve model (LCM) was applied and various covariates were assessed to improve the regression model. Results. A quadratic LCM was applied for the first 4 assessments to predict final examination performance. None of the covariates examined significantly impacted the regression model fit except metacognitive self-regulation, which explained some of the variability in the rate of learning. There were some correlations between personality type and attitudes towards team learning, with introverts having a lower opinion of team-learning than extroverts. Conclusion. The LCM could readily describe the learning curve. Extroverted and introverted personality types had the same learning performance even though preference for team-learning was lower in introverts. Other personality traits, study skills, or practice did not significantly contribute to the learning variability in this course. PMID:25861101

An exploratory analysis of personality, attitudes, and study skills on the learning curve within a team-based learning environment.

PubMed

Persky, Adam M; Henry, Teague; Campbell, Ashley

2015-03-25

To examine factors that determine the interindividual variability of learning within a team-based learning environment. Students in a pharmacokinetics course were given 4 interim, low-stakes cumulative assessments throughout the semester and a cumulative final examination. Students' Myers-Briggs personality type was assessed, as well as their study skills, motivations, and attitudes towards team-learning. A latent curve model (LCM) was applied and various covariates were assessed to improve the regression model. A quadratic LCM was applied for the first 4 assessments to predict final examination performance. None of the covariates examined significantly impacted the regression model fit except metacognitive self-regulation, which explained some of the variability in the rate of learning. There were some correlations between personality type and attitudes towards team learning, with introverts having a lower opinion of team-learning than extroverts. The LCM could readily describe the learning curve. Extroverted and introverted personality types had the same learning performance even though preference for team-learning was lower in introverts. Other personality traits, study skills, or practice did not significantly contribute to the learning variability in this course.

Determining delayed admission to intensive care unit for mechanically ventilated patients in the emergency department.

PubMed

Hung, Shih-Chiang; Kung, Chia-Te; Hung, Chih-Wei; Liu, Ber-Ming; Liu, Jien-Wei; Chew, Ghee; Chuang, Hung-Yi; Lee, Wen-Huei; Lee, Tzu-Chi

2014-08-23

The adverse effects of delayed admission to the intensive care unit (ICU) have been recognized in previous studies. However, the definitions of delayed admission varies across studies. This study proposed a model to define "delayed admission", and explored the effect of ICU-waiting time on patients' outcome. This retrospective cohort study included non-traumatic adult patients on mechanical ventilation in the emergency department (ED), from July 2009 to June 2010. The primary outcomes measures were 21-ventilator-day mortality and prolonged hospital stays (over 30 days). Models of Cox regression and logistic regression were used for multivariate analysis. The non-delayed ICU-waiting was defined as a period in which the time effect on mortality was not statistically significant in a Cox regression model. To identify a suitable cut-off point between "delayed" and "non-delayed", subsets from the overall data were made based on ICU-waiting time and the hazard ratio of ICU-waiting hour in each subset was iteratively calculated. The cut-off time was then used to evaluate the impact of delayed ICU admission on mortality and prolonged length of hospital stay. The final analysis included 1,242 patients. The time effect on mortality emerged after 4 hours, thus we deduced ICU-waiting time in ED > 4 hours as delayed. By logistic regression analysis, delayed ICU admission affected the outcomes of 21 ventilator-days mortality and prolonged hospital stay, with odds ratio of 1.41 (95% confidence interval, 1.05 to 1.89) and 1.56 (95% confidence interval, 1.07 to 2.27) respectively. For patients on mechanical ventilation at the ED, delayed ICU admission is associated with higher probability of mortality and additional resource expenditure. A benchmark waiting time of no more than 4 hours for ICU admission is recommended.

Application of Temperature Sensitivities During Iterative Strain-Gage Balance Calibration Analysis

NASA Technical Reports Server (NTRS)

Ulbrich, N.

2011-01-01

A new method is discussed that may be used to correct wind tunnel strain-gage balance load predictions for the influence of residual temperature effects at the location of the strain-gages. The method was designed for the iterative analysis technique that is used in the aerospace testing community to predict balance loads from strain-gage outputs during a wind tunnel test. The new method implicitly applies temperature corrections to the gage outputs during the load iteration process. Therefore, it can use uncorrected gage outputs directly as input for the load calculations. The new method is applied in several steps. First, balance calibration data is analyzed in the usual manner assuming that the balance temperature was kept constant during the calibration. Then, the temperature difference relative to the calibration temperature is introduced as a new independent variable for each strain--gage output. Therefore, sensors must exist near the strain--gages so that the required temperature differences can be measured during the wind tunnel test. In addition, the format of the regression coefficient matrix needs to be extended so that it can support the new independent variables. In the next step, the extended regression coefficient matrix of the original calibration data is modified by using the manufacturer specified temperature sensitivity of each strain--gage as the regression coefficient of the corresponding temperature difference variable. Finally, the modified regression coefficient matrix is converted to a data reduction matrix that the iterative analysis technique needs for the calculation of balance loads. Original calibration data and modified check load data of NASA's MC60D balance are used to illustrate the new method.

Generating linear regression model to predict motor functions by use of laser range finder during TUG.

PubMed

Adachi, Daiki; Nishiguchi, Shu; Fukutani, Naoto; Hotta, Takayuki; Tashiro, Yuto; Morino, Saori; Shirooka, Hidehiko; Nozaki, Yuma; Hirata, Hinako; Yamaguchi, Moe; Yorozu, Ayanori; Takahashi, Masaki; Aoyama, Tomoki

2017-05-01

The purpose of this study was to investigate which spatial and temporal parameters of the Timed Up and Go (TUG) test are associated with motor function in elderly individuals. This study included 99 community-dwelling women aged 72.9 ± 6.3 years. Step length, step width, single support time, variability of the aforementioned parameters, gait velocity, cadence, reaction time from starting signal to first step, and minimum distance between the foot and a marker placed to 3 in front of the chair were measured using our analysis system. The 10-m walk test, five times sit-to-stand (FTSTS) test, and one-leg standing (OLS) test were used to assess motor function. Stepwise multivariate linear regression analysis was used to determine which TUG test parameters were associated with each motor function test. Finally, we calculated a predictive model for each motor function test using each regression coefficient. In stepwise linear regression analysis, step length and cadence were significantly associated with the 10-m walk test, FTSTS and OLS test. Reaction time was associated with the FTSTS test, and step width was associated with the OLS test. Each predictive model showed a strong correlation with the 10-m walk test and OLS test (P < 0.01), which was not significant higher correlation than TUG test time. We showed which TUG test parameters were associated with each motor function test. Moreover, the TUG test time regarded as the lower extremity function and mobility has strong predictive ability in each motor function test. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

PREDICTION OF MALIGNANT BREAST LESIONS FROM MRI FEATURES: A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION TECHNIQUES

PubMed Central

McLaren, Christine E.; Chen, Wen-Pin; Nie, Ke; Su, Min-Ying

2009-01-01

Rationale and Objectives Dynamic contrast enhanced MRI (DCE-MRI) is a clinical imaging modality for detection and diagnosis of breast lesions. Analytical methods were compared for diagnostic feature selection and performance of lesion classification to differentiate between malignant and benign lesions in patients. Materials and Methods The study included 43 malignant and 28 benign histologically-proven lesions. Eight morphological parameters, ten gray level co-occurrence matrices (GLCM) texture features, and fourteen Laws’ texture features were obtained using automated lesion segmentation and quantitative feature extraction. Artificial neural network (ANN) and logistic regression analysis were compared for selection of the best predictors of malignant lesions among the normalized features. Results Using ANN, the final four selected features were compactness, energy, homogeneity, and Law_LS, with area under the receiver operating characteristic curve (AUC) = 0.82, and accuracy = 0.76. The diagnostic performance of these 4-features computed on the basis of logistic regression yielded AUC = 0.80 (95% CI, 0.688 to 0.905), similar to that of ANN. The analysis also shows that the odds of a malignant lesion decreased by 48% (95% CI, 25% to 92%) for every increase of 1 SD in the Law_LS feature, adjusted for differences in compactness, energy, and homogeneity. Using logistic regression with z-score transformation, a model comprised of compactness, NRL entropy, and gray level sum average was selected, and it had the highest overall accuracy of 0.75 among all models, with AUC = 0.77 (95% CI, 0.660 to 0.880). When logistic modeling of transformations using the Box-Cox method was performed, the most parsimonious model with predictors, compactness and Law_LS, had an AUC of 0.79 (95% CI, 0.672 to 0.898). Conclusion The diagnostic performance of models selected by ANN and logistic regression was similar. The analytic methods were found to be roughly equivalent in terms of predictive ability when a small number of variables were chosen. The robust ANN methodology utilizes a sophisticated non-linear model, while logistic regression analysis provides insightful information to enhance interpretation of the model features. PMID:19409817

Bayesian survival analysis in clinical trials: What methods are used in practice?

PubMed

Brard, Caroline; Le Teuff, Gwénaël; Le Deley, Marie-Cécile; Hampson, Lisa V

2017-02-01

Background Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment effect was based on historical data in only four trials. Decision rules were pre-defined in eight cases when trials used Bayesian monitoring, and in only one case when trials adopted a Bayesian approach to the final analysis. Conclusion Few trials implemented a Bayesian survival analysis and few incorporated external data into priors. There is scope to improve the quality of reporting of Bayesian methods in survival trials. Extension of the Consolidated Standards of Reporting Trials statement for reporting Bayesian clinical trials is recommended.

Risk factors for amendment in type, duration and setting of prescribed outpatient parenteral antimicrobial therapy (OPAT) for adult patients with cellulitis: a retrospective cohort study and CART analysis.

PubMed

Quirke, Michael; Curran, Emma May; O'Kelly, Patrick; Moran, Ruth; Daly, Eimear; Aylward, Seamus; McElvaney, Gerry; Wakai, Abel

2018-01-01

To measure the percentage rate and risk factors for amendment in the type, duration and setting of outpatient parenteral antimicrobial therapy ( OPAT) for the treatment of cellulitis. A retrospective cohort study of adult patients receiving OPAT for cellulitis was performed. Treatment amendment (TA) was defined as hospital admission or change in antibiotic therapy in order to achieve clinical response. Multivariable logistic regression (MVLR) and classification and regression tree (CART) analysis were performed. There were 307 patients enrolled. TA occurred in 36 patients (11.7%). Significant risk factors for TA on MVLR were increased age, increased Numerical Pain Scale Score (NPSS) and immunocompromise. The median OPAT duration was 7 days. Increased age, heart rate and C reactive protein were associated with treatment prolongation. CART analysis selected age <64.5 years, female gender and NPSS <2.5 in the final model, generating a low-sensitivity (27.8%), high-specificity (97.1%) decision tree. Increased age, NPSS and immunocompromise were associated with OPAT amendment. These identified risk factors can be used to support an evidence-based approach to patient selection for OPAT in cellulitis. The CART algorithm has good specificity but lacks sensitivity and is shown to be inferior in this study to logistic regression modelling. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

How much do hazard mitigation plans cost? An analysis of federal grant data.

PubMed

Jackman, Andrea M; Beruvides, Mario G

2013-01-01

Under the Disaster Mitigation Act of 2000 and Federal Emergency Management Agency's subsequent Interim Final Rule, the requirement was placed on local governments to author and gain approval for a Hazard Mitigation Plan (HMP) for the areas under their jurisdiction. Low completion percentages for HMPs--less than one-third of eligible governments--were found by an analysis conducted 3 years after the final deadline for the aforementioned legislation took place. Follow-up studies showed little improvement at 5 and 8 years after the deadline. It was hypothesized that the cost of a HMP is a significant factor in determining whether or not a plan is completed. A study was conducted using Boolean Matrix Analysis methods to determine what, if any, characteristics of a certain community will most influence the cost of a HMP. The frequency of natural hazards experienced by the planning area, the number of jurisdictions participating in the HMEP, the population, and population density were found to significantly affect cost. These variables were used in a regression analysis to determine their predictive power for cost. It was found that along with two interaction terms, the variables explain approximately half the variation in HMP cost.

Identification and functional analysis of risk-related microRNAs for the prognosis of patients with bladder urothelial carcinoma.

PubMed

Gao, Ji; Li, Hongyan; Liu, Lei; Song, Lide; Lv, Yanting; Han, Yuping

2017-12-01

The aim of the present study was to investigate risk-related microRNAs (miRs) for bladder urothelial carcinoma (BUC) prognosis. Clinical and microRNA expression data downloaded from the Cancer Genome Atlas were utilized for survival analysis. Risk factor estimation was performed using Cox's proportional regression analysis. A microRNA-regulated target gene network was constructed and presented using Cytoscape. In addition, the Database for Annotation, Visualization and Integrated Discovery was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment, followed by protein-protein interaction (PPI) network analysis. Finally, the K-clique method was applied to analyze sub-pathways. A total of 16 significant microRNAs, including hsa-miR-3622a and hsa-miR-29a, were identified (P<0.05). Following Cox's proportional regression analysis, hsa-miR-29a was screened as a prognostic marker of BUC risk (P=0.0449). A regulation network of hsa-miR-29a comprising 417 target genes was constructed. These target genes were primarily enriched in GO terms, including collagen fibril organization, extracellular matrix (ECM) organization and pathways, such as focal adhesion (P<0.05). A PPI network including 197 genes and 510 interactions, was constructed. The top 21 genes in the network module were enriched in GO terms, including collagen fibril organization and pathways, such as ECM receptor interaction (P<0.05). Finally, 4 sub-pathways of cysteine and methionine metabolism, including paths 00270_4, 00270_1, 00270_2 and 00270_5, were obtained (P<0.01) and identified to be enriched through DNA (cytosine-5)-methyltransferase ( DNMT)3A, DNMT3B , methionine adenosyltransferase 2α ( MAT2A ) and spermine synthase ( SMS ). The identified microRNAs, particularly hsa-miR-29a and its 4 associated target genes DNMT3A, DNMT3B, MAT2A and SMS , may participate in the prognostic risk mechanism of BUC.

Long-term prognosis of patients with acute myocardial infarction due to unprotected left main coronary artery disease: a single-centre experience over 14 years.

PubMed

Xu, Li; Sun, Hao; Wang, Le-Feng; Yang, Xin-Chun; Li, Kui-Bao; Zhang, Da-Peng; Wang, Hong-Shi; Li, Wei-Ming

2016-07-01

Acute myocardial infarction (AMI) due to unprotected left main coronary artery (ULMCA) disease is clinically catastrophic although it has a low incidence. Studies on the long-term prognosis of these patients are rare. From January 1999 to September 2013, 55 patients whose infarct-related artery was the ULMCA were enrolled. Clinical, angiographic and interventional data was collected. Short-term and long-term clinical follow-up results as well as prognostic determinants during hospitalisation and follow-up were analysed. Cardiogenic shock (CS) occurred in 30 (54.5%) patients. During hospitalisation, 22 (40.0%) patients died. Multivariate logistic regression analysis showed that CS (odds ratio [OR] 5.86; p = 0.03), collateral circulation of Grade 2 or 3 (OR 0.14; p = 0.02) and final flow of thrombolysis in myocardial infarction (TIMI) Grade 3 (OR 0.05; p = 0.03) correlated with death during hospitalisation. 33 patients survived to discharge; another seven patients died during the follow-up period of 44.6 ± 31.3 (median 60, range 0.67-117.00) months. The overall mortality rate was 52.7% (n = 29). Kaplan-Meier analysis showed that the total cumulative survival rate was 30.7%. Cox multivariate regression analysis showed that CS during hospitalisation was the only predictor of overall mortality (hazard ratio 4.07, 95% confidence interval 1.40-11.83; p = 0.01). AMI caused by ULMCA lesions is complicated by high incidence of CS and mortality. CS, poor collateral blood flow and failure to restore final flow of TIMI Grade 3 correlated with death during hospitalisation. CS is the only predictor of long-term overall mortality. Copyright: © Singapore Medical Association.

Robust regression for large-scale neuroimaging studies.

PubMed

Fritsch, Virgile; Da Mota, Benoit; Loth, Eva; Varoquaux, Gaël; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Brühl, Rüdiger; Butzek, Brigitte; Conrod, Patricia; Flor, Herta; Garavan, Hugh; Lemaitre, Hervé; Mann, Karl; Nees, Frauke; Paus, Tomas; Schad, Daniel J; Schümann, Gunter; Frouin, Vincent; Poline, Jean-Baptiste; Thirion, Bertrand

2015-05-01

Multi-subject datasets used in neuroimaging group studies have a complex structure, as they exhibit non-stationary statistical properties across regions and display various artifacts. While studies with small sample sizes can rarely be shown to deviate from standard hypotheses (such as the normality of the residuals) due to the poor sensitivity of normality tests with low degrees of freedom, large-scale studies (e.g. >100 subjects) exhibit more obvious deviations from these hypotheses and call for more refined models for statistical inference. Here, we demonstrate the benefits of robust regression as a tool for analyzing large neuroimaging cohorts. First, we use an analytic test based on robust parameter estimates; based on simulations, this procedure is shown to provide an accurate statistical control without resorting to permutations. Second, we show that robust regression yields more detections than standard algorithms using as an example an imaging genetics study with 392 subjects. Third, we show that robust regression can avoid false positives in a large-scale analysis of brain-behavior relationships with over 1500 subjects. Finally we embed robust regression in the Randomized Parcellation Based Inference (RPBI) method and demonstrate that this combination further improves the sensitivity of tests carried out across the whole brain. Altogether, our results show that robust procedures provide important advantages in large-scale neuroimaging group studies. Copyright © 2015 Elsevier Inc. All rights reserved.

Incremental online learning in high dimensions.

PubMed

Vijayakumar, Sethu; D'Souza, Aaron; Schaal, Stefan

2005-12-01

Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear function approximation in high-dimensional spaces with redundant and irrelevant input dimensions. At its core, it employs nonparametric regression with locally linear models. In order to stay computationally efficient and numerically robust, each local model performs the regression analysis with a small number of univariate regressions in selected directions in input space in the spirit of partial least squares regression. We discuss when and how local learning techniques can successfully work in high-dimensional spaces and review the various techniques for local dimensionality reduction before finally deriving the LWPR algorithm. The properties of LWPR are that it (1) learns rapidly with second-order learning methods based on incremental training, (2) uses statistically sound stochastic leave-one-out cross validation for learning without the need to memorize training data, (3) adjusts its weighting kernels based on only local information in order to minimize the danger of negative interference of incremental learning, (4) has a computational complexity that is linear in the number of inputs, and (5) can deal with a large number of-possibly redundant-inputs, as shown in various empirical evaluations with up to 90 dimensional data sets. For a probabilistic interpretation, predictive variance and confidence intervals are derived. To our knowledge, LWPR is the first truly incremental spatially localized learning method that can successfully and efficiently operate in very high-dimensional spaces.

The regression discontinuity design showed to be a valid alternative to a randomized controlled trial for estimating treatment effects.

PubMed

Maas, Iris L; Nolte, Sandra; Walter, Otto B; Berger, Thomas; Hautzinger, Martin; Hohagen, Fritz; Lutz, Wolfgang; Meyer, Björn; Schröder, Johanna; Späth, Christina; Klein, Jan Philipp; Moritz, Steffen; Rose, Matthias

2017-02-01

To compare treatment effect estimates obtained from a regression discontinuity (RD) design with results from an actual randomized controlled trial (RCT). Data from an RCT (EVIDENT), which studied the effect of an Internet intervention on depressive symptoms measured with the Patient Health Questionnaire (PHQ-9), were used to perform an RD analysis, in which treatment allocation was determined by a cutoff value at baseline (PHQ-9 = 10). A linear regression model was fitted to the data, selecting participants above the cutoff who had received the intervention (n = 317) and control participants below the cutoff (n = 187). Outcome was PHQ-9 sum score 12 weeks after baseline. Robustness of the effect estimate was studied; the estimate was compared with the RCT treatment effect. The final regression model showed a regression coefficient of -2.29 [95% confidence interval (CI): -3.72 to -.85] compared with a treatment effect found in the RCT of -1.57 (95% CI: -2.07 to -1.07). Although the estimates obtained from two designs are not equal, their confidence intervals overlap, suggesting that an RD design can be a valid alternative for RCTs. This finding is particularly important for situations where an RCT may not be feasible or ethical as is often the case in clinical research settings. Copyright © 2016 Elsevier Inc. All rights reserved.

Motivation and Self-Management Behavior of the Individuals With Chronic Low Back Pain.

PubMed

Jung, Mi Jung; Jeong, Younhee

2016-01-01

Self-management behavior is an important component for successful pain management in individuals with chronic low back pain. Motivation has been considered as an effective way to change behavior. Because there are other physical, social, and psychological factors affecting individuals with pain, it is necessary to identify the main effect of motivation on self-management behavior without the influence of those factors. The purpose of this study was to investigate the effect of motivation on self-management in controlling pain, depression, and social support. We used a nonexperimental, cross-sectional, descriptive design with mediation analysis and included 120 participants' data in the final analysis. We also used hierarchical multiple regression to test the effect of motivation, and multiple regression analysis and Sobel test were used to examine the mediating effect. Motivation itself accounted for 23.4% of the variance in self-management, F(1, 118) = 35.003, p < .001. After controlling covariates, motivation was also a significant factor for self-management. In the mediation analysis, motivation completely mediated the relationship between education and self-management, z = 2.292, p = .021. Motivation is an important part of self-management, and self-management education is not effective without motivation. The results of our study suggest that nurses incorporate motivation in nursing intervention, rather than only giving information.

Final height in elite male artistic gymnasts.

PubMed

Georgopoulos, Neoklis A; Theodoropoulou, Anastasia; Roupas, Nikolaos D; Armeni, Anastasia K; Koukkou, Eftychia; Leglise, Michel; Markou, Kostas B

2012-01-01

Elite male artistic gymnasts (AG) are exposed to high levels of physical and psychological stress during adolescence and experience a significant late maturation in both linear growth and pubertal development. The aim of the present study was to determine the impact of intensive physical training on the adult final height in elite male AG. This study is unique in character, as all variables were measured on the field of competition. The study was prospective and longitudinal; however, the current analysis of data is cross-sectional. Data from 86 elite male AG were obtained during the gymnastics competitions of European and World Championships. Clinical evaluation included height and weight measurements, as well as assessment of pubic hair and genital development according to Tanner's stages of pubertal development. The laboratory investigation included determination of skeletal maturation. All athletes completed a questionnaire that included questions on personal (onset and intensity of training, number of competitions per year) and family data (paternal and maternal heights). Male AG were below the 50th percentile for both final height and weight. Elite male AG had final height standard deviation score (SDS) lower than their genetic predisposition. Final height SDS was correlated positively with target height SDS (r = 0.430, p < 0.001) and weight SDS (r = 0.477, p < 0.001) and negatively to the intensity of training (r = -0.252, p = 0.022). The main factors influencing final height, by multiple regression analysis were weight SDS (p < 0.001) and target height SDS (p = 0.003). In elite maleAG, final height falls short of genetic predisposition, still well within normal limits. Considering medical and psychological risks in general, and based on the results of this research project, the International Federation of Gymnastics has increased the age limit for participants in international gymnastics competitions by 1 year.

The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents

NASA Astrophysics Data System (ADS)

Valizadeh, Maryam; Sohrabi, Mahmoud Reza

2018-03-01

In the present study, artificial neural networks (ANNs) and support vector regression (SVR) as intelligent methods coupled with UV spectroscopy for simultaneous quantitative determination of Dorzolamide (DOR) and Timolol (TIM) in eye drop. Several synthetic mixtures were analyzed for validating the proposed methods. At first, neural network time series, which one type of network from the artificial neural network was employed and its efficiency was evaluated. Afterwards, the radial basis network was applied as another neural network. Results showed that the performance of this method is suitable for predicting. Finally, support vector regression was proposed to construct the Zilomole prediction model. Also, root mean square error (RMSE) and mean recovery (%) were calculated for SVR method. Moreover, the proposed methods were compared to the high-performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them. Also, the effect of interferences was investigated in spike solutions.

A simple method for processing data with least square method

NASA Astrophysics Data System (ADS)

Wang, Chunyan; Qi, Liqun; Chen, Yongxiang; Pang, Guangning

2017-08-01

The least square method is widely used in data processing and error estimation. The mathematical method has become an essential technique for parameter estimation, data processing, regression analysis and experimental data fitting, and has become a criterion tool for statistical inference. In measurement data analysis, the distribution of complex rules is usually based on the least square principle, i.e., the use of matrix to solve the final estimate and to improve its accuracy. In this paper, a new method is presented for the solution of the method which is based on algebraic computation and is relatively straightforward and easy to understand. The practicability of this method is described by a concrete example.

Nutritional status and weight gain in pregnant women.

PubMed

Sato, Ana Paula Sayuri; Fujimori, Elizabeth

2012-01-01

This study described the nutritional status of 228 pregnant women and the influence of this on birth weight. This is a retrospective study, developed in a health center in the municipality of São Paulo, with data obtained from medical records. Linear regression analysis was carried out. An association was verified between the initial and final nutritional status (p<0.001). The mean of total weight gain in the pregnant women who began the pregnancy underweight was higher compared those who started overweight/obese (p=0.005). Weight gain was insufficient for 43.4% of the pregnant women with adequate initial weight and for 36.4% of all the pregnant women studied. However, 37.1% of those who began the pregnancy overweight/obese finished with excessive weight gain, a condition that ultimately affected almost a quarter of the pregnant women. Anemia and low birth weight were uncommon, however, in the linear regression analysis, birth weight was associated with weight gain (p<0.05). The study highlights the importance of nutritional care before and during pregnancy to promote maternal-infant health.

Monitoring of bone regeneration process by means of texture analysis

NASA Astrophysics Data System (ADS)

Kokkinou, E.; Boniatis, I.; Costaridou, L.; Saridis, A.; Panagiotopoulos, E.; Panayiotakis, G.

2009-09-01

An image analysis method is proposed for the monitoring of the regeneration of the tibial bone. For this purpose, 130 digitized radiographs of 13 patients, who had undergone tibial lengthening by the Ilizarov method, were studied. For each patient, 10 radiographs, taken at an equal number of postoperative successive time moments, were available. Employing available software, 3 Regions Of Interest (ROIs), corresponding to the: (a) upper, (b) central, and (c) lower aspect of the gap, where bone regeneration was expected to occur, were determined on each radiograph. Employing custom developed algorithms: (i) a number of textural features were generated from each of the ROIs, and (ii) a texture-feature based regression model was designed for the quantitative monitoring of the bone regeneration process. Statistically significant differences (p < 0.05) were derived for the initial and the final textural features values, generated from the first and the last postoperatively obtained radiographs, respectively. A quadratic polynomial regression equation fitted data adequately (r2 = 0.9, p < 0.001). The suggested method may contribute to the monitoring of the tibial bone regeneration process.

Prevalence of and risk factors for depressive symptoms among people living with HIV/AIDS receiving antiretroviral treatment in Wuhan, China: a short report.

PubMed

Rong, Hu; Nianhua, Xie; Jun, Xu; Lianguo, Ruan; Si, Wu; Sheng, Wei; Heng, Guo; Xia, Wang

2017-12-01

We aimed to explore the prevalence of and risk factors for depressive symptoms (DS) among people living with HIV/AIDS (PLWHA) receiving antiretroviral treatment (ART) in Wuhan, Hubei, China. A cross-sectional study evaluating adult PLWHA receiving ART in nine designated clinical hospitals was conducted from October to December 2015. The validated Beck Depression Inventory (BDI) was used to assess DS in eligible participants. Socio-demographical, epidemiological and clinical data were directly extracted from the case reporting database of the China HIV/AIDS Information Network. Multinomial regression analysis was used to explore the risk factors for DS. 394 participants were finally included in all analyses. 40.3% were found to have DS with 13.7% having mild DS and 26.6% having moderate to severe DS. The results of multinomial regression analysis suggested that being married or living with a partner, recent experience of ART-related side effects, and/or history of HCV infection were positively associated with mild DS, while increasing age was positively associated with moderate to severe DS.

Seroprevalence and Risk Factors of Chlamydia abortus Infection in Tibetan Sheep in Gansu Province, Northwest China

PubMed Central

Qin, Si-Yuan; Yin, Ming-Yang; Cong, Wei; Zhou, Dong-Hui; Zhang, Xiao-Xuan; Zhao, Quan; Zhu, Xing-Quan; Zhou, Ji-Zhang; Qian, Ai-Dong

2014-01-01

Chlamydia abortus, an important pathogen in a variety of animals, is associated with abortion in sheep. In the present study, 1732 blood samples, collected from Tibetan sheep between June 2013 and April 2014, were examined by the indirect hemagglutination (IHA) test, aiming to evaluate the seroprevalence and risk factors of C. abortus infection in Tibetan sheep. 323 of 1732 (18.65%) samples were seropositive for C. abortus antibodies at the cut-off of 1 : 16. A multivariate logistic regression analysis was used to evaluate the risk factors associated with seroprevalence, which could provide foundation to prevent and control C. abortus infection in Tibetan sheep. Gender of Tibetan sheep was left out of the final model because it is not significant in the logistic regression analysis (P > 0.05). Region, season, and age were considered as major risk factors associated with C. abortus infection in Tibetan sheep. Our study revealed a widespread and high prevalence of C. abortus infection in Tibetan sheep in Gansu province, northwest China, with higher exposure risk in different seasons and ages and distinct geographical distribution. PMID:25401129

A quantitative study of factors influencing quality of life in rural Mexican women diagnosed with HIV.

PubMed

Holtz, Carol; Sowell, Richard; VanBrackle, Lewis; Velasquez, Gabriela; Hernandez-Alonso, Virginia

2014-01-01

This quantitative study explored the level of Quality of Life (QoL) in indigenous Mexican women and identified psychosocial factors that significantly influenced their QoL, using face-to-face interviews with 101 women accessing care in an HIV clinic in Oaxaca, Mexico. Variables included demographic characteristics, levels of depression, coping style, family functioning, HIV-related beliefs, and QoL. Descriptive statistics were used to analyze participant characteristics, and women's scores on data collection instruments. Pearson's R correlational statistics were used to determine the level of significance between study variables. Multiple regression analysis examined all variables that were significantly related to QoL. Pearson's correlational analysis of relationships between Spirituality, Educating Self about HIV, Family Functioning, Emotional Support, Physical Care, and Staying Positive demonstrated positive correlation to QoL. Stigma, depression, and avoidance coping were significantly and negatively associated with QoL. The final regression model indicated that depression and avoidance coping were the best predictor variables for QoL. Copyright © 2014 Association of Nurses in AIDS Care. Published by Elsevier Inc. All rights reserved.

Relationship between body composition and postural control in prepubertal overweight/obese children: A cross-sectional study.

PubMed

Villarrasa-Sapiña, Israel; Álvarez-Pitti, Julio; Cabeza-Ruiz, Ruth; Redón, Pau; Lurbe, Empar; García-Massó, Xavier

2018-02-01

Excess body weight during childhood causes reduced motor functionality and problems in postural control, a negative influence which has been reported in the literature. Nevertheless, no information regarding the effect of body composition on the postural control of overweight and obese children is available. The objective of this study was therefore to establish these relationships. A cross-sectional design was used to establish relationships between body composition and postural control variables obtained in bipedal eyes-open and eyes-closed conditions in twenty-two children. Centre of pressure signals were analysed in the temporal and frequency domains. Pearson correlations were applied to establish relationships between variables. Principal component analysis was applied to the body composition variables to avoid potential multicollinearity in the regression models. These principal components were used to perform a multiple linear regression analysis, from which regression models were obtained to predict postural control. Height and leg mass were the body composition variables that showed the highest correlation with postural control. Multiple regression models were also obtained and several of these models showed a higher correlation coefficient in predicting postural control than simple correlations. These models revealed that leg and trunk mass were good predictors of postural control. More equations were found in the eyes-open than eyes-closed condition. Body weight and height are negatively correlated with postural control. However, leg and trunk mass are better postural control predictors than arm or body mass. Finally, body composition variables are more useful in predicting postural control when the eyes are open. Copyright © 2017 Elsevier Ltd. All rights reserved.

Quality Reporting of Multivariable Regression Models in Observational Studies: Review of a Representative Sample of Articles Published in Biomedical Journals.

PubMed

Real, Jordi; Forné, Carles; Roso-Llorach, Albert; Martínez-Sánchez, Jose M

2016-05-01

Controlling for confounders is a crucial step in analytical observational studies, and multivariable models are widely used as statistical adjustment techniques. However, the validation of the assumptions of the multivariable regression models (MRMs) should be made clear in scientific reporting. The objective of this study is to review the quality of statistical reporting of the most commonly used MRMs (logistic, linear, and Cox regression) that were applied in analytical observational studies published between 2003 and 2014 by journals indexed in MEDLINE.Review of a representative sample of articles indexed in MEDLINE (n = 428) with observational design and use of MRMs (logistic, linear, and Cox regression). We assessed the quality of reporting about: model assumptions and goodness-of-fit, interactions, sensitivity analysis, crude and adjusted effect estimate, and specification of more than 1 adjusted model.The tests of underlying assumptions or goodness-of-fit of the MRMs used were described in 26.2% (95% CI: 22.0-30.3) of the articles and 18.5% (95% CI: 14.8-22.1) reported the interaction analysis. Reporting of all items assessed was higher in articles published in journals with a higher impact factor.A low percentage of articles indexed in MEDLINE that used multivariable techniques provided information demonstrating rigorous application of the model selected as an adjustment method. Given the importance of these methods to the final results and conclusions of observational studies, greater rigor is required in reporting the use of MRMs in the scientific literature.

Emotional intelligence and academic performance of medical undergraduates: a cross-sectional study in a selected university in Sri Lanka.

PubMed

Wijekoon, Chandrani Nirmala; Amaratunge, Heshan; de Silva, Yashica; Senanayake, Solith; Jayawardane, Pradeepa; Senarath, Upul

2017-09-25

Emotional intelligence (EI) has been linked with academic and professional success. Such data are scarce in Sri Lanka. This study was conducted to describe the pattern of EI, to determine its predictors and to determine the effect of EI on academic performance at the final MBBS examination, in medical undergraduates of a Sri Lankan university. This is a cross-sectional study in a selected university, involving those who did final MBBS examination in 2016. Consecutive sampling was done. EI was assessed with self-administered Genos Emotional Intelligence Full Version (7 domains; 70 questions equally weighted; total score 350). Socio-demographic data were obtained using a self-administered questionnaire. Academic performance was assessed with final MBBS results in the first attempt. Of 148 eligible students 130 responded (response rate-88%); 61.5% were females; mean age was 26.3 ± 1 years. Mean total EI score was 241.5 (females-245.5, males-235.1; p = 0.045).Among different domains, mean score was highest for Emotional Self-Awareness (36.8/50) and lowest for Emotional Expression (32.6/50). Multiple linear regression analysis indicated that having good family support (p = 0.002), socializing well in university (p = 0.024) and being satisfied with facilities available for learning (p = 0.002), were independent predictors of EI. At the final MBBS examination 51.6% obtained classes, 31.5% passed the examination without classes and 16.9% got repeated. Females had better academic performance than males (p = 0.009). Mean EI of second-class upper division, second-class lower division, pass and repeat groups were 249.4, 246.6, 240.2 and 226.9, respectively (with one-way ANOVA p = 0.015). After adjusting for gender, ordinal regression analysis indicated that, total EI score was an independent predictor of final MBBS results [β-0.018 (95% CI 0.005-0.031); p = 0.006]. In the study population, both EI and academic performance were higher among females. Independent of gender, academic performance was better in those who were more emotionally intelligent. Several psychosocial factors were found to be independent predictors of EI. These results suggest that emotional skills development might enhance academic performance of medical undergraduates in Sri Lanka. Further research is needed in this under-explored area.

Estimating flood magnitude and frequency at gaged and ungaged sites on streams in Alaska and conterminous basins in Canada, based on data through water year 2012

USGS Publications Warehouse

Curran, Janet H.; Barth, Nancy A.; Veilleux, Andrea G.; Ourso, Robert T.

2016-03-16

Estimates of the magnitude and frequency of floods are needed across Alaska for engineering design of transportation and water-conveyance structures, flood-insurance studies, flood-plain management, and other water-resource purposes. This report updates methods for estimating flood magnitude and frequency in Alaska and conterminous basins in Canada. Annual peak-flow data through water year 2012 were compiled from 387 streamgages on unregulated streams with at least 10 years of record. Flood-frequency estimates were computed for each streamgage using the Expected Moments Algorithm to fit a Pearson Type III distribution to the logarithms of annual peak flows. A multiple Grubbs-Beck test was used to identify potentially influential low floods in the time series of peak flows for censoring in the flood frequency analysis.For two new regional skew areas, flood-frequency estimates using station skew were computed for stations with at least 25 years of record for use in a Bayesian least-squares regression analysis to determine a regional skew value. The consideration of basin characteristics as explanatory variables for regional skew resulted in improvements in precision too small to warrant the additional model complexity, and a constant model was adopted. Regional Skew Area 1 in eastern-central Alaska had a regional skew of 0.54 and an average variance of prediction of 0.45, corresponding to an effective record length of 22 years. Regional Skew Area 2, encompassing coastal areas bordering the Gulf of Alaska, had a regional skew of 0.18 and an average variance of prediction of 0.12, corresponding to an effective record length of 59 years. Station flood-frequency estimates for study sites in regional skew areas were then recomputed using a weighted skew incorporating the station skew and regional skew. In a new regional skew exclusion area outside the regional skew areas, the density of long-record streamgages was too sparse for regional analysis and station skew was used for all estimates. Final station flood frequency estimates for all study streamgages are presented for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities.Regional multiple-regression analysis was used to produce equations for estimating flood frequency statistics from explanatory basin characteristics. Basin characteristics, including physical and climatic variables, were updated for all study streamgages using a geographical information system and geospatial source data. Screening for similar-sized nested basins eliminated hydrologically redundant sites, and screening for eligibility for analysis of explanatory variables eliminated regulated peaks, outburst peaks, and sites with indeterminate basin characteristics. An ordinary least‑squares regression used flood-frequency statistics and basin characteristics for 341 streamgages (284 in Alaska and 57 in Canada) to determine the most suitable combination of basin characteristics for a flood-frequency regression model and to explore regional grouping of streamgages for explaining variability in flood-frequency statistics across the study area. The most suitable model for explaining flood frequency used drainage area and mean annual precipitation as explanatory variables for the entire study area as a region. Final regression equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability discharge in Alaska and conterminous basins in Canada were developed using a generalized least-squares regression. The average standard error of prediction for the regression equations for the various annual exceedance probabilities ranged from 69 to 82 percent, and the pseudo-coefficient of determination (pseudo-R2) ranged from 85 to 91 percent.The regional regression equations from this study were incorporated into the U.S. Geological Survey StreamStats program for a limited area of the State—the Cook Inlet Basin. StreamStats is a national web-based geographic information system application that facilitates retrieval of streamflow statistics and associated information. StreamStats retrieves published data for gaged sites and, for user-selected ungaged sites, delineates drainage areas from topographic and hydrographic data, computes basin characteristics, and computes flood frequency estimates using the regional regression equations.

Dimensional analysis of detrimental ozone generation by positive wire-to-plate corona discharge in air

NASA Astrophysics Data System (ADS)

Bo, Z.; Chen, J. H.

2010-02-01

The dimensional analysis technique is used to formulate a correlation between ozone generation rate and various parameters that are important in the design and operation of positive wire-to-plate corona discharges in indoor air. The dimensionless relation is determined by linear regression analysis based on the results from 36 laboratory-scale experiments. The derived equation is validated by experimental data and a numerical model published in the literature. Applications of such derived equation are illustrated through an example selection of the appropriate set of operating conditions in the design/operation of a photocopier to follow the federal regulations of ozone emission. Finally, a new current-voltage characteristic equation is proposed for positive wire-to-plate corona discharges based on the derived dimensionless equation.

Principal component regression analysis with SPSS.

PubMed

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

2003-06-01

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

Assessing student understanding of measurement and uncertainty

NASA Astrophysics Data System (ADS)

Jirungnimitsakul, S.; Wattanakasiwich, P.

2017-09-01

The objectives of this study were to develop and assess student understanding of measurement and uncertainty. A test has been adapted and translated from the Laboratory Data Analysis Instrument (LDAI) test, consists of 25 questions focused on three topics including measures of central tendency, experimental errors and uncertainties, and fitting regression lines. The test was evaluated its content validity by three physics experts in teaching physics laboratory. In the pilot study, Thai LDAI was administered to 93 freshmen enrolled in a fundamental physics laboratory course. The final draft of the test was administered to three groups—45 freshmen taking fundamental physics laboratory, 16 sophomores taking intermediated physics laboratory and 21 juniors taking advanced physics laboratory at Chiang Mai University. As results, we found that the freshmen had difficulties in experimental errors and uncertainties. Most students had problems with fitting regression lines. These results will be used to improve teaching and learning physics laboratory for physics students in the department.

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

PubMed Central

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

2015-01-01

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

Short and long-term career plans of final year dental students in the United Arab Emirates.

PubMed

Rashid, Hazim H; Ghotane, Swapnil G; Abufanas, Salem H; Gallagher, Jennifer E

2013-08-13

New dental schools have been established to train dentists in many parts of the world. This study examines the future dental workforce from the first dental school in the United Arab Emirates [UAE]; the aim of this study was to explore the short and long-term career aspirations of the final year dental students in the UAE in relation to their demography. Final year dental students of the Ajman University's College of Dentistry (n=87) were invited to participate in a self-completion questionnaire survey. Descriptive analysis, chi-square tests, and binary logistic regression analysis were carried out on career aspirations using SPSS v20. Eighty-two percent of students (n=71) responded, the majority of whom were female (65%; n=46). Ethnicity was reported as: 'other Arab' (61%; n=43), 'Emirati' (17%, n=12), and 'Other' (21%, n=15). In the short-term, 41% (n=29) expressed a desire to work in government training centres, with Emirati students significantly more likely to do so (p=0.002). 'Financial stability' (80%; n=57) and 'gaining professional experience' (76%; n=54) emerged as the most important influences on their short-term career plans. The vast majority of students wished to specialise in dentistry (92%; n=65) in the longer term; logistic regression analysis revealed that the odds of specialising in the most popular specialties of Orthodontics and Oral and Maxillofacial Surgery were less for the 'Other' ethnic group when compared with 'Emirati' students (0.26; 95% CI 0.068-0.989; p=0.04). Almost three-quarters of the students overall (72%; n=51) intended to work full-time. 'High income/financial security' (97%; n=69), 'standard of living' (97%; n=69), 'work/life balance' (94%; n=67), and 'professional fulfilment' (87%; n=62) were reported by the students as the most influential items affecting their long-term professional career choices. The findings suggest that students aspire to make a long-term contribution to the profession and there is a high level of interest in specialisation with a desire to achieve financial stability and quality of life.

Army College Fund Cost-Effectiveness Study

DTIC Science & Technology

1990-11-01

Section A.2 presents a theory of enlistment supply to provide a basis for specifying the regression model , The model Is specified in Section A.3, which...Supplementary materials are included in the final four sections. Section A.6 provides annual trends in the regression model variables. Estimates of the model ...millions, A.S. ESTIMATION OF A YOUTH EARNINGS FORECASTING MODEL Civilian pay is an important explanatory variable in the regression model . Previous

Educational intervention on physical restraint use in long-term care facilities - Systematic review and meta-analysis.

PubMed

Lan, Shao-Huan; Lu, Li-Chin; Lan, Shou-Jen; Chen, Jong-Chen; Wu, Wen-Jun; Chang, Shen-Peng; Lin, Long-Yau

2017-08-01

"Physical restraint" formerly used as a measure of protection for psychiatric patients is now widely used. However, existing studies showed that physical restraint not only has inadequate effect of protection but also has negative effects on residents. To analyzes the impact of educational program on the physical restraint use in long-term care facilities. A systematic review with meta-analysis and meta-regression. Eight databases, including Cochrane Library, ProQuest, PubMed, EMBASE, EBSCO, Web of Science, Ovid Medline and Physiotherapy Evidence Database (PEDro), were searched up to January 2017. Eligible studies were classified by intervention and accessed for quality using the Quality Assessment Tool for quantitative studies. Sixteen research articles were eligible in the final review; 10 randomize control trail studies were included in the analysis. The meta-analysis revealed that the use of physical restraint was significantly less often in the experimental (education) group (OR = 0.55, 95% CI: 0.39 to 0.78, p < 0.001) compared to the control group. Meta-regression revealed the period of post education would have decreased the effect of the restraint educational program (β: 0.08, p = 0.002); instead, the longer education period and more times of education would have a stronger effect of reducing the use of physical restraint (β: -0.07, p < 0.001; β: -0.04, p = 0.056). The educational program had an effect on the reduced use of physical restraint. The results of meta-regression suggest that long-term care facilities should provide a continuous education program of physical restraint for caregivers. Copyright © 2017. Published by Elsevier Taiwan.

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

PubMed

Shteingart, Hanan; Loewenstein, Yonatan

2016-01-01

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

Flippin' Fluid Mechanics - Quasi-experimental Pre-test and Post-test Comparison Using Two Groups

NASA Astrophysics Data System (ADS)

Webster, D. R.; Majerich, D. M.; Luo, J.

2014-11-01

A flipped classroom approach has been implemented in an undergraduate fluid mechanics course. Students watch short on-line videos before class, participate in active in-class problem solving (in dyads), and complete individualized on-line quizzes weekly. In-class activities are designed to achieve a trifecta of: 1. developing problem solving skills, 2. learning subject content, and 3. developing inquiry skills. The instructor and assistants provide critical ``just-in-time tutoring'' during the in-class problem solving sessions. Comparisons are made with a simultaneous section offered in a traditional mode by a different instructor. Regression analysis was used to control for differences among students and to quantify the effect of the flipped fluid mechanics course. The dependent variable was the students' combined final examination and post-concept inventory scores and the independent variables were pre-concept inventory score, gender, major, course section, and (incoming) GPA. The R-square equaled 0.45 indicating that the included variables explain 45% of the variation in the dependent variable. The regression results indicated that if the student took the flipped fluid mechanics course, the dependent variable (i.e., combined final exam and post-concept inventory scores) was raised by 7.25 points. Interestingly, the comparison group reported significantly more often that their course emphasized memorization than did the flipped classroom group.

Increasing maternal healthcare use in Rwanda: implications for child nutrition and survival.

PubMed

Pierce, Hayley; Heaton, Tim B; Hoffmann, John

2014-04-01

Rwanda has made great progress in improving maternal utilization of health care through coordination of external aid and more efficient health policy. Using data from the 2005 and 2010 Rwandan Demographic and Health Surveys, we examine three related questions regarding the impact of expansion of health care in Rwanda. First, did the increased use of health center deliveries apply to women across varying levels of education, economic status, and area of residency? Second, did the benefits associated with being delivered at a health center diminish as utilization became more widespread? Finally, did inequality in child outcomes decline as a result of increased health care utilization? Propensity score matching was used to address the selectivity that arises when choosing to deliver at a hospital. In addition, the regression models include a linear model to predict child nutritional status and Cox regression to predict child survival. The analysis shows that the largest increases in delivery at a health center occur among less educated, less wealthy, and rural Rwandan women. In addition, delivery at a health center is associated with better nutritional status and survival and the benefit is not diminished following the dramatic increase in use of health centers. Finally, educational, economic and residential inequality in child survival and nutrition did not decline. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Development of equations to predict the influence of floor space on average daily gain, average daily feed intake and gain : feed ratio of finishing pigs.

PubMed

Flohr, J R; Dritz, S S; Tokach, M D; Woodworth, J C; DeRouchey, J M; Goodband, R D

2018-05-01

Floor space allowance for pigs has substantial effects on pig growth and welfare. Data from 30 papers examining the influence of floor space allowance on the growth of finishing pigs was used in a meta-analysis to develop alternative prediction equations for average daily gain (ADG), average daily feed intake (ADFI) and gain : feed ratio (G : F). Treatment means were compiled in a database that contained 30 papers for ADG and 28 papers for ADFI and G : F. The predictor variables evaluated were floor space (m2/pig), k (floor space/final BW0.67), Initial BW, Final BW, feed space (pigs per feeder hole), water space (pigs per waterer), group size (pigs per pen), gender, floor type and study length (d). Multivariable general linear mixed model regression equations were used. Floor space treatments within each experiment were the observational and experimental unit. The optimum equations to predict ADG, ADFI and G : F were: ADG, g=337.57+(16 468×k)-(237 350×k 2)-(3.1209×initial BW (kg))+(2.569×final BW (kg))+(71.6918×k×initial BW (kg)); ADFI, g=833.41+(24 785×k)-(388 998×k 2)-(3.0027×initial BW (kg))+(11.246×final BW (kg))+(187.61×k×initial BW (kg)); G : F=predicted ADG/predicted ADFI. Overall, the meta-analysis indicates that BW is an important predictor of ADG and ADFI even after computing the constant coefficient k, which utilizes final BW in its calculation. This suggests including initial and final BW improves the prediction over using k as a predictor alone. In addition, the analysis also indicated that G : F of finishing pigs is influenced by floor space allowance, whereas individual studies have concluded variable results.

Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries

PubMed Central

Fan, Shou-Zen; Abbod, Maysam F.

2018-01-01

Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the proposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels. Furthermore, the Random Forest output was extensively linear in relation to BIS, thus with better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients’ anaesthetic level during surgeries. PMID:29844970

Information-theoretic metric as a tool to investigate nonclassical correlations

NASA Astrophysics Data System (ADS)

Rudolph, Alexander L.; Lamine, Brahim; Joyce, Michael; Vignolles, Hélène; Consiglio, David

2014-06-01

We report on a project to introduce interactive learning strategies (ILS) to physics classes at the Université Pierre et Marie Curie, one of the leading science universities in France. In Spring 2012, instructors in two large introductory classes, first-year, second-semester mechanics, and second-year introductory electricity and magnetism, enrolling approximately 500 and 250 students, respectively, introduced ILS into some, but not all, of the sections of each class. The specific ILS utilized were think-pair-share questions and Peer Instruction in the main lecture classrooms, and University of Washington Tutorials for Introductory Physics in recitation sections. Pre- and postinstruction assessments [Force Concept Inventory (FCI) and Conceptual Survey of Electricity and Magnetism (CSEM), respectively] were given, along with a series of demographic questions. Since not all lecture or recitation sections in these classes used ILS, we were able to compare the results of the FCI and CSEM between interactive and noninteractive classes taught simultaneously with the same curriculum. We also analyzed final exam results, as well as the results of student and instructor attitude surveys between classes. In our analysis, we argue that multiple linear regression modeling is superior to other common analysis tools, including normalized gain. Our results show that ILS are effective at improving student learning by all measures used: research-validated concept inventories and final exam scores, on both conceptual and traditional problem-solving questions. Multiple linear regression analysis reveals that interactivity in the classroom is a significant predictor of student learning, showing a similar or stronger relationship with student learning than such ascribed characteristics as parents’ education, and achieved characteristics such as grade point average and hours studied per week. Analysis of student and instructor attitudes shows that both groups believe that ILS improve student learning in the physics classroom and increase student engagement and motivation. All of the instructors who used ILS in this study plan to continue their use.

For public service or money: understanding geographical imbalances in the health workforce.

PubMed

Serneels, Pieter; Lindelow, Magnus; Montalvo, Jose G; Barr, Abigail

2007-05-01

Geographical imbalances in the health workforce have been a consistent feature of nearly all health systems, and especially in developing countries. In this paper we investigate the willingness to work in a rural area among final year nursing and medical students in Ethiopia. Analysing data obtained from contingent valuation questions for final year students from three medical schools and eight nursing schools, we find that there is substantial heterogeneity in the willingness to serve in rural areas. Using both ordinary least squares and maximum likelihood regression analysis, we find that household consumption and the student's motivation to help the poor are the main determinants of willingness to work in a rural area. We carry out a simulation on how much it would cost to get a target proportion of health workers to take up a rural post.

Regression Analysis by Example. 5th Edition

ERIC Educational Resources Information Center

Chatterjee, Samprit; Hadi, Ali S.

2012-01-01

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…

On Becoming Trauma-Informed: Role of the Adverse Childhood Experiences Survey in Tertiary Child and Adolescent Mental Health Services and the Association with Standard Measures of Impairment and Severity.

PubMed

Rahman, Abdul; Perri, Andrea; Deegan, Avril; Kuntz, Jennifer; Cawthorpe, David

2018-01-01

There is a movement toward trauma-informed, trauma-focused psychiatric treatment. To examine Adverse Childhood Experiences (ACE) survey items by sex and by total scores by sex vs clinical measures of impairment to examine the clinical utility of the ACE survey as an index of trauma in a child and adolescent mental health care setting. Descriptive, polychoric factor analysis and regression analyses were employed to analyze cross-sectional ACE surveys (N = 2833) and registration-linked data using past admissions (N = 10,400) collected from November 2016 to March 2017 related to clinical data (28 independent variables), taking into account multicollinearity. Distinct ACE items emerged for males, females, and those with self-identified sex and for ACE total scores in regression analysis. In hierarchical regression analysis, the final models consisting of standard clinical measures and demographic and system variables (eg, repeated admissions) were associated with substantial ACE total score variance for females (44%) and males (38%). Inadequate sample size foreclosed on developing a reduced multivariable model for the self-identified sex group. The ACE scores relate to independent clinical measures and system and demographic variables. There are implications for clinical practice. For example, a child presenting with anxiety and a high ACE score likely requires treatment that is different from a child presenting with anxiety and an ACE score of zero. The ACE survey score is an important index of presenting clinical status that guides patient care planning and intervention in the progress toward a trauma-focused system of care.

A Development of Nonstationary Regional Frequency Analysis Model with Large-scale Climate Information: Its Application to Korean Watershed

NASA Astrophysics Data System (ADS)

Kim, Jin-Young; Kwon, Hyun-Han; Kim, Hung-Soo

2015-04-01

The existing regional frequency analysis has disadvantages in that it is difficult to consider geographical characteristics in estimating areal rainfall. In this regard, this study aims to develop a hierarchical Bayesian model based nonstationary regional frequency analysis in that spatial patterns of the design rainfall with geographical information (e.g. latitude, longitude and altitude) are explicitly incorporated. This study assumes that the parameters of Gumbel (or GEV distribution) are a function of geographical characteristics within a general linear regression framework. Posterior distribution of the regression parameters are estimated by Bayesian Markov Chain Monte Carlo (MCMC) method, and the identified functional relationship is used to spatially interpolate the parameters of the distributions by using digital elevation models (DEM) as inputs. The proposed model is applied to derive design rainfalls over the entire Han-river watershed. It was found that the proposed Bayesian regional frequency analysis model showed similar results compared to L-moment based regional frequency analysis. In addition, the model showed an advantage in terms of quantifying uncertainty of the design rainfall and estimating the area rainfall considering geographical information. Finally, comprehensive discussion on design rainfall in the context of nonstationary will be presented. KEYWORDS: Regional frequency analysis, Nonstationary, Spatial information, Bayesian Acknowledgement This research was supported by a grant (14AWMP-B082564-01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

How the study of online collaborative learning can guide teachers and predict students' performance in a medical course.

PubMed

Saqr, Mohammed; Fors, Uno; Tedre, Matti

2018-02-06

Collaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. Although the benefits of collaborative learning have long been recognized, it is still rarely studied by social network analysis (SNA) in medical education, and the relationship of parameters that can be obtained via SNA with students' performance remains largely unknown. The aim of this work was to assess the potential of SNA for studying online collaborative clinical case discussions in a medical course and to find out which activities correlate with better performance and help predict final grade or explain variance in performance. Interaction data were extracted from the learning management system (LMS) forum module of the Surgery course in Qassim University, College of Medicine. The data were analyzed using social network analysis. The analysis included visual as well as a statistical analysis. Correlation with students' performance was calculated, and automatic linear regression was used to predict students' performance. By using social network analysis, we were able to analyze a large number of interactions in online collaborative discussions and gain an overall insight of the course social structure, track the knowledge flow and the interaction patterns, as well as identify the active participants and the prominent discussion moderators. When augmented with calculated network parameters, SNA offered an accurate view of the course network, each user's position, and level of connectedness. Results from correlation coefficients, linear regression, and logistic regression indicated that a student's position and role in information relay in online case discussions, combined with the strength of that student's network (social capital), can be used as predictors of performance in relevant settings. By using social network analysis, researchers can analyze the social structure of an online course and reveal important information about students' and teachers' interactions that can be valuable in guiding teachers, improve students' engagement, and contribute to learning analytics insights.

A regression-kriging model for estimation of rainfall in the Laohahe basin

NASA Astrophysics Data System (ADS)

Wang, Hong; Ren, Li L.; Liu, Gao H.

2009-10-01

This paper presents a multivariate geostatistical algorithm called regression-kriging (RK) for predicting the spatial distribution of rainfall by incorporating five topographic/geographic factors of latitude, longitude, altitude, slope and aspect. The technique is illustrated using rainfall data collected at 52 rain gauges from the Laohahe basis in northeast China during 1986-2005 . Rainfall data from 44 stations were selected for modeling and the remaining 8 stations were used for model validation. To eliminate multicollinearity, the five explanatory factors were first transformed using factor analysis with three Principal Components (PCs) extracted. The rainfall data were then fitted using step-wise regression and residuals interpolated using SK. The regression coefficients were estimated by generalized least squares (GLS), which takes the spatial heteroskedasticity between rainfall and PCs into account. Finally, the rainfall prediction based on RK was compared with that predicted from ordinary kriging (OK) and ordinary least squares (OLS) multiple regression (MR). For correlated topographic factors are taken into account, RK improves the efficiency of predictions. RK achieved a lower relative root mean square error (RMSE) (44.67%) than MR (49.23%) and OK (73.60%) and a lower bias than MR and OK (23.82 versus 30.89 and 32.15 mm) for annual rainfall. It is much more effective for the wet season than for the dry season. RK is suitable for estimation of rainfall in areas where there are no stations nearby and where topography has a major influence on rainfall.

Latent transition analysis of pre-service teachers' efficacy in mathematics and science

NASA Astrophysics Data System (ADS)

Ward, Elizabeth Kennedy

This study modeled changes in pre-service teacher efficacy in mathematics and science over the course of the final year of teacher preparation using latent transition analysis (LTA), a longitudinal form of analysis that builds on two modeling traditions (latent class analysis (LCA) and auto-regressive modeling). Data were collected using the STEBI-B, MTEBI-r, and the ABNTMS instruments. The findings suggest that LTA is a viable technique for use in teacher efficacy research. Teacher efficacy is modeled as a construct with two dimensions: personal teaching efficacy (PTE) and outcome expectancy (OE). Findings suggest that the mathematics and science teaching efficacy (PTE) of pre-service teachers is a multi-class phenomena. The analyses revealed a four-class model of PTE at the beginning and end of the final year of teacher training. Results indicate that when pre-service teachers transition between classes, they tend to move from a lower efficacy class into a higher efficacy class. In addition, the findings suggest that time-varying variables (attitudes and beliefs) and time-invariant variables (previous coursework, previous experiences, and teacher perceptions) are statistically significant predictors of efficacy class membership. Further, analyses suggest that the measures used to assess outcome expectancy are not suitable for LCA and LTA procedures.

The impact of pneumatic tube system on routine laboratory parameters: a systematic review and meta-analysis.

PubMed

Kapoula, Georgia V; Kontou, Panagiota I; Bagos, Pantelis G

2017-10-26

Pneumatic tube system (PTS) is a widely used method of transporting blood samples in hospitals. The aim of this study was to evaluate the effects of the PTS transport in certain routine laboratory parameters as it has been implicated with hemolysis. A systematic review and a meta-analysis were conducted. PubMed and Scopus databases were searched (up until November 2016) to identify prospective studies evaluating the impact of PTS transport in hematological, biochemical and coagulation measurements. The random-effects model was used in the meta-analysis utilizing the mean difference (MD). Heterogeneity was quantitatively assessed using the Cohran's Q and the I2 index. Subgroup analysis, meta-regression analysis, sensitivity analysis, cumulative meta-analysis and assessment of publication bias were performed for all outcomes. From a total of 282 studies identified by the searching procedure, 24 were finally included in the meta-analysis. The meta-analysis yielded statistically significant results for potassium (K) [MD=0.04 mmol/L; 95% confidence interval (CI)=0.015-0.065; p=0.002], lactate dehydrogenase (LDH) (MD=10.343 U/L; 95% CI=6.132-14.554; p<10-4) and aspartate aminotransferase (AST) (MD=1.023 IU/L; 95% CI=0.344-1.702; p=0.003). Subgroup analysis and random-effects meta-regression analysis according to the speed and distance of the samples traveled via the PTS revealed that there is relation between the rate and the distance of PTS with the measurements of K, LDH, white blood cells and red blood cells. This meta-analysis suggests that PTS may be associated with alterations in K, LDH and AST measurements. Although these findings may not have any significant clinical effect on laboratory results, it is wise that each hospital validates their PTS.

The association between meteorological factors and road traffic injuries: a case analysis from Shantou city, China

PubMed Central

Gao, Jinghong; Chen, Xiaojun; Woodward, Alistair; Liu, Xiaobo; Wu, Haixia; Lu, Yaogui; Li, Liping; Liu, Qiyong

2016-01-01

Few studies examined the associations of meteorological factors with road traffic injuries (RTIs). The purpose of the present study was to quantify the contributions of meteorological factors to RTI cases treated at a tertiary level hospital in Shantou city, China. A time-series diagram was employed to illustrate the time trends and seasonal variation of RTIs, and correlation analysis and multiple linear regression analysis were conducted to investigate the relationships between meteorological parameters and RTIs. RTIs followed a seasonal pattern as more cases occurred during summer and winter months. RTIs are positively correlated with temperature and sunshine duration, while negatively associated with wind speed. Temperature, sunshine hour and wind speed were included in the final linear model with regression coefficients of 0.65 (t = 2.36, P = 0.019), 2.23 (t = 2.72, P = 0.007) and −27.66 (t = −5.67, P < 0.001), respectively, accounting for 19.93% of the total variation of RTI cases. The findings can help us better understand the associations between meteorological factors and RTIs, and with potential contributions to the development and implementation of regional level evidence-based weather-responsive traffic management system in the future. PMID:27853316

H. Pylori as a predictor of marginal ulceration: A nationwide analysis.

PubMed

Schulman, Allison R; Abougergi, Marwan S; Thompson, Christopher C

2017-03-01

Helicobacter pylori has been implicated as a risk factor for development of marginal ulceration following gastric bypass, although studies have been small and yielded conflicting results. This study sought to determine the relationship between H. pylori infection and development of marginal ulceration following bariatric surgery in a nationwide analysis. This was a retrospective cohort study using the 2012 Nationwide Inpatient Sample (NIS) database. Discharges with ICD-9-CM code indicating marginal ulceration and a secondary ICD-9-CM code for bariatric surgery were included. Primary outcome was incidence of marginal ulceration. A stepwise forward selection model was used to build the multivariate logistic regression model based on known risk factors. A P value of 0.05 was considered significant. There were 253,765 patients who met inclusion criteria. Prevalence of marginal ulceration was 3.90%. Of those patients found to have marginal ulceration, 31.20% of patients were H. pylori-positive. Final multivariate regression analysis revealed that H. pylori was the strongest independent predictor of marginal ulceration. H. pylori is an independent predictor of marginal ulceration using a large national database. Preoperative testing for and eradication of H. pylori prior to bariatric surgery may be an important preventive measure to reduce the incidence of ulcer development. © 2017 The Obesity Society.

Exploring the relationships between free-time management and boredom in leisure.

PubMed

Wang, Wei-Ching; Wu, Chung-Chi; Wu, Chang-Yang; Huan, Tzung-Cheng

2012-04-01

The purpose of the study was to examine the relations of five dimensions of free-time management (including goal setting and evaluating, technique, values, immediate response, and scheduling) with leisure boredom, and whether these factors could predict leisure boredom. A total of 500 undergraduates from a university in southern Taiwan were surveyed with 403 usable questionnaires was returned. Pearson correlation analysis revealed that five dimensions of free-time management had significant negative relationships with leisure boredom. Furthermore, the results of stepwise regression analysis revealed that four dimensions of free-time management were significant contributors to leisure boredom. Finally, we suggested students can avoid boredom by properly planning and organizing leisure time and applying techniques for managing leisure time.

Evaluation of driver fatigue on two channels of EEG data.

PubMed

Li, Wei; He, Qi-chang; Fan, Xiu-min; Fei, Zhi-min

2012-01-11

Electroencephalogram (EEG) data is an effective indicator to evaluate driver fatigue. The 16 channels of EEG data are collected and transformed into three bands (θ, α, and β) in the current paper. First, 12 types of energy parameters are computed based on the EEG data. Then, Grey Relational Analysis (GRA) is introduced to identify the optimal indicator of driver fatigue, after which, the number of significant electrodes is reduced using Kernel Principle Component Analysis (KPCA). Finally, the evaluation model for driver fatigue is established with the regression equation based on the EEG data from two significant electrodes (Fp1 and O1). The experimental results verify that the model is effective in evaluating driver fatigue. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Analysis of perceived risk among construction workers: a cross-cultural study and reflection on the Hofstede model.

PubMed

Martinez-Fiestas, Myriam; Rodríguez-Garzón, Ignacio; Delgado-Padial, Antonio; Lucas-Ruiz, Valeriano

2017-09-01

This article presents a cross-cultural study on perceived risk in the construction industry. Worker samples from three different countries were studied: Spain, Peru and Nicaragua. The main goal was to explain how construction workers perceive their occupational hazard and to analyze how this is related to their national culture. The model used to measure perceived risk was the psychometric paradigm. The results show three very similar profiles, indicating that risk perception is independent of nationality. A cultural analysis was conducted using the Hofstede model. The results of this analysis and the relation to perceived risk showed that risk perception in construction is independent of national culture. Finally, a multiple lineal regression analysis was conducted to determine what qualitative attributes could predict the global quantitative size of risk perception. All of the findings have important implications regarding the management of safety in the workplace.

Complexities and potential pitfalls of clinical study design and data analysis in assisted reproduction.

PubMed

Patounakis, George; Hill, Micah J

2018-06-01

The purpose of the current review is to describe the common pitfalls in design and statistical analysis of reproductive medicine studies. It serves to guide both authors and reviewers toward reducing the incidence of spurious statistical results and erroneous conclusions. The large amount of data gathered in IVF cycles leads to problems with multiplicity, multicollinearity, and over fitting of regression models. Furthermore, the use of the word 'trend' to describe nonsignificant results has increased in recent years. Finally, methods to accurately account for female age in infertility research models are becoming more common and necessary. The pitfalls of study design and analysis reviewed provide a framework for authors and reviewers to approach clinical research in the field of reproductive medicine. By providing a more rigorous approach to study design and analysis, the literature in reproductive medicine will have more reliable conclusions that can stand the test of time.

Air Pollutants, Climate, and the Prevalence of Pediatric Asthma in Urban Areas of China

PubMed Central

Zhang, Juanjuan; Yan, Li; Fu, Wenlong; Yi, Jing; Chen, Yuzhi; Liu, Chuanhe; Xu, Dongqun; Wang, Qiang

2016-01-01

Background. Prevalence of childhood asthma varies significantly among regions, while its reasons are not clear yet with only a few studies reporting relevant causes for this variation. Objective. To investigate the potential role of city-average levels of air pollutants and climatic factors in order to distinguish differences in asthma prevalence in China and explain their reasons. Methods. Data pertaining to 10,777 asthmatic patients were obtained from the third nationwide survey of childhood asthma in China's urban areas. Annual mean concentrations of air pollutants and other climatic factors were obtained for the same period from several government departments. Data analysis was implemented with descriptive statistics, Pearson correlation coefficient, and multiple regression analysis. Results. Pearson correlation analysis showed that the situation of childhood asthma was strongly linked with SO2, relative humidity, and hours of sunshine (p < 0.05). Multiple regression analysis indicated that, among the predictor variables in the final step, SO2 was found to be the most powerful predictor variable amongst all (β = −19.572, p < 0.05). Furthermore, results had shown that hours of sunshine (β = −0.014, p < 0.05) was a significant component summary predictor variable. Conclusion. The findings of this study do not suggest that air pollutants or climate, at least in terms of children, plays a major role in explaining regional differences in asthma prevalence in China. PMID:27556031

Factors Associated with Salmonella Prevalence in U.S. Swine Grower-Finisher Operations, 2012.

PubMed

Bjork, Kathe E; Fields, Victoria; Garber, Lindsey P; Kopral, Christine A

2018-05-15

Nontyphoidal Salmonella is an important foodborne pathogen with diverse serotypes occurring in animal and human populations. The prevalence of the organism on swine farms has been associated with numerous risk factors, and although there are strong veterinary public health controls for preventing Salmonella from entering food, there remains interest in eradicating or controlling the organism in the preharvest environment. In this study, using data collected via the U.S. Department of Agriculture (USDA) National Animal Health Monitoring System Swine 2012 study, we describe nontyphoidal Salmonella and specific serotype prevalence on U.S. grower-finisher swine operations and investigate associations between Salmonella detection and numerous factors via multiple correspondence analysis (MCA) and regression analysis. MCA plots, complementary to univariate analyses, display relationships between covariates and Salmonella detection at the farm level. In the univariate analysis, Salmonella detection varied with feed characteristics and farm management practices, reports of diseases on farms and vaccinations administered, and administration of certain antimicrobials. Results from the univariate analysis reinforce the importance of biosecurity in managing diseases and pathogens such as Salmonella on farms. All multivariable regression models for the likelihood of Salmonella detection were strongly affected by multicollinearity among variables, and only one variable, pelleted feed preparation, remained in the final model. The study was limited by its cross-sectional nature, timelines of data collection, and reliance on operator-reported data via a convenience sample.

Paramedic-Initiated Home Care Referrals and Use of Home Care and Emergency Medical Services.

PubMed

Verma, Amol A; Klich, John; Thurston, Adam; Scantlebury, Jordan; Kiss, Alex; Seddon, Gayle; Sinha, Samir K

2018-01-01

We examined the association between paramedic-initiated home care referrals and utilization of home care, 9-1-1, and Emergency Department (ED) services. This was a retrospective cohort study of individuals who received a paramedic-initiated home care referral after a 9-1-1 call between January 1, 2011 and December 31, 2012 in Toronto, Ontario, Canada. Home care, 9-1-1, and ED utilization were compared in the 6 months before and after home care referral. Nonparametric longitudinal regression was performed to assess changes in hours of home care service use and zero-inflated Poisson regression was performed to assess changes in the number of 9-1-1 calls and ambulance transports to ED. During the 24-month study period, 2,382 individuals received a paramedic-initiated home care referral. After excluding individuals who died, were hospitalized, or were admitted to a nursing home, the final study cohort was 1,851. The proportion of the study population receiving home care services increased from 18.2% to 42.5% after referral, representing 450 additional people receiving services. In longitudinal regression analysis, there was an increase of 17.4 hours in total services per person in the six months after referral (95% CI: 1.7-33.1, p = 0.03). The mean number of 9-1-1 calls per person was 1.44 (SD 9.58) before home care referral and 1.20 (SD 7.04) after home care referral in the overall study cohort. This represented a 10% reduction in 9-1-1 calls (95% CI: 7-13%, p < 0.001) in Poisson regression analysis. The mean number of ambulance transports to ED per person was 0.91 (SD 8.90) before home care referral and 0.79 (SD 6.27) after home care referral, representing a 7% reduction (95% CI: 3-11%, p < 0.001) in Poisson regression analysis. When only the participants with complete paramedic and home care records were included in the analysis, the reductions in 9-1-1 calls and ambulance transports to ED were attenuated but remained statistically significant. Paramedic-initiated home care referrals in Toronto were associated with improved access to and use of home care services and may have been associated with reduced 9-1-1 calls and ambulance transports to ED.

Optimum pelvic incidence minus lumbar lordosis value can be determined by individual pelvic incidence.

PubMed

Inami, Satoshi; Moridaira, Hiroshi; Takeuchi, Daisaku; Shiba, Yo; Nohara, Yutaka; Taneichi, Hiroshi

2016-11-01

Adult spinal deformity (ASD) classification showing that ideal pelvic incidence minus lumbar lordosis (PI-LL) value is within 10° has been received widely. But no study has focused on the optimum level of PI-LL value that reflects wide variety in PI among patients. This study was conducted to determine the optimum PI-LL value specific to an individual's PI in postoperative ASD patients. 48 postoperative ASD patients were recruited. Spino-pelvic parameters and Oswestry Disability Index (ODI) were measured at the final follow-up. Factors associated with good clinical results were determined by stepwise multiple regression model using the ODI. The patients with ODI under the 75th percentile cutoff were designated into the "good" health related quality of life (HRQOL) group. In this group, the relationship between the PI-LL and PI was assessed by regression analysis. Multiple regression analysis revealed PI-LL as significant parameters associated with ODI. Thirty-six patients with an ODI <22 points (75th percentile cutoff) were categorized into a good HRQOL group, and linear regression models demonstrated the following equation: PI-LL = 0.41PI-11.12 (r = 0.45, P = 0.0059). On the basis of this equation, in the patients with a PI = 50°, the PI-LL is 9°. Whereas in those with a PI = 30°, the optimum PI-LL is calculated to be as low as 1°. In those with a PI = 80°, PI-LL is estimated at 22°. Consequently, an optimum PI-LL is inconsistent in that it depends on the individual PI.

Parameters of Models of Structural Transformations in Alloy Steel Under Welding Thermal Cycle

NASA Astrophysics Data System (ADS)

Kurkin, A. S.; Makarov, E. L.; Kurkin, A. B.; Rubtsov, D. E.; Rubtsov, M. E.

2017-05-01

A mathematical model of structural transformations in an alloy steel under the thermal cycle of multipass welding is suggested for computer implementation. The minimum necessary set of parameters for describing the transformations under heating and cooling is determined. Ferritic-pearlitic, bainitic and martensitic transformations under cooling of a steel are considered. A method for deriving the necessary temperature and time parameters of the model from the chemical composition of the steel is described. Published data are used to derive regression models of the temperature ranges and parameters of transformation kinetics in alloy steels. It is shown that the disadvantages of the active visual methods of analysis of the final phase composition of steels are responsible for inaccuracy and mismatch of published data. The hardness of a specimen, which correlates with some other mechanical properties of the material, is chosen as the most objective and reproducible criterion of the final phase composition. The models developed are checked by a comparative analysis of computational results and experimental data on the hardness of 140 alloy steels after cooling at various rates.

Increasing URM Undergraduate Student Success through Assessment-Driven Interventions: A Multiyear Study Using Freshman-Level General Biology as a Model System.

PubMed

Carmichael, Mary C; St Clair, Candace; Edwards, Andrea M; Barrett, Peter; McFerrin, Harris; Davenport, Ian; Awad, Mohamed; Kundu, Anup; Ireland, Shubha Kale

2016-01-01

Xavier University of Louisiana leads the nation in awarding BS degrees in the biological sciences to African-American students. In this multiyear study with ∼5500 participants, data-driven interventions were adopted to improve student academic performance in a freshman-level general biology course. The three hour-long exams were common and administered concurrently to all students. New exam questions were developed using Bloom's taxonomy, and exam results were analyzed statistically with validated assessment tools. All but the comprehensive final exam were returned to students for self-evaluation and remediation. Among other approaches, course rigor was monitored by using an identical set of 60 questions on the final exam across 10 semesters. Analysis of the identical sets of 60 final exam questions revealed that overall averages increased from 72.9% (2010) to 83.5% (2015). Regression analysis demonstrated a statistically significant correlation between high-risk students and their averages on the 60 questions. Additional analysis demonstrated statistically significant improvements for at least one letter grade from midterm to final and a 20% increase in the course pass rates over time, also for the high-risk population. These results support the hypothesis that our data-driven interventions and assessment techniques are successful in improving student retention, particularly for our academically at-risk students. © 2016 M. C. Carmichael et al. CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Distorted Perceptions of Competence and Incompetence Are More than Regression Effects

ERIC Educational Resources Information Center

Albanese, M.; Dottl, S.; Mejicano, G.; Zakowski, L.; Seibert, C.; Van Eyck, S.; Prucha, C.

2006-01-01

Students inaccurately assess their own skills, especially high- or low-performers on exams. This study assessed whether regression effects account for this observation. After completing the Infection and Immunity course final exam (IIF), second year medical students (N = 143) estimated their performance on the IIF in terms of percent correct and…

Impact of volunteer-related and methodology-related factors on the reproducibility of brachial artery flow-mediated vasodilation: analysis of 672 individual repeated measurements.

PubMed

van Mil, Anke C C M; Greyling, Arno; Zock, Peter L; Geleijnse, Johanna M; Hopman, Maria T; Mensink, Ronald P; Reesink, Koen D; Green, Daniel J; Ghiadoni, Lorenzo; Thijssen, Dick H

2016-09-01

Brachial artery flow-mediated dilation (FMD) is a popular technique to examine endothelial function in humans. Identifying volunteer and methodological factors related to variation in FMD is important to improve measurement accuracy and applicability. Volunteer-related and methodology-related parameters were collected in 672 volunteers from eight affiliated centres worldwide who underwent repeated measures of FMD. All centres adopted contemporary expert-consensus guidelines for FMD assessment. After calculating the coefficient of variation (%) of the FMD for each individual, we constructed quartiles (n = 168 per quartile). Based on two regression models (volunteer-related factors and methodology-related factors), statistically significant components of these two models were added to a final regression model (calculated as β-coefficient and R). This allowed us to identify factors that independently contributed to the variation in FMD%. Median coefficient of variation was 17.5%, with healthy volunteers demonstrating a coefficient of variation 9.3%. Regression models revealed age (β = 0.248, P < 0.001), hypertension (β = 0.104, P < 0.001), dyslipidemia (β = 0.331, P < 0.001), time between measurements (β = 0.318, P < 0.001), lab experience (β = -0.133, P < 0.001) and baseline FMD% (β = 0.082, P < 0.05) as contributors to the coefficient of variation. After including all significant factors in the final model, we found that time between measurements, hypertension, baseline FMD% and lab experience with FMD independently predicted brachial artery variability (total R = 0.202). Although FMD% showed good reproducibility, larger variation was observed in conditions with longer time between measurements, hypertension, less experience and lower baseline FMD%. Accounting for these factors may improve FMD% variability.

Assessing Principal Component Regression Prediction of Neurochemicals Detected with Fast-Scan Cyclic Voltammetry

PubMed Central

2011-01-01

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook’s distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards. PMID:21966586

Lifespan development of pro- and anti-saccades: multiple regression models for point estimates.

PubMed

Klein, Christoph; Foerster, Friedrich; Hartnegg, Klaus; Fischer, Burkhart

2005-12-07

The comparative study of anti- and pro-saccade task performance contributes to our functional understanding of the frontal lobes, their alterations in psychiatric or neurological populations, and their changes during the life span. In the present study, we apply regression analysis to model life span developmental effects on various pro- and anti-saccade task parameters, using data of a non-representative sample of 327 participants aged 9 to 88 years. Development up to the age of about 27 years was dominated by curvilinear rather than linear effects of age. Furthermore, the largest developmental differences were found for intra-subject variability measures and the anti-saccade task parameters. Ageing, by contrast, had the shape of a global linear decline of the investigated saccade functions, lacking the differential effects of age observed during development. While these results do support the assumption that frontal lobe functions can be distinguished from other functions by their strong and protracted development, they do not confirm the assumption of disproportionate deterioration of frontal lobe functions with ageing. We finally show that the regression models applied here to quantify life span developmental effects can also be used for individual predictions in applied research contexts or clinical practice.

Semiparametric regression analysis of interval-censored competing risks data.

PubMed

Mao, Lu; Lin, Dan-Yu; Zeng, Donglin

2017-09-01

Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not observed directly but rather is known to lie in an interval between two examinations. We formulate the effects of possibly time-varying (external) covariates on the cumulative incidence or sub-distribution function of competing risks (i.e., the marginal probability of failure from a specific cause) through a broad class of semiparametric regression models that captures both proportional and non-proportional hazards structures for the sub-distribution. We allow each subject to have an arbitrary number of examinations and accommodate missing information on the cause of failure. We consider nonparametric maximum likelihood estimation and devise a fast and stable EM-type algorithm for its computation. We then establish the consistency, asymptotic normality, and semiparametric efficiency of the resulting estimators for the regression parameters by appealing to modern empirical process theory. In addition, we show through extensive simulation studies that the proposed methods perform well in realistic situations. Finally, we provide an application to a study on HIV-1 infection with different viral subtypes. © 2017, The International Biometric Society.

Use of a pretest strategy for physical therapist assistant programs to predict success rate on the national physical therapy exam.

PubMed

Sloas, Stacey B; Keith, Becky; Whitehead, Malcolm T

2013-01-01

This study investigated a pretest strategy that identified physical therapist assistant (PTA) students who were at risk of failure on the National Physical Therapy Examination (NPTE). Program assessment data from five cohorts of PTA students (2005-2009) were used to develop a stepwise multiple regression formula that predicted first-time NPTE licensure scores. Data used included the Nelson-Denny Reading Test, grades from eight core courses, grade point average upon admission to the program, and scores from three mock NPTE exams given during the program. Pearson correlation coefficients were calculated between each of the 15 variables and NPTE scores. Stepwise multiple regression analysis was performed using data collected at the ends of the first, second, and third (final) semesters of the program. Data from the class of 2010 were then used to validate the formula. The end-of-program formula accounted for the greatest variance (57%) in predicted scores. Those students scoring below a predicted scaled score of 620 were identified to be at risk of failure of the licensure exam. These students were counseled, and a remedial plan was developed based on regression predictions prior to them sitting for the licensure exam.

Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry.

PubMed

Keithley, Richard B; Wightman, R Mark

2011-06-07

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook's distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards.

Quantification of rare earth elements using laser-induced breakdown spectroscopy

DOE PAGES

Martin, Madhavi; Martin, Rodger C.; Allman, Steve; ...

2015-10-21

In this paper, a study of the optical emission as a function of concentration of laser-ablated yttrium (Y) and of six rare earth elements, europium (Eu), gadolinium (Gd), lanthanum (La), praseodymium (Pr), neodymium (Nd), and samarium (Sm), has been evaluated using the laser-induced breakdown spectroscopy (LIBS) technique. Statistical methodology using multivariate analysis has been used to obtain the sampling errors, coefficient of regression, calibration, and cross-validation of measurements as they relate to the LIBS analysis in graphite-matrix pellets that were doped with elements at several concentrations. Each element (in oxide form) was mixed in the graphite matrix in percentages rangingmore » from 1% to 50% by weight and the LIBS spectra obtained for each composition as well as for pure oxide samples. Finally, a single pellet was mixed with all the elements in equal oxide masses to determine if we can identify the elemental peaks in a mixed pellet. This dataset is relevant for future application to studies of fission product content and distribution in irradiated nuclear fuels. These results demonstrate that LIBS technique is inherently well suited for the future challenge of in situ analysis of nuclear materials. Finally, these studies also show that LIBS spectral analysis using statistical methodology can provide quantitative results and suggest an approach in future to the far more challenging multielemental analysis of ~ 20 primary elements in high-burnup nuclear reactor fuel.« less

Visualizing variations in organizational safety culture across an inter-hospital multifaceted workforce.

PubMed

Kobuse, Hiroe; Morishima, Toshitaka; Tanaka, Masayuki; Murakami, Genki; Hirose, Masahiro; Imanaka, Yuichi

2014-06-01

To develop a reliable and valid questionnaire that can distinguish features of organizational culture for patient safety across subgroups such as hospitals, professions, management/non-management positions and units/wards. We developed a Hospital Organizational Culture Questionnaire based on a conceptual framework incorporating items from a review of existing literature. The questionnaire was administered to hospital staff including doctors, nurses, allied health personnel, and administrative staff at six public hospitals in Japan. Reliability and validity were assessed through exploratory factor analysis, multitrait scaling analysis, Cronbach's alpha coefficient and multiple regression analysis using staff-perceived achievement of safety as the response variable. Discriminative power across subgroups was assessed with radar chart profiling. Of the 3304 hospital staff surveyed, 2924 (88.5%) responded. After exploratory factor analysis and multitrait analysis, the finalized questionnaire was composed of 24 items in the following eight dimensions: improvement orientation, passion for mission, professional growth, resource allocation prioritization, inter-sectional collaboration, responsibility and authority, teamwork, and information sharing. Construct validity and internal consistency of dimensions were confirmed with multitrait analysis and Cronbach's alpha coefficients, respectively. Multiple regression analysis showed that improvement orientation, passion for mission, resource allocation prioritization and information sharing were significantly associated with higher achievement in safety practices. Our questionnaire tool was able to distinguish features of safety culture among different subgroups. Our questionnaire demonstrated excellent validity and reliability, and revealed distinct cultural patterns among different subgroups. Quantitative assessment of organizational safety culture with this tool may further the understanding of associated characteristics of each subgroup and provide insight into organizational readiness for patient safety improvement. © 2014 John Wiley & Sons, Ltd.

Increased risk for complications following removal of hardware in patients with liver disease, pilon or pelvic fractures: A regression analysis.

PubMed

Brown, Bryan D; Steinert, Justin N; Stelzer, John W; Yoon, Richard S; Langford, Joshua R; Koval, Kenneth J

2017-12-01

Indications for removing orthopedic hardware on an elective basis varies widely. Although viewed as a relatively benign procedure, there is a lack of data regarding overall complication rates after fracture fixation. The purpose of this study is to determine the overall short-term complication rate for elective removal of orthopedic hardware after fracture fixation and to identify associated risk factors. Adult patients indicated for elective hardware removal after fracture fixation between July 2012 and July 2016 were screened for inclusion. Inclusion criteria included patients with hardware related pain and/or impaired cosmesis with complete medical and radiographic records and at least 3-month follow-up. Exclusion criteria were those patients indicated for hardware removal for a diagnosis of malunion, non-union, and/or infection. Data collected included patient age, gender, anatomic location of hardware removed, body mass index, ASA score, and comorbidities. Overall complications, as well as complications requiring revision surgery were recorded. Statistical analysis was performed with SPSS 20.0, and included univariate and multivariate regression analysis. 391 patients (418 procedures) were included for analysis. Overall complication rates were 8.4%, with a 3.6% revision surgery rate. Univariate regression analysis revealed that patients who had liver disease were at significant risk for complication (p=0.001) and revision surgery (p=0.036). Multivariate regression analysis showed that: 1) patients who had liver disease were at significant risk of overall complication (p=0.001) and revision surgery (p=0.039); 2) Removal of hardware following fixation for a pilon had significantly increased risk for complication (p=0.012), but not revision surgery (p=0.43); and 3) Removal of hardware for pelvic fixation had a significantly increased risk for revision surgery (p=0.017). Removal of hardware following fracture fixation is not a risk-free procedure. Patients with liver disease are at increased risk for complications, including increased risk for needing revision surgery following hardware removal. Patients having hardware removed following fixation for pilon fractures also are at increased risk for complication, although they may not require a return trip to the operating room. Finally, removal of pelvic hardware is associated with a higher return to the operating room. Copyright © 2017 Elsevier Ltd. All rights reserved.

Pre-hospital electrocardiogram triage with telemedicine near halves time to treatment in STEMI: A meta-analysis and meta-regression analysis of non-randomized studies.

PubMed

Brunetti, Natale Daniele; De Gennaro, Luisa; Correale, Michele; Santoro, Francesco; Caldarola, Pasquale; Gaglione, Antonio; Di Biase, Matteo

2017-04-01

A shorter time to treatment has been shown to be associated with lower mortality rates in acute myocardial infarction (AMI). Several strategies have been adopted with the aim to reduce any delay in diagnosis of AMI: pre-hospital triage with telemedicine is one of such strategies. We therefore aimed to measure the real effect of pre-hospital triage with telemedicine in case of AMI in a meta-analysis study. We performed a meta-analysis of non-randomized studies with the aim to quantify the exact reduction of time to treatment achieved by pre-hospital triage with telemedicine. Data were pooled and compared by relative time reduction and 95% C.I.s. A meta-regression analysis was performed in order to find possible predictors of shorter time to treatment. Eleven studies were selected and finally evaluated in the study. The overall relative reduction of time to treatment with pre-hospital triage and telemedicine was -38/-40% (p<0.001). Absolute time reduction was significantly correlated to time to treatment in the control groups (p<0.001), while relative time reduction was independent. A non-significant trend toward shorter relative time reductions was observed over years. Pre-hospital triage with telemedicine is associated with a near halved time to treatment in AMI. The benefit is larger in terms of absolute time to treatment reduction in populations with larger delays to treatment. Copyright © 2017 Elsevier B.V. All rights reserved.

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

PubMed

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

2014-06-19

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

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…

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach.

PubMed

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate - adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research.

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach

PubMed Central

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Introduction Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. Aim The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Methods Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate – adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Results Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. Conclusion To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research. PMID:26080057

Evaluation of functional outcome of the floating knee injury using multivariate analysis.

PubMed

Yokoyama, Kazuhiko; Tsukamoto, Tatsuro; Aoki, Shinichi; Wakita, Ryuji; Uchino, Masataka; Noumi, Takashi; Fukushima, Nobuaki; Itoman, Moritoshi

2002-11-01

The objective of this study is to evaluate significant contributing factors affecting the functional prognosis of floating knee injuries using multivariate analysis. A total of 68 floating knee injuries (67 patients) were treated at Kitasato University Hospital from 1986 to 1999. Both the femoral fractures and the tibial fractures were managed surgically by various methods. The functional results of these injuries were evaluated using the grading system of Karlström and Olerud. Follow-up periods ranged from 2 to 19 years (mean 50.2 months) after the original injury. We defined satisfactory (S) outcomes as those cases with excellent or good results and unsatisfactory (US) outcomes as those cases with acceptable or poor results. Logistic regression analysis was used as a multivariate analysis, and the dependent variables were defined as a satisfactory outcome or as an unsatisfactory outcome. The explanatory variables were predicting factors influencing the functional outcome such as age at trauma, gender, severity of soft-tissue injury in the femur and the tibia, AO fracture grade in the femur and the tibia, Fraser type (type I or type II), Injury Severity Score (ISS), and fixation time after injury (less than 1 week or more than 1 week) in the femur and the tibia. The final functional results were as follows: 25 cases had excellent results, 15 cases good results, 16 cases acceptable results, and 12 cases poor results. The predictive logistic regression equation was as follows: Log 1-p/p = 3.12-1.52 x Fraser type - 1.65 x severity of soft-tissue injury in the tibia - 1.31 x fixation time after injury in the tibia - 0.821 x AO fracture grade in the tibia + 1.025 x fixation time after injury in the femur - 0.687 x AO fracture grade in the femur ( p=0.01). Among the variables, Fraser type and the severity of soft-tissue injury in the tibia were significantly related to the final result. The multivariate analysis showed that both the involvement of the knee joint and the severity grade of soft-tissue injury in the tibia represented significant risk factors of poor outcome in floating knee injuries in this study.

The Role of Habit and Perceived Control on Health Behavior among Pregnant Women.

PubMed

Mullan, Barbara; Henderson, Joanna; Kothe, Emily; Allom, Vanessa; Orbell, Sheina; Hamilton, Kyra

2016-05-01

Many pregnant women do not adhere to physical activity and dietary recommendations. Research investigating what psychological processes might predict physical activity and healthy eating (fruit and vegetable consumption) during pregnancy is scant. We explored the role of intention, habit, and perceived behavioral control as predictors of physical activity and healthy eating. Pregnant women (N = 195, Mage = 30.17, SDage = 4.46) completed questionnaires at 2 time points. At Time 1, participants completed measures of intention, habit, and perceived behavioral control. At Time 2, participants reported on their behavior (physical activity and healthy eating) within the intervening week. Regression analysis determined whether Time 1 variables predicted behavior at Time 2. Interaction terms also were tested. Final regression models indicated that only intention and habit explained significant variance in physical activity, whereas habit and the interaction between intention and habit explained significant variance in healthy eating. Simple slopes analysis indicated that the relationship between intention and healthy eating behavior was only significant at high levels of habit. Findings highlight the influence of habit on behavior and suggest that automaticity interventions may be useful in changing health behaviors during pregnancy.

Optimisation of the formulation of a bubble bath by a chemometric approach market segmentation and optimisation.

PubMed

Marengo, Emilio; Robotti, Elisa; Gennaro, Maria Carla; Bertetto, Mariella

2003-03-01

The optimisation of the formulation of a commercial bubble bath was performed by chemometric analysis of Panel Tests results. A first Panel Test was performed to choose the best essence, among four proposed to the consumers; the best essence chosen was used in the revised commercial bubble bath. Afterwards, the effect of changing the amount of four components (the amount of primary surfactant, the essence, the hydratant and the colouring agent) of the bubble bath was studied by a fractional factorial design. The segmentation of the bubble bath market was performed by a second Panel Test, in which the consumers were requested to evaluate the samples coming from the experimental design. The results were then treated by Principal Component Analysis. The market had two segments: people preferring a product with a rich formulation and people preferring a poor product. The final target, i.e. the optimisation of the formulation for each segment, was obtained by the calculation of regression models relating the subjective evaluations given by the Panel and the compositions of the samples. The regression models allowed to identify the best formulations for the two segments ofthe market.

Factors associated with happiness in the elderly persons living in the community.

PubMed

Luchesi, Bruna Moretti; de Oliveira, Nathalia Alves; de Morais, Daiene; de Paula Pessoa, Rebeca Mendes; Pavarini, Sofia Cristina I; Chagas, Marcos Hortes N

2018-01-01

The aim of the present study was to evaluate factors associated with happiness in a sample of Brazilian older adults. A study was conducted with 263 elderly people in the area of coverage of a family health unit located in the state of São Paulo, Brazil. The Subjective Happiness Scale was used to measure happiness, the final score of which determined one of three outcomes: not happy, intermediate, and happy. Disability, sociodemographic characteristics, and psychological, cognitive, and physical factors were considered for the multinomial logistic regression analysis. Statistically significant differences were found among the three groups regarding satisfaction with life, disability, social phobia, anxiety, depression, and frailty (p≤0.05). In the multinomial regression analysis, being "not happy" was significantly associated with satisfaction with life (RRR: 0.53), depression (RRR: 1.46), social phobia (RRR: 1.24), and age (RRR: 1.06). The present findings indicate that psychological factors and age influence the levels of happiness in older adults living in the community. Furthermore, better screening, diagnosis, and treatment of mental health disorders could increase the feeling of happiness among older adults. Copyright © 2017 Elsevier B.V. All rights reserved.

Risk Factors for Problem Gambling in California: Demographics, Comorbidities and Gambling Participation.

PubMed

Volberg, Rachel A; McNamara, Lauren M; Carris, Kari L

2018-06-01

While population surveys have been carried out in numerous jurisdictions internationally, little has been done to assess the relative strength of different risk factors that may contribute to the development of problem gambling. This is an important preparatory step for future research on the etiology of problem gambling. Using data from the 2006 California Problem Gambling Prevalence Survey, a telephone survey of adult California residents that used the NODS to assess respondents for gambling problems, binary logistic regression analysis was used to identify demographic characteristics, health-related behaviors, and gambling participation variables that statistically predicted the odds of being a problem or pathological gambler. In a separate approach, linear regression analysis was used to assess the impact of changes in these variables on the severity of the disorder. In both of the final models, the greatest statistical predictor of problem gambling status was past year Internet gambling. Furthermore, the unique finding of a significant interaction between physical or mental disability, Internet gambling, and problem gambling highlights the importance of exploring the interactions between different forms of gambling, the experience of mental and physical health issues, and the development of problem gambling using a longitudinal lens.

Seroprevalence and risk factors of Chlamydia abortus infection in free-ranging white yaks in China.

PubMed

Qin, Si-Yuan; Huang, Si-Yang; Yin, Ming-Yang; Tan, Qi-Dong; Liu, Guang-Xue; Zhou, Dong-Hui; Zhu, Xing-Quan; Zhou, Ji-Zhang; Qian, Ai-Dong

2015-01-20

Chlamydia is gram-negative obligate bacteria which causes a wide variety of diseases in humans and animals. To date, there are a few reports about the seroprevalence of Chlamydia and the risk factors associated with Chlamydia infection in yaks in the world. In this study, 974 blood samples were collected from white yaks (Bos grunniens) in Tianzhu Tibetan Autonomous County, Gansu province, northwest China from June 2013 to April 2014. Antibodies against Chlamydia abortus were examined by the indirect hemagglutination (IHA) test, and 158 of 974 (16.22%) white yaks were seropositive for C. abortus antibodies at the cut-off of 1:16. The risk factors associated with seroprevalence were evaluated by a multivariate logistic regression analysis. Region, gender and age of white yak were left out of the final model, due to its insignificance in the logistic regression analysis (P > 0.05). However, season was considered as a major risk factor associated with C. abortus infection in white yaks. To our knowledge, this is the first survey of C. abortus seroprevalence in white yaks in China, which extends the host range for C. abortus and has important implications for public health and the local Tibetan economy.

Initial depressive episodes affect the risk of suicide attempts in Korean patients with bipolar disorder.

PubMed

Ryu, Vin; Jon, Duk-In; Cho, Hyun Sang; Kim, Se Joo; Lee, Eun; Kim, Eun Joo; Seok, Jeong-Ho

2010-09-01

Suicide is a major concern for increasing mortality in bipolar patients, but risk factors for suicide in bipolar disorder remain complex, including Korean patients. Medical records of bipolar patients were retrospectively reviewed to detect significant clinical characteristics associated with suicide attempts. A total of 579 medical records were retrospectively reviewed. Bipolar patients were divided into two groups with the presence of a history of suicide attempts. We compared demographic characteristics and clinical features between the two groups using an analysis of covariance and chi-square tests. Finally, logistic regression was performed to evaluate significant risk factors associated with suicide attempts in bipolar disorder. The prevalence of suicide attempt was 13.1% in our patient group. The presence of a depressive first episode was significantly different between attempters and nonattempters. Logistic regression analysis revealed that depressive first episodes and bipolar II disorder were significantly associated with suicide attempts in those patients. Clinicians should consider the polarity of the first mood episode when evaluating suicide risk in bipolar patients. This study has some limitations as a retrospective study and further studies with a prospective design are needed to replicate and evaluate risk factors for suicide in patients with bipolar disorder.

Regional L-Moment-Based Flood Frequency Analysis in the Upper Vistula River Basin, Poland

NASA Astrophysics Data System (ADS)

Rutkowska, A.; Żelazny, M.; Kohnová, S.; Łyp, M.; Banasik, K.

2017-02-01

The Upper Vistula River basin was divided into pooling groups with similar dimensionless frequency distributions of annual maximum river discharge. The cluster analysis and the Hosking and Wallis (HW) L-moment-based method were used to divide the set of 52 mid-sized catchments into disjoint clusters with similar morphometric, land use, and rainfall variables, and to test the homogeneity within clusters. Finally, three and four pooling groups were obtained alternatively. Two methods for identification of the regional distribution function were used, the HW method and the method of Kjeldsen and Prosdocimi based on a bivariate extension of the HW measure. Subsequently, the flood quantile estimates were calculated using the index flood method. The ordinary least squares (OLS) and the generalised least squares (GLS) regression techniques were used to relate the index flood to catchment characteristics. Predictive performance of the regression scheme for the southern part of the Upper Vistula River basin was improved by using GLS instead of OLS. The results of the study can be recommended for the estimation of flood quantiles at ungauged sites, in flood risk mapping applications, and in engineering hydrology to help design flood protection structures.

[Gaussian process regression and its application in near-infrared spectroscopy analysis].

PubMed

Feng, Ai-Ming; Fang, Li-Min; Lin, Min

2011-06-01

Gaussian process (GP) is applied in the present paper as a chemometric method to explore the complicated relationship between the near infrared (NIR) spectra and ingredients. After the outliers were detected by Monte Carlo cross validation (MCCV) method and removed from dataset, different preprocessing methods, such as multiplicative scatter correction (MSC), smoothing and derivate, were tried for the best performance of the models. Furthermore, uninformative variable elimination (UVE) was introduced as a variable selection technique and the characteristic wavelengths obtained were further employed as input for modeling. A public dataset with 80 NIR spectra of corn was introduced as an example for evaluating the new algorithm. The optimal models for oil, starch and protein were obtained by the GP regression method. The performance of the final models were evaluated according to the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (r). The models give good calibration ability with r values above 0.99 and the prediction ability is also satisfactory with r values higher than 0.96. The overall results demonstrate that GP algorithm is an effective chemometric method and is promising for the NIR analysis.

Social determinants of childhood asthma symptoms: an ecological study in urban Latin America.

PubMed

Fattore, Gisel L; Santos, Carlos A T; Barreto, Mauricio L

2014-04-01

Asthma is an important public health problem in urban Latin America. This study aimed to analyze the role of socioeconomic and environmental factors as potential determinants of asthma symptoms prevalence in children from Latin American (LA) urban centers. We selected 31 LA urban centers with complete data, and an ecological analysis was performed. According to our theoretical framework, the explanatory variables were classified in three levels: distal, intermediate, and proximate. The association between variables in the three levels and prevalence of asthma symptoms was examined by bivariate and multivariate linear regression analysis weighed by sample size. In a second stage, we fitted several linear regression models introducing sequentially the variables according to the predefined hierarchy. In the final hierarchical model Gini Index, crowding, sanitation, variation in infant mortality rates and homicide rates, explained great part of the variance in asthma prevalence between centers (R(2) = 75.0 %). We found a strong association between socioeconomic and environmental variables and prevalence of asthma symptoms in LA urban children, and according to our hierarchical framework and the results found we suggest that social inequalities (measured by the Gini Index) is a central determinant to explain high prevalence of asthma in LA.

The joint moderating effect of health consciousness and healthy lifestyle on consumers' willingness to use functional foods in Taiwan.

PubMed

Chen, Mei-Fang

2011-08-01

Functional foods marketed as promoting health or reducing the risk of disease open a promising avenue for consumers to pursue a healthier life. Despite the stable growth in functional foods in Taiwan, at present little is known about whether or not consumers with varying degrees of health consciousness and different healthy lifestyles will have dissimilar attitudes toward functional foods and will vary in their willingness to use them. Regression analysis of this empirical study verifies that consumers' attitudes toward functional foods do have an impact on their willingness to use such foods. Moreover, moderated regression analysis (MRA) reveals that the joint moderator of health consciousness and healthy lifestyle indeed exerts an impact on consumers' willingness to consume functional foods. Finally, one-way ANOVA tests show that there are some differences between the consumers of the "Healthy Life Attentive" group and those of the "Healthy Life Inattentive" one both in attitudes toward and in willingness to consume functional foods. The empirical results and findings from this study would be valuable for the marketers in the functional food industry to formulate marketing communication strategies and facilitate this industry's development. Copyright © 2011 Elsevier Ltd. All rights reserved.

Regression estimators for generic health-related quality of life and quality-adjusted life years.

PubMed

Basu, Anirban; Manca, Andrea

2012-01-01

To develop regression models for outcomes with truncated supports, such as health-related quality of life (HRQoL) data, and account for features typical of such data such as a skewed distribution, spikes at 1 or 0, and heteroskedasticity. Regression estimators based on features of the Beta distribution. First, both a single equation and a 2-part model are presented, along with estimation algorithms based on maximum-likelihood, quasi-likelihood, and Bayesian Markov-chain Monte Carlo methods. A novel Bayesian quasi-likelihood estimator is proposed. Second, a simulation exercise is presented to assess the performance of the proposed estimators against ordinary least squares (OLS) regression for a variety of HRQoL distributions that are encountered in practice. Finally, the performance of the proposed estimators is assessed by using them to quantify the treatment effect on QALYs in the EVALUATE hysterectomy trial. Overall model fit is studied using several goodness-of-fit tests such as Pearson's correlation test, link and reset tests, and a modified Hosmer-Lemeshow test. The simulation results indicate that the proposed methods are more robust in estimating covariate effects than OLS, especially when the effects are large or the HRQoL distribution has a large spike at 1. Quasi-likelihood techniques are more robust than maximum likelihood estimators. When applied to the EVALUATE trial, all but the maximum likelihood estimators produce unbiased estimates of the treatment effect. One and 2-part Beta regression models provide flexible approaches to regress the outcomes with truncated supports, such as HRQoL, on covariates, after accounting for many idiosyncratic features of the outcomes distribution. This work will provide applied researchers with a practical set of tools to model outcomes in cost-effectiveness analysis.

Exploring relationships between Dairy Herd Improvement monitors of performance and the Transition Cow Index in Wisconsin dairy herds.

PubMed

Schultz, K K; Bennett, T B; Nordlund, K V; Döpfer, D; Cook, N B

2016-09-01

Transition cow management has been tracked via the Transition Cow Index (TCI; AgSource Cooperative Services, Verona, WI) since 2006. Transition Cow Index was developed to measure the difference between actual and predicted milk yield at first test day to evaluate the relative success of the transition period program. This project aimed to assess TCI in relation to all commonly used Dairy Herd Improvement (DHI) metrics available through AgSource Cooperative Services. Regression analysis was used to isolate variables that were relevant to TCI, and then principal components analysis and network analysis were used to determine the relative strength and relatedness among variables. Finally, cluster analysis was used to segregate herds based on similarity of relevant variables. The DHI data were obtained from 2,131 Wisconsin dairy herds with test-day mean ≥30 cows, which were tested ≥10 times throughout the 2014 calendar year. The original list of 940 DHI variables was reduced through expert-driven selection and regression analysis to 23 variables. The K-means cluster analysis produced 5 distinct clusters. Descriptive statistics were calculated for the 23 variables per cluster grouping. Using principal components analysis, cluster analysis, and network analysis, 4 parameters were isolated as most relevant to TCI; these were energy-corrected milk, 3 measures of intramammary infection (dry cow cure rate, linear somatic cell count score in primiparous cows, and new infection rate), peak ratio, and days in milk at peak milk production. These variables together with cow and newborn calf survival measures form a group of metrics that can be used to assist in the evaluation of overall transition period performance. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

ERIC Educational Resources Information Center

Azen, Razia; Traxel, Nicole

2009-01-01

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…

The Impact of Prior Programming Knowledge on Lecture Attendance and Final Exam

ERIC Educational Resources Information Center

Veerasamy, Ashok Kumar; D'Souza, Daryl; Lindén, Rolf; Laakso, Mikko-Jussi

2018-01-01

In this article, we report the results of the impact of prior programming knowledge (PPK) on lecture attendance (LA) and on subsequent final programming exam performance in a university level introductory programming course. This study used Spearman's rank correlation coefficient, multiple regression, Kruskal-Wallis, and Bonferroni correction…

On Becoming Trauma-Informed: Role of the Adverse Childhood Experiences Survey in Tertiary Child and Adolescent Mental Health Services and the Association with Standard Measures of Impairment and Severity

PubMed Central

Rahman, Abdul; Perri, Andrea; Deegan, Avril; Kuntz, Jennifer; Cawthorpe, David

2018-01-01

Context There is a movement toward trauma-informed, trauma-focused psychiatric treatment. Objective To examine Adverse Childhood Experiences (ACE) survey items by sex and by total scores by sex vs clinical measures of impairment to examine the clinical utility of the ACE survey as an index of trauma in a child and adolescent mental health care setting. Design Descriptive, polychoric factor analysis and regression analyses were employed to analyze cross-sectional ACE surveys (N = 2833) and registration-linked data using past admissions (N = 10,400) collected from November 2016 to March 2017 related to clinical data (28 independent variables), taking into account multicollinearity. Results Distinct ACE items emerged for males, females, and those with self-identified sex and for ACE total scores in regression analysis. In hierarchical regression analysis, the final models consisting of standard clinical measures and demographic and system variables (eg, repeated admissions) were associated with substantial ACE total score variance for females (44%) and males (38%). Inadequate sample size foreclosed on developing a reduced multivariable model for the self-identified sex group. Conclusion The ACE scores relate to independent clinical measures and system and demographic variables. There are implications for clinical practice. For example, a child presenting with anxiety and a high ACE score likely requires treatment that is different from a child presenting with anxiety and an ACE score of zero. The ACE survey score is an important index of presenting clinical status that guides patient care planning and intervention in the progress toward a trauma-focused system of care. PMID:29401055

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

PubMed

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

2014-05-01

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

Masculinity, alexithymia, and fear of intimacy as predictors of UK men's attitudes towards seeking professional psychological help.

PubMed

Sullivan, Luke; Camic, Paul M; Brown, June S L

2015-02-01

Men's reluctance to access health care services has been under researched even though it has been identified as a potentially important predictor of poorer health outcomes amongst men. Male gender role socialization and male development may be important in accounting for men's underutilization of mental health services in the United Kingdom. A cross-sectional online survey was used to administer standardized self-report measures that were subject to regression analysis. Five hundred and eighty-one men from the UK general population completed the survey, and 536 participants formed the final regression analysis. Men who score higher on measures of traditional masculine ideology, normative alexithymia, and fear of intimacy reported more negative attitudes towards seeking professional psychological help. Normative alexithymia fully mediated the effect of fear of intimacy on attitudes towards professional help seeking. In the final regression model, education significantly accounted for a proportion of unique variance in men's help-seeking attitudes. Hypothesized consequences of male emotional and interpersonal development and male gender role socialization were associated with men's attitudes towards seeking psychological help. These are important factors which could help to improve help seeking and mental health outcomes for men. Limitations of this study and implications for future research are discussed. Statement of contribution What is already known on this subject? Men are less likely to seek help for physical and psychological problems and have poorer health outcomes across nearly all major illness and injury. Men's reluctance to access health care services is believed to be a major contributory factor to poorer health outcomes for men. What does the study add? The study is a large-scale survey of UK men's attitudes towards professional psychological help seeking. Results provide evidence that hypothesized consequences of male gender role socialization and dominant masculine norms are associated with men's attitudes towards seeking professional psychological help. Attitudes towards psychological help seeking were associated with masculinity, alexithymia, and intimacy. Alexithymia fully mediated the effect of intimacy on men's attitudes towards psychological help seeking. Promoting help seeking in men could improve men's emotional well-being and interpersonal functioning. © 2014 The British Psychological Society.

Correlational analysis of neck/shoulder pain and low back pain with the use of digital products, physical activity and psychological status among adolescents in Shanghai.

PubMed

Shan, Zhi; Deng, Guoying; Li, Jipeng; Li, Yangyang; Zhang, Yongxing; Zhao, Qinghua

2013-01-01

This study investigates the neck/shoulder pain (NSP) and low back pain (LBP) among current high school students in Shanghai and explores the relationship between these pains and their possible influences, including digital products, physical activity, and psychological status. An anonymous self-assessment was administered to 3,600 students across 30 high schools in Shanghai. This questionnaire examined the prevalence of NSP and LBP and the level of physical activity as well as the use of mobile phones, personal computers (PC) and tablet computers (Tablet). The CES-D (Center for Epidemiological Studies Depression) scale was also included in the survey. The survey data were analyzed using the chi-square test, univariate logistic analyses and a multivariate logistic regression model. Three thousand sixteen valid questionnaires were received including 1,460 (48.41%) from male respondents and 1,556 (51.59%) from female respondents. The high school students in this study showed NSP and LBP rates of 40.8% and 33.1%, respectively, and the prevalence of both influenced by the student's grade, use of digital products, and mental status; these factors affected the rates of NSP and LBP to varying degrees. The multivariate logistic regression analysis revealed that Gender, grade, soreness after exercise, PC using habits, tablet use, sitting time after school and academic stress entered the final model of NSP, while the final model of LBP consisted of gender, grade, soreness after exercise, PC using habits, mobile phone use, sitting time after school, academic stress and CES-D score. High school students in Shanghai showed high prevalence of NSP and LBP that were closely related to multiple factors. Appropriate interventions should be implemented to reduce the occurrences of NSP and LBP.

Analysis of the relationship between community characteristics and depression using geographically weighted regression.

PubMed

Choi, Hyungyun; Kim, Ho

2017-01-01

Achieving national health equity is currently a pressing issue. Large regional variations in the health determinants are observed. Depression, one of the most common mental disorders, has large variations in incidence among different populations, and thus must be regionally analyzed. The present study aimed at analyzing regional disparities in depressive symptoms and identifying the health determinants that require regional interventions. Using health indicators of depression in the Korea Community Health Survey 2011 and 2013, the Moran's I was calculated for each variable to assess spatial autocorrelation, and a validated geographically weighted regression analysis using ArcGIS version 10.1 of different domains: health behavior, morbidity, and the social and physical environments were created, and the final model included a combination of significant variables in these models. In the health behavior domain, the weekly breakfast intake frequency of 1-2 times was the most significantly correlated with depression in all regions, followed by exposure to secondhand smoke and the level of perceived stress in some regions. In the morbidity domain, the rate of lifetime diagnosis of myocardial infarction was the most significantly correlated with depression. In the social and physical environment domain, the trust environment within the local community was highly correlated with depression, showing that lower the level of trust, higher was the level of depression. A final model was constructed and analyzed using highly influential variables from each domain. The models were divided into two groups according to the significance of correlation of each variable with the experience of depression symptoms. The indicators of the regional health status are significantly associated with the incidence of depressive symptoms within a region. The significance of this correlation varied across regions.

Accuracy of Bayes and Logistic Regression Subscale Probabilities for Educational and Certification Tests

ERIC Educational Resources Information Center

Rudner, Lawrence

2016-01-01

In the machine learning literature, it is commonly accepted as fact that as calibration sample sizes increase, Naïve Bayes classifiers initially outperform Logistic Regression classifiers in terms of classification accuracy. Applied to subtests from an on-line final examination and from a highly regarded certification examination, this study shows…

Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression

ERIC Educational Resources Information Center

Anderson, Joan L.

2006-01-01

Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models…

Severity of mental illness as a result of multiple childhood adversities: US National Epidemiologic Survey.

PubMed

Curran, Emma; Adamson, Gary; Stringer, Maurice; Rosato, Michael; Leavey, Gerard

2016-05-01

To examine patterns of childhood adversity, their long-term consequences and the combined effect of different childhood adversity patterns as predictors of subsequent psychopathology. Secondary analysis of data from the US National Epidemiologic Survey on alcohol and related conditions. Using latent class analysis to identify childhood adversity profiles; and using multinomial logistic regression to validate and further explore these profiles with a range of associated demographic and household characteristics. Finally, confirmatory factor analysis substantiated initial latent class analysis findings by investigating a range of mental health diagnoses. Latent class analysis generated a three-class model of childhood adversity in which 60 % of participants were allocated to a low adversity class; 14 % to a global adversities class (reporting exposures for all the derived latent classes); and 26 % to a domestic emotional and physical abuse class (exposed to a range of childhood adversities). Confirmatory Factor analysis defined an internalising-externalising spectrum to represent lifetime reporting patterns of mental health disorders. Using logistic regression, both adversity groups showed specific gender and race/ethnicity differences, related family discord and increased psychopathology. We identified underlying patterns in the exposure to childhood adversity and associated mental health. These findings are informative in their description of the configuration of adversities, rather than focusing solely on the cumulative aspect of experience. Amelioration of longer-term negative consequences requires early identification of psychopathology risk factors that can inform protective and preventive interventions. This study highlights the utility of screening for childhood adversities when individuals present with symptoms of psychiatric disorders.

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

PubMed

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

2017-10-01

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

Predictive factors for postoperative visual function of primary chronic rhegmatogenous retinal detachment after scleral buckling.

PubMed

Fang, Wei; Li, Jiu-Ke; Jin, Xiao-Hong; Dai, Yuan-Min; Li, Yu-Min

2016-01-01

To evaluate predictive factors for postoperative visual function of primary chronic rhegmatgenous retinal detachment (RRD) after sclera buckling (SB). Totally 48 patients (51 eyes) with primary chronic RRD were included in this prospective interventional clinical cases study, which underwent SB alone from June 2008 to December 2014. Age, sex, symptoms duration, detached extension, retinal hole position, size, type, fovea on/off, proliferative vitreoretinopathy (PVR), posterior vitreous detachment (PVD), baseline best corrected visual acuity (BCVA), operative duration, follow up duration, final BCVA were measured. Pearson correlation analysis, Spearman correlation analysis and multivariate linear stepwise regression were used to confirm predictive factors for better final visual acuity. Student's t-test, Wilcoxon two-sample test, Chi-square test and logistic stepwise regression were used to confirm predictive factors for better vision improvement. Baseline BCVA was 0.8313±0.6911 logMAR and final BCVA was 0.4761±0.4956 logMAR. Primary surgical success rate was 92.16% (47/51). Correlation analyses revealed shorter symptoms duration (r=0.3850, P=0.0053), less detached area (r=0.5489, P<0.0001), fovea (r=0.4605, P=0.0007), no PVR (r=0.3138, P=0.0250), better baseline BCVA (r=0.7291, P<0.0001), shorter operative duration (r=0.3233, P=0.0207) and longer follow up (r=-0.3358, P=0.0160) were related with better final BCVA, while independent predictive factors were better baseline BCVA [partial R-square (PR(2))=0.5316, P<0.0001], shorter symptoms duration (PR(2)=0.0609, P=0.0101), longer follow up duration (PR(2)=0.0278, P=0.0477) and shorter operative duration (PR(2)=0.0338, P=0.0350). Patients with vision improvement took up 49.02% (25/51). Univariate and multivariate analyses both revealed predictive factors for better vision improvement were better baseline vision [odds ratio (OR) =50.369, P=0.0041] and longer follow up duration (OR=1.144, P=0.0067). Independent predictive factors for better visual outcome of primary chronic RRD after SB are better baseline BCVA, shorter symptoms duration, shorter operative duration and longer follow up duration, while independent predictive factors for better vision improvement after operation are better baseline vision and longer follow up duration.

Zinc movement in sewage-sludge-treated soils as influenced by soil properties, irrigation water quality, and soil moisture level

USGS Publications Warehouse

Welch, J.E.; Lund, L.J.

1989-01-01

A soil column study was conducted to assess the movement of Zn in sewage-sludge-amended soils. Varables investigated were soil properties, irrigation water quality, and soil moisture level. Bulk samples of the surface layer of six soil series were packed into columns, 10.2 cm in diameter and 110 cm in length. An anaerobically digested municipal sewage sludge was incorporated into the top 20 cm of each column at a rate of 300 mg ha-1. The columns were maintained at moisture levels of saturation and unsaturation and were leached with two waters of different quality. At the termination of leaching, the columns were cut open and the soil was sectioned and analyzed. Zinc movement was evaluated by mass balance accounting and correlation and regression analysis. Zinc movement in the unsaturated columns ranged from 3 to 30 cm, with a mean of 10 cm. The difference in irrigation water quality did not have an effect on Zn movement. Most of the Zn applied to the unsaturated columns remained in the sludge-amended soil layer (96.1 to 99.6%, with a mean of 98.1%). The major portion of Zn leached from the sludge-amended soil layer accumulated in the 0- to 3-cm depth (35.7 to 100%, with a mean of 73.6%). The mean final soil pH values decreased in the order: saturated columns = sludge-amended soil layer > untreated soils > unsaturated columns. Total Zn leached from the sludge-amended soil layer was correlated negatively at P = 0.001 with final pH (r = -0.85). Depth of Zn movement was correlated negatively at P = 0.001 with final pH (r = -0.91). Multiple linear regression analysis showed that the final pH accounted for 72% of the variation in the total amounts of Zn leached from the sludge-amended soil layer of the unsaturated columns and accounted for 82% of the variation in the depth of Zn movement among the unsaturated columns. A significant correlation was not found between Zn and organic carbon in soil solutions, but a negative correlation significant at P = 0.001 was found between pH and Zn (r = -0.61).

Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression.

PubMed

Mones, Letif; Bernstein, Noam; Csányi, Gábor

2016-10-11

Practical free energy reconstruction algorithms involve three separate tasks: biasing, measuring some observable, and finally reconstructing the free energy surface from those measurements. In more than one dimension, adaptive schemes make it possible to explore only relatively low lying regions of the landscape by progressively building up the bias toward the negative of the free energy surface so that free energy barriers are eliminated. Most schemes use the final bias as their best estimate of the free energy surface. We show that large gains in computational efficiency, as measured by the reduction of time to solution, can be obtained by separating the bias used for dynamics from the final free energy reconstruction itself. We find that biasing with metadynamics, measuring a free energy gradient estimator, and reconstructing using Gaussian process regression can give an order of magnitude reduction in computational cost.

Predictors of student success in entry-level science courses

NASA Astrophysics Data System (ADS)

Singh, Mamta K.

Although the educational evaluation process is useful and valuable and is supported by the Higher Education Act, a strong research base for program evaluation of college entry-level science courses is still lacking. Studies in science disciplines such as, biology, chemistry, and physics have addressed various affective and demographic factors and their relationships to student achievement. However, the literature contains little information that specifically addresses student biology content knowledge skills (basics and higher order thinking skills) and identifies factors that affect students' success in entry-level college science courses. These gate-keeping courses require detailed evaluation if the goal of an institution is to increase students' performance and success in these courses. These factors are, in fact, a stepping stone for increasing the number of graduates in Science, Technology, Engineering, and Mathematics (STEM) majors. The present study measured students' biology content knowledge and investigated students' performance and success in college biology, chemistry, and physics entry-level courses. Seven variables---gender, ethnicity, high school Grade Point Average (GPA), high school science, college major, school financial aid support, and work hours were used as independent variables and course final performance as a dichotomous dependent variable. The sample comprised voluntary student participants in entry-level science courses. The study attempted to explore eight research questions. Content knowledge assessments, demographic information analysis, multiple regression analysis, and binary logistic regression analysis were used to address research questions. The results suggested that high school GPA was a consistently good predictor of students' performance and success in entry-level science courses. Additionally, high school chemistry was a significant predictor variable for student success in entry-level biology and chemistry courses. Similarly, students' performance and success in entry-level physics courses were influenced by high school physics. Finally, the study developed student success equation with high school GAP and high school chemistry as good predictors of students' success in entry-level science courses.

Prediction of rectal temperature using non-invasive physiologic variable measurements in hair pregnant ewes subjected to natural conditions of heat stress.

PubMed

Vicente-Pérez, Ricardo; Avendaño-Reyes, Leonel; Mejía-Vázquez, Ángel; Álvarez-Valenzuela, F Daniel; Correa-Calderón, Abelardo; Mellado, Miguel; Meza-Herrera, Cesar A; Guerra-Liera, Juan E; Robinson, P H; Macías-Cruz, Ulises

2016-01-01

Rectal temperature (RT) is the foremost physiological variable indicating if an animal is suffering hyperthermia. However, this variable is traditionally measured by invasive methods, which may compromise animal welfare. Models to predict RT have been developed for growing pigs and lactating dairy cows, but not for pregnant heat-stressed ewes. Our aim was to develop a prediction equation for RT using non-invasive physiological variables in pregnant ewes under heat stress. A total of 192 records of respiratory frequency (RF) and hair coat temperature in various body regions (i.e., head, rump, flank, shoulder, and belly) obtained from 24 Katahdin × Pelibuey pregnant multiparous ewes were collected during the last third of gestation (i.e., d 100 to lambing) with a 15 d sampling interval. Hair coat temperatures were taken using infrared thermal imaging technology. Initially, a Pearson correlation analysis examined the relationship among variables, and then multiple linear regression analysis was used to develop the prediction equations. All predictor variables were positively correlated (P<0.01; r=0.59-0.67) with RT. The adjusted equation which best predicted RT (P<0.01; Radj(2)=56.15%; CV=0.65%) included as predictors RF and head and belly temperatures. Comparison of predicted and observed values for RT indicates a suitable agreement (P<0.01) between them with moderate accuracy (Radj(2)=56.15%) when RT was calculated with the adjusted equation. In general, the final equation does not violate any assumption of multiple regression analysis. The RT in heat-stressed pregnant ewes can be predicted with an adequate accuracy using non-invasive physiologic variables, and the final equation was: RT=35.57+0.004 (RF)+0.067 (heat temperature)+0.028 (belly temperature). Copyright © 2015 Elsevier Ltd. All rights reserved.

Spontaneous regression of curve in immature idiopathic scoliosis - does spinal column play a role to balance? An observation with literature review.

PubMed

Modi, Hitesh N; Suh, Seung-Woo; Yang, Jae-Hyuk; Hong, Jae-Young; Venkatesh, Kp; Muzaffar, Nasir

2010-11-04

Child with mild scoliosis is always a subject of interest for most orthopaedic surgeons regarding progression. Literature described Hueter-Volkmann theory regarding disc and vertebral wedging, and muscular imbalance for the progression of adolescent idiopathic scoliosis. However, many authors reported spontaneous resolution of curves also without any reason for that and the rate of resolution reported is almost 25%. Purpose of this study was to question the role of paraspinal muscle tuning/balancing mechanism, especially in patients with idiopathic scoliosis with early mild curve, for spontaneous regression or progression as well as changing pattern of curves. An observational study of serial radiograms in 169 idiopathic scoliosis children (with minimum follow-up one year) was carried. All children with Cobb angle < 25° and who were diagnosed for the first time were selected. As a sign of immaturity at the time of diagnosis, all children had Risser sign 0. No treatment was given to entire study group. Children were divided in three groups at final follow-up: Group A, B and C as children with regression, no change and progression of their curves, respectively. Additionally changes in the pattern of curve were also noted. Average age was 9.2 years at first visit and 10.11 years at final follow-up with an average follow-up of 21 months. 32.5% (55/169), 41.4% (70/169) and 26% (44/169) children exhibited regression, no change and progression in their curves, respectively. 46.1% of children (78/169) showed changing pattern of their curves during the follow-up visits before it settled down to final curve. Comparing final fate of curve with side of curve and number of curves it did not show any relationship (p > 0.05) in our study population. Possible reason for changing patterns could be better explained by the tuning/balancing mechanism of spinal column that makes an effort to balance the spine and result into spontaneous regression or prevent further progression of curve. If this which we called as "tuning/balancing mechanism" fails, curve will ultimately progress.

Characterisation of volatile profile and sensory analysis of fresh-cut "Radicchio di Chioggia" stored in air or modified atmosphere.

PubMed

Cozzolino, Rosaria; Martignetti, Antonella; Pellicano, Mario Paolo; Stocchero, Matteo; Cefola, Maria; Pace, Bernardo; De Giulio, Beatrice

2016-02-01

The volatile profile of two hybrids of "Radicchio di Chioggia", Corelli and Botticelli, stored in air or passive modified atmosphere (MAP) during 12 days of cold storage, was monitored by solid phase micro-extraction (SPME) GC-MS. Botticelli samples were also subjected to sensory analysis. Totally, 61 volatile organic compounds (VOCs) were identified in the headspace of radicchio samples. Principal component analysis (PCA) showed that fresh product possessed a metabolic content similar to that of the MAP samples after 5 and 8 days of storage. Projection to latent structures by partial least squares (PLS) regression analysis showed the volatiles content of the samples varied depending only on the packaging conditions. Specifically, 12 metabolites describing the time evolution and explaining the effects of the different storage conditions were highlighted. Finally, a PCA analysis revealed that VOCs profile significantly correlated with sensory attributes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Role of macular hole angle in macular hole closure.

PubMed

Chhablani, Jay; Khodani, Mitali; Hussein, Abdullah; Bondalapati, Sailaja; Rao, Harsha B; Narayanan, Raja; Sudhalkar, Aditya

2015-12-01

To evaluate correlation of various spectral-domain optical coherence tomography (SD-OCT) parameters including macular hole angle as well as various indices with anatomical and visual outcomes after idiopathic macular hole repair surgery. Retrospective study of 137 eyes of 137 patients who underwent idiopathic macular hole repair surgery between January 2008 and January 2014 was performed. Various qualitative parameters such as presence of vitreomacular traction, epiretinal membrane and cystic edges at the macular hole as well as quantitative parameters such as maximum diameter on the apex of the hole, minimum diameter between edges, nasal and temporal vertical height, longest base diameter and macular hole angle between the retinal edge and the retinal pigment epithelium were noted. Indices including hole form factor, Macular Hole Index (MHI), Diameter Hole Index and Tractional Hole Index (THI) were calculated. Univariate and multivariate regression analysis was performed separately for final visual acuity (VA) and type of closure as dependent variable in relation to SD-OCT parameters as independent variables. On multivariate regression only minimum diameter between edges (p≤0.01) and longest base diameter (p≤0.03) were correlated significantly with both, type 1 closure and final VA. Among the indices, significant correlation of MHI (p=0.009) was noted with type of closure and that of THI with final VA (p=0.017). Our study shows no significant correlation between macular hole angle and hole closure. Minimum diameter between the edges and longest diameter of the hole are best predictors of hole closure and postoperative VA. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Study for Updated Gout Classification Criteria (SUGAR): identification of features to classify gout

PubMed Central

Taylor, William J.; Fransen, Jaap; Jansen, Tim L.; Dalbeth, Nicola; Schumacher, H. Ralph; Brown, Melanie; Louthrenoo, Worawit; Vazquez-Mellado, Janitzia; Eliseev, Maxim; McCarthy, Geraldine; Stamp, Lisa K.; Perez-Ruiz, Fernando; Sivera, Francisca; Ea, Hang-Korng; Gerritsen, Martijn; Scire, Carlo; Cavagna, Lorenzo; Lin, Chingtsai; Chou, Yin-Yi; Tausche, Anne-Kathrin; Vargas-Santos, Ana Beatriz; Janssen, Matthijs; Chen, Jiunn-Horng; Slot, Ole; Cimmino, Marco A.; Uhlig, Till; Neogi, Tuhina

2015-01-01

Objective To determine which clinical, laboratory and imaging features most accurately distinguished gout from non-gout. Methods A cross-sectional study of consecutive rheumatology clinic patients with at least one swollen joint or subcutaneous tophus. Gout was defined by synovial fluid or tophus aspirate microscopy by certified examiners in all patients. The sample was randomly divided into a model development (2/3) and test sample (1/3). Univariate and multivariate association between clinical features and MSU-defined gout was determined using logistic regression modelling. Shrinkage of regression weights was performed to prevent over-fitting of the final model. Latent class analysis was conducted to identify patterns of joint involvement. Results In total, 983 patients were included. Gout was present in 509 (52%). In the development sample (n=653), these features were selected for the final model (multivariate OR) joint erythema (2.13), difficulty walking (7.34), time to maximal pain < 24 hours (1.32), resolution by 2 weeks (3.58), tophus (7.29), MTP1 ever involved (2.30), location of currently tender joints: Other foot/ankle (2.28), MTP1 (2.82), serum urate level > 6 mg/dl (0.36 mmol/l) (3.35), ultrasound double contour sign (7.23), Xray erosion or cyst (2.49). The final model performed adequately in the test set with no evidence of misfit, high discrimination and predictive ability. MTP1 involvement was the most common joint pattern (39.4%) in gout cases. Conclusion Ten key discriminating features have been identified for further evaluation for new gout classification criteria. Ultrasound findings and degree of uricemia add discriminating value, and will significantly contribute to more accurate classification criteria. PMID:25777045

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.

Paradoxical Behavior of Granger Causality

NASA Astrophysics Data System (ADS)

Witt, Annette; Battaglia, Demian; Gail, Alexander

2013-03-01

Granger causality is a standard tool for the description of directed interaction of network components and is popular in many scientific fields including econometrics, neuroscience and climate science. For time series that can be modeled as bivariate auto-regressive processes we analytically derive an expression for spectrally decomposed Granger Causality (SDGC) and show that this quantity depends only on two out of four groups of model parameters. Then we present examples of such processes whose SDGC expose paradoxical behavior in the sense that causality is high for frequency ranges with low spectral power. For avoiding misinterpretations of Granger causality analysis we propose to complement it by partial spectral analysis. Our findings are illustrated by an example from brain electrophysiology. Finally, we draw implications for the conventional definition of Granger causality. Bernstein Center for Computational Neuroscience Goettingen

[Predictors of Resilience in Adolescents with Leukemia].

PubMed

Hong, Sung Sil; Park, Ho Ran

2015-08-01

The purpose of this study was to identify the factors relating to resilience for adolescents with leukemia and examine the relationship between these factors. From June to September in 2014, 199 adolescents aged 11 to 21 participated in the study as they visited the out-patient clinic at C university hospital for follow-up care. To verify the predictors and the effects of resilience, uncertainty, symptom distress, perceived social support, spiritual perspective, defensive coping, courageous coping, hope, and self-transcendence were measured. Collected data were analyzed using hierarchical regression analysis with the SAS statistics program. The final regression model showed that courageous coping, hope, and self-transcendence were significant predictors related to resilience in adolescents with leukemia and explained for 63% of the variance in resilience. The findings indicate that adolescent-oriented intervention programs enhancing courageous coping, hope, and self-transcendence should be provide for adolescents with leukemia in order to overcome illness-related stress and support physical, psychological and social adjustment.

Quantitative appraisal of the Amyloid Imaging Taskforce appropriate use criteria for amyloid-PET.

PubMed

Altomare, Daniele; Ferrari, Clarissa; Festari, Cristina; Guerra, Ugo Paolo; Muscio, Cristina; Padovani, Alessandro; Frisoni, Giovanni B; Boccardi, Marina

2018-04-18

We test the hypothesis that amyloid-PET prescriptions, considered appropriate based on the Amyloid Imaging Taskforce (AIT) criteria, lead to greater clinical utility than AIT-inappropriate prescriptions. We compared the clinical utility between patients who underwent amyloid-PET appropriately or inappropriately and among the subgroups of patients defined by the AIT criteria. Finally, we performed logistic regressions to identify variables associated with clinical utility. We identified 171 AIT-appropriate and 67 AIT-inappropriate patients. AIT-appropriate and AIT-inappropriate cases did not differ in any outcomes of clinical utility (P > .05). Subgroup analysis denoted both expected and unexpected results. The logistic regressions outlined the primary role of clinical picture and clinical or neuropsychological profile in identifying patients benefitting from amyloid-PET. Contrary to our hypothesis, also AIT-inappropriate prescriptions were associated with clinical utility. Clinical or neuropsychological variables, not taken into account by the AIT criteria, may help further refine criteria for appropriateness. Copyright © 2018. Published by Elsevier Inc.

Gender Role Conflict, Interest in Casual Sex, and Relationship Satisfaction Among Gay Men

PubMed Central

Sanchez, Fráncisco J.; Bocklandt, Sven; Vilain, Eric

2010-01-01

This study compared single (n = 129) and partnered gay men (n = 114) to determine if they differed in their concerns over traditional masculine roles and interest in casual sex, and to measure the relationship between concerns over masculine roles and interest in casual sex. Additionally, a regression model to predict relationship satisfaction was tested. Participants were recruited at two Southern California Gay Pride festivals. Group comparisons showed single men were more restrictive in their affectionate behavior with other men (effect-size r = .14) and were more interested in casual sex than partnered men (effect-size r = .13); and partnered men were more concerned with being successful, powerful, and competitive than single men (effect-size r = .20). Different masculine roles were predictive of interest in casual sex among the two groups of men. Finally, a hierarchical regression analysis found that interest in casual sex and the length of one’s current relationship served as unique predictors of relationship satisfaction among the partnered gay men (Cohen’s f2 = .52). PMID:20721305

Comprehensive evaluation system of intelligent urban growth

NASA Astrophysics Data System (ADS)

Li, Lian-Yan; Ren, Xiao-Bin

2017-06-01

With the rapid urbanization of the world, urban planning has become increasingly important and necessary to ensure people have access to equitable and sustainable homes, resources and jobs.This article is to talk about building an intelligent city evaluation system.First,using System Analysis Model(SAM) which concludes literature data analysis and stepwise regression analysis to describe intelligent growth scientifically and obtain the evaluation index. Then,using the improved entropy method to obtain the weight of the evaluation index.Afterwards, establishing a complete Smart Growth Comprehensive Evaluation Model(SGCEM).Finally,testing the correctness of the model.Choosing Otago(New Zealand )and Yumen(China) as research object by data mining and SGCEM model,then we get Yumen and Otago’s rational degree’s values are 0.3485 and 0.5376 respectively. It’s believed that the Otago’s smart level is higher,and it is found that the estimated value of rationality is consistent with the reality.

The lexical development of children with hearing impairment and associated factors.

PubMed

Penna, Leticia Macedo; Lemos, Stela Maris Aguiar; Alves, Cláudia Regina Lindgren

2014-01-01

This study aimed at analyzing the association between the lexical development of children with hearing impairment and their psychosocial and socioeconomic characteristics and medical history. An analytic transversal study was conducted in an Auditive Health Attention Service. One hundred and ten children from 6 to 10 years old using hearing aids and presenting hearing loss that ranged from light to deep levels were evaluated. All children were subjected to oral, written language and auditory perception tests. Parents answered a structured questionnaire to collect data from their medical history and socioeconomic status, and questionnaires about the features of the family environment and psychosocial characteristics. Multivariate analysis was performed by logistic regression, being the initial model composed by variables with p<0,20 in the univariate analysis. In the final model, we adopted a significance level of 5%. The final model of the multivariate analysis showed an association between the performance on the vocabulary test and the results of phonemic discrimination test (OR=0.81; 95%CI 0.73-0.89). The results show the importance of stimulating the auditory processing, particularly the phonemic discrimination skill, throughout the rehabilitation process of children with hearing impairment. This stimulation can enhance lexical development and minimize the metalanguage and learning difficulties often observed in these children.

A simple prognostic model for overall survival in metastatic renal cell carcinoma.

PubMed

Assi, Hazem I; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis.

A simple prognostic model for overall survival in metastatic renal cell carcinoma

PubMed Central

Assi, Hazem I.; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

Introduction: The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. Methods: We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. Results: There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. Conclusions: In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis. PMID:27217858

Parametric study and performance analysis of hybrid rocket motors with double-tube configuration

NASA Astrophysics Data System (ADS)

Yu, Nanjia; Zhao, Bo; Lorente, Arnau Pons; Wang, Jue

2017-03-01

The practical implementation of hybrid rocket motors has historically been hampered by the slow regression rate of the solid fuel. In recent years, the research on advanced injector designs has achieved notable results in the enhancement of the regression rate and combustion efficiency of hybrid rockets. Following this path, this work studies a new configuration called double-tube characterized by injecting the gaseous oxidizer through a head end injector and an inner tube with injector holes distributed along the motor longitudinal axis. This design has demonstrated a significant potential for improving the performance of hybrid rockets by means of a better mixing of the species achieved through a customized injection of the oxidizer. Indeed, the CFD analysis of the double-tube configuration has revealed that this design may increase the regression rate over 50% with respect to the same motor with a conventional axial showerhead injector. However, in order to fully exploit the advantages of the double-tube concept, it is necessary to acquire a deeper understanding of the influence of the different design parameters in the overall performance. In this way, a parametric study is carried out taking into account the variation of the oxidizer mass flux rate, the ratio of oxidizer mass flow rate injected through the inner tube to the total oxidizer mass flow rate, and injection angle. The data for the analysis have been gathered from a large series of three-dimensional numerical simulations that considered the changes in the design parameters. The propellant combination adopted consists of gaseous oxygen as oxidizer and high-density polyethylene as solid fuel. Furthermore, the numerical model comprises Navier-Stokes equations, k-ε turbulence model, eddy-dissipation combustion model and solid-fuel pyrolysis, which is computed through user-defined functions. This numerical model was previously validated by analyzing the computational and experimental results obtained for conventional hybrid rocket designs. In addition, a performance analysis is conducted in order to evaluate the influence in the performance provoked by the possible growth of the diameter of the inner fuel grain holes during the motor operation. The latter phenomenon is known as burn through holes. Finally, after a statistical analysis of the data, a regression rate expression as a function of the design parameters is obtained.

Short and long-term career plans of final year dental students in the United Arab Emirates

PubMed Central

2013-01-01

Background New dental schools have been established to train dentists in many parts of the world. This study examines the future dental workforce from the first dental school in the United Arab Emirates [UAE]; the aim of this study was to explore the short and long-term career aspirations of the final year dental students in the UAE in relation to their demography. Method Final year dental students of the Ajman University’s College of Dentistry (n=87) were invited to participate in a self-completion questionnaire survey. Descriptive analysis, chi-square tests, and binary logistic regression analysis were carried out on career aspirations using SPSS v20. Results Eighty-two percent of students (n=71) responded, the majority of whom were female (65%; n=46). Ethnicity was reported as: ‘other Arab’ (61%; n=43), ‘Emirati’ (17%, n=12), and ‘Other’ (21%, n=15). In the short-term, 41% (n=29) expressed a desire to work in government training centres, with Emirati students significantly more likely to do so (p=0.002). ‘Financial stability’ (80%; n=57) and ‘gaining professional experience’ (76%; n=54) emerged as the most important influences on their short-term career plans. The vast majority of students wished to specialise in dentistry (92%; n=65) in the longer term; logistic regression analysis revealed that the odds of specialising in the most popular specialties of Orthodontics and Oral and Maxillofacial Surgery were less for the ‘Other’ ethnic group when compared with ‘Emirati’ students (0.26; 95% CI 0.068-0.989; p=0.04). Almost three-quarters of the students overall (72%; n=51) intended to work full-time. ‘High income/financial security’ (97%; n=69), ‘standard of living’ (97%; n=69), ‘work/life balance’ (94%; n=67), and ‘professional fulfilment’ (87%; n=62) were reported by the students as the most influential items affecting their long-term professional career choices. Conclusion The findings suggest that students aspire to make a long-term contribution to the profession and there is a high level of interest in specialisation with a desire to achieve financial stability and quality of life. PMID:23937862

Regression: The Apple Does Not Fall Far From the Tree.

PubMed

Vetter, Thomas R; Schober, Patrick

2018-05-15

Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.

A systematic evaluation of normalization methods in quantitative label-free proteomics.

PubMed

Välikangas, Tommi; Suomi, Tomi; Elo, Laura L

2018-01-01

To date, mass spectrometry (MS) data remain inherently biased as a result of reasons ranging from sample handling to differences caused by the instrumentation. Normalization is the process that aims to account for the bias and make samples more comparable. The selection of a proper normalization method is a pivotal task for the reliability of the downstream analysis and results. Many normalization methods commonly used in proteomics have been adapted from the DNA microarray techniques. Previous studies comparing normalization methods in proteomics have focused mainly on intragroup variation. In this study, several popular and widely used normalization methods representing different strategies in normalization are evaluated using three spike-in and one experimental mouse label-free proteomic data sets. The normalization methods are evaluated in terms of their ability to reduce variation between technical replicates, their effect on differential expression analysis and their effect on the estimation of logarithmic fold changes. Additionally, we examined whether normalizing the whole data globally or in segments for the differential expression analysis has an effect on the performance of the normalization methods. We found that variance stabilization normalization (Vsn) reduced variation the most between technical replicates in all examined data sets. Vsn also performed consistently well in the differential expression analysis. Linear regression normalization and local regression normalization performed also systematically well. Finally, we discuss the choice of a normalization method and some qualities of a suitable normalization method in the light of the results of our evaluation. © The Author 2016. Published by Oxford University Press.

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.

SigEMD: A powerful method for differential gene expression analysis in single-cell RNA sequencing data.

PubMed

Wang, Tianyu; Nabavi, Sheida

2018-04-24

Differential gene expression analysis is one of the significant efforts in single cell RNA sequencing (scRNAseq) analysis to discover the specific changes in expression levels of individual cell types. Since scRNAseq exhibits multimodality, large amounts of zero counts, and sparsity, it is different from the traditional bulk RNA sequencing (RNAseq) data. The new challenges of scRNAseq data promote the development of new methods for identifying differentially expressed (DE) genes. In this study, we proposed a new method, SigEMD, that combines a data imputation approach, a logistic regression model and a nonparametric method based on the Earth Mover's Distance, to precisely and efficiently identify DE genes in scRNAseq data. The regression model and data imputation are used to reduce the impact of large amounts of zero counts, and the nonparametric method is used to improve the sensitivity of detecting DE genes from multimodal scRNAseq data. By additionally employing gene interaction network information to adjust the final states of DE genes, we further reduce the false positives of calling DE genes. We used simulated datasets and real datasets to evaluate the detection accuracy of the proposed method and to compare its performance with those of other differential expression analysis methods. Results indicate that the proposed method has an overall powerful performance in terms of precision in detection, sensitivity, and specificity. Copyright © 2018 Elsevier Inc. All rights reserved.

[A novel approach to NIR spectral quantitative analysis: semi-supervised least-squares support vector regression machine].

PubMed

Li, Lin; Xu, Shuo; An, Xin; Zhang, Lu-Da

2011-10-01

In near infrared spectral quantitative analysis, the precision of measured samples' chemical values is the theoretical limit of those of quantitative analysis with mathematical models. However, the number of samples that can obtain accurately their chemical values is few. Many models exclude the amount of samples without chemical values, and consider only these samples with chemical values when modeling sample compositions' contents. To address this problem, a semi-supervised LS-SVR (S2 LS-SVR) model is proposed on the basis of LS-SVR, which can utilize samples without chemical values as well as those with chemical values. Similar to the LS-SVR, to train this model is equivalent to solving a linear system. Finally, the samples of flue-cured tobacco were taken as experimental material, and corresponding quantitative analysis models were constructed for four sample compositions' content(total sugar, reducing sugar, total nitrogen and nicotine) with PLS regression, LS-SVR and S2 LS-SVR. For the S2 LS-SVR model, the average relative errors between actual values and predicted ones for the four sample compositions' contents are 6.62%, 7.56%, 6.11% and 8.20%, respectively, and the correlation coefficients are 0.974 1, 0.973 3, 0.923 0 and 0.948 6, respectively. Experimental results show the S2 LS-SVR model outperforms the other two, which verifies the feasibility and efficiency of the S2 LS-SVR model.

Factors predictive of the onset and duration of action of local anesthesia in mandibular third-molar surgery: a prospective study.

PubMed

Al-Shayyab, Mohammad H; Baqain, Zaid H

2018-04-01

The aim of this study was to assess the influence of patients' and surgical variables on the onset and duration of action of local anesthesia (LA) in mandibular third-molar (M3) surgery. Patients scheduled for mandibular M3 surgery were considered for inclusion in this prospective cohort study. Patients' and surgical variables were recorded. Two per cent (2%) lidocaine with 1:100,000 epinephrine was used to block the nerves for extraction of mandibular M3. Then, the onset of action and duration of LA were monitored. Univariate analysis and multivariate regression analysis were used to analyze the data. The final cohort included 88 subjects (32 men and 56 women; mean age ± SD = 29.3 ± 12.3 yr). With univariate analysis, age, gender, body mass index (BMI), smoking quantity and duration, operation time, and 'volume of local anesthetic needed' significantly influenced the onset of action and duration of LA. Multivariate regression revealed that age and smoking quantity were the only statistically significant predictors of the onset of action of LA, whereas age, smoking quantity, and 'volume of local anesthetic needed' were the only statistically significant predictors of duration of LA. Further studies are recommended to uncover other predictors of the onset of action and duration of LA. © 2018 Eur J Oral Sci.

Risk prediction for myocardial infarction via generalized functional regression models.

PubMed

Ieva, Francesca; Paganoni, Anna M

2016-08-01

In this paper, we propose a generalized functional linear regression model for a binary outcome indicating the presence/absence of a cardiac disease with multivariate functional data among the relevant predictors. In particular, the motivating aim is the analysis of electrocardiographic traces of patients whose pre-hospital electrocardiogram (ECG) has been sent to 118 Dispatch Center of Milan (the Italian free-toll number for emergencies) by life support personnel of the basic rescue units. The statistical analysis starts with a preprocessing of ECGs treated as multivariate functional data. The signals are reconstructed from noisy observations. The biological variability is then removed by a nonlinear registration procedure based on landmarks. Thus, in order to perform a data-driven dimensional reduction, a multivariate functional principal component analysis is carried out on the variance-covariance matrix of the reconstructed and registered ECGs and their first derivatives. We use the scores of the Principal Components decomposition as covariates in a generalized linear model to predict the presence of the disease in a new patient. Hence, a new semi-automatic diagnostic procedure is proposed to estimate the risk of infarction (in the case of interest, the probability of being affected by Left Bundle Brunch Block). The performance of this classification method is evaluated and compared with other methods proposed in literature. Finally, the robustness of the procedure is checked via leave-j-out techniques. © The Author(s) 2013.

The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques

NASA Astrophysics Data System (ADS)

Deng, Chengbin; Wu, Changshan

2013-12-01

Urban impervious surface information is essential for urban and environmental applications at the regional/national scales. As a popular image processing technique, spectral mixture analysis (SMA) has rarely been applied to coarse-resolution imagery due to the difficulty of deriving endmember spectra using traditional endmember selection methods, particularly within heterogeneous urban environments. To address this problem, we derived endmember signatures through a least squares solution (LSS) technique with known abundances of sample pixels, and integrated these endmember signatures into SMA for mapping large-scale impervious surface fraction. In addition, with the same sample set, we carried out objective comparative analyses among SMA (i.e. fully constrained and unconstrained SMA) and machine learning (i.e. Cubist regression tree and Random Forests) techniques. Analysis of results suggests three major conclusions. First, with the extrapolated endmember spectra from stratified random training samples, the SMA approaches performed relatively well, as indicated by small MAE values. Second, Random Forests yields more reliable results than Cubist regression tree, and its accuracy is improved with increased sample sizes. Finally, comparative analyses suggest a tentative guide for selecting an optimal approach for large-scale fractional imperviousness estimation: unconstrained SMA might be a favorable option with a small number of samples, while Random Forests might be preferred if a large number of samples are available.

Association of vitamin C with the risk of age-related cataract: a meta-analysis.

PubMed

Wei, Lin; Liang, Ge; Cai, Chunmei; Lv, Jin

2016-05-01

Whether vitamin C is a protective factor for age-related cataract remains unclear. Thus, we conducted a meta-analysis to summarize the evidence from epidemiological studies of vitamin C and the risk of age-related cataract. Pertinent studies were identified by searching in PubMed and in Webscience. The random effect model was used to combine the results. Meta-regression and subgroups analyses were used to explore potential sources of between-study heterogeneity. Publication bias was estimated using Egger's regression asymmetry test. Finally, 15 articles with 20 studies for vitamin C intake and eight articles with 10 studies for serum ascorbate were included in this meta-analysis. The relative risk (RR) and 95% confidence interval of cataract for the highest versus the lowest category of vitamin C intake was 0.814 (0.707-0.938), and the associations were significant in America and Asia. Significant association of cataract risk with highest versus the lowest category of serum ascorbate was found in general [0.704 (0.564-0.879)]. Inverse associations were also found between serum ascorbate and nuclear cataract and posterior subcapsular cataract. Higher vitamin C intake and serum ascorbate might be inversely associated with risk of cataract. Vitamin C intake should be advocated for the primary prevention of cataract. © 2015 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

Using runoff slope-break to determine dominate factors of runoff decline in Hutuo River Basin, North China.

PubMed

Tian, Fei; Yang, Yonghui; Han, Shumin

2009-01-01

Water resources in North China have declined sharply in recent years. Low runoff (especially in the mountain areas) has been identified as the main factor. Hutuo River Basin (HRB), a typical up-stream basin in North China with two subcatchments (Ye and Hutuo River Catchments), was investigated in this study. Mann-Kendall test was used to determine the general trend of precipitation and runoff for 1960-1999. Then Sequential Mann-Kendall test was used to establish runoff slope-break from which the beginning point of sharp decline in runoff was determined. Finally, regression analysis was done to illustrate runoff decline via comparison of precipitation-runoff correlation for the period prior to and after sharp runoff decline. This was further verified by analysis of rainy season peak runoff flows. The results are as follows: (1) annual runoff decline in the basin is significant while that of precipitation is insignificant at alpha=0.05 confidence level; (2) sharp decline in runoff in Ye River Catchment (YRC) occurred in 1968 while that in Hutuo River Catchment (HRC) occurred in 1978; (3) based on the regression analysis, human activity has the highest impact on runoff decline in the basin. As runoff slope-breaks in both Catchments strongly coincided with increase in agricultural activity, agricultural water use is considered the dominate factor of runoff decline in the study area.

Applied Multiple Linear Regression: A General Research Strategy

ERIC Educational Resources Information Center

Smith, Brandon B.

1969-01-01

Illustrates some of the basic concepts and procedures for using regression analysis in experimental design, analysis of variance, analysis of covariance, and curvilinear regression. Applications to evaluation of instruction and vocational education programs are illustrated. (GR)

Personality traits and life satisfaction among online game players.

PubMed

Chen, Lily Shui-Lien; Tu, Hill Hung-Jen; Wang, Edward Shih-Tse

2008-04-01

The DFC Intelligence predicts worldwide online game revenues will reach $9.8 billion by 2009, making online gaming a mainstream recreational activity. Understanding online game player personality traits is therefore important. This study researches the relationship between personality traits and life satisfaction in online game players. Taipei, Taiwan, is the study location, with questionnaire surveys conducted in cyber cafe shops. Multiple regression analysis studies the causal relationship between personality traits and life satisfaction in online game players. The result shows that neuroticism has significant negative influence on life satisfaction. Both openness and conscientiousness have significant positive influence on life satisfaction. Finally, implications for leisure practice and further research are discussed.

The Determinants of Federal and State Enforcement of Workplace Safety Regulations: OSHA Inspections 1990-2010*

PubMed Central

Jung, Juergen

2013-01-01

We explore the determinants of inspection outcomes across 1.6 million Occupational Safety and Health Agency (OSHA) audits from 1990 through 2010. We find that discretion in enforcement differs in state and federally conducted inspections. State agencies are more sensitive to local economic conditions, finding fewer standard violations and fewer serious violations as unemployment increases. Larger companies receive greater lenience in multiple dimensions. Inspector issued fines and final fines, after negotiated reductions, are both smaller during Republican presidencies. Quantile regression analysis reveals that Presidential and Congressional party affiliations have their greatest impact on the largest negotiated reductions in fines. PMID:24659856

Magnitude of flood flows for selected annual exceedance probabilities for streams in Massachusetts

USGS Publications Warehouse

Zarriello, Phillip J.

2017-05-11

The U.S. Geological Survey, in cooperation with the Massachusetts Department of Transportation, determined the magnitude of flood flows at selected annual exceedance prob­abilities (AEPs) at streamgages in Massachusetts and from these data developed equations for estimating flood flows at ungaged locations in the State. Flood magnitudes were deter­mined for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEPs at 220 streamgages, 125 of which are in Massachusetts and 95 are in the adjacent States of Connecticut, New Hamp­shire, New York, Rhode Island, and Vermont. AEP flood flows were computed for streamgages using the expected moments algorithm weighted with a recently computed regional skew­ness coefficient for New England.Regional regression equations were developed to estimate the magnitude of floods for selected AEP flows at ungaged sites from 199 selected streamgages and for 60 potential explanatory basin characteristics. AEP flows for 21 of the 125 streamgages in Massachusetts were not used in the final regional regression analysis, primarily because of regulation or redundancy. The final regression equations used general­ized least squares methods to account for streamgage record length and correlation. Drainage area, mean basin elevation, and basin storage explained 86 to 93 percent of the variance in flood magnitude from the 50- to 0.2-percent AEPs, respec­tively. The estimates of AEP flows at streamgages can be improved by using a weighted estimate that is based on the magnitude of the flood and associated uncertainty from the at-site analysis and the regional regression equations. Weighting procedures for estimating AEP flows at an ungaged site on a gaged stream also are provided that improve estimates of flood flows at the ungaged site when hydrologic characteristics do not abruptly change.Urbanization expressed as the percentage of imperviousness provided some explanatory power in the regional regression; however, it was not statistically significant at the 95-percent confidence level for any of the AEPs examined. The effect of urbanization on flood flows indicates a complex interaction with other basin characteristics. Another complicating factor is the assumption of stationarity, that is, the assumption that annual peak flows exhibit no significant trend over time. The results of the analysis show that stationarity does not prevail at all of the streamgages. About 27 percent of streamgages in Massachusetts and about 42 percent of streamgages in adjacent States with 20 or more years of systematic record used in the study show a significant positive trend at the 95-percent confidence level. The remaining streamgages had both positive and negative trends, but the trends were not statistically significant. Trends were shown to vary over time. In particular, during the past decade (2004–2013), peak flows were persistently above normal, which may give the impression of positive trends. Only continued monitoring will provide the information needed to determine whether recent increases in annual peak flows are a normal oscillation or a true trend.The analysis used 37 years of additional data obtained since the last comprehensive study of flood flows in Massa­chusetts. In addition, new methods for computing flood flows at streamgages and regionalization improved estimates of flood magnitudes at gaged and ungaged locations and better defined the uncertainty of the estimates of AEP floods.

Predictive value of clinical scoring and simplified gait analysis for acetabulum fractures.

PubMed

Braun, Benedikt J; Wrona, Julian; Veith, Nils T; Rollman, Mika; Orth, Marcel; Herath, Steven C; Holstein, Jörg H; Pohlemann, Tim

2016-12-01

Fractures of the acetabulum show a high, long-term complication rate. The aim of the present study was to determine the predictive value of clinical scoring and standardized, simplified gait analysis on the outcome after these fractures. Forty-one patients with acetabular fractures treated between 2008 and 2013 and available, standardized video recorded aftercare were identified from a prospective database. A visual gait score was used to determine the patients walking abilities 6-m postoperatively. Clinical (Merle d'Aubigne and Postel score, visual analogue scale pain, EQ5d) and radiological scoring (Kellgren-Lawrence score, postoperative computed tomography, and Matta classification) were used to perform correlation and multivariate regression analysis. The average patient age was 48 y (range, 15-82 y), six female patients were included in the study. Mean follow-up was 1.6 y (range, 1-2 y). Moderate correlation between the gait score and outcome (versus EQ5d: r s  = 0.477; versus Merle d'Aubigne: r s  = 0.444; versus Kellgren-Lawrence: r s  = -0.533), as well as high correlation between the Merle d'Aubigne score and outcome were seen (versus EQ5d: r s  = 0.575; versus Merle d'Aubigne: r s  = 0.776; versus Kellgren-Lawrence: r s  = -0.419). Using a multivariate regression model, the 6 m gait score (B = -0.299; P < 0.05) and early osteoarthritis development (B = 1.026; P < 0.05) were determined as predictors of final osteoarthritis. A good fit of the regression model was seen (R 2  = 904). Easy and available clinical scoring (gait score/Merle d'Aubigne) can predict short-term radiological and functional outcome after acetabular fractures with sufficient accuracy. Decisions on further treatment and interventions could be based on simplified gait analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

Dual oxidase 1: A predictive tool for the prognosis of hepatocellular carcinoma patients.

PubMed

Chen, Shengsen; Ling, Qingxia; Yu, Kangkang; Huang, Chong; Li, Ning; Zheng, Jianming; Bao, Suxia; Cheng, Qi; Zhu, Mengqi; Chen, Mingquan

2016-06-01

Dual oxidase 1 (DUOX1), which is the main source of reactive oxygen species (ROS) production in the airway, can be silenced in human lung cancer and hepatocellular carcinomas. However, the prognostic value of DUOX1 expression in hepatocellular carcinoma patients is still unclear. We investigated the prognostic value of DUOX1 expression in liver cancer patients. DUOX1 mRNA expression was determined in tumor tissues and non-tumor tissues by real‑time PCR. For evaluation of the prognostic value of DUOX1 expression, Kaplan-Meier method and Cox's proportional hazards model (univariate analysis and multivariate analysis) were employed. A simple risk score was devised by using significant variables obtained from the Cox's regression analysis to further predict the HCC patient prognosis. We observed a reduced DUOX1 mRNA level in the cancer tissues in comparison to the non‑cancer tissues. More importantly, Kaplan-Meier analysis showed that patients with high DUOX1 expression had longer disease-free survival and overall survival compared with those with low expression of DUOX1. Cox's regression analysis indicated that DUOX1 expression, age, and intrahepatic metastasis may be significant prognostic factors for disease-free survival and overall survival. Finally, we found that patients with total scores of >2 and >1 were more likely to relapse and succumb to the disease than patients whose total scores were ≤2 and ≤1. In conclusion, DUOX1 expression in liver tumors is a potential prognostic tool for patients. The risk scoring system is useful for predicting the survival of liver cancer patients after tumor resection.

Analysis of wave III of brain stem auditory evoked potential waveforms during microvascular decompression of cranial nerve VII for hemifacial spasm.

PubMed

Thirumala, Parthasarathy D; Krishnaiah, Balaji; Crammond, Donald J; Habeych, Miguel E; Balzer, Jeffrey R

2014-04-01

Intraoperative monitoring of brain stem auditory evoked potential during microvascular decompression (MVD) prevent hearing loss (HL). Previous studies have shown that changes in wave III (wIII) are an early and sensitive sign of auditory nerve injury. To evaluate the changes of amplitude and latency of wIII of brain stem auditory evoked potential during MVD and its association with postoperative HL. Hearing loss was classified by American Academy of Otolaryngology - Head and Neck Surgery (AAO-HNS) criteria, based on changes in pure tone audiometry and speech discrimination score. Retrospective analysis of wIII in patients who underwent intraoperative monitoring with brain stem auditory evoked potential during MVD was performed. A univariate logistic regression analysis was performed on independent variables amplitude of wIII and latency of wIII at change max and On-Skin, or a final recording at the time of skin closure. A further analysis for the same variables was performed adjusting for the loss of wave. The latency of wIII was not found to be significantly different between groups I and II. The amplitude of wIII was significantly decreased in the group with HL. Regression analysis did not find any increased odds of HL with changes in the amplitude of wIII. Changes in wave III did not increase the odds of HL in patients who underwent brain stem auditory evoked potential s during MVD. This information might be valuable to evaluate the value of wIII as an alarm criterion during MVD to prevent HL.

Curcumin downregulates human tumor necrosis factor-α levels: A systematic review and meta-analysis ofrandomized controlled trials.

PubMed

Sahebkar, Amirhossein; Cicero, Arrigo F G; Simental-Mendía, Luis E; Aggarwal, Bharat B; Gupta, Subash C

2016-05-01

Tumor necrosis factor-α (TNF-α) is a key inflammatory mediator and its reduction is a therapeutic target in several inflammatory diseases. Curcumin, a bioactive polyphenol from turmeric, has been shown in several preclinical studies to block TNF-α effectively. However, clinical evidence has not been fully conclusive. The aim of the present meta-analysis was to evaluate the efficacy of curcumin supplementation on circulating levels of TNF-α in randomized controlled trials (RCTs). The search included PubMed-Medline, Scopus, Web of Science and Google Scholar databases by up to September 21, 2015, to identify RCTs investigating the impact of curcumin on circulating TNF-α concentration. Quantitative data synthesis was performed using a random-effects model, with weighed mean difference (WMD) and 95% confidence interval (CI) as summary statistics. Meta-regression and leave-one-out sensitivity analyses were performed to assess the modifiers of treatment response. Eight RCTs comprising nine treatment arms were finally selected for the meta-analysis. There was a significant reduction of circulating TNF-α concentrations following curcumin supplementation (WMD: -4.69pg/mL, 95% CI: -7.10, -2.28, p<0.001). This effect size was robust in sensitivity analysis. Meta-regression did not suggest any significant association between the circulating TNF-α-lowering effects of curcumin with either dose or duration (slope: 0.197; 95% CI: -1.73, 2.12; p=0.841) of treatment. This meta-analysis of RCTs suggested a significant effect of curcumin in lowering circulating TNF-α concentration. Copyright © 2016 Elsevier Ltd. All rights reserved.

Decision tree analysis to stratify risk of de novo non-melanoma skin cancer following liver transplantation.

PubMed

Tanaka, Tomohiro; Voigt, Michael D

2018-03-01

Non-melanoma skin cancer (NMSC) is the most common de novo malignancy in liver transplant (LT) recipients; it behaves more aggressively and it increases mortality. We used decision tree analysis to develop a tool to stratify and quantify risk of NMSC in LT recipients. We performed Cox regression analysis to identify which predictive variables to enter into the decision tree analysis. Data were from the Organ Procurement Transplant Network (OPTN) STAR files of September 2016 (n = 102984). NMSC developed in 4556 of the 105984 recipients, a mean of 5.6 years after transplant. The 5/10/20-year rates of NMSC were 2.9/6.3/13.5%, respectively. Cox regression identified male gender, Caucasian race, age, body mass index (BMI) at LT, and sirolimus use as key predictive or protective factors for NMSC. These factors were entered into a decision tree analysis. The final tree stratified non-Caucasians as low risk (0.8%), and Caucasian males > 47 years, BMI < 40 who did not receive sirolimus, as high risk (7.3% cumulative incidence of NMSC). The predictions in the derivation set were almost identical to those in the validation set (r 2  = 0.971, p < 0.0001). Cumulative incidence of NMSC in low, moderate and high risk groups at 5/10/20 year was 0.5/1.2/3.3, 2.1/4.8/11.7 and 5.6/11.6/23.1% (p < 0.0001). The decision tree model accurately stratifies the risk of developing NMSC in the long-term after LT.

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

PubMed

Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

2016-04-01

Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure surfaces that can be applied in large population-based studies. To address this need, we developed a land use regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus.

PubMed

Kadiyala, Akhil; Kaur, Devinder; Kumar, Ashok

2013-02-01

The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), 0.3-0.4 microm sized particle numbers, 0.4-0.5 microm sized particle numbers, particulate matter (PM) concentrations less than 1.0 microm (PM10), and PM concentrations less than 2.5 microm (PM2.5) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH. First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based back-propagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic-algorithm-based neural network IAQ models outperformed the traditional ANN methods of the back-propagation and the radial basis function networks. The novelty of this research is the development of a novel approach to modeling vehicular indoor air quality by integration of the advanced methods of genetic algorithms, regression trees, and the analysis of variance for the monitored in-vehicle gaseous and particulate matter contaminants, and comparing the results obtained from using the developed approach with conventional artificial intelligence techniques of back propagation networks and radial basis function networks. This study validated the newly developed approach using holdout and threefold cross-validation methods. These results are of great interest to scientists, researchers, and the public in understanding the various aspects of modeling an indoor microenvironment. This methodology can easily be extended to other fields of study also.

Parameter estimation in Cox models with missing failure indicators and the OPPERA study.

PubMed

Brownstein, Naomi C; Cai, Jianwen; Slade, Gary D; Bair, Eric

2015-12-30

In a prospective cohort study, examining all participants for incidence of the condition of interest may be prohibitively expensive. For example, the "gold standard" for diagnosing temporomandibular disorder (TMD) is a physical examination by a trained clinician. In large studies, examining all participants in this manner is infeasible. Instead, it is common to use questionnaires to screen for incidence of TMD and perform the "gold standard" examination only on participants who screen positively. Unfortunately, some participants may leave the study before receiving the "gold standard" examination. Within the framework of survival analysis, this results in missing failure indicators. Motivated by the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, a large cohort study of TMD, we propose a method for parameter estimation in survival models with missing failure indicators. We estimate the probability of being an incident case for those lacking a "gold standard" examination using logistic regression. These estimated probabilities are used to generate multiple imputations of case status for each missing examination that are combined with observed data in appropriate regression models. The variance introduced by the procedure is estimated using multiple imputation. The method can be used to estimate both regression coefficients in Cox proportional hazard models as well as incidence rates using Poisson regression. We simulate data with missing failure indicators and show that our method performs as well as or better than competing methods. Finally, we apply the proposed method to data from the OPPERA study. Copyright © 2015 John Wiley & Sons, Ltd.

Application and interpretation of functional data analysis techniques to differential scanning calorimetry data from lupus patients.

PubMed

Kendrick, Sarah K; Zheng, Qi; Garbett, Nichola C; Brock, Guy N

2017-01-01

DSC is used to determine thermally-induced conformational changes of biomolecules within a blood plasma sample. Recent research has indicated that DSC curves (or thermograms) may have different characteristics based on disease status and, thus, may be useful as a monitoring and diagnostic tool for some diseases. Since thermograms are curves measured over a range of temperature values, they are considered functional data. In this paper we apply functional data analysis techniques to analyze differential scanning calorimetry (DSC) data from individuals from the Lupus Family Registry and Repository (LFRR). The aim was to assess the effect of lupus disease status as well as additional covariates on the thermogram profiles, and use FD analysis methods to create models for classifying lupus vs. control patients on the basis of the thermogram curves. Thermograms were collected for 300 lupus patients and 300 controls without lupus who were matched with diseased individuals based on sex, race, and age. First, functional regression with a functional response (DSC) and categorical predictor (disease status) was used to determine how thermogram curve structure varied according to disease status and other covariates including sex, race, and year of birth. Next, functional logistic regression with disease status as the response and functional principal component analysis (FPCA) scores as the predictors was used to model the effect of thermogram structure on disease status prediction. The prediction accuracy for patients with Osteoarthritis and Rheumatoid Arthritis but without Lupus was also calculated to determine the ability of the classifier to differentiate between Lupus and other diseases. Data were divided 1000 times into separate 2/3 training and 1/3 test data for evaluation of predictions. Finally, derivatives of thermogram curves were included in the models to determine whether they aided in prediction of disease status. Functional regression with thermogram as a functional response and disease status as predictor showed a clear separation in thermogram curve structure between cases and controls. The logistic regression model with FPCA scores as the predictors gave the most accurate results with a mean 79.22% correct classification rate with a mean sensitivity = 79.70%, and specificity = 81.48%. The model correctly classified OA and RA patients without Lupus as controls at a rate of 75.92% on average with a mean sensitivity = 79.70% and specificity = 77.6%. Regression models including FPCA scores for derivative curves did not perform as well, nor did regression models including covariates. Changes in thermograms observed in the disease state likely reflect covalent modifications of plasma proteins or changes in large protein-protein interacting networks resulting in the stabilization of plasma proteins towards thermal denaturation. By relating functional principal components from thermograms to disease status, our Functional Principal Component Analysis model provides results that are more easily interpretable compared to prior studies. Further, the model could also potentially be coupled with other biomarkers to improve diagnostic classification for lupus.

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges

PubMed Central

Goldstein, Benjamin A.; Navar, Ann Marie; Carter, Rickey E.

2017-01-01

Abstract Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. PMID:27436868

Simulation of urban land surface temperature based on sub-pixel land cover in a coastal city

NASA Astrophysics Data System (ADS)

Zhao, Xiaofeng; Deng, Lei; Feng, Huihui; Zhao, Yanchuang

2014-11-01

The sub-pixel urban land cover has been proved to have obvious correlations with land surface temperature (LST). Yet these relationships have seldom been used to simulate LST. In this study we provided a new approach of urban LST simulation based on sub-pixel land cover modeling. Landsat TM/ETM+ images of Xiamen city, China on both the January of 2002 and 2007 were used to acquire land cover and then extract the transformation rule using logistic regression. The transformation possibility was taken as its percent in the same pixel after normalization. And cellular automata were used to acquire simulated sub-pixel land cover on 2007 and 2017. On the other hand, the correlations between retrieved LST and sub-pixel land cover achieved by spectral mixture analysis in 2002 were examined and a regression model was built. Then the regression model was used on simulated 2007 land cover to model the LST of 2007. Finally the LST of 2017 was simulated for urban planning and management. The results showed that our method is useful in LST simulation. Although the simulation accuracy is not quite satisfactory, it provides an important idea and a good start in the modeling of urban LST.

Internalized stigma among psychiatric outpatients: Associations with quality of life, functioning, hope and self-esteem.

PubMed

Picco, Louisa; Pang, Shirlene; Lau, Ying Wen; Jeyagurunathan, Anitha; Satghare, Pratika; Abdin, Edimansyah; Vaingankar, Janhavi Ajit; Lim, Susan; Poh, Chee Lien; Chong, Siow Ann; Subramaniam, Mythily

2016-12-30

This study aimed to: (i) determine the prevalence, socio-demographic and clinical correlates of internalized stigma and (ii) explore the association between internalized stigma and quality of life, general functioning, hope and self-esteem, among a multi-ethnic Asian population of patients with mental disorders. This cross-sectional, survey recruited adult patients (n=280) who were seeking treatment at outpatient and affiliated clinics of the only tertiary psychiatric hospital in Singapore. Internalized stigma was measured using the Internalized Stigma of Mental Illness scale. 43.6% experienced moderate to high internalized stigma. After making adjustments in multiple logistic regression analysis, results revealed there were no significant socio-demographic or clinical correlates relating to internalized stigma. Individual logistic regression models found a negative relationship between quality of life, self-esteem, general functioning and internalized stigma whereby lower scores were associated with higher internalized stigma. In the final regression model, which included all psychosocial variables together, self-esteem was the only variable significantly and negatively associated with internalized stigma. The results of this study contribute to our understanding of the role internalized stigma plays in patients with mental illness, and the impact it can have on psychosocial aspects of their lives. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

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.

Analysis of dietary intake of selected metals in the NHEXAS-Maryland investigation.

PubMed Central

Ryan, P B; Scanlon, K A; MacIntosh, D L

2001-01-01

As part of a large pilot investigation of multimedia exposure to several classes of environmental contaminants, the National Human Exposure Assessment Survey (NHEXAS)-Maryland study, we collected 388 semiquantitative food checklists and duplicate diet solid food samples, analyzed for arsenic, cadmium, chromium, and lead concentrations, from 80 individuals in Maryland in 1995-1996 in a repeated measures design. Here we explore several methods to infer foods most strongly associated with concentrations of these metals observed in the duplicate diet in our data set. We employed two techniques in which logarithmically transformed metal concentrations in the duplicate diet were regressed on individual food item consumption using algorithms designed to identify the foods most associated with the observed duplicate diet concentrations. We also employed an alternative strategy in which foods to be used as independent variables in regression were selected using data collected in national food consumption and residue surveys, with regression procedures proceeding with the selected foods in a similar manner. The concordance of foods selected as major predictors among these three techniques is noteworthy and is discussed. Finally, the Dietary Exposure Potential Model (DEPM) was used with the Dietary Checklist data to predict duplicate diet concentrations within our sample. A comparison between the predicted values and those observed gave R(2) values of 0.180, 0.206, and 0.076 for As, Cd, and Pb, respectively (p < 0.0001 in all cases). We discuss the significance of these observations and the implications for dietary-exposure-based risk analysis and dietary intake epidemiology. PMID:11266320

A new technique for spectrophotometric determination of pseudoephedrine and guaifenesin in syrup and synthetic mixture.

PubMed

Riahi, Siavash; Hadiloo, Farshad; Milani, Seyed Mohammad R; Davarkhah, Nazila; Ganjali, Mohammad R; Norouzi, Parviz; Seyfi, Payam

2011-05-01

The accuracy in predicting different chemometric methods was compared when applied on ordinary UV spectra and first order derivative spectra. Principal component regression (PCR) and partial least squares with one dependent variable (PLS1) and two dependent variables (PLS2) were applied on spectral data of pharmaceutical formula containing pseudoephedrine (PDP) and guaifenesin (GFN). The ability to derivative in resolved overlapping spectra chloropheniramine maleate was evaluated when multivariate methods are adopted for analysis of two component mixtures without using any chemical pretreatment. The chemometrics models were tested on an external validation dataset and finally applied to the analysis of pharmaceuticals. Significant advantages were found in analysis of the real samples when the calibration models from derivative spectra were used. It should also be mentioned that the proposed method is a simple and rapid way requiring no preliminary separation steps and can be used easily for the analysis of these compounds, especially in quality control laboratories. Copyright © 2011 John Wiley & Sons, Ltd.

Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

PubMed Central

Qian, Guoqi; Wu, Yuehua; Ferrari, Davide; Qiao, Puxue; Hollande, Frédéric

2016-01-01

Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method. PMID:27212939

Maintenance Operations in Mission Oriented Protective Posture Level IV (MOPPIV)

DTIC Science & Technology

1987-10-01

Repair FADAC Printed Circuit Board ............. 6 3. Data Analysis Techniques ............................. 6 a. Multiple Linear Regression... ANALYSIS /DISCUSSION ............................... 12 1. Exa-ple of Regression Analysis ..................... 12 S2. Regression results for all tasks...6 * TABLE 9. Task Grouping for Analysis ........................ 7 "TABXLE 10. Remove/Replace H60A3 Power Pack................. 8 TABLE

Creep-Rupture Data Analysis - Engineering Application of Regression Techniques. Ph.D. Thesis - North Carolina State Univ.

NASA Technical Reports Server (NTRS)

Rummler, D. R.

1976-01-01

The results are presented of investigations to apply regression techniques to the development of methodology for creep-rupture data analysis. Regression analysis techniques are applied to the explicit description of the creep behavior of materials for space shuttle thermal protection systems. A regression analysis technique is compared with five parametric methods for analyzing three simulated and twenty real data sets, and a computer program for the evaluation of creep-rupture data is presented.

Resting-state functional magnetic resonance imaging: the impact of regression analysis.

PubMed

Yeh, Chia-Jung; Tseng, Yu-Sheng; Lin, Yi-Ru; Tsai, Shang-Yueh; Huang, Teng-Yi

2015-01-01

To investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis. Twenty volunteers (10 men and 10 women; aged 23.4 ± 1.5 years) participated in the experiments. We used node analysis and functional connectivity mapping to assess the brain default mode network by using five combinations of regression methods. The results show that regressing the global mean plays a major role in the preprocessing steps. When a global regression method is applied, the values of functional connectivity are significantly lower (P ≤ .01) than those calculated without a global regression. This step increases inter-subject variation and produces anticorrelated brain areas. rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear. Copyright © 2014 by the American Society of Neuroimaging.

The estimated effect of mass or footprint reduction in recent light-duty vehicles on U.S. societal fatality risk per vehicle mile traveled.

PubMed

Wenzel, Tom

2013-10-01

The National Highway Traffic Safety Administration (NHTSA) recently updated its 2003 and 2010 logistic regression analyses of the effect of a reduction in light-duty vehicle mass on US societal fatality risk per vehicle mile traveled (VMT; Kahane, 2012). Societal fatality risk includes the risk to both the occupants of the case vehicle as well as any crash partner or pedestrians. The current analysis is the most thorough investigation of this issue to date. This paper replicates the Kahane analysis and extends it by testing the sensitivity of his results to changes in the definition of risk, and the data and control variables used in the regression models. An assessment by Lawrence Berkeley National Laboratory (LBNL) indicates that the estimated effect of mass reduction on risk is smaller than in Kahane's previous studies, and is statistically non-significant for all but the lightest cars (Wenzel, 2012a). The estimated effects of a reduction in mass or footprint (i.e. wheelbase times track width) are small relative to other vehicle, driver, and crash variables used in the regression models. The recent historical correlation between mass and footprint is not so large to prohibit including both variables in the same regression model; excluding footprint from the model, i.e. allowing footprint to decrease with mass, increases the estimated detrimental effect of mass reduction on risk in cars and crossover utility vehicles (CUVs)/minivans, but has virtually no effect on light trucks. Analysis by footprint deciles indicates that risk does not consistently increase with reduced mass for vehicles of similar footprint. Finally, the estimated effects of mass and footprint reduction are sensitive to the measure of exposure used (fatalities per induced exposure crash, rather than per VMT), as well as other changes in the data or control variables used. It appears that the safety penalty from lower mass can be mitigated with careful vehicle design, and that manufacturers can reduce mass as a strategy to increase their vehicles' fuel economy and reduce greenhouse gas emissions without necessarily compromising societal safety. Published by Elsevier Ltd.

Standards for Standardized Logistic Regression Coefficients

ERIC Educational Resources Information Center

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

PubMed

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

A meta-analysis of association between glutathione S-transferase M1 gene polymorphism and Parkinson's disease susceptibility.

PubMed

Weikang, Chen; Jie, Li; Likang, Lan; Weiwen, Qiu; Liping, Lu

2016-01-01

The aim of this meta-analysis was to evaluate whether there was an association between glutathione S-transferase M1(GSTM1)gene polymorphism and Parkinson's disease (PD) susceptibility by pooling published data. We performed comprehensive electronic database search for articles published between February12,2015 and April30 2016. The published case-control or cohort studies related to GSTM1 gene polymorphism and Parkinson's disease susceptibility were screened, reviewed, and included in this meta-analysis. The correlation between GSTM1 gene polymorphism and PD susceptibility was expressed by odds ratio (OR) and its corresponding 95% confidence interval (95%CI). Publication bias was evaluated by Begg's funnel plot and Egger's line regression test. All analysis was done by stata11.0 software. After searching the PubMed, EMBASE, and CNKI databases, seventeen case-control studies with 3,538 PD and 5,180 controls were included in the final meta-analysis. The data was pooled by a fixed-effect model for lack of statistical heterogeneity across the studies; the results showed GSTM1 null expression can significant increase the susceptibility of PD (OR=1.11, 95% CI:1.01-1.21, P<0.05). Subgroup analysis indicated GSTM1 gene polymorphism was associated with PD susceptibility in the Caucasian ethnic group (OR=1.15, 95% CI:1.05-1.27, P<0.05) but not in the Asian ethnic group (OR=0.89, 95% CI:0.70-1.12, P>0.05). Begg's funnel plot and Egger's line regression test showed no significant publication bias. Based on the present evidence, GSTM1 null expression can significant increase the susceptibility of PD in persons of Caucasian ethnicity.

Measuring the statistical validity of summary meta‐analysis and meta‐regression results for use in clinical practice

PubMed Central

Riley, Richard D.

2017-01-01

An important question for clinicians appraising a meta‐analysis is: are the findings likely to be valid in their own practice—does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity—where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple (‘leave‐one‐out’) cross‐validation technique, we demonstrate how we may test meta‐analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution. We compare this with the usual approach of investigating heterogeneity in meta‐analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta‐analysis and a tailored meta‐regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within‐study variance, between‐study variance, study sample size, and the number of studies in the meta‐analysis. Finally, we apply Vn to two published meta‐analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta‐analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28620945

Measuring the statistical validity of summary meta-analysis and meta-regression results for use in clinical practice.

PubMed

Willis, Brian H; Riley, Richard D

2017-09-20

An important question for clinicians appraising a meta-analysis is: are the findings likely to be valid in their own practice-does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity-where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple ('leave-one-out') cross-validation technique, we demonstrate how we may test meta-analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution. We compare this with the usual approach of investigating heterogeneity in meta-analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta-analysis and a tailored meta-regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within-study variance, between-study variance, study sample size, and the number of studies in the meta-analysis. Finally, we apply Vn to two published meta-analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta-analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Design, innovation, and rural creative places: Are the arts the cherry on top, or the secret sauce?

PubMed

Wojan, Timothy R; Nichols, Bonnie

2018-01-01

Creative class theory explains the positive relationship between the arts and commercial innovation as the mutual attraction of artists and other creative workers by an unobserved creative milieu. This study explores alternative theories for rural settings, by analyzing establishment-level survey data combined with data on the local arts scene. The study identifies the local contextual factors associated with a strong design orientation, and estimates the impact that a strong design orientation has on the local economy. Data on innovation and design come from a nationally representative sample of establishments in tradable industries. Latent class analysis allows identifying unobserved subpopulations comprised of establishments with different design and innovation orientations. Logistic regression allows estimating the association between an establishment's design orientation and local contextual factors. A quantile instrumental variable regression allows assessing the robustness of the logistic regression results with respect to endogeneity. An estimate of design orientation at the local level derived from the survey is used to examine variation in economic performance during the period of recovery from the Great Recession (2010-2014). Three distinct innovation (substantive, nominal, and non-innovators) and design orientations (design-integrated, "design last finish," and no systematic approach to design) are identified. Innovation- and design-intensive establishments were identified in both rural and urban areas. Rural design-integrated establishments tended to locate in counties with more highly educated workforces and containing at least one performing arts organization. A quantile instrumental variable regression confirmed that the logistic regression result is robust to endogeneity concerns. Finally, rural areas characterized by design-integrated establishments experienced faster growth in wages relative to rural areas characterized by establishments using no systematic approach to design.

Design, innovation, and rural creative places: Are the arts the cherry on top, or the secret sauce?

PubMed Central

Nichols, Bonnie

2018-01-01

Objective Creative class theory explains the positive relationship between the arts and commercial innovation as the mutual attraction of artists and other creative workers by an unobserved creative milieu. This study explores alternative theories for rural settings, by analyzing establishment-level survey data combined with data on the local arts scene. The study identifies the local contextual factors associated with a strong design orientation, and estimates the impact that a strong design orientation has on the local economy. Method Data on innovation and design come from a nationally representative sample of establishments in tradable industries. Latent class analysis allows identifying unobserved subpopulations comprised of establishments with different design and innovation orientations. Logistic regression allows estimating the association between an establishment’s design orientation and local contextual factors. A quantile instrumental variable regression allows assessing the robustness of the logistic regression results with respect to endogeneity. An estimate of design orientation at the local level derived from the survey is used to examine variation in economic performance during the period of recovery from the Great Recession (2010–2014). Results Three distinct innovation (substantive, nominal, and non-innovators) and design orientations (design-integrated, “design last finish,” and no systematic approach to design) are identified. Innovation- and design-intensive establishments were identified in both rural and urban areas. Rural design-integrated establishments tended to locate in counties with more highly educated workforces and containing at least one performing arts organization. A quantile instrumental variable regression confirmed that the logistic regression result is robust to endogeneity concerns. Finally, rural areas characterized by design-integrated establishments experienced faster growth in wages relative to rural areas characterized by establishments using no systematic approach to design. PMID:29489884

An improved multiple linear regression and data analysis computer program package

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

Relationship between negative mental adjustment to cancer and distress in thyroid cancer patients.

PubMed

Seok, Jeong-Ho; Choi, Won-Jung; Lee, Yong Sang; Park, Cheong Soo; Oh, Young-Ja; Kim, Jong-Sun; Chang, Hang-Seok

2013-05-01

Previous studies have reported that over a third of cancer patients experience significant psychological distress with diagnosis and treatment of cancer. Mental adjustment to cancer as well as other biologic and demographic factors may be associated with their distress. We investigated the relationship between mental adjustment and distress in patients with thyroid cancer prior to thyroidectomy. One hundred and fifty-two thyroid cancer patients were included in the final analysis. After global distress levels were screened with a distress thermometer, patients were evaluated concerning mental adjustment to cancer, as well as demographic and cancer-related characteristics. A thyroid function test was also performed. Regression analysis was performed to discern significant factors associated with distress in thyroid cancer patients. Our regression model was significant and explained 38.5% of the total variance in distress of this patient group. Anxious-preoccupation and helpless-hopeless factors on the mental adjustment to cancer scale were significantly associated with distress in thyroid cancer patients. Negative emotional response to cancer diagnosis may be associated with distress in thyroid cancer patients awaiting thyroidectomy. Screening of mental coping strategies at the beginning of cancer treatment may predict psychological distress in cancer patients. Further studies on the efficacy of psychiatric intervention during cancer treatment may be needed for patients showing maladaptive psychological responses to cancer.

Oxidative desulfurization: kinetic modelling.

PubMed

Dhir, S; Uppaluri, R; Purkait, M K

2009-01-30

Increasing environmental legislations coupled with enhanced production of petroleum products demand, the deployment of novel technologies to remove organic sulfur efficiently. This work represents the kinetic modeling of ODS using H(2)O(2) over tungsten-containing layered double hydroxide (LDH) using the experimental data provided by Hulea et al. [V. Hulea, A.L. Maciuca, F. Fajula, E. Dumitriu, Catalytic oxidation of thiophenes and thioethers with hydrogen peroxide in the presence of W-containing layered double hydroxides, Appl. Catal. A: Gen. 313 (2) (2006) 200-207]. The kinetic modeling approach in this work initially targets the scope of the generation of a superstructure of micro-kinetic reaction schemes and models assuming Langmuir-Hinshelwood (LH) and Eley-Rideal (ER) mechanisms. Subsequently, the screening and selection of above models is initially based on profile-based elimination of incompetent schemes followed by non-linear regression search performed using the Levenberg-Marquardt algorithm (LMA) for the chosen models. The above analysis inferred that Eley-Rideal mechanism describes the kinetic behavior of ODS process using tungsten-containing LDH, with adsorption of reactant and intermediate product only taking place on the catalyst surface. Finally, an economic index is presented that scopes the economic aspects of the novel catalytic technology with the parameters obtained during regression analysis to conclude that the cost factor for the catalyst is 0.0062-0.04759 US $ per barrel.

Impact of Depression, Fatigue, and Global Measure of Cortical Volume on Cognitive Impairment in Multiple Sclerosis

PubMed Central

De Cola, Maria Cristina; D'Aleo, Giangaetano; Sessa, Edoardo; Marino, Silvia

2015-01-01

Objective. To investigate the influence of demographic and clinical variables, such as depression, fatigue, and quantitative MRI marker on cognitive performances in a sample of patients affected by multiple sclerosis (MS). Methods. 60 MS patients (52 relapsing remitting and 8 primary progressive) underwent neuropsychological assessments using Rao's Brief Repeatable Battery of Neuropsychological Tests (BRB-N), the Beck Depression Inventory-second edition (BDI-II), and the Fatigue Severity Scale (FSS). We performed magnetic resonance imaging to all subjects using a 3 T scanner and obtained tissue-specific volumes (normalized brain volume and cortical brain volume). We used Student's t-test to compare depressed and nondepressed MS patients. Finally, we performed a multivariate regression analysis in order to assess possible predictors of patients' cognitive outcome among demographic and clinical variables. Results. 27.12% of the sample (16/59) was cognitively impaired, especially in tasks requiring attention and information processing speed. From between group comparison, we find that depressed patients had worse performances on BRB-N score, greater disability and disease duration, and brain volume decrease. According to multiple regression analysis, the BDI-II score was a significant predictor for most of the neuropsychological tests. Conclusions. Our findings suggest that the presence of depressive symptoms is an important determinant of cognitive performance in MS patients. PMID:25861633

Statistical analysis of mixed recurrent event data with application to cancer survivor study

PubMed Central

Zhu, Liang; Tong, Xingwei; Zhao, Hui; Sun, Jianguo; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L.

2014-01-01

Event history studies occur in many fields including economics, medical studies and social science. In such studies concerning some recurrent events, two types of data have been extensively discussed in the literature. One is recurrent event data that arise if study subjects are monitored or observed continuously. In this case, the observed information provides the times of all occurrences of the recurrent events of interest. The other is panel count data, which occur if the subjects are monitored or observed only periodically. This can happen if the continuous observation is too expensive or not practical and in this case, only the numbers of occurrences of the events between subsequent observation times are available. In this paper, we discuss a third type of data, which is a mixture of recurrent event and panel count data and for which there exists little literature. For regression analysis of such data, a marginal mean model is presented and we propose an estimating equation-based approach for estimation of regression parameters. A simulation study is conducted to assess the finite sample performance of the proposed methodology and indicates that it works well for practical situations. Finally it is applied to a motivating study on childhood cancer survivors. PMID:23139023

Demographic and clinical features related to perceived discrimination in schizophrenia.

PubMed

Fresán, Ana; Robles-García, Rebeca; Madrigal, Eduardo; Tovilla-Zarate, Carlos-Alfonso; Martínez-López, Nicolás; Arango de Montis, Iván

2018-04-01

Perceived discrimination contributes to the development of internalized stigma among those with schizophrenia. Evidence on demographic and clinical factors related to the perception of discrimination among this population is both contradictory and scarce in low- and middle-income countries. Accordingly, the main purpose of this study is to determine the demographic and clinical factors predicting the perception of discrimination among Mexican patients with schizophrenia. Two hundred and seventeen adults with paranoid schizophrenia completed an interview on their demographic status and clinical characteristics. Symptom severity was assessed using the Positive and Negative Syndrome Scale; and perceived discrimination using 13 items from the King's Internalized Stigma Scale. Bivariate linear associations were determined to identify the variables of interest to be included in a linear regression analysis. Years of education, age of illness onset and length of hospitalization were associated with discrimination. However, only age of illness onset and length of hospitalization emerged as predictors of perceived discrimination in the final regression analysis, with longer length of hospitalization being the independent variable with the greatest contribution. Fortunately, this is a modifiable factor regarding the perception of discrimination and self-stigma. Strategies for achieving this as part of community-based mental health care are also discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Depression in non-Korean women residing in South Korea following marriage to Korean men.

PubMed

Kim, Hyun-Sil; Kim, Hun-Soo

2013-06-01

The purpose of the study was to examine the roles of acculturative stress, life satisfaction, and language literacy in depression in non-Korean women residing in South Korea following marriage to Korean men. A cross-sectional study was performed, using an anonymous, self-reporting questionnaire. A total of 173 women were selected using a proportional stratified random sampling method. The relation between acculturation, depression, language literacy, life satisfaction and socio-demographic variables and the predictors of depression among participants were analyzed. The analysis included descriptive statistics and hierarchical multiple regression. Of the participants, 9.2% had depression, which was almost twice the rate of depression found in the general Korean population. In hierarchical multiple regression analysis, acculturative stress (beta=-.325, P<.001) and life satisfaction (beta=-.282, P=.003) were significantly associated with the level of depression. This final model was statistically significant and life satisfaction, acculturative stress, language literacy accounted for 31.0% (adjusted R(2)) of the variance in the depression score (P<.001). Elevated acculturative stress and less life satisfaction were significantly associated with a higher level of depression in migrant wives in Korea. Implications for practice and research are discussed. Copyright © 2013 Elsevier Inc. All rights reserved.

Compressive strength of human openwedges: a selection method

NASA Astrophysics Data System (ADS)

Follet, H.; Gotteland, M.; Bardonnet, R.; Sfarghiu, A. M.; Peyrot, J.; Rumelhart, C.

2004-02-01

A series of 44 samples of bone wedges of human origin, intended for allograft openwedge osteotomy and obtained without particular precautions during hip arthroplasty were re-examined. After viral inactivity chemical treatment, lyophilisation and radio-sterilisation (intended to produce optimal health safety), the compressive strength, independent of age, sex and the height of the sample (or angle of cut), proved to be too widely dispersed [ 10{-}158 MPa] in the first study. We propose a method for selecting samples which takes into account their geometry (width, length, thicknesses, cortical surface area). Statistical methods (Principal Components Analysis PCA, Hierarchical Cluster Analysis, Multilinear regression) allowed final selection of 29 samples having a mean compressive strength σ_{max} =103 MPa ± 26 and with variation [ 61{-}158 MPa] . These results are equivalent or greater than average materials currently used in openwedge osteotomy.

Detection of Bi-Directionality in Strain-Gage Balance Calibration Data

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert

2012-01-01

An indicator variable was developed for both visualization and detection of bi-directionality in wind tunnel strain-gage balance calibration data. First, the calculation of the indicator variable is explained in detail. Then, a criterion is discussed that may be used to decide which gage outputs of a balance have bi- directional behavior. The result of this analysis could be used, for example, to justify the selection of certain absolute value or other even function terms in the regression model of gage outputs whenever the Iterative Method is chosen for the balance calibration data analysis. Calibration data of NASA s MK40 Task balance is analyzed to illustrate both the calculation of the indicator variable and the application of the proposed criterion. Finally, bi directionality characteristics of typical multi piece, hybrid, single piece, and semispan balances are determined and discussed.

The future is in the numbers: the power of predictive analysis in the biomedical educational environment.

PubMed

Gullo, Charles A

2016-01-01

Biomedical programs have a potential treasure trove of data they can mine to assist admissions committees in identification of students who are likely to do well and help educational committees in the identification of students who are likely to do poorly on standardized national exams and who may need remediation. In this article, we provide a step-by-step approach that schools can utilize to generate data that are useful when predicting the future performance of current students in any given program. We discuss the use of linear regression analysis as the means of generating that data and highlight some of the limitations. Finally, we lament on how the combination of these institution-specific data sets are not being fully utilized at the national level where these data could greatly assist programs at large.

Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey)

NASA Astrophysics Data System (ADS)

Ozdemir, Adnan

2011-07-01

SummaryThe purpose of this study is to produce a groundwater spring potential map of the Sultan Mountains in central Turkey, based on a logistic regression method within a Geographic Information System (GIS) environment. Using field surveys, the locations of the springs (440 springs) were determined in the study area. In this study, 17 spring-related factors were used in the analysis: geology, relative permeability, land use/land cover, precipitation, elevation, slope, aspect, total curvature, plan curvature, profile curvature, wetness index, stream power index, sediment transport capacity index, distance to drainage, distance to fault, drainage density, and fault density map. The coefficients of the predictor variables were estimated using binary logistic regression analysis and were used to calculate the groundwater spring potential for the entire study area. The accuracy of the final spring potential map was evaluated based on the observed springs. The accuracy of the model was evaluated by calculating the relative operating characteristics. The area value of the relative operating characteristic curve model was found to be 0.82. These results indicate that the model is a good estimator of the spring potential in the study area. The spring potential map shows that the areas of very low, low, moderate and high groundwater spring potential classes are 105.586 km 2 (28.99%), 74.271 km 2 (19.906%), 101.203 km 2 (27.14%), and 90.05 km 2 (24.671%), respectively. The interpretations of the potential map showed that stream power index, relative permeability of lithologies, geology, elevation, aspect, wetness index, plan curvature, and drainage density play major roles in spring occurrence and distribution in the Sultan Mountains. The logistic regression approach has not yet been used to delineate groundwater potential zones. In this study, the logistic regression method was used to locate potential zones for groundwater springs in the Sultan Mountains. The evolved model was found to be in strong agreement with the available groundwater spring test data. Hence, this method can be used routinely in groundwater exploration under favourable conditions.

[A SAS marco program for batch processing of univariate Cox regression analysis for great database].

PubMed

Yang, Rendong; Xiong, Jie; Peng, Yangqin; Peng, Xiaoning; Zeng, Xiaomin

2015-02-01

To realize batch processing of univariate Cox regression analysis for great database by SAS marco program. We wrote a SAS macro program, which can filter, integrate, and export P values to Excel by SAS9.2. The program was used for screening survival correlated RNA molecules of ovarian cancer. A SAS marco program could finish the batch processing of univariate Cox regression analysis, the selection and export of the results. The SAS macro program has potential applications in reducing the workload of statistical analysis and providing a basis for batch processing of univariate Cox regression analysis.

Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models

ERIC Educational Resources Information Center

Shieh, Gwowen

2009-01-01

In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…

Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

USDA-ARS?s Scientific Manuscript database

Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

Are Scores From NBME Subject Examinations Valid Measures of Knowledge Acquired During Clinical Clerkships?

PubMed

Ryan, Michael S; Bishop, Steven; Browning, Joel; Anand, Rahul J; Waterhouse, Elizabeth; Rigby, Fidelma; Al-Mateen, Cheryl S; Lee, Clifton; Bradner, Melissa; Colbert-Getz, Jorie M

2017-06-01

The National Board of Medical Examiners' Clinical Science Subject Examinations are a component used by most U.S. medical schools to determine clerkship grades. The purpose of this study was to examine the validity of this practice. This was a retrospective cohort study of medical students at the Virginia Commonwealth University School of Medicine who completed clerkships in 2012 through 2014. Linear regression was used to determine how well United States Medical Licensing Examination Step 1 scores predicted Subject Examination scores in seven clerkships. The authors then substituted each student's Subject Examination standard scores with his or her Step 1 standard score. Clerkship grades based on the Step 1 substitution were compared with actual grades with the Wilcoxon rank test. A total of 2,777 Subject Examination scores from 432 students were included in the analysis. Step 1 scores significantly predicted between 23% and 44% of the variance in Subject Examination scores, P < .001 for all clerkship regression equations. Mean differences between expected and actual Subject Examination scores were small (≤ 0.2 points). There was a match between 73% of Step 1 substituted final clerkship grades and actual final clerkship grades. The results of this study suggest that performance on Step 1 can be used to identify and counsel students at risk for poor performance on the Subject Examinations. In addition, these findings call into the question the validity of using scores from Subject Examinations as a high-stakes assessment of learning in individual clerkships.

Factors Impacting Growth in Infants with Single Ventricle Physiology: A Report from Pediatric Heart Network Infant Single Ventricle Trial

PubMed Central

Williams, Richard V.; Zak, Victor; Ravishankar, Chitra; Altmann, Karen; Anderson, Jeffrey; Atz, Andrew M.; Dunbar-Masterson, Carolyn; Ghanayem, Nancy; Lambert, Linda; Lurito, Karen; Medoff-Cooper, Barbara; Margossian, Renee; Pemberton, Victoria L.; Russell, Jennifer; Stylianou, Mario; Hsu, Daphne

2011-01-01

Objectives To describe growth patterns in infants with single ventricle physiology and determine factors influencing growth. Study design Data from 230 subjects enrolled in the Pediatric Heart Network Infant Single Ventricle Enalapril Trial were used to assess factors influencing change in weight-for-age z-score (Δz) from study enrollment (0.7 ± 0.4 months) to pre-superior cavopulmonary connection (SCPC) (5.1 ± 1.8 months, period 1), and pre-SCPC to final study visit (14.1 ± 0.9 months, period 2). Predictor variables included patient characteristics, feeding regimen, clinical center, and medical factors during neonatal (period 1) and SCPC hospitalizations (period 2). Univariate regression analysis was performed, followed by backward stepwise regression and bootstrapping reliability to inform a final multivariable model. Results Weights were available for 197/230 subjects for period 1 and 173/197 for period 2. For period 1, greater gestational age, younger age at study enrollment, tube feeding at neonatal discharge, and clinical center were associated with a greater negative Δz (poorer growth) in multivariable modeling (adjusted R2 = 0.39, p < 0.001). For period 2, younger age at SCPC and greater daily caloric intake were associated with greater positive Δz (better growth) (R2 = 0.10, p = 0.002). Conclusions Aggressive nutritional support and earlier SCPC are modifiable factors associated with a favorable change in weight-for-age z-score. PMID:21784436

Effectiveness of the New Hampshire stream-gaging network in providing regional streamflow information

USGS Publications Warehouse

Olson, Scott A.

2003-01-01

The stream-gaging network in New Hampshire was analyzed for its effectiveness in providing regional information on peak-flood flow, mean-flow, and low-flow frequency. The data available for analysis were from stream-gaging stations in New Hampshire and selected stations in adjacent States. The principles of generalized-least-squares regression analysis were applied to develop regional regression equations that relate streamflow-frequency characteristics to watershed characteristics. Regression equations were developed for (1) the instantaneous peak flow with a 100-year recurrence interval, (2) the mean-annual flow, and (3) the 7-day, 10-year low flow. Active and discontinued stream-gaging stations with 10 or more years of flow data were used to develop the regression equations. Each stream-gaging station in the network was evaluated and ranked on the basis of how much the data from that station contributed to the cost-weighted sampling-error component of the regression equation. The potential effect of data from proposed and new stream-gaging stations on the sampling error also was evaluated. The stream-gaging network was evaluated for conditions in water year 2000 and for estimated conditions under various network strategies if an additional 5 years and 20 years of streamflow data were collected. The effectiveness of the stream-gaging network in providing regional streamflow information could be improved for all three flow characteristics with the collection of additional flow data, both temporally and spatially. With additional years of data collection, the greatest reduction in the average sampling error of the regional regression equations was found for the peak- and low-flow characteristics. In general, additional data collection at stream-gaging stations with unregulated flow, relatively short-term record (less than 20 years), and drainage areas smaller than 45 square miles contributed the largest cost-weighted reduction to the average sampling error of the regional estimating equations. The results of the network analyses can be used to prioritize the continued operation of active stations, the reactivation of discontinued stations, or the activation of new stations to maximize the regional information content provided by the stream-gaging network. Final decisions regarding altering the New Hampshire stream-gaging network would require the consideration of the many uses of the streamflow data serving local, State, and Federal interests.

Predicting the risk of patients with biopsy Gleason score 6 to harbor a higher grade cancer.

PubMed

Gofrit, Ofer N; Zorn, Kevin C; Taxy, Jerome B; Lin, Shang; Zagaja, Gregory P; Steinberg, Gary D; Shalhav, Arieh L

2007-11-01

Prostate cancer Gleason score 3 + 3 = 6 is currently the most common score assigned on prostatic biopsies. We analyzed the clinical variables that predict the likelihood of a patient with biopsy Gleason score 6 to harbor a higher grade tumor. The study population consisted of 448 patients with a mean age of 59.1 years who underwent radical prostatectomy between February 2003 to October 2006 for Gleason score 6 adenocarcinoma. The effect of preoperative variables on the probability of a Gleason score upgrade on final pathological evaluation was evaluated using logistic regression, and classification and regression tree analysis. Gleason score upgrade was found in 91 of 448 patients (20.3%). Logistic regression showed that only serum prostate specific antigen and the greatest percent of cancer in a core were significantly associated with a score upgrade (p = 0.0014 and 0.023, respectively). Classification and regression tree analysis showed that the risk of a Gleason score upgrade was 62% when serum prostate specific antigen was higher than 12 ng/ml and 18% when serum prostate specific antigen was 12 ng/ml or less. In patients with serum prostate specific antigen lower than 12 ng/ml the risk of a score upgrade could be dichotomized at a greatest percent of cancer in a core of 5%. The risk was 22.6% and 10.5% when the greatest percent of cancer in a core was higher than 5% and 5% or lower, respectively. The probability of patients with a prostate biopsy Gleason score of 6 to conceal a Gleason score of 7 or higher can be predicted using serum prostate specific antigen and the greatest percent of cancer in a core. With these parameters it is possible to predict upgrade rates as high as 62% and as low as 10.5%.

The Impact and Implication of Peripheral Vascular Leakage on Ultra-Widefield Fluorescein Angiography in Uveitis.

PubMed

Thomas, Akshay S; Redd, Travis; Campbell, John P; Palejwala, Neal V; Baynham, Justin T; Suhler, Eric B; Rosenbaum, James T; Lin, Phoebe

2017-10-16

To study if peripheral vascular leakage (PVL) on ultra-widefield fluorescein angiography (UWFFA) prognosticates complications of uveitis or necessitates treatment augmentation. Retrospective cohort study of uveitis patients imaged with UWFFA and ≥1 yr of follow-up. We included 73 eyes of 42 patients with uveitis. There was no difference in baseline, intermediate, final visual acuity (p = 0.47-0.95) or rates of cystoid macular edema (CME) (p = 0.37-0.87) in eyes with PVL vs. those without. Eyes with PVL receiving baseline treatment augmentation were more likely to have baseline CME but were not more likely to have impaired visual acuity at final follow-up. PVL was independently associated with treatment augmentation on generalized estimating equation analysis with multivariable linear regression (OR: 4.39, p = 0.015). PVL did not confer an increased risk of impaired VA or CME at ≥1 yr follow-up but was possibly an independent driver of treatment augmentation.

Relationship between long working hours and depression: a 3-year longitudinal study of clerical workers.

PubMed

Amagasa, Takashi; Nakayama, Takeo

2013-08-01

To clarify how long working hours affect the likelihood of current and future depression. Using data from four repeated measurements collected from 218 clerical workers, four models associating work-related factors to the depressive mood scale were established. The final model was constructed after comparing and testing the goodness-of-fit index using structural equation modeling. Multiple logistic regression analysis was also performed. The final model showed the best fit (normed fit index = 0.908; goodness-of-fit index = 0.936; root-mean-square error of approximation = 0.018). Its standardized total effect indicated that long working hours affected depression at the time of evaluation and 1 to 3 years later. The odds ratio for depression risk was 14.7 in employees who were not long-hours overworked according to the initial survey but who were long-hours overworked according to the second survey. Long working hours increase current and future risks of depression.

On the Effectiveness of Security Countermeasures for Critical Infrastructures.

PubMed

Hausken, Kjell; He, Fei

2016-04-01

A game-theoretic model is developed where an infrastructure of N targets is protected against terrorism threats. An original threat score is determined by the terrorist's threat against each target and the government's inherent protection level and original protection. The final threat score is impacted by the government's additional protection. We investigate and verify the effectiveness of countermeasures using empirical data and two methods. The first is to estimate the model's parameter values to minimize the sum of the squared differences between the government's additional resource investment predicted by the model and the empirical data. The second is to develop a multivariate regression model where the final threat score varies approximately linearly relative to the original threat score, sectors, and threat scenarios, and depends nonlinearly on the additional resource investment. The model and method are offered as tools, and as a way of thinking, to determine optimal resource investments across vulnerable targets subject to terrorism threats. © 2014 Society for Risk Analysis.

Key team physical and technical performance indicators indicative of team quality in the soccer Chinese super league.

PubMed

Yang, Gai; Leicht, Anthony S; Lago, Carlos; Gómez, Miguel-Ángel

2018-01-01

The aim of this study was to identify the key physical and technical performance variables related to team quality in the Chinese Super League (CSL). Teams' performance variables were collected from 240 matches and analysed via analysis of variance between end-of-season-ranked groups and multinomial logistic regression. Significant physical performance differences between groups were identified for sprinting (top-ranked group vs. upper-middle-ranked group) and total distance covered without possession (upper and upper-middle-ranked groups and lower-ranked group). For technical performance, teams in the top-ranked group exhibited a significantly greater amount of possession in opponent's half, number of entry passes in the final 1/3 of the field and the Penalty Area, and 50-50 challenges than lower-ranked teams. Finally, time of possession increased the probability of a win compared with a draw. The current study identified key performance indicators that differentiated end-season team quality within the CSL.

Development of a User Interface for a Regression Analysis Software Tool

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred; Volden, Thomas R.

2010-01-01

An easy-to -use user interface was implemented in a highly automated regression analysis tool. The user interface was developed from the start to run on computers that use the Windows, Macintosh, Linux, or UNIX operating system. Many user interface features were specifically designed such that a novice or inexperienced user can apply the regression analysis tool with confidence. Therefore, the user interface s design minimizes interactive input from the user. In addition, reasonable default combinations are assigned to those analysis settings that influence the outcome of the regression analysis. These default combinations will lead to a successful regression analysis result for most experimental data sets. The user interface comes in two versions. The text user interface version is used for the ongoing development of the regression analysis tool. The official release of the regression analysis tool, on the other hand, has a graphical user interface that is more efficient to use. This graphical user interface displays all input file names, output file names, and analysis settings for a specific software application mode on a single screen which makes it easier to generate reliable analysis results and to perform input parameter studies. An object-oriented approach was used for the development of the graphical user interface. This choice keeps future software maintenance costs to a reasonable limit. Examples of both the text user interface and graphical user interface are discussed in order to illustrate the user interface s overall design approach.

Regression Analysis and the Sociological Imagination

ERIC Educational Resources Information Center

De Maio, Fernando

2014-01-01

Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.

Knowledge, attitudes and practices of hypertensive patients towards prevention and early detection of chronic kidney disease: a cross sectional study from Palestine.

PubMed

Sa'adeh, Hala H; Darwazeh, Razan N; Khalil, Amani A; Zyoud, Sa'ed H

2018-01-01

Hypertension is the second most common cause of chronic kidney disease (CKD). Therefore, the aims of the study were to assess the knowledge, attitudes and practices (KAP) of hypertensive patients towards prevention and early detection of CKD, and to determine the clinical and socio-demographic factors, which affect the KAP regarding prevention of CKD. A cross-sectional study was held using the CKD screening Index to assess the KAP of 374 hypertensive patients who were selected from multiple primary healthcare centers in Nablus, Palestine. The CKD Screening Index is formed of three scales. First, the knowledge scale was a dichotomous scale of 30 items, while the attitude scale used 5-point Likert-type scale for 18 items and finally the practice scale was measured using 4-point Likert-type scale for 12 items. Multiple linear regression analysis was used to determine the association between clinical and socio-demographic factors and practices. In total, 374 hypertensive patients participated in the study. The mean age of participants was 59.14 ± 10.4 years, (range 26-85). The median (interquartile range) of the knowledge, attitude, and practice scores of hypertensive patients towards prevention and early detection of CKD were 20 (16-23), 69 (65-72), and 39 (36-42), respectively. In multiple linear regression analysis, patients age < 65 years ( p  < 0.001) and patients with high education level ( p  = 0.009) were the only factors significantly associated with higher knowledge scores. Additionally, patients age < 65 years ( p  = 0.007), patients with high income ( p  = 0.005), and patients with high knowledge score ( p  < 0.001) were the only factors significantly associated with higher attitude scores. Furthermore, regression analysis showed that patients with higher total knowledge ( p  = 0.001) as well as higher total attitudes scores towards CKD prevention ( p  < 0.001), male gender ( p  = 0.048), and patients with normal body mass index (BMI) ( p  = 0.026) were statistically significantly associated with higher practice score towards CKD prevention. Among hypertensive patients, higher scores for total knowledge and attitudes toward prevention, male sex, and normal BMI were associated with modestly higher scores for prevention practices. Finally the findings may encourage healthcare workers to give better counseling to improve knowledge.

Telangiectatic focal nodular hyperplasia of the liver in an infant with spontaneous regression: a case report.

PubMed

Hirakawa, Hitoshi; Ueno, Shigeru; Matuda, Hiromitu; Hinoki, Tomoya; Kato, Yuko

2009-04-20

A distinctive mass in the liver in a two-month-old girl with elevated serum alpha-fetoprotein (AFP) level was diagnosed as telangiectatic focal nodular hyperplasia (FNH) after biopsy. The tumor spontaneously regressed and finally became no longer detectable by any imaging study within normal range of AFP. The nature of this novel entity and its management are discussed based on literature review.

How many stakes are required to measure the mass balance of a glacier?

USGS Publications Warehouse

Fountain, A.G.; Vecchia, A.

1999-01-01

Glacier mass balance is estimated for South Cascade Glacier and Maclure Glacier using a one-dimensional regression of mass balance with altitude as an alternative to the traditional approach of contouring mass balance values. One attractive feature of regression is that it can be applied to sparse data sets where contouring is not possible and can provide an objective error of the resulting estimate. Regression methods yielded mass balance values equivalent to contouring methods. The effect of the number of mass balance measurements on the final value for the glacier showed that sample sizes as small as five stakes provided reasonable estimates, although the error estimates were greater than for larger sample sizes. Different spatial patterns of measurement locations showed no appreciable influence on the final value as long as different surface altitudes were intermittently sampled over the altitude range of the glacier. Two different regression equations were examined, a quadratic, and a piecewise linear spline, and comparison of results showed little sensitivity to the type of equation. These results point to the dominant effect of the gradient of mass balance with altitude of alpine glaciers compared to transverse variations. The number of mass balance measurements required to determine the glacier balance appears to be scale invariant for small glaciers and five to ten stakes are sufficient.

Distant stereoacuity in children with anisometropic amblyopia.

PubMed

Chung, Yeon Woong; Park, Shin Hae; Shin, Sun Young

2017-09-01

To characterize changes in distant stereoacuity using Frisby-Davis Distance test (FD2) and Distant Randot test (DR) during treatment for anisometropic amblyopia, to determine factors that influence posttreatment stereoacuity and to compare the two distant stereotests. Fifty-eight anisometropic amblyopic patients with an interocular difference of ≥1.00 diopter who achieved the visual acuity 20/20 following amblyopia treatment were retrospectively included. Stereoacuity using FD2 and DR for distant and Titmus test for near measurement were assessed and compared at the initial, intermediate, and final visit. Multivariate regression models were used to identify factors associated with initial and final stereoacuity. The two distant stereotests revealed a significant improvement in distant stereoacuity after successful amblyopia treatment. Distant stereoacuity using FD2 showed the greatest improvement during the follow up period. The number of nil scores was higher in DR than FD2 at each period. In multivariate analysis, better final stereoacuity was associated with better initial amblyopic eye acuity in both distant stereotests, but not in the Titmus test. Comparing the two distant stereotests, final stereoacuity using FD2 was associated with initial stereoacuity and was moderately related with the Titmus test at each period, but final stereoacuity using DR was not. Distant stereoacuity measured with both FD2 and DR showed significant improvement when the visual acuity of the amblyopic eye achieved 20/20. Changes in distant stereoacuity by FD2 and DR during the amblyopia treatment were somewhat different.

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Effect of the full implementation of the European Working Time Directive on operative training in adult cardiac surgery.

PubMed

Mahesh, Balakrishnan; Sharples, Linda; Codispoti, Massimiliano

2014-01-01

Surgical specialties rely on practice and apprenticeship to acquire technical skills. In 2009, the final reduction in working hours to 48 per week, in accordance with the European Working Time Directive (EWTD), has also led to an expansion in the number of trainees. We examined the effect of these changes on operative training in a single high-volume [>1500 procedures/year] adult cardiac surgical center. Setting: A single high-volume [>1500 procedures/year] adult cardiac surgical center. Design: Consecutive data were prospectively collected into a database and retrospectively analyzed. Procedures and Main Outcome Measures: Between January 2006 and August 2010, 6688 consecutive adult cardiac surgical procedures were analyzed. The proportion of cases offered for surgical training were compared for 2 non-overlapping consecutive time periods: 4504 procedures were performed before the final implementation of the EWTD (Phase 1: January 2006-December 2008) and 2184 procedures after the final implementation of the EWTD (Phase 2: January 2009-August 2010). Other predictors of training considered in the analysis were grade of trainee, logistic European system for cardiac operative risk evaluation (EuroSCORE), type of surgical procedure, weekend or late procedure, and consultant. Logistic regression analysis was used to determine the predictors of training cases (procedure performed by trainee) and to evaluate the effect of the EWTD on operative surgical training after correcting for confounding factors. Proportion of training cases rose from 34.6% (1558/4504) during Phase 1 to 43.6% (953/2184) in Phase 2 (p < 0.0001), despite higher mean logistic EuroSCORE [4.29 (6.8) during Phase 1 vs 4.95 (7.2) during Phase 2, p < 0.0001] and higher proportion of cases performed out of hours [153 (3.4) during Phase 1 vs 116 (5.3) during Phase 2, p < 0.0001]. During Phase 1, senior trainees (last 2 years of training) performed 803 (17.8%) procedures, whereas other trainees (first 4 years of training) performed 755(16.8%) cases. During Phase 2, senior trainees performed 763 (34.9%) procedures, whereas other trainees performed 190 (8.7%) cases (p < 0.0001). Independent positive predictors of training cases emerging from the multivariable logistic regression model included consultant in charge, final EWTD, and senior trainees. Independent negative predictors of training cases included logistic EuroSCORE, out-of-hours' procedures, and surgery other than coronary artery bypass grafts. Implementation of the final phase of EWTD has not decreased training in a high-volume center. The positive adjustment of trainers' attitudes and efforts to match trainees' needs allow maintenance of adequate training, despite reduction in working hours and increasing patients' risk profile. Copyright © 2014 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Serial in-office laser treatment of vocal fold leukoplakia: Disease control and voice outcomes.

PubMed

Koss, Shira L; Baxter, Peter; Panossian, Haig; Woo, Peak; Pitman, Michael J

2017-07-01

Although vocal fold (VF) leukoplakia is commonly treated with in-office laser, there is no data on its long-term effectiveness. This study hypothesizes that VF leukoplakia treated by serial in-office laser results in long-term disease control with maintenance of voice and minimal morbidity. Retrospective review (2008-2015). Forty-six patients with VF leukoplakia treated by in-office KTP (potassium titanyl phosphate) or PDL (pulsed dye laser) were included. Median follow-up from final laser treatment was 19.6 months. Main outcomes included: 1) rate of disease control, 2) percentage of disease regression using ImageJ analysis. Secondary outcomes included vocal assessment using the Voice Handicap Index-10 (VHI-10). Patients underwent a median of 2 (range: 1-6) in-office laser treatments. Time between treatments was median 7.6 months. After final treatment, 19 patients (41.3%) had no disease; two patients (4.3%) progressed to invasive cancer; overall disease regression was median 77.1% (P < 0.001); and VHI-10 score decreased by median 5 (P = 0.037). Thirty-one patients (67.4%) were responders (controlled with in-office treatment only); failures were 13 patients (28.3%) who required operative intervention and two patients (4%) who underwent radiation. Compared to responders, failures demonstrated significantly shorter duration between treatments (median 2.3 vs. 8.9 months, P = 0.038) and significantly less regression (median 49.3% vs. 100%, P = 0.006). Serial outpatient KTP or PDL treatment of VF leukoplakia is effective for disease control with minimal morbidity and preservation of voice quality. We suggest that patients requiring repeated in-office treatment every 6 months may benefit from earlier operative intervention; other factors associated with in-office success remain unclear. 4. Laryngoscope, 127:1644-1651, 2017. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Classification and regression tree (CART) analysis of endometrial carcinoma: Seeing the forest for the trees.

PubMed

Barlin, Joyce N; Zhou, Qin; St Clair, Caryn M; Iasonos, Alexia; Soslow, Robert A; Alektiar, Kaled M; Hensley, Martee L; Leitao, Mario M; Barakat, Richard R; Abu-Rustum, Nadeem R

2013-09-01

The objectives of the study are to evaluate which clinicopathologic factors influenced overall survival (OS) in endometrial carcinoma and to determine if the surgical effort to assess para-aortic (PA) lymph nodes (LNs) at initial staging surgery impacts OS. All patients diagnosed with endometrial cancer from 1/1993-12/2011 who had LNs excised were included. PALN assessment was defined by the identification of one or more PALNs on final pathology. A multivariate analysis was performed to assess the effect of PALNs on OS. A form of recursive partitioning called classification and regression tree (CART) analysis was implemented. Variables included: age, stage, tumor subtype, grade, myometrial invasion, total LNs removed, evaluation of PALNs, and adjuvant chemotherapy. The cohort included 1920 patients, with a median age of 62 years. The median number of LNs removed was 16 (range, 1-99). The removal of PALNs was not associated with OS (P=0.450). Using the CART hierarchically, stage I vs. stages II-IV and grades 1-2 vs. grade 3 emerged as predictors of OS. If the tree was allowed to grow, further branching was based on age and myometrial invasion. Total number of LNs removed and assessment of PALNs as defined in this study were not predictive of OS. This innovative CART analysis emphasized the importance of proper stage assignment and a binary grading system in impacting OS. Notably, the total number of LNs removed and specific evaluation of PALNs as defined in this study were not important predictors of OS. Copyright © 2013 Elsevier Inc. All rights reserved.

Association of Sociodemographic Factors, Smoking-Related Beliefs, and Smoking Restrictions With Intention to Quit Smoking in Korean Adults: Findings From the ITC Korea Survey

PubMed Central

Myung, Seung-Kwon; Seo, Hong Gwan; Cheong, Yoo-Seock; Park, Sohee; Lee, Wonkyong B; Fong, Geoffrey T

2012-01-01

Background Few studies have reported the factors associated with intention to quit smoking among Korean adult smokers. This study aimed to examine sociodemographic characteristics, smoking-related beliefs, and smoking-restriction variables associated with intention to quit smoking among Korean adult smokers. Methods We used data from the International Tobacco Control Korea Survey, which was conducted from November through December 2005 by using random-digit dialing and computer-assisted telephone interviewing of male and female smokers aged 19 years or older in 16 metropolitan areas and provinces of Korea. We performed univariate analysis and multiple logistic regression analysis to identify predictors of intention to quit. Results A total of 995 respondents were included in the final analysis. Of those, 74.9% (n = 745) intended to quit smoking. In univariate analyses, smokers with an intention to quit were younger, smoked fewer cigarettes per day, had a higher annual income, were more educated, were more likely to have a religious affiliation, drank less alcohol per week, were less likely to have self-exempting beliefs, and were more likely to have self-efficacy beliefs regarding quitting, to believe that smoking had damaged their health, and to report that smoking was never allowed anywhere in their home. In multiple logistic regression analysis, higher education level, having a religious affiliation, and a higher self-efficacy regarding quitting were significantly associated with intention to quit. Conclusions Sociodemographic factors, smoking-related beliefs, and smoking restrictions at home were associated with intention to quit smoking among Korean adults. PMID:22186157

Association of sociodemographic factors, smoking-related beliefs, and smoking restrictions with intention to quit smoking in Korean adults: findings from the ITC Korea Survey.

PubMed

Myung, Seung-Kwon; Seo, Hong Gwan; Cheong, Yoo-Seock; Park, Sohee; Lee, Wonkyong B; Fong, Geoffrey T

2012-01-01

Few studies have reported the factors associated with intention to quit smoking among Korean adult smokers. This study aimed to examine sociodemographic characteristics, smoking-related beliefs, and smoking-restriction variables associated with intention to quit smoking among Korean adult smokers. We used data from the International Tobacco Control Korea Survey, which was conducted from November through December 2005 by using random-digit dialing and computer-assisted telephone interviewing of male and female smokers aged 19 years or older in 16 metropolitan areas and provinces of Korea. We performed univariate analysis and multiple logistic regression analysis to identify predictors of intention to quit. A total of 995 respondents were included in the final analysis. Of those, 74.9% (n = 745) intended to quit smoking. In univariate analyses, smokers with an intention to quit were younger, smoked fewer cigarettes per day, had a higher annual income, were more educated, were more likely to have a religious affiliation, drank less alcohol per week, were less likely to have self-exempting beliefs, and were more likely to have self-efficacy beliefs regarding quitting, to believe that smoking had damaged their health, and to report that smoking was never allowed anywhere in their home. In multiple logistic regression analysis, higher education level, having a religious affiliation, and a higher self-efficacy regarding quitting were significantly associated with intention to quit. Sociodemographic factors, smoking-related beliefs, and smoking restrictions at home were associated with intention to quit smoking among Korean adults.

Calibration of diatom-pH-alkalinity methodology for the interpretation of the sedimentary record in Emerald Lake Integrated watershed study. Final report, 6 May 1985-10 October 1986

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

Holmes, R.W.

1986-10-10

The present study was designed to establish quantitative relationships between lake air-equilibrated pH, alkalinity, and diatoms occurring in the surface sediments in high-elevation Sierra Nevada Lakes. These relationships provided the necessary information to develop predictive equations relating lake pH to the composition of surface-sediment diatom assemblages in 27 study lakes. Using the Hustedt diatom pH classification system, Index B of Renberg and Hellberg, and multiple linear regression analysis, two equations were developed which predict lake pH from the relative abundance of sediment diatoms occurring in each of four diatom pH groupings.

Association of fruit and vegetables with the risk of nasopharyngeal cancer: Evidence from a meta-analysis

PubMed Central

Jin, Jian; Ouyang, Zhiguo; Wang, Zhaoyan

2014-01-01

Quantification of the association between the intake of vegetables and fruit and risk of nasopharyngeal cancer (NPC) is controversial. Thus, we conducted a meta-analysis to assess the relationship between vegetables and fruit and NPC risk. Pertinent studies were identified by a search in PubMed, Web of Knowledge and Wan Fang Med Online. Random-effects models were used to calculate summary relative risks (RRs) and the corresponding 95% confidence intervals (CIs). Publication bias was estimated using Egger's regression asymmetry test. Finally, 15 articles comprising 8208 NPC cases were included in this meta-analysis. The combined results showed that there was significant association between vegetables and fruit intake and NPC risk. The pooled RRs were 0.60 (95% CI = 0.47–0.76) for vegetables and 0.63 (95% CI = 0.56–0.70) for fruit. No publication bias was detected. Our analysis indicated that intake of vegetables and fruit may have a protective effect on NPC. Since the potential biases and confounders could not be ruled out completely in this meta-analysis, further studies are needed. PMID:25008797

A Two-Stage Method to Determine Optimal Product Sampling considering Dynamic Potential Market

PubMed Central

Hu, Zhineng; Lu, Wei; Han, Bing

2015-01-01

This paper develops an optimization model for the diffusion effects of free samples under dynamic changes in potential market based on the characteristics of independent product and presents a two-stage method to figure out the sampling level. The impact analysis of the key factors on the sampling level shows that the increase of the external coefficient or internal coefficient has a negative influence on the sampling level. And the changing rate of the potential market has no significant influence on the sampling level whereas the repeat purchase has a positive one. Using logistic analysis and regression analysis, the global sensitivity analysis gives a whole analysis of the interaction of all parameters, which provides a two-stage method to estimate the impact of the relevant parameters in the case of inaccuracy of the parameters and to be able to construct a 95% confidence interval for the predicted sampling level. Finally, the paper provides the operational steps to improve the accuracy of the parameter estimation and an innovational way to estimate the sampling level. PMID:25821847

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…

RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

DTIC Science & Technology

This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)

Prediction of reported consumption of selected fat-containing foods.

PubMed

Tuorila, H; Pangborn, R M

1988-10-01

A total of 100 American females (mean age = 20.8 years) completed a questionnaire, in which their beliefs, evaluations, liking and consumption (frequency, consumption compared to others, intention to consume) of milk, cheese, ice cream, chocolate and "high-fat foods" were measured. For the design and analysis, the basic frame of reference was the Fishbein-Ajzen model of reasoned action, but the final analyses were carried out with stepwise multiple regression analysis. In addition to the components of the Fishbein-Ajzen model, beliefs and evaluations were used as independent variables. On the average, subjects reported liking all the products but not "high-fat foods", and thought that milk and cheese were "good for you" whereas the remaining items were "bad for you". Principal component analysis for beliefs revealed factors related to pleasantness/benefit aspects, to health and weight concern and to the "functionality" of the foods. In stepwise multiple regression analyses, liking was the predominant predictor of reported consumption for all the foods, but various belief factors, particularly those related to concern with weight, also significantly predicted consumption. Social factors played only a minor role. The multiple R's of the predictive functions varied from 0.49 to 0.74. The fact that all four foods studied elicited individual sets of beliefs and belief structures, and that none of them was rated similar to the generic "high-fat foods", emphasizes that consumers attach meaning to integrated food entities rather than to ingredients.

Technological, Pedagogical, and Content Knowledge (TPACK): An Educational Landscape for Tertiary Science Faculty

NASA Astrophysics Data System (ADS)

Lavadia, Linda

Earlier studies concluded that technology's strength is in supporting student learning rather than as an instrument for content delivery (Angeli & Valanides, 2014). Current research espouses the merits of the Technological Pedagogical Content Knowledge (TPACK) framework as a guide for educators' reflections about technology integration within the context of content and instructional practice. Grounded by two theoretical frameworks, TPACK (Mishra & Koehler, 2006; 2008) and Rogers' (1983, 1995) theory of diffusion of innovation, the purpose of this mixed-methods research was two-fold: to explore the perceived competencies of tertiary science faculty at higher education institutions with respect to their integration of technology within the constructs of pedagogical practice and content learning and to analyze whether these perceived competencies may serve as predictive factors for technology adoption level. The literature review included past research that served as models for the Sci-TPACK instrument. Twenty-nine professors of tertiary science courses participated in an online Likert survey, and four professors provided in-depth interviews on their TPACK practices. Quantitative analysis of data consisted of descriptive and reliability statistics, calculations of means for each of the seven scales or domains of TPACK, and regression analysis. Open-ended questions on the Likert survey and individual interviews provided recurrent themes of the qualitative data. Final results revealed that the participants integrate technology into pedagogy and content through a myriad of TPACK practices. Regression analysis supported perceived TPACK competencies as predictive factors for technology adoption level.

Effective role of lady health workers in immunization of children in Pakistan.

PubMed

Afzal, Saira; Naeem, Azka; Shahid, Unaiza; Noor Syed, Wajiha; Khan, Urva; Misal Zaidi, Nayyar

2016-01-01

To determine the association of Lady Health Worker's role with immunization of children in Pakistan. Secondary analysis was conducted on data obtained from Pakistan's Demographic and Health Survey. Children who did not receive all doses of vaccines were considered incompletely immunized or vice versa. The association between determinants was assessed by simple and multivariable binary logistic regression. The mothers and fathers had a mean age of 32.7 (SD+8.6) years and 37.9 (SD +10.1) years, respectively. Age of mother greater than 35 (OR=0.93; 95% CI:0.70-1.25); born in Baluchistan (OR=3.47,95% CI:2.21-5.49); rural area dwellers (OR=2.04; 95% CI:1.65-2.51); female gender (OR=1.06; 95% CI:0.87-1.29); birth order (of last born child) greater than 7 (OR=2.21, 95% CI:1.60-3.06); delivered at home (OR=2.20, 95% CI:1.76-2.74); long distance to health care facility (OR=2.66, 95% CI:2.16-3.28); and no LHW visit in last 12 months (OR=1.91, CI:1.48-2.47) were significantly associated with incomplete immunization in bivariate analysis. In final model of multinomial regression analysis the absence of visit by LHW in last 12 months was the most significant factor when all risk factors were analyzed in last model. This study has concluded that visit of LHW in last 12 months was significantly associated with immunization.

[Awareness of health co-benefits of carbon emissions reduction in urban residents in Beijing: a cross-sectional survey].

PubMed

Gao, J H; Zhang, Y; Wang, J; Chen, H J; Zhang, G B; Liu, X B; Wu, H X; Li, J; Li, J; Liu, Q Y

2017-05-10

Objective: To understand the awareness of the health co-benefits of carbon emission reduction in urban residents in Beijing and the influencing factors, and provide information for policy decision on carbon emission reduction and health education campaigns. Methods: Four communities were selected randomly from Fangshan, Haidian, Huairou and Dongcheng districts of Beijing, respectively. The sample size was estimated by using Kish-Leslie formula for descriptive analysis. 90 participants were recruited from each community. χ (2) test was conducted to examine the associations between socio-demographic variables and individuals' awareness of the health co-benefits of carbon emission reduction. Ordinal logistic regression analysis was performed to investigate the factors influencing the awareness about the health co-benefits. Results: In 369 participants surveyed, 12.7 % reported they knew the health co-benefits of carbon emission reduction. The final logistic regression analysis revealed that age ( OR =0.98), attitude to climate warming ( OR =0.72) and air pollution ( OR =1.59), family monthly average income ( OR =1.27), and low carbon lifestyle ( OR =2.36) were important factors influencing their awareness of the health co-benefits of carbon emission reduction. Conclusion: The awareness of the health co-benefits of carbon emissions reduction were influenced by people' socio-demographic characteristics (age and family income), concerns about air pollution and climate warming, and low carbon lifestyle. It is necessary to take these factors into consideration in future development and implementation of carbon emission reduction policies and related health education campaigns.

[Detection and evaluation of the role of sarcopenia in elderly patients with cancer treated with chemotherapy. ONCOSARCO project].

PubMed

Molina Garrido, Maria José; Guillén Ponce, Carmen; Fernández Félix, Borja Manuel; Muñoz Sánchez, Maria Del Mar; Soriano Rodríguez, Maria Del Carmen; Olaverri Hernández, Amaya; Santiago Crespo, Jose Antonio

To develop a predictive model of toxicity to chemotherapy in elderly patients with cancer, using the variables associated with sarcopenia, and to identify which of these parameters, sarcopenia or frailty, is the best predictor of toxicity to chemotherapy in the elderly. A prospective observational study with patients ≥70 years treated with chemotherapy in the Cancer Unit for the Elderly, in the Medical Oncology Section of the Hospital Virgen de la Luz de Cuenca. The following tests will be performed by each patient before chemotherapy: muscle strength (handgrip, cylindrical handgrip, pinch gauge, hip flexion, knee extension), muscle mass (skeletal muscle mass index), and physical function (gait speed and 5STS test). The occurrence of severe toxicity will be recorded over a period of 4 months of chemotherapy treatment. It will be evaluated, using logistic regression analysis, whether sarcopenia (defined by the European Working Group on Sarcopenia in Older People) or frailty (defined by the phenotype of frailty) is the best predictor of chemotherapy toxicity. Using a multinomial logistic regression analysis, we will try to create the first model to predict toxicity to chemotherapy in elderly patients with diagnosis of cancer, based on the definition of sarcopenia. It is expected that the final analysis of this project will be useful to detect predictive factors of toxicity to chemotherapy in elderly patients with cancer. Copyright © 2016 SEGG. Publicado por Elsevier España, S.L.U. All rights reserved.

[Comparison of application of Cochran-Armitage trend test and linear regression analysis for rate trend analysis in epidemiology study].

PubMed

Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H

2017-05-10

We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P value

Developing global regression models for metabolite concentration prediction regardless of cell line.

PubMed

André, Silvère; Lagresle, Sylvain; Da Sliva, Anthony; Heimendinger, Pierre; Hannas, Zahia; Calvosa, Éric; Duponchel, Ludovic

2017-11-01

Following the Process Analytical Technology (PAT) of the Food and Drug Administration (FDA), drug manufacturers are encouraged to develop innovative techniques in order to monitor and understand their processes in a better way. Within this framework, it has been demonstrated that Raman spectroscopy coupled with chemometric tools allow to predict critical parameters of mammalian cell cultures in-line and in real time. However, the development of robust and predictive regression models clearly requires many batches in order to take into account inter-batch variability and enhance models accuracy. Nevertheless, this heavy procedure has to be repeated for every new line of cell culture involving many resources. This is why we propose in this paper to develop global regression models taking into account different cell lines. Such models are finally transferred to any culture of the cells involved. This article first demonstrates the feasibility of developing regression models, not only for mammalian cell lines (CHO and HeLa cell cultures), but also for insect cell lines (Sf9 cell cultures). Then global regression models are generated, based on CHO cells, HeLa cells, and Sf9 cells. Finally, these models are evaluated considering a fourth cell line(HEK cells). In addition to suitable predictions of glucose and lactate concentration of HEK cell cultures, we expose that by adding a single HEK-cell culture to the calibration set, the predictive ability of the regression models are substantially increased. In this way, we demonstrate that using global models, it is not necessary to consider many cultures of a new cell line in order to obtain accurate models. Biotechnol. Bioeng. 2017;114: 2550-2559. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

A primer for biomedical scientists on how to execute model II linear regression analysis.

PubMed

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

Water quality parameter measurement using spectral signatures

NASA Technical Reports Server (NTRS)

White, P. E.

1973-01-01

Regression analysis is applied to the problem of measuring water quality parameters from remote sensing spectral signature data. The equations necessary to perform regression analysis are presented and methods of testing the strength and reliability of a regression are described. An efficient algorithm for selecting an optimal subset of the independent variables available for a regression is also presented.

Optical scatterometry of quarter-micron patterns using neural regression

NASA Astrophysics Data System (ADS)

Bischoff, Joerg; Bauer, Joachim J.; Haak, Ulrich; Hutschenreuther, Lutz; Truckenbrodt, Horst

1998-06-01

With shrinking dimensions and increasing chip areas, a rapid and non-destructive full wafer characterization after every patterning cycle is an inevitable necessity. In former publications it was shown that Optical Scatterometry (OS) has the potential to push the attainable feature limits of optical techniques from 0.8 . . . 0.5 microns for imaging methods down to 0.1 micron and below. Thus the demands of future metrology can be met. Basically being a nonimaging method, OS combines light scatter (or diffraction) measurements with modern data analysis schemes to solve the inverse scatter issue. For very fine patterns with lambda-to-pitch ratios grater than one, the specular reflected light versus the incidence angle is recorded. Usually, the data analysis comprises two steps -- a training cycle connected the a rigorous forward modeling and the prediction itself. Until now, two data analysis schemes are usually applied -- the multivariate regression based Partial Least Squares method (PLS) and a look-up-table technique which is also referred to as Minimum Mean Square Error approach (MMSE). Both methods are afflicted with serious drawbacks. On the one hand, the prediction accuracy of multivariate regression schemes degrades with larger parameter ranges due to the linearization properties of the method. On the other hand, look-up-table methods are rather time consuming during prediction thus prolonging the processing time and reducing the throughput. An alternate method is an Artificial Neural Network (ANN) based regression which combines the advantages of multivariate regression and MMSE. Due to the versatility of a neural network, not only can its structure be adapted more properly to the scatter problem, but also the nonlinearity of the neuronal transfer functions mimic the nonlinear behavior of optical diffraction processes more adequately. In spite of these pleasant properties, the prediction speed of ANN regression is comparable with that of the PLS-method. In this paper, the viability and performance of ANN-regression will be demonstrated with the example of sub-quarter-micron resist metrology. To this end, 0.25 micrometer line/space patterns have been printed in positive photoresist by means of DUV projection lithography. In order to evaluate the total metrology chain from light scatter measurement through data analysis, a thorough modeling has been performed. Assuming a trapezoidal shape of the developed resist profile, a training data set was generated by means of the Rigorous Coupled Wave Approach (RCWA). After training the model, a second data set was computed and deteriorated by Gaussian noise to imitate real measuring conditions. Then, these data have been fed into the models established before resulting in a Standard Error of Prediction (SEP) which corresponds to the measuring accuracy. Even with putting only little effort in the design of a back-propagation network, the ANN is clearly superior to the PLS-method. Depending on whether a network with one or two hidden layers was used, accuracy gains between 2 and 5 can be achieved compared with PLS regression. Furthermore, the ANN is less noise sensitive, for there is only a doubling of the SEP at 5% noise for ANN whereas for PLS the accuracy degrades rapidly with increasing noise. The accuracy gain also depends on the light polarization and on the measured parameters. Finally, these results have been proven experimentally, where the OS-results are in good accordance with the profiles obtained from cross- sectioning micrographs.

Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.

PubMed

Chen, Jian; Chen, Jie; Ding, Hong-Yan; Pan, Qin-Shi; Hong, Wan-Dong; Xu, Gang; Yu, Fang-You; Wang, Yu-Min

2015-01-01

The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05% (200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18 g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67 g /L), long time of hospitalization (≥14 days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model (0.829±0.019) was higher than that of LR model (0.756±0.021). The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

Immunohistological Analysis of Intracoronary Thrombus Aspirate in STEMI Patients: Clinical Implications of Pathological Findings.

PubMed

Blasco, Ana; Bellas, Carmen; Goicolea, Leyre; Muñiz, Ana; Abraira, Víctor; Royuela, Ana; Mingo, Susana; Oteo, Juan Francisco; García-Touchard, Arturo; Goicolea, Francisco Javier

2017-03-01

Thrombus aspiration allows analysis of intracoronary material in patients with ST-segment elevation myocardial infarction. Our objective was to characterize this material by immunohistology and to study its possible association with patient progress. This study analyzed a prospective cohort of 142 patients undergoing primary angioplasty with positive coronary aspiration. Histological examination of aspirated samples included immunohistochemistry stains for the detection of plaque fragments. The statistical analysis comprised histological variables (thrombus age, degree of inflammation, presence of plaque), the patients' clinical and angiographic features, estimation of survival curves, and logistic regression analysis. Among the histological markers, only the presence of plaque (63% of samples) was associated with postinfarction clinical events. Factors associated with 5-year event-free survival were the presence of plaque in the aspirate (82.2% vs 66.0%; P = .033), smoking (82.5% smokers vs 66.7% nonsmokers; P = .036), culprit coronary artery (83.3% circumflex or right coronary artery vs 68.5% anterior descending artery; P = .042), final angiographic flow (80.8% II-III vs 30.0% 0-I; P < .001) and left ventricular ejection fraction ≥ 35% at discharge (83.7% vs 26.7%; P < .001). On multivariable Cox regression analysis with these variables, independent predictors of event-free survival were the presence of plaque (hazard ratio, 0.37; 95%CI, 0.18-0.77; P = .008), and left ventricular ejection fraction (hazard ratio, 0.92; 95%CI, 0.88-0.95; P < .001). The presence of plaque in the coronary aspirate of patients with ST elevation myocardial infarction may be an independent prognostic marker. CD68 immunohistochemical stain is a good method for plaque detection. Copyright © 2016 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.

Exposure-response analysis of alectinib in crizotinib-resistant ALK-positive non-small cell lung cancer.

PubMed

Morcos, Peter N; Nueesch, Eveline; Jaminion, Felix; Guerini, Elena; Hsu, Joy C; Bordogna, Walter; Balas, Bogdana; Mercier, Francois

2018-05-10

Alectinib is a selective and potent anaplastic lymphoma kinase (ALK) inhibitor that is active in the central nervous system (CNS). Alectinib demonstrated robust efficacy in a pooled analysis of two single-arm, open-label phase II studies (NP28673, NCT01801111; NP28761, NCT01871805) in crizotinib-resistant ALK-positive non-small-cell lung cancer (NSCLC): median overall survival (OS) 29.1 months (95% confidence interval [CI]: 21.3-39.0) for alectinib 600 mg twice daily (BID). We investigated exposure-response relationships from final pooled phase II OS and safety data to assess alectinib dose selection. A semi-parametric Cox proportional hazards model analyzed relationships between individual median observed steady-state trough concentrations (C trough,ss ) for combined exposure of alectinib and its major metabolite (M4), baseline covariates (demographics and disease characteristics) and OS. Univariate logistic regression analysis analyzed relationships between C trough,ss and incidence of adverse events (AEs: serious and Grade ≥ 3). Overall, 92% of patients (n = 207/225) had C trough,ss data and were included in the analysis. No statistically significant relationship was found between C trough,ss and OS following alectinib treatment. The only baseline covariates that statistically influenced OS were baseline tumor size and prior crizotinib treatment duration. Larger baseline tumor size and shorter prior crizotinib treatment were both associated with shorter OS. Logistic regression confirmed no significant relationship between C trough,ss and AEs. Alectinib 600 mg BID provides systemic exposures at plateau of response for OS while maintaining a well-tolerated safety profile. This analysis confirms alectinib 600 mg BID as the recommended global dose for patients with crizotinib-resistant ALK-positive NSCLC.

Population Dynamics of Dactylella oviparasitica and Heterodera schachtii: Toward a Decision Model for Sugar Beet Planting

PubMed Central

Yang, Jiue-in; Benecke, Scott; Jeske, Daniel R.; Rocha, Fernando S.; Smith Becker, Jennifer; Timper, Patricia; Ole Becker, J.

2012-01-01

A series of experiments were performed to examine the population dynamics of the sugarbeet cyst nematode, Heterodera schachtii, and the nematophagus fungus Dactylella oviparasitica. After two nematode generations, the population densities of H. schachtii were measured in relation to various initial infestation densities of both D. oviparasitica and H. schachtii. In general, higher initial population densities of D. oviparasitica were associated with lower final population densities of H. schachtii. Regression models showed that the initial densities of D. oviparasitica were only significant when predicting the final densities of H. schachtii J2 and eggs as well as fungal egg parasitism, while the initial densities of J2 were significant for all final H. schachtii population density measurements. We also showed that the densities of H. schachtii-associated D. oviparasitica fluctuate greatly, with rRNA gene numbers going from zero in most field-soil-collected cysts to an average of 4.24 x 108 in mature females isolated directly from root surfaces. Finally, phylogenetic analysis of rRNA genes suggested that D. oviparasitica belongs to a clade of nematophagous fungi that includes Arkansas Fungus strain L (ARF-L) and that these fungi are widely distributed. We anticipate that these findings will provide foundational data facilitating the development of more effective decision models for sugar beet planting. PMID:23481664

Characterizing the spatial distribution of ambient ultrafine particles in Toronto, Canada: A land use regression model.

PubMed

Weichenthal, Scott; Van Ryswyk, Keith; Goldstein, Alon; Shekarrizfard, Maryam; Hatzopoulou, Marianne

2016-01-01

Exposure models are needed to evaluate the chronic health effects of ambient ultrafine particles (<0.1 μm) (UFPs). We developed a land use regression model for ambient UFPs in Toronto, Canada using mobile monitoring data collected during summer/winter 2010-2011. In total, 405 road segments were included in the analysis. The final model explained 67% of the spatial variation in mean UFPs and included terms for the logarithm of distances to highways, major roads, the central business district, Pearson airport, and bus routes as well as variables for the number of on-street trees, parks, open space, and the length of bus routes within a 100 m buffer. There was no systematic difference between measured and predicted values when the model was evaluated in an external dataset, although the R(2) value decreased (R(2) = 50%). This model will be used to evaluate the chronic health effects of UFPs using population-based cohorts in the Toronto area. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

The ties that bind what is known to the recall of what is new.

PubMed

Nelson, D L; Zhang, N

2000-12-01

Cued recall success varies with what people know and with what they do during an episode. This paper focuses on prior knowledge and disentangles the relative effects of 10 features of words and their relationships on cued recall. Results are reported for correlational and multiple regression analyses of data obtained from free association norms and from 29 experiments. The 10 features were only weakly correlated with each other in the norms and, with notable exceptions, in the experiments. The regression analysis indicated that forward cue-to-target strength explained the most variance, followed by backward target-to-cue strength. Target connectivity and set size explained the next most variance, along with mediated cue-to-target strength. Finally, frequency, concreteness, shared associate strength, and cue set size also contributed significantly to recall. Taken together, indices of prior word knowledge explain 49% of the recall variance. Theoretically driven equations that use free association to predict cued recall were also evaluated. Each equation was designed to condense multiple indices of word interconnectivity into a single predictor.

The weighted priors approach for combining expert opinions in logistic regression experiments

DOE PAGES

Quinlan, Kevin R.; Anderson-Cook, Christine M.; Myers, Kary L.

2017-04-24

When modeling the reliability of a system or component, it is not uncommon for more than one expert to provide very different prior estimates of the expected reliability as a function of an explanatory variable such as age or temperature. Our goal in this paper is to incorporate all information from the experts when choosing a design about which units to test. Bayesian design of experiments has been shown to be very successful for generalized linear models, including logistic regression models. We use this approach to develop methodology for the case where there are several potentially non-overlapping priors under consideration.more » While multiple priors have been used for analysis in the past, they have never been used in a design context. The Weighted Priors method performs well for a broad range of true underlying model parameter choices and is more robust when compared to other reasonable design choices. Finally, we illustrate the method through multiple scenarios and a motivating example. Additional figures for this article are available in the online supplementary information.« less

Heat rejection efficiency research of new energy automobile radiators

NASA Astrophysics Data System (ADS)

Ma, W. S.; Shen, W. X.; Zhang, L. W.

2018-03-01

The driving system of new energy vehicle has larger heat load than conventional engine. How to ensure the heat dissipation performance of the cooling system is the focus of the design of new energy vehicle thermal management system. In this paper, the heat dissipation efficiency of the radiator of the hybrid electric vehicle is taken as the research object, the heat dissipation efficiency of the radiator of the new energy vehicle is studied through the multi-working-condition enthalpy difference test. In this paper, the test method in the current standard QC/T 468-2010 “automobile radiator” is taken, but not limited to the test conditions specified in the standard, 5 types of automobile radiator are chosen, each of them is tested 20 times in simulated condition of different wind speed and engine inlet temperature. Finally, regression analysis is carried out for the test results, and regression equation describing the relationship of radiator heat dissipation heat dissipation efficiency air side flow rate cooling medium velocity and inlet air temperature is obtained, and the influence rule is systematically discussed.

Identifying and quantifying secondhand smoke in multiunit homes with tobacco smoke odor complaints

NASA Astrophysics Data System (ADS)

Dacunto, Philip J.; Cheng, Kai-Chung; Acevedo-Bolton, Viviana; Klepeis, Neil E.; Repace, James L.; Ott, Wayne R.; Hildemann, Lynn M.

2013-06-01

Accurate identification and quantification of the secondhand tobacco smoke (SHS) that drifts between multiunit homes (MUHs) is essential for assessing resident exposure and health risk. We collected 24 gaseous and particle measurements over 6-9 day monitoring periods in five nonsmoking MUHs with reported SHS intrusion problems. Nicotine tracer sampling showed evidence of SHS intrusion in all five homes during the monitoring period; logistic regression and chemical mass balance (CMB) analysis enabled identification and quantification of some of the precise periods of SHS entry. Logistic regression models identified SHS in eight periods when residents complained of SHS odor, and CMB provided estimates of SHS magnitude in six of these eight periods. Both approaches properly identified or apportioned all six cooking periods used as no-SHS controls. Finally, both approaches enabled identification and/or apportionment of suspected SHS in five additional periods when residents did not report smelling smoke. The time resolution of this methodology goes beyond sampling methods involving single tracers (such as nicotine), enabling the precise identification of the magnitude and duration of SHS intrusion, which is essential for accurate assessment of human exposure.

Can the displacement of a conservatively treated distal radius fracture be predicted at the beginning of treatment?

PubMed Central

Einsiedel, T.; Freund, W.; Sander, S.; Trnavac, S.; Gebhard, F.

2008-01-01

The aim of this study was to investigate whether the final displacement of conservatively treated distal radius fractures can be predicted after primary reduction. We analysed the radiographic documents of 311 patients with a conservatively treated distal radius fracture at the time of injury, after reduction and after bony consolidation. We measured the dorsal angulation (DA), the radial angle (RA) and the radial shortening (RS) at each time point. The parameters were analysed separately for metaphyseally “stable” (A2, C1) and “unstable” (A3, C2, C3) fractures, according to the AO classification system. Spearman’s rank correlations and regression functions were determined for the analysis. The highest correlations were found for the DA between the time points ‘reduction’ and ‘complete healing’ (r = 0.75) and for the RA between the time points ‘reduction’ and ‘complete healing’ (r = 0.80). The DA and the RA after complete healing can be predicted from the regression functions. PMID:18504577

The weighted priors approach for combining expert opinions in logistic regression experiments

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

Quinlan, Kevin R.; Anderson-Cook, Christine M.; Myers, Kary L.

When modeling the reliability of a system or component, it is not uncommon for more than one expert to provide very different prior estimates of the expected reliability as a function of an explanatory variable such as age or temperature. Our goal in this paper is to incorporate all information from the experts when choosing a design about which units to test. Bayesian design of experiments has been shown to be very successful for generalized linear models, including logistic regression models. We use this approach to develop methodology for the case where there are several potentially non-overlapping priors under consideration.more » While multiple priors have been used for analysis in the past, they have never been used in a design context. The Weighted Priors method performs well for a broad range of true underlying model parameter choices and is more robust when compared to other reasonable design choices. Finally, we illustrate the method through multiple scenarios and a motivating example. Additional figures for this article are available in the online supplementary information.« less

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Handling nonnormality and variance heterogeneity for quantitative sublethal toxicity tests.

PubMed

Ritz, Christian; Van der Vliet, Leana

2009-09-01

The advantages of using regression-based techniques to derive endpoints from environmental toxicity data are clear, and slowly, this superior analytical technique is gaining acceptance. As use of regression-based analysis becomes more widespread, some of the associated nuances and potential problems come into sharper focus. Looking at data sets that cover a broad spectrum of standard test species, we noticed that some model fits to data failed to meet two key assumptions-variance homogeneity and normality-that are necessary for correct statistical analysis via regression-based techniques. Failure to meet these assumptions often is caused by reduced variance at the concentrations showing severe adverse effects. Although commonly used with linear regression analysis, transformation of the response variable only is not appropriate when fitting data using nonlinear regression techniques. Through analysis of sample data sets, including Lemna minor, Eisenia andrei (terrestrial earthworm), and algae, we show that both the so-called Box-Cox transformation and use of the Poisson distribution can help to correct variance heterogeneity and nonnormality and so allow nonlinear regression analysis to be implemented. Both the Box-Cox transformation and the Poisson distribution can be readily implemented into existing protocols for statistical analysis. By correcting for nonnormality and variance heterogeneity, these two statistical tools can be used to encourage the transition to regression-based analysis and the depreciation of less-desirable and less-flexible analytical techniques, such as linear interpolation.

A simplified computational fluid-dynamic approach to the oxidizer injector design in hybrid rockets

NASA Astrophysics Data System (ADS)

Di Martino, Giuseppe D.; Malgieri, Paolo; Carmicino, Carmine; Savino, Raffaele

2016-12-01

Fuel regression rate in hybrid rockets is non-negligibly affected by the oxidizer injection pattern. In this paper a simplified computational approach developed in an attempt to optimize the oxidizer injector design is discussed. Numerical simulations of the thermo-fluid-dynamic field in a hybrid rocket are carried out, with a commercial solver, to investigate into several injection configurations with the aim of increasing the fuel regression rate and minimizing the consumption unevenness, but still favoring the establishment of flow recirculation at the motor head end, which is generated with an axial nozzle injector and has been demonstrated to promote combustion stability, and both larger efficiency and regression rate. All the computations have been performed on the configuration of a lab-scale hybrid rocket motor available at the propulsion laboratory of the University of Naples with typical operating conditions. After a preliminary comparison between the two baseline limiting cases of an axial subsonic nozzle injector and a uniform injection through the prechamber, a parametric analysis has been carried out by varying the oxidizer jet flow divergence angle, as well as the grain port diameter and the oxidizer mass flux to study the effect of the flow divergence on heat transfer distribution over the fuel surface. Some experimental firing test data are presented, and, under the hypothesis that fuel regression rate and surface heat flux are proportional, the measured fuel consumption axial profiles are compared with the predicted surface heat flux showing fairly good agreement, which allowed validating the employed design approach. Finally an optimized injector design is proposed.

Testing in Microbiome-Profiling Studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test

PubMed Central

Zhao, Ni; Chen, Jun; Carroll, Ian M.; Ringel-Kulka, Tamar; Epstein, Michael P.; Zhou, Hua; Zhou, Jin J.; Ringel, Yehuda; Li, Hongzhe; Wu, Michael C.

2015-01-01

High-throughput sequencing technology has enabled population-based studies of the role of the human microbiome in disease etiology and exposure response. Distance-based analysis is a popular strategy for evaluating the overall association between microbiome diversity and outcome, wherein the phylogenetic distance between individuals’ microbiome profiles is computed and tested for association via permutation. Despite their practical popularity, distance-based approaches suffer from important challenges, especially in selecting the best distance and extending the methods to alternative outcomes, such as survival outcomes. We propose the microbiome regression-based kernel association test (MiRKAT), which directly regresses the outcome on the microbiome profiles via the semi-parametric kernel machine regression framework. MiRKAT allows for easy covariate adjustment and extension to alternative outcomes while non-parametrically modeling the microbiome through a kernel that incorporates phylogenetic distance. It uses a variance-component score statistic to test for the association with analytical p value calculation. The model also allows simultaneous examination of multiple distances, alleviating the problem of choosing the best distance. Our simulations demonstrated that MiRKAT provides correctly controlled type I error and adequate power in detecting overall association. “Optimal” MiRKAT, which considers multiple candidate distances, is robust in that it suffers from little power loss in comparison to when the best distance is used and can achieve tremendous power gain in comparison to when a poor distance is chosen. Finally, we applied MiRKAT to real microbiome datasets to show that microbial communities are associated with smoking and with fecal protease levels after confounders are controlled for. PMID:25957468

Linear and evolutionary polynomial regression models to forecast coastal dynamics: Comparison and reliability assessment

NASA Astrophysics Data System (ADS)

Bruno, Delia Evelina; Barca, Emanuele; Goncalves, Rodrigo Mikosz; de Araujo Queiroz, Heithor Alexandre; Berardi, Luigi; Passarella, Giuseppe

2018-01-01

In this paper, the Evolutionary Polynomial Regression data modelling strategy has been applied to study small scale, short-term coastal morphodynamics, given its capability for treating a wide database of known information, non-linearly. Simple linear and multilinear regression models were also applied to achieve a balance between the computational load and reliability of estimations of the three models. In fact, even though it is easy to imagine that the more complex the model, the more the prediction improves, sometimes a "slight" worsening of estimations can be accepted in exchange for the time saved in data organization and computational load. The models' outcomes were validated through a detailed statistical, error analysis, which revealed a slightly better estimation of the polynomial model with respect to the multilinear model, as expected. On the other hand, even though the data organization was identical for the two models, the multilinear one required a simpler simulation setting and a faster run time. Finally, the most reliable evolutionary polynomial regression model was used in order to make some conjecture about the uncertainty increase with the extension of extrapolation time of the estimation. The overlapping rate between the confidence band of the mean of the known coast position and the prediction band of the estimated position can be a good index of the weakness in producing reliable estimations when the extrapolation time increases too much. The proposed models and tests have been applied to a coastal sector located nearby Torre Colimena in the Apulia region, south Italy.

Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan T.

2012-01-01

Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…

A Quality Assessment Tool for Non-Specialist Users of Regression Analysis

ERIC Educational Resources Information Center

Argyrous, George

2015-01-01

This paper illustrates the use of a quality assessment tool for regression analysis. It is designed for non-specialist "consumers" of evidence, such as policy makers. The tool provides a series of questions such consumers of evidence can ask to interrogate regression analysis, and is illustrated with reference to a recent study published…

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

PubMed

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Visual grading characteristics and ordinal regression analysis during optimisation of CT head examinations.

PubMed

Zarb, Francis; McEntee, Mark F; Rainford, Louise

2015-06-01

To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists (n = 6) performed VGA followed by VGC and ordinal regression analysis. VGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations (including trauma) in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases. The combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings. • There is need for scientifically based image quality evaluation during CT optimisation. • VGC and ordinal regression analysis in combination led to better informed clinical decisions. • VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.

A Meta-Regression Analysis of the Effectiveness of Mosquito Nets for Malaria Control: The Value of Long-Lasting Insecticide Nets

PubMed Central

Yang, Gi-geun; Pham, Anh

2018-01-01

Long-lasting insecticidal nets (LLINs) have been widely used as an effective alternative to conventional insecticide-treated nets (ITNs) for over a decade. Due to the growing number of field trials and interventions reporting the effectiveness of LLINs in controlling malaria, there is a need to systematically review the literature on LLINs and ITNs to examine the relative effectiveness and characteristics of both insecticide nettings. A systematic review of over 2000 scholarly articles published since the year 2000 was conducted. The odds ratios (ORs) of insecticidal net effectiveness in reducing malaria were recorded. The final dataset included 26 articles for meta-regression analysis, with a sample size of 154 subgroup observations. While there is substantial heterogeneity in study characteristics and effect size, we found that the overall OR for reducing malaria by LLIN use was 0.44 (95% CI = 0.41–0.48, p < 0.01) indicating a risk reduction of 56%, while ITNs were slightly less effective with an OR of 0.59 (95% CI = 0.57–0.61, p <0.01). A meta-regression model confirms that LLINs are significantly more effective than ITNs in the prevention of malaria, when controlling for other covariates. For both types of nets, protective efficacy was greater in high transmission areas when nets were used for an extended period. However, cross-sectional studies may overestimate the effect of the nets. The results surprisingly suggest that nets are less effective in protecting children under the age of five, which may be due to differences in child behavior or inadequate coverage. Compared to a previous meta-analysis, insecticide-treated nets appear to have improved their efficacy despite the risks of insecticide resistance. These findings have practical implications for policymakers seeking effective malaria control strategies. PMID:29562673

Long-Term Vegetation Trends Detected In Northern Canada Using Landsat Image Stacks

NASA Astrophysics Data System (ADS)

Fraser, R.; Olthof, I.; Carrière, M.; Deschamps, A.; Pouliot, D.

2011-12-01

Evidence of recent productivity increases in arctic vegetation comes from a variety of sources. At local scales, long-term plot measurements in North America are beginning to record increases in vascular plant cover and biomass. At landscape scales, expansion and densification of shrubs has been observed using repeat oblique photographs. Finally, continental-scale increases in vegetation "greenness" have been documented based on analysis of coarse resolution (≥ 1 km) NOAA-AVHRR satellite imagery. In this study we investigated intermediate, regional-level changes occurring in tundra vegetation since 1984 using the Landsat TM and ETM+ satellite image archive. Four study areas averaging 13,619 km2 were located over widely distributed national parks in northern Canada (Ivvavik, Sirmilik, Torngat Mountains, and Wapusk). Time-series image stacks of 16-41 growing-season Landsat scenes from overlapping WRS-2 frames were acquired spanning periods of 17-25 years. Each pixel's unique temporal database of clear-sky values was then analyzed for trends in four indices (NDVI, Tasseled Cap Brightness, Greenness and Wetness) using robust linear regression. The trends were further related to changes in the fractional cover of functional vegetation types using regression tree models trained with plot data and high resolution (≤ 10 m) satellite imagery. We found all four study areas to have a larger proportion of significant (p<0.05) positive greenness trends (range 6.1-25.5%) by comparison to negative trends (range 0.3-4.1%). For the three study areas where regression tree models could be derived, consistent trends of increasing shrub or vascular fractional cover and decreasing bare cover were predicted. The Landsat-based observations were associated with warming trends in each park over the analysis periods. Many of the major changes observed could be corroborated using published studies or field observations.

Ballistic limit regression analysis for Space Station Freedom meteoroid and space debris protection system

NASA Technical Reports Server (NTRS)

Jolly, William H.

1992-01-01

Relationships defining the ballistic limit of Space Station Freedom's (SSF) dual wall protection systems have been determined. These functions were regressed from empirical data found in Marshall Space Flight Center's (MSFC) Hypervelocity Impact Testing Summary (HITS) for the velocity range between three and seven kilometers per second. A stepwise linear least squares regression was used to determine the coefficients of several expressions that define a ballistic limit surface. Using statistical significance indicators and graphical comparisons to other limit curves, a final set of expressions is recommended for potential use in Probability of No Critical Flaw (PNCF) calculations for Space Station. The three equations listed below represent the mean curves for normal, 45 degree, and 65 degree obliquity ballistic limits, respectively, for a dual wall protection system consisting of a thin 6061-T6 aluminum bumper spaced 4.0 inches from a .125 inches thick 2219-T87 rear wall with multiple layer thermal insulation installed between the two walls. Normal obliquity is d(sub c) = 1.0514 v(exp 0.2983 t(sub 1)(exp 0.5228). Forty-five degree obliquity is d(sub c) = 0.8591 v(exp 0.0428) t(sub 1)(exp 0.2063). Sixty-five degree obliquity is d(sub c) = 0.2824 v(exp 0.1986) t(sub 1)(exp -0.3874). Plots of these curves are provided. A sensitivity study on the effects of using these new equations in the probability of no critical flaw analysis indicated a negligible increase in the performance of the dual wall protection system for SSF over the current baseline. The magnitude of the increase was 0.17 percent over 25 years on the MB-7 configuration run with the Bumper II program code.

Multivariate random regression analysis for body weight and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus).

PubMed

He, Jie; Zhao, Yunfeng; Zhao, Jingli; Gao, Jin; Han, Dandan; Xu, Pao; Yang, Runqing

2017-11-02

Because of their high economic importance, growth traits in fish are under continuous improvement. For growth traits that are recorded at multiple time-points in life, the use of univariate and multivariate animal models is limited because of the variable and irregular timing of these measures. Thus, the univariate random regression model (RRM) was introduced for the genetic analysis of dynamic growth traits in fish breeding. We used a multivariate random regression model (MRRM) to analyze genetic changes in growth traits recorded at multiple time-point of genetically-improved farmed tilapia. Legendre polynomials of different orders were applied to characterize the influences of fixed and random effects on growth trajectories. The final MRRM was determined by optimizing the univariate RRM for the analyzed traits separately via penalizing adaptively the likelihood statistical criterion, which is superior to both the Akaike information criterion and the Bayesian information criterion. In the selected MRRM, the additive genetic effects were modeled by Legendre polynomials of three orders for body weight (BWE) and body length (BL) and of two orders for body depth (BD). By using the covariance functions of the MRRM, estimated heritabilities were between 0.086 and 0.628 for BWE, 0.155 and 0.556 for BL, and 0.056 and 0.607 for BD. Only heritabilities for BD measured from 60 to 140 days of age were consistently higher than those estimated by the univariate RRM. All genetic correlations between growth time-points exceeded 0.5 for either single or pairwise time-points. Moreover, correlations between early and late growth time-points were lower. Thus, for phenotypes that are measured repeatedly in aquaculture, an MRRM can enhance the efficiency of the comprehensive selection for BWE and the main morphological traits.

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.

Prediction model of critical weight loss in cancer patients during particle therapy.

PubMed

Zhang, Zhihong; Zhu, Yu; Zhang, Lijuan; Wang, Ziying; Wan, Hongwei

2018-01-01

The objective of this study is to investigate the predictors of critical weight loss in cancer patients receiving particle therapy, and build a prediction model based on its predictive factors. Patients receiving particle therapy were enroled between June 2015 and June 2016. Body weight was measured at the start and end of particle therapy. Association between critical weight loss (defined as >5%) during particle therapy and patients' demographic, clinical characteristic, pre-therapeutic nutrition risk screening (NRS 2002) and BMI were evaluated by logistic regression and decision tree analysis. Finally, 375 cancer patients receiving particle therapy were included. Mean weight loss was 0.55 kg, and 11.5% of patients experienced critical weight loss during particle therapy. The main predictors of critical weight loss during particle therapy were head and neck tumour location, total radiation dose ≥70 Gy on the primary tumour, and without post-surgery, as indicated by both logistic regression and decision tree analysis. Prediction model that includes tumour locations, total radiation dose and post-surgery had a good predictive ability, with the area under receiver operating characteristic curve 0.79 (95% CI: 0.71-0.88) and 0.78 (95% CI: 0.69-0.86) for decision tree and logistic regression model, respectively. Cancer patients with head and neck tumour location, total radiation dose ≥70 Gy and without post-surgery were at higher risk of critical weight loss during particle therapy, and early intensive nutrition counselling or intervention should be target at this population. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Ballistic limit regression analysis for Space Station Freedom meteoroid and space debris protection system

NASA Astrophysics Data System (ADS)

Jolly, William H.

1992-05-01

Relationships defining the ballistic limit of Space Station Freedom's (SSF) dual wall protection systems have been determined. These functions were regressed from empirical data found in Marshall Space Flight Center's (MSFC) Hypervelocity Impact Testing Summary (HITS) for the velocity range between three and seven kilometers per second. A stepwise linear least squares regression was used to determine the coefficients of several expressions that define a ballistic limit surface. Using statistical significance indicators and graphical comparisons to other limit curves, a final set of expressions is recommended for potential use in Probability of No Critical Flaw (PNCF) calculations for Space Station. The three equations listed below represent the mean curves for normal, 45 degree, and 65 degree obliquity ballistic limits, respectively, for a dual wall protection system consisting of a thin 6061-T6 aluminum bumper spaced 4.0 inches from a .125 inches thick 2219-T87 rear wall with multiple layer thermal insulation installed between the two walls. Normal obliquity is d(sub c) = 1.0514 v(exp 0.2983 t(sub 1)(exp 0.5228). Forty-five degree obliquity is d(sub c) = 0.8591 v(exp 0.0428) t(sub 1)(exp 0.2063). Sixty-five degree obliquity is d(sub c) = 0.2824 v(exp 0.1986) t(sub 1)(exp -0.3874). Plots of these curves are provided. A sensitivity study on the effects of using these new equations in the probability of no critical flaw analysis indicated a negligible increase in the performance of the dual wall protection system for SSF over the current baseline. The magnitude of the increase was 0.17 percent over 25 years on the MB-7 configuration run with the Bumper II program code.

AHP based Anthropometric Analysis of University Hall Bed Design in Bangladesh

NASA Astrophysics Data System (ADS)

Halder, Pobitra; Sarker, Eity; Karmaker, Chitralekha

2018-05-01

In university hall, different types of bed are used for providing sleeping environment to the students. Although there are wide variations in the design of students' bed in Bangladeshi university hall, none of them are designed properly considering the anthropometric data. In this study, four anthropometric measurements related to normal students' bed dimensions were measured from 300 students from a public university hall in Bangladesh. The feedbacks regarding different health problems and their reasons were collected from considering practical situations of the students and gathering experts' opinions. Chi-square test showed that back pain, blood circulation problem, fatigue, comfort, and sleeping problem are related to students' anthropometric measurements. The analytic hierarchy process (AHP) analysis identified students' bed length as the most responsible attribute for ergonomic problems of the students. Finally, the linear regression and correlation analysis suggested the bed dimensions based on stature of the students. This study can be a helpful guideline for industrial engineers and manufacturers in designing more comfortable students' bed.

A meta-analysis of interleukin-10-1082 promoter genetic polymorphism associated with atherosclerotic risk.

PubMed

Chao, Li; Lei, Huang; Fei, Jin

2014-01-01

This meta-analysis was conducted to assess the relationship between interleukin-10-1082 G/A single nucleotide polymorphism with atherosclerosis (AS) risk. The databases of PubMed, EMBASE, Chinese National Knowledge Infrastructure and Wan-Fang were searched from January 2000 to January 2014. 16 studies (involving 7779 cases and 7271 controls) were finally included. Each eligible study was scored for quality assessment. We adopted the most probably appropriate genetic model (recessive model) after carefully calculation. Between study heterogeneity was explored by subgroup analysis and publication bias was estimated by Begg's funnel plot and Egger's regression test. Statistically significant association was observed between AA genotype with overall AS risk, being mainly in coronary heart disease and stroke subgroups among Asian population, and peripheral artery disease (PAD) subgroup among Caucasians. Interleukin-10-1082 AA genotype is associated with increased overall AS risk. AA carriers of Asians seem to be more susceptible to coronary artery disease and stroke, and Caucasians are more susceptible to PAD.

Digital Correlation Microwave Polarimetry: Analysis and Demonstration

NASA Technical Reports Server (NTRS)

Piepmeier, J. R.; Gasiewski, A. J.; Krebs, Carolyn A. (Technical Monitor)

2000-01-01

The design, analysis, and demonstration of a digital-correlation microwave polarimeter for use in earth remote sensing is presented. We begin with an analysis of three-level digital correlation and develop the correlator transfer function and radiometric sensitivity. A fifth-order polynomial regression is derived for inverting the digital correlation coefficient into the analog statistic. In addition, the effects of quantizer threshold asymmetry and hysteresis are discussed. A two-look unpolarized calibration scheme is developed for identifying correlation offsets. The developed theory and calibration method are verified using a 10.7 GHz and a 37.0 GHz polarimeter. The polarimeters are based upon 1-GS/s three-level digital correlators and measure the first three Stokes parameters. Through experiment, the radiometric sensitivity is shown to approach the theoretical as derived earlier in the paper and the two-look unpolarized calibration method is successfully compared with results using a polarimetric scheme. Finally, sample data from an aircraft experiment demonstrates that the polarimeter is highly-useful for ocean wind-vector measurement.

Data mining-based coefficient of influence factors optimization of test paper reliability

NASA Astrophysics Data System (ADS)

Xu, Peiyao; Jiang, Huiping; Wei, Jieyao

2018-05-01

Test is a significant part of the teaching process. It demonstrates the final outcome of school teaching through teachers' teaching level and students' scores. The analysis of test paper is a complex operation that has the characteristics of non-linear relation in the length of the paper, time duration and the degree of difficulty. It is therefore difficult to optimize the coefficient of influence factors under different conditions in order to get text papers with clearly higher reliability with general methods [1]. With data mining techniques like Support Vector Regression (SVR) and Genetic Algorithm (GA), we can model the test paper analysis and optimize the coefficient of impact factors for higher reliability. It's easy to find that the combination of SVR and GA can get an effective advance in reliability from the test results. The optimal coefficient of influence factors optimization has a practicability in actual application, and the whole optimizing operation can offer model basis for test paper analysis.

Bagging Voronoi classifiers for clustering spatial functional data

NASA Astrophysics Data System (ADS)

Secchi, Piercesare; Vantini, Simone; Vitelli, Valeria

2013-06-01

We propose a bagging strategy based on random Voronoi tessellations for the exploration of geo-referenced functional data, suitable for different purposes (e.g., classification, regression, dimensional reduction, …). Urged by an application to environmental data contained in the Surface Solar Energy database, we focus in particular on the problem of clustering functional data indexed by the sites of a spatial finite lattice. We thus illustrate our strategy by implementing a specific algorithm whose rationale is to (i) replace the original data set with a reduced one, composed by local representatives of neighborhoods covering the entire investigated area; (ii) analyze the local representatives; (iii) repeat the previous analysis many times for different reduced data sets associated to randomly generated different sets of neighborhoods, thus obtaining many different weak formulations of the analysis; (iv) finally, bag together the weak analyses to obtain a conclusive strong analysis. Through an extensive simulation study, we show that this new procedure - which does not require an explicit model for spatial dependence - is statistically and computationally efficient.

Discomfort Evaluation of Truck Ingress/Egress Motions Based on Biomechanical Analysis

PubMed Central

Choi, Nam-Chul; Lee, Sang Hun

2015-01-01

This paper presents a quantitative discomfort evaluation method based on biomechanical analysis results for human body movement, as well as its application to an assessment of the discomfort for truck ingress and egress. In this study, the motions of a human subject entering and exiting truck cabins with different types, numbers, and heights of footsteps were first measured using an optical motion capture system and load sensors. Next, the maximum voluntary contraction (MVC) ratios of the muscles were calculated through a biomechanical analysis of the musculoskeletal human model for the captured motion. Finally, the objective discomfort was evaluated using the proposed discomfort model based on the MVC ratios. To validate this new discomfort assessment method, human subject experiments were performed to investigate the subjective discomfort levels through a questionnaire for comparison with the objective discomfort levels. The validation results showed that the correlation between the objective and subjective discomforts was significant and could be described by a linear regression model. PMID:26067194

REGRESSION ANALYSIS OF SEA-SURFACE-TEMPERATURE PATTERNS FOR THE NORTH PACIFIC OCEAN.

DTIC Science & Technology

SEA WATER, *SURFACE TEMPERATURE, *OCEANOGRAPHIC DATA, PACIFIC OCEAN, REGRESSION ANALYSIS , STATISTICAL ANALYSIS, UNDERWATER EQUIPMENT, DETECTION, UNDERWATER COMMUNICATIONS, DISTRIBUTION, THERMAL PROPERTIES, COMPUTERS.

The process and utility of classification and regression tree methodology in nursing research

PubMed Central

Kuhn, Lisa; Page, Karen; Ward, John; Worrall-Carter, Linda

2014-01-01

Aim This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Background Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Design Discussion paper. Data sources English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984–2013. Discussion Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Implications for Nursing Research Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Conclusion Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions. PMID:24237048

The process and utility of classification and regression tree methodology in nursing research.

PubMed

Kuhn, Lisa; Page, Karen; Ward, John; Worrall-Carter, Linda

2014-06-01

This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Discussion paper. English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984-2013. Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions. © 2013 The Authors. Journal of Advanced Nursing Published by John Wiley & Sons Ltd.

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

PubMed

Hoch, Jeffrey S; Dewa, Carolyn S

2014-04-01

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

Science and Engineering Ph.D. Students' Career Outcomes, by Gender.

PubMed

Conti, Annamaria; Visentin, Fabiana

2015-01-01

We examine differences in the careers of men and women Ph.D.s from two major European universities. Having performed regression analysis, we find that women are more likely than men to be employed in public administration when the alternatives are either academia or industry. Between the latter two alternatives, women are more likely to be employed in academia. These gender differences persist after accounting for Ph.D.s' and their supervisors' characteristics. Gender gaps are smaller for Ph.D.s with large research outputs and for those who conducted applied research. Restricting the analysis to Ph.D.s who pursued postdoc training, women are less likely than men to be employed in highly ranked universities, even after controlling for their research outputs. Finally, we find gender differences in Ph.D.s' appointment to professorship, which are explained by the Ph.D.s' publication output and the quality of their postdoc training.

Bayesian Group Bridge for Bi-level Variable Selection.

PubMed

Mallick, Himel; Yi, Nengjun

2017-06-01

A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties.

Predicting Positive Education Outcomes for Emerging Adults in Mental Health Systems of Care.

PubMed

Brennan, Eileen M; Nygren, Peggy; Stephens, Robert L; Croskey, Adrienne

2016-10-01

Emerging adults who receive services based on positive youth development models have shown an ability to shape their own life course to achieve positive goals. This paper reports secondary data analysis from the Longitudinal Child and Family Outcome Study including 248 culturally diverse youth ages 17 through 22 receiving mental health services in systems of care. After 12 months of services, school performance was positively related to youth ratings of school functioning and service participation and satisfaction. Regression analysis revealed ratings of young peoples' perceptions of school functioning, and their experience in services added to the significant prediction of satisfactory school performance, even controlling for sex and attendance. Finally, in addition to expected predictors, participation in planning their own services significantly predicted enrollment in higher education for those who finished high school. Findings suggest that programs and practices based on positive youth development approaches can improve educational outcomes for emerging adults.

Science and Engineering Ph.D. Students’ Career Outcomes, by Gender

PubMed Central

2015-01-01

We examine differences in the careers of men and women Ph.D.s from two major European universities. Having performed regression analysis, we find that women are more likely than men to be employed in public administration when the alternatives are either academia or industry. Between the latter two alternatives, women are more likely to be employed in academia. These gender differences persist after accounting for Ph.D.s’ and their supervisors’ characteristics. Gender gaps are smaller for Ph.D.s with large research outputs and for those who conducted applied research. Restricting the analysis to Ph.D.s who pursued postdoc training, women are less likely than men to be employed in highly ranked universities, even after controlling for their research outputs. Finally, we find gender differences in Ph.D.s’ appointment to professorship, which are explained by the Ph.D.s’ publication output and the quality of their postdoc training. PMID:26244797

Analysis of ethnic disparities in workers' compensation claims using data linkage.

PubMed

Friedman, Lee S; Ruestow, Peter; Forst, Linda

2012-10-01

The overall goal of this research project was to assess ethnic disparities in monetary compensation among construction workers injured on the job through the linkage of medical records and workers' compensation data. Probabilistic linkage of medical records with workers' compensation claim data. In the final multivariable robust regression model, compensation was $5824 higher (P = 0.030; 95% confidence interval: 551 to 11,097) for white non-Hispanic workers than for other ethnic groups when controlling for injury severity, affected body region, type of injury, average weekly wage, weeks of temporary total disability, percent permanent partial disability, death, or attorney use. The analysis indicates that white non-Hispanic construction workers are awarded higher monetary settlements despite the observation that for specific injuries the mean temporary total disability and permanent partial disability were equivalent to or lower than those in Hispanic and black construction workers.

The future is in the numbers: the power of predictive analysis in the biomedical educational environment

PubMed Central

Gullo, Charles A.

2016-01-01

Biomedical programs have a potential treasure trove of data they can mine to assist admissions committees in identification of students who are likely to do well and help educational committees in the identification of students who are likely to do poorly on standardized national exams and who may need remediation. In this article, we provide a step-by-step approach that schools can utilize to generate data that are useful when predicting the future performance of current students in any given program. We discuss the use of linear regression analysis as the means of generating that data and highlight some of the limitations. Finally, we lament on how the combination of these institution-specific data sets are not being fully utilized at the national level where these data could greatly assist programs at large. PMID:27374246

A Prospective Evaluation of High-Resolution CT Parameters in Predicting Extracorporeal Shockwave Lithotripsy Success for Upper Urinary Tract Calculi.

PubMed

Abdelhamid, Mahmoud; Mosharafa, Ashraf A; Ibrahim, Hamdy; Selim, Hany M; Hamed, Mohamed; Elghoneimy, Mohamed N; Salem, Hosny K; Abdelazim, Mohamed S; Badawy, Hesham

2016-11-01

To evaluate the ability of noncontrast CT parameters (stone size, stone attenuation, and skin-to-stone distance [SSD]) to predict the outcome of extracorporeal shockwave lithotripsy (SWL) in a prospective cohort of patients with renal and upper ureteric stones. Patients with stones 5 to 20 mm were prospectively enrolled from 2011 to 2014. Patients had NCCT with recording of stone size, stone mean attenuation, and SSD, as well as various stone and patient parameters. The numbers of needed sessions as well as the final outcome were determined, with SWL failure defined as residual fragments >3 mm. Predictors of SWL failure were assessed by multiple regression analysis. Two hundred twenty patients (mean ± standard deviation [SD] age 41.5 ± 12.4 years) underwent SWL. Mean ± SD stone size was 11.3 ± 4.1 mm, while mean ± SD stone attenuation was 795.1 ± 340.4 HU. Mean ± SD SSD was 9.4 ± 2.1 cm. The average number of sessions was 1.64. SWL was effective in 186 (84.5%) patients (group A), while 34 (15.5%) patients had significant residual fragments (>3 mm). On univariate analysis, predictors of SWL failure included stone attenuation >1000 HU, older age, higher body mass index, higher attenuation value, larger stone size, and longer SSD. Increased SSD and higher stone attenuation retained their significance as independent predictors of SWL failure (p < 0.05) on multiple regression analysis both after first session and as final SWL outcome. A positive correlation was found between number of SWL sessions and mean stone attenuation (r = 0.6, p < 0.001) and SSD (r = 4, p < 0.001). Stone mean attenuation and SSD on noncontrast CT are significant independent predictors of SWL outcome in patients with renal and ureteric stones. These parameters should be included in clinical decision algorithms for patients with urolithiasis. For patients with stones having mean attenuation of >1000 HU and/or large SSDs, alternatives to SWL should be considered.

Renal function changes after percutaneous nephrolithotomy in patients with renal calculi with a solitary kidney compared to bilateral kidneys.

PubMed

Shi, Xiaolei; Peng, Yonghan; Li, Ling; Li, Xiao; Wang, Qi; Zhang, Wei; Dong, Hao; Shen, Rong; Lu, Chaoyue; Liu, Min; Gao, Xiaofeng; Sun, Yinghao

2018-05-26

To evaluate renal function changes and risk factors for acute kidney injury (AKI) after percutaneous nephrolithotomy (PCNL) in patients with renal calculi with a solitary kidney (SK) or normal bilateral kidneys (BKs). Between 2012 and 2016, 859 patients undergoing PCNL were retrospectively reviewed at Changhai Hospital. In all, 53 patients with a SK were paired with 53 patients with normal BKs via a propensity score-matched analysis. Data for the following variables were collected: age, sex, body mass index, stone size, distribution, operation time, perioperative outcomes, and complications. The complications were graded according to the modified Clavien-Dindo system. Univariable and multivariable logistic regression models were constructed to evaluate risk factors for predicting AKI. The SK and BKs groups were comparable in terms of age, sex ratio, stone size, stone location distribution, comorbidities, and American Society of Anesthesiologists Physical Status classification. The initial and final stone-free rates were comparable between the SK and BKs groups (initial: 52.83% vs 58.49%, P = 0.696; final: 84.91% vs 92.45%, P = 0.359). There was no difference between the two groups for complications, according to the Clavien-Dindo grades. The estimated glomerular filtration rate (eGFR) increased dramatically after the stone burden was immediately relieved, and during the 6-month follow-up eGFR was lower in the SK group compared with the BKs group. We found a modest improvement in renal function immediately after PCNL in the BKs group, and renal function gain was delayed in the SK group. Through logistic regression analysis, we discovered that a SK, preoperative creatinine and diabetes were independent risk factors for predicting AKI after PCNL. Considering the overall complication rates, PCNL is generally a safe procedure for treating renal calculi amongst patients with a SK or normal BKs. Follow-up renal function analysis showed a modest improvement in patients of both groups. Compared to patients with normal BKs, patients with a SK were more likely to develop AKI after PCNL. © 2018 The Authors BJU International © 2018 BJU International Published by John Wiley & Sons Ltd.

Alzheimer's Disease Detection by Pseudo Zernike Moment and Linear Regression Classification.

PubMed

Wang, Shui-Hua; Du, Sidan; Zhang, Yin; Phillips, Preetha; Wu, Le-Nan; Chen, Xian-Qing; Zhang, Yu-Dong

2017-01-01

This study presents an improved method based on "Gorji et al. Neuroscience. 2015" by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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.

Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection.

PubMed

Muhlestein, Whitney E; Akagi, Dallin S; Kallos, Justiss A; Morone, Peter J; Weaver, Kyle D; Thompson, Reid C; Chambless, Lola B

2018-04-01

Objective  Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. Materials and Methods  A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Results  Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p  = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Conclusion  Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

PubMed

Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E

2017-06-14

Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

Seasonal mean pressure reconstruction for the North Atlantic (1750 1850) based on early marine data

NASA Astrophysics Data System (ADS)

Gallego, D.; Garcia-Herrera, R.; Ribera, P.; Jones, P. D.

2005-12-01

Measurements of wind strength and direction abstracted from European ships' logbooks during the recently finished CLIWOC project have been used to produce the first gridded Sea Level Pressure (SLP) reconstruction for the 1750-1850 period over the North Atlantic based solely on marine data. The reconstruction is based on a spatial regression analysis calibrated by using data taken from the ICOADS database. An objective methodology has been developed to select the optimal calibration period and spatial domain of the reconstruction by testing several thousands of possible models. The finally selected area, limited by the performance of the regression equations and by the availability of data, covers the region between 28° N and 52° N close to the European coast and between 28° N and 44° N in the open Ocean. The results provide a direct measure of the strength and extension of the Azores High during the 101 years of the study period. The comparison with the recent land-based SLP reconstruction by Luterbacher et al. (2002) indicates the presence of a common signal. The interannual variability of the CLIWOC reconstructions is rather high due to the current scarcity of abstracted wind data in the areas with best response in the regression. Guidelines are proposed to optimize the efficiency of future abstraction work.

Seasonal mean pressure reconstruction for the North Atlantic (1750 1850) based on early marine data

NASA Astrophysics Data System (ADS)

Gallego, D.; Garcia-Herrera, R.; Ribera, P.; Jones, P. D.

2005-08-01

Measures of wind strength and direction abstracted from European ships' logbooks during the recently finished CLIWOC project have been used to produce the first gridded Sea Level Pressure (SLP) reconstruction for the 1750-1850 period over the North Atlantic based solely on marine data. The reconstruction is based on a spatial regression analysis calibrated by using data taken from the ICOADS database. An objective methodology has been developed to select the optimal calibration period and spatial domain of the reconstruction by testing several thousands of possible models. The finally selected area, limited by the performance of the regression equations and by the availability of data, covers the region between 28°N and 52°N close to the European coast and between 28°N and 44°N in the open Ocean. The results provide a direct measure of the strength and extension of the Azores High during the 101 years of the study period. The comparison with the recent land-based SLP reconstruction by Luterbacher et al. (2002) indicates the presence of a common signal. The interannual variability of the CLIWOC reconstructions is rather high due to the current scarcity of abstracted wind data in the areas with best response in the regression. Guidelines are proposed to optimize the efficiency of future abstraction work.

Analysis of the inter- and extracellular formation of platinum nanoparticles by Fusarium oxysporum f. sp. lycopersici using response surface methodology

NASA Astrophysics Data System (ADS)

Riddin, T. L.; Gericke, M.; Whiteley, C. G.

2006-07-01

Fusarium oxysporum fungal strain was screened and found to be successful for the inter- and extracellular production of platinum nanoparticles. Nanoparticle formation was visually observed, over time, by the colour of the extracellular solution and/or the fungal biomass turning from yellow to dark brown, and their concentration was determined from the amount of residual hexachloroplatinic acid measured from a standard curve at 456 nm. The extracellular nanoparticles were characterized by transmission electron microscopy. Nanoparticles of varying size (10-100 nm) and shape (hexagons, pentagons, circles, squares, rectangles) were produced at both extracellular and intercellular levels by the Fusarium oxysporum. The particles precipitate out of solution and bioaccumulate by nucleation either intercellularly, on the cell wall/membrane, or extracellularly in the surrounding medium. The importance of pH, temperature and hexachloroplatinic acid (H2PtCl6) concentration in nanoparticle formation was examined through the use of a statistical response surface methodology. Only the extracellular production of nanoparticles proved to be statistically significant, with a concentration yield of 4.85 mg l-1 estimated by a first-order regression model. From a second-order polynomial regression, the predicted yield of nanoparticles increased to 5.66 mg l-1 and, after a backward step, regression gave a final model with a yield of 6.59 mg l-1.

Correlational Analysis of neck/shoulder Pain and Low Back Pain with the Use of Digital Products, Physical Activity and Psychological Status among Adolescents in Shanghai

PubMed Central

Li, Jipeng; Li, Yangyang; Zhang, Yongxing; Zhao, Qinghua

2013-01-01

Purpose This study investigates the neck/shoulder pain (NSP) and low back pain (LBP) among current high school students in Shanghai and explores the relationship between these pains and their possible influences, including digital products, physical activity, and psychological status. Methods An anonymous self-assessment was administered to 3,600 students across 30 high schools in Shanghai. This questionnaire examined the prevalence of NSP and LBP and the level of physical activity as well as the use of mobile phones, personal computers (PC) and tablet computers (Tablet). The CES-D (Center for Epidemiological Studies Depression) scale was also included in the survey. The survey data were analyzed using the chi-square test, univariate logistic analyses and a multivariate logistic regression model. Results Three thousand sixteen valid questionnaires were received including 1,460 (48.41%) from male respondents and 1,556 (51.59%) from female respondents. The high school students in this study showed NSP and LBP rates of 40.8% and 33.1%, respectively, and the prevalence of both influenced by the student’s grade, use of digital products, and mental status; these factors affected the rates of NSP and LBP to varying degrees. The multivariate logistic regression analysis revealed that Gender, grade, soreness after exercise, PC using habits, tablet use, sitting time after school and academic stress entered the final model of NSP, while the final model of LBP consisted of gender, grade, soreness after exercise, PC using habits, mobile phone use, sitting time after school, academic stress and CES-D score. Conclusions High school students in Shanghai showed high prevalence of NSP and LBP that were closely related to multiple factors. Appropriate interventions should be implemented to reduce the occurrences of NSP and LBP. PMID:24147114

Implications of deregulation in natural gas industry on utility risks and returns

NASA Astrophysics Data System (ADS)

Addepalli, Rajendra P.

This thesis examines the changes in risk and required return on capital for local distribution utility companies in the increasingly competitive natural gas industry. The deregulation in the industry impacts the LDCs in several ways. First, with the introduction of competition consumers have been given choices among suppliers besides the traditional monopoly, the local utility, for purchasing their natural gas supply needs. Second, with the introduction of competition, some of the interstate pipelines were stuck with 'Take Or Pay' contracts and other costs that resulted in 'stranded costs', which have been passed on to customers of the pipeline including the LDCs. Third, the new obligation for the LDCs to purchase gas from the market, as opposed to buying it from pipelines and passing on the costs to its customers, brought opportunities and risks as well. Finally, with the introduction of competition, in some states LDCs have been allowed to enter into unregulated ventures to increase their profits. In the thesis we first develop a multifactor model (MFM) to explain historical common stock returns of individual utilities and of utility portfolios. We use 'rolling regression' analysis to analyze how different variables explain the variation in stock returns over time. Second, we conduct event studies to analyze the events in the deregulation process that had significant impacts on the LDC returns. Finally we assess the changes in risk and required return on capital for the LDCs over a 15 year time frame, covering the deregulation period. We employ four aspects in the examination of risk and return profile of the utilities: measuring (a) changes in required return on common equity and Weighted Average Cost of Capital, (b) changes in risk premium (WACC less an interest rate proxy), (c) changes in utility bond ratings, and (d) changes in dividend payments, new debt and equity issuances. We perform regression analysis to explain the changes in the required WACC using new security issuances, dividend payments and revenues of the companies.

Periodontal health and treatment needs among hospitalized chronic psychiatric patients in Istanbul, Turkey.

PubMed

Gurbuz, O; Alatas, G; Kurt, E; Dogan, F; Issever, H

2011-03-01

The aim of the study was to evaluate the periodontal health and treatment needs of chronically hospitalized psychiatric patients in Istanbul, Turkey. The subjects' periodontal health was recorded by the CPI (Community Periodontal Index) method. Of the 330 patients examined, 179 (52.5%) were males and 151 (47.5%) females. The mean age of the patients was 49.2 +/- 11.7 years. The majority (61.8%) was diagnosed with schizophrenia and 30.6% diagnosed with mental retardation. The mean length of hospitalization was 16.0 +/- 10.9 years. Healthy periodontal tissues (CPI 0) were found in 8.8% of the subjects. Bleeding on probing (CPI 1) was recorded in 6.3%, and dental calculus (CPI 2) in 51.8% of the subjects. These were determined as the worst findings. Altogether, 33% of the subjects had deep periodontal pockets, 14.2% with at least one 4- to 5-mm pocket (CPI 3), and 18.8% with at least one 6-mm pocket (CPI 4). The stepwise logistic regression analysis, between the final CPI score and seven variables including age, gender, psychiatric diagnosis, length of hospitalization, degree of helplessness, tooth brushing habits and smoking, showed that irregular tooth brushing habits and male gender were significant contributors to having a final CPI score of 2 or more. The regression analysis also showed that tooth brushing habits remained as an explanatory variable in CPI 0 coded subjects; helplessness and psychiatric diagnosis (mental retardation) in CPI 2; tooth brushing habits and psychiatric diagnosis (schizophrenia) in CPI 3; and only helplessness in CPI 4. The present study underlines a considerable need for prevention and treatment of periodontal disease among chronic psychiatric patients in Istanbul. Efforts need to be focused above all on raising this population's awareness of the importance of oral hygiene and on early diagnosis of periodontal problems.

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Can early postoperative intraocular pressure predict success following mitomycin-C augmented trabeculectomy in primary angle-closure glaucoma.

PubMed

Rong, S S; Feng, M Y; Wang, N; Meng, H; Thomas, R; Fan, S; Wang, R; Wang, X; Tang, X; Liang, Y B

2013-03-01

To evaluate the association between early and late postoperative intraocular pressure (IOP) and determine if early postoperative IOP can predict the surgical outcome. A total of 165 consecutive patients with primary angle-closure glaucoma (PACG) undergoing primary mitomycin-C-augmented trabeculectomy underwent a comprehensive eye examination before surgery and were followed-up on days 1, 7, 14, and 30, and months 3, 6, 12, and 18. IOPs on days 1, 7, 14, and 30 were stratified into groups A (<10 mm Hg), B (≥10 and <15 mm Hg), C (≥15 and <20 mm Hg), and D (≥20 mm Hg). Differences between groups were analyzed using analysis of variance (ANOVA) and Fisher's exact test. Multivariable regression was used to exam the predictive ability of early IOP for final outcome. The mean age was 62.5±7.9 years and 41.21% (n=68) were males. Stratified by IOP on days 1, 7, 14, and 30, respectively, mean IOPs at month 18 were different among groups A, B, C, and D (ANOVA, P=0.047, P=0.033, P=0.008, and P<0.001, respectively). Once the IOPs were settled with interventions on day 7 a higher IOP level was associated with decreasing success rate under different outcome definitions, final IOP <15 mm Hg (Fisher's exact P=0.001) and <20 mm Hg (P=0.039) without medication. Multiple regression showed early IOP predicted final IOP independently from baseline variables. A cutoff value of 13.5 mm Hg on day 7 achieved an accuracy of 80.0 and 57.1% in predicting IOP<15 mm Hg without medication and failure after surgery, respectively. The IOP at 18 months following primary antifibrotic-augmented trabeculectomy in PACG patients is associated with and predicted by the postoperative IOPs at 1 month. Control of early IOP to 13.5 or less may provide better outcomes.

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

NASA Astrophysics Data System (ADS)

Tang, Jie; Liu, Rong; Zhang, Yue-Li; Liu, Mou-Ze; Hu, Yong-Fang; Shao, Ming-Jie; Zhu, Li-Jun; Xin, Hua-Wen; Feng, Gui-Wen; Shang, Wen-Jun; Meng, Xiang-Guang; Zhang, Li-Rong; Ming, Ying-Zi; Zhang, Wei

2017-02-01

Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

A Note on the Relationship between the Number of Indicators and Their Reliability in Detecting Regression Coefficients in Latent Regression Analysis

ERIC Educational Resources Information Center

Dolan, Conor V.; Wicherts, Jelte M.; Molenaar, Peter C. M.

2004-01-01

We consider the question of how variation in the number and reliability of indicators affects the power to reject the hypothesis that the regression coefficients are zero in latent linear regression analysis. We show that power remains constant as long as the coefficient of determination remains unchanged. Any increase in the number of indicators…

Model selection for logistic regression models

NASA Astrophysics Data System (ADS)

Duller, Christine

2012-09-01

Model selection for logistic regression models decides which of some given potential regressors have an effect and hence should be included in the final model. The second interesting question is whether a certain factor is heterogeneous among some subsets, i.e. whether the model should include a random intercept or not. In this paper these questions will be answered with classical as well as with Bayesian methods. The application show some results of recent research projects in medicine and business administration.

Markovian prediction of future values for food grains in the economic survey

NASA Astrophysics Data System (ADS)

Sathish, S.; Khadar Babu, S. K.

2017-11-01

Now-a-days prediction and forecasting are plays a vital role in research. For prediction, regression is useful to predict the future value and current value on production process. In this paper, we assume food grain production exhibit Markov chain dependency and time homogeneity. The economic generative performance evaluation the balance time artificial fertilization different level in Estrusdetection using a daily Markov chain model. Finally, Markov process prediction gives better performance compare with Regression model.

The Multivariate Regression Statistics Strategy to Investigate Content-Effect Correlation of Multiple Components in Traditional Chinese Medicine Based on a Partial Least Squares Method.

PubMed

Peng, Ying; Li, Su-Ning; Pei, Xuexue; Hao, Kun

2018-03-01

Amultivariate regression statisticstrategy was developed to clarify multi-components content-effect correlation ofpanaxginseng saponins extract and predict the pharmacological effect by components content. In example 1, firstly, we compared pharmacological effects between panax ginseng saponins extract and individual saponin combinations. Secondly, we examined the anti-platelet aggregation effect in seven different saponin combinations of ginsenoside Rb1, Rg1, Rh, Rd, Ra3 and notoginsenoside R1. Finally, the correlation between anti-platelet aggregation and the content of multiple components was analyzed by a partial least squares algorithm. In example 2, firstly, 18 common peaks were identified in ten different batches of panax ginseng saponins extracts from different origins. Then, we investigated the anti-myocardial ischemia reperfusion injury effects of the ten different panax ginseng saponins extracts. Finally, the correlation between the fingerprints and the cardioprotective effects was analyzed by a partial least squares algorithm. Both in example 1 and 2, the relationship between the components content and pharmacological effect was modeled well by the partial least squares regression equations. Importantly, the predicted effect curve was close to the observed data of dot marked on the partial least squares regression model. This study has given evidences that themulti-component content is a promising information for predicting the pharmacological effects of traditional Chinese medicine.

Semi-automatic assessment of skin capillary density: proof of principle and validation.

PubMed

Gronenschild, E H B M; Muris, D M J; Schram, M T; Karaca, U; Stehouwer, C D A; Houben, A J H M

2013-11-01

Skin capillary density and recruitment have been proven to be relevant measures of microvascular function. Unfortunately, the assessment of skin capillary density from movie files is very time-consuming, since this is done manually. This impedes the use of this technique in large-scale studies. We aimed to develop a (semi-) automated assessment of skin capillary density. CapiAna (Capillary Analysis) is a newly developed semi-automatic image analysis application. The technique involves four steps: 1) movement correction, 2) selection of the frame range and positioning of the region of interest (ROI), 3) automatic detection of capillaries, and 4) manual correction of detected capillaries. To gain insight into the performance of the technique, skin capillary density was measured in twenty participants (ten women; mean age 56.2 [42-72] years). To investigate the agreement between CapiAna and the classic manual counting procedure, we used weighted Deming regression and Bland-Altman analyses. In addition, intra- and inter-observer coefficients of variation (CVs), and differences in analysis time were assessed. We found a good agreement between CapiAna and the classic manual method, with a Pearson's correlation coefficient (r) of 0.95 (P<0.001) and a Deming regression coefficient of 1.01 (95%CI: 0.91; 1.10). In addition, we found no significant differences between the two methods, with an intercept of the Deming regression of 1.75 (-6.04; 9.54), while the Bland-Altman analysis showed a mean difference (bias) of 2.0 (-13.5; 18.4) capillaries/mm(2). The intra- and inter-observer CVs of CapiAna were 2.5% and 5.6% respectively, while for the classic manual counting procedure these were 3.2% and 7.2%, respectively. Finally, the analysis time for CapiAna ranged between 25 and 35min versus 80 and 95min for the manual counting procedure. We have developed a semi-automatic image analysis application (CapiAna) for the assessment of skin capillary density, which agrees well with the classic manual counting procedure, is time-saving, and has a better reproducibility as compared to the classic manual counting procedure. As a result, the use of skin capillaroscopy is feasible in large-scale studies, which importantly extends the possibilities to perform microcirculation research in humans. © 2013.

Value of Construction Company and its Dependence on Significant Variables

NASA Astrophysics Data System (ADS)

Vítková, E.; Hromádka, V.; Ondrušková, E.

2017-10-01

The paper deals with the value of the construction company assessment respecting usable approaches and determinable variables. The reasons of the value of the construction company assessment are different, but the most important reasons are the sale or the purchase of the company, the liquidation of the company, the fusion of the company with another subject or the others. According the reason of the value assessment it is possible to determine theoretically different approaches for valuation, mainly it concerns about the yield method of valuation and the proprietary method of valuation. Both approaches are dependant of detailed input variables, which quality will influence the final assessment of the company´s value. The main objective of the paper is to suggest, according to the analysis, possible ways of input variables, mainly in the form of expected cash-flows or the profit, determination. The paper is focused mainly on methods of time series analysis, regression analysis and mathematical simulation utilization. As the output, the results of the analysis on the case study will be demonstrated.

Personality traits and psychotic symptoms in recent onset of psychosis patients.

PubMed

Sevilla-Llewellyn-Jones, Julia; Cano-Domínguez, Pablo; de-Luis-Matilla, Antonia; Peñuelas-Calvo, Inmaculada; Espina-Eizaguirre, Alberto; Moreno-Kustner, Berta; Ochoa, Susana

2017-04-01

Personality in patients with psychosis, and particularly its relation to psychotic symptoms in recent onset of psychosis (ROP) patients, is understudied. The aims of this research were to study the relation between dimensional and categorical clinical personality traits and symptoms, as well as the effects that symptoms, sex and age have on clinically significant personality traits. Data for these analyses were obtained from 94 ROP patients. The Millon Clinical Multiaxial Inventory and the Positive and Negative Syndrome Scale were used to assess personality and symptoms. Correlational Analysis, Mann-Whitney test, and, finally, logistic regression were carried out. The negative dimension was higher in patients with schizoid traits. The excited dimension was lower for those with avoidant and depressive traits. The anxiety and depression dimension was higher for patients with dependent traits. The positive dimension was lower for patients with histrionic and higher for patients with compulsive traits. Logistic regression demonstrated that gender and the positive and negative dimensions explained 35.9% of the variance of the schizoid trait. The excited dimension explained 9.1% of the variance of avoidant trait. The anxiety and depression dimension and age explained 31.3% of the dependent trait. Gender explained 11.6% of the histrionic trait, 14.5% of the narcissistic trait and 11.6% of the paranoid trait. Finally gender and positive dimension explained 16.1% of the compulsive trait. The study highlights the importance of studying personality in patients with psychosis as it broadens understating of the patients themselves and the symptoms suffered. Copyright © 2017 Elsevier Inc. All rights reserved.

Contribution of neurocognition to 18-month employment outcomes in first-episode psychosis.

PubMed

Karambelas, George J; Cotton, Sue M; Farhall, John; Killackey, Eóin; Allott, Kelly A

2017-10-27

To examine whether baseline neurocognition predicts vocational outcomes over 18 months in patients with first-episode psychosis enrolled in a randomized controlled trial of Individual Placement and Support or treatment as usual. One-hundred and thirty-four first-episode psychosis participants completed an extensive neurocognitive battery. Principal axis factor analysis using PROMAX rotation was used to determine the underlying structure of the battery. Setwise (hierarchical) multiple linear and logistic regressions were used to examine predictors of (1) total hours employed over 18 months and (2) employment status, respectively. Neurocognition factors were entered in the models after accounting for age, gender, premorbid IQ, negative symptoms, treatment group allocation and employment status at baseline. Five neurocognitive factors were extracted: (1) processing speed, (2) verbal learning and memory, (3) knowledge and reasoning, (4) attention and working memory and (5) visual organization and memory. Employment status over 18 months was not significantly predicted by any of the predictors in the final model. Total hours employed over 18 months were significantly predicted by gender (P = .027), negative symptoms (P = .032) and verbal learning and memory (P = .040). Every step of the regression model was a significant predictor of total hours worked overall (final model: P = .013). Verbal learning and memory, negative symptoms and gender were implicated in duration of employment in first-episode psychosis. The other neurocognitive domains did not significantly contribute to the prediction of vocational outcomes over 18 months. Interventions targeting verbal memory may improve vocational outcomes in early psychosis. © 2017 John Wiley & Sons Australia, Ltd.

The Relationship Between Reported Sleep Quality and Sleep Hygiene in Italian and American Adolescents

PubMed Central

LeBourgeois, Monique K.; Giannotti, Flavia; Cortesi, Flavia; Wolfson, Amy R.; Harsh, John

2014-01-01

Objective The purpose of the study was to examine the relationship between self-reported sleep quality and sleep hygiene in Italian and American adolescents and to assess whether sleep-hygiene practices mediate the relationship between culture and sleep quality. Methods Two nonprobability samples were collected from public schools in Rome, Italy, and Hattiesburg, Mississippi. Students completed the following self-report measures: Adolescent Sleep-Wake Scale, Adolescent Sleep Hygiene Scale, Pubertal Developmental Scale, and Morningness/Eveningness Scale. Results The final sample included 776 Italian and 572 American adolescents 12 to 17 years old. Italian adolescents reported much better sleep hygiene and substantially better sleep quality than American adolescents. A moderate-to-strong linear relationship was found between sleep hygiene and sleep quality in both samples. Separate hierarchical multiple regression analyses were performed on both samples. Demographic and individual characteristics explained a significant proportion of the variance in sleep quality (Italians: 18%; Americans: 25%), and the addition of sleep-hygiene domains explained significantly more variance in sleep quality (Italians: 17%; Americans: 16%). A final hierarchical multiple regression analysis with both samples combined showed that culture (Italy versus United States) only explained 0.8% of the variance in sleep quality after controlling for sleep hygiene and all other variables. Conclusions Cross-cultural differences in sleep quality, for the most part, were due to differences in sleep-hygiene practices. Sleep hygiene is an important predictor of sleep quality in Italian and American adolescents, thus supporting the implementation and evaluation of educational programs on good sleep-hygiene practices. PMID:15866860

Effect of warming temperatures on US wheat yields.

PubMed

Tack, Jesse; Barkley, Andrew; Nalley, Lawton Lanier

2015-06-02

Climate change is expected to increase future temperatures, potentially resulting in reduced crop production in many key production regions. Research quantifying the complex relationship between weather variables and wheat yields is rapidly growing, and recent advances have used a variety of model specifications that differ in how temperature data are included in the statistical yield equation. A unique data set that combines Kansas wheat variety field trial outcomes for 1985-2013 with location-specific weather data is used to analyze the effect of weather on wheat yield using regression analysis. Our results indicate that the effect of temperature exposure varies across the September-May growing season. The largest drivers of yield loss are freezing temperatures in the Fall and extreme heat events in the Spring. We also find that the overall effect of warming on yields is negative, even after accounting for the benefits of reduced exposure to freezing temperatures. Our analysis indicates that there exists a tradeoff between average (mean) yield and ability to resist extreme heat across varieties. More-recently released varieties are less able to resist heat than older lines. Our results also indicate that warming effects would be partially offset by increased rainfall in the Spring. Finally, we find that the method used to construct measures of temperature exposure matters for both the predictive performance of the regression model and the forecasted warming impacts on yields.

[Possibility and predictors of successful cigarette purchase attempts by 201 primary school students in Guangzhou, China].

PubMed

Wen, Xiao-zhong; Huang, Jian-hua; Chen, Wei-qing; Liang, Cai-hua; Han, Ke; Ling, Wen-hua

2007-01-01

To explore the access to tobacco and exam the predictors of successful tobacco purchase attempts among Chinese minors. A simulative trial of purchasing cigarettes was participated by 201 sixth grade students to assess the prevalence of illegal cigarette sales to minors in Guangzhou. Methods of Chi-square and unconditional logistic regression were used to identify the significant predictors,with the result of tobacco purchase as the dependent variable and the characteristics of stores, retailers and minors as the independent variables. A total of 165 students succeeded in purchasing cigarettes but 36 failed, and the percentage of successful purchase attempts was 82. 1% . Data from univariate analysis indicated that 9 factors were significantly associated with students' success in purchasing cigarettes. They were age and height of the purchasers, types of stores, seller's gender and age, posting cigarette advertisements,showing warning signs of 'no cigarette selling to minors' ,asking buyer's age,and asking whom you buy the cigarettes for. The results of multivariable analysis showed that only three variables entering the final logistic regression: the age of students, the type of stores, and showing warning signs of 'no cigarette selling to minors'. Chinese minors have easy access to purchasing cigarettes, especially in groceries and small markets. Selling cigarettes by sellers to minors should be monitored and managed in the future.

[Depressive symptoms among medical intern students in a Brazilian public university].

PubMed

Costa, Edméa Fontes de Oliva; Santana, Ygo Santos; Santos, Ana Teresa Rodrigues de Abreu; Martins, Luiz Antonio Nogueira; Melo, Enaldo Vieira de; Andrade, Tarcísio Matos de

2012-01-01

To estimate, among Medical School intern students, the prevalence of depressive symptoms and their severity, as well as associated factors. Cross-sectional study in May 2008, with a representative sample of medical intern students (n = 84) from Universidade Federal de Sergipe (UFS). Beck Depression Inventory (BDI) and a structured questionnaire containing information on sociodemographic variables, teaching-learning process, and personal aspects were used. The exploratory data analysis was performed by descriptive and inferential statistics. Finally, the analysis of multiple variables by logistic regression and the calculation of simple and adjusted ORs with their respective 95% confidence intervals were performed. The general prevalence was 40.5%, with 1.2% (95% CI: 0.0-6.5) of severe depressive symptoms; 4.8% (95% CI: 1.3-11.7) of moderate depressive symptoms; and 34.5% (95% CI: 24.5-45.7) of mild depressive symptoms. The logistic regression revealed the variables with a major impact associated with the emergence of depressive symptoms: thoughts of dropping out (OR 6.24; p = 0.002); emotional stress (OR 7.43;p = 0.0004); and average academic performance (OR 4.74; p = 0.0001). The high prevalence of depressive symptoms in the study population was associated with variables related to the teaching-learning process and personal aspects, suggesting immediate preemptive measures regarding Medical School graduation and student care are required.

Fertilizer nitrogen, soil chemical properties, and their determinacy on rice yield: Evidence from 92 paddy fields of a large-scale farm in the Kanto Region of Japan

NASA Astrophysics Data System (ADS)

Li, D.; Nanseki, T.; Chomei, Y.; Yokota, S.

2017-07-01

Rice, a staple crop in Japan, is at risk of decreasing production and its yield highly depends on soil fertility. This study aimed to investigate determinants of rice yield, from the perspectives of fertilizer nitrogen and soil chemical properties. The data were sampled in 2014 and 2015 from 92 peat soil paddy fields on a large-scale farm located in the Kanto Region of Japan. The rice variety used was the most widely planted Koshihikari in Japan. Regression analysis indicated that fertilizer nitrogen significantly affected the yield, with a significant sustained effect to the subsequent year. Twelve soil chemical properties, including pH, cation exchange capacity, content of pyridine base elements, phosphoric acid, and silicic acid, were estimated. In addition to silicic acid, magnesia, in forms of its exchangeable content, saturation, and ratios to potassium and lime, positively affected the yield, while phosphoric acid negatively affected the yield. We assessed the soil chemical properties by soil quality index and principal component analysis. Positive effects were identified for both approaches, with the former performing better in explaining the rice yield. For soil quality index, the individual standardized soil properties and margins for improvement were indicated for each paddy field. Finally, multivariate regression on the principal components identified the most significant properties.

On identifying relationships between the flood scaling exponent and basin attributes.

PubMed

Medhi, Hemanta; Tripathi, Shivam

2015-07-01

Floods are known to exhibit self-similarity and follow scaling laws that form the basis of regional flood frequency analysis. However, the relationship between basin attributes and the scaling behavior of floods is still not fully understood. Identifying these relationships is essential for drawing connections between hydrological processes in a basin and the flood response of the basin. The existing studies mostly rely on simulation models to draw these connections. This paper proposes a new methodology that draws connections between basin attributes and the flood scaling exponents by using observed data. In the proposed methodology, region-of-influence approach is used to delineate homogeneous regions for each gaging station. Ordinary least squares regression is then applied to estimate flood scaling exponents for each homogeneous region, and finally stepwise regression is used to identify basin attributes that affect flood scaling exponents. The effectiveness of the proposed methodology is tested by applying it to data from river basins in the United States. The results suggest that flood scaling exponent is small for regions having (i) large abstractions from precipitation in the form of large soil moisture storages and high evapotranspiration losses, and (ii) large fractions of overland flow compared to base flow, i.e., regions having fast-responding basins. Analysis of simple scaling and multiscaling of floods showed evidence of simple scaling for regions in which the snowfall dominates the total precipitation.

Shrinkage Degree in $L_{2}$ -Rescale Boosting for Regression.

PubMed

Xu, Lin; Lin, Shaobo; Wang, Yao; Xu, Zongben

2017-08-01

L 2 -rescale boosting ( L 2 -RBoosting) is a variant of L 2 -Boosting, which can essentially improve the generalization performance of L 2 -Boosting. The key feature of L 2 -RBoosting lies in introducing a shrinkage degree to rescale the ensemble estimate in each iteration. Thus, the shrinkage degree determines the performance of L 2 -RBoosting. The aim of this paper is to develop a concrete analysis concerning how to determine the shrinkage degree in L 2 -RBoosting. We propose two feasible ways to select the shrinkage degree. The first one is to parameterize the shrinkage degree and the other one is to develop a data-driven approach. After rigorously analyzing the importance of the shrinkage degree in L 2 -RBoosting, we compare the pros and cons of the proposed methods. We find that although these approaches can reach the same learning rates, the structure of the final estimator of the parameterized approach is better, which sometimes yields a better generalization capability when the number of sample is finite. With this, we recommend to parameterize the shrinkage degree of L 2 -RBoosting. We also present an adaptive parameter-selection strategy for shrinkage degree and verify its feasibility through both theoretical analysis and numerical verification. The obtained results enhance the understanding of L 2 -RBoosting and give guidance on how to use it for regression tasks.

Cross-sectional study of variables associated with length of stay and ICU need in open Roux-En-Y gastric bypass surgery for morbid obese patients: an exploratory analysis based on the Public Health System administrative database (Datasus) in Brazil.

PubMed

Asano, Elio Fernando; Rasera, Irineu; Shiraga, Elisabete Cristina

2012-12-01

This is an exploratory analysis of potential variables associated with open Roux-en-Y gastric bypass (RYGB) surgery hospitalization resource use pattern. Cross-sectional study based on an administrative database (DATASUS) records. Inclusion criteria were adult patients undergoing RYGB between Jan/2008 and Jun/2011. Dependent variables were length of stay (LoS) and ICU need. Independent variables were: gender, age, region, hospital volume, surgery at certified center of excellence (CoE) by the Surgical Review Corporation (SRC), teaching hospital, and year of hospitalization. Univariate and multivariate analysis (logistic regression for ICU need and linear regression for length of stay) were performed. Data from 13,069 surgeries were analyzed. In crude analysis, hospital volume was the most impactful variable associated with log-transformed LoS (1.312 ± 0.302 high volume vs. 1.670 ± 0.581 low volume, p < 0.001), whereas for ICU need it was certified CoE (odds ratio (OR), 0.016; 95% confidence interval (CI), 0.010-0.026). After adjustment by logistic regression, certified CoE remained as the strongest predictor of ICU need (OR, 0.011; 95% CI, 0.007-0.018), followed by hospital volume (OR, 3.096; 95% CI, 2.861-3.350). Age group, male gender, and teaching hospital were also significantly associated (p < 0.001). For log-transformed LoS, final model includes hospital volume (coefficient, -0.223; 95% CI, -0.250 to -0.196) and teaching hospital (coefficient, 0.375; 95% CI, 0.351-0.398). Region of Brazil was not associated with any of the outcomes. High-volume hospital was the strongest predictor for shorter LoS, whereas SRC certification was the strongest predictor of lower ICU need. Public health policies targeting an increase of efficiency and patient access to the procedure should take into account these results.

Associations of blood lead, cadmium, and mercury with estimated glomerular filtration rate in the Korean general population: Analysis of 2008-2010 Korean National Health and Nutrition Examination Survey data

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

Kim, Yangho; Lee, Byung-Kook, E-mail: bklee@sch.ac.kr

Introduction: The objective of this study was to evaluate associations between blood lead, cadmium, and mercury levels with estimated glomerular filtration rate in a general population of South Korean adults. Methods: This was a cross-sectional study based on data obtained in the Korean National Health and Nutrition Examination Survey (KNHANES) (2008-2010). The final analytical sample consisted of 5924 participants. Estimated glomerular filtration rate (eGFR) was calculated using the MDRD Study equation as an indicator of glomerular function. Results: In multiple linear regression analysis of log2-transformed blood lead as a continuous variable on eGFR, after adjusting for covariates including cadmium andmore » mercury, the difference in eGFR levels associated with doubling of blood lead were -2.624 mL/min per 1.73 m Superscript-Two (95% CI: -3.803 to -1.445). In multiple linear regression analysis using quartiles of blood lead as the independent variable, the difference in eGFR levels comparing participants in the highest versus the lowest quartiles of blood lead was -3.835 mL/min per 1.73 m Superscript-Two (95% CI: -5.730 to -1.939). In a multiple linear regression analysis using blood cadmium and mercury, as continuous or categorical variables, as independent variables, neither metal was a significant predictor of eGFR. Odds ratios (ORs) and 95% CI values for reduced eGFR calculated for log2-transformed blood metals and quartiles of the three metals showed similar trends after adjustment for covariates. Discussion: In this large, representative sample of South Korean adults, elevated blood lead level was consistently associated with lower eGFR levels and with the prevalence of reduced eGFR even in blood lead levels below 10 {mu}g/dL. In conclusion, elevated blood lead level was associated with lower eGFR in a Korean general population, supporting the role of lead as a risk factor for chronic kidney disease.« less

Moderation analysis using a two-level regression model.

PubMed

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

Multiple Correlation versus Multiple Regression.

ERIC Educational Resources Information Center

Huberty, Carl J.

2003-01-01

Describes differences between multiple correlation analysis (MCA) and multiple regression analysis (MRA), showing how these approaches involve different research questions and study designs, different inferential approaches, different analysis strategies, and different reported information. (SLD)

Functional Relationships and Regression Analysis.

ERIC Educational Resources Information Center

Preece, Peter F. W.

1978-01-01

Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…

Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression

ERIC Educational Resources Information Center

Beckstead, Jason W.

2012-01-01

The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…

General Nature of Multicollinearity in Multiple Regression Analysis.

ERIC Educational Resources Information Center

Liu, Richard

1981-01-01

Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)

Logistic Regression: Concept and Application

ERIC Educational Resources Information Center

Cokluk, Omay

2010-01-01

The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…

The impact of moderate wine consumption on the risk of developing prostate cancer.

PubMed

Vartolomei, Mihai Dorin; Kimura, Shoji; Ferro, Matteo; Foerster, Beat; Abufaraj, Mohammad; Briganti, Alberto; Karakiewicz, Pierre I; Shariat, Shahrokh F

2018-01-01

To investigate the impact of moderate wine consumption on the risk of prostate cancer (PCa). We focused on the differential effect of moderate consumption of red versus white wine. This study was a meta-analysis that includes data from case-control and cohort studies. A systematic search of Web of Science, Medline/PubMed, and Cochrane library was performed on December 1, 2017. Studies were deemed eligible if they assessed the risk of PCa due to red, white, or any wine using multivariable logistic regression analysis. We performed a formal meta-analysis for the risk of PCa according to moderate wine and wine type consumption (white or red). Heterogeneity between studies was assessed using Cochrane's Q test and I 2 statistics. Publication bias was assessed using Egger's regression test. A total of 930 abstracts and titles were initially identified. After removal of duplicates, reviews, and conference abstracts, 83 full-text original articles were screened. Seventeen studies (611,169 subjects) were included for final evaluation and fulfilled the inclusion criteria. In the case of moderate wine consumption: the pooled risk ratio (RR) for the risk of PCa was 0.98 (95% CI 0.92-1.05, p =0.57) in the multivariable analysis. Moderate white wine consumption increased the risk of PCa with a pooled RR of 1.26 (95% CI 1.10-1.43, p =0.001) in the multi-variable analysis. Meanwhile, moderate red wine consumption had a protective role reducing the risk by 12% (RR 0.88, 95% CI 0.78-0.999, p =0.047) in the multivariable analysis that comprised 222,447 subjects. In this meta-analysis, moderate wine consumption did not impact the risk of PCa. Interestingly, regarding the type of wine, moderate consumption of white wine increased the risk of PCa, whereas moderate consumption of red wine had a protective effect. Further analyses are needed to assess the differential molecular effect of white and red wine conferring their impact on PCa risk.

Predictive value of spectral-domain optical coherence tomography features in assessment of visual prognosis in eyes with acute welding arc maculopathy.

PubMed

Zhang, Chunxia; Dang, Guangfu; Zhao, Tianmei; Wang, DongLin; Su, Yan; Qu, Yi

2018-04-12

To observe spectral-domain optical coherence tomography (SD-OCT) features and to determine whether baseline OCT features can be used as predictors of visual acuity outcome in eyes with acute welding arc maculopathy. This retrospective study enrolled twenty-two eyes of eleven subjects with acute welding arc maculopathy. All subjects were evaluated by SD-OCT at baseline and final visit. The involved parameters included best-corrected visual acuity (BCVA), central macular thickness (CMT), the length of ellipsoid zone (EZ) defects, the greatest linear dimension (GLD) of outer retinal lesions, EZ reflectivity and relative EZ reflectivity (defined as the ratio of EZ reflectivity to retinal pigment epithelium reflectivity on OCT). Acute welding arc maculopathy was presented as abnormal hyperreflectivity, hyporeflectivity and defects of outer retinal layer in fovea on OCT. Compared with baseline, BCVA improved significantly accompanied by decreased GLD of outer retinal lesions and the length of EZ defects at final visit (P = 0.0004, P < 0.0001 and P < 0.0001, respectively). No significant changes were shown on CMT (P = 0.248). In multivariate regression analysis, final BCVA was associated with baseline BCVA and the length of EZ defects (P = 0.012 and P = 0.045, respectively). However, EZ reflectivity and relative EZ reflectivity were not associated with final BCVA (P > 0.05). In conclusion, SD-OCT images clearly reveal morphological changes in outer retinal layer in acute welding arc maculopathy. The baseline BCVA and length of EZ defects are the strongest predictors of final BCVA.

Applying Regression Analysis to Problems in Institutional Research.

ERIC Educational Resources Information Center

Bohannon, Tom R.

1988-01-01

Regression analysis is one of the most frequently used statistical techniques in institutional research. Principles of least squares, model building, residual analysis, influence statistics, and multi-collinearity are described and illustrated. (Author/MSE)

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies

PubMed Central

Vatcheva, Kristina P.; Lee, MinJae; McCormick, Joseph B.; Rahbar, Mohammad H.

2016-01-01

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis. PMID:27274911

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies.

PubMed

Vatcheva, Kristina P; Lee, MinJae; McCormick, Joseph B; Rahbar, Mohammad H

2016-04-01

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.

Frailty Markers and Treatment Decisions in Patients Seen in Oncogeriatric Clinics: Results from the ASRO Pilot Study.

PubMed

Farcet, Anaïs; de Decker, Laure; Pauly, Vanessa; Rousseau, Frédérique; Bergman, Howard; Molines, Catherine; Retornaz, Frédérique

2016-01-01

Comprehensive Geriatric Assessment (CGA) is the gold standard to help oncologists select the best cancer treatment for their older patients. Some authors have suggested that the concept of frailty could be a more useful approach in this population. We investigated whether frailty markers are associated with treatment recommendations in an oncogeriatric clinic. This prospective study included 70 years and older patients with solid tumors and referred for an oncogeriatric assessment. The CGA included nine domains: autonomy, comorbidities, medication, cognition, nutrition, mood, neurosensory deficits, falls, and social status. Five frailty markers were assessed (nutrition, physical activity, energy, mobility, and strength). Patients were categorized as Frail (three or more frailty markers), pre-frail (one or two frailty markers), or not-frail (no frailty marker). Treatment recommendations were classified into two categories: standard treatment with and without any changes and supportive/palliative care. Multiple logistic regression models were used to analyze factors associated with treatment recommendations. 217 patients, mean age 83 years (± Standard deviation (SD) 5.3), were included. In the univariate analysis, number of frailty markers, grip strength, physical activity, mobility, nutrition, energy, autonomy, depression, Eastern Cooperative Oncology Group Scale of Performance Status (ECOG-PS), and falls were significantly associated with final treatment recommendations. In the multivariate analysis, the number of frailty markers and basic Activities of Daily Living (ADL) were significantly associated with final treatment recommendations (p<0.001 and p = 0.010, respectively). Frailty markers are associated with final treatment recommendations in older cancer patients. Longitudinal studies are warranted to better determine their use in a geriatric oncology setting.

Influence of genetic, biological and pharmacological factors on levodopa dose in Parkinson's disease.

PubMed

Altmann, Vivian; Schumacher-Schuh, Artur F; Rieck, Mariana; Callegari-Jacques, Sidia M; Rieder, Carlos R M; Hutz, Mara H

2016-04-01

Levodopa is first-line treatment of Parkinson's disease motor symptoms but, dose response is highly variable. Therefore, the aim of this study was to determine how much levodopa dose could be explained by biological, pharmacological and genetic factors. A total of 224 Parkinson's disease patients were genotyped for SV2C and SLC6A3 polymorphisms by allelic discrimination assays. Comedication, demographic and clinical data were also assessed. All variables with p < 0.20 were included in a multiple regression analysis for dose prediction. The final model explained 23% of dose variation (F = 11.54; p < 0.000001). Although a good prediction model was obtained, it still needs to be tested in an independent sample to be validated.

Personality and emotional intelligence in teacher burnout.

PubMed

Pishghadam, Reza; Sahebjam, Samaneh

2012-03-01

This paper aims to investigate the relationship between teacher's personality types, emotional intelligence and burnout and to predict the burnout levels of 147 teachers in the city of Mashhad (Iran). To this end, we have used three inventories: Maslach Burnout Inventory (MBI), NEO Five Factor Inventory (NEO-FFI), and Emotional Quotient Inventory (EQ-I). We used Homogeneity Analysis and Multiple Linear Regression to analyze the data. The results exhibited a significant relationship between personality types and emotional intelligence and the three dimensions of burnout. It was indicated that the best predictors for emotional exhaustion were neuroticism and extroversion, for depersonalization were intrapersonal scale of emotional intelligence and agreeableness, and for personal accomplishment were interpersonal scale and conscientiousness. Finally, the results were discussed in the context of teacher burnout.

Stepwise versus Hierarchical Regression: Pros and Cons

ERIC Educational Resources Information Center

Lewis, Mitzi

2007-01-01

Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…

Interpreting Bivariate Regression Coefficients: Going beyond the Average

ERIC Educational Resources Information Center

Halcoussis, Dennis; Phillips, G. Michael

2010-01-01

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

Regression Commonality Analysis: A Technique for Quantitative Theory Building

ERIC Educational Resources Information Center

Nimon, Kim; Reio, Thomas G., Jr.

2011-01-01

When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…

Precision Efficacy Analysis for Regression.

ERIC Educational Resources Information Center

Brooks, Gordon P.

When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…

Diagnostic accuracy of contrast-enhanced ultrasound in assessing the therapeutic response to radio frequency ablation for liver tumors: systematic review and meta-analysis.

PubMed

Xuan, Min; Zhou, Fengsheng; Ding, Yan; Zhu, Qiaoying; Dong, Ji; Zhou, Hao; Cheng, Jun; Jiang, Xiao; Wu, Pengxi

2018-04-01

To review the diagnostic accuracy of contrast-enhanced ultrasound (CEUS) used to detect residual or recurrent liver tumors after radiofrequency ablation (RFA). This technique uses contrast-enhanced computer tomography or/and contrast-enhanced magnetic resonance imaging as the gold standard of investigation. MEDLINE, EMBASE, and COCHRANE were systematically searched for all potentially eligible studies comparing CEUS with the reference standard that follows RFA. Risk of bias and applicability concerns were addressed by adopting the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Pooled point estimates for sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratios (DOR) with 95% CI were computed before plotting the sROC (summary receiver operating characteristic) curve. Meta-regression and subgroup analysis were used to identify the source of the heterogeneity that was detected. Publication bias was evaluated using Deeks' funnel plot asymmetry test. Ten eligible studies on 1162 lesions that occurred between 2001 and 2016 were included in the final analysis. The quality of the included studies assessed by the QUADAS-2 tool was considered reasonable. The pooled sensitivity and specificity of CEUS in detecting residual or recurrent liver tumors had the following values: 0.90 (95% CI 0.85-0.94) and 1.00 (95% CI 0.99-1.00), respectively. Overall DOR was 420.10 (95% CI 142.30-1240.20). The sources of heterogeneity could not be precisely identified by meta-regression or subgroup analysis. No evidence of publication bias was found. This study confirmed that CEUS exhibits high sensitivity and specificity in assessing therapeutic responses to RFA for liver tumors.

Exposure to fluoride in drinking water and hip fracture risk: a meta-analysis of observational studies.

PubMed

Yin, Xin-Hai; Huang, Guang-Lei; Lin, Du-Ren; Wan, Cheng-Cheng; Wang, Ya-Dong; Song, Ju-Kun; Xu, Ping

2015-01-01

Many observational studies have shown that exposure to fluoride in drinking water is associated with hip fracture risk. However, the findings are varied or even contradictory. In this work, we performed a meta-analysis to assess the relationship between fluoride exposure and hip fracture risk. PubMed and EMBASE databases were searched to identify relevant observational studies from the time of inception until March 2014 without restrictions. Data from the included studies were extracted and analyzed by two authors. Summary relative risks (RRs) with corresponding 95% confidence intervals (CIs) were pooled using random- or fixed-effects models as appropriate. Sensitivity analyses and meta-regression were conducted to explore possible explanations for heterogeneity. Finally, publication bias was assessed. Fourteen observational studies involving thirteen cohort studies and one case-control study were included in the meta-analysis. Exposure to fluoride in drinking water does not significantly increase the incidence of hip fracture (RRs, 1.05; 95% CIs, 0.96-1.15). Sensitivity analyses based on adjustment for covariates, effect measure, country, sex, sample size, quality of Newcastle-Ottawa Scale scores, and follow-up period validated the strength of the results. Meta-regression showed that country, gender, quality of Newcastle-Ottawa Scale scores, adjustment for covariates and sample size were not sources of heterogeneity. Little evidence of publication bias was observed. The present meta-analysis suggests that chronic fluoride exposure from drinking water does not significantly increase the risk of hip fracture. Given the potential confounding factors and exposure misclassification, further large-scale, high-quality studies are needed to evaluate the association between exposure to fluoride in drinking water and hip fracture risk.

Exposure to Fluoride in Drinking Water and Hip Fracture Risk: A Meta-Analysis of Observational Studies

PubMed Central

Yin, Xin-Hai; Huang, Guang-Lei; Lin, Du-Ren; Wan, Cheng-Cheng; Wang, Ya-Dong; Song, Ju-Kun; Xu, Ping

2015-01-01

Background Many observational studies have shown that exposure to fluoride in drinking water is associated with hip fracture risk. However, the findings are varied or even contradictory. In this work, we performed a meta-analysis to assess the relationship between fluoride exposure and hip fracture risk. Methods PubMed and EMBASE databases were searched to identify relevant observational studies from the time of inception until March 2014 without restrictions. Data from the included studies were extracted and analyzed by two authors. Summary relative risks (RRs) with corresponding 95% confidence intervals (CIs) were pooled using random- or fixed-effects models as appropriate. Sensitivity analyses and meta-regression were conducted to explore possible explanations for heterogeneity. Finally, publication bias was assessed. Results Fourteen observational studies involving thirteen cohort studies and one case-control study were included in the meta-analysis. Exposure to fluoride in drinking water does not significantly increase the incidence of hip fracture (RRs, 1.05; 95% CIs, 0.96–1.15). Sensitivity analyses based on adjustment for covariates, effect measure, country, sex, sample size, quality of Newcastle–Ottawa Scale scores, and follow-up period validated the strength of the results. Meta-regression showed that country, gender, quality of Newcastle–Ottawa Scale scores, adjustment for covariates and sample size were not sources of heterogeneity. Little evidence of publication bias was observed. Conclusion The present meta-analysis suggests that chronic fluoride exposure from drinking water does not significantly increase the risk of hip fracture. Given the potential confounding factors and exposure misclassification, further large-scale, high-quality studies are needed to evaluate the association between exposure to fluoride in drinking water and hip fracture risk. PMID:26020536

A comparison of logistic regression analysis and an artificial neural network using the BI-RADS lexicon for ultrasonography in conjunction with introbserver variability.

PubMed

Kim, Sun Mi; Han, Heon; Park, Jeong Mi; Choi, Yoon Jung; Yoon, Hoi Soo; Sohn, Jung Hee; Baek, Moon Hee; Kim, Yoon Nam; Chae, Young Moon; June, Jeon Jong; Lee, Jiwon; Jeon, Yong Hwan

2012-10-01

To determine which Breast Imaging Reporting and Data System (BI-RADS) descriptors for ultrasound are predictors for breast cancer using logistic regression (LR) analysis in conjunction with interobserver variability between breast radiologists, and to compare the performance of artificial neural network (ANN) and LR models in differentiation of benign and malignant breast masses. Five breast radiologists retrospectively reviewed 140 breast masses and described each lesion using BI-RADS lexicon and categorized final assessments. Interobserver agreements between the observers were measured by kappa statistics. The radiologists' responses for BI-RADS were pooled. The data were divided randomly into train (n = 70) and test sets (n = 70). Using train set, optimal independent variables were determined by using LR analysis with forward stepwise selection. The LR and ANN models were constructed with the optimal independent variables and the biopsy results as dependent variable. Performances of the models and radiologists were evaluated on the test set using receiver-operating characteristic (ROC) analysis. Among BI-RADS descriptors, margin and boundary were determined as the predictors according to stepwise LR showing moderate interobserver agreement. Area under the ROC curves (AUC) for both of LR and ANN were 0.87 (95% CI, 0.77-0.94). AUCs for the five radiologists ranged 0.79-0.91. There was no significant difference in AUC values among the LR, ANN, and radiologists (p > 0.05). Margin and boundary were found as statistically significant predictors with good interobserver agreement. Use of the LR and ANN showed similar performance to that of the radiologists for differentiation of benign and malignant breast masses.

Effects of sense of coherence on depressive symptoms after employment in the Japan Self-Defense Force among male young adults.

PubMed

Kobayashi, Tohru

2017-01-01

Objective　The present study aimed to explore the effects of sense of coherence (SOC) on depressive symptoms after employment in the Japan Self-Defense Force among male young adults.Methods　In April 2013, 953 new male members of the Japan Ground Self-Defense Force (JGSDF; age range: 18-24 years) participated in this study. Depressive symptoms were assessed using the 20-item version of the Center for Epidemiologic Studies Depression scale (CES-D), which defines a score of 16 or greater as indicating the presence of depressive symptoms. The SOC score was assessed using a 13-item version (SOC-13), in which a score of 59 or greater is as assigned to the high score group. A second survey was conducted two months later, in June of 2013. For the analysis, we selected participants without depressive symptoms at the baseline survey. The association between SOC scores at baseline and the onset of depressive symptoms was examined using a logistic regression analysis.Results　The final analysis was conducted on data on 389 new male members of the JGSDF. The logistic regression analysis showed a significant reduction in the onset of depressive symptoms among the group with high SOC scores (odds ratios: 0.59, 95% confidence interval=0.35-0.98) as compared with that observed in the group with low SOC scores.Conclusions　The present study clarified that SOC among male young adults has a buffering effect on the risk of developing depressive symptoms after employment in the Japan Self-Defense Force. Our results may be useful for improving the mental health of new employees.

Investigation of the Prevalence of Obesity in Iran: a Systematic Review and Meta-Analysis Study.

PubMed

Rahmani, Asghar; Sayehmiri, Kourosh; Asadollahi, Khairollah; Sarokhani, Diana; Islami, Farhad; Sarokhani, Mandana

2015-10-01

Obesity is one of the main public health problems which underlie many chronic illnesses and socioeconomic difficulties. According to the literature review, there are limited data on the prevalence of obesity in different parts of Iran as well as its trend and prevalence among different age and gender groups. The aim of this study was to estimate the obesity prevalence in Iran using meta-analysis. All the corresponding articles published in the external and internal journals, final reports of research projects, articles of related congresses and the reference index of the correlated papers published between 1995 and 2010 were collected via the electronic research engines (PubMed, Scopus, SID, Magiran, IranMedex). Data were analyzed using meta-analysis (random effects model) and meta-regression). A total of 144 articles with the sample size of 377858 people (134588 males and 164858 females) were enrolled in the study. The prevalence of obesity in populations above the age of 18 was estimated as 21.7% (CI 95%: 18.5% - 25%) and in populations below 18 as 6.1% (CI 95%: 6.8%-5.4%). Meta-regression analysis showed an ascending trend in the prevalence of obesity in Iran. The prevalence rates of obesity according to the BMI index, NCHC and percentile above 95 were 17.4%, 7.6% and 7.4%, respectively. The BMI mean was 19.3 in populations below the age of 18 (CI 95%: 17-21.6) and 25.2 in those above the age of 18 (CI 95%: 27.1-23.3). Considering the increasing rate of obesity in Iran and its effects on the public health, corresponding health authorities should revise the obesity preventive programs and, using public health interventions, reduce the rate of obesity in the country.

Colorectal cancer mortality trends in Serbia during 1991-2010: an age-period-cohort analysis and a joinpoint regression analysis.

PubMed

Ilic, Milena; Ilic, Irena

2016-06-22

For both men and women worldwide, colorectal cancer is among the leading causes of cancer-related death. This study aimed to assess the mortality trends of colorectal cancer in Serbia between 1991 and 2010, prior to the introduction of population-based screening. Joinpoint regression analysis was used to estimate average annual percent change (AAPC) with the corresponding 95% confidence interval (CI). Furthermore, age-period-cohort analysis was performed to examine the effects of birth cohort and calendar period on the observed temporal trends. We observed a significantly increased trend in colorectal cancer mortality in Serbia during the study period (AAPC = 1.6%, 95% CI 1.3%-1.8%). Colorectal cancer showed an increased mortality trend in both men (AAPC = 2.0%, 95% CI 1.7%-2.2%) and women (AAPC = 1.0%, 95% CI 0.6%-1.4%). The temporal trend of colorectal cancer mortality was significantly affected by birth cohort (P < 0.05), whereas the study period did not significantly affect the trend (P = 0.072). Colorectal cancer mortality increased for the first several birth cohorts in Serbia (from 1916 to 1955), followed by downward flexion for people born after the 1960s. According to comparability test, overall mortality trends for colon cancer and rectal and anal cancer were not parallel (the final selected model rejected parallelism, P < 0.05). We found that colorectal cancer mortality in Serbia increased considerably over the past two decades. Mortality increased particularly in men, but the trends were different according to age group and subsite. In Serbia, interventions to reduce colorectal cancer burden, especially the implementation of a national screening program, as well as treatment improvements and measures to encourage the adoption of a healthy lifestyle, are needed.

Comparison of athlete-coach perceptions of internal and external load markers for elite junior tennis training.

PubMed

Murphy, Alistair P; Duffield, Rob; Kellett, Aaron; Reid, Machar

2014-09-01

To investigate the discrepancy between coach and athlete perceptions of internal load and notational analysis of external load in elite junior tennis. Fourteen elite junior tennis players and 6 international coaches were recruited. Ratings of perceived exertion (RPEs) were recorded for individual drills and whole sessions, along with a rating of mental exertion, coach rating of intended session exertion, and athlete heart rate (HR). Furthermore, total stroke count and unforced-error count were notated using video coding after each session, alongside coach and athlete estimations of shots and errors made. Finally, regression analyses explained the variance in the criterion variables of athlete and coach RPE. Repeated-measures analyses of variance and interclass correlation coefficients revealed that coaches significantly (P < .01) underestimated athlete session RPE, with only moderate correlation (r = .59) demonstrated between coach and athlete. However, athlete drill RPE (P = .14; r = .71) and mental exertion (P = .44; r = .68) were comparable and substantially correlated. No significant differences in estimated stroke count were evident between athlete and coach (P = .21), athlete notational analysis (P = .06), or coach notational analysis (P = .49). Coaches estimated significantly greater unforced errors than either athletes or notational analysis (P < .01). Regression analyses found that 54.5% of variance in coach RPE was explained by intended session exertion and coach drill RPE, while drill RPE and peak HR explained 45.3% of the variance in athlete session RPE. Coaches misinterpreted session RPE but not drill RPE, while inaccurately monitoring error counts. Improved understanding of external- and internal-load monitoring may help coach-athlete relationships in individual sports like tennis avoid maladaptive training.

Estimation of 1RM for knee extension based on the maximal isometric muscle strength and body composition.

PubMed

Kanada, Yoshikiyo; Sakurai, Hiroaki; Sugiura, Yoshito; Arai, Tomoaki; Koyama, Soichiro; Tanabe, Shigeo

2017-11-01

[Purpose] To create a regression formula in order to estimate 1RM for knee extensors, based on the maximal isometric muscle strength measured using a hand-held dynamometer and data regarding the body composition. [Subjects and Methods] Measurement was performed in 21 healthy males in their twenties to thirties. Single regression analysis was performed, with measurement values representing 1RM and the maximal isometric muscle strength as dependent and independent variables, respectively. Furthermore, multiple regression analysis was performed, with data regarding the body composition incorporated as another independent variable, in addition to the maximal isometric muscle strength. [Results] Through single regression analysis with the maximal isometric muscle strength as an independent variable, the following regression formula was created: 1RM (kg)=0.714 + 0.783 × maximal isometric muscle strength (kgf). On multiple regression analysis, only the total muscle mass was extracted. [Conclusion] A highly accurate regression formula to estimate 1RM was created based on both the maximal isometric muscle strength and body composition. Using a hand-held dynamometer and body composition analyzer, it was possible to measure these items in a short time, and obtain clinically useful results.

Determining the influence of past development experience on the cost of strategic ballistic missile development

NASA Astrophysics Data System (ADS)

Whitney, Dwight E.

The influence of learning in the form of past relevant experience was examined in data collected for strategic ballistic missiles developed by the United States. A total of twenty-four new missiles were developed and entered service between 1954 and 1990. Missile development costs were collected and analyzed by regression analysis using the learning curve model with factors for past experience and other relevant cost estimating relationships. The purpose of the study was to determine whether prior development experience was a factor in the development cost of these like systems. Of the twenty-four missiles in the population, development costs for twelve of the missiles were collected from the literature. Since the costs were found to be segmented by military service, a discrete input variable for military service was used as one of the cost estimating relationships. Because there were only two US Navy samples, too few to analyze for segmentation and learning rate, they were excluded from the final analysis. The final analysis was on a sample of ten out of eighteen US Army and US Air Force missiles within the population. The result of the analysis found past experience to be a statistically significant factor in describing the development cost of the US Army and US Air Force missiles. The influence equated to a 0.86 progress ratio, indicating prior development experience had a positive (cost-reducing) influence on their development cost. Based on the result, it was concluded that prior development experience was a factor in the development cost of these systems.

Local polynomial estimation of heteroscedasticity in a multivariate linear regression model and its applications in economics.

PubMed

Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan

2012-01-01

Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.

Soft-sensing model of temperature for aluminum reduction cell on improved twin support vector regression

NASA Astrophysics Data System (ADS)

Li, Tao

2018-06-01

The complexity of aluminum electrolysis process leads the temperature for aluminum reduction cells hard to measure directly. However, temperature is the control center of aluminum production. To solve this problem, combining some aluminum plant's practice data, this paper presents a Soft-sensing model of temperature for aluminum electrolysis process on Improved Twin Support Vector Regression (ITSVR). ITSVR eliminates the slow learning speed of Support Vector Regression (SVR) and the over-fit risk of Twin Support Vector Regression (TSVR) by introducing a regularization term into the objective function of TSVR, which ensures the structural risk minimization principle and lower computational complexity. Finally, the model with some other parameters as auxiliary variable, predicts the temperature by ITSVR. The simulation result shows Soft-sensing model based on ITSVR has short time-consuming and better generalization.

[Application of negative binomial regression and modified Poisson regression in the research of risk factors for injury frequency].

PubMed

Cao, Qingqing; Wu, Zhenqiang; Sun, Ying; Wang, Tiezhu; Han, Tengwei; Gu, Chaomei; Sun, Yehuan

2011-11-01

To Eexplore the application of negative binomial regression and modified Poisson regression analysis in analyzing the influential factors for injury frequency and the risk factors leading to the increase of injury frequency. 2917 primary and secondary school students were selected from Hefei by cluster random sampling method and surveyed by questionnaire. The data on the count event-based injuries used to fitted modified Poisson regression and negative binomial regression model. The risk factors incurring the increase of unintentional injury frequency for juvenile students was explored, so as to probe the efficiency of these two models in studying the influential factors for injury frequency. The Poisson model existed over-dispersion (P < 0.0001) based on testing by the Lagrangemultiplier. Therefore, the over-dispersion dispersed data using a modified Poisson regression and negative binomial regression model, was fitted better. respectively. Both showed that male gender, younger age, father working outside of the hometown, the level of the guardian being above junior high school and smoking might be the results of higher injury frequencies. On a tendency of clustered frequency data on injury event, both the modified Poisson regression analysis and negative binomial regression analysis can be used. However, based on our data, the modified Poisson regression fitted better and this model could give a more accurate interpretation of relevant factors affecting the frequency of injury.

Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules

PubMed Central

Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules’ 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p < 0.05 were embodied in a logistic regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively. PMID:29228030

Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules.

PubMed

Pang, Tiantian; Huang, Leidan; Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Gong, Xuehao; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules' 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p < 0.05 were embodied in a logistic regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively.

The end-Permian mass extinction: A complex, multicausal extinction

NASA Technical Reports Server (NTRS)

Erwin, D. H.

1994-01-01

The end-Permian mass extinction was the most extensive in the history of life and remains one of the most complex. Understanding its causes is particularly important because it anchors the putative 26-m.y. pattern of periodic extinction. However, there is no good evidence for an impact and this extinction appears to be more complex than others, involving at least three phases. The first began with the onset of a marine regression during the Late Permian and resulting elimination of most marine basins, reduction in habitat area, and increased climatic instability; the first pulse of tetrapod extinctions occurred in South Africa at this time. The second phase involved increased regression in many areas (although apparently not in South China) and heightened climatic instability and environmental degradation. Release of gas hydrates, oxidation of marine carbon, and the eruption of the Siberian flood basalts occurred during this phase. The final phase of the extinction episode began with the earliest Triassic marine regression and destruction of nearshore continental habitats. Some evidence suggests oceanic anoxia may have developed during the final phase of the extinction, although it appears to have been insufficient to the sole cause of the extinction.

Construction and analysis of a modular model of caspase activation in apoptosis

PubMed Central

Harrington, Heather A; Ho, Kenneth L; Ghosh, Samik; Tung, KC

2008-01-01

Background A key physiological mechanism employed by multicellular organisms is apoptosis, or programmed cell death. Apoptosis is triggered by the activation of caspases in response to both extracellular (extrinsic) and intracellular (intrinsic) signals. The extrinsic and intrinsic pathways are characterized by the formation of the death-inducing signaling complex (DISC) and the apoptosome, respectively; both the DISC and the apoptosome are oligomers with complex formation dynamics. Additionally, the extrinsic and intrinsic pathways are coupled through the mitochondrial apoptosis-induced channel via the Bcl-2 family of proteins. Results A model of caspase activation is constructed and analyzed. The apoptosis signaling network is simplified through modularization methodologies and equilibrium abstractions for three functional modules. The mathematical model is composed of a system of ordinary differential equations which is numerically solved. Multiple linear regression analysis investigates the role of each module and reduced models are constructed to identify key contributions of the extrinsic and intrinsic pathways in triggering apoptosis for different cell lines. Conclusion Through linear regression techniques, we identified the feedbacks, dissociation of complexes, and negative regulators as the key components in apoptosis. The analysis and reduced models for our model formulation reveal that the chosen cell lines predominately exhibit strong extrinsic caspase, typical of type I cell, behavior. Furthermore, under the simplified model framework, the selected cells lines exhibit different modes by which caspase activation may occur. Finally the proposed modularized model of apoptosis may generalize behavior for additional cells and tissues, specifically identifying and predicting components responsible for the transition from type I to type II cell behavior. PMID:19077196

Discriminating gastric cancer and gastric ulcer using human plasma amino acid metabolic profile.

PubMed

Jing, Fangyu; Hu, Xin; Cao, Yunfeng; Xu, Minghao; Wang, Yuanyuan; Jing, Yu; Hu, Xiaodan; Gao, Yu; Zhu, Zhitu

2018-06-01

Patients with gastric ulcer (GU) have a significantly higher risk of developing gastric cancer (GC), especially within 2 years after diagnosis. The main way to improve the prognosis of GC is to predict the tumorigenesis and metastasis in the early stage. The objective of this study was to demonstrate the ability of human plasma amino acid metabolic profile for discriminating GC and GU. In this study, we first used liquid chromatography-tandem mass spectrometry technique to characterize the plasma amino acid metabolism in GC and GU patients. Plasma samples were collected from 84 GC patients and 82 GU patients, and 22 amino acids were detected in each patient. Partial least squares-discriminant analysis model was performed to analyze the data of these amino acids. We observed seven differential amino acids between GC and GU. A regression analysis model was established using these seven amino acids. Finally, a panel of five differential amino acids, including glutamine, ornithine, histidine, arginine and tryptophan, was identified for discriminating GC and GU with good specificity and sensitivity. The receiver operating characteristic curve was used to evaluate diagnostic ability of the regression model and area under the curve was 0.922. In conclusion, this study demonstrated the potential values of plasma amino acid metabolic profile and metabolomic analysis technique in assisting diagnosis of GC. More studies are needed to highlight the theoretical strengths of metabolomics to understand the potential metabolic mechanisms in GC. © 2018 IUBMB Life, 70(6):553-562, 2018. © 2018 International Union of Biochemistry and Molecular Biology.

The association of ethnicity with electronically measured adherence to inhaled corticosteroids in children.

PubMed

Vasbinder, Erwin; Dahhan, Nordin; Wolf, Bart; Zoer, Jan; Blankman, Ellen; Bosman, Diederik; van Dijk, Liset; van den Bemt, Patricia

2013-03-01

To investigate the association of ethnicity with objectively, electronically measured adherence to inhaled corticosteroids (ICS) in a multicultural population of children with asthma in the city of Amsterdam. The study was designed as a prospective, observational multicenter study in which adherence to ICS and potential risk factors for adherence to ICS were measured in a cohort of Moroccan and native Dutch children with asthma. Electronic adherence measurements were performed for 3 months per patient using a Real Time Medication Monitoring (RTMM) system. Ethnicity and other potential risk factors, such as socio-economic status, asthma control and parental medication beliefs, were extracted from medical records or parent interviews. The association between adherence and ethnicity was analysed using multivariate linear regression analysis. A total of 90 children (aged 1-11 years) were included in the study and data of 87 children were used for analysis. Average adherence to ICS was 49.3 %. Native Dutch children showed higher adherence to ICS than Moroccan children (55.9 vs. 42.5 %, respectively; p = 0.044, univariate analysis). After correction for confounders (>3 annual visits to the paediatric outpatient clinic, regular use of a spacer during inhalation), the final regression model showed that ethnicity was independently associated with adherence (p = 0.028). In our Western European population of inner city children with asthma, poor adherence to ICS was a serious problem, and even somewhat more so in ethnic minorities. Paediatricians involved in asthma treatment should be aware of these cultural differences in medication-taking behaviour, but further studies are needed to elucidate the causal mechanism.

CHILDHOOD DEPRESSION. Exploring the association between family violence and other psychosocial factors in low-income Brazilian schoolchildren

PubMed Central

2012-01-01

Background Childhood depression affects the morbidity, mortality and life functions of children. Individual, family and environmental factors have been documented as psychosocial risk factors for childhood depression, especially family violence, which results in inadequate support, low family cohesion and poor communication. This study investigates the association between psychosocial depression factors in low-income schoolchildren and reveals the potential trouble spots, highlighting several forms of violence that take place within the family context. Methods The study was based on a cross-sectional analysis of 464 schoolchildren aged between 6 and 10, selected by random sampling from a city in the state of Rio de Janeiro, Brazil. Socio-economic, family and individual variables were investigated on the strength of the caregivers’ information and organized in blocks for analysis. A binary logistic regression model was applied, according to hierarchical blocks. Results The final hierarchical regression analysis showed that the following variables are potential psychosocial factors associated with depression in childhood: average/poor relationship with the father (OR 3.24, 95% CI 1.32-7.94), high frequency of victimization by psychological violence (humiliation) (OR 6.13, 95% CI 2.06-18.31), parental divorce (OR 2.89, 95% CI 1.14-7.32) and externalizing behavior problems (OR 3.53 IC 1.51-8.23). Conclusions The results point to multiple determinants of depressive behavior in children, as well as the potential contribution of psychological family violence. The study also reveals potential key targets for early intervention, especially for children from highly vulnerable families. PMID:22776354

Corticospinal excitability as a predictor of functional gains at the affected upper limb following robotic training in chronic stroke survivors

PubMed Central

Milot, Marie-Hélène; Spencer, Steven J.; Chan, Vicky; Allington, James P.; Klein, Julius; Chou, Cathy; Pearson-Fuhrhop, Kristin; Bobrow, James E.; Reinkensmeyer, David J.; Cramer, Steven C.

2014-01-01

Background Robotic training can help improve function of a paretic limb following a stroke, but individuals respond differently to the training. A predictor of functional gains might improve the ability to select those individuals more likely to benefit from robot based therapy. Studies evaluating predictors of functional improvement after a robotic training are scarce. One study has found that white matter tract integrity predicts functional gains following a robotic training of the hand and wrist. Objective Determine the predictive ability of behavioral and brain measures to improve selection of individuals for robotic training. Methods Twenty subjects with chronic stroke participated in an 8-week course of robotic exoskeletal training for the arm. Before training, a clinical evaluation, fMRI, diffusion tensor imaging, and transcranial magnetic stimulation (TMS) were each measured as predictors. Final functional gain was defined as change in the Box and Block Test (BBT). Measures significant in bivariate analysis were fed into a multivariate linear regression model. Results Training was associated with an average gain of 6±5 blocks on the BBT (p<0.0001). Bivariate analysis revealed that lower baseline motor evoked potential (MEP) amplitude on TMS, and lower laterality M1 index on fMRI each significantly correlated with greater BBT change. In the multivariate linear regression analysis, baseline MEP magnitude was the only measure that remained significant. Conclusion Subjects with lower baseline MEP magnitude benefited the most from robotic training of the affected arm. These subjects might have reserve remaining for the training to boost corticospinal excitability, translating into functional gains. PMID:24642382

Which kind of psychometrics is adequate for patient satisfaction questionnaires?

PubMed

Konerding, Uwe

2016-01-01

The construction and psychometric analysis of patient satisfaction questionnaires are discussed. The discussion is based upon the classification of multi-item questionnaires into scales or indices. Scales consist of items that describe the effects of the latent psychological variable to be measured, and indices consist of items that describe the causes of this variable. Whether patient satisfaction questionnaires should be constructed and analyzed as scales or as indices depends upon the purpose for which these questionnaires are required. If the final aim is improving care with regard to patients' preferences, then these questionnaires should be constructed and analyzed as indices. This implies two requirements: 1) items for patient satisfaction questionnaires should be selected in such a way that the universe of possible causes of patient satisfaction is covered optimally and 2) Cronbach's alpha, principal component analysis, exploratory factor analysis, confirmatory factor analysis, and analyses with models from item response theory, such as the Rasch Model, should not be applied for psychometric analyses. Instead, multivariate regression analyses with a direct rating of patient satisfaction as the dependent variable and the individual questionnaire items as independent variables should be performed. The coefficients produced by such an analysis can be applied for selecting the best items and for weighting the selected items when a sum score is determined. The lower boundaries of the validity of the unweighted and the weighted sum scores can be estimated by their correlations with the direct satisfaction rating. While the first requirement is fulfilled in the majority of the previous patient satisfaction questionnaires, the second one deviates from previous practice. Hence, if patient satisfaction is actually measured with the final aim of improving care with regard to patients' preferences, then future practice should be changed so that the second requirement is also fulfilled.

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

PubMed Central

Jiang, Feng; Han, Ji-zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods. PMID:29623088

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

PubMed

Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000-2015 using quantile and multiple line regression models

NASA Astrophysics Data System (ADS)

Zhao, Wei; Fan, Shaojia; Guo, Hai; Gao, Bo; Sun, Jiaren; Chen, Laiguo

2016-11-01

The quantile regression (QR) method has been increasingly introduced to atmospheric environmental studies to explore the non-linear relationship between local meteorological conditions and ozone mixing ratios. In this study, we applied QR for the first time, together with multiple linear regression (MLR), to analyze the dominant meteorological parameters influencing the mean, 10th percentile, 90th percentile and 99th percentile of maximum daily 8-h average (MDA8) ozone concentrations in 2000-2015 in Hong Kong. The dominance analysis (DA) was used to assess the relative importance of meteorological variables in the regression models. Results showed that the MLR models worked better at suburban and rural sites than at urban sites, and worked better in winter than in summer. QR models performed better in summer for 99th and 90th percentiles and performed better in autumn and winter for 10th percentile. And QR models also performed better in suburban and rural areas for 10th percentile. The top 3 dominant variables associated with MDA8 ozone concentrations, changing with seasons and regions, were frequently associated with the six meteorological parameters: boundary layer height, humidity, wind direction, surface solar radiation, total cloud cover and sea level pressure. Temperature rarely became a significant variable in any season, which could partly explain the peak of monthly average ozone concentrations in October in Hong Kong. And we found the effect of solar radiation would be enhanced during extremely ozone pollution episodes (i.e., the 99th percentile). Finally, meteorological effects on MDA8 ozone had no significant changes before and after the 2010 Asian Games.

Online sparse Gaussian process based human motion intent learning for an electrically actuated lower extremity exoskeleton.

PubMed

Long, Yi; Du, Zhi-Jiang; Chen, Chao-Feng; Dong, Wei; Wang, Wei-Dong

2017-07-01

The most important step for lower extremity exoskeleton is to infer human motion intent (HMI), which contributes to achieve human exoskeleton collaboration. Since the user is in the control loop, the relationship between human robot interaction (HRI) information and HMI is nonlinear and complicated, which is difficult to be modeled by using mathematical approaches. The nonlinear approximation can be learned by using machine learning approaches. Gaussian Process (GP) regression is suitable for high-dimensional and small-sample nonlinear regression problems. GP regression is restrictive for large data sets due to its computation complexity. In this paper, an online sparse GP algorithm is constructed to learn the HMI. The original training dataset is collected when the user wears the exoskeleton system with friction compensation to perform unconstrained movement as far as possible. The dataset has two kinds of data, i.e., (1) physical HRI, which is collected by torque sensors placed at the interaction cuffs for the active joints, i.e., knee joints; (2) joint angular position, which is measured by optical position sensors. To reduce the computation complexity of GP, grey relational analysis (GRA) is utilized to specify the original dataset and provide the final training dataset. Those hyper-parameters are optimized offline by maximizing marginal likelihood and will be applied into online GP regression algorithm. The HMI, i.e., angular position of human joints, will be regarded as the reference trajectory for the mechanical legs. To verify the effectiveness of the proposed algorithm, experiments are performed on a subject at a natural speed. The experimental results show the HMI can be obtained in real time, which can be extended and employed in the similar exoskeleton systems.

Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis.

PubMed

Dietrich, Stefan; Floegel, Anna; Troll, Martina; Kühn, Tilman; Rathmann, Wolfgang; Peters, Anette; Sookthai, Disorn; von Bergen, Martin; Kaaks, Rudolf; Adamski, Jerzy; Prehn, Cornelia; Boeing, Heiner; Schulze, Matthias B; Illig, Thomas; Pischon, Tobias; Knüppel, Sven; Wang-Sattler, Rui; Drogan, Dagmar

2016-10-01

The application of metabolomics in prospective cohort studies is statistically challenging. Given the importance of appropriate statistical methods for selection of disease-associated metabolites in highly correlated complex data, we combined random survival forest (RSF) with an automated backward elimination procedure that addresses such issues. Our RSF approach was illustrated with data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study, with concentrations of 127 serum metabolites as exposure variables and time to development of type 2 diabetes mellitus (T2D) as outcome variable. Out of this data set, Cox regression with a stepwise selection method was recently published. Replication of methodical comparison (RSF and Cox regression) was conducted in two independent cohorts. Finally, the R-code for implementing the metabolite selection procedure into the RSF-syntax is provided. The application of the RSF approach in EPIC-Potsdam resulted in the identification of 16 incident T2D-associated metabolites which slightly improved prediction of T2D when used in addition to traditional T2D risk factors and also when used together with classical biomarkers. The identified metabolites partly agreed with previous findings using Cox regression, though RSF selected a higher number of highly correlated metabolites. The RSF method appeared to be a promising approach for identification of disease-associated variables in complex data with time to event as outcome. The demonstrated RSF approach provides comparable findings as the generally used Cox regression, but also addresses the problem of multicollinearity and is suitable for high-dimensional data. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS)

NASA Astrophysics Data System (ADS)

Emamgolizadeh, S.; Bateni, S. M.; Shahsavani, D.; Ashrafi, T.; Ghorbani, H.

2015-10-01

The soil cation exchange capacity (CEC) is one of the main soil chemical properties, which is required in various fields such as environmental and agricultural engineering as well as soil science. In situ measurement of CEC is time consuming and costly. Hence, numerous studies have used traditional regression-based techniques to estimate CEC from more easily measurable soil parameters (e.g., soil texture, organic matter (OM), and pH). However, these models may not be able to adequately capture the complex and highly nonlinear relationship between CEC and its influential soil variables. In this study, Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) were employed to estimate CEC from more readily measurable soil physical and chemical variables (e.g., OM, clay, and pH) by developing functional relations. The GEP- and MARS-based functional relations were tested at two field sites in Iran. Results showed that GEP and MARS can provide reliable estimates of CEC. Also, it was found that the MARS model (with root-mean-square-error (RMSE) of 0.318 Cmol+ kg-1 and correlation coefficient (R2) of 0.864) generated slightly better results than the GEP model (with RMSE of 0.270 Cmol+ kg-1 and R2 of 0.807). The performance of GEP and MARS models was compared with two existing approaches, namely artificial neural network (ANN) and multiple linear regression (MLR). The comparison indicated that MARS and GEP outperformed the MLP model, but they did not perform as good as ANN. Finally, a sensitivity analysis was conducted to determine the most and the least influential variables affecting CEC. It was found that OM and pH have the most and least significant effect on CEC, respectively.

The effects of climate change on harp seals (Pagophilus groenlandicus).

PubMed

Johnston, David W; Bowers, Matthew T; Friedlaender, Ari S; Lavigne, David M

2012-01-01

Harp seals (Pagophilus groenlandicus) have evolved life history strategies to exploit seasonal sea ice as a breeding platform. As such, individuals are prepared to deal with fluctuations in the quantity and quality of ice in their breeding areas. It remains unclear, however, how shifts in climate may affect seal populations. The present study assesses the effects of climate change on harp seals through three linked analyses. First, we tested the effects of short-term climate variability on young-of-the year harp seal mortality using a linear regression of sea ice cover in the Gulf of St. Lawrence against stranding rates of dead harp seals in the region during 1992 to 2010. A similar regression of stranding rates and North Atlantic Oscillation (NAO) index values was also conducted. These analyses revealed negative correlations between both ice cover and NAO conditions and seal mortality, indicating that lighter ice cover and lower NAO values result in higher mortality. A retrospective cross-correlation analysis of NAO conditions and sea ice cover from 1978 to 2011 revealed that NAO-related changes in sea ice may have contributed to the depletion of seals on the east coast of Canada during 1950 to 1972, and to their recovery during 1973 to 2000. This historical retrospective also reveals opposite links between neonatal mortality in harp seals in the Northeast Atlantic and NAO phase. Finally, an assessment of the long-term trends in sea ice cover in the breeding regions of harp seals across the entire North Atlantic during 1979 through 2011 using multiple linear regression models and mixed effects linear regression models revealed that sea ice cover in all harp seal breeding regions has been declining by as much as 6 percent per decade over the time series of available satellite data.

The Effects of Climate Change on Harp Seals (Pagophilus groenlandicus)

PubMed Central

Johnston, David W.; Bowers, Matthew T.; Friedlaender, Ari S.; Lavigne, David M.

2012-01-01

Harp seals (Pagophilus groenlandicus) have evolved life history strategies to exploit seasonal sea ice as a breeding platform. As such, individuals are prepared to deal with fluctuations in the quantity and quality of ice in their breeding areas. It remains unclear, however, how shifts in climate may affect seal populations. The present study assesses the effects of climate change on harp seals through three linked analyses. First, we tested the effects of short-term climate variability on young-of-the year harp seal mortality using a linear regression of sea ice cover in the Gulf of St. Lawrence against stranding rates of dead harp seals in the region during 1992 to 2010. A similar regression of stranding rates and North Atlantic Oscillation (NAO) index values was also conducted. These analyses revealed negative correlations between both ice cover and NAO conditions and seal mortality, indicating that lighter ice cover and lower NAO values result in higher mortality. A retrospective cross-correlation analysis of NAO conditions and sea ice cover from 1978 to 2011 revealed that NAO-related changes in sea ice may have contributed to the depletion of seals on the east coast of Canada during 1950 to 1972, and to their recovery during 1973 to 2000. This historical retrospective also reveals opposite links between neonatal mortality in harp seals in the Northeast Atlantic and NAO phase. Finally, an assessment of the long-term trends in sea ice cover in the breeding regions of harp seals across the entire North Atlantic during 1979 through 2011 using multiple linear regression models and mixed effects linear regression models revealed that sea ice cover in all harp seal breeding regions has been declining by as much as 6 percent per decade over the time series of available satellite data. PMID:22238591

Vegetation Monitoring with Gaussian Processes and Latent Force Models

NASA Astrophysics Data System (ADS)

Camps-Valls, Gustau; Svendsen, Daniel; Martino, Luca; Campos, Manuel; Luengo, David

2017-04-01

Monitoring vegetation by biophysical parameter retrieval from Earth observation data is a challenging problem, where machine learning is currently a key player. Neural networks, kernel methods, and Gaussian Process (GP) regression have excelled in parameter retrieval tasks at both local and global scales. GP regression is based on solid Bayesian statistics, yield efficient and accurate parameter estimates, and provides interesting advantages over competing machine learning approaches such as confidence intervals. However, GP models are hampered by lack of interpretability, that prevented the widespread adoption by a larger community. In this presentation we will summarize some of our latest developments to address this issue. We will review the main characteristics of GPs and their advantages in vegetation monitoring standard applications. Then, three advanced GP models will be introduced. First, we will derive sensitivity maps for the GP predictive function that allows us to obtain feature ranking from the model and to assess the influence of examples in the solution. Second, we will introduce a Joint GP (JGP) model that combines in situ measurements and simulated radiative transfer data in a single GP model. The JGP regression provides more sensible confidence intervals for the predictions, respects the physics of the underlying processes, and allows for transferability across time and space. Finally, a latent force model (LFM) for GP modeling that encodes ordinary differential equations to blend data-driven modeling and physical models of the system is presented. The LFM performs multi-output regression, adapts to the signal characteristics, is able to cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. Empirical evidence of the performance of these models will be presented through illustrative examples.

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.

The Precision Efficacy Analysis for Regression Sample Size Method.

ERIC Educational Resources Information Center

Brooks, Gordon P.; Barcikowski, Robert S.

The general purpose of this study was to examine the efficiency of the Precision Efficacy Analysis for Regression (PEAR) method for choosing appropriate sample sizes in regression studies used for precision. The PEAR method, which is based on the algebraic manipulation of an accepted cross-validity formula, essentially uses an effect size to…

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Monthly streamflow forecasting in the Rhine basin

NASA Astrophysics Data System (ADS)

Schick, Simon; Rössler, Ole; Weingartner, Rolf

2017-04-01

Forecasting seasonal streamflow of the Rhine river is of societal relevance as the Rhine is an important water way and water resource in Western Europe. The present study investigates the predictability of monthly mean streamflow at lead times of zero, one, and two months with the focus on potential benefits by the integration of seasonal climate predictions. Specifically, we use seasonal predictions of precipitation and surface air temperature released by the European Centre for Medium-Range Weather Forecasts (ECMWF) for a regression analysis. In order to disentangle forecast uncertainty, the 'Reverse Ensemble Streamflow Prediction' framework is adapted here to the context of regression: By using appropriate subsets of predictors the regression model is constrained to either the initial conditions, the meteorological forcing, or both. An operational application is mimicked by equipping the model with the seasonal climate predictions provided by ECMWF. Finally, to mitigate the spatial aggregation of the meteorological fields the model is also applied at the subcatchment scale, and the resulting predictions are combined afterwards. The hindcast experiment is carried out for the period 1982-2011 in cross validation mode at two gauging stations, namely the Rhine at Lobith and Basel. The results show that monthly forecasts are skillful with respect to climatology only at zero lead time. In addition, at zero lead time the integration of seasonal climate predictions decreases the mean absolute error by 5 to 10 percentage compared to forecasts which are solely based on initial conditions. This reduction most likely is induced by the seasonal prediction of precipitation and not air temperature. The study is completed by bench marking the regression model with runoff simulations from ECMWFs seasonal forecast system. By simply using basin averages followed by a linear bias correction, these runoff simulations translate well to monthly streamflow. Though the regression model is only slightly outperformed, we argue that runoff out of the land surface component of seasonal climate forecasting systems is an interesting option when it comes to seasonal streamflow forecasting in large river basins.

Effect of Contact Damage on the Strength of Ceramic Materials.

DTIC Science & Technology

1982-10-01

variables that are important to erosion, and a multivariate , linear regression analysis is used to fit the data to the dimensional analysis. The...of Equations 7 and 8 by a multivariable regression analysis (room tem- perature data) Exponent Regression Standard error Computed coefficient of...1980) 593. WEAVER, Proc. Brit. Ceram. Soc. 22 (1973) 125. 39. P. W. BRIDGMAN, "Dimensional Analaysis ", (Yale 18. R. W. RICE, S. W. FREIMAN and P. F

Accessing and constructing driving data to develop fuel consumption forecast model

NASA Astrophysics Data System (ADS)

Yamashita, Rei-Jo; Yao, Hsiu-Hsen; Hung, Shih-Wei; Hackman, Acquah

2018-02-01

In this study, we develop a forecasting models, to estimate fuel consumption based on the driving behavior, in which vehicles and routes are known. First, the driving data are collected via telematics and OBDII. Then, the driving fuel consumption formula is used to calculate the estimate fuel consumption, and driving behavior indicators are generated for analysis. Based on statistical analysis method, the driving fuel consumption forecasting model is constructed. Some field experiment results were done in this study to generate hundreds of driving behavior indicators. Based on data mining approach, the Pearson coefficient correlation analysis is used to filter highly fuel consumption related DBIs. Only highly correlated DBI will be used in the model. These DBIs are divided into four classes: speed class, acceleration class, Left/Right/U-turn class and the other category. We then use K-means cluster analysis to group to the driver class and the route class. Finally, more than 12 aggregate models are generated by those highly correlated DBIs, using the neural network model and regression analysis. Based on Mean Absolute Percentage Error (MAPE) to evaluate from the developed AMs. The best MAPE values among these AM is below 5%.

Common pitfalls in statistical analysis: Linear regression analysis

PubMed Central

Aggarwal, Rakesh; Ranganathan, Priya

2017-01-01

In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis. PMID:28447022

The impact of bariatric surgery on pulmonary function: a meta-analysis.

PubMed

Alsumali, Adnan; Al-Hawag, Ali; Bairdain, Sigrid; Eguale, Tewodros

2018-02-01

Morbid obesity may affect several body systems and cause ill effects to the cardiovascular, hepatobiliary, endocrine, and mental health systems. However, the impact on the pulmonary system and pulmonary function has been debated in the literature. A systematic review and meta-analysis for studies that have evaluated the impact of bariatric surgery on pulmonary function were pooled for this analysis. PubMed, Cochrane, and Embase databases were evaluated through September 31, 2016. They were used as the primary search engine for studies evaluating the impact pre- and post-bariatric surgery on pulmonary function. Pooled effect estimates were calculated using random-effects model. Twenty-three studies with 1013 participants were included in the final meta-analysis. Only 8 studies had intervention and control groups with different time points, but 15 studies had matched groups with different time points. Overall, pulmonary function score was significantly improved after bariatric surgery, with a pooled standardized mean difference of .59 (95% confidence interval: .46-.73). Heterogeneity test was performed by using Cochran's Q test (I 2 = 46%; P heterogeneity = .10). Subgroup analysis and univariate meta-regression based on study quality, age, presurgery body mass index, postsurgery body mass index, study design, female patients only, study continent, asthmatic patients in the study, and the type of bariatric surgery confirmed no statistically significant difference among these groups (P value>.05 for all). A multivariate meta-regression model, which adjusted simultaneously for these same covariates, did not change the results (P value > .05 overall). Assessment of publication bias was done visually and by Begg's rank correlation test and indicated the absence of publication bias (asymmetric shape was observed and P = .34). This meta-analysis shows that bariatric surgery significantly improved overall pulmonary functions score for morbid obesity. Copyright © 2018 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.

Influencing factors of alexithymia in Chinese medical students: a cross-sectional study.

PubMed

Zhu, Yaxin; Luo, Ting; Liu, Jie; Qu, Bo

2017-04-04

A much higher prevalence of alexithymia has been reported in medical students compared with the general population, and alexithymia is a risk factor that increases vulnerability to mental disorders. Our aim was to evaluate the level of alexithymia in Chinese medical students and to explore its influencing factors. A cross-sectional study of 1,950 medical students at Shenyang Medical College was conducted in May 2014 to evaluate alexithymia in medical students using the Chinese version of the 20-item Toronto Alexithymia Scale (TAS-20). The reliability of the questionnaire was assessed by Cronbach's α coefficient and mean inter-item correlations. Confirmatory factor analysis (CFA) was used to evaluate construct validity. The relationships between alexithymia and influencing factors were examined using Student's t-test, analysis of variance, and multiple linear regression analysis. Statistical analysis was performed using SPSS 21.0. Of the 1,950 medical students, 1,886 (96.7%) completed questionnaires. Overall, Cronbach's α coefficient of the TAS-20 questionnaire was 0.868. The results of CFA showed that the original three-factor structure produced an acceptable fit to the data. By univariate analysis, gender, grade (academic year of study), smoking behavior, alcohol use, physical activity, history of living with parents during childhood, and childhood trauma were influencing factors of TAS-20 scores (p < 0.05). Multiple linear regression analysis showed that gender, physical activity, grade, living with parents, and childhood trauma also had statistically significant association with total TAS-20 score (p < 0.05). Gender, physical activity, grade, history of living with parents during childhood, and childhood trauma were all factors determining the level of alexithymia. To prevent alexithymia, it will be advisable to promote adequate physical activity and pay greater attention to male medical students and those who are in the final year of training.

The Health-Related Quality of Life for Patients with Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS).

PubMed

Falk Hvidberg, Michael; Brinth, Louise Schouborg; Olesen, Anne V; Petersen, Karin D; Ehlers, Lars

2015-01-01

Myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) is a common, severe condition affecting 0.2 to 0.4 per cent of the population. Even so, no recent international EQ-5D based health-related quality of life (HRQoL) estimates exist for ME/CFS patients. The main purpose of this study was to estimate HRQoL scores using the EQ-5D-3L with Danish time trade-off tariffs. Secondary, the aims were to explore whether the results are not influenced by other conditions using regression, to compare the estimates to 20 other conditions and finally to present ME/CFS patient characteristics for use in clinical practice. All members of the Danish ME/CFS Patient Association in 2013 (n=319) were asked to fill out a questionnaire including the EQ-5D-3L. From these, 105 ME/CFS patients were identified and gave valid responses. Unadjusted EQ-5D-3L means were calculated and compared to the population mean as well as to the mean of 20 other conditions. Furthermore, adjusted estimates were calculated using ordinary least squares (OLS) regression, adjusting for gender, age, education, and co-morbidity of 18 self-reported conditions. Data from the North Denmark Health Profile 2010 was used as population reference in the regression analysis (n=23,392). The unadjusted EQ-5D-3L mean of ME/CFS was 0.47 [0.41-0.53] compared to a population mean of 0.85 [0.84-0.86]. The OLS regression estimated a disutility of -0.29 [-0.21;-0.34] for ME/CFS patients in this study. The characteristics of ME/CFS patients are different from the population with respect to gender, relationship, employment etc. The EQ-5D-3L-based HRQoL of ME/CFS is significantly lower than the population mean and the lowest of all the compared conditions. The adjusted analysis confirms that poor HRQoL of ME/CFS is distinctly different from and not a proxy of the other included conditions. However, further studies are needed to exclude the possible selection bias of the current study.

Quality of life in breast cancer patients--a quantile regression analysis.

PubMed

Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma

2008-01-01

Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.

The microcomputer scientific software series 2: general linear model--regression.

Treesearch

Harold M. Rauscher

1983-01-01

The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

USAF (United States Air Force) Stability and Control DATCOM (Data Compendium)

DTIC Science & Technology

1978-04-01

regression analysis involves the study of a group of variables to determine their effect on a given parameter. Because of the empirical nature of this...regression analysis of mathematical statistics. In general, a regression analysis involves the study of a group of variables to determine their effect on a...Excperiment, OSR TN 58-114, MIT Fluid Dynamics Research Group Rapt. 57-5, 1957. (U) 90. Kennet, H., and Ashley, H.: Review of Unsteady Aerodynamic Studies in

Ileus in children presenting with diarrhea and severe acute malnutrition: A chart review

PubMed Central

Shahid, Abu SMSB; Shahunja, K. M.; Bardhan, Pradip Kumar; Faruque, Abu Syeed Golam; Shahrin, Lubaba; Das, Sumon Kumar; Barua, Dipesh Kumar; Hossain, Md Iqbal; Ahmed, Tahmeed

2017-01-01

Background Severely malnourished children aged under five years requiring hospital admission for diarrheal illness frequently develop ileus during hospitalization with often fatal outcomes. However, there is no data on risk factors and outcome of ileus in such children. We intended to evaluate predictive factors for ileus during hospitalization and their outcomes. Methodology/Principal findings This was a retrospective chart review that enrolled severely malnourished children under five years old with diarrhea, admitted to the Dhaka Hospital of the International Centre for Diarrhoeal Disease Research, Bangladesh between April 2011 and August 2012. We used electronic database to have our chart abstraction from previously admitted children in the hospital. The clinical and laboratory characteristics of children with (cases = 45), and without ileus (controls = 261) were compared. Cases were first identified by observation of abnormal bowel sounds on physical examination and confirmed with abdominal radiographs. For this comparison, Chi-square test was used to measure the difference in proportion, Student’s t-test to calculate the difference in mean for normally distributed data and Mann-Whitney test for data that were not normally distributed. Finally, in identifying independent risk factors for ileus, logistical regression analysis was performed. Ileus was defined if a child developed abdominal distension and had hyperactive or sluggish or absent bowel sound and a radiologic evidence of abdominal gas-fluid level during hospitalization. Logistic regression analysis adjusting for potential confounders revealed that the independent risk factors for admission for ileus were reluctance to feed (odds ratio [OR] = 3.22, 95% confidence interval [CI] = 1.24–8.39, p = 0.02), septic shock (OR = 3.62, 95% CI = 1.247–8.95, p<0.01), and hypokalemia (OR = 1.99, 95% CI = 1.03–3.86, p = 0.04). Mortality was significantly higher in cases compared to controls (22% vs. 8%, p<0.01) in univariate analysis; however, in multivariable regression analysis, after adjusting for potential confounders such as septic shock, no association was found between ileus and death (OR = 2.05, 95% CI = 0.68–6.14, p = 0.20). In a separate regression analysis model, after adjusting for potential confounders such as ileus, reluctance to feed, hypokalemia, hypocalcemia, and blood transfusion, septic shock (OR = 168.84, 95% CI = 19.27–1479.17, p<0.01) emerged as the only independent predictor of death in severely malnourished diarrheal children. Conclusions/Significance This study suggests that the identification of simple independent admission risk factors for ileus and risk factors for death in hospitalized severely malnourished diarrheal children may prompt clinicians to be more vigilant in managing these conditions, especially in resource-limited settings in order to decrease ileus and ileus-related fatal outcomes in such children. PMID:28493871

Ileus in children presenting with diarrhea and severe acute malnutrition: A chart review.

PubMed

Chisti, Mohammod Jobayer; Shahid, Abu Smsb; Shahunja, K M; Bardhan, Pradip Kumar; Faruque, Abu Syeed Golam; Shahrin, Lubaba; Das, Sumon Kumar; Barua, Dipesh Kumar; Hossain, Md Iqbal; Ahmed, Tahmeed

2017-05-01

Severely malnourished children aged under five years requiring hospital admission for diarrheal illness frequently develop ileus during hospitalization with often fatal outcomes. However, there is no data on risk factors and outcome of ileus in such children. We intended to evaluate predictive factors for ileus during hospitalization and their outcomes. This was a retrospective chart review that enrolled severely malnourished children under five years old with diarrhea, admitted to the Dhaka Hospital of the International Centre for Diarrhoeal Disease Research, Bangladesh between April 2011 and August 2012. We used electronic database to have our chart abstraction from previously admitted children in the hospital. The clinical and laboratory characteristics of children with (cases = 45), and without ileus (controls = 261) were compared. Cases were first identified by observation of abnormal bowel sounds on physical examination and confirmed with abdominal radiographs. For this comparison, Chi-square test was used to measure the difference in proportion, Student's t-test to calculate the difference in mean for normally distributed data and Mann-Whitney test for data that were not normally distributed. Finally, in identifying independent risk factors for ileus, logistical regression analysis was performed. Ileus was defined if a child developed abdominal distension and had hyperactive or sluggish or absent bowel sound and a radiologic evidence of abdominal gas-fluid level during hospitalization. Logistic regression analysis adjusting for potential confounders revealed that the independent risk factors for admission for ileus were reluctance to feed (odds ratio [OR] = 3.22, 95% confidence interval [CI] = 1.24-8.39, p = 0.02), septic shock (OR = 3.62, 95% CI = 1.247-8.95, p<0.01), and hypokalemia (OR = 1.99, 95% CI = 1.03-3.86, p = 0.04). Mortality was significantly higher in cases compared to controls (22% vs. 8%, p<0.01) in univariate analysis; however, in multivariable regression analysis, after adjusting for potential confounders such as septic shock, no association was found between ileus and death (OR = 2.05, 95% CI = 0.68-6.14, p = 0.20). In a separate regression analysis model, after adjusting for potential confounders such as ileus, reluctance to feed, hypokalemia, hypocalcemia, and blood transfusion, septic shock (OR = 168.84, 95% CI = 19.27-1479.17, p<0.01) emerged as the only independent predictor of death in severely malnourished diarrheal children. This study suggests that the identification of simple independent admission risk factors for ileus and risk factors for death in hospitalized severely malnourished diarrheal children may prompt clinicians to be more vigilant in managing these conditions, especially in resource-limited settings in order to decrease ileus and ileus-related fatal outcomes in such children.