SEMIPARAMETRIC QUANTILE REGRESSION WITH HIGH-DIMENSIONAL COVARIATES

PubMed Central

Zhu, Liping; Huang, Mian; Li, Runze

2012-01-01

This paper is concerned with quantile regression for a semiparametric regression model, in which both the conditional mean and conditional variance function of the response given the covariates admit a single-index structure. This semiparametric regression model enables us to reduce the dimension of the covariates and simultaneously retains the flexibility of nonparametric regression. Under mild conditions, we show that the simple linear quantile regression offers a consistent estimate of the index parameter vector. This is a surprising and interesting result because the single-index model is possibly misspecified under the linear quantile regression. With a root-n consistent estimate of the index vector, one may employ a local polynomial regression technique to estimate the conditional quantile function. This procedure is computationally efficient, which is very appealing in high-dimensional data analysis. We show that the resulting estimator of the quantile function performs asymptotically as efficiently as if the true value of the index vector were known. The methodologies are demonstrated through comprehensive simulation studies and an application to a real dataset. PMID:24501536

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.

A Method of Calculating Functional Independence Measure at Discharge from Functional Independence Measure Effectiveness Predicted by Multiple Regression Analysis Has a High Degree of Predictive Accuracy.

PubMed

Tokunaga, Makoto; Watanabe, Susumu; Sonoda, Shigeru

2017-09-01

Multiple linear regression analysis is often used to predict the outcome of stroke rehabilitation. However, the predictive accuracy may not be satisfactory. The objective of this study was to elucidate the predictive accuracy of a method of calculating motor Functional Independence Measure (mFIM) at discharge from mFIM effectiveness predicted by multiple regression analysis. The subjects were 505 patients with stroke who were hospitalized in a convalescent rehabilitation hospital. The formula "mFIM at discharge = mFIM effectiveness × (91 points - mFIM at admission) + mFIM at admission" was used. By including the predicted mFIM effectiveness obtained through multiple regression analysis in this formula, we obtained the predicted mFIM at discharge (A). We also used multiple regression analysis to directly predict mFIM at discharge (B). The correlation between the predicted and the measured values of mFIM at discharge was compared between A and B. The correlation coefficients were .916 for A and .878 for B. Calculating mFIM at discharge from mFIM effectiveness predicted by multiple regression analysis had a higher degree of predictive accuracy of mFIM at discharge than that directly predicted. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression

PubMed Central

2014-01-01

Background Support vector regression (SVR) and Gaussian process regression (GPR) were used for the analysis of electroanalytical experimental data to estimate diffusion coefficients. Results For simulated cyclic voltammograms based on the EC, Eqr, and EqrC mechanisms these regression algorithms in combination with nonlinear kernel/covariance functions yielded diffusion coefficients with higher accuracy as compared to the standard approach of calculating diffusion coefficients relying on the Nicholson-Shain equation. The level of accuracy achieved by SVR and GPR is virtually independent of the rate constants governing the respective reaction steps. Further, the reduction of high-dimensional voltammetric signals by manual selection of typical voltammetric peak features decreased the performance of both regression algorithms compared to a reduction by downsampling or principal component analysis. After training on simulated data sets, diffusion coefficients were estimated by the regression algorithms for experimental data comprising voltammetric signals for three organometallic complexes. Conclusions Estimated diffusion coefficients closely matched the values determined by the parameter fitting method, but reduced the required computational time considerably for one of the reaction mechanisms. The automated processing of voltammograms according to the regression algorithms yields better results than the conventional analysis of peak-related data. PMID:24987463

MicroCT angiography detects vascular formation and regression in skin wound healing

PubMed Central

Urao, Norifumi; Okonkwo, Uzoagu A.; Fang, Milie M.; Zhuang, Zhen W.; Koh, Timothy J.; DiPietro, Luisa A.

2016-01-01

Properly regulated angiogenesis and arteriogenesis are essential for effective wound healing. Tissue injury induces robust new vessel formation and subsequent vessel maturation, which involves vessel regression and remodeling. Although formation of functional vasculature is essential for healing, alterations in vascular structure over the time course of skin wound healing are not well understood. Here, using high-resolution ex vivo X-ray micro-computed tomography (microCT), we describe the vascular network during healing of skin excisional wounds with highly detailed three-dimensional (3D) reconstructed images and associated quantitative analysis. We found that relative vessel volume, surface area and branching number are significantly decreased in wounds from day 7 to day 14 and 21. Segmentation and skeletonization analysis of selected branches from high-resolution images as small as 2.5 μm voxel size show that branching orders are decreased in the wound vessels during healing. In histological analysis, we found that the contrast agent fills mainly arterioles, but not small capillaries nor large veins. In summary, high-resolution microCT revealed dynamic alterations of vessel structures during wound healing. This technique may be useful as a key tool in the study of the formation and regression of wound vessels. PMID:27009591

Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs. NBER Working Paper No. 20405

ERIC Educational Resources Information Center

Gelman, Andrew; Imbens, Guido

2014-01-01

It is common in regression discontinuity analysis to control for high order (third, fourth, or higher) polynomials of the forcing variable. We argue that estimators for causal effects based on such methods can be misleading, and we recommend researchers do not use them, and instead use estimators based on local linear or quadratic polynomials or…

The microbiological profile and presence of bloodstream infection influence mortality rates in necrotizing fasciitis

PubMed Central

2011-01-01

Introduction Necrotizing fasciitis (NF) is a life threatening infectious disease with a high mortality rate. We carried out a microbiological characterization of the causative pathogens. We investigated the correlation of mortality in NF with bloodstream infection and with the presence of co-morbidities. Methods In this retrospective study, we analyzed 323 patients who presented with necrotizing fasciitis at two different institutions. Bloodstream infection (BSI) was defined as a positive blood culture result. The patients were categorized as survivors and non-survivors. Eleven clinically important variables which were statistically significant by univariate analysis were selected for multivariate regression analysis and a stepwise logistic regression model was developed to determine the association between BSI and mortality. Results Univariate logistic regression analysis showed that patients with hypotension, heart disease, liver disease, presence of Vibrio spp. in wound cultures, presence of fungus in wound cultures, and presence of Streptococcus group A, Aeromonas spp. or Vibrio spp. in blood cultures, had a significantly higher risk of in-hospital mortality. Our multivariate logistic regression analysis showed a higher risk of mortality in patients with pre-existing conditions like hypotension, heart disease, and liver disease. Multivariate logistic regression analysis also showed that presence of Vibrio spp in wound cultures, and presence of Streptococcus Group A in blood cultures were associated with a high risk of mortality while debridement > = 3 was associated with improved survival. Conclusions Mortality in patients with necrotizing fasciitis was significantly associated with the presence of Vibrio in wound cultures and Streptococcus group A in blood cultures. PMID:21693053

Satellite remote sensing of fine particulate air pollutants over Indian mega cities

NASA Astrophysics Data System (ADS)

Sreekanth, V.; Mahesh, B.; Niranjan, K.

2017-11-01

In the backdrop of the need for high spatio-temporal resolution data on PM2.5 mass concentrations for health and epidemiological studies over India, empirical relations between Aerosol Optical Depth (AOD) and PM2.5 mass concentrations are established over five Indian mega cities. These relations are sought to predict the surface PM2.5 mass concentrations from high resolution columnar AOD datasets. Current study utilizes multi-city public domain PM2.5 data (from US Consulate and Embassy's air monitoring program) and MODIS AOD, spanning for almost four years. PM2.5 is found to be positively correlated with AOD. Station-wise linear regression analysis has shown spatially varying regression coefficients. Similar analysis has been repeated by eliminating data from the elevated aerosol prone seasons, which has improved the correlation coefficient. The impact of the day to day variability in the local meteorological conditions on the AOD-PM2.5 relationship has been explored by performing a multiple regression analysis. A cross-validation approach for the multiple regression analysis considering three years of data as training dataset and one-year data as validation dataset yielded an R value of ∼0.63. The study was concluded by discussing the factors which can improve the relationship.

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

NASA Astrophysics Data System (ADS)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

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

PubMed

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

2007-07-01

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

Distributed collaborative probabilistic design for turbine blade-tip radial running clearance using support vector machine of regression

NASA Astrophysics Data System (ADS)

Fei, Cheng-Wei; Bai, Guang-Chen

2014-12-01

To improve the computational precision and efficiency of probabilistic design for mechanical dynamic assembly like the blade-tip radial running clearance (BTRRC) of gas turbine, a distribution collaborative probabilistic design method-based support vector machine of regression (SR)(called as DCSRM) is proposed by integrating distribution collaborative response surface method and support vector machine regression model. The mathematical model of DCSRM is established and the probabilistic design idea of DCSRM is introduced. The dynamic assembly probabilistic design of aeroengine high-pressure turbine (HPT) BTRRC is accomplished to verify the proposed DCSRM. The analysis results reveal that the optimal static blade-tip clearance of HPT is gained for designing BTRRC, and improving the performance and reliability of aeroengine. The comparison of methods shows that the DCSRM has high computational accuracy and high computational efficiency in BTRRC probabilistic analysis. The present research offers an effective way for the reliability design of mechanical dynamic assembly and enriches mechanical reliability theory and method.

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis

PubMed Central

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Background: Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. Methods: In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. Results: The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Conclusion: Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended. PMID:26793655

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

PubMed

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Abdominal girth, vertebral column length, and spread of spinal anesthesia in 30 minutes after plain bupivacaine 5 mg/mL.

PubMed

Zhou, Qing-he; Xiao, Wang-pin; Shen, Ying-yan

2014-07-01

The spread of spinal anesthesia is highly unpredictable. In patients with increased abdominal girth and short stature, a greater cephalad spread after a fixed amount of subarachnoidally administered plain bupivacaine is often observed. We hypothesized that there is a strong correlation between abdominal girth/vertebral column length and cephalad spread. Age, weight, height, body mass index, abdominal girth, and vertebral column length were recorded for 114 patients. The L3-L4 interspace was entered, and 3 mL of 0.5% plain bupivacaine was injected into the subarachnoid space. The cephalad spread (loss of temperature sensation and loss of pinprick discrimination) was assessed 30 minutes after intrathecal injection. Linear regression analysis was performed for age, weight, height, body mass index, abdominal girth, vertebral column length, and the spread of spinal anesthesia, and the combined linear contribution of age up to 55 years, weight, height, abdominal girth, and vertebral column length was tested by multiple regression analysis. Linear regression analysis showed that there was a significant univariate correlation among all 6 patient characteristics evaluated and the spread of spinal anesthesia (all P < 0.039) except for age and loss of temperature sensation (P > 0.068). Multiple regression analysis showed that abdominal girth and the vertebral column length were the key determinants for spinal anesthesia spread (both P < 0.0001), whereas age, weight, and height could be omitted without changing the results (all P > 0.059, all 95% confidence limits < 0.372). Multiple regression analysis revealed that the combination of a patient's 5 general characteristics, especially abdominal girth and vertebral column length, had a high predictive value for the spread of spinal anesthesia after a given dose of plain bupivacaine.

MicroCT angiography detects vascular formation and regression in skin wound healing.

PubMed

Urao, Norifumi; Okonkwo, Uzoagu A; Fang, Milie M; Zhuang, Zhen W; Koh, Timothy J; DiPietro, Luisa A

2016-07-01

Properly regulated angiogenesis and arteriogenesis are essential for effective wound healing. Tissue injury induces robust new vessel formation and subsequent vessel maturation, which involves vessel regression and remodeling. Although formation of functional vasculature is essential for healing, alterations in vascular structure over the time course of skin wound healing are not well understood. Here, using high-resolution ex vivo X-ray micro-computed tomography (microCT), we describe the vascular network during healing of skin excisional wounds with highly detailed three-dimensional (3D) reconstructed images and associated quantitative analysis. We found that relative vessel volume, surface area and branching number are significantly decreased in wounds from day 7 to days 14 and 21. Segmentation and skeletonization analysis of selected branches from high-resolution images as small as 2.5μm voxel size show that branching orders are decreased in the wound vessels during healing. In histological analysis, we found that the contrast agent fills mainly arterioles, but not small capillaries nor large veins. In summary, high-resolution microCT revealed dynamic alterations of vessel structures during wound healing. This technique may be useful as a key tool in the study of the formation and regression of wound vessels. Copyright © 2016 Elsevier Inc. All rights reserved.

Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Kong, Shish-Jeng; Chang, Chih-Hua

2018-03-01

The turbidity (TB) of a water body varies with time and space. Water quality is traditionally estimated via linear regression based on satellite images. However, estimating and mapping water quality require a spatio-temporal nonstationary model, while TB mapping necessitates the use of geographically and temporally weighted regression (GTWR) and geographically weighted regression (GWR) models, both of which are more precise than linear regression. Given the temporal nonstationary models for mapping water quality, GTWR offers the best option for estimating regional water quality. Compared with GWR, GTWR provides highly reliable information for water quality mapping, boasts a relatively high goodness of fit, improves the explanation of variance from 44% to 87%, and shows a sufficient space-time explanatory power. The seasonal patterns of TB and the main spatial patterns of TB variability can be identified using the estimated TB maps from GTWR and by conducting an empirical orthogonal function (EOF) analysis.

INNOVATIVE INSTRUMENTATION AND ANALYSIS OF THE TEMPERATURE MEASUREMENT FOR HIGH TEMPERATURE GASIFICATION

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

Seong W. Lee

During this reporting period, the literature survey including the gasifier temperature measurement literature, the ultrasonic application and its background study in cleaning application, and spray coating process are completed. The gasifier simulator (cold model) testing has been successfully conducted. Four factors (blower voltage, ultrasonic application, injection time intervals, particle weight) were considered as significant factors that affect the temperature measurement. The Analysis of Variance (ANOVA) was applied to analyze the test data. The analysis shows that all four factors are significant to the temperature measurements in the gasifier simulator (cold model). The regression analysis for the case with the normalizedmore » room temperature shows that linear model fits the temperature data with 82% accuracy (18% error). The regression analysis for the case without the normalized room temperature shows 72.5% accuracy (27.5% error). The nonlinear regression analysis indicates a better fit than that of the linear regression. The nonlinear regression model's accuracy is 88.7% (11.3% error) for normalized room temperature case, which is better than the linear regression analysis. The hot model thermocouple sleeve design and fabrication are completed. The gasifier simulator (hot model) design and the fabrication are completed. The system tests of the gasifier simulator (hot model) have been conducted and some modifications have been made. Based on the system tests and results analysis, the gasifier simulator (hot model) has met the proposed design requirement and the ready for system test. The ultrasonic cleaning method is under evaluation and will be further studied for the gasifier simulator (hot model) application. The progress of this project has been on schedule.« less

[On the effectiveness of the homeopathic remedy Arnica montana].

PubMed

Lüdtke, Rainer; Hacke, Daniela

2005-11-01

Arnica montana is a homeopathic remedy often prescribed after traumata and injuries. To assess whether Arnica is effective beyond placebo and to identify factors which support or contradict this effectiveness. All prospective, controlled trials on the effectiveness of homeopathic Arnica were included. Overall effectiveness was assessed by meta-analysis and meta-regression techniques. 68 comparisons from 49 clinical trials show a significant effectiveness of Arnica in traumatic injuries in random effects meta-analysis (odds ratio [OR], 0.36; 95% confidence interval [CI], 0.24-0.55), but not in meta-regression models (OR, 0.37; CI, 0.11-1.24). We found no evidence for publication bias. Studies from Medline-listed journals and high-quality studies are less likely to report positive results (p = 0.0006 and p = 0.0167). The hypothesis that homeopathic Arnica is effective could neither be proved nor rejected. All trials were highly heterogeneous, meta-regression does not help to explain this heterogeneity substantially.

Efficacy of Statin Therapy in Inducing Coronary Plaque Regression in Patients with Low Baseline Cholesterol Levels

PubMed Central

Nozue, Tsuyoshi; Yamamoto, Shingo; Tohyama, Shinichi; Fukui, Kazuki; Umezawa, Shigeo; Onishi, Yuko; Kunishima, Tomoyuki; Sato, Akira; Miyake, Shogo; Morino, Yoshihiro; Yamauchi, Takao; Muramatsu, Toshiya; Hibi, Kiyoshi; Terashima, Mitsuyasu; Suzuki, Hiroshi; Michishita, Ichiro

2016-01-01

Aim: The efficacy of statin therapy in inducing coronary plaque regression may depend on baseline cholesterol levels. We aimed to determine the efficacy of statin therapy in inducing coronary plaque regression in statin-naïve patients with low cholesterol levels using serial intravascular ultrasound (IVUS) data from the treatment with statin on atheroma regression evaluated by virtual histology IVUS (TRUTH) study. Methods: The TRUTH study is a prospective, multicenter trial, comparing the efficacies of pitavastatin and pravastatin in coronary plaque regression in 164 patients. All patients were statin-naïve and received statin therapy only after study enrollment. The primary endpoint was the observation of coronary plaque progression, despite statin therapy. Results: Serial IVUS data, at baseline and after an 8-month follow-up, were available for 119 patients. The patients were divided into three groups based on non-high-density lipoprotein cholesterol (HDL-C) levels—low: ≤ 140 mg/dl, n = 38; moderate: 141–169 mg/dl, n = 42; and high: ≥ 170 mg/dl, n = 39. Coronary plaque progression was noted in the low cholesterol group, whereas plaque regression was noted in the moderate and high cholesterol groups [%Δplaque volume: 2.3 ± 7.4 vs. − 2.7 ± 10.7 vs. − 3.2 ± 7.5, p = 0.004 (analysis of variance)]. After adjusting for all variables, a low non-HDLC level (≤ 140 mg/dl) was identified as an independent predictor of coronary plaque progression [odds ratio, 3.7; 95% confidence interval, 1.5–9.1, p = 0.004]. Conclusion: Serial IVUS data analysis indicated that statin therapy was less effective in inducing coronary plaque regression in patients with low cholesterol levels but more effective in those with high cholesterol levels at baseline. University Hospital Medical Information Network (UMIN) (UMIN ID: C000000311). PMID:27040362

SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression *

PubMed Central

Sun, Qiang; Zhu, Hongtu; Liu, Yufeng; Ibrahim, Joseph G.

2014-01-01

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods. PMID:26527844

Bayesian Analysis of High Dimensional Classification

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Subhadeep; Liang, Faming

2009-12-01

Modern data mining and bioinformatics have presented an important playground for statistical learning techniques, where the number of input variables is possibly much larger than the sample size of the training data. In supervised learning, logistic regression or probit regression can be used to model a binary output and form perceptron classification rules based on Bayesian inference. In these cases , there is a lot of interest in searching for sparse model in High Dimensional regression(/classification) setup. we first discuss two common challenges for analyzing high dimensional data. The first one is the curse of dimensionality. The complexity of many existing algorithms scale exponentially with the dimensionality of the space and by virtue of that algorithms soon become computationally intractable and therefore inapplicable in many real applications. secondly, multicollinearities among the predictors which severely slowdown the algorithm. In order to make Bayesian analysis operational in high dimension we propose a novel 'Hierarchical stochastic approximation monte carlo algorithm' (HSAMC), which overcomes the curse of dimensionality, multicollinearity of predictors in high dimension and also it possesses the self-adjusting mechanism to avoid the local minima separated by high energy barriers. Models and methods are illustrated by simulation inspired from from the feild of genomics. Numerical results indicate that HSAMC can work as a general model selection sampler in high dimensional complex model space.

Quantile Regression in the Study of Developmental Sciences

PubMed Central

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression. PMID:24329596

Estimate the contribution of incubation parameters influence egg hatchability using multiple linear regression analysis

PubMed Central

Khalil, Mohamed H.; Shebl, Mostafa K.; Kosba, Mohamed A.; El-Sabrout, Karim; Zaki, Nesma

2016-01-01

Aim: This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens’ eggs. Materials and Methods: Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. Results: The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. Conclusion: A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens. PMID:27651666

Practical aspects of estimating energy components in rodents

PubMed Central

van Klinken, Jan B.; van den Berg, Sjoerd A. A.; van Dijk, Ko Willems

2013-01-01

Recently there has been an increasing interest in exploiting computational and statistical techniques for the purpose of component analysis of indirect calorimetry data. Using these methods it becomes possible to dissect daily energy expenditure into its components and to assess the dynamic response of the resting metabolic rate (RMR) to nutritional and pharmacological manipulations. To perform robust component analysis, however, is not straightforward and typically requires the tuning of parameters and the preprocessing of data. Moreover the degree of accuracy that can be attained by these methods depends on the configuration of the system, which must be properly taken into account when setting up experimental studies. Here, we review the methods of Kalman filtering, linear, and penalized spline regression, and minimal energy expenditure estimation in the context of component analysis and discuss their results on high resolution datasets from mice and rats. In addition, we investigate the effect of the sample time, the accuracy of the activity sensor, and the washout time of the chamber on the estimation accuracy. We found that on the high resolution data there was a strong correlation between the results of Kalman filtering and penalized spline (P-spline) regression, except for the activity respiratory quotient (RQ). For low resolution data the basal metabolic rate (BMR) and resting RQ could still be estimated accurately with P-spline regression, having a strong correlation with the high resolution estimate (R2 > 0.997; sample time of 9 min). In contrast, the thermic effect of food (TEF) and activity related energy expenditure (AEE) were more sensitive to a reduction in the sample rate (R2 > 0.97). In conclusion, for component analysis on data generated by single channel systems with continuous data acquisition both Kalman filtering and P-spline regression can be used, while for low resolution data from multichannel systems P-spline regression gives more robust results. PMID:23641217

Local regression type methods applied to the study of geophysics and high frequency financial data

NASA Astrophysics Data System (ADS)

Mariani, M. C.; Basu, K.

2014-09-01

In this work we applied locally weighted scatterplot smoothing techniques (Lowess/Loess) to Geophysical and high frequency financial data. We first analyze and apply this technique to the California earthquake geological data. A spatial analysis was performed to show that the estimation of the earthquake magnitude at a fixed location is very accurate up to the relative error of 0.01%. We also applied the same method to a high frequency data set arising in the financial sector and obtained similar satisfactory results. The application of this approach to the two different data sets demonstrates that the overall method is accurate and efficient, and the Lowess approach is much more desirable than the Loess method. The previous works studied the time series analysis; in this paper our local regression models perform a spatial analysis for the geophysics data providing different information. For the high frequency data, our models estimate the curve of best fit where data are dependent on time.

Prediction of hearing outcomes by multiple regression analysis in patients with idiopathic sudden sensorineural hearing loss.

PubMed

Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki

2014-12-01

This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.

Bias due to two-stage residual-outcome regression analysis in genetic association studies.

PubMed

Demissie, Serkalem; Cupples, L Adrienne

2011-11-01

Association studies of risk factors and complex diseases require careful assessment of potential confounding factors. Two-stage regression analysis, sometimes referred to as residual- or adjusted-outcome analysis, has been increasingly used in association studies of single nucleotide polymorphisms (SNPs) and quantitative traits. In this analysis, first, a residual-outcome is calculated from a regression of the outcome variable on covariates and then the relationship between the adjusted-outcome and the SNP is evaluated by a simple linear regression of the adjusted-outcome on the SNP. In this article, we examine the performance of this two-stage analysis as compared with multiple linear regression (MLR) analysis. Our findings show that when a SNP and a covariate are correlated, the two-stage approach results in biased genotypic effect and loss of power. Bias is always toward the null and increases with the squared-correlation between the SNP and the covariate (). For example, for , 0.1, and 0.5, two-stage analysis results in, respectively, 0, 10, and 50% attenuation in the SNP effect. As expected, MLR was always unbiased. Since individual SNPs often show little or no correlation with covariates, a two-stage analysis is expected to perform as well as MLR in many genetic studies; however, it produces considerably different results from MLR and may lead to incorrect conclusions when independent variables are highly correlated. While a useful alternative to MLR under , the two -stage approach has serious limitations. Its use as a simple substitute for MLR should be avoided. © 2011 Wiley Periodicals, Inc.

Forest type mapping of the Interior West

Treesearch

Bonnie Ruefenacht; Gretchen G. Moisen; Jock A. Blackard

2004-01-01

This paper develops techniques for the mapping of forest types in Arizona, New Mexico, and Wyoming. The methods involve regression-tree modeling using a variety of remote sensing and GIS layers along with Forest Inventory Analysis (FIA) point data. Regression-tree modeling is a fast and efficient technique of estimating variables for large data sets with high accuracy...

Determinants of Academic Attainment in the United States: A Quantile Regression Analysis of Test Scores

ERIC Educational Resources Information Center

Haile, Getinet Astatike; Nguyen, Anh Ngoc

2008-01-01

We investigate the determinants of high school students' academic attainment in mathematics, reading and science in the United States; focusing particularly on possible differential impacts of ethnicity and family background across the distribution of test scores. Using data from the NELS2000 and employing quantile regression, we find two…

INNOVATIVE INSTRUMENTATION AND ANALYSIS OF THE TEMPERATURE MEASUREMENT FOR HIGH TEMPERATURE GASIFICATION

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

Seong W. Lee

2004-10-01

The systematic tests of the gasifier simulator on the clean thermocouple were completed in this reporting period. Within the systematic tests on the clean thermocouple, five (5) factors were considered as the experimental parameters including air flow rate, water flow rate, fine dust particle amount, ammonia addition and high/low frequency device (electric motor). The fractional factorial design method was used in the experiment design with sixteen (16) data sets of readings. Analysis of Variances (ANOVA) was applied to the results from systematic tests. The ANOVA results show that the un-balanced motor vibration frequency did not have the significant impact onmore » the temperature changes in the gasifier simulator. For the fine dust particles testing, the amount of fine dust particles has significant impact to the temperature measurements in the gasifier simulator. The effects of the air and water on the temperature measurements show the same results as reported in the previous report. The ammonia concentration was included as an experimental parameter for the reducing environment in this reporting period. The ammonia concentration does not seem to be a significant factor on the temperature changes. The linear regression analysis was applied to the temperature reading with five (5) factors. The accuracy of the linear regression is relatively low, which is less than 10% accuracy. Nonlinear regression was also conducted to the temperature reading with the same factors. Since the experiments were designed in two (2) levels, the nonlinear regression is not very effective with the dataset (16 readings). An extra central point test was conducted. With the data of the center point testing, the accuracy of the nonlinear regression is much better than the linear regression.« less

Sparse partial least squares regression for simultaneous dimension reduction and variable selection

PubMed Central

Chun, Hyonho; Keleş, Sündüz

2010-01-01

Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data. PMID:20107611

Integrated analysis of DNA-methylation and gene expression using high-dimensional penalized regression: a cohort study on bone mineral density in postmenopausal women.

PubMed

Lien, Tonje G; Borgan, Ørnulf; Reppe, Sjur; Gautvik, Kaare; Glad, Ingrid Kristine

2018-03-07

Using high-dimensional penalized regression we studied genome-wide DNA-methylation in bone biopsies of 80 postmenopausal women in relation to their bone mineral density (BMD). The women showed BMD varying from severely osteoporotic to normal. Global gene expression data from the same individuals was available, and since DNA-methylation often affects gene expression, the overall aim of this paper was to include both of these omics data sets into an integrated analysis. The classical penalized regression uses one penalty, but we incorporated individual penalties for each of the DNA-methylation sites. These individual penalties were guided by the strength of association between DNA-methylations and gene transcript levels. DNA-methylations that were highly associated to one or more transcripts got lower penalties and were therefore favored compared to DNA-methylations showing less association to expression. Because of the complex pathways and interactions among genes, we investigated both the association between DNA-methylations and their corresponding cis gene, as well as the association between DNA-methylations and trans-located genes. Two integrating penalized methods were used: first, an adaptive group-regularized ridge regression, and secondly, variable selection was performed through a modified version of the weighted lasso. When information from gene expressions was integrated, predictive performance was considerably improved, in terms of predictive mean square error, compared to classical penalized regression without data integration. We found a 14.7% improvement in the ridge regression case and a 17% improvement for the lasso case. Our version of the weighted lasso with data integration found a list of 22 interesting methylation sites. Several corresponded to genes that are known to be important in bone formation. Using BMD as response and these 22 methylation sites as covariates, least square regression analyses resulted in R 2 =0.726, comparable to an average R 2 =0.438 for 10000 randomly selected groups of DNA-methylations with group size 22. Two recent types of penalized regression methods were adapted to integrate DNA-methylation and their association to gene expression in the analysis of bone mineral density. In both cases predictions clearly benefit from including the additional information on gene expressions.

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.

Predicting Student Engagement in Online High Schools

ERIC Educational Resources Information Center

Vieira, Christopher James

2013-01-01

The purpose of this study was to analyze student engagement in online high schools based on demographic information of high school students using a mixed methods research design. Key findings through a multiple regression analysis and Pearson correlation coefficient suggest that although the majority of participants in the study are highly engaged…

Influences on Academic Achievement Across High and Low Income Countries: A Re-Analysis of IEA Data.

ERIC Educational Resources Information Center

Heyneman, S.; Loxley, W.

Previous international studies of science achievement put the data through a process of winnowing to decide which variables to keep in the final regressions. Variables were allowed to enter the final regressions if they met a minimum beta coefficient criterion of 0.05 averaged across rich and poor countries alike. The criterion was an average…

Hybrid ICA-Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals.

PubMed

Mannan, Malik M Naeem; Jeong, Myung Y; Kamran, Muhammad A

2016-01-01

Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG.

Hybrid ICA—Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals

PubMed Central

Mannan, Malik M. Naeem; Jeong, Myung Y.; Kamran, Muhammad A.

2016-01-01

Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG. PMID:27199714

A Comparison of Various MRA Methods Applied to Longitudinal Evaluation Studies in Vocational Education.

ERIC Educational Resources Information Center

Kapes, Jerome T.; And Others

Three models of multiple regression analysis (MRA): single equation, commonality analysis, and path analysis, were applied to longitudinal data from the Pennsylvania Vocational Development Study. Variables influencing weekly income of vocational education students one year after high school graduation were examined: grade point averages (grades…

The role of empathy and emotional intelligence in nurses' communication attitudes using regression models and fuzzy-set qualitative comparative analysis models.

PubMed

Giménez-Espert, María Del Carmen; Prado-Gascó, Vicente Javier

2018-03-01

To analyse link between empathy and emotional intelligence as a predictor of nurses' attitudes towards communication while comparing the contribution of emotional aspects and attitudinal elements on potential behaviour. Nurses' attitudes towards communication, empathy and emotional intelligence are key skills for nurses involved in patient care. There are currently no studies analysing this link, and its investigation is needed because attitudes may influence communication behaviours. Correlational study. To attain this goal, self-reported instruments (attitudes towards communication of nurses, trait emotional intelligence (Trait Emotional Meta-Mood Scale) and Jefferson Scale of Nursing Empathy (Jefferson Scale Nursing Empathy) were collected from 460 nurses between September 2015-February 2016. Two different analytical methodologies were used: traditional regression models and fuzzy-set qualitative comparative analysis models. The results of the regression model suggest that cognitive dimensions of attitude are a significant and positive predictor of the behavioural dimension. The perspective-taking dimension of empathy and the emotional-clarity dimension of emotional intelligence were significant positive predictors of the dimensions of attitudes towards communication, except for the affective dimension (for which the association was negative). The results of the fuzzy-set qualitative comparative analysis models confirm that the combination of high levels of cognitive dimension of attitudes, perspective-taking and emotional clarity explained high levels of the behavioural dimension of attitude. Empathy and emotional intelligence are predictors of nurses' attitudes towards communication, and the cognitive dimension of attitude is a good predictor of the behavioural dimension of attitudes towards communication of nurses in both regression models and fuzzy-set qualitative comparative analysis. In general, the fuzzy-set qualitative comparative analysis models appear to be better predictors than the regression models are. To evaluate current practices, establish intervention strategies and evaluate their effectiveness. The evaluation of these variables and their relationships are important in creating a satisfied and sustainable workforce and improving quality of care and patient health. © 2018 John Wiley & Sons Ltd.

A diagnostic analysis of the VVP single-doppler retrieval technique

NASA Technical Reports Server (NTRS)

Boccippio, Dennis J.

1995-01-01

A diagnostic analysis of the VVP (volume velocity processing) retrieval method is presented, with emphasis on understanding the technique as a linear, multivariate regression. Similarities and differences to the velocity-azimuth display and extended velocity-azimuth display retrieval techniques are discussed, using this framework. Conventional regression diagnostics are then employed to quantitatively determine situations in which the VVP technique is likely to fail. An algorithm for preparation and analysis of a robust VVP retrieval is developed and applied to synthetic and actual datasets with high temporal and spatial resolution. A fundamental (but quantifiable) limitation to some forms of VVP analysis is inadequate sampling dispersion in the n space of the multivariate regression, manifest as a collinearity between the basis functions of some fitted parameters. Such collinearity may be present either in the definition of these basis functions or in their realization in a given sampling configuration. This nonorthogonality may cause numerical instability, variance inflation (decrease in robustness), and increased sensitivity to bias from neglected wind components. It is shown that these effects prevent the application of VVP to small azimuthal sectors of data. The behavior of the VVP regression is further diagnosed over a wide range of sampling constraints, and reasonable sector limits are established.

Methods for estimating the magnitude and frequency of peak streamflows at ungaged sites in and near the Oklahoma Panhandle

USGS Publications Warehouse

Smith, S. Jerrod; Lewis, Jason M.; Graves, Grant M.

2015-09-28

Generalized-least-squares multiple-linear regression analysis was used to formulate regression relations between peak-streamflow frequency statistics and basin characteristics. Contributing drainage area was the only basin characteristic determined to be statistically significant for all percentage of annual exceedance probabilities and was the only basin characteristic used in regional regression equations for estimating peak-streamflow frequency statistics on unregulated streams in and near the Oklahoma Panhandle. The regression model pseudo-coefficient of determination, converted to percent, for the Oklahoma Panhandle regional regression equations ranged from about 38 to 63 percent. The standard errors of prediction and the standard model errors for the Oklahoma Panhandle regional regression equations ranged from about 84 to 148 percent and from about 76 to 138 percent, respectively. These errors were comparable to those reported for regional peak-streamflow frequency regression equations for the High Plains areas of Texas and Colorado. The root mean square errors for the Oklahoma Panhandle regional regression equations (ranging from 3,170 to 92,000 cubic feet per second) were less than the root mean square errors for the Oklahoma statewide regression equations (ranging from 18,900 to 412,000 cubic feet per second); therefore, the Oklahoma Panhandle regional regression equations produce more accurate peak-streamflow statistic estimates for the irrigated period of record in the Oklahoma Panhandle than do the Oklahoma statewide regression equations. The regression equations developed in this report are applicable to streams that are not substantially affected by regulation, impoundment, or surface-water withdrawals. These regression equations are intended for use for stream sites with contributing drainage areas less than or equal to about 2,060 square miles, the maximum value for the independent variable used in the regression analysis.

Impact of Texas high school science teacher credentials on student performance in high school science

NASA Astrophysics Data System (ADS)

George, Anna Ray Bayless

A study was conducted to determine the relationship between the credentials held by science teachers who taught at a school that administered the Science Texas Assessment on Knowledge and Skills (Science TAKS), the state standardized exam in science, at grade 11 and student performance on a state standardized exam in science administered in grade 11. Years of teaching experience, teacher certification type(s), highest degree level held, teacher and school demographic information, and the percentage of students who met the passing standard on the Science TAKS were obtained through a public records request to the Texas Education Agency (TEA) and the State Board for Educator Certification (SBEC). Analysis was performed through the use of canonical correlation analysis and multiple linear regression analysis. The results of the multiple linear regression analysis indicate that a larger percentage of students met the passing standard on the Science TAKS state attended schools in which a large portion of the high school science teachers held post baccalaureate degrees, elementary and physical science certifications, and had 11-20 years of teaching experience.

High Expression of EphA4 Predicted Lesser Degree of Tumor Regression after Neoadjuvant Chemoradiotherapy in Rectal Cancer.

PubMed

Lin, Ching-Yih; Lee, Ying-En; Tian, Yu-Feng; Sun, Ding-Ping; Sheu, Ming-Jen; Lin, Chen-Yi; Li, Chien-Feng; Lee, Sung-Wei; Lin, Li-Ching; Chang, I-Wei; Wang, Chieh-Tien; He, Hong-Lin

2017-01-01

Background: Numerous transmembrane receptor tyrosine kinase pathways have been found to play an important role in tumor progression in some cancers. This study was aimed to evaluate the clinical impact of Eph receptor A4 (EphA4) in patients with rectal cancer treated with neoadjuvant concurrent chemoradiotherapy (CCRT) combined with mesorectal excision, with special emphasis on tumor regression. Methods: Analysis of the publicly available expression profiling dataset of rectal cancer disclosed that EphA4 was the top-ranking, significantly upregulated, transmembrane receptor tyrosine kinase pathway-associated gene in the non-responders to CCRT, compared with the responders. Immunohistochemical study was conducted to assess the EphA4 expression in pre-treatment biopsy specimens from 172 rectal cancer patients without distant metastasis. The relationships between EphA4 expression and various clinicopathological factors or survival were statistically analyzed. Results: EphA4 expression was significantly associated with vascular invasion ( P =0.015), post-treatment depth of tumor invasion ( P =0.006), pre-treatment and post-treatment lymph node metastasis ( P =0.004 and P =0.011, respectively). More importantly, high EphA4 expression was significantly predictive for lesser degree of tumor regression after CCRT ( P =0.031). At univariate analysis, high EphA4 expression was a negative prognosticator for disease-specific survival ( P =0.0009) and metastasis-free survival ( P =0.0001). At multivariate analysis, high expression of EphA4 still served as an independent adverse prognostic factor for disease-specific survival (HR, 2.528; 95% CI, 1.131-5.651; P =0.024) and metastasis-free survival (HR, 3.908; 95% CI, 1.590-9.601; P =0.003). Conclusion: High expression of EphA4 predicted lesser degree of tumor regression after CCRT and served as an independent negative prognostic factor in patients with rectal cancer.

Classification and regression tree analysis of acute-on-chronic hepatitis B liver failure: Seeing the forest for the trees.

PubMed

Shi, K-Q; Zhou, Y-Y; Yan, H-D; Li, H; Wu, F-L; Xie, Y-Y; Braddock, M; Lin, X-Y; Zheng, M-H

2017-02-01

At present, there is no ideal model for predicting the short-term outcome of patients with acute-on-chronic hepatitis B liver failure (ACHBLF). This study aimed to establish and validate a prognostic model by using the classification and regression tree (CART) analysis. A total of 1047 patients from two separate medical centres with suspected ACHBLF were screened in the study, which were recognized as derivation cohort and validation cohort, respectively. CART analysis was applied to predict the 3-month mortality of patients with ACHBLF. The accuracy of the CART model was tested using the area under the receiver operating characteristic curve, which was compared with the model for end-stage liver disease (MELD) score and a new logistic regression model. CART analysis identified four variables as prognostic factors of ACHBLF: total bilirubin, age, serum sodium and INR, and three distinct risk groups: low risk (4.2%), intermediate risk (30.2%-53.2%) and high risk (81.4%-96.9%). The new logistic regression model was constructed with four independent factors, including age, total bilirubin, serum sodium and prothrombin activity by multivariate logistic regression analysis. The performances of the CART model (0.896), similar to the logistic regression model (0.914, P=.382), exceeded that of MELD score (0.667, P<.001). The results were confirmed in the validation cohort. We have developed and validated a novel CART model superior to MELD for predicting three-month mortality of patients with ACHBLF. Thus, the CART model could facilitate medical decision-making and provide clinicians with a validated practical bedside tool for ACHBLF risk stratification. © 2016 John Wiley & Sons Ltd.

High-Dimensional Heteroscedastic Regression with an Application to eQTL Data Analysis

PubMed Central

Daye, Z. John; Chen, Jinbo; Li, Hongzhe

2011-01-01

Summary We consider the problem of high-dimensional regression under non-constant error variances. Despite being a common phenomenon in biological applications, heteroscedasticity has, so far, been largely ignored in high-dimensional analysis of genomic data sets. We propose a new methodology that allows non-constant error variances for high-dimensional estimation and model selection. Our method incorporates heteroscedasticity by simultaneously modeling both the mean and variance components via a novel doubly regularized approach. Extensive Monte Carlo simulations indicate that our proposed procedure can result in better estimation and variable selection than existing methods when heteroscedasticity arises from the presence of predictors explaining error variances and outliers. Further, we demonstrate the presence of heteroscedasticity in and apply our method to an expression quantitative trait loci (eQTLs) study of 112 yeast segregants. The new procedure can automatically account for heteroscedasticity in identifying the eQTLs that are associated with gene expression variations and lead to smaller prediction errors. These results demonstrate the importance of considering heteroscedasticity in eQTL data analysis. PMID:22547833

A Systematic Review and Meta-Regression Analysis of Lung Cancer Risk and Inorganic Arsenic in Drinking Water.

PubMed

Lamm, Steven H; Ferdosi, Hamid; Dissen, Elisabeth K; Li, Ji; Ahn, Jaeil

2015-12-07

High levels (> 200 µg/L) of inorganic arsenic in drinking water are known to be a cause of human lung cancer, but the evidence at lower levels is uncertain. We have sought the epidemiological studies that have examined the dose-response relationship between arsenic levels in drinking water and the risk of lung cancer over a range that includes both high and low levels of arsenic. Regression analysis, based on six studies identified from an electronic search, examined the relationship between the log of the relative risk and the log of the arsenic exposure over a range of 1-1000 µg/L. The best-fitting continuous meta-regression model was sought and found to be a no-constant linear-quadratic analysis where both the risk and the exposure had been logarithmically transformed. This yielded both a statistically significant positive coefficient for the quadratic term and a statistically significant negative coefficient for the linear term. Sub-analyses by study design yielded results that were similar for both ecological studies and non-ecological studies. Statistically significant X-intercepts consistently found no increased level of risk at approximately 100-150 µg/L arsenic.

Contributions of sociodemographic factors to criminal behavior

PubMed Central

Mundia, Lawrence; Matzin, Rohani; Mahalle, Salwa; Hamid, Malai Hayati; Osman, Ratna Suriani

2016-01-01

We explored the extent to which prisoner sociodemographic variables (age, education, marital status, employment, and whether their parents were married or not) influenced offending in 64 randomly selected Brunei inmates, comprising both sexes. A quantitative field survey design ideal for the type of participants used in a prison context was employed to investigate the problem. Hierarchical multiple regression analysis with backward elimination identified prisoner marital status and age groups as significantly related to offending. Furthermore, hierarchical multinomial logistic regression analysis with backward elimination indicated that prisoners’ age, primary level education, marital status, employment status, and parental marital status as significantly related to stealing offenses with high odds ratios. All 29 nonrecidivists were false negatives and predicted to reoffend upon release. Similarly, all 33 recidivists were projected to reoffend after release. Hierarchical binary logistic regression analysis revealed age groups (24–29 years and 30–35 years), employed prisoner, and primary level education as variables with high likelihood trends for reoffending. The results suggested that prisoner interventions (educational, counseling, and psychotherapy) in Brunei should treat not only antisocial personality, psychopathy, and mental health problems but also sociodemographic factors. The study generated offending patterns, trends, and norms that may inform subsequent investigations on Brunei prisoners. PMID:27382342

A Systematic Review and Meta-Regression Analysis of Lung Cancer Risk and Inorganic Arsenic in Drinking Water

PubMed Central

Lamm, Steven H.; Ferdosi, Hamid; Dissen, Elisabeth K.; Li, Ji; Ahn, Jaeil

2015-01-01

High levels (> 200 µg/L) of inorganic arsenic in drinking water are known to be a cause of human lung cancer, but the evidence at lower levels is uncertain. We have sought the epidemiological studies that have examined the dose-response relationship between arsenic levels in drinking water and the risk of lung cancer over a range that includes both high and low levels of arsenic. Regression analysis, based on six studies identified from an electronic search, examined the relationship between the log of the relative risk and the log of the arsenic exposure over a range of 1–1000 µg/L. The best-fitting continuous meta-regression model was sought and found to be a no-constant linear-quadratic analysis where both the risk and the exposure had been logarithmically transformed. This yielded both a statistically significant positive coefficient for the quadratic term and a statistically significant negative coefficient for the linear term. Sub-analyses by study design yielded results that were similar for both ecological studies and non-ecological studies. Statistically significant X-intercepts consistently found no increased level of risk at approximately 100–150 µg/L arsenic. PMID:26690190

A dynamic regression analysis tool for quantitative assessment of bacterial growth written in Python.

PubMed

Hoeflinger, Jennifer L; Hoeflinger, Daniel E; Miller, Michael J

2017-01-01

Herein, an open-source method to generate quantitative bacterial growth data from high-throughput microplate assays is described. The bacterial lag time, maximum specific growth rate, doubling time and delta OD are reported. Our method was validated by carbohydrate utilization of lactobacilli, and visual inspection revealed 94% of regressions were deemed excellent. Copyright © 2016 Elsevier B.V. All rights reserved.

Predicting Student Success on the Texas Chemistry STAAR Test: A Logistic Regression Analysis

ERIC Educational Resources Information Center

Johnson, William L.; Johnson, Annabel M.; Johnson, Jared

2012-01-01

Background: The context is the new Texas STAAR end-of-course testing program. Purpose: The authors developed a logistic regression model to predict who would pass-or-fail the new Texas chemistry STAAR end-of-course exam. Setting: Robert E. Lee High School (5A) with an enrollment of 2700 students, Tyler, Texas. Date of the study was the 2011-2012…

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

PubMed

Erdogan, Saffet

2009-10-01

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

The 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.

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.

Reflectance measurements for the detection and mapping of soil limitations

NASA Technical Reports Server (NTRS)

Benson, L. A.; Frazee, C. J.

1973-01-01

During 1971 and 1972 research was conducted on two fallow fields in the proposed Oahe Irrigation Project to investigate the relationship between the tonal variations observed on aerial photographs and the principal soil limitations of the area. A grid sampling procedure was used to collected detailed field data during the 1972 growing season. The field data was compared to imagery collected on May 14, 1971 at 3050 meters altitude. The imagery and field data were initially evaluated by a visual analysis. Correlation and regression analysis revealed a highly significant correlation and regression analysis revealed a highly significant correlation between the digitized color infrared film data and soil properties such as organic matter content, color, depth to carbonates, bulk density and reflectivity. Computer classification of the multiemulsion film data resulted in maps delineating the areas containing claypan and erosion limitations. Reflectance data from the red spectral band provided the best results.

Mangrove canopy density analysis using Sentinel-2A imagery satellite data

NASA Astrophysics Data System (ADS)

Wachid, M. N.; Hapsara, R. P.; Cahyo, R. D.; Wahyu, G. N.; Syarif, A. M.; Umarhadi, D. A.; Fitriani, A. N.; Ramadhanningrum, D. P.; Widyatmanti, W.

2017-06-01

Teluk Jor has alluvium surface sediment that came from volcanic materials. Sea wave that relatively calm and the closed beach shape support the existence of mangrove forest at Teluk Jor. Sentinel-2A imagery has a good spatial and spectral resolution for mangrove density study. The regression between samples and the NDVI values of Sentinel-2A used to analyze the mangrove canopy density. Mangrove canopy density was identified using field survey with transect method. The regression analysis shows field data and NDVI value has correlation R=0.7739 and coefficient of determination R2=0.5989. The result of the analysis shows area of low density 397,900 m2, moderate density 336,200 m2, the high density has 110,300 m2 and very high density has 500 m2. This research also found that mangrove genus in Teluk Jor consists of Rhizopora, Ceriops, Aegiceras and Sonneratia.

Evaluation of methodology for the analysis of 'time-to-event' data in pharmacogenomic genome-wide association studies.

PubMed

Syed, Hamzah; Jorgensen, Andrea L; Morris, Andrew P

2016-06-01

To evaluate the power to detect associations between SNPs and time-to-event outcomes across a range of pharmacogenomic study designs while comparing alternative regression approaches. Simulations were conducted to compare Cox proportional hazards modeling accounting for censoring and logistic regression modeling of a dichotomized outcome at the end of the study. The Cox proportional hazards model was demonstrated to be more powerful than the logistic regression analysis. The difference in power between the approaches was highly dependent on the rate of censoring. Initial evaluation of single-nucleotide polymorphism association signals using computationally efficient software with dichotomized outcomes provides an effective screening tool for some design scenarios, and thus has important implications for the development of analytical protocols in pharmacogenomic studies.

[Sociodemographic context of homicide in Mexico City: a spatial analysis].

PubMed

Fuentes Flores, César; Sánchez Salinas, Omar

2015-12-01

Investigate the spatial distribution pattern of the homicide rate and its relation to sociodemographic features in the Benito Juárez, Coyoacán, and Cuauhtémoc districts of Mexico City in 2010. Inferential cross-sectional study that uses spatial analysis methods to study the spatial association of the homicide rate and demographic features. Spatial association was determined through the location quotient, multiple regression analysis, and the use of geographically weighted regression. Homicides show a heterogeneous location pattern with high rates in areas with non-residential land use, low population density, and low marginalization. Spatial analysis tools are powerful instruments for the design of prevention- and recreation-focused public safety policies that aim to reduce mortality from external causes such as homicides.

Geometric dimension model of virtual astronaut body for ergonomic analysis of man-machine space system

NASA Astrophysics Data System (ADS)

Qianxiang, Zhou

2012-07-01

It is very important to clarify the geometric characteristic of human body segment and constitute analysis model for ergonomic design and the application of ergonomic virtual human. The typical anthropometric data of 1122 Chinese men aged 20-35 years were collected using three-dimensional laser scanner for human body. According to the correlation between different parameters, curve fitting were made between seven trunk parameters and ten body parameters with the SPSS 16.0 software. It can be concluded that hip circumference and shoulder breadth are the most important parameters in the models and the two parameters have high correlation with the others parameters of human body. By comparison with the conventional regressive curves, the present regression equation with the seven trunk parameters is more accurate to forecast the geometric dimensions of head, neck, height and the four limbs with high precision. Therefore, it is greatly valuable for ergonomic design and analysis of man-machine system.This result will be very useful to astronaut body model analysis and application.

The use of bulk collectors in monitoring wet deposition at high-altitude sites in winter

USGS Publications Warehouse

Ranalli, A.J.; Turk, J.T.; Campbell, D.H.

1997-01-01

Concentrations of dissolved ions from samples collected by wet/dry collectors were compared to those collected by bulk collectors at Halfmoon Creek and Ned Wilson Lake in western Colorado to determine if bulk collectors can be used to monitor wet deposition chemistry in remote, high-altitude regions in winter. Hydrogen-ion concentration was significantly lower (p 0.05) at Halfmoon Creek. Wet deposition concentrations were predicated from bulk deposition concentrations through linear regression analysis. Results indicate that anions (chloride, nitrate and sulfate) can be predicted with a high degree of confidence. Lack of significant differences between seasonal (winter and summer) ratios of bulk to wet deposition concentrations indicates that at sites where operation of a wet/dry collector during the winter is not practical, wet deposition concentrations can be predicted from bulk collector samples through regression analysis of wet and bulk deposition data collected during the summer.

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models.

PubMed

Riley, Richard D; Ensor, Joie; Jackson, Dan; Burke, Danielle L

2017-01-01

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

Multivariate time series analysis of neuroscience data: some challenges and opportunities.

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

Neuroimaging data may be viewed as high-dimensional multivariate time series, and analyzed using techniques from regression analysis, time series analysis and spatiotemporal analysis. We discuss issues related to data quality, model specification, estimation, interpretation, dimensionality and causality. Some recent research areas addressing aspects of some recurring challenges are introduced. Copyright © 2015 Elsevier Ltd. All rights reserved.

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…

Influence of storage conditions on the stability of monomeric anthocyanins studied by reversed-phase high-performance liquid chromatography.

PubMed

Morais, Helena; Ramos, Cristina; Forgács, Esther; Cserháti, Tibor; Oliviera, José

2002-04-25

The effect of light, storage time and temperature on the decomposition rate of monomeric anthocyanin pigments extracted from skins of grape (Vitis vinifera var. Red globe) was determined by reversed-phase high-performance liquid chromatography (RP-HPLC). The impact of various storage conditions on the pigment stability was assessed by stepwise regression analysis. RP-HPLC separated well the five anthocyanins identified and proved the presence of other unidentified pigments at lower concentrations. Stepwise regression analysis confirmed that the overall decomposition rate of monomeric anthocyanins, peonidin-3-glucoside and malvidin-3-glucoside significantly depended on the time and temperature of storage, the effect of storage time being the most important. The presence or absence of light exerted a negligible impact on the decomposition rate.

Approximate median regression for complex survey data with skewed response.

PubMed

Fraser, Raphael André; Lipsitz, Stuart R; Sinha, Debajyoti; Fitzmaurice, Garrett M; Pan, Yi

2016-12-01

The ready availability of public-use data from various large national complex surveys has immense potential for the assessment of population characteristics using regression models. Complex surveys can be used to identify risk factors for important diseases such as cancer. Existing statistical methods based on estimating equations and/or utilizing resampling methods are often not valid with survey data due to complex survey design features. That is, stratification, multistage sampling, and weighting. In this article, we accommodate these design features in the analysis of highly skewed response variables arising from large complex surveys. Specifically, we propose a double-transform-both-sides (DTBS)'based estimating equations approach to estimate the median regression parameters of the highly skewed response; the DTBS approach applies the same Box-Cox type transformation twice to both the outcome and regression function. The usual sandwich variance estimate can be used in our approach, whereas a resampling approach would be needed for a pseudo-likelihood based on minimizing absolute deviations (MAD). Furthermore, the approach is relatively robust to the true underlying distribution, and has much smaller mean square error than a MAD approach. The method is motivated by an analysis of laboratory data on urinary iodine (UI) concentration from the National Health and Nutrition Examination Survey. © 2016, The International Biometric Society.

Approximate Median Regression for Complex Survey Data with Skewed Response

PubMed Central

Fraser, Raphael André; Lipsitz, Stuart R.; Sinha, Debajyoti; Fitzmaurice, Garrett M.; Pan, Yi

2016-01-01

Summary The ready availability of public-use data from various large national complex surveys has immense potential for the assessment of population characteristics using regression models. Complex surveys can be used to identify risk factors for important diseases such as cancer. Existing statistical methods based on estimating equations and/or utilizing resampling methods are often not valid with survey data due to complex survey design features. That is, stratification, multistage sampling and weighting. In this paper, we accommodate these design features in the analysis of highly skewed response variables arising from large complex surveys. Specifically, we propose a double-transform-both-sides (DTBS) based estimating equations approach to estimate the median regression parameters of the highly skewed response; the DTBS approach applies the same Box-Cox type transformation twice to both the outcome and regression function. The usual sandwich variance estimate can be used in our approach, whereas a resampling approach would be needed for a pseudo-likelihood based on minimizing absolute deviations (MAD). Furthermore, the approach is relatively robust to the true underlying distribution, and has much smaller mean square error than a MAD approach. The method is motivated by an analysis of laboratory data on urinary iodine (UI) concentration from the National Health and Nutrition Examination Survey. PMID:27062562

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.

Support vector methods for survival analysis: a comparison between ranking and regression approaches.

PubMed

Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K

2011-10-01

To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods including only regression or both regression and ranking constraints on clinical data. On high dimensional data, the former model performs better. However, this approach does not have a theoretical link with standard statistical models for survival data. This link can be made by means of transformation models when ranking constraints are included. Copyright © 2011 Elsevier B.V. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Öktem, H.

2012-01-01

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

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.

Optimizing Prophylactic CPAP in Patients Without Obstructive Sleep Apnoea for High-Risk Abdominal Surgeries: A Meta-regression Analysis.

PubMed

Singh, Preet Mohinder; Borle, Anuradha; Shah, Dipal; Sinha, Ashish; Makkar, Jeetinder Kaur; Trikha, Anjan; Goudra, Basavana Gouda

2016-04-01

Prophylactic continuous positive airway pressure (CPAP) can prevent pulmonary adverse events following upper abdominal surgeries. The present meta-regression evaluates and quantifies the effect of degree/duration of (CPAP) on the incidence of postoperative pulmonary events. Medical databases were searched for randomized controlled trials involving adult patients, comparing the outcome in those receiving prophylactic postoperative CPAP versus no CPAP, undergoing high-risk abdominal surgeries. Our meta-analysis evaluated the relationship between the postoperative pulmonary complications and the use of CPAP. Furthermore, meta-regression was used to quantify the effect of cumulative duration and degree of CPAP on the measured outcomes. Seventy-three potentially relevant studies were identified, of which 11 had appropriate data, allowing us to compare a total of 362 and 363 patients in CPAP and control groups, respectively. Qualitatively, Odds ratio for CPAP showed protective effect for pneumonia [0.39 (0.19-0.78)], atelectasis [0.51 (0.32-0.80)] and pulmonary complications [0.37 (0.24-0.56)] with zero heterogeneity. For prevention of pulmonary complications, odds ratio was better for continuous than intermittent CPAP. Meta-regression demonstrated a positive correlation between the degree of CPAP and the incidence of pneumonia with a regression coefficient of +0.61 (95 % CI 0.02-1.21, P = 0.048, τ (2) = 0.078, r (2) = 7.87 %). Overall, adverse effects were similar with or without the use of CPAP. Prophylactic postoperative use of continuous CPAP significantly reduces the incidence of postoperative pneumonia, atelectasis and pulmonary complications in patients undergoing high-risk abdominal surgeries. Quantitatively, increasing the CPAP levels does not necessarily enhance the protective effect against pneumonia. Instead, protective effect diminishes with increasing degree of CPAP.

Association of nitrogen compounds in drinking water with incidence of esophageal squamous cell carcinoma in Shexian, China.

PubMed

Zhang, Nan; Yu, Cao; Wen, Denggui; Chen, Jun; Ling, Yiwei; Terajima, Kenshi; Akazawa, Kohei; Shan, Baoen; Wang, Shijie

2012-01-01

The incidence of esophageal squamous cell carcinoma (ESCC), which is the eighth most common malignancy worldwide, is highest in China. The purpose of this study was to investigate the association between nitrogen compounds in drinking water with the incidence of ESCC by geographical spatial analysis. The incidence of ESCC is high in Shexian county, China, and environmental factors, particularly nitrogen-contaminated drinking water, are the main suspected risk factors. This study focuses on three nitrogen compounds in drinking water, namely, nitrates, nitrites, and ammonia, all of which are derived mainly from domestic garbage and agricultural fertilizer. The study surveyed 48 villages in the Shexian area with a total population of 54,716 (661 adults with ESCC and 54,055 non-cancer subjects). Hot-spot analysis was used to identify spatial clusters with a high incidence of ESCC and a high concentration of nitrogen compounds. Logistic regression analysis was used to detect risk factors for ESCC incidence. Most areas with high concentrations of nitrate nitrogen in drinking water had a high incidence of ESCC. Correlation analysis revealed a significant positive relationship between nitrate concentration and ESCC (P = 0.01). Logistic regression analysis also confirmed that nitrate nitrogen has a significantly higher odds ratio. The results indicate that nitrate nitrogen is associated with ESCC incidence in Shexian county. In conclusion, high concentrations of nitrate nitrogen in drinking water may be a significant risk factor for the incidence of ESCC.

Authoritative School Climate and High School Dropout Rates

ERIC Educational Resources Information Center

Jia, Yuane; Konold, Timothy R.; Cornell, Dewey

2016-01-01

This study tested the association between school-wide measures of an authoritative school climate and high school dropout rates in a statewide sample of 315 high schools. Regression models at the school level of analysis used teacher and student measures of disciplinary structure, student support, and academic expectations to predict overall high…

The Production of Health, An Exploratory Study

ERIC Educational Resources Information Center

Auster, Richard; and others

1969-01-01

The relationship of mortality of whites to both medical care and environmental variables is examined in a regression analysis. Environmental factors are found to be more important in relation to death rate than medical care. High education is associated with low death rates and high income is associated with high death rates. (Author/AP)

Identifying Aspects of Parental Involvement that Affect the Academic Achievement of High School Students

ERIC Educational Resources Information Center

Roulette-McIntyre, Ovella; Bagaka's, Joshua G.; Drake, Daniel D.

2005-01-01

This study identified parental practices that relate positively to high school students' academic performance. Parents of 643 high school students participated in the study. Data analysis, using a multiple linear regression model, shows parent-school connection, student gender, and race are significant predictors of student academic performance.…

Non-stationary hydrologic frequency analysis using B-spline quantile regression

NASA Astrophysics Data System (ADS)

Nasri, B.; Bouezmarni, T.; St-Hilaire, A.; Ouarda, T. B. M. J.

2017-11-01

Hydrologic frequency analysis is commonly used by engineers and hydrologists to provide the basic information on planning, design and management of hydraulic and water resources systems under the assumption of stationarity. However, with increasing evidence of climate change, it is possible that the assumption of stationarity, which is prerequisite for traditional frequency analysis and hence, the results of conventional analysis would become questionable. In this study, we consider a framework for frequency analysis of extremes based on B-Spline quantile regression which allows to model data in the presence of non-stationarity and/or dependence on covariates with linear and non-linear dependence. A Markov Chain Monte Carlo (MCMC) algorithm was used to estimate quantiles and their posterior distributions. A coefficient of determination and Bayesian information criterion (BIC) for quantile regression are used in order to select the best model, i.e. for each quantile, we choose the degree and number of knots of the adequate B-spline quantile regression model. The method is applied to annual maximum and minimum streamflow records in Ontario, Canada. Climate indices are considered to describe the non-stationarity in the variable of interest and to estimate the quantiles in this case. The results show large differences between the non-stationary quantiles and their stationary equivalents for an annual maximum and minimum discharge with high annual non-exceedance probabilities.

The comparison of robust partial least squares regression with robust principal component regression on a real

NASA Astrophysics Data System (ADS)

Polat, Esra; Gunay, Suleyman

2013-10-01

One of the problems encountered in Multiple Linear Regression (MLR) is multicollinearity, which causes the overestimation of the regression parameters and increase of the variance of these parameters. Hence, in case of multicollinearity presents, biased estimation procedures such as classical Principal Component Regression (CPCR) and Partial Least Squares Regression (PLSR) are then performed. SIMPLS algorithm is the leading PLSR algorithm because of its speed, efficiency and results are easier to interpret. However, both of the CPCR and SIMPLS yield very unreliable results when the data set contains outlying observations. Therefore, Hubert and Vanden Branden (2003) have been presented a robust PCR (RPCR) method and a robust PLSR (RPLSR) method called RSIMPLS. In RPCR, firstly, a robust Principal Component Analysis (PCA) method for high-dimensional data on the independent variables is applied, then, the dependent variables are regressed on the scores using a robust regression method. RSIMPLS has been constructed from a robust covariance matrix for high-dimensional data and robust linear regression. The purpose of this study is to show the usage of RPCR and RSIMPLS methods on an econometric data set, hence, making a comparison of two methods on an inflation model of Turkey. The considered methods have been compared in terms of predictive ability and goodness of fit by using a robust Root Mean Squared Error of Cross-validation (R-RMSECV), a robust R2 value and Robust Component Selection (RCS) statistic.

The Impact of Managerial Coaching on Learning Outcomes within the Team Context: An Analysis

ERIC Educational Resources Information Center

Hagen, Marcia; Aguilar, Mariya Gavrilova

2012-01-01

This study investigates the relationship between coaching expertise, project difficulty, and team empowerment on team learning outcomes within the context of a high-performance work team. Variables were tested using multiple regression analysis. The data were analyzed for two groups--team leaders and team members--using t-tests, factor analysis,…

Cluster Analysis of Campylobacter jejuni Genotypes Isolated from Small and Medium-Sized Mammalian Wildlife and Bovine Livestock from Ontario Farms.

PubMed

Viswanathan, M; Pearl, D L; Taboada, E N; Parmley, E J; Mutschall, S K; Jardine, C M

2017-05-01

Using data collected from a cross-sectional study of 25 farms (eight beef, eight swine and nine dairy) in 2010, we assessed clustering of molecular subtypes of C. jejuni based on a Campylobacter-specific 40 gene comparative genomic fingerprinting assay (CGF40) subtypes, using unweighted pair-group method with arithmetic mean (UPGMA) analysis, and multiple correspondence analysis. Exact logistic regression was used to determine which genes differentiate wildlife and livestock subtypes in our study population. A total of 33 bovine livestock (17 beef and 16 dairy), 26 wildlife (20 raccoon (Procyon lotor), five skunk (Mephitis mephitis) and one mouse (Peromyscus spp.) C. jejuni isolates were subtyped using CGF40. Dendrogram analysis, based on UPGMA, showed distinct branches separating bovine livestock and mammalian wildlife isolates. Furthermore, two-dimensional multiple correspondence analysis was highly concordant with dendrogram analysis showing clear differentiation between livestock and wildlife CGF40 subtypes. Based on multilevel logistic regression models with a random intercept for farm of origin, we found that isolates in general, and raccoons more specifically, were significantly more likely to be part of the wildlife branch. Exact logistic regression conducted gene by gene revealed 15 genes that were predictive of whether an isolate was of wildlife or bovine livestock isolate origin. Both multiple correspondence analysis and exact logistic regression revealed that in most cases, the presence of a particular gene (13 of 15) was associated with an isolate being of livestock rather than wildlife origin. In conclusion, the evidence gained from dendrogram analysis, multiple correspondence analysis and exact logistic regression indicates that mammalian wildlife carry CGF40 subtypes of C. jejuni distinct from those carried by bovine livestock. Future studies focused on source attribution of C. jejuni in human infections will help determine whether wildlife transmit Campylobacter jejuni directly to humans. © 2016 Blackwell Verlag GmbH.

Multiple correlates of cigarette use among high school students.

PubMed

McDermott, R J; Sarvela, P D; Hoalt, P N; Bajracharya, S M; Marty, P J; Emery, E M

1992-04-01

A cross-sectional survey research design measured factors related to cigarette use among 2,212 senior high school students. Results showed 14.3% of the sample smoked cigarettes at least occasionally, with 5.3% reporting they were daily smokers. About 12.8% indicated they were ex-smokers. Males and females smoked at almost equal rates, and the percentage of 10th grade student smokers was slightly higher (16.4%) than the percentage of juniors and seniors who smoked. Approximately 22% of Hispanic students, 15% of Caucasian students, and 4.5% of African-American students reported smoking cigarettes at least occasionally. An initial regression analysis used 21 variables to predict cigarette smoking. A more parsimonious regression model (R2 = .28), using variables from the initial regression analysis with significance levels of .01 or less, indicated the most important predictors of cigarette use were ethnic group, attitude toward females who smoke, close friends' use of cigarettes, personal use of marijuana, best friend's use of cigarettes, personal use of alcohol, and school self-esteem. Implications for school health programs are addressed.

Comparison of partial least squares and random forests for evaluating relationship between phenolics and bioactivities of Neptunia oleracea.

PubMed

Lee, Soo Yee; Mediani, Ahmed; Maulidiani, Maulidiani; Khatib, Alfi; Ismail, Intan Safinar; Zawawi, Norhasnida; Abas, Faridah

2018-01-01

Neptunia oleracea is a plant consumed as a vegetable and which has been used as a folk remedy for several diseases. Herein, two regression models (partial least squares, PLS; and random forest, RF) in a metabolomics approach were compared and applied to the evaluation of the relationship between phenolics and bioactivities of N. oleracea. In addition, the effects of different extraction conditions on the phenolic constituents were assessed by pattern recognition analysis. Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities. Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Multi-Target Regression via Robust Low-Rank Learning.

PubMed

Zhen, Xiantong; Yu, Mengyang; He, Xiaofei; Li, Shuo

2018-02-01

Multi-target regression has recently regained great popularity due to its capability of simultaneously learning multiple relevant regression tasks and its wide applications in data mining, computer vision and medical image analysis, while great challenges arise from jointly handling inter-target correlations and input-output relationships. In this paper, we propose Multi-layer Multi-target Regression (MMR) which enables simultaneously modeling intrinsic inter-target correlations and nonlinear input-output relationships in a general framework via robust low-rank learning. Specifically, the MMR can explicitly encode inter-target correlations in a structure matrix by matrix elastic nets (MEN); the MMR can work in conjunction with the kernel trick to effectively disentangle highly complex nonlinear input-output relationships; the MMR can be efficiently solved by a new alternating optimization algorithm with guaranteed convergence. The MMR leverages the strength of kernel methods for nonlinear feature learning and the structural advantage of multi-layer learning architectures for inter-target correlation modeling. More importantly, it offers a new multi-layer learning paradigm for multi-target regression which is endowed with high generality, flexibility and expressive ability. Extensive experimental evaluation on 18 diverse real-world datasets demonstrates that our MMR can achieve consistently high performance and outperforms representative state-of-the-art algorithms, which shows its great effectiveness and generality for multivariate prediction.

Modeling time-to-event (survival) data using classification tree analysis.

PubMed

Linden, Ariel; Yarnold, Paul R

2017-12-01

Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (called censored) are accounted for in the analysis. Cox regression is the standard method for analysing censored data, but the assumptions required of these models are easily violated. In this paper, we introduce classification tree analysis (CTA) as a flexible alternative for modelling censored data. Classification tree analysis is a "decision-tree"-like classification model that provides parsimonious, transparent (ie, easy to visually display and interpret) decision rules that maximize predictive accuracy, derives exact P values via permutation tests, and evaluates model cross-generalizability. Using empirical data, we identify all statistically valid, reproducible, longitudinally consistent, and cross-generalizable CTA survival models and then compare their predictive accuracy to estimates derived via Cox regression and an unadjusted naïve model. Model performance is assessed using integrated Brier scores and a comparison between estimated survival curves. The Cox regression model best predicts average incidence of the outcome over time, whereas CTA survival models best predict either relatively high, or low, incidence of the outcome over time. Classification tree analysis survival models offer many advantages over Cox regression, such as explicit maximization of predictive accuracy, parsimony, statistical robustness, and transparency. Therefore, researchers interested in accurate prognoses and clear decision rules should consider developing models using the CTA-survival framework. © 2017 John Wiley & Sons, Ltd.

Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery

NASA Astrophysics Data System (ADS)

Monteiro, Sildomar Takahashi; Minekawa, Yohei; Kosugi, Yukio; Akazawa, Tsuneya; Oda, Kunio

Hyperspectral image data provides a powerful tool for non-destructive crop analysis. This paper investigates a hyperspectral image data-processing method to predict the sweetness and amino acid content of soybean crops. Regression models based on artificial neural networks were developed in order to calculate the level of sucrose, glucose, fructose, and nitrogen concentrations, which can be related to the sweetness and amino acid content of vegetables. A performance analysis was conducted comparing regression models obtained using different preprocessing methods, namely, raw reflectance, second derivative, and principal components analysis. This method is demonstrated using high-resolution hyperspectral data of wavelengths ranging from the visible to the near infrared acquired from an experimental field of green vegetable soybeans. The best predictions were achieved using a nonlinear regression model of the second derivative transformed dataset. Glucose could be predicted with greater accuracy, followed by sucrose, fructose and nitrogen. The proposed method provides the possibility to provide relatively accurate maps predicting the chemical content of soybean crop fields.

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.

To Leave or Not to Leave? A Regression Discontinuity Analysis of the Impact of Failing High School Exit Exam. CEE DP 107

ERIC Educational Resources Information Center

Ou, Dongshu

2009-01-01

This paper presents new empirical evidence on whether failing the high school exit exam increases the chance of exiting from high school "prior to high school completion". More importantly, the author discusses the potentially different impacts of failing the High School Exit Exams (HSEE) on students with limited English proficiency,…

Monitoring heavy metal Cr in soil based on hyperspectral data using regression analysis

NASA Astrophysics Data System (ADS)

Zhang, Ningyu; Xu, Fuyun; Zhuang, Shidong; He, Changwei

2016-10-01

Heavy metal pollution in soils is one of the most critical problems in the global ecology and environment safety nowadays. Hyperspectral remote sensing and its application is capable of high speed, low cost, less risk and less damage, and provides a good method for detecting heavy metals in soil. This paper proposed a new idea of applying regression analysis of stepwise multiple regression between the spectral data and monitoring the amount of heavy metal Cr by sample points in soil for environmental protection. In the measurement, a FieldSpec HandHeld spectroradiometer is used to collect reflectance spectra of sample points over the wavelength range of 325-1075 nm. Then the spectral data measured by the spectroradiometer is preprocessed to reduced the influence of the external factors, and the preprocessed methods include first-order differential equation, second-order differential equation and continuum removal method. The algorithms of stepwise multiple regression are established accordingly, and the accuracy of each equation is tested. The results showed that the accuracy of first-order differential equation works best, which makes it feasible to predict the content of heavy metal Cr by using stepwise multiple regression.

Identification of extremely premature infants at high risk of rehospitalization.

PubMed

Ambalavanan, Namasivayam; Carlo, Waldemar A; McDonald, Scott A; Yao, Qing; Das, Abhik; Higgins, Rosemary D

2011-11-01

Extremely low birth weight infants often require rehospitalization during infancy. Our objective was to identify at the time of discharge which extremely low birth weight infants are at higher risk for rehospitalization. Data from extremely low birth weight infants in Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network centers from 2002-2005 were analyzed. The primary outcome was rehospitalization by the 18- to 22-month follow-up, and secondary outcome was rehospitalization for respiratory causes in the first year. Using variables and odds ratios identified by stepwise logistic regression, scoring systems were developed with scores proportional to odds ratios. Classification and regression-tree analysis was performed by recursive partitioning and automatic selection of optimal cutoff points of variables. A total of 3787 infants were evaluated (mean ± SD birth weight: 787 ± 136 g; gestational age: 26 ± 2 weeks; 48% male, 42% black). Forty-five percent of the infants were rehospitalized by 18 to 22 months; 14.7% were rehospitalized for respiratory causes in the first year. Both regression models (area under the curve: 0.63) and classification and regression-tree models (mean misclassification rate: 40%-42%) were moderately accurate. Predictors for the primary outcome by regression were shunt surgery for hydrocephalus, hospital stay of >120 days for pulmonary reasons, necrotizing enterocolitis stage II or higher or spontaneous gastrointestinal perforation, higher fraction of inspired oxygen at 36 weeks, and male gender. By classification and regression-tree analysis, infants with hospital stays of >120 days for pulmonary reasons had a 66% rehospitalization rate compared with 42% without such a stay. The scoring systems and classification and regression-tree analysis models identified infants at higher risk of rehospitalization and might assist planning for care after discharge.

Identification of Extremely Premature Infants at High Risk of Rehospitalization

PubMed Central

Carlo, Waldemar A.; McDonald, Scott A.; Yao, Qing; Das, Abhik; Higgins, Rosemary D.

2011-01-01

OBJECTIVE: Extremely low birth weight infants often require rehospitalization during infancy. Our objective was to identify at the time of discharge which extremely low birth weight infants are at higher risk for rehospitalization. METHODS: Data from extremely low birth weight infants in Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network centers from 2002–2005 were analyzed. The primary outcome was rehospitalization by the 18- to 22-month follow-up, and secondary outcome was rehospitalization for respiratory causes in the first year. Using variables and odds ratios identified by stepwise logistic regression, scoring systems were developed with scores proportional to odds ratios. Classification and regression-tree analysis was performed by recursive partitioning and automatic selection of optimal cutoff points of variables. RESULTS: A total of 3787 infants were evaluated (mean ± SD birth weight: 787 ± 136 g; gestational age: 26 ± 2 weeks; 48% male, 42% black). Forty-five percent of the infants were rehospitalized by 18 to 22 months; 14.7% were rehospitalized for respiratory causes in the first year. Both regression models (area under the curve: 0.63) and classification and regression-tree models (mean misclassification rate: 40%–42%) were moderately accurate. Predictors for the primary outcome by regression were shunt surgery for hydrocephalus, hospital stay of >120 days for pulmonary reasons, necrotizing enterocolitis stage II or higher or spontaneous gastrointestinal perforation, higher fraction of inspired oxygen at 36 weeks, and male gender. By classification and regression-tree analysis, infants with hospital stays of >120 days for pulmonary reasons had a 66% rehospitalization rate compared with 42% without such a stay. CONCLUSIONS: The scoring systems and classification and regression-tree analysis models identified infants at higher risk of rehospitalization and might assist planning for care after discharge. PMID:22007016

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.

High- and Low-Achieving Fraternity Environments at a Selective Institution: Their Influence on Members' Binge Drinking and GPA

ERIC Educational Resources Information Center

Maholchic-Nelson, Suzy

2010-01-01

This correlational study tested the efficacy of the social-ecological theory (Moos, 1979) by employing the University Residential Environmental Scale and multiple regression analysis to examine the influences of personal attributes (SAT, parents' level of education, race/ethnicity, and high school drinking) and environmental factors (high/low…

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.

Job Satisfaction of High School Journalism Educators.

ERIC Educational Resources Information Center

Dvorak, Jack; Phillips, Kay D.

Four research questions are posed to explore the job satisfaction of high school journalism educators. A national random sample of 669 respondents shows that journalism educators are generally satisfied with their jobs--more so than teachers in other disciplines. Multiple regression analysis using Herzberg's motivation-hygiene theory as a…

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…

Demonstration of a Fiber Optic Regression Probe in a High-Temperature Flow

NASA Technical Reports Server (NTRS)

Korman, Valentin; Polzin, Kurt

2011-01-01

The capability to provide localized, real-time monitoring of material regression rates in various applications has the potential to provide a new stream of data for development testing of various components and systems, as well as serving as a monitoring tool in flight applications. These applications include, but are not limited to, the regression of a combusting solid fuel surface, the ablation of the throat in a chemical rocket or the heat shield of an aeroshell, and the monitoring of erosion in long-life plasma thrusters. The rate of regression in the first application is very fast, while the second and third are increasingly slower. A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor is optical, using two different, co-located fiber-optics to perform the regression measurement. The disparate optical transmission properties of the two fiber-optics makes it possible to measure the regression rate by monitoring the relative light attenuation through the fibers. As the fibers regress along with the parent material in which they are embedded, the relative light intensities through the two fibers changes, providing a measure of the regression rate. The optical nature of the system makes it relatively easy to use in a variety of harsh, high temperature environments, and it is also unaffected by the presence of electric and magnetic fields. In addition, the sensor could be used to perform optical spectroscopy on the light emitted by a process and collected by fibers, giving localized measurements of various properties. The capability to perform an in-situ measurement of material regression rates is useful in addressing a variety of physical issues in various applications. An in-situ measurement allows for real-time data regarding the erosion rates, providing a quick method for empirically anchoring any analysis geared towards lifetime qualification. Erosion rate data over an operating envelope could also be useful in the modeling detailed physical processes. The sensor has been embedded in many regressing media to demonstrate the capabilities in a number of regressing environments. In the present work, sensors were installed in the eroding/regressing throat region of a converging-diverging flow, with the working gas heated to high temperatures by means of a high-pressure arc discharge at steady-state discharge power levels up to 500 kW. The amount of regression observed in each material sample was quantified using a later profilometer, which was compared to the in-situ erosion measurements to demonstrate the efficacy of the measurement technique in very harsh, high-temperature environments.

Spatial regression analysis on 32 years of total column ozone data

NASA Astrophysics Data System (ADS)

Knibbe, J. S.; van der A, R. J.; de Laat, A. T. J.

2014-08-01

Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979-2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) ozone data (2009-2010). The two-dimensionality in this data set allows us to perform the regressions locally and investigate spatial patterns of regression coefficients and their explanatory power. Seasonal dependencies of ozone on regressors are included in the analysis. A new physically oriented model is developed to parameterize stratospheric ozone. Ozone variations on nonseasonal timescales are parameterized by explanatory variables describing the solar cycle, stratospheric aerosols, the quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO) and stratospheric alternative halogens which are parameterized by the effective equivalent stratospheric chlorine (EESC). For several explanatory variables, seasonally adjusted versions of these explanatory variables are constructed to account for the difference in their effect on ozone throughout the year. To account for seasonal variation in ozone, explanatory variables describing the polar vortex, geopotential height, potential vorticity and average day length are included. Results of this regression model are compared to that of a similar analysis based on a more commonly applied statistically oriented model. The physically oriented model provides spatial patterns in the regression results for each explanatory variable. The EESC has a significant depleting effect on ozone at mid- and high latitudes, the solar cycle affects ozone positively mostly in the Southern Hemisphere, stratospheric aerosols affect ozone negatively at high northern latitudes, the effect of QBO is positive and negative in the tropics and mid- to high latitudes, respectively, and ENSO affects ozone negatively between 30° N and 30° S, particularly over the Pacific. The contribution of explanatory variables describing seasonal ozone variation is generally large at mid- to high latitudes. We observe ozone increases with potential vorticity and day length and ozone decreases with geopotential height and variable ozone effects due to the polar vortex in regions to the north and south of the polar vortices. Recovery of ozone is identified globally. However, recovery rates and uncertainties strongly depend on choices that can be made in defining the explanatory variables. The application of several trend models, each with their own pros and cons, yields a large range of recovery rate estimates. Overall these results suggest that care has to be taken in determining ozone recovery rates, in particular for the Antarctic ozone hole.

Forecasting models for sugi (Cryptomeria japonica D. Don) pollen count showing an alternate dispersal rhythm.

PubMed

Ito, Yukiko; Hattori, Reiko; Mase, Hiroki; Watanabe, Masako; Shiotani, Itaru

2008-12-01

Pollen information is indispensable for allergic individuals and clinicians. This study aimed to develop forecasting models for the total annual count of airborne pollen grains based on data monitored over the last 20 years at the Mie Chuo Medical Center, Tsu, Mie, Japan. Airborne pollen grains were collected using a Durham sampler. Total annual pollen count and pollen count from October to December (OD pollen count) of the previous year were transformed to logarithms. Regression analysis of the total pollen count was performed using variables such as the OD pollen count and the maximum temperature for mid-July of the previous year. Time series analysis revealed an alternate rhythm of the series of total pollen count. The alternate rhythm consisted of a cyclic alternation of an "on" year (high pollen count) and an "off" year (low pollen count). This rhythm was used as a dummy variable in regression equations. Of the three models involving the OD pollen count, a multiple regression equation that included the alternate rhythm variable and the interaction of this rhythm with OD pollen count showed a high coefficient of determination (0.844). Of the three models involving the maximum temperature for mid-July, those including the alternate rhythm variable and the interaction of this rhythm with maximum temperature had the highest coefficient of determination (0.925). An alternate pollen dispersal rhythm represented by a dummy variable in the multiple regression analysis plays a key role in improving forecasting models for the total annual sugi pollen count.

The Alcohol Dehydrogenase Kinetics Laboratory: Enhanced Data Analysis and Student-Designed Mini-Projects

ERIC Educational Resources Information Center

Silverstein, Todd P.

2016-01-01

A highly instructive, wide-ranging laboratory project in which students study the effects of various parameters on the enzymatic activity of alcohol dehydrogenase has been adapted for the upper-division biochemistry and physical biochemistry laboratory. Our two main goals were to provide enhanced data analysis, featuring nonlinear regression, and…

New insights into old methods for identifying causal rare variants.

PubMed

Wang, Haitian; Huang, Chien-Hsun; Lo, Shaw-Hwa; Zheng, Tian; Hu, Inchi

2011-11-29

The advance of high-throughput next-generation sequencing technology makes possible the analysis of rare variants. However, the investigation of rare variants in unrelated-individuals data sets faces the challenge of low power, and most methods circumvent the difficulty by using various collapsing procedures based on genes, pathways, or gene clusters. We suggest a new way to identify causal rare variants using the F-statistic and sliced inverse regression. The procedure is tested on the data set provided by the Genetic Analysis Workshop 17 (GAW17). After preliminary data reduction, we ranked markers according to their F-statistic values. Top-ranked markers were then subjected to sliced inverse regression, and those with higher absolute coefficients in the most significant sliced inverse regression direction were selected. The procedure yields good false discovery rates for the GAW17 data and thus is a promising method for future study on rare variants.

Analyses of polycyclic aromatic hydrocarbon (PAH) and chiral-PAH analogues-methyl-β-cyclodextrin guest-host inclusion complexes by fluorescence spectrophotometry and multivariate regression analysis.

PubMed

Greene, LaVana; Elzey, Brianda; Franklin, Mariah; Fakayode, Sayo O

2017-03-05

The negative health impact of polycyclic aromatic hydrocarbons (PAHs) and differences in pharmacological activity of enantiomers of chiral molecules in humans highlights the need for analysis of PAHs and their chiral analogue molecules in humans. Herein, the first use of cyclodextrin guest-host inclusion complexation, fluorescence spectrophotometry, and chemometric approach to PAH (anthracene) and chiral-PAH analogue derivatives (1-(9-anthryl)-2,2,2-triflouroethanol (TFE)) analyses are reported. The binding constants (K b ), stoichiometry (n), and thermodynamic properties (Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS)) of anthracene and enantiomers of TFE-methyl-β-cyclodextrin (Me-β-CD) guest-host complexes were also determined. Chemometric partial-least-square (PLS) regression analysis of emission spectra data of Me-β-CD-guest-host inclusion complexes was used for the determination of anthracene and TFE enantiomer concentrations in Me-β-CD-guest-host inclusion complex samples. The values of calculated K b and negative ΔG suggest the thermodynamic favorability of anthracene-Me-β-CD and enantiomeric of TFE-Me-β-CD inclusion complexation reactions. However, anthracene-Me-β-CD and enantiomer TFE-Me-β-CD inclusion complexations showed notable differences in the binding affinity behaviors and thermodynamic properties. The PLS regression analysis resulted in square-correlation-coefficients of 0.997530 or better and a low LOD of 3.81×10 -7 M for anthracene and 3.48×10 -8 M for TFE enantiomers at physiological conditions. Most importantly, PLS regression accurately determined the anthracene and TFE enantiomer concentrations with an average low error of 2.31% for anthracene, 4.44% for R-TFE and 3.60% for S-TFE. The results of the study are highly significant because of its high sensitivity and accuracy for analysis of PAH and chiral PAH analogue derivatives without the need of an expensive chiral column, enantiomeric resolution, or use of a polarized light. Published by Elsevier B.V.

Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study.

PubMed

Lee, Poh Foong; Kan, Donica Pei Xin; Croarkin, Paul; Phang, Cheng Kar; Doruk, Deniz

2018-01-01

There is an unmet need for practical and reliable biomarkers for mood disorders in young adults. Identifying the brain activity associated with the early signs of depressive disorders could have important diagnostic and therapeutic implications. In this study we sought to investigate the EEG characteristics in young adults with newly identified depressive symptoms. Based on the initial screening, a total of 100 participants (n = 50 euthymic, n = 50 depressive) underwent 32-channel EEG acquisition. Simple logistic regression and C-statistic were used to explore if EEG power could be used to discriminate between the groups. The strongest EEG predictors of mood using multivariate logistic regression models. Simple logistic regression analysis with subsequent C-statistics revealed that only high-alpha and beta power originating from the left central cortex (C3) have a reliable discriminative value (ROC curve >0.7 (70%)) for differentiating the depressive group from the euthymic group. Multivariate regression analysis showed that the single most significant predictor of group (depressive vs. euthymic) is the high-alpha power over C3 (p = 0.03). The present findings suggest that EEG is a useful tool in the identification of neurophysiological correlates of depressive symptoms in young adults with no previous psychiatric history. Our results could guide future studies investigating the early neurophysiological changes and surrogate outcomes in depression. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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…

Serum S100B Is Related to Illness Duration and Clinical Symptoms in Schizophrenia—A Meta-Regression Analysis

PubMed Central

Schümberg, Katharina; Polyakova, Maryna; Steiner, Johann; Schroeter, Matthias L.

2016-01-01

S100B has been linked to glial pathology in several psychiatric disorders. Previous studies found higher S100B serum levels in patients with schizophrenia compared to healthy controls, and a number of covariates influencing the size of this effect have been proposed in the literature. Here, we conducted a meta-analysis and meta-regression analysis on alterations of serum S100B in schizophrenia in comparison with healthy control subjects. The meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to guarantee a high quality and reproducibility. With strict inclusion criteria 19 original studies could be included in the quantitative meta-analysis, comprising a total of 766 patients and 607 healthy control subjects. The meta-analysis confirmed higher values of the glial serum marker S100B in schizophrenia if compared with control subjects. Meta-regression analyses revealed significant effects of illness duration and clinical symptomatology, in particular the total score of the Positive and Negative Syndrome Scale (PANSS), on serum S100B levels in schizophrenia. In sum, results confirm glial pathology in schizophrenia that is modulated by illness duration and related to clinical symptomatology. Further studies are needed to investigate mechanisms and mediating factors related to these findings. PMID:26941608

High depressive symptomatology among older community-dwelling Mexican Americans: the impact of immigration.

PubMed

Gerst, Kerstin; Al-Ghatrif, Majd; Beard, Holly A; Samper-Ternent, Rafael; Markides, Kyriakos S

2010-04-01

This analysis explores nativity differences in depressive symptoms among very old (75+) community-dwelling Mexican Americans. Cross-sectional analysis using the fifth wave (2004-2005) of the Hispanic Established Population for the Epidemiological Study of the Elderly (Hispanic EPESE). The sample consisted of 1699 non-institutionalized Mexican American men and women aged 75 years and above. Depressive symptoms were measured by the Center for Epidemiological Studies Depression Scale (CES-D). Logistic regression was used to predict high depressive symptoms (CES-D score 16 or higher) and multinomial logistic regression was used to predict sub-threshold, moderate, and high depressive symptoms. Results showed that elders born in Mexico had higher odds of more depressive symptoms compared to otherwise similar Mexican Americans born in the US. Age of arrival, gender, and other covariates did not modify that risk. The findings suggest that older Mexican American immigrants are at higher risk of depressive symptomatology compared to persons born in the US, which has significant implications for research, policy, and clinical practice.

Higher-Than-Conventional Radiation Doses in Localized Prostate Cancer Treatment: A Meta-analysis of Randomized, Controlled Trials

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

Viani, Gustavo Arruda; Stefano, Eduardo Jose; Afonso, Sergio Luis

2009-08-01

Purpose: To determine in a meta-analysis whether the outcomes in men with localized prostate cancer treated with high-dose radiotherapy (HDRT) are better than those in men treated with conventional-dose radiotherapy (CDRT), by quantifying the effect of the total dose of radiotherapy on biochemical control (BC). Methods and Materials: The MEDLINE, EMBASE, CANCERLIT, and Cochrane Library databases, as well as the proceedings of annual meetings, were systematically searched to identify randomized, controlled studies comparing HDRT with CDRT for localized prostate cancer. To evaluate the dose-response relationship, we conducted a meta-regression analysis of BC ratios by means of weighted linear regression. Results:more » Seven RCTs with a total patient population of 2812 were identified that met the study criteria. Pooled results from these RCTs showed a significant reduction in the incidence of biochemical failure in those patients with prostate cancer treated with HDRT (p < 0.0001). However, there was no difference in the mortality rate (p = 0.38) and specific prostate cancer mortality rates (p = 0.45) between the groups receiving HDRT and CDRT. However, there were more cases of late Grade >2 gastrointestinal toxicity after HDRT than after CDRT. In the subgroup analysis, patients classified as being at low (p = 0.007), intermediate (p < 0.0001), and high risk (p < 0.0001) of biochemical failure all showed a benefit from HDRT. The meta-regression analysis also detected a linear correlation between the total dose of radiotherapy and biochemical failure (BC = -67.3 + [1.8 x radiotherapy total dose in Gy]; p = 0.04). Conclusions: Our meta-analysis showed that HDRT is superior to CDRT in preventing biochemical failure in low-, intermediate-, and high-risk prostate cancer patients, suggesting that this should be offered as a treatment for all patients, regardless of their risk status.« less

Independent Prognostic Factors for Acute Organophosphorus Pesticide Poisoning.

PubMed

Tang, Weidong; Ruan, Feng; Chen, Qi; Chen, Suping; Shao, Xuebo; Gao, Jianbo; Zhang, Mao

2016-07-01

Acute organophosphorus pesticide poisoning (AOPP) is becoming a significant problem and a potential cause of human mortality because of the abuse of organophosphate compounds. This study aims to determine the independent prognostic factors of AOPP by using multivariate logistic regression analysis. The clinical data for 71 subjects with AOPP admitted to our hospital were retrospectively analyzed. This information included the Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, admission blood cholinesterase levels, 6-h post-admission blood cholinesterase levels, cholinesterase activity, blood pH, and other factors. Univariate analysis and multivariate logistic regression analyses were conducted to identify all prognostic factors and independent prognostic factors, respectively. A receiver operating characteristic curve was plotted to analyze the testing power of independent prognostic factors. Twelve of 71 subjects died. Admission blood lactate levels, 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, blood pH, and APACHE II scores were identified as prognostic factors for AOPP according to the univariate analysis, whereas only 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, and blood pH were independent prognostic factors identified by multivariate logistic regression analysis. The receiver operating characteristic analysis suggested that post-admission 6-h lactate clearance rates were of moderate diagnostic value. High 6-h post-admission blood lactate levels, low blood pH, and low post-admission 6-h lactate clearance rates were independent prognostic factors identified by multivariate logistic regression analysis. Copyright © 2016 by Daedalus Enterprises.

Continuous water-quality monitoring and regression analysis to estimate constituent concentrations and loads in the Sheyenne River, North Dakota, 1980-2006

USGS Publications Warehouse

Ryberg, Karen R.

2007-01-01

This report presents the results of a study by the U.S. Geological Survey, done in cooperation with the North Dakota State Water Commission, to estimate water-quality constituent concentrations at seven sites on the Sheyenne River, N. Dak. Regression analysis of water-quality data collected in 1980-2006 was used to estimate concentrations for hardness, dissolved solids, calcium, magnesium, sodium, and sulfate. The explanatory variables examined for the regression relations were continuously monitored streamflow, specific conductance, and water temperature. For the conditions observed in 1980-2006, streamflow was a significant explanatory variable for some constituents. Specific conductance was a significant explanatory variable for all of the constituents, and water temperature was not a statistically significant explanatory variable for any of the constituents in this study. The regression relations were evaluated using common measures of variability, including R2, the proportion of variability in the estimated constituent concentration explained by the explanatory variables and regression equation. R2 values ranged from 0.784 for calcium to 0.997 for dissolved solids. The regression relations also were evaluated by calculating the median relative percentage difference (RPD) between measured constituent concentration and the constituent concentration estimated by the regression equations. Median RPDs ranged from 1.7 for dissolved solids to 11.5 for sulfate. The regression relations also may be used to estimate daily constituent loads. The relations should be monitored for change over time, especially at sites 2 and 3 which have a short period of record. In addition, caution should be used when the Sheyenne River is affected by ice or when upstream sites are affected by isolated storm runoff. Almost all of the outliers and highly influential samples removed from the analysis were made during periods when the Sheyenne River might be affected by ice.

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

PubMed Central

Libiger, Ondrej; Schork, Nicholas J.

2015-01-01

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

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®

Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy

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

Dean, Jamie A., E-mail: jamie.dean@icr.ac.uk; Wong, Kee H.; Gay, Hiram

Purpose: Current normal tissue complication probability modeling using logistic regression suffers from bias and high uncertainty in the presence of highly correlated radiation therapy (RT) dose data. This hinders robust estimates of dose-response associations and, hence, optimal normal tissue–sparing strategies from being elucidated. Using functional data analysis (FDA) to reduce the dimensionality of the dose data could overcome this limitation. Methods and Materials: FDA was applied to modeling of severe acute mucositis and dysphagia resulting from head and neck RT. Functional partial least squares regression (FPLS) and functional principal component analysis were used for dimensionality reduction of the dose-volume histogrammore » data. The reduced dose data were input into functional logistic regression models (functional partial least squares–logistic regression [FPLS-LR] and functional principal component–logistic regression [FPC-LR]) along with clinical data. This approach was compared with penalized logistic regression (PLR) in terms of predictive performance and the significance of treatment covariate–response associations, assessed using bootstrapping. Results: The area under the receiver operating characteristic curve for the PLR, FPC-LR, and FPLS-LR models was 0.65, 0.69, and 0.67, respectively, for mucositis (internal validation) and 0.81, 0.83, and 0.83, respectively, for dysphagia (external validation). The calibration slopes/intercepts for the PLR, FPC-LR, and FPLS-LR models were 1.6/−0.67, 0.45/0.47, and 0.40/0.49, respectively, for mucositis (internal validation) and 2.5/−0.96, 0.79/−0.04, and 0.79/0.00, respectively, for dysphagia (external validation). The bootstrapped odds ratios indicated significant associations between RT dose and severe toxicity in the mucositis and dysphagia FDA models. Cisplatin was significantly associated with severe dysphagia in the FDA models. None of the covariates was significantly associated with severe toxicity in the PLR models. Dose levels greater than approximately 1.0 Gy/fraction were most strongly associated with severe acute mucositis and dysphagia in the FDA models. Conclusions: FPLS and functional principal component analysis marginally improved predictive performance compared with PLR and provided robust dose-response associations. FDA is recommended for use in normal tissue complication probability modeling.« less

Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy.

PubMed

Dean, Jamie A; Wong, Kee H; Gay, Hiram; Welsh, Liam C; Jones, Ann-Britt; Schick, Ulrike; Oh, Jung Hun; Apte, Aditya; Newbold, Kate L; Bhide, Shreerang A; Harrington, Kevin J; Deasy, Joseph O; Nutting, Christopher M; Gulliford, Sarah L

2016-11-15

Current normal tissue complication probability modeling using logistic regression suffers from bias and high uncertainty in the presence of highly correlated radiation therapy (RT) dose data. This hinders robust estimates of dose-response associations and, hence, optimal normal tissue-sparing strategies from being elucidated. Using functional data analysis (FDA) to reduce the dimensionality of the dose data could overcome this limitation. FDA was applied to modeling of severe acute mucositis and dysphagia resulting from head and neck RT. Functional partial least squares regression (FPLS) and functional principal component analysis were used for dimensionality reduction of the dose-volume histogram data. The reduced dose data were input into functional logistic regression models (functional partial least squares-logistic regression [FPLS-LR] and functional principal component-logistic regression [FPC-LR]) along with clinical data. This approach was compared with penalized logistic regression (PLR) in terms of predictive performance and the significance of treatment covariate-response associations, assessed using bootstrapping. The area under the receiver operating characteristic curve for the PLR, FPC-LR, and FPLS-LR models was 0.65, 0.69, and 0.67, respectively, for mucositis (internal validation) and 0.81, 0.83, and 0.83, respectively, for dysphagia (external validation). The calibration slopes/intercepts for the PLR, FPC-LR, and FPLS-LR models were 1.6/-0.67, 0.45/0.47, and 0.40/0.49, respectively, for mucositis (internal validation) and 2.5/-0.96, 0.79/-0.04, and 0.79/0.00, respectively, for dysphagia (external validation). The bootstrapped odds ratios indicated significant associations between RT dose and severe toxicity in the mucositis and dysphagia FDA models. Cisplatin was significantly associated with severe dysphagia in the FDA models. None of the covariates was significantly associated with severe toxicity in the PLR models. Dose levels greater than approximately 1.0 Gy/fraction were most strongly associated with severe acute mucositis and dysphagia in the FDA models. FPLS and functional principal component analysis marginally improved predictive performance compared with PLR and provided robust dose-response associations. FDA is recommended for use in normal tissue complication probability modeling. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Factors Associated with Participation in Employment for High School Leavers with Autism

ERIC Educational Resources Information Center

Chiang, Hsu-Min; Cheung, Ying Kuen; Li, Huacheng; Tsai, Luke Y.

2013-01-01

This study aimed to identify the factors associated with participation in employment for high school leavers with autism. A secondary data analysis of the National Longitudinal Transition Study 2 (NLTS2) data was performed. Potential factors were assessed using a weighted multivariate logistic regression. This study found that annual household…

Predictors of Music Sight-Reading Ability in High School Wind Players

ERIC Educational Resources Information Center

Gromko, Joyce Eastlund

2004-01-01

The purpose of this study, grounded in near-transfer theory, was to investigate relationships among music sight-reading and tonal and rhythmic audiation, visual field articulation, spatial orientation and visualization, and achievement in math concepts and reading comprehension. A regression analysis with data from four high schools (N = 98) in…

Factors Affecting University Entrants' Performance in High-Stakes Tests: A Multiple Regression Analysis

ERIC Educational Resources Information Center

Uy, Chin; Manalo, Ronaldo A.; Cabauatan, Ronaldo R.

2015-01-01

In the Philippines, students seeking admission to a university are usually required to meet certain entrance requirements, including passing the entrance examinations with questions on IQ and English, mathematics, and science. This paper aims to determine the factors that affect the performance of entrants into business programmes in high-stakes…

Prevalence of hepatitis C virus infection among high-risk groups in Iran: a systematic review and meta-analysis.

PubMed

Nematollahi, S; Ayubi, E; Almasi-Hashiani, A; Mansori, K; Moradi, Y; Veisani, Y; Jenabi, E; Gholamaliei, B; Khazaei, S

2018-06-20

Determination of the true burden of hepatitis C virus (HCV) infection among high-risk groups relies heavily on occurrence measures such as prevalence, which are vital for implementation of preventive action plans. Nevertheless, up-to-date data on the prevalence of HCV infection remain scarce in Iran. This study aimed to review the relevant literature systematically and determine the pooled prevalence of HCV infection among high-risk groups in Iran. Systematic review & meta-analysis. In 2016, electronic scientific databases including PubMed, Scopus, Web of Science and local databases were searched using a detailed search strategy with language restricted to English and Farsi. The reference lists of the studies included in this review were also screened. Data were reviewed and extracted independently by two authors. A random effects model was used to estimate the pooled prevalence. Sources of heterogeneity among the studies were determined using subgroup analysis and meta-regression. In total, 1817 records were identified in the initial search, and 46 records were included in the meta-analysis. The overall prevalence of HCV among high-risk groups was 32.3%. The prevalence was 41.3% in injection drug users (IDUs), 22.9% in prisoners, 16.2% in drug-dependent individuals and 24.6% in drug-dependent prisoners. Subgroup and meta-regression analyses revealed that geographical location and year of publication were the probable sources of heterogeneity. This meta-analysis found a high prevalence of HCV among high-risk groups in Iran, particularly among IDUs. There is a need for prevention strategies to reduce the burden of HCV infection among high-risk groups, particularly IDUs. Copyright © 2018 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Prevalence of difficult venous access and associated risk factors in highly complex hospitalised patients.

PubMed

Armenteros-Yeguas, Victoria; Gárate-Echenique, Lucía; Tomás-López, Maria Aranzazu; Cristóbal-Domínguez, Estíbaliz; Moreno-de Gusmão, Breno; Miranda-Serrano, Erika; Moraza-Dulanto, Maria Inmaculada

2017-12-01

To estimate the prevalence of difficult venous access in complex patients with multimorbidity and to identify associated risk factors. In highly complex patients, factors like ageing, the need for frequent use of irritant medication and multiple venous catheterisations to complete treatment could contribute to exhaustion of venous access. A cross-sectional study was conducted. 'Highly complex' patients (n = 135) were recruited from March 2013-November 2013. The main study variable was the prevalence of difficult venous access, assessed using one of the following criteria: (1) a history of difficulties obtaining venous access based on more than two attempts to insert an intravenous line and (2) no visible or palpable veins. Other factors potentially associated with the risk of difficult access were also measured (age, gender and chronic illnesses). Univariate analysis was performed for each potential risk factor. Factors with p < 0·2 were then included in multivariable logistic regression analysis. Odds ratios were also calculated. The prevalence of difficult venous access was 59·3%. The univariate logistic regression analysis indicated that gender, a history of vascular access complications and osteoarticular disease were significantly associated with difficult venous access. The multivariable logistic regression showed that only gender was an independent risk factor and the odds ratios was 2·85. The prevalence of difficult venous access is high in this population. Gender (female) is the only independent risk factor associated with this. Previous history of several attempts at catheter insertion is an important criterion in the assessment of difficult venous access. The prevalence of difficult venous access in complex patients is 59·3%. Significant risk factors include being female and a history of complications related to vascular access. © 2017 John Wiley & Sons Ltd.

The effectiveness of drinking and driving policies for different alcohol-related fatalities: a quantile regression analysis.

PubMed

Ying, Yung-Hsiang; Wu, Chin-Chih; Chang, Koyin

2013-09-27

To understand the impact of drinking and driving laws on drinking and driving fatality rates, this study explored the different effects these laws have on areas with varying severity rates for drinking and driving. Unlike previous studies, this study employed quantile regression analysis. Empirical results showed that policies based on local conditions must be used to effectively reduce drinking and driving fatality rates; that is, different measures should be adopted to target the specific conditions in various regions. For areas with low fatality rates (low quantiles), people's habits and attitudes toward alcohol should be emphasized instead of transportation safety laws because "preemptive regulations" are more effective. For areas with high fatality rates (or high quantiles), "ex-post regulations" are more effective, and impact these areas approximately 0.01% to 0.05% more than they do areas with low fatality rates.

The Effectiveness of Drinking and Driving Policies for Different Alcohol-Related Fatalities: A Quantile Regression Analysis

PubMed Central

Ying, Yung-Hsiang; Wu, Chin-Chih; Chang, Koyin

2013-01-01

To understand the impact of drinking and driving laws on drinking and driving fatality rates, this study explored the different effects these laws have on areas with varying severity rates for drinking and driving. Unlike previous studies, this study employed quantile regression analysis. Empirical results showed that policies based on local conditions must be used to effectively reduce drinking and driving fatality rates; that is, different measures should be adopted to target the specific conditions in various regions. For areas with low fatality rates (low quantiles), people’s habits and attitudes toward alcohol should be emphasized instead of transportation safety laws because “preemptive regulations” are more effective. For areas with high fatality rates (or high quantiles), “ex-post regulations” are more effective, and impact these areas approximately 0.01% to 0.05% more than they do areas with low fatality rates. PMID:24084673

High-level language ability in healthy individuals and its relationship with verbal working memory.

PubMed

Antonsson, Malin; Longoni, Francesca; Einald, Christina; Hallberg, Lina; Kurt, Gabriella; Larsson, Kajsa; Nilsson, Tina; Hartelius, Lena

2016-01-01

The aims of the study were to investigate healthy subjects' performance on a clinical test of high-level language (HLL) and how it is related to demographic characteristics and verbal working memory (VWM). One hundred healthy subjects (20-79 years old) were assessed with the Swedish BeSS test (Laakso, Brunnegård, Hartelius, & Ahlsén, 2000) and two digit span tasks. Relationships between the demographic variables, VWM and BeSS were investigated both with bivariate correlations and multiple regression analysis. The results present the norms for BeSS. The correlations and multiple regression analysis show that demographic variables had limited influence on test performance. Measures of VWM were moderately related to total BeSS score and weakly to moderately correlated with five of the seven subtests. To conclude, education has an influence on the test as a whole but measures of VWM stood out as the most robust predictor of HLL.

A Regional Analysis of Non-Methane Hydrocarbons And Meteorology of The Rural Southeast United States

DTIC Science & Technology

1996-01-01

Zt is an ARIMA time series. This is a typical regression model , except that it allows for autocorrelation in the error term Z. In this work, an ARMA...data=folder; var residual; run; II Statistical output of 1992 regression model on 1993 ozone data ARIMA Procedure Maximum Likelihood Estimation Approx...at each of the sites, and to show the effect of synoptic meteorology on high ozone by examining NOAA daily weather maps and climatic data

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.

Construction of a pathological risk model of occult lymph node metastases for prognostication by semi-automated image analysis of tumor budding in early-stage oral squamous cell carcinoma

PubMed Central

Pedersen, Nicklas Juel; Jensen, David Hebbelstrup; Lelkaitis, Giedrius; Kiss, Katalin; Charabi, Birgitte; Specht, Lena; von Buchwald, Christian

2017-01-01

It is challenging to identify at diagnosis those patients with early oral squamous cell carcinoma (OSCC), who have a poor prognosis and those that have a high risk of harboring occult lymph node metastases. The aim of this study was to develop a standardized and objective digital scoring method to evaluate the predictive value of tumor budding. We developed a semi-automated image-analysis algorithm, Digital Tumor Bud Count (DTBC), to evaluate tumor budding. The algorithm was tested in 222 consecutive patients with early-stage OSCC and major endpoints were overall (OS) and progression free survival (PFS). We subsequently constructed and cross-validated a binary logistic regression model and evaluated its clinical utility by decision curve analysis. A high DTBC was an independent predictor of both poor OS and PFS in a multivariate Cox regression model. The logistic regression model was able to identify patients with occult lymph node metastases with an area under the curve (AUC) of 0.83 (95% CI: 0.78–0.89, P <0.001) and a 10-fold cross-validated AUC of 0.79. Compared to other known histopathological risk factors, the DTBC had a higher diagnostic accuracy. The proposed, novel risk model could be used as a guide to identify patients who would benefit from an up-front neck dissection. PMID:28212555

The increase in symptoms of anxiety and depressed mood among Icelandic adolescents: time trend between 2006 and 2016.

PubMed

Thorisdottir, Ingibjorg E; Asgeirsdottir, Bryndis B; Sigurvinsdottir, Rannveig; Allegrante, John P; Sigfusdottir, Inga D

2017-10-01

Both research and popular media reports suggest that adolescent mental health has been deteriorating across societies with advanced economies. This study sought to describe the trends in self-reported symptoms of depressed mood and anxiety among Icelandic adolescents. Data for this study come from repeated, cross-sectional, population-based school surveys of 43 482 Icelandic adolescents in 9th and 10th grade, with six waves of pooled data from 2006 to 2016. We used analysis of variance, linear regression and binomial logistic regression to examine trends in symptom scores of anxiety and depressed mood over time. Gender differences in trends of high symptoms were also tested for interactions. Linear regression analysis showed a significant linear increase over the course of the study period in mean symptoms of anxiety and depressed mood for girls only; however, symptoms of anxiety among boys decreased. The proportion of adolescents reporting high depressive symptoms increased by 1.6% for boys and 6.8% for girls; the proportion of those reporting high anxiety symptoms increased by 1.3% for boys and 8.6% for girls. Over the study period, the odds for reporting high depressive symptoms and high anxiety symptoms were significantly higher for both genders. Girls were more likely to report high symptoms of anxiety and depressed mood than boys. Self-reported symptoms of anxiety and depressed mood have increased over time among Icelandic adolescents. Our findings suggest that future research needs to look beyond mean changes and examine the trends among those adolescents who report high symptoms of emotional distress. © The Author 2017. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

Information and Communication Technology (ICT) Usage and Achievement of Turkish Students in Pisa 2006

ERIC Educational Resources Information Center

Aypay, Ahmet

2010-01-01

The purpose of this study is to examine the ICT usage and academic achievement of Turkish students in PISA 2006 data. The sample of the study included 4942 students from 160 schools. Frequencies, independent samples t-tests, ANOVAs, pearson correlation coefficients, exploratory factor analysis, and regression analysis were used. A high percentage…

A Model for Predicting Student Performance on High-Stakes Assessment

ERIC Educational Resources Information Center

Dammann, Matthew Walter

2010-01-01

This research study examined the use of student achievement on reading and math state assessments to predict success on the science state assessment. Multiple regression analysis was utilized to test the prediction for all students in grades 5 and 8 in a mid-Atlantic state. The prediction model developed from the analysis explored the combined…

Individual risk factors for deep infection and compromised fracture healing after intramedullary nailing of tibial shaft fractures: a single centre experience of 480 patients.

PubMed

Metsemakers, W-J; Handojo, K; Reynders, P; Sermon, A; Vanderschot, P; Nijs, S

2015-04-01

Despite modern advances in the treatment of tibial shaft fractures, complications including nonunion, malunion, and infection remain relatively frequent. A better understanding of these injuries and its complications could lead to prevention rather than treatment strategies. A retrospective study was performed to identify risk factors for deep infection and compromised fracture healing after intramedullary nailing (IMN) of tibial shaft fractures. Between January 2000 and January 2012, 480 consecutive patients with 486 tibial shaft fractures were enrolled in the study. Statistical analysis was performed to determine predictors of deep infection and compromised fracture healing. Compromised fracture healing was subdivided in delayed union and nonunion. The following independent variables were selected for analysis: age, sex, smoking, obesity, diabetes, American Society of Anaesthesiologists (ASA) classification, polytrauma, fracture type, open fractures, Gustilo type, primary external fixation (EF), time to nailing (TTN) and reaming. As primary statistical evaluation we performed a univariate analysis, followed by a multiple logistic regression model. Univariate regression analysis revealed similar risk factors for delayed union and nonunion, including fracture type, open fractures and Gustilo type. Factors affecting the occurrence of deep infection in this model were primary EF, a prolonged TTN, open fractures and Gustilo type. Multiple logistic regression analysis revealed polytrauma as the single risk factor for nonunion. With respect to delayed union, no risk factors could be identified. In the same statistical model, deep infection was correlated with primary EF. The purpose of this study was to evaluate risk factors of poor outcome after IMN of tibial shaft fractures. The univariate regression analysis showed that the nature of complications after tibial shaft nailing could be multifactorial. This was not confirmed in a multiple logistic regression model, which only revealed polytrauma and primary EF as risk factors for nonunion and deep infection, respectively. Future strategies should focus on prevention in high-risk populations such as polytrauma patients treated with EF. Copyright © 2014 Elsevier Ltd. All rights reserved.

Resting state fMRI reveals a default mode dissociation between retrosplenial and medial prefrontal subnetworks in ASD despite motion scrubbing.

PubMed

Starck, Tuomo; Nikkinen, Juha; Rahko, Jukka; Remes, Jukka; Hurtig, Tuula; Haapsamo, Helena; Jussila, Katja; Kuusikko-Gauffin, Sanna; Mattila, Marja-Leena; Jansson-Verkasalo, Eira; Pauls, David L; Ebeling, Hanna; Moilanen, Irma; Tervonen, Osmo; Kiviniemi, Vesa J

2013-01-01

In resting state functional magnetic resonance imaging (fMRI) studies of autism spectrum disorders (ASDs) decreased frontal-posterior functional connectivity is a persistent finding. However, the picture of the default mode network (DMN) hypoconnectivity remains incomplete. In addition, the functional connectivity analyses have been shown to be susceptible even to subtle motion. DMN hypoconnectivity in ASD has been specifically called for re-evaluation with stringent motion correction, which we aimed to conduct by so-called scrubbing. A rich set of default mode subnetworks can be obtained with high dimensional group independent component analysis (ICA) which can potentially provide more detailed view of the connectivity alterations. We compared the DMN connectivity in high-functioning adolescents with ASDs to typically developing controls using ICA dual-regression with decompositions from typical to high dimensionality. Dual-regression analysis within DMN subnetworks did not reveal alterations but connectivity between anterior and posterior DMN subnetworks was decreased in ASD. The results were very similar with and without motion scrubbing thus indicating the efficacy of the conventional motion correction methods combined with ICA dual-regression. Specific dissociation between DMN subnetworks was revealed on high ICA dimensionality, where networks centered at the medial prefrontal cortex and retrosplenial cortex showed weakened coupling in adolescents with ASDs compared to typically developing control participants. Generally the results speak for disruption in the anterior-posterior DMN interplay on the network level whereas local functional connectivity in DMN seems relatively unaltered.

Poor sleep quality predicts decreased cognitive function independently of chronic mountain sickness score in young soldiers with polycythemia stationed in Tibet.

PubMed

Kong, Fan-Yi; Li, Qiang; Liu, Shi-Xiang

2011-01-01

Little is known about the association between poor sleep and cognitive function in people with polycythemia at high altitude. The aim of this study was to survey the sleep quality of individuals with polycythemia at high altitude and determine its association with cognitive abilities. We surveyed 230 soldiers stationed in Tibet (all men; mean age 21-52±4.30 yr) at altitudes ranging from 3658 to 3996 m. All participants were given a blood tests for hemoglobin level and a questionnaire survey of cognitive function. Polycythemia was defined as excessive erythrocytosis (Hb≥21 g/dL in men or ≥19 g/dL in women). Poor sleepers were defined as having a global Pittsburgh Sleep Quality Index score (PSQI)>5. Cognitive abilities were determined by the Chinese revision of the Wechsler Adult Intelligence Scale and the Benton Visual Retention Test. Multiple linear regression analysis was used to determine the association between the PSQI and cognitive function. Logistic regression analysis was performed to determine the independent effect of sleep quality on cognitive function. The global PSQI score of enrolled participants was 8.14±3.79. Seventy-five (32.6%) soldiers were diagnosed with polycythemia. The proportion of poor sleepers was 1.45 times greater in those with polycythemia compared with those without polycythemia [95% (confidence interval) CI 1.82-2.56], and they had a statistically significant lower score for cognitive function. Multiple linear regression analysis showed that the global PSQI score was negatively associated with IQ (β=0.11, 95% CI -0.16 to -0.05) and digit symbol scores (β=0.66, 95% CI -0.86 to -0.44). Poor sleep quality was determined to be an independent predictor of impaired IQ [odds ratio (OR) 1.59, 95% CI 1.30-1.95] and digit symbol score (OR 1.18, 95% CI 1.07-1.31) in logistic regression analysis. The present study showed that for young soldiers with polycythemia at high altitude impaired subjective sleep quality was an independent predictor of decreased cognitive function, especially IQ and verbal short-term memory.

Adolescent religiosity and attitudes to HIV and AIDS in Ghana.

PubMed

Amoako-Agyeman, Kofi Nyame

2012-11-01

This study investigated the relationships between adolescent religiosity and attitudes to HIV/AIDS based on two major techniques of analysis, factor and regression analysis towards informing preventive school education strategies. Using cross-sectional data of 448 adolescents in junior high school, the study incorporated survey in a self-administered questionnaire and sought to identify underlying factors that affect pupils' responses, delineate the pattern of relationships between variables and select models which best explain and predict relationships among variables. A seven-factor solution described the 'attitude' construct including abstinence and protection, and six for 'religiosity'. The results showed relatively high levels of religiosity and a preference for private religiosity as opposed to organisational religiosity. The regression analysis produced significant relationships between factors of attitudes to HIV/AIDS and of religiosity. Adolescent with very high private religiosity are more likely to abstain from sex but less likely to use condoms once they initiate: protection is inversely related to religiosity. The findings suggest that religious-based adolescent interventions should focus on intrinsic religiosity. Additionally, increasing HIV prevention information and incorporating culturally relevant and socially acceptable values might lend support to improved adolescent school-based HIV/AIDS prevention programmes.

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)

Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model.

PubMed

Ma, Jing; Yu, Jiong; Hao, Guangshu; Wang, Dan; Sun, Yanni; Lu, Jianxin; Cao, Hongcui; Lin, Feiyan

2017-02-20

The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people. A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL). The back propagation artificial neural network (BP-ANN) was applied to develop prediction models of fs-TG and fs-TC. The results showed there was significant difference in biochemical indexes between healthy people and overweight people. The correlation analysis showed fs-TG was related to weight, height, blood glucose, and indexes of liver and kidney function; while fs-TC was correlated with age, indexes of liver function (P < 0.01). The MRL analysis indicated regression equations of fs-TG and fs-TC both had statistic significant (P < 0.01) when included independent indexes. The BP-ANN model of fs-TG reached training goal at 59 epoch, while fs-TC model achieved high prediction accuracy after training 1000 epoch. In conclusions, there was high relationship of fs-TG and fs-TC with weight, height, age, blood glucose, indexes of liver function and kidney function. Based on related variables, the indexes of fs-TG and fs-TC can be predicted by BP-ANN models in overweight people.

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.

Racial/ethnic and educational differences in the estimated odds of recent nitrite use among adult household residents in the United States: an illustration of matching and conditional logistic regression.

PubMed

Delva, J; Spencer, M S; Lin, J K

2000-01-01

This article compares estimates of the relative odds of nitrite use obtained from weighted unconditional logistic regression with estimates obtained from conditional logistic regression after post-stratification and matching of cases with controls by neighborhood of residence. We illustrate these methods by comparing the odds associated with nitrite use among adults of four racial/ethnic groups, with and without a high school education. We used aggregated data from the 1994-B through 1996 National Household Survey on Drug Abuse (NHSDA). Difference between the methods and implications for analysis and inference are discussed.

Self-reported work ability and work performance in workers with chronic nonspecific musculoskeletal pain.

PubMed

de Vries, Haitze J; Reneman, Michiel F; Groothoff, Johan W; Geertzen, Jan H B; Brouwer, Sandra

2013-03-01

To assess self-reported work ability and work performance of workers who stay at work despite chronic nonspecific musculoskeletal pain (CMP), and to explore which variables were associated with these outcomes. In a cross-sectional study we assessed work ability (Work Ability Index, single item scale 0-10) and work performance (Health and Work Performance Questionnaire, scale 0-10) among 119 workers who continued work while having CMP. Scores of work ability and work performance were categorized into excellent (10), good (9), moderate (8) and poor (0-7). Hierarchical multiple regression and logistic regression analysis was used to analyze the relation of socio-demographic, pain-related, personal- and work-related variables with work ability and work performance. Mean work ability and work performance were 7.1 and 7.7 (poor to moderate). Hierarchical multiple regression analysis revealed that higher work ability scores were associated with lower age, better general health perception, and higher pain self-efficacy beliefs (R(2) = 42 %). Higher work performance was associated with lower age, higher pain self-efficacy beliefs, lower physical work demand category and part-time work (R(2) = 37 %). Logistic regression analysis revealed that work ability ≥8 was significantly explained by age (OR = 0.90), general health perception (OR = 1.04) and pain self-efficacy (OR = 1.15). Work performance ≥8 was explained by pain self-efficacy (OR = 1.11). Many workers with CMP who stay at work report poor to moderate work ability and work performance. Our findings suggest that a subgroup of workers with CMP can stay at work with high work ability and performance, especially when they have high beliefs of pain self-efficacy. Our results further show that not the pain itself, but personal and work-related factors relate to work ability and work performance.

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

PubMed

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

2017-09-01

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

Physical activity and selected cardiovascular risk factors in middle-aged male personnel of self-defense forces.

PubMed

Sakuta, Hidenari; Suzuki, Takashi

2006-01-01

We cross-sectionally analyzed the association between duration of physical activity and the presence of selected cardiovascular risk factors in the middle-aged male personnel of the Self-Defense Forces who underwent retirement check-up (n = 974). In a univariate regression analysis, duration of high intensity physical activity but not that of moderate or low intensity physical activity inversely correlated with body mass index (BMI), triglyceride, fasting plasma glucose, white blood cell (WBC) count and systolic blood pressure. No intensity categories of physical activity correlated with total cholesterol. In a multivariate logistic regression analysis adjusted for lifestyle factors and the rank, the odds ratio per 1 h/wk increase in high intensity physical activity was .88 (95% confidence interval (CI) .80-.97; P=.007) for the presence of obesity (BMI 25.0 kg/m2), .88 (95% CI .81-.95; P = .002) for hypertrigly ceridemia, .87 (95% CI .76-.99; P=.034) for type 2 diabetes, and .90 (95% CI .82-.99; P=.037) for hypertension. Neither hypercholesterolemia nor high WBC count (> or = 6,900/microl) was associated with high intensity physical activity. High intensity physical activity inversely correlated with traditional cardiovascular risk factors in the middle-aged men.

External Tank Liquid Hydrogen (LH2) Prepress Regression Analysis Independent Review Technical Consultation Report

NASA Technical Reports Server (NTRS)

Parsons, Vickie s.

2009-01-01

The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.

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.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

The effects of designation and volume of neonatal care on mortality and morbidity outcomes of very preterm infants in England: retrospective population-based cohort study

PubMed Central

Watson, S I; Arulampalam, W; Petrou, S; Marlow, N; Morgan, A S; Draper, E S; Santhakumaran, S; Modi, N

2014-01-01

Objective To examine the effects of designation and volume of neonatal care at the hospital of birth on mortality and morbidity outcomes in very preterm infants in a managed clinical network setting. Design A retrospective, population-based analysis of operational clinical data using adjusted logistic regression and instrumental variables (IV) analyses. Setting 165 National Health Service neonatal units in England contributing data to the National Neonatal Research Database at the Neonatal Data Analysis Unit and participating in the Neonatal Economic, Staffing and Clinical Outcomes Project. Participants 20 554 infants born at <33 weeks completed gestation (17 995 born at 27–32 weeks; 2559 born at <27 weeks), admitted to neonatal care and either discharged or died, over the period 1 January 2009–31 December 2011. Intervention Tertiary designation or high-volume neonatal care at the hospital of birth. Outcomes Neonatal mortality, any in-hospital mortality, surgery for necrotising enterocolitis, surgery for retinopathy of prematurity, bronchopulmonary dysplasia and postmenstrual age at discharge. Results Infants born at <33 weeks gestation and admitted to a high-volume neonatal unit at the hospital of birth were at reduced odds of neonatal mortality (IV regression odds ratio (OR) 0.70, 95% CI 0.53 to 0.92) and any in-hospital mortality (IV regression OR 0.68, 95% CI 0.54 to 0.85). The effect of volume on any in-hospital mortality was most acute among infants born at <27 weeks gestation (IV regression OR 0.51, 95% CI 0.33 to 0.79). A negative association between tertiary-level unit designation and mortality was also observed with adjusted logistic regression for infants born at <27 weeks gestation. Conclusions High-volume neonatal care provided at the hospital of birth may protect against in-hospital mortality in very preterm infants. Future developments of neonatal services should promote delivery of very preterm infants at hospitals with high-volume neonatal units. PMID:25001393

The effects of designation and volume of neonatal care on mortality and morbidity outcomes of very preterm infants in England: retrospective population-based cohort study.

PubMed

Watson, S I; Arulampalam, W; Petrou, S; Marlow, N; Morgan, A S; Draper, E S; Santhakumaran, S; Modi, N

2014-07-07

To examine the effects of designation and volume of neonatal care at the hospital of birth on mortality and morbidity outcomes in very preterm infants in a managed clinical network setting. A retrospective, population-based analysis of operational clinical data using adjusted logistic regression and instrumental variables (IV) analyses. 165 National Health Service neonatal units in England contributing data to the National Neonatal Research Database at the Neonatal Data Analysis Unit and participating in the Neonatal Economic, Staffing and Clinical Outcomes Project. 20 554 infants born at <33 weeks completed gestation (17 995 born at 27-32 weeks; 2559 born at <27 weeks), admitted to neonatal care and either discharged or died, over the period 1 January 2009-31 December 2011. Tertiary designation or high-volume neonatal care at the hospital of birth. Neonatal mortality, any in-hospital mortality, surgery for necrotising enterocolitis, surgery for retinopathy of prematurity, bronchopulmonary dysplasia and postmenstrual age at discharge. Infants born at <33 weeks gestation and admitted to a high-volume neonatal unit at the hospital of birth were at reduced odds of neonatal mortality (IV regression odds ratio (OR) 0.70, 95% CI 0.53 to 0.92) and any in-hospital mortality (IV regression OR 0.68, 95% CI 0.54 to 0.85). The effect of volume on any in-hospital mortality was most acute among infants born at <27 weeks gestation (IV regression OR 0.51, 95% CI 0.33 to 0.79). A negative association between tertiary-level unit designation and mortality was also observed with adjusted logistic regression for infants born at <27 weeks gestation. High-volume neonatal care provided at the hospital of birth may protect against in-hospital mortality in very preterm infants. Future developments of neonatal services should promote delivery of very preterm infants at hospitals with high-volume neonatal units. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

The Counseling Opportunity Structure: Examining Correlates of Four-Year College-Going Rates

ERIC Educational Resources Information Center

Engberg, Mark E.; Gilbert, Aliza J.

2014-01-01

This study examines the relationships between the normative and resource dimensions of a high school counseling department and four-year college-going rates. Utilizing data from the High School Longitudinal Study of 2009 (HSLS: 09), we employ multiple regression and latent class analysis to identify salient factors related to the college-going…

Predicting Academic Success in First-Year Mathematics Courses Using ACT Mathematics Scores and High School Grade Point Average

ERIC Educational Resources Information Center

Mayo, Sandra Sims

2012-01-01

Improving college performance and retention is a daunting task for colleges and universities. Many institutions are taking action to increase retention rates by exploring their academic programs. Regression analysis was used to compare the effectiveness of ACT mathematics scores, high school grade point averages (HSGPA), and demographic factors…

Neural Network and Regression Approximations in High Speed Civil Transport Aircraft Design Optimization

NASA Technical Reports Server (NTRS)

Patniak, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.

1998-01-01

Nonlinear mathematical-programming-based design optimization can be an elegant method. However, the calculations required to generate the merit function, constraints, and their gradients, which are frequently required, can make the process computational intensive. The computational burden can be greatly reduced by using approximating analyzers derived from an original analyzer utilizing neural networks and linear regression methods. The experience gained from using both of these approximation methods in the design optimization of a high speed civil transport aircraft is the subject of this paper. The Langley Research Center's Flight Optimization System was selected for the aircraft analysis. This software was exercised to generate a set of training data with which a neural network and a regression method were trained, thereby producing the two approximating analyzers. The derived analyzers were coupled to the Lewis Research Center's CometBoards test bed to provide the optimization capability. With the combined software, both approximation methods were examined for use in aircraft design optimization, and both performed satisfactorily. The CPU time for solution of the problem, which had been measured in hours, was reduced to minutes with the neural network approximation and to seconds with the regression method. Instability encountered in the aircraft analysis software at certain design points was also eliminated. On the other hand, there were costs and difficulties associated with training the approximating analyzers. The CPU time required to generate the input-output pairs and to train the approximating analyzers was seven times that required for solution of the problem.

p.Q192R SNP of PON1 seems not to be Associated with Carotid Atherosclerosis Risk Factors in an Asymptomatic and Normolipidemic Brazilian Population Sample

PubMed Central

Scherrer, Daniel Zanetti; Zago, Vanessa Helena de Souza; Vieira, Isabela Calanca; Parra, Eliane Soler; Panzoldo, Natália Baratella; Alexandre, Fernanda; Secolin, Rodrigo; Baracat, Jamal; Quintão, Eder Carlos Rocha; de Faria, Eliana Cotta

2015-01-01

Background Evidences suggest that paraoxonase 1 (PON1) confers important antioxidant and anti-inflammatory properties when associated with high-density lipoprotein (HDL). Objective To investigate the relationships between p.Q192R SNP of PON1, biochemical parameters and carotid atherosclerosis in an asymptomatic, normolipidemic Brazilian population sample. Methods We studied 584 volunteers (females n = 326, males n = 258; 19-75 years of age). Total genomic DNA was extracted and SNP was detected in the TaqMan® SNP OpenArray® genotyping platform (Applied Biosystems, Foster City, CA). Plasma lipoproteins and apolipoproteins were determined and PON1 activity was measured using paraoxon as a substrate. High-resolution β-mode ultrasonography was used to measure cIMT and the presence of carotid atherosclerotic plaques in a subgroup of individuals (n = 317). Results The presence of p.192Q was associated with a significant increase in PON1 activity (RR = 12.30 (11.38); RQ = 46.96 (22.35); QQ = 85.35 (24.83) μmol/min; p < 0.0001), HDL-C (RR= 45 (37); RQ = 62 (39); QQ = 69 (29) mg/dL; p < 0.001) and apo A-I (RR = 140.76 ± 36.39; RQ = 147.62 ± 36.92; QQ = 147.49 ± 36.65 mg/dL; p = 0.019). Stepwise regression analysis revealed that heterozygous and p.192Q carriers influenced by 58% PON1 activity towards paraoxon. The univariate linear regression analysis demonstrated that p.Q192R SNP was not associated with mean cIMT; as a result, in the multiple regression analysis, no variables were selected with 5% significance. In logistic regression analysis, the studied parameters were not associated with the presence of carotid plaques. Conclusion In low-risk individuals, the presence of the p.192Q variant of PON1 is associated with a beneficial plasma lipid profile but not with carotid atherosclerosis. PMID:26039660

Clinical benefit from pharmacological elevation of high-density lipoprotein cholesterol: meta-regression analysis.

PubMed

Hourcade-Potelleret, F; Laporte, S; Lehnert, V; Delmar, P; Benghozi, Renée; Torriani, U; Koch, R; Mismetti, P

2015-06-01

Epidemiological evidence that the risk of coronary heart disease is inversely associated with the level of high-density lipoprotein cholesterol (HDL-C) has motivated several phase III programmes with cholesteryl ester transfer protein (CETP) inhibitors. To assess alternative methods to predict clinical response of CETP inhibitors. Meta-regression analysis on raising HDL-C drugs (statins, fibrates, niacin) in randomised controlled trials. 51 trials in secondary prevention with a total of 167,311 patients for a follow-up >1 year where HDL-C was measured at baseline and during treatment. The meta-regression analysis showed no significant association between change in HDL-C (treatment vs comparator) and log risk ratio (RR) of clinical endpoint (non-fatal myocardial infarction or cardiac death). CETP inhibitors data are consistent with this finding (RR: 1.03; P5-P95: 0.99-1.21). A prespecified sensitivity analysis by drug class suggested that the strength of relationship might differ between pharmacological groups. A significant association for both statins (p<0.02, log RR=-0.169-0.0499*HDL-C change, R(2)=0.21) and niacin (p=0.02, log RR=1.07-0.185*HDL-C change, R(2)=0.61) but not fibrates (p=0.18, log RR=-0.367+0.077*HDL-C change, R(2)=0.40) was shown. However, the association was no longer detectable after adjustment for low-density lipoprotein cholesterol for statins or exclusion of open trials for niacin. Meta-regression suggested that CETP inhibitors might not influence coronary risk. The relation between change in HDL-C level and clinical endpoint may be drug dependent, which limits the use of HDL-C as a surrogate marker of coronary events. Other markers of HDL function may be more relevant. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

p.Q192R SNP of PON1 seems not to be Associated with Carotid Atherosclerosis Risk Factors in an Asymptomatic and Normolipidemic Brazilian Population Sample.

PubMed

Scherrer, Daniel Zanetti; Zago, Vanessa Helena de Souza; Vieira, Isabela Calanca; Parra, Eliane Soler; Panzoldo, Natália Baratella; Alexandre, Fernanda; Secolin, Rodrigo; Baracat, Jamal; Quintão, Eder Carlos Rocha; Faria, Eliana Cotta de

2015-07-01

Evidences suggest that paraoxonase 1 (PON1) confers important antioxidant and anti-inflammatory properties when associated with high-density lipoprotein (HDL). To investigate the relationships between p.Q192R SNP of PON1, biochemical parameters and carotid atherosclerosis in an asymptomatic, normolipidemic Brazilian population sample. We studied 584 volunteers (females n = 326, males n = 258; 19-75 years of age). Total genomic DNA was extracted and SNP was detected in the TaqMan® SNP OpenArray® genotyping platform (Applied Biosystems, Foster City, CA). Plasma lipoproteins and apolipoproteins were determined and PON1 activity was measured using paraoxon as a substrate. High-resolution β-mode ultrasonography was used to measure cIMT and the presence of carotid atherosclerotic plaques in a subgroup of individuals (n = 317). The presence of p.192Q was associated with a significant increase in PON1 activity (RR = 12.30 (11.38); RQ = 46.96 (22.35); QQ = 85.35 (24.83) μmol/min; p < 0.0001), HDL-C (RR= 45 (37); RQ = 62 (39); QQ = 69 (29) mg/dL; p < 0.001) and apo A-I (RR = 140.76 ± 36.39; RQ = 147.62 ± 36.92; QQ = 147.49 ± 36.65 mg/dL; p = 0.019). Stepwise regression analysis revealed that heterozygous and p.192Q carriers influenced by 58% PON1 activity towards paraoxon. The univariate linear regression analysis demonstrated that p.Q192R SNP was not associated with mean cIMT; as a result, in the multiple regression analysis, no variables were selected with 5% significance. In logistic regression analysis, the studied parameters were not associated with the presence of carotid plaques. In low-risk individuals, the presence of the p.192Q variant of PON1 is associated with a beneficial plasma lipid profile but not with carotid atherosclerosis.

The moderating effect of teamwork perceptions on the organizational politics--job satisfaction relationship.

PubMed

Valle, M; Witt, L A

2001-06-01

By using regression analyses on data from 355 full-time employees of a customer-service organization in the eastern United States, the authors tested the hypothesis that perceptions of organizational politics are more strongly related to job dissatisfaction among individuals who perceive low levels of teamwork importance than among those who perceive high levels of teamwork importance. Hierarchical moderated regression analysis of the data revealed that the moderating effect of teamwork importance was most relevant at average-to-high levels of perceived politics. That finding supports the assertion that one way to address the negative impact of organizational politics is to try to ensure that employees value teamwork.

Effective psychological and psychosocial approaches to reduce repetition of self-harm: a systematic review, meta-analysis and meta-regression

PubMed Central

Robinson, Jo; Spittal, Matthew J; Carter, Greg

2016-01-01

Objective To examine the efficacy of psychological and psychosocial interventions for reductions in repeated self-harm. Design We conducted a systematic review, meta-analysis and meta-regression to examine the efficacy of psychological and psychosocial interventions to reduce repeat self-harm in adults. We included a sensitivity analysis of studies with a low risk of bias for the meta-analysis. For the meta-regression, we examined whether the type, intensity (primary analyses) and other components of intervention or methodology (secondary analyses) modified the overall intervention effect. Data sources A comprehensive search of MEDLINE, PsycInfo and EMBASE (from 1999 to June 2016) was performed. Eligibility criteria for selecting studies Randomised controlled trials of psychological and psychosocial interventions for adult self-harm patients. Results Forty-five trials were included with data available from 36 (7354 participants) for the primary analysis. Meta-analysis showed a significant benefit of all psychological and psychosocial interventions combined (risk ratio 0.84; 95% CI 0.74 to 0.96; number needed to treat=33); however, sensitivity analyses showed that this benefit was non-significant when restricted to a limited number of high-quality studies. Meta-regression showed that the type of intervention did not modify the treatment effects. Conclusions Consideration of a psychological or psychosocial intervention over and above treatment as usual is worthwhile; with the public health benefits of ensuring that this practice is widely adopted potentially worth the investment. However, the specific type and nature of the intervention that should be delivered is not yet clear. Cognitive–behavioural therapy or interventions with an interpersonal focus and targeted on the precipitants to self-harm may be the best candidates on the current evidence. Further research is required. PMID:27660314

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-05-01

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

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

Prediction models for clustered data: comparison of a random intercept and standard regression model

PubMed Central

2013-01-01

Background When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Methods Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. Results The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. Conclusion The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters. PMID:23414436

Prediction models for clustered data: comparison of a random intercept and standard regression model.

PubMed

Bouwmeester, Walter; Twisk, Jos W R; Kappen, Teus H; van Klei, Wilton A; Moons, Karel G M; Vergouwe, Yvonne

2013-02-15

When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters.

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.

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.

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

PubMed

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

2016-01-01

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

The perception of the relationship between environment and health according to data from Italian Behavioural Risk Factor Surveillance System (PASSI).

PubMed

Sampaolo, Letizia; Tommaso, Giulia; Gherardi, Bianca; Carrozzi, Giuliano; Freni Sterrantino, Anna; Ottone, Marta; Goldoni, Carlo Alberto; Bertozzi, Nicoletta; Scaringi, Meri; Bolognesi, Lara; Masocco, Maria; Salmaso, Stefania; Lauriola, Paolo

2017-01-01

"OBJECTIVES: to identify groups of people in relation to the perception of environmental risk and to assess the main characteristics using data collected in the environmental module of the surveillance network Italian Behavioral Risk Factor Surveillance System (PASSI). perceptive profiles were identified using a latent class analysis; later they were included as outcome in multinomial logistic regression models to assess the association between environmental risk perception and demographic, health, socio-economic and behavioural variables. the latent class analysis allowed to split the sample in "worried", "indifferent", and "positive" people. The multinomial logistic regression model showed that the "worried" profile typically includes people of Italian nationality, living in highly urbanized areas, with a high level of education, and with economic difficulties; they pay special attention to their own health and fitness, but they have a negative perception of their own psychophysical state. the application of advanced statistical analysis enable to appraise PASSI data in order to characterize the perception of environmental risk, making the planning of interventions related to risk communication possible. ".

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.

Comparison of two occurrence risk assessment methods for collapse gully erosion ——A case study in Guangdong province

NASA Astrophysics Data System (ADS)

Sun, K.; Cheng, D. B.; He, J. J.; Zhao, Y. L.

2018-02-01

Collapse gully erosion is a specific type of soil erosion in the red soil region of southern China, and early warning and prevention of the occurrence of collapse gully erosion is very important. Based on the idea of risk assessment, this research, taking Guangdong province as an example, adopt the information acquisition analysis and the logistic regression analysis, to discuss the feasibility for collapse gully erosion risk assessment in regional scale, and compare the applicability of the different risk assessment methods. The results show that in the Guangdong province, the risk degree of collapse gully erosion occurrence is high in northeastern and western area, and relatively low in southwestern and central part. The comparing analysis of the different risk assessment methods on collapse gully also indicated that the risk distribution patterns from the different methods were basically consistent. However, the accuracy of risk map from the information acquisition analysis method was slightly better than that from the logistic regression analysis method.

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.

[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...

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.

PubMed

Gong, Xiajing; Hu, Meng; Zhao, Liang

2018-05-01

Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time-to-event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high-dimensional data featured by a large number of predictor variables. Our results showed that ML-based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high-dimensional data. The prediction performances of ML-based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML-based methods provide a powerful tool for time-to-event analysis, with a built-in capacity for high-dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. © 2018 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Integrative Analysis of High-throughput Cancer Studies with Contrasted Penalization

PubMed Central

Shi, Xingjie; Liu, Jin; Huang, Jian; Zhou, Yong; Shia, BenChang; Ma, Shuangge

2015-01-01

In cancer studies with high-throughput genetic and genomic measurements, integrative analysis provides a way to effectively pool and analyze heterogeneous raw data from multiple independent studies and outperforms “classic” meta-analysis and single-dataset analysis. When marker selection is of interest, the genetic basis of multiple datasets can be described using the homogeneity model or the heterogeneity model. In this study, we consider marker selection under the heterogeneity model, which includes the homogeneity model as a special case and can be more flexible. Penalization methods have been developed in the literature for marker selection. This study advances from the published ones by introducing the contrast penalties, which can accommodate the within- and across-dataset structures of covariates/regression coefficients and, by doing so, further improve marker selection performance. Specifically, we develop a penalization method that accommodates the across-dataset structures by smoothing over regression coefficients. An effective iterative algorithm, which calls an inner coordinate descent iteration, is developed. Simulation shows that the proposed method outperforms the benchmark with more accurate marker identification. The analysis of breast cancer and lung cancer prognosis studies with gene expression measurements shows that the proposed method identifies genes different from those using the benchmark and has better prediction performance. PMID:24395534

Analysis of the labor productivity of enterprises via quantile regression

NASA Astrophysics Data System (ADS)

Türkan, Semra

2017-07-01

In this study, we have analyzed the factors that affect the performance of Turkey's Top 500 Industrial Enterprises using quantile regression. The variable about labor productivity of enterprises is considered as dependent variable, the variableabout assets is considered as independent variable. The distribution of labor productivity of enterprises is right-skewed. If the dependent distribution is skewed, linear regression could not catch important aspects of the relationships between the dependent variable and its predictors due to modeling only the conditional mean. Hence, the quantile regression, which allows modelingany quantilesof the dependent distribution, including the median,appears to be useful. It examines whether relationships between dependent and independent variables are different for low, medium, and high percentiles. As a result of analyzing data, the effect of total assets is relatively constant over the entire distribution, except the upper tail. It hasa moderately stronger effect in the upper tail.

Multiple regression for physiological data analysis: the problem of multicollinearity.

PubMed

Slinker, B K; Glantz, S A

1985-07-01

Multiple linear regression, in which several predictor variables are related to a response variable, is a powerful statistical tool for gaining quantitative insight into complex in vivo physiological systems. For these insights to be correct, all predictor variables must be uncorrelated. However, in many physiological experiments the predictor variables cannot be precisely controlled and thus change in parallel (i.e., they are highly correlated). There is a redundancy of information about the response, a situation called multicollinearity, that leads to numerical problems in estimating the parameters in regression equations; the parameters are often of incorrect magnitude or sign or have large standard errors. Although multicollinearity can be avoided with good experimental design, not all interesting physiological questions can be studied without encountering multicollinearity. In these cases various ad hoc procedures have been proposed to mitigate multicollinearity. Although many of these procedures are controversial, they can be helpful in applying multiple linear regression to some physiological problems.

Temperature dependence of nucleation rate in a binary solid solution

NASA Astrophysics Data System (ADS)

Wang, H. Y.; Philippe, T.; Duguay, S.; Blavette, D.

2012-12-01

The influence of regression (partial dissolution) effects on the temperature dependence of nucleation rate in a binary solid solution has been studied theoretically. The results of the analysis are compared with the predictions of the simplest Volmer-Weber theory. Regression effects are shown to have a strong influence on the shape of the curve of nucleation rate versus temperature. The temperature TM at which the maximum rate of nucleation occurs is found to be lowered, particularly for low interfacial energy (coherent precipitation) and high-mobility species (e.g. interstitial atoms).

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

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

Nonlinear Trimodal Regression Analysis of Radiodensitometric Distributions to Quantify Sarcopenic and Sequelae Muscle Degeneration

PubMed Central

Árnadóttir, Í.; Gíslason, M. K.; Carraro, U.

2016-01-01

Muscle degeneration has been consistently identified as an independent risk factor for high mortality in both aging populations and individuals suffering from neuromuscular pathology or injury. While there is much extant literature on its quantification and correlation to comorbidities, a quantitative gold standard for analyses in this regard remains undefined. Herein, we hypothesize that rigorously quantifying entire radiodensitometric distributions elicits more muscle quality information than average values reported in extant methods. This study reports the development and utility of a nonlinear trimodal regression analysis method utilized on radiodensitometric distributions of upper leg muscles from CT scans of a healthy young adult, a healthy elderly subject, and a spinal cord injury patient. The method was then employed with a THA cohort to assess pre- and postsurgical differences in their healthy and operative legs. Results from the initial representative models elicited high degrees of correlation to HU distributions, and regression parameters highlighted physiologically evident differences between subjects. Furthermore, results from the THA cohort echoed physiological justification and indicated significant improvements in muscle quality in both legs following surgery. Altogether, these results highlight the utility of novel parameters from entire HU distributions that could provide insight into the optimal quantification of muscle degeneration. PMID:28115982

Exhaled volatile organic compounds in individuals with a history of high altitude pulmonary edema and varying hypoxia-induced responses.

PubMed

Figueroa, Jennifer A; Mansoor, Jim K; Allen, Roblee P; Davis, Cristina E; Walby, William F; Aksenov, Alexander A; Zhao, Weixiang; Lewis, William R; Schelegle, Edward S

2015-04-20

With ascent to altitude, certain individuals are susceptible to high altitude pulmonary edema (HAPE), which in turn can cause disability and even death. The ability to identify individuals at risk of HAPE prior to ascent is poor. The present study examined the profile of volatile organic compounds (VOC) in exhaled breath condensate (EBC) and pulmonary artery systolic pressures (PASP) before and after exposure to normobaric hypoxia (12% O2) in healthy males with and without a history of HAPE (Hx HAPE, n = 5; Control, n = 11). In addition, hypoxic ventilatory response (HVR), and PASP response to normoxic exercise were also measured. Auto-regression/partial least square regression of whole gas chromatography/mass spectrometry (GC/MS) data and binary logistic regression (BLR) of individual GC peaks and physiologic parameters resulted in models that separate individual subjects into their groups with variable success. The result of BLR analysis highlights HVR, PASP response to hypoxia and the amount of benzyl alcohol and dimethylbenzaldehyde dimethyl in expired breath as markers of HAPE history. These findings indicate the utility of EBC VOC analysis to discriminate between individuals with and without a history of HAPE and identified potential novel biomarkers that correlated with physiological responses to hypoxia.

English Language Learners' (ELLs) Science, Technology, Engineering, Math (STEM) Course-Taking, Achievement and Attainment in College

ERIC Educational Resources Information Center

Shi, Qi

2017-01-01

Using data from the Educational Longitudinal Study: 2002, the present study examined the effects of demographic variables, high school math course-taking and high school GPA on ELL students' STEM course-taking, achievement and attainment in college. Regression analysis showed female ELL students were more likely to take more STEM courses and get…

Analysis of oscillatory motion of a light airplane at high values of lift coefficient

NASA Technical Reports Server (NTRS)

Batterson, J. G.

1983-01-01

A modified stepwise regression is applied to flight data from a light research air-plane operating at high angles at attack. The well-known phenomenon referred to as buckling or porpoising is analyzed and modeled using both power series and spline expansions of the aerodynamic force and moment coefficients associated with the longitudinal equations of motion.

Use of Case History Data for the Development of Equations in Predicting High Risk, Reading Disabled Students.

ERIC Educational Resources Information Center

Stratton, Beverly D.; And Others

Demographic data on 92 subjects identified as having reading problems were used to develop equations useful in identifying high risk, reading disabled students. Multiple linear regression analysis of the data indicated that reading disability (1) had a significant positive relationship with birth order and number of siblings; (2) had a positive…

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.

Engine With Regression and Neural Network Approximators Designed

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Hopkins, Dale A.

2001-01-01

At the NASA Glenn Research Center, the NASA engine performance program (NEPP, ref. 1) and the design optimization testbed COMETBOARDS (ref. 2) with regression and neural network analysis-approximators have been coupled to obtain a preliminary engine design methodology. The solution to a high-bypass-ratio subsonic waverotor-topped turbofan engine, which is shown in the preceding figure, was obtained by the simulation depicted in the following figure. This engine is made of 16 components mounted on two shafts with 21 flow stations. The engine is designed for a flight envelope with 47 operating points. The design optimization utilized both neural network and regression approximations, along with the cascade strategy (ref. 3). The cascade used three algorithms in sequence: the method of feasible directions, the sequence of unconstrained minimizations technique, and sequential quadratic programming. The normalized optimum thrusts obtained by the three methods are shown in the following figure: the cascade algorithm with regression approximation is represented by a triangle, a circle is shown for the neural network solution, and a solid line indicates original NEPP results. The solutions obtained from both approximate methods lie within one standard deviation of the benchmark solution for each operating point. The simulation improved the maximum thrust by 5 percent. The performance of the linear regression and neural network methods as alternate engine analyzers was found to be satisfactory for the analysis and operation optimization of air-breathing propulsion engines (ref. 4).

Study Heterogeneity and Estimation of Prevalence of Primary Aldosteronism: A Systematic Review and Meta-Regression Analysis.

PubMed

Käyser, Sabine C; Dekkers, Tanja; Groenewoud, Hans J; van der Wilt, Gert Jan; Carel Bakx, J; van der Wel, Mark C; Hermus, Ad R; Lenders, Jacques W; Deinum, Jaap

2016-07-01

For health care planning and allocation of resources, realistic estimation of the prevalence of primary aldosteronism is necessary. Reported prevalences of primary aldosteronism are highly variable, possibly due to study heterogeneity. Our objective was to identify and explain heterogeneity in studies that aimed to establish the prevalence of primary aldosteronism in hypertensive patients. PubMed, EMBASE, Web of Science, Cochrane Library, and reference lists from January 1, 1990, to January 31, 2015, were used as data sources. Description of an adult hypertensive patient population with confirmed diagnosis of primary aldosteronism was included in this study. Dual extraction and quality assessment were the forms of data extraction. Thirty-nine studies provided data on 42 510 patients (nine studies, 5896 patients from primary care). Prevalence estimates varied from 3.2% to 12.7% in primary care and from 1% to 29.8% in referral centers. Heterogeneity was too high to establish point estimates (I(2) = 57.6% in primary care; 97.1% in referral centers). Meta-regression analysis showed higher prevalences in studies 1) published after 2000, 2) from Australia, 3) aimed at assessing prevalence of secondary hypertension, 4) that were retrospective, 5) that selected consecutive patients, and 6) not using a screening test. All studies had minor or major flaws. This study demonstrates that it is pointless to claim low or high prevalence of primary aldosteronism based on published reports. Because of the significant impact of a diagnosis of primary aldosteronism on health care resources and the necessary facilities, our findings urge for a prevalence study whose design takes into account the factors identified in the meta-regression analysis.

Subsonic Aircraft With Regression and Neural-Network Approximators Designed

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Hopkins, Dale A.

2004-01-01

At the NASA Glenn Research Center, NASA Langley Research Center's Flight Optimization System (FLOPS) and the design optimization testbed COMETBOARDS with regression and neural-network-analysis approximators have been coupled to obtain a preliminary aircraft design methodology. For a subsonic aircraft, the optimal design, that is the airframe-engine combination, is obtained by the simulation. The aircraft is powered by two high-bypass-ratio engines with a nominal thrust of about 35,000 lbf. It is to carry 150 passengers at a cruise speed of Mach 0.8 over a range of 3000 n mi and to operate on a 6000-ft runway. The aircraft design utilized a neural network and a regression-approximations-based analysis tool, along with a multioptimizer cascade algorithm that uses sequential linear programming, sequential quadratic programming, the method of feasible directions, and then sequential quadratic programming again. Optimal aircraft weight versus the number of design iterations is shown. The central processing unit (CPU) time to solution is given. It is shown that the regression-method-based analyzer exhibited a smoother convergence pattern than the FLOPS code. The optimum weight obtained by the approximation technique and the FLOPS code differed by 1.3 percent. Prediction by the approximation technique exhibited no error for the aircraft wing area and turbine entry temperature, whereas it was within 2 percent for most other parameters. Cascade strategy was required by FLOPS as well as the approximators. The regression method had a tendency to hug the data points, whereas the neural network exhibited a propensity to follow a mean path. The performance of the neural network and regression methods was considered adequate. It was at about the same level for small, standard, and large models with redundancy ratios (defined as the number of input-output pairs to the number of unknown coefficients) of 14, 28, and 57, respectively. In an SGI octane workstation (Silicon Graphics, Inc., Mountainview, CA), the regression training required a fraction of a CPU second, whereas neural network training was between 1 and 9 min, as given. For a single analysis cycle, the 3-sec CPU time required by the FLOPS code was reduced to milliseconds by the approximators. For design calculations, the time with the FLOPS code was 34 min. It was reduced to 2 sec with the regression method and to 4 min by the neural network technique. The performance of the regression and neural network methods was found to be satisfactory for the analysis and design optimization of the subsonic aircraft.

High energy injury is a risk factor for preoperative venous thromboembolism in the patients with hip fractures: A prospective observational study.

PubMed

Park, Jin Sup; Jang, Jae Hoon; Park, Ki Young; Moon, Nam Hoon

2018-06-01

The purpose of this study was to identify the incidence of preoperative venous thromboembolism (VTE), and determine if high energy hip fracture affects preoperative VTE occurrence. Three-hundred nine patients (244 low and 61 high energy injuries) treated between March 2015 and March 2017 were included in this study. Indirect multidetector computed tomographic venography for the detection of preoperative VTE was performed at admission. The incidence of preoperative VTE was compared between high and low energy injury hip fractures. Logistic regression analysis was used to identify independent risk factors for preoperative VTE. The overall incidence of preoperative VTE was 18.4% (56 of 305 patients). Preoperative VTE was identified in 17 (27.9%) and 39 (16.0%) patients in the high and low energy injury groups, respectively (p = 0.034). Multivariate logistic regression analysis showed that high energy injury, history of VTE, and myeloproliferative disease were significant predictive factors of preoperative VTE (OR = 2.451; 95% CI = 1.227-4.896, OR = 11.174; 95% CI = 3.500-35.673, OR = 6.936; 95% CI = 1.641-29.321, respectively) CONCLUSION: Because high energy hip fracture is significantly associated with preoperative VTE occurrence, preoperative evaluation and proper thromboprophylaxis should be performed for patients with a high-energy hip fracture. Copyright © 2018 Elsevier Ltd. All rights reserved.

Interactions between cadmium and decabrominated diphenyl ether on blood cells count in rats-Multiple factorial regression analysis.

PubMed

Curcic, Marijana; Buha, Aleksandra; Stankovic, Sanja; Milovanovic, Vesna; Bulat, Zorica; Đukić-Ćosić, Danijela; Antonijević, Evica; Vučinić, Slavica; Matović, Vesna; Antonijevic, Biljana

2017-02-01

The objective of this study was to assess toxicity of Cd and BDE-209 mixture on haematological parameters in subacutely exposed rats and to determine the presence and type of interactions between these two chemicals using multiple factorial regression analysis. Furthermore, for the assessment of interaction type, an isobologram based methodology was applied and compared with multiple factorial regression analysis. Chemicals were given by oral gavage to the male Wistar rats weighing 200-240g for 28days. Animals were divided in 16 groups (8/group): control vehiculum group, three groups of rats were treated with 2.5, 7.5 or 15mg Cd/kg/day. These doses were chosen on the bases of literature data and reflect relatively high Cd environmental exposure, three groups of rats were treated with 1000, 2000 or 4000mg BDE-209/kg/bw/day, doses proved to induce toxic effects in rats. Furthermore, nine groups of animals were treated with different mixtures of Cd and BDE-209 containing doses of Cd and BDE-209 stated above. Blood samples were taken at the end of experiment and red blood cells, white blood cells and platelets counts were determined. For interaction assessment multiple factorial regression analysis and fitted isobologram approach were used. In this study, we focused on multiple factorial regression analysis as a method for interaction assessment. We also investigated the interactions between Cd and BDE-209 by the derived model for the description of the obtained fitted isobologram curves. Current study indicated that co-exposure to Cd and BDE-209 can result in significant decrease in RBC count, increase in WBC count and decrease in PLT count, when compared with controls. Multiple factorial regression analysis used for the assessment of interactions type between Cd and BDE-209 indicated synergism for the effect on RBC count and no interactions i.e. additivity for the effects on WBC and PLT counts. On the other hand, isobologram based approach showed slight antagonism for the effects on RBC and WBC while no interactions were proved for the joint effect on PLT count. These results confirm that the assessment of interactions between chemicals in the mixture greatly depends on the concept or method used for this evaluation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

Predictors of exercise capacity following exercise-based rehabilitation in patients with coronary heart disease and heart failure: A meta-regression analysis.

PubMed

Uddin, Jamal; Zwisler, Ann-Dorthe; Lewinter, Christian; Moniruzzaman, Mohammad; Lund, Ken; Tang, Lars H; Taylor, Rod S

2016-05-01

The aim of this study was to undertake a comprehensive assessment of the patient, intervention and trial-level factors that may predict exercise capacity following exercise-based rehabilitation in patients with coronary heart disease and heart failure. Meta-analysis and meta-regression analysis. Randomized controlled trials of exercise-based rehabilitation were identified from three published systematic reviews. Exercise capacity was pooled across trials using random effects meta-analysis, and meta-regression used to examine the association between exercise capacity and a range of patient (e.g. age), intervention (e.g. exercise frequency) and trial (e.g. risk of bias) factors. 55 trials (61 exercise-control comparisons, 7553 patients) were included. Following exercise-based rehabilitation compared to control, overall exercise capacity was on average 0.95 (95% CI: 0.76-1.41) standard deviation units higher, and in trials reporting maximum oxygen uptake (VO2max) was 3.3 ml/kg.min(-1) (95% CI: 2.6-4.0) higher. There was evidence of a high level of statistical heterogeneity across trials (I(2) statistic > 50%). In multivariable meta-regression analysis, only exercise intervention intensity was found to be significantly associated with VO2max (P = 0.04); those trials with the highest average exercise intensity had the largest mean post-rehabilitation VO2max compared to control. We found considerable heterogeneity across randomized controlled trials in the magnitude of improvement in exercise capacity following exercise-based rehabilitation compared to control among patients with coronary heart disease or heart failure. Whilst higher exercise intensities were associated with a greater level of post-rehabilitation exercise capacity, there was no strong evidence to support other intervention, patient or trial factors to be predictive. © The European Society of Cardiology 2015.

A Multilevel Multivariate Analysis of Academic Performances in College Based on NCAA Student-Athletes

ERIC Educational Resources Information Center

McArdle, John J.; Paskus, Thomas S.; Boker, Steven M.

2013-01-01

This is an application of contemporary multilevel regression modeling to the prediction of academic performances of 1st-year college students. At a first level of analysis, the data come from N greater than 16,000 students who were college freshman in 1994-1995 and who were also participants in high-level college athletics. At a second level of…

Analysis of Variables to Predict First Year Persistence Using Logistic Regression Analysis at the University of South Florida

ERIC Educational Resources Information Center

Miller, T. E.; Herreid, C. H.

2008-01-01

This article presents a project intended to produce a model for predicting the risk of attrition of individual students enrolled at the University of South Florida. The project is premised upon the principle that college student attrition is as highly individual and personal as any other aspect of the college-going experience. Students make…

A new multiple regression model to identify multi-family houses with a high prevalence of sick building symptoms "SBS", within the healthy sustainable house study in Stockholm (3H).

PubMed

Engvall, Karin; Hult, M; Corner, R; Lampa, E; Norbäck, D; Emenius, G

2010-01-01

The aim was to develop a new model to identify residential buildings with higher frequencies of "SBS" than expected, "risk buildings". In 2005, 481 multi-family buildings with 10,506 dwellings in Stockholm were studied by a new stratified random sampling. A standardised self-administered questionnaire was used to assess "SBS", atopy and personal factors. The response rate was 73%. Statistical analysis was performed by multiple logistic regressions. Dwellers owning their building reported less "SBS" than those renting. There was a strong relationship between socio-economic factors and ownership. The regression model, ended up with high explanatory values for age, gender, atopy and ownership. Applying our model, 9% of all residential buildings in Stockholm were classified as "risk buildings" with the highest proportion in houses built 1961-1975 (26%) and lowest in houses built 1985-1990 (4%). To identify "risk buildings", it is necessary to adjust for ownership and population characteristics.

Internalized homophobia, mental health, sexual behaviors, and outness of gay/bisexual men from Southwest China.

PubMed

Xu, Wenjian; Zheng, Lijun; Xu, Yin; Zheng, Yong

2017-02-17

Social attitudes toward male homosexuality in China so far are still not optimistic. Sexual minorities in China have reported high levels of internalized homophobia. This Internet-based study examined the associations among internalized homophobia, mental health, sexual behaviors, and outness among 435 gay/bisexual men in Southwest China from 2014 to 2015. Latent profile analysis, confirmatory factor analysis, univariate logistic regression, and separate multivariate logistic regression analyses were conducted. This descriptive study found the Internalized Homophobia Scale to be suitable for use in China. The sample demonstrated a high prevalence of internalized homophobia. Latent profile analysis suggested a 2-class solution as optimal, and a high level of internalized homophobia was significantly associated with greater psychological distress (Wald = 6.49, AOR = 1.66), transactional sex during the previous 6 months (Wald = 5.23, AOR = 2.77), more sexual compulsions (Wald = 14.05, AOR = 2.12), and the concealment of sexual identity from others (Wald = 30.70, AOR = 0.30) and parents (Wald = 6.72, AOR = 0.49). These findings contribute to our understanding of internalized homophobia in China, and highlight the need to decrease gay-related psychological stress/distress and improve public health services.

The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity.

PubMed

Wardenaar, K J; van Loo, H M; Cai, T; Fava, M; Gruber, M J; Li, J; de Jonge, P; Nierenberg, A A; Petukhova, M V; Rose, S; Sampson, N A; Schoevers, R A; Wilcox, M A; Alonso, J; Bromet, E J; Bunting, B; Florescu, S E; Fukao, A; Gureje, O; Hu, C; Huang, Y Q; Karam, A N; Levinson, D; Medina Mora, M E; Posada-Villa, J; Scott, K M; Taib, N I; Viana, M C; Xavier, M; Zarkov, Z; Kessler, R C

2014-11-01

Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question. Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes. Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6-72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors. Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.

Assessing host-specificity of Escherichia coli using a supervised learning logic-regression-based analysis of single nucleotide polymorphisms in intergenic regions.

PubMed

Zhi, Shuai; Li, Qiaozhi; Yasui, Yutaka; Edge, Thomas; Topp, Edward; Neumann, Norman F

2015-11-01

Host specificity in E. coli is widely debated. Herein, we used supervised learning logic-regression-based analysis of intergenic DNA sequence variability in E. coli in an attempt to identify single nucleotide polymorphism (SNP) biomarkers of E. coli that are associated with natural selection and evolution toward host specificity. Seven-hundred and eighty strains of E. coli were isolated from 15 different animal hosts. We utilized logic regression for analyzing DNA sequence data of three intergenic regions (flanked by the genes uspC-flhDC, csgBAC-csgDEFG, and asnS-ompF) to identify genetic biomarkers that could potentially discriminate E. coli based on host sources. Across 15 different animal hosts, logic regression successfully discriminated E. coli based on animal host source with relatively high specificity (i.e., among the samples of the non-target animal host, the proportion that correctly did not have the host-specific marker pattern) and sensitivity (i.e., among the samples from a given animal host, the proportion that correctly had the host-specific marker pattern), even after fivefold cross validation. Permutation tests confirmed that for most animals, host specific intergenic biomarkers identified by logic regression in E. coli were significantly associated with animal host source. The highest level of biomarker sensitivity was observed in deer isolates, with 82% of all deer E. coli isolates displaying a unique SNP pattern that was 98% specific to deer. Fifty-three percent of human isolates displayed a unique biomarker pattern that was 98% specific to humans. Twenty-nine percent of cattle isolates displayed a unique biomarker that was 97% specific to cattle. Interestingly, even within a related host group (i.e., Family: Canidae [domestic dogs and coyotes]), highly specific SNP biomarkers (98% and 99% specificity for dog and coyotes, respectively) were observed, with 21% of dog E. coli isolates displaying a unique dog biomarker and 61% of coyote isolates displaying a unique coyote biomarker. Application of a supervised learning method, such as logic regression, to DNA sequence analysis at certain intergenic regions demonstrates that some E. coli strains may evolve to become host-specific. Copyright © 2015 Elsevier Inc. All rights reserved.

The effect of biological movement variability on the performance of the golf swing in high- and low-handicapped players.

PubMed

Bradshaw, Elizabeth J; Keogh, Justin W L; Hume, Patria A; Maulder, Peter S; Nortje, Jacques; Marnewick, Michel

2009-06-01

The purpose of this study was to examine the role of neuromotor noise on golf swing performance in high- and low-handicap players. Selected two-dimensional kinematic measures of 20 male golfers (n=10 per high- or low-handicap group) performing 10 golf swings with a 5-iron club was obtained through video analysis. Neuromotor noise was calculated by deducting the standard error of the measurement from the coefficient of variation obtained from intra-individual analysis. Statistical methods included linear regression analysis and one-way analysis of variance using SPSS. Absolute invariance in the key technical positions (e.g., at the top of the backswing) of the golf swing appears to be a more favorable technique for skilled performance.

Polymorphisms within the FANCA gene associate with premature ovarian failure in Korean women.

PubMed

Pyun, Jung-A; Kim, Sunshin; Cha, Dong Hyun; Kwack, KyuBum

2014-05-01

This study investigated whether polymorphisms within the Fanconi anemia complementation group A (FANCA) gene contribute to the increased risk of premature ovarian failure (POF) in Korean women. Ninety-eight women with POF and 218 controls participated in this study. Genomic DNA from peripheral blood was isolated, and GoldenGate genotyping assay was used to identify single nucleotide polymorphisms (SNPs) within the FANCA gene. Two significant SNPs (rs1006547 and rs2239359; P < 0.05) were identified by logistic regression analysis, but results were insignificant after Bonferroni correction. Six SNPs formed a linkage disequilibrium block, and three main haplotypes were found. Two of three haplotypes (AAAGAA and GGGAGG) distributed highly in the POF group, whereas the remaining haplotype (GGAAGG) distributed highly in the control group by logistic regression analysis (highest odds ratio, 2.515; 95% CI, 1.515-4.175; P = 0.00036). Our observations suggest that genetic variations in the FANCA gene may increase the risk for POF in Korean women.

Serum dehydroepiandrosterone sulphate, psychosocial factors and musculoskeletal pain in workers.

PubMed

Marinelli, A; Prodi, A; Pesel, G; Ronchese, F; Bovenzi, M; Negro, C; Larese Filon, F

2017-12-30

The serum level of dehydroepiandrosterone sulphate (DHEA-S) has been suggested as a biological marker of stress. To assess the association between serum DHEA-S, psychosocial factors and musculoskeletal (MS) pain in university workers. The study population included voluntary workers at the scientific departments of the University of Trieste (Italy) who underwent periodical health surveillance from January 2011 to June 2012. DHEA-S level was analysed in serum. The assessment tools included the General Health Questionnaire (GHQ) and a modified Nordic musculoskeletal symptoms questionnaire. The relation between DHEA-S, individual characteristics, pain perception and psychological factors was assessed by means of multivariable linear regression analysis. There were 189 study participants. The study population was characterized by high reward and low effort. Pain perception in the neck, shoulder, upper limbs, upper back and lower back was reported by 42, 32, 19, 29 and 43% of people, respectively. In multivariable regression analysis, gender, age and pain perception in the shoulder and upper limbs were significantly related to serum DHEA-S. Effort and overcommitment were related to shoulder and neck pain but not to DHEA-S. The GHQ score was associated with pain perception in different body sites and inversely to DHEA-S but significance was lost in multivariable regression analysis. DHEA-S was associated with age, gender and perception of MS pain, while effort-reward imbalance dimensions and GHQ score failed to reach the statistical significance in multivariable regression analysis. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oup.com

A local equation for differential diagnosis of β-thalassemia trait and iron deficiency anemia by logistic regression analysis in Southeast Iran.

PubMed

Sargolzaie, Narjes; Miri-Moghaddam, Ebrahim

2014-01-01

The most common differential diagnosis of β-thalassemia (β-thal) trait is iron deficiency anemia. Several red blood cell equations were introduced during different studies for differential diagnosis between β-thal trait and iron deficiency anemia. Due to genetic variations in different regions, these equations cannot be useful in all population. The aim of this study was to determine a native equation with high accuracy for differential diagnosis of β-thal trait and iron deficiency anemia for the Sistan and Baluchestan population by logistic regression analysis. We selected 77 iron deficiency anemia and 100 β-thal trait cases. We used binary logistic regression analysis and determined best equations for probability prediction of β-thal trait against iron deficiency anemia in our population. We compared diagnostic values and receiver operative characteristic (ROC) curve related to this equation and another 10 published equations in discriminating β-thal trait and iron deficiency anemia. The binary logistic regression analysis determined the best equation for best probability prediction of β-thal trait against iron deficiency anemia with area under curve (AUC) 0.998. Based on ROC curves and AUC, Green & King, England & Frazer, and then Sirdah indices, respectively, had the most accuracy after our equation. We suggest that to get the best equation and cut-off in each region, one needs to evaluate specific information of each region, specifically in areas where populations are homogeneous, to provide a specific formula for differentiating between β-thal trait and iron deficiency anemia.

Evaluation of a complex intervention to improve primary care prescribing: a phase IV segmented regression interrupted time series analysis

PubMed Central

MacBride-Stewart, Sean; Marwick, Charis; Houston, Neil; Watt, Iain; Patton, Andrea; Guthrie, Bruce

2017-01-01

Background It is uncertain whether improvements in primary care high-risk prescribing seen in research trials can be realised in the real-world setting. Aim To evaluate the impact of a 1-year system-wide phase IV prescribing safety improvement initiative, which included education, feedback, support to identify patients to review, and small financial incentives. Design and setting An interrupted time series analysis of targeted high-risk prescribing in all 56 general practices in NHS Forth Valley, Scotland, was performed. In 2013–2014, this focused on high-risk non-steroidal anti-inflammatory drugs (NSAIDs) in older people and NSAIDs with oral anticoagulants; in 2014–2015, it focused on antipsychotics in older people. Method The primary analysis used segmented regression analysis to estimate impact at the end of the intervention, and 12 months later. The secondary analysis used difference-in-difference methods to compare Forth Valley changes with those in NHS Greater Glasgow and Clyde (GGC). Results In the primary analysis, downward trends for all three NSAID measures that were existent before the intervention statistically significantly steepened following implementation of the intervention. At the end of the intervention period, 1221 fewer patients than expected were prescribed a high-risk NSAID. In contrast, antipsychotic prescribing in older people increased slowly over time, with no intervention-associated change. In the secondary analysis, reductions at the end of the intervention period in all three NSAID measures were statistically significantly greater in NHS Forth Valley than in NHS GGC, but only significantly greater for two of these measures 12 months after the intervention finished. Conclusion There were substantial and sustained reductions in the high-risk prescribing of NSAIDs, although with some waning of effect 12 months after the intervention ceased. The same intervention had no effect on antipsychotic prescribing in older people. PMID:28347986

Evaluation of a complex intervention to improve primary care prescribing: a phase IV segmented regression interrupted time series analysis.

PubMed

MacBride-Stewart, Sean; Marwick, Charis; Houston, Neil; Watt, Iain; Patton, Andrea; Guthrie, Bruce

2017-05-01

It is uncertain whether improvements in primary care high-risk prescribing seen in research trials can be realised in the real-world setting. To evaluate the impact of a 1-year system-wide phase IV prescribing safety improvement initiative, which included education, feedback, support to identify patients to review, and small financial incentives. An interrupted time series analysis of targeted high-risk prescribing in all 56 general practices in NHS Forth Valley, Scotland, was performed. In 2013-2014, this focused on high-risk non-steroidal anti-inflammatory drugs (NSAIDs) in older people and NSAIDs with oral anticoagulants; in 2014-2015, it focused on antipsychotics in older people. The primary analysis used segmented regression analysis to estimate impact at the end of the intervention, and 12 months later. The secondary analysis used difference-in-difference methods to compare Forth Valley changes with those in NHS Greater Glasgow and Clyde (GGC). In the primary analysis, downward trends for all three NSAID measures that were existent before the intervention statistically significantly steepened following implementation of the intervention. At the end of the intervention period, 1221 fewer patients than expected were prescribed a high-risk NSAID. In contrast, antipsychotic prescribing in older people increased slowly over time, with no intervention-associated change. In the secondary analysis, reductions at the end of the intervention period in all three NSAID measures were statistically significantly greater in NHS Forth Valley than in NHS GGC, but only significantly greater for two of these measures 12 months after the intervention finished. There were substantial and sustained reductions in the high-risk prescribing of NSAIDs, although with some waning of effect 12 months after the intervention ceased. The same intervention had no effect on antipsychotic prescribing in older people. © British Journal of General Practice 2017.

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)

[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

Factors associated with trait anger level of juvenile offenders in Hubei province: A binary logistic regression analysis.

PubMed

Tang, Li-Na; Ye, Xiao-Zhou; Yan, Qiu-Ge; Chang, Hong-Juan; Ma, Yu-Qiao; Liu, De-Bin; Li, Zhi-Gen; Yu, Yi-Zhen

2017-02-01

The risk factors of high trait anger of juvenile offenders were explored through questionnaire study in a youth correctional facility of Hubei province, China. A total of 1090 juvenile offenders in Hubei province were investigated by self-compiled social-demographic questionnaire, Childhood Trauma Questionnaire (CTQ), and State-Trait Anger Expression Inventory-II (STAXI-II). The risk factors were analyzed by chi-square tests, correlation analysis, and binary logistic regression analysis with SPSS 19.0. A total of 1082 copies of valid questionnaires were collected. High trait anger group (n=316) was defined as those who scored in the upper 27th percentile of STAXI-II trait anger scale (TAS), and the rest were defined as low trait anger group (n=766). The risk factors associated with high level of trait anger included: childhood emotional abuse, childhood sexual abuse, step family, frequent drug abuse, and frequent internet using (P<0.05 or P<0.01). Birth sequence, number of sibling, ranking in the family, identity of the main care-taker, the education level of care-taker, educational style of care-taker, family income, relationship between parents, social atmosphere of local area, frequent drinking, and frequent smoking did not predict to high level of trait anger (P>0.05). It was suggested that traumatic experience in childhood and unhealthy life style may significantly increase the level of trait anger in adulthood. The risk factors of high trait anger and their effects should be taken into consideration seriously.

Grey and white matter differences in brain energy metabolism in first episode schizophrenia: 31P-MRS chemical shift imaging at 4 Tesla.

PubMed

Jensen, J Eric; Miller, Jodi; Williamson, Peter C; Neufeld, Richard W J; Menon, Ravi S; Malla, Ashok; Manchanda, Rahul; Schaefer, Betsy; Densmore, Maria; Drost, Dick J

2006-03-31

Altered high energy and membrane metabolism, measured with phosphorus magnetic resonance spectroscopy (31P-MRS), has been inconsistently reported in schizophrenic patients in several anatomical brain regions implicated in the pathophysiology of this illness, with little attention to the effects of brain tissue type on the results. Tissue regression analysis correlates brain tissue type to measured metabolite levels, allowing for the extraction of "pure" estimated grey and white matter compartment metabolite levels. We use this tissue analysis technique on a clinical dataset of first episode schizophrenic patients and matched controls to investigate the effect of brain tissue specificity on altered energy and membrane metabolism. In vivo brain spectra from two regions, (a) the fronto-temporal-striatal region and (b) the frontal-lobes, were analyzed from 12 first episode schizophrenic patients and 11 matched controls from a (31)P chemical shift imaging (CSI) study at 4 Tesla (T) field strength. Tissue regression analyses using voxels from each region were performed relating metabolite levels to tissue content, examining phosphorus metabolite levels in grey and white matter compartments. Compared with controls, the first episode schizophrenic patient group showed significantly increased adenosine triphosphate levels (B-ATP) in white matter and decreased B-ATP levels in grey matter in the fronto-temporal-striatal region. No significant metabolite level differences were found in grey or white matter compartments in the frontal cortex. Tissue regression analysis reveals grey and white matter specific aberrations in high-energy phosphates in first episode schizophrenia. Although past studies report inconsistent regional differences in high-energy phosphate levels in schizophrenia, the present analysis suggests more widespread differences that seem to be strongly related to tissue type. Our data suggest that differences in grey and white matter tissue content between past studies may account for some of the variance in the literature.

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.

The 2011 heat wave in Greater Houston: Effects of land use on temperature.

PubMed

Zhou, Weihe; Ji, Shuang; Chen, Tsun-Hsuan; Hou, Yi; Zhang, Kai

2014-11-01

Effects of land use on temperatures during severe heat waves have been rarely studied. This paper examines land use-temperature associations during the 2011 heat wave in Greater Houston. We obtained high resolution of satellite-derived land use data from the US National Land Cover Database, and temperature observations at 138 weather stations from Weather Underground, Inc (WU) during the August of 2011, which was the hottest month in Houston since 1889. Land use regression and quantile regression methods were applied to the monthly averages of daily maximum/mean/minimum temperatures and 114 land use-related predictors. Although selected variables vary with temperature metric, distance to the coastline consistently appears among all models. Other variables are generally related to high developed intensity, open water or wetlands. In addition, our quantile regression analysis shows that distance to the coastline and high developed intensity areas have larger impacts on daily average temperatures at higher quantiles, and open water area has greater impacts on daily minimum temperatures at lower quantiles. By utilizing both land use regression and quantile regression on a recent heat wave in one of the largest US metropolitan areas, this paper provides a new perspective on the impacts of land use on temperatures. Our models can provide estimates of heat exposures for epidemiological studies, and our findings can be combined with demographic variables, air conditioning and relevant diseases information to identify 'hot spots' of population vulnerability for public health interventions to reduce heat-related health effects during heat waves. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis.

PubMed

Song, Seung Yeob; Lee, Young Koung; Kim, In-Jung

2016-01-01

A high-throughput screening system for Citrus lines were established with higher sugar and acid contents using Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. FT-IR spectra confirmed typical spectral differences between the frequency regions of 950-1100 cm(-1), 1300-1500 cm(-1), and 1500-1700 cm(-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate five Citrus lines into three separate clusters corresponding to their taxonomic relationships. The quantitative predictive modeling of sugar and acid contents from Citrus fruits was established using partial least square regression algorithms from FT-IR spectra. The regression coefficients (R(2)) between predicted values and estimated sugar and acid content values were 0.99. These results demonstrate that by using FT-IR spectra and applying quantitative prediction modeling to Citrus sugar and acid contents, excellent Citrus lines can be early detected with greater accuracy. Copyright © 2015 Elsevier Ltd. 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.

[An investigation on job burnout of medical personnel in a top three hospital].

PubMed

Li, Y Y; Li, L P

2016-05-20

To investigate job burnout status of medical Personnel in a top three hospitals, in order to provide basic data for intervention of the hospital management. A total of 549 doctors and nurses were assessed by Maslach Burnout Inventory-Human Service Survey (MBI-HSS). SPSS 19.0 software package was applied to data description and analysis, including univariate analysis and orderly classification Logistic regression analysis. The rate of high job burnout of doctors and nurses are 36.3% and 42.8% respectively. Female subjects got higher scores (29.4±13.5) on emotional exhaustion than male subjects (26.2±12.8) compared with.Doctors got lower scores (28.2±15.9) on emotional exhaustion and higher scores (31.4±9.3) on personal accomplishment than nurses.Compared with subjects with higher professional title, young subjects with primary professional title got lower scores on personal accomplishment.Subjects with 11-20 years working age got the highest scores on depersonalization.Among all the test departments, medical personnel of emergency department got the highest scores (31.9±12.6) on emotional exhaustion,while the lowest scores (28.1±8.0) on personal accomplishment. According to the results of orderly classification Logistic regression analysis, age, job type,professional qualifications and clinical departments type entered the regression model. Physical resources and emotional resources of medical personnel are overdraft so that they got some high degree of job burnout.Much more attention should be paid to professional mental health of nurses,and personnel who at low age,got low professional titles.Positive measures should be provided, including management mechanism,organizational culture, occupational protection and psychological intervention.

Objective Quantification of Pre-and Postphonosurgery Vocal Fold Vibratory Characteristics Using High-Speed Videoendoscopy and a Harmonic Waveform Model

ERIC Educational Resources Information Center

Ikuma, Takeshi; Kunduk, Melda; McWhorter, Andrew J.

2014-01-01

Purpose: The model-based quantitative analysis of high-speed videoendoscopy (HSV) data at a low frame rate of 2,000 frames per second was assessed for its clinical adequacy. Stepwise regression was employed to evaluate the HSV parameters using harmonic models and their relationships to the Voice Handicap Index (VHI). Also, the model-based HSV…

A Regression Analysis of South Carolina Algebra I End-of-Course Exam Scores by Schedule Type

ERIC Educational Resources Information Center

Smith, Dawn M.

2017-01-01

The purpose of this study was to examine the relationship between scheduling and first-year-high-school students' exam scores on the South Carolina Algebra I End-of-Course (EOC) assessment. The study compared existing empirical data from two southeastern high schools from the same school district using 4 X 4 block schedules from 2011-2014 and…

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.

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.

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.

Heat and moisture exchangers (HMEs) and heated humidifiers (HHs) in adult critically ill patients: a systematic review, meta-analysis and meta-regression of randomized controlled trials.

PubMed

Vargas, Maria; Chiumello, Davide; Sutherasan, Yuda; Ball, Lorenzo; Esquinas, Antonio M; Pelosi, Paolo; Servillo, Giuseppe

2017-05-29

The aims of this systematic review and meta-analysis of randomized controlled trials are to evaluate the effects of active heated humidifiers (HHs) and moisture exchangers (HMEs) in preventing artificial airway occlusion and pneumonia, and on mortality in adult critically ill patients. In addition, we planned to perform a meta-regression analysis to evaluate the relationship between the incidence of artificial airway occlusion, pneumonia and mortality and clinical features of adult critically ill patients. Computerized databases were searched for randomized controlled trials (RCTs) comparing HHs and HMEs and reporting artificial airway occlusion, pneumonia and mortality as predefined outcomes. Relative risk (RR), 95% confidence interval for each outcome and I 2 were estimated for each outcome. Furthermore, weighted random-effect meta-regression analysis was performed to test the relationship between the effect size on each considered outcome and covariates. Eighteen RCTs and 2442 adult critically ill patients were included in the analysis. The incidence of artificial airway occlusion (RR = 1.853; 95% CI 0.792-4.338), pneumonia (RR = 932; 95% CI 0.730-1.190) and mortality (RR = 1.023; 95% CI 0.878-1.192) were not different in patients treated with HMEs and HHs. However, in the subgroup analyses the incidence of airway occlusion was higher in HMEs compared with HHs with non-heated wire (RR = 3.776; 95% CI 1.560-9.143). According to the meta-regression, the effect size in the treatment group on artificial airway occlusion was influenced by the percentage of patients with pneumonia (β = -0.058; p = 0.027; favors HMEs in studies with high prevalence of pneumonia), and a trend was observed for an effect of the duration of mechanical ventilation (MV) (β = -0.108; p = 0.054; favors HMEs in studies with longer MV time). In this meta-analysis we found no superiority of HMEs and HHs, in terms of artificial airway occlusion, pneumonia and mortality. A trend favoring HMEs was observed in studies including a high percentage of patients with pneumonia diagnosis at admission and those with prolonged MV. However, the choice of humidifiers should be made according to the clinical context, trying to avoid possible complications and reaching the appropriate performance at lower costs.

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

PubMed

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

2018-03-13

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

Simultaneous determination of estrogens (ethinylestradiol and norgestimate) concentrations in human and bovine serum albumin by use of fluorescence spectroscopy and multivariate regression analysis.

PubMed

Hordge, LaQuana N; McDaniel, Kiara L; Jones, Derick D; Fakayode, Sayo O

2016-05-15

The endocrine disruption property of estrogens necessitates the immediate need for effective monitoring and development of analytical protocols for their analyses in biological and human specimens. This study explores the first combined utility of a steady-state fluorescence spectroscopy and multivariate partial-least-square (PLS) regression analysis for the simultaneous determination of two estrogens (17α-ethinylestradiol (EE) and norgestimate (NOR)) concentrations in bovine serum albumin (BSA) and human serum albumin (HSA) samples. The influence of EE and NOR concentrations and temperature on the emission spectra of EE-HSA EE-BSA, NOR-HSA, and NOR-BSA complexes was also investigated. The binding of EE with HSA and BSA resulted in increase in emission characteristics of HSA and BSA and a significant blue spectra shift. In contrast, the interaction of NOR with HSA and BSA quenched the emission characteristics of HSA and BSA. The observed emission spectral shifts preclude the effective use of traditional univariate regression analysis of fluorescent data for the determination of EE and NOR concentrations in HSA and BSA samples. Multivariate partial-least-squares (PLS) regression analysis was utilized to correlate the changes in emission spectra with EE and NOR concentrations in HSA and BSA samples. The figures-of-merit of the developed PLS regression models were excellent, with limits of detection as low as 1.6×10(-8) M for EE and 2.4×10(-7) M for NOR and good linearity (R(2)>0.994985). The PLS models correctly predicted EE and NOR concentrations in independent validation HSA and BSA samples with a root-mean-square-percent-relative-error (RMS%RE) of less than 6.0% at physiological condition. On the contrary, the use of univariate regression resulted in poor predictions of EE and NOR in HSA and BSA samples, with RMS%RE larger than 40% at physiological conditions. High accuracy, low sensitivity, simplicity, low-cost with no prior analyte extraction or separation required makes this method promising, compelling, and attractive alternative for the rapid determination of estrogen concentrations in biomedical and biological specimens, pharmaceuticals, or environmental samples. Published by Elsevier B.V.

[Predicting the probability of development and progression of primary open angle glaucoma by regression modeling].

PubMed

Likhvantseva, V G; Sokolov, V A; Levanova, O N; Kovelenova, I V

2018-01-01

Prediction of the clinical course of primary open-angle glaucoma (POAG) is one of the main directions in solving the problem of vision loss prevention and stabilization of the pathological process. Simple statistical methods of correlation analysis show the extent of each risk factor's impact, but do not indicate the total impact of these factors in personalized combinations. The relationships between the risk factors is subject to correlation and regression analysis. The regression equation represents the dependence of the mathematical expectation of the resulting sign on the combination of factor signs. To develop a technique for predicting the probability of development and progression of primary open-angle glaucoma based on a personalized combination of risk factors by linear multivariate regression analysis. The study included 66 patients (23 female and 43 male; 132 eyes) with newly diagnosed primary open-angle glaucoma. The control group consisted of 14 patients (8 male and 6 female). Standard ophthalmic examination was supplemented with biochemical study of lacrimal fluid. Concentration of matrix metalloproteinase MMP-2 and MMP-9 in tear fluid in both eyes was determined using 'sandwich' enzyme-linked immunosorbent assay (ELISA) method. The study resulted in the development of regression equations and step-by-step multivariate logistic models that can help calculate the risk of development and progression of POAG. Those models are based on expert evaluation of clinical and instrumental indicators of hydrodynamic disturbances (coefficient of outflow ease - C, volume of intraocular fluid secretion - F, fluctuation of intraocular pressure), as well as personalized morphometric parameters of the retina (central retinal thickness in the macular area) and concentration of MMP-2 and MMP-9 in the tear film. The newly developed regression equations are highly informative and can be a reliable tool for studying of the influence vector and assessment of pathogenic potential of the independent risk factors in specific personalized combinations.

Variable Selection for Regression Models of Percentile Flows

NASA Astrophysics Data System (ADS)

Fouad, G.

2017-12-01

Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high degree of multicollinearity, possibly illustrating the co-evolution of climatic and physiographic conditions. Given the ineffectiveness of many variables used here, future work should develop new variables that target specific processes associated with percentile flows.

Prevalence of vitamin D deficiency and associated factors in women and newborns in the immediate postpartum period

PubMed Central

do Prado, Mara Rúbia Maciel Cardoso; Oliveira, Fabiana de Cássia Carvalho; Assis, Karine Franklin; Ribeiro, Sarah Aparecida Vieira; do Prado, Pedro Paulo; Sant'Ana, Luciana Ferreira da Rocha; Priore, Silvia Eloiza; Franceschini, Sylvia do Carmo Castro

2015-01-01

Abstract Objective: To assess the prevalence of vitamin D deficiency and its associated factors in women and their newborns in the postpartum period. Methods: This cross-sectional study evaluated vitamin D deficiency/insufficiency in 226 women and their newborns in Viçosa (Minas Gerais, BR) between December 2011 and November 2012. Cord blood and venous maternal blood were collected to evaluate the following biochemical parameters: vitamin D, alkaline phosphatase, calcium, phosphorus and parathyroid hormone. Poisson regression analysis, with a confidence interval of 95%, was applied to assess vitamin D deficiency and its associated factors. Multiple linear regression analysis was performed to identify factors associated with 25(OH)D deficiency in the newborns and women from the study. The criteria for variable inclusion in the multiple linear regression model was the association with the dependent variable in the simple linear regression analysis, considering p<0.20. Significance level was α <5%. Results: From 226 women included, 200 (88.5%) were 20-44 years old; the median age was 28 years. Deficient/insufficient levels of vitamin D were found in 192 (85%) women and in 182 (80.5%) neonates. The maternal 25(OH)D and alkaline phosphatase levels were independently associated with vitamin D deficiency in infants. Conclusions: This study identified a high prevalence of vitamin D deficiency and insufficiency in women and newborns and the association between maternal nutritional status of vitamin D and their infants' vitamin D status. PMID:26100593

Statistical research using the multiple regression analysis in areas of the cast hipereutectoid steel rolls manufacturing

NASA Astrophysics Data System (ADS)

Kiss, I.; Alexa, V.; Serban, S.; Rackov, M.; Čavić, M.

2018-01-01

The cast hipereutectoid steel (usually named Adamite) is a roll manufacturing destined material, having mechanical, chemical properties and Carbon [C] content of which stands between steelandiron, along-withitsalloyelements such as Nickel [Ni], Chrome [Cr], Molybdenum [Mo] and/or other alloy elements. Adamite Rolls are basically alloy steel rolls (a kind of high carbon steel) having hardness ranging from 40 to 55 degrees Shore C, with Carbon [C] percentage ranging from 1.35% until to 2% (usually between 1.2˜2.3%), the extra Carbon [C] and the special alloying element giving an extra wear resistance and strength. First of all the Adamite roll’s prominent feature is the small variation in hardness of the working surface, and has a good abrasion resistance and bite performance. This paper reviews key aspects of roll material properties and presents an analysis of the influences of chemical composition upon the mechanical properties (hardness) of the cast hipereutectoid steel rolls (Adamite). Using the multiple regression analysis (the double and triple regression equations), some mathematical correlations between the cast hipereutectoid steel rolls’ chemical composition and the obtained hardness are presented. In this work several results and evidence obtained by actual experiments are presented. Thus, several variation boundaries for the chemical composition of cast hipereutectoid steel rolls, in view the obtaining the proper values of the hardness, are revealed. For the multiple regression equations, correlation coefficients and graphical representations the software Matlab was used.

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.

The effect of high leverage points on the logistic ridge regression estimator having multicollinearity

NASA Astrophysics Data System (ADS)

Ariffin, Syaiba Balqish; Midi, Habshah

2014-06-01

This article is concerned with the performance of logistic ridge regression estimation technique in the presence of multicollinearity and high leverage points. In logistic regression, multicollinearity exists among predictors and in the information matrix. The maximum likelihood estimator suffers a huge setback in the presence of multicollinearity which cause regression estimates to have unduly large standard errors. To remedy this problem, a logistic ridge regression estimator is put forward. It is evident that the logistic ridge regression estimator outperforms the maximum likelihood approach for handling multicollinearity. The effect of high leverage points are then investigated on the performance of the logistic ridge regression estimator through real data set and simulation study. The findings signify that logistic ridge regression estimator fails to provide better parameter estimates in the presence of both high leverage points and multicollinearity.

Multiple regression analysis in modelling of carbon dioxide emissions by energy consumption use in Malaysia

NASA Astrophysics Data System (ADS)

Keat, Sim Chong; Chun, Beh Boon; San, Lim Hwee; Jafri, Mohd Zubir Mat

2015-04-01

Climate change due to carbon dioxide (CO2) emissions is one of the most complex challenges threatening our planet. This issue considered as a great and international concern that primary attributed from different fossil fuels. In this paper, regression model is used for analyzing the causal relationship among CO2 emissions based on the energy consumption in Malaysia using time series data for the period of 1980-2010. The equations were developed using regression model based on the eight major sources that contribute to the CO2 emissions such as non energy, Liquefied Petroleum Gas (LPG), diesel, kerosene, refinery gas, Aviation Turbine Fuel (ATF) and Aviation Gasoline (AV Gas), fuel oil and motor petrol. The related data partly used for predict the regression model (1980-2000) and partly used for validate the regression model (2001-2010). The results of the prediction model with the measured data showed a high correlation coefficient (R2=0.9544), indicating the model's accuracy and efficiency. These results are accurate and can be used in early warning of the population to comply with air quality standards.

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.

Bayesian propensity scores for high-dimensional causal inference: A comparison of drug-eluting to bare-metal coronary stents.

PubMed

Spertus, Jacob V; Normand, Sharon-Lise T

2018-04-23

High-dimensional data provide many potential confounders that may bolster the plausibility of the ignorability assumption in causal inference problems. Propensity score methods are powerful causal inference tools, which are popular in health care research and are particularly useful for high-dimensional data. Recent interest has surrounded a Bayesian treatment of propensity scores in order to flexibly model the treatment assignment mechanism and summarize posterior quantities while incorporating variance from the treatment model. We discuss methods for Bayesian propensity score analysis of binary treatments, focusing on modern methods for high-dimensional Bayesian regression and the propagation of uncertainty. We introduce a novel and simple estimator for the average treatment effect that capitalizes on conjugacy of the beta and binomial distributions. Through simulations, we show the utility of horseshoe priors and Bayesian additive regression trees paired with our new estimator, while demonstrating the importance of including variance from the treatment regression model. An application to cardiac stent data with almost 500 confounders and 9000 patients illustrates approaches and facilitates comparison with existing alternatives. As measured by a falsifiability endpoint, we improved confounder adjustment compared with past observational research of the same problem. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Logistic regression trees for initial selection of interesting loci in case-control studies

PubMed Central

Nickolov, Radoslav Z; Milanov, Valentin B

2007-01-01

Modern genetic epidemiology faces the challenge of dealing with hundreds of thousands of genetic markers. The selection of a small initial subset of interesting markers for further investigation can greatly facilitate genetic studies. In this contribution we suggest the use of a logistic regression tree algorithm known as logistic tree with unbiased selection. Using the simulated data provided for Genetic Analysis Workshop 15, we show how this algorithm, with incorporation of multifactor dimensionality reduction method, can reduce an initial large pool of markers to a small set that includes the interesting markers with high probability. PMID:18466557

Visual abilities distinguish pitchers from hitters in professional baseball.

PubMed

Klemish, David; Ramger, Benjamin; Vittetoe, Kelly; Reiter, Jerome P; Tokdar, Surya T; Appelbaum, Lawrence Gregory

2018-01-01

This study aimed to evaluate the possibility that differences in sensorimotor abilities exist between hitters and pitchers in a large cohort of baseball players of varying levels of experience. Secondary data analysis was performed on 9 sensorimotor tasks comprising the Nike Sensory Station assessment battery. Bayesian hierarchical regression modelling was applied to test for differences between pitchers and hitters in data from 566 baseball players (112 high school, 85 college, 369 professional) collected at 20 testing centres. Explanatory variables including height, handedness, eye dominance, concussion history, and player position were modelled along with age curves using basis regression splines. Regression analyses revealed better performance for hitters relative to pitchers at the professional level in the visual clarity and depth perception tasks, but these differences did not exist at the high school or college levels. No significant differences were observed in the other 7 measures of sensorimotor capabilities included in the test battery, and no systematic biases were found between the testing centres. These findings, indicating that professional-level hitters have better visual acuity and depth perception than professional-level pitchers, affirm the notion that highly experienced athletes have differing perceptual skills. Findings are discussed in relation to deliberate practice theory.

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.

Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center

PubMed Central

Mehra, Tarun; Müller, Christian Thomas Benedikt; Volbracht, Jörk; Seifert, Burkhardt; Moos, Rudolf

2015-01-01

Principles Case weights of Diagnosis Related Groups (DRGs) are determined by the average cost of cases from a previous billing period. However, a significant amount of cases are largely over- or underfunded. We therefore decided to analyze earning outliers of our hospital as to search for predictors enabling a better grouping under SwissDRG. Methods 28,893 inpatient cases without additional private insurance discharged from our hospital in 2012 were included in our analysis. Outliers were defined by the interquartile range method. Predictors for deficit and profit outliers were determined with logistic regressions. Predictors were shortlisted with the LASSO regularized logistic regression method and compared to results of Random forest analysis. 10 of these parameters were selected for quantile regression analysis as to quantify their impact on earnings. Results Psychiatric diagnosis and admission as an emergency case were significant predictors for higher deficit with negative regression coefficients for all analyzed quantiles (p<0.001). Admission from an external health care provider was a significant predictor for a higher deficit in all but the 90% quantile (p<0.001 for Q10, Q20, Q50, Q80 and p = 0.0017 for Q90). Burns predicted higher earnings for cases which were favorably remunerated (p<0.001 for the 90% quantile). Osteoporosis predicted a higher deficit in the most underfunded cases, but did not predict differences in earnings for balanced or profitable cases (Q10 and Q20: p<0.00, Q50: p = 0.10, Q80: p = 0.88 and Q90: p = 0.52). ICU stay, mechanical and patient clinical complexity level score (PCCL) predicted higher losses at the 10% quantile but also higher profits at the 90% quantile (p<0.001). Conclusion We suggest considering psychiatric diagnosis, admission as an emergencay case and admission from an external health care provider as DRG split criteria as they predict large, consistent and significant losses. PMID:26517545

Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.

PubMed

Mehra, Tarun; Müller, Christian Thomas Benedikt; Volbracht, Jörk; Seifert, Burkhardt; Moos, Rudolf

2015-01-01

Case weights of Diagnosis Related Groups (DRGs) are determined by the average cost of cases from a previous billing period. However, a significant amount of cases are largely over- or underfunded. We therefore decided to analyze earning outliers of our hospital as to search for predictors enabling a better grouping under SwissDRG. 28,893 inpatient cases without additional private insurance discharged from our hospital in 2012 were included in our analysis. Outliers were defined by the interquartile range method. Predictors for deficit and profit outliers were determined with logistic regressions. Predictors were shortlisted with the LASSO regularized logistic regression method and compared to results of Random forest analysis. 10 of these parameters were selected for quantile regression analysis as to quantify their impact on earnings. Psychiatric diagnosis and admission as an emergency case were significant predictors for higher deficit with negative regression coefficients for all analyzed quantiles (p<0.001). Admission from an external health care provider was a significant predictor for a higher deficit in all but the 90% quantile (p<0.001 for Q10, Q20, Q50, Q80 and p = 0.0017 for Q90). Burns predicted higher earnings for cases which were favorably remunerated (p<0.001 for the 90% quantile). Osteoporosis predicted a higher deficit in the most underfunded cases, but did not predict differences in earnings for balanced or profitable cases (Q10 and Q20: p<0.00, Q50: p = 0.10, Q80: p = 0.88 and Q90: p = 0.52). ICU stay, mechanical and patient clinical complexity level score (PCCL) predicted higher losses at the 10% quantile but also higher profits at the 90% quantile (p<0.001). We suggest considering psychiatric diagnosis, admission as an emergency case and admission from an external health care provider as DRG split criteria as they predict large, consistent and significant losses.

Epistasis analysis for quantitative traits by functional regression model.

PubMed

Zhang, Futao; Boerwinkle, Eric; Xiong, Momiao

2014-06-01

The critical barrier in interaction analysis for rare variants is that most traditional statistical methods for testing interactions were originally designed for testing the interaction between common variants and are difficult to apply to rare variants because of their prohibitive computational time and poor ability. The great challenges for successful detection of interactions with next-generation sequencing (NGS) data are (1) lack of methods for interaction analysis with rare variants, (2) severe multiple testing, and (3) time-consuming computations. To meet these challenges, we shift the paradigm of interaction analysis between two loci to interaction analysis between two sets of loci or genomic regions and collectively test interactions between all possible pairs of SNPs within two genomic regions. In other words, we take a genome region as a basic unit of interaction analysis and use high-dimensional data reduction and functional data analysis techniques to develop a novel functional regression model to collectively test interactions between all possible pairs of single nucleotide polymorphisms (SNPs) within two genome regions. By intensive simulations, we demonstrate that the functional regression models for interaction analysis of the quantitative trait have the correct type 1 error rates and a much better ability to detect interactions than the current pairwise interaction analysis. The proposed method was applied to exome sequence data from the NHLBI's Exome Sequencing Project (ESP) and CHARGE-S study. We discovered 27 pairs of genes showing significant interactions after applying the Bonferroni correction (P-values < 4.58 × 10(-10)) in the ESP, and 11 were replicated in the CHARGE-S study. © 2014 Zhang et al.; Published by Cold Spring Harbor Laboratory Press.

Predictors of Incomes. AIR Forum 1981 Paper.

ERIC Educational Resources Information Center

Witmer, David R.

Income predictions that provide some indication of the potential value of attending college are considered. Standard multiple regression analysis of data describing the income experiences of men 25 years old and older were used to determine differences in incomes of high school and college graduates. Information on the gross national product was…

The Impact of Consumer Credentialism on Employee and Entrepreneur Returns to Higher Education.

ERIC Educational Resources Information Center

Tucker, Irvin B., III

1987-01-01

Examines the relative importance of education credentials in consumer perceptions of self-employed business people. Using 1980 national cross-sectional data on goods- and service-producing occupations, the regression analysis shows that highly educated entrepreneurs are not influenced by consumer credentialism. Includes 17 references. (MLH)

A Logistic Regression Analysis of Score Sending and College Matching among High School Students

ERIC Educational Resources Information Center

Oates, Krystle S.

2015-01-01

College decisions are often the result of a variety of influences related to student background characteristics, academic characteristics, college preferences and college aspirations. College counselors recommend that students choose a variety of schools, especially schools where the general student body matches the academic achievement of…

Pesticides in Urban Multiunit Dwellings: Hazard IdentificationUsing Classification and Regression Tree (CART) Analysis

EPA Science Inventory

Many units in public housing or other low-income urban dwellings may have elevated pesticide residues, given recurring infestation, but it would be logistically and economically infeasible to sample a large number of units to identify highly exposed households to design interven...

Teacher Salaries and Teacher Aptitude: An Analysis Using Quantile Regressions

ERIC Educational Resources Information Center

Gilpin, Gregory A.

2012-01-01

This study investigates the relationship between salaries and scholastic aptitude for full-time public high school humanities and mathematics/sciences teachers. For identification, we rely on variation in salaries between adjacent school districts within the same state. The results indicate that teacher aptitude is positively correlated with…

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

PubMed

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

2015-01-01

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

Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data

PubMed Central

Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D.; Nichols, Thomas E.

2017-01-01

Summary Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. PMID:28498564

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…

Decreased levels of sRAGE in follicular fluid from patients with PCOS.

PubMed

Wang, BiJun; Li, Jing; Yang, QingLing; Zhang, FuLi; Hao, MengMeng; Guo, YiHong

2017-03-01

This study aimed to explore the association between soluble receptor for advanced glycation end products (sRAGE) levels in follicular fluid and the number of oocytes retrieved and to evaluate the effect of sRAGE on vascular endothelial growth factor (VEGF) in granulosa cells in patients with polycystic ovarian syndrome (PCOS). Two sets of experiments were performed in this study. In part one, sRAGE and VEGF protein levels in follicular fluid samples from 39 patients with PCOS and 35 non-PCOS patients were measured by ELISA. In part two, ovarian granulosa cells were isolated from an additional 10 patients with PCOS and cultured. VEGF and SP1 mRNA and protein levels, as well as pAKT levels, were detected by real-time PCR and Western blotting after cultured cells were treated with different concentrations of sRAGE. Compared with the non-PCOS patients, patients with PCOS had lower sRAGE levels in follicular fluid. Multi-adjusted regression analysis showed that high sRAGE levels in follicular fluid predicted a lower Gn dose, more oocytes retrieved, and a better IVF outcome in the non-PCOS group. Logistic regression analysis showed that higher sRAGE levels predicted favorably IVF outcomes in the non-PCOS group. Multi-adjusted regression analysis also showed that high sRAGE levels in follicular fluid predicted a lower Gn dose in the PCOS group. Treating granulosa cells isolated from patients with PCOS with recombinant sRAGE decreased VEGF and SP1 mRNA and protein expression and pAKT levels in a dose-dependent manner. © 2017 Society for Reproduction and Fertility.

Psychological factors influence the gastroesophageal reflux disease (GERD) and their effect on quality of life among firefighters in South Korea.

PubMed

Jang, Seung-Ho; Ryu, Han-Seung; Choi, Suck-Chei; Lee, Sang-Yeol

2016-10-01

The purpose of this study was to examine psychosocial factors related to gastroesophageal reflux disease (GERD) and their effects on quality of life (QOL) in firefighters. Data were collected from 1217 firefighters in a Korean province. We measured psychological symptoms using the scale. In order to observe the influence of the high-risk group on occupational stress, we conduct logistic multiple linear regression. The correlation between psychological factors and QOL was also analyzed and performed a hierarchical regression analysis. GERD was observed in 32.2% of subjects. Subjects with GERD showed higher depressive symptom, anxiety and occupational stress scores, and lower self-esteem and QOL scores relative to those observed in GERD - negative subject. GERD risk was higher for the following occupational stress subcategories: job demand, lack of reward, interpersonal conflict, and occupational climate. The stepwise regression analysis showed that depressive symptoms, occupational stress, self-esteem, and anxiety were the best predictors of QOL. The results suggest that psychological and medical approaches should be combined in GERD assessment.

Prediction of strontium bromide laser efficiency using cluster and decision tree analysis

NASA Astrophysics Data System (ADS)

Iliev, Iliycho; Gocheva-Ilieva, Snezhana; Kulin, Chavdar

2018-01-01

Subject of investigation is a new high-powered strontium bromide (SrBr2) vapor laser emitting in multiline region of wavelengths. The laser is an alternative to the atom strontium lasers and electron free lasers, especially at the line 6.45 μm which line is used in surgery for medical processing of biological tissues and bones with minimal damage. In this paper the experimental data from measurements of operational and output characteristics of the laser are statistically processed by means of cluster analysis and tree-based regression techniques. The aim is to extract the more important relationships and dependences from the available data which influence the increase of the overall laser efficiency. There are constructed and analyzed a set of cluster models. It is shown by using different cluster methods that the seven investigated operational characteristics (laser tube diameter, length, supplied electrical power, and others) and laser efficiency are combined in 2 clusters. By the built regression tree models using Classification and Regression Trees (CART) technique there are obtained dependences to predict the values of efficiency, and especially the maximum efficiency with over 95% accuracy.

HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS

PubMed Central

Wang, Shuang; Zhang, Yuchen; Dai, Wenrui; Lauter, Kristin; Kim, Miran; Tang, Yuzhe; Xiong, Hongkai; Jiang, Xiaoqian

2016-01-01

Motivation: Genome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individual’s privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size. Results: We target at the algorithm design aiming at reducing the computational and storage costs to learn a homomorphic exact logistic regression model (i.e. evaluate P-values of coefficients), where the circuit depth is proportional to the logarithmic scale of data size. We evaluate the algorithm performance using rare Kawasaki Disease datasets. Availability and implementation: Download HEALER at http://research.ucsd-dbmi.org/HEALER/ Contact: shw070@ucsd.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26446135

Psychological factors influence the gastroesophageal reflux disease (GERD) and their effect on quality of life among firefighters in South Korea

PubMed Central

Jang, Seung-Ho; Ryu, Han-Seung; Choi, Suck-Chei; Lee, Sang-Yeol

2016-01-01

Objectives The purpose of this study was to examine psychosocial factors related to gastroesophageal reflux disease (GERD) and their effects on quality of life (QOL) in firefighters. Methods Data were collected from 1217 firefighters in a Korean province. We measured psychological symptoms using the scale. In order to observe the influence of the high-risk group on occupational stress, we conduct logistic multiple linear regression. The correlation between psychological factors and QOL was also analyzed and performed a hierarchical regression analysis. Results GERD was observed in 32.2% of subjects. Subjects with GERD showed higher depressive symptom, anxiety and occupational stress scores, and lower self-esteem and QOL scores relative to those observed in GERD – negative subject. GERD risk was higher for the following occupational stress subcategories: job demand, lack of reward, interpersonal conflict, and occupational climate. The stepwise regression analysis showed that depressive symptoms, occupational stress, self-esteem, and anxiety were the best predictors of QOL. Conclusions The results suggest that psychological and medical approaches should be combined in GERD assessment. PMID:27691373

Discrimination and characterization of strawberry juice based on electronic nose and tongue: comparison of different juice processing approaches by LDA, PLSR, RF, and SVM.

PubMed

Qiu, Shanshan; Wang, Jun; Gao, Liping

2014-07-09

An electronic nose (E-nose) and an electronic tongue (E-tongue) have been used to characterize five types of strawberry juices based on processing approaches (i.e., microwave pasteurization, steam blanching, high temperature short time pasteurization, frozen-thawed, and freshly squeezed). Juice quality parameters (vitamin C, pH, total soluble solid, total acid, and sugar/acid ratio) were detected by traditional measuring methods. Multivariate statistical methods (linear discriminant analysis (LDA) and partial least squares regression (PLSR)) and neural networks (Random Forest (RF) and Support Vector Machines) were employed to qualitative classification and quantitative regression. E-tongue system reached higher accuracy rates than E-nose did, and the simultaneous utilization did have an advantage in LDA classification and PLSR regression. According to cross-validation, RF has shown outstanding and indisputable performances in the qualitative and quantitative analysis. This work indicates that the simultaneous utilization of E-nose and E-tongue can discriminate processed fruit juices and predict quality parameters successfully for the beverage industry.

Baseline placental growth factor levels for the prediction of benefit from early aspirin prophylaxis for preeclampsia prevention.

PubMed

Moore, Gaea S; Allshouse, Amanda A; Winn, Virginia D; Galan, Henry L; Heyborne, Kent D

2015-10-01

Placental growth factor (PlGF) levels early in pregnancy are lower in women who ultimately develop preeclampsia. Early initiation of low-dose aspirin reduces preeclampsia risk in some high risk women. We hypothesized that low PlGF levels may identify women at increased risk for preeclampsia who would benefit from aspirin. Secondary analysis of the MFMU High-Risk Aspirin study including singleton pregnancies randomized to aspirin 60mg/d (n=102) or placebo (n=72), with PlGF collected at 13w 0d-16w 6d. Within the placebo group, we estimated the probability of preeclampsia by PlGF level using logistic regression analysis, then determined a potential PlGF threshold for preeclampsia prediction using ROC analysis. We performed logistic regression modeling for potential confounders. ROC analysis indicated 87.71pg/ml as the threshold between high and low PlGF for preeclampsia-prediction. Within the placebo group high PlGF weakly predicted preeclampsia (AUC 0.653, sensitivity/specificity 63%/66%). We noted a 2.6-fold reduction in preeclampsia with aspirin in the high-PlGF group (12.15% aspirin vs 32.14% placebo, p=0.057), but no significant differences in preeclampsia in the low PlGF group (21.74% vs 15.91%, p=0.445). Unlike other studies, we found that high rather than low PlGF levels were associated with an increased preeclampsia risk. Low PlGF neither identified women at increased risk of preeclampsia nor women who benefitted from aspirin. Further research is needed to determine whether aspirin is beneficial in women with high PlGF, and whether the paradigm linking low PlGF and preeclampsia needs to be reevaluated. High-risk women with low baseline PlGF, a risk factor for preeclampsia, did not benefit from early initiation of low-dose aspirin. Copyright © 2015 International Society for the Study of Hypertension in Pregnancy. Published by Elsevier B.V. All rights reserved.

Relationship between serum bilirubin concentrations and diabetic nephropathy in Shanghai Han's patients with type 1 diabetes mellitus.

PubMed

Li, Xu; Zhang, Lei; Chen, Haibing; Guo, Kaifeng; Yu, Haoyong; Zhou, Jian; Li, Ming; Li, Qing; Li, Lianxi; Yin, Jun; Liu, Fang; Bao, Yuqian; Han, Junfeng; Jia, Weiping

2017-03-31

Recent studies highlight a negative association between total bilirubin concentrations and albuminuria in patients with type 2 diabetes mellitus. Our study evaluated the relationship between bilirubin concentrations and the prevalence of diabetic nephropathy (DN) in Chinese patients with type 1 diabetes mellitus (T1DM). A total of 258 patients with T1DM were recruited and bilirubin concentrations were compared between patients with or without diabetic nephropathy. Multiple stepwise regression analysis was used to examine the relationship between bilirubin concentrations and 24 h urinary microalbumin. Binary logistic regression analysis was performed to assess independent risk factors for diabetic nephropathy. Participants were divided into four groups according to the quartile of total bilirubin concentrations (Q1, 0.20-0.60; Q2, 0.60-0.80; Q3, 0.80-1.00; Q4, 1.00-1.90 mg/dL) and the chi-square test was used to compare the prevalence of DN in patients with T1DM. The median bilirubin level was 0.56 (interquartile: 0.43-0.68 mg/dL) in the DN group, significantly lower than in the non-DN group (0.70 [interquartile: 0.58-0.89 mg/dL], P < 0.001). Spearman's correlational analysis showed bilirubin concentrations were inversely correlated with 24 h urinary microalbumin (r = -0.13, P < 0.05) and multiple stepwise regression analysis showed bilirubin concentrations were independently associated with 24 h urinary microalbumin. In logistic regression analysis, bilirubin concentrations were significantly inversely associated with nephropathy. In addition, in stratified analysis, from the first to the fourth quartile group, increased bilirubin concentrations were associated with decreased prevalence of DN from 21.90% to 2.00%. High bilirubin concentrations are independently and negatively associated with albuminuria and the prevalence of DN in patients with T1DM.

Reference-Free Removal of EEG-fMRI Ballistocardiogram Artifacts with Harmonic Regression

PubMed Central

Krishnaswamy, Pavitra; Bonmassar, Giorgio; Poulsen, Catherine; Pierce, Eric T; Purdon, Patrick L.; Brown, Emery N.

2016-01-01

Combining electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) offers the potential for imaging brain activity with high spatial and temporal resolution. This potential remains limited by the significant ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation-related head movement within the magnetic field. We model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts. Our analysis paradigm accounts for time-frequency overlap between the BCG artifacts and neurophysiologic EEG signals, and tracks the spatiotemporal variations in both the artifact and the signal. We evaluate performance on: simulated oscillatory and evoked responses constructed with realistic artifacts; actual anesthesia-induced oscillatory recordings; and actual visual evoked potential recordings. In each case, the local harmonic regression analysis effectively removes the BCG artifacts, and recovers the neurophysiologic EEG signals. We further show that our algorithm outperforms commonly used reference-based and component analysis techniques, particularly in low SNR conditions, the presence of significant time-frequency overlap between the artifact and the signal, and/or large spatiotemporal variations in the BCG. Because our algorithm does not require reference signals and has low computational complexity, it offers a practical tool for removing BCG artifacts from EEG data recorded in combination with fMRI. PMID:26151100

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

PubMed

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

2012-06-01

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

Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

2017-05-01

The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for maximal response. For the calculation of the regression coefficients, dispersion and correlation coefficients, the software Matlab was used.

Predicting Scientific Understanding of Prospective Elementary Teachers: Role of Gender, Education Level, Courses in Science, and Attitudes Toward Science and Mathematics

NASA Astrophysics Data System (ADS)

Kumar, David D.; Morris, John D.

2005-12-01

A multiple regression analysis of the relationship between prospective teachers' scientific understanding and Gender, Education Level (High School, College), Courses in Science (Biology, Chemistry, Physics, Earth Science, Astronomy, and Agriculture), Attitude Towards Science, and Attitude Towards Mathematics is reported. Undergraduate elementary science students ( N = 176) in an urban doctoral-level university in the United States participated in this study. The results of this study showed Gender, completion of courses in High School Chemistry and Physics, College Chemistry and Physics, and Attitudes Toward Mathematics and Science significantly correlated with scientific understanding. Based on a regression model, Gender, and College Chemistry and Physics experiences added significant predictive accuracy to scientific understanding among prospective elementary teachers compared to the other variables.

Machine vision and appearance based learning

NASA Astrophysics Data System (ADS)

Bernstein, Alexander

2017-03-01

Smart algorithms are used in Machine vision to organize or extract high-level information from the available data. The resulted high-level understanding the content of images received from certain visual sensing system and belonged to an appearance space can be only a key first step in solving various specific tasks such as mobile robot navigation in uncertain environments, road detection in autonomous driving systems, etc. Appearance-based learning has become very popular in the field of machine vision. In general, the appearance of a scene is a function of the scene content, the lighting conditions, and the camera position. Mobile robots localization problem in machine learning framework via appearance space analysis is considered. This problem is reduced to certain regression on an appearance manifold problem, and newly regression on manifolds methods are used for its solution.

Assessing the risk of bovine fasciolosis using linear regression analysis for the state of Rio Grande do Sul, Brazil.

PubMed

Silva, Ana Elisa Pereira; Freitas, Corina da Costa; Dutra, Luciano Vieira; Molento, Marcelo Beltrão

2016-02-15

Fasciola hepatica is the causative agent of fasciolosis, a disease that triggers a chronic inflammatory process in the liver affecting mainly ruminants and other animals including humans. In Brazil, F. hepatica occurs in larger numbers in the most Southern state of Rio Grande do Sul. The objective of this study was to estimate areas at risk using an eight-year (2002-2010) time series of climatic and environmental variables that best relate to the disease using a linear regression method to municipalities in the state of Rio Grande do Sul. The positivity index of the disease, which is the rate of infected animal per slaughtered animal, was divided into three risk classes: low, medium and high. The accuracy of the known sample classification on the confusion matrix for the low, medium and high rates produced by the estimated model presented values between 39 and 88% depending of the year. The regression analysis showed the importance of the time-based data for the construction of the model, considering the two variables of the previous year of the event (positivity index and maximum temperature). The generated data is important for epidemiological and parasite control studies mainly because F. hepatica is an infection that can last from months to years. Copyright © 2015 Elsevier B.V. All rights reserved.

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

PubMed

Wilks, Scott E; Croom, Beth

2008-05-01

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

A Meta-analysis on Resting State High-frequency Heart Rate Variability in Bulimia Nervosa.

PubMed

Peschel, Stephanie K V; Feeling, Nicole R; Vögele, Claus; Kaess, Michael; Thayer, Julian F; Koenig, Julian

2016-09-01

Autonomic nervous system function is altered in eating disorders. We aimed to quantify differences in resting state vagal activity, indexed by high-frequency heart rate variability comparing patients with bulimia nervosa (BN) and healthy controls. A systematic search of the literature to identify studies eligible for inclusion and meta-analytical methods were applied. Meta-regression was used to identify potential covariates. Eight studies reporting measures of resting high-frequency heart rate variability in individuals with BN (n = 137) and controls (n = 190) were included. Random-effects meta-analysis revealed a sizeable main effect (Z = 2.22, p = .03; Hedge's g = 0.52, 95% CI [0.06;0.98]) indicating higher resting state vagal activity in individuals with BN. Meta-regression showed that body mass index and medication intake are significant covariates. Findings suggest higher vagal activity in BN at rest, particularly in unmedicated samples with lower body mass index. Potential mechanisms underlying these findings and implications for routine clinical care are discussed. Copyright © 2016 John Wiley & Sons, Ltd and Eating Disorders Association. Copyright © 2016 John Wiley & Sons, Ltd and Eating Disorders Association.

Dietary patterns in the Avon Longitudinal Study of Parents and Children

PubMed Central

Jones, Louise R.; Northstone, Kate

2015-01-01

Publications from the Avon Longitudinal Study of Parents and Children that used empirically derived dietary patterns were reviewed. The relationships of dietary patterns with socioeconomic background and childhood development were examined. Diet was assessed using food frequency questionnaires and food records. Three statistical methods were used: principal components analysis, cluster analysis, and reduced rank regression. Throughout childhood, children and parents have similar dietary patterns. The “health-conscious” and “traditional” patterns were associated with high intakes of fruits and/or vegetables and better nutrient profiles than the “processed” patterns. There was evidence of tracking in childhood diet, with the “health-conscious” patterns tracking most strongly, followed by the “processed” pattern. An “energy-dense, low-fiber, high-fat” dietary pattern was extracted using reduced rank regression; high scores on this pattern were associated with increasing adiposity. Maternal education was a strong determinant of pattern score or cluster membership; low educational attainment was associated with higher scores on processed, energy-dense patterns in both parents and children. The Avon Longitudinal Study of Parents and Children has provided unique insights into the value of empirically derived dietary patterns and has demonstrated that they are a useful tool in nutritional epidemiology. PMID:26395343

Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project

PubMed Central

Kandala, Sridhar; Nolan, Dan; Laumann, Timothy O.; Power, Jonathan D.; Adeyemo, Babatunde; Harms, Michael P.; Petersen, Steven E.; Barch, Deanna M.

2016-01-01

Abstract Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high-motion time points and for high-motion participants. This inflation occurred across the brain, suggesting the presence of globally distributed artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance-dependent spatially specific artifacts. We evaluated several denoising methods: censoring high-motion time points, motion regression, the FMRIB independent component analysis-based X-noiseifier (FIX), and mean grayordinate time series regression (MGTR; as a proxy for global signal regression). The results suggest that FIX denoising reduced both types of artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifacts, eliminating neither type. All denoising strategies left differences between high- and low-motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially specific and globally distributed artifacts, and the most effective approach to address both types of motion-correlated artifacts was a combination of FIX and MGTR. PMID:27571276

Metrics to Compare Aircraft Operating and Support Costs in the Department of Defense

DTIC Science & Technology

2015-01-01

a phenomenon in regression analysis called multicollinear - ity, which makes problematic the interpretation of the coefficient esti- mates of highly...indicating a very high amount of multicollinearity and suggesting that the magnitude of the coefficients on those variables should be treated with caution... multicollinearity between these independent variables, one must be cautious when interpreting the statistical relationship between flying hours and cost. The

Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology: Replication of a Prospective Experience Sampling Analysis

PubMed Central

Hasmi, Laila; Drukker, Marjan; Guloksuz, Sinan; Menne-Lothmann, Claudia; Decoster, Jeroen; van Winkel, Ruud; Collip, Dina; Delespaul, Philippe; De Hert, Marc; Derom, Catherine; Thiery, Evert; Jacobs, Nele; Rutten, Bart P. F.; Wichers, Marieke; van Os, Jim

2017-01-01

Background: The network analysis of intensive time series data collected using the Experience Sampling Method (ESM) may provide vital information in gaining insight into the link between emotion regulation and vulnerability to psychopathology. The aim of this study was to apply the network approach to investigate whether genetic liability (GL) to psychopathology and childhood trauma (CT) are associated with the network structure of the emotions “cheerful,” “insecure,” “relaxed,” “anxious,” “irritated,” and “down”—collected using the ESM method. Methods: Using data from a population-based sample of twin pairs and siblings (704 individuals), we examined whether momentary emotion network structures differed across strata of CT and GL. GL was determined empirically using the level of psychopathology in monozygotic and dizygotic co-twins. Network models were generated using multilevel time-lagged regression analysis and were compared across three strata (low, medium, and high) of CT and GL, respectively. Permutations were utilized to calculate p values and compare regressions coefficients, density, and centrality indices. Regression coefficients were presented as connections, while variables represented the nodes in the network. Results: In comparison to the low GL stratum, the high GL stratum had significantly denser overall (p = 0.018) and negative affect network density (p < 0.001). The medium GL stratum also showed a directionally similar (in-between high and low GL strata) but statistically inconclusive association with network density. In contrast to GL, the results of the CT analysis were less conclusive, with increased positive affect density (p = 0.021) and overall density (p = 0.042) in the high CT stratum compared to the medium CT stratum but not to the low CT stratum. The individual node comparisons across strata of GL and CT yielded only very few significant results, after adjusting for multiple testing. Conclusions: The present findings demonstrate that the network approach may have some value in understanding the relation between established risk factors for mental disorders (particularly GL) and the dynamic interplay between emotions. The present finding partially replicates an earlier analysis, suggesting it may be instructive to model negative emotional dynamics as a function of genetic influence. PMID:29163289

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.

Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions.

PubMed

Drouard, Vincent; Horaud, Radu; Deleforge, Antoine; Ba, Sileye; Evangelidis, Georgios

2017-03-01

Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging, because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment errors. We propose to use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena. We describe in detail the mapping method that combines the merits of unsupervised manifold learning techniques and of mixtures of regressions. We validate our method with three publicly available data sets and we thoroughly benchmark four variants of the proposed algorithm with several state-of-the-art head-pose estimation methods.

Ridge regression for predicting elastic moduli and hardness of calcium aluminosilicate glasses

NASA Astrophysics Data System (ADS)

Deng, Yifan; Zeng, Huidan; Jiang, Yejia; Chen, Guorong; Chen, Jianding; Sun, Luyi

2018-03-01

It is of great significance to design glasses with satisfactory mechanical properties predictively through modeling. Among various modeling methods, data-driven modeling is such a reliable approach that can dramatically shorten research duration, cut research cost and accelerate the development of glass materials. In this work, the ridge regression (RR) analysis was used to construct regression models for predicting the compositional dependence of CaO-Al2O3-SiO2 glass elastic moduli (Shear, Bulk, and Young’s moduli) and hardness based on the ternary diagram of the compositions. The property prediction over a large glass composition space was accomplished with known experimental data of various compositions in the literature, and the simulated results are in good agreement with the measured ones. This regression model can serve as a facile and effective tool for studying the relationship between the compositions and the property, enabling high-efficient design of glasses to meet the requirements for specific elasticity and hardness.

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…

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…

Hyper-Spectral Image Analysis With Partially Latent Regression and Spatial Markov Dependencies

NASA Astrophysics Data System (ADS)

Deleforge, Antoine; Forbes, Florence; Ba, Sileye; Horaud, Radu

2015-09-01

Hyper-spectral data can be analyzed to recover physical properties at large planetary scales. This involves resolving inverse problems which can be addressed within machine learning, with the advantage that, once a relationship between physical parameters and spectra has been established in a data-driven fashion, the learned relationship can be used to estimate physical parameters for new hyper-spectral observations. Within this framework, we propose a spatially-constrained and partially-latent regression method which maps high-dimensional inputs (hyper-spectral images) onto low-dimensional responses (physical parameters such as the local chemical composition of the soil). The proposed regression model comprises two key features. Firstly, it combines a Gaussian mixture of locally-linear mappings (GLLiM) with a partially-latent response model. While the former makes high-dimensional regression tractable, the latter enables to deal with physical parameters that cannot be observed or, more generally, with data contaminated by experimental artifacts that cannot be explained with noise models. Secondly, spatial constraints are introduced in the model through a Markov random field (MRF) prior which provides a spatial structure to the Gaussian-mixture hidden variables. Experiments conducted on a database composed of remotely sensed observations collected from the Mars planet by the Mars Express orbiter demonstrate the effectiveness of the proposed model.

Association between sociability and diffusion tensor imaging in BALB/cJ mice.

PubMed

Kim, Sungheon; Pickup, Stephen; Fairless, Andrew H; Ittyerah, Ranjit; Dow, Holly C; Abel, Ted; Brodkin, Edward S; Poptani, Harish

2012-01-01

The purpose of this study was to use high-resolution diffusion tensor imaging (DTI) to investigate the association between DTI metrics and sociability in BALB/c inbred mice. The sociability of prepubescent (30-day-old) BALB/cJ mice was operationally defined as the time that the mice spent sniffing a stimulus mouse in a social choice test. High-resolution ex vivo DTI data on 12 BALB/cJ mouse brains were acquired using a 9.4-T vertical-bore magnet. Regression analysis was conducted to investigate the association between DTI metrics and sociability. Significant positive regression (p < 0.001) between social sniffing time and fractional anisotropy was found in 10 regions located in the thalamic nuclei, zona incerta/substantia nigra, visual/orbital/somatosensory cortices and entorhinal cortex. In addition, significant negative regression (p < 0.001) between social sniffing time and mean diffusivity was found in five areas located in the sensory cortex, motor cortex, external capsule and amygdaloid region. In all regions showing significant regression with either the mean diffusivity or fractional anisotropy, the tertiary eigenvalue correlated negatively with the social sniffing time. This study demonstrates the feasibility of using DTI to detect brain regions associated with sociability in a mouse model system. Copyright © 2011 John Wiley & Sons, Ltd.

Statistical downscaling modeling with quantile regression using lasso to estimate extreme rainfall

NASA Astrophysics Data System (ADS)

Santri, Dewi; Wigena, Aji Hamim; Djuraidah, Anik

2016-02-01

Rainfall is one of the climatic elements with high diversity and has many negative impacts especially extreme rainfall. Therefore, there are several methods that required to minimize the damage that may occur. So far, Global circulation models (GCM) are the best method to forecast global climate changes include extreme rainfall. Statistical downscaling (SD) is a technique to develop the relationship between GCM output as a global-scale independent variables and rainfall as a local- scale response variable. Using GCM method will have many difficulties when assessed against observations because GCM has high dimension and multicollinearity between the variables. The common method that used to handle this problem is principal components analysis (PCA) and partial least squares regression. The new method that can be used is lasso. Lasso has advantages in simultaneuosly controlling the variance of the fitted coefficients and performing automatic variable selection. Quantile regression is a method that can be used to detect extreme rainfall in dry and wet extreme. Objective of this study is modeling SD using quantile regression with lasso to predict extreme rainfall in Indramayu. The results showed that the estimation of extreme rainfall (extreme wet in January, February and December) in Indramayu could be predicted properly by the model at quantile 90th.

A test of Hirschi's social bonding theory: juvenile delinquency in the high schools of Ankara, Turkey.

PubMed

Ozbay, Ozden; Ozcan, Yusuf Ziya

2006-12-01

Travis Hirschi's social bonding theory has mostly been tested in the West. In this study, the theory is tested on juvenile delinquency in a developing country, Turkey. Data were gathered from 1,710 high school students in Ankara by using two-stage stratified cluster sampling. Factor analysis was employed to determine the dimensions of juvenile delinquency (assault, school delinquency, and public disturbance), and regression analysis was used to test the theory. Similar to some other traditional societies, the social bonding theory plays an important role in the explanation of juvenile delinquency in Turkey.

Using Remote Sensing Data to Evaluate Surface Soil Properties in Alabama Ultisols

NASA Technical Reports Server (NTRS)

Sullivan, Dana G.; Shaw, Joey N.; Rickman, Doug; Mask, Paul L.; Luvall, Jeff

2005-01-01

Evaluation of surface soil properties via remote sensing could facilitate soil survey mapping, erosion prediction and allocation of agrochemicals for precision management. The objective of this study was to evaluate the relationship between soil spectral signature and surface soil properties in conventionally managed row crop systems. High-resolution RS data were acquired over bare fields in the Coastal Plain, Appalachian Plateau, and Ridge and Valley provinces of Alabama using the Airborne Terrestrial Applications Sensor multispectral scanner. Soils ranged from sandy Kandiudults to fine textured Rhodudults. Surface soil samples (0-1 cm) were collected from 163 sampling points for soil organic carbon, particle size distribution, and citrate dithionite extractable iron content. Surface roughness, soil water content, and crusting were also measured during sampling. Two methods of analysis were evaluated: 1) multiple linear regression using common spectral band ratios, and 2) partial least squares regression. Our data show that thermal infrared spectra are highly, linearly related to soil organic carbon, sand and clay content. Soil organic carbon content was the most difficult to quantify in these highly weathered systems, where soil organic carbon was generally less than 1.2%. Estimates of sand and clay content were best using partial least squares regression at the Valley site, explaining 42-59% of the variability. In the Coastal Plain, sandy surfaces prone to crusting limited estimates of sand and clay content via partial least squares and regression with common band ratios. Estimates of iron oxide content were a function of mineralogy and best accomplished using specific band ratios, with regression explaining 36-65% of the variability at the Valley and Coastal Plain sites, respectively.

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)

Application of Regression-Discontinuity Analysis in Pharmaceutical Health Services Research

PubMed Central

Zuckerman, Ilene H; Lee, Euni; Wutoh, Anthony K; Xue, Zhenyi; Stuart, Bruce

2006-01-01

Objective To demonstrate how a relatively underused design, regression-discontinuity (RD), can provide robust estimates of intervention effects when stronger designs are impossible to implement. Data Sources/Study Setting Administrative claims from a Mid-Atlantic state Medicaid program were used to evaluate the effectiveness of an educational drug utilization review intervention. Study Design Quasi-experimental design. Data Collection/Extraction Methods A drug utilization review study was conducted to evaluate a letter intervention to physicians treating Medicaid children with potentially excessive use of short-acting β2-agonist inhalers (SAB). The outcome measure is change in seasonally-adjusted SAB use 5 months pre- and postintervention. To determine if the intervention reduced monthly SAB utilization, results from an RD analysis are compared to findings from a pretest–posttest design using repeated-measure ANOVA. Principal Findings Both analyses indicated that the intervention significantly reduced SAB use among the high users. Average monthly SAB use declined by 0.9 canisters per month (p<.001) according to the repeated-measure ANOVA and by 0.2 canisters per month (p<.001) from RD analysis. Conclusions Regression-discontinuity design is a useful quasi-experimental methodology that has significant advantages in internal validity compared to other pre–post designs when assessing interventions in which subjects' assignment is based on cutoff scores for a critical variable. PMID:16584464

Investigation of marital satisfaction and its relationship with job stress and general health of nurses in Qazvin, Iran.

PubMed

Azimian, Jalil; Piran, Pegah; Jahanihashemi, Hassan; Dehghankar, Leila

2017-04-01

Pressures in nursing can affect family life and marital problems, disrupt common social problems, increase work-family conflicts and endanger people's general health. To determine marital satisfaction and its relationship with job stress and general health of nurses. This descriptive and cross-sectional study was done in 2015 in medical educational centers of Qazvin by using an ENRICH marital satisfaction scale and General Health and Job Stress questionnaires completed by 123 nurses. Analysis was done by SPSS version 19 using descriptive and analytical statistics (Pearson correlation, t-test, ANOVA, Chi-square, regression line, multiple regression analysis). The findings showed that 64.4% of nurses had marital satisfaction. There was significant relationship between age (p=0.03), job experience (p=0.01), age of spouse (p=0.01) and marital satisfaction. The results showed that there was a significant relationship between marital satisfaction and general health (p<0.0001). Multiple regression analysis showed that there was a significant relationship between depression (p=0.012) and anxiety (p=0.001) with marital satisfaction. Due to high levels of job stress and disorder in general health of nurses and low marital satisfaction by running health promotion programs and paying attention to its dimensions can help work and family health of nurses.

A 3-Year Study of Predictive Factors for Positive and Negative Appendicectomies.

PubMed

Chang, Dwayne T S; Maluda, Melissa; Lee, Lisa; Premaratne, Chandrasiri; Khamhing, Srisongham

2018-03-06

Early and accurate identification or exclusion of acute appendicitis is the key to avoid the morbidity of delayed treatment for true appendicitis or unnecessary appendicectomy, respectively. We aim (i) to identify potential predictive factors for positive and negative appendicectomies; and (ii) to analyse the use of ultrasound scans (US) and computed tomography (CT) scans for acute appendicitis. All appendicectomies that took place at our hospital from the 1st of January 2013 to the 31st of December 2015 were retrospectively recorded. Test results of potential predictive factors of acute appendicitis were recorded. Statistical analysis was performed using Fisher exact test, logistic regression analysis, sensitivity, specificity, and positive and negative predictive values calculation. 208 patients were included in this study. 184 patients had histologically proven acute appendicitis. The other 24 patients had either nonappendicitis pathology or normal appendix. Logistic regression analysis showed statistically significant associations between appendicitis and white cell count, neutrophil count, C-reactive protein, and bilirubin. Neutrophil count was the test with the highest sensitivity and negative predictive values, whereas bilirubin was the test with the highest specificity and positive predictive values (PPV). US and CT scans had high sensitivity and PPV for diagnosing appendicitis. No single test was sufficient to diagnose or exclude acute appendicitis by itself. Combining tests with high sensitivity (abnormal neutrophil count, and US and CT scans) and high specificity (raised bilirubin) may predict acute appendicitis more accurately.

Morningness-eveningness questionnaire score and metabolic parameters in patients with type 2 diabetes mellitus.

PubMed

Osonoi, Yusuke; Mita, Tomoya; Osonoi, Takeshi; Saito, Miyoko; Tamasawa, Atsuko; Nakayama, Shiho; Someya, Yuki; Ishida, Hidenori; Kanazawa, Akio; Gosho, Masahiko; Fujitani, Yoshio; Watada, Hirotaka

2014-11-01

"Morningness" and "Eveningness" represent lifestyle patterns including sleep-wake patterns. Although previous studies described a relationship between the morningness-eveningness trait and glycemic control in patients with type 2 diabetes mellitus (T2DM), the mechanism underlying this association remains unknown. The study participants comprised 725 Japanese T2DM outpatients free of history of cardiovascular diseases. Various lifestyles were analyzed using self-reported questionnaires, including morningness-eveningness questionnaire (MEQ). The relationships between morningness-eveningness trait and various biochemical parameters were investigated by linear regression analysis and logistic regression analysis. We classified the study patients into three groups, morning type (n=117), neither type (n=424) and evening type (n=184). Subjects of the evening type had high levels of alanine aminotransferase, triglyceride, fasting blood glucose and HbA1c and low high-density lipoprotein-cholesterol level in a model adjusted for age and gender. Furthermore, multivariate analysis showed that the evening type was associated with high HbA1c and estimated glomerular filtration rate even after adjustment for other lifestyle factors known to affect metabolic control. The results suggest that T2DM patients with eveningness trait are under inadequate metabolic control independent of other lifestyle factors. Thus, the evening trait of T2DM patients represents an important target for intervention to ensure appropriate metabolic function.

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.

Building factorial regression models to explain and predict nitrate concentrations in groundwater under agricultural land

NASA Astrophysics Data System (ADS)

Stigter, T. Y.; Ribeiro, L.; Dill, A. M. M. Carvalho

2008-07-01

SummaryFactorial regression models, based on correspondence analysis, are built to explain the high nitrate concentrations in groundwater beneath an agricultural area in the south of Portugal, exceeding 300 mg/l, as a function of chemical variables, electrical conductivity (EC), land use and hydrogeological setting. Two important advantages of the proposed methodology are that qualitative parameters can be involved in the regression analysis and that multicollinearity is avoided. Regression is performed on eigenvectors extracted from the data similarity matrix, the first of which clearly reveals the impact of agricultural practices and hydrogeological setting on the groundwater chemistry of the study area. Significant correlation exists between response variable NO3- and explanatory variables Ca 2+, Cl -, SO42-, depth to water, aquifer media and land use. Substituting Cl - by the EC results in the most accurate regression model for nitrate, when disregarding the four largest outliers (model A). When built solely on land use and hydrogeological setting, the regression model (model B) is less accurate but more interesting from a practical viewpoint, as it is based on easily obtainable data and can be used to predict nitrate concentrations in groundwater in other areas with similar conditions. This is particularly useful for conservative contaminants, where risk and vulnerability assessment methods, based on assumed rather than established correlations, generally produce erroneous results. Another purpose of the models can be to predict the future evolution of nitrate concentrations under influence of changes in land use or fertilization practices, which occur in compliance with policies such as the Nitrates Directive. Model B predicts a 40% decrease in nitrate concentrations in groundwater of the study area, when horticulture is replaced by other land use with much lower fertilization and irrigation rates.

Application of third molar development and eruption models in estimating dental age in Malay sub-adults.

PubMed

Mohd Yusof, Mohd Yusmiaidil Putera; Cauwels, Rita; Deschepper, Ellen; Martens, Luc

2015-08-01

The third molar development (TMD) has been widely utilized as one of the radiographic method for dental age estimation. By using the same radiograph of the same individual, third molar eruption (TME) information can be incorporated to the TMD regression model. This study aims to evaluate the performance of dental age estimation in individual method models and the combined model (TMD and TME) based on the classic regressions of multiple linear and principal component analysis. A sample of 705 digital panoramic radiographs of Malay sub-adults aged between 14.1 and 23.8 years was collected. The techniques described by Gleiser and Hunt (modified by Kohler) and Olze were employed to stage the TMD and TME, respectively. The data was divided to develop three respective models based on the two regressions of multiple linear and principal component analysis. The trained models were then validated on the test sample and the accuracy of age prediction was compared between each model. The coefficient of determination (R²) and root mean square error (RMSE) were calculated. In both genders, adjusted R² yielded an increment in the linear regressions of combined model as compared to the individual models. The overall decrease in RMSE was detected in combined model as compared to TMD (0.03-0.06) and TME (0.2-0.8). In principal component regression, low value of adjusted R(2) and high RMSE except in male were exhibited in combined model. Dental age estimation is better predicted using combined model in multiple linear regression models. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Plant leaf chlorophyll content retrieval based on a field imaging spectroscopy system.

PubMed

Liu, Bo; Yue, Yue-Min; Li, Ru; Shen, Wen-Jing; Wang, Ke-Lin

2014-10-23

A field imaging spectrometer system (FISS; 380-870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%-35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector.

Plant Leaf Chlorophyll Content Retrieval Based on a Field Imaging Spectroscopy System

PubMed Central

Liu, Bo; Yue, Yue-Min; Li, Ru; Shen, Wen-Jing; Wang, Ke-Lin

2014-01-01

A field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%–35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector. PMID:25341439

Burnout and Associated Factors among Iranian Emergency Medicine Practitioners.

PubMed

Jalili, Mohammad; Sadeghipour Roodsari, Gholamreza; Bassir Nia, Anahita

2013-09-01

Emergency physicians are at risk of burnout, which can affect their mental health, as well as patient care. We assessed burnout level among Iranian emergency physicians and investigated demographic, work-related factors and stressors associated with higher burnout. In a cross-sectional study, we surveyed all 188 emergency medicine residents and practitioners in Iran. We measured burnout using 22-item Maslach Burnout Inventory assessing emotional exhaustion, depersonalization and personal accomplishment, also demographic factors, work related factors and sources of stress in emergency department using anonymous self-administered questionnaire. Descriptive analysis, univariate analysis to evaluate association with higher score of burnout, and multivariate logistic regression analysis to predict high burnout in 3 subscales was performed. Totally, 165 questionnaires were filled (response rate: 88%; mean age: 33.6 years, 91% male). Mean burnout scores were 22.94 for emotional exhaustion (95% CI=20.78-25.01; moderate), 9.3 for depersonalization (95% CI=8.24-10.36; moderate to high), and 31.47 for personal accomplishment (95% CI=29.87-33.07; moderate to high). Frequent reported sources of stress were shortage of equipment, problem with work physical environment, and relationship with other services. All 19 sources of stress were associated with higher score of emotional exhaustion and depersonalization; while twelve out of 19 were significantly associated with lower level of personal accomplishment. In logistic regression model, the significant predictors for high emotional exhaustion were work overload, feeling of insecurity for future career and difficulties to balance professional and private life. Burnout is high among Iranian emergency medicine practitioners and some interventions can be proposed to reduce stress.

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.

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…

The application of neural network model to the simulation nitrous oxide emission in the hydro-fluctuation belt of Three Gorges Reservoir

NASA Astrophysics Data System (ADS)

Song, Lanlan

2017-04-01

Nitrous oxide is much more potent greenhouse gas than carbon dioxide. However, the estimation of N2O flux is usually clouded with uncertainty, mainly due to high spatial and temporal variations. This hampers the development of general mechanistic models for N2O emission as well, as most previously developed models were empirical or exhibited low predictability with numerous assumptions. In this study, we tested General Regression Neural Networks (GRNN) as an alternative to classic empirical models for simulating N2O emission in riparian zones of Reservoirs. GRNN and nonlinear regression (NLR) were applied to estimate the N2O flux of 1-year observations in riparian zones of Three Gorge Reservoir. NLR resulted in lower prediction power and higher residuals compared to GRNN. Although nonlinear regression model estimated similar average values of N2O, it could not capture the fluctuation patterns accurately. In contrast, GRNN model achieved a fairly high predictability, with an R2 of 0.59 for model validation, 0.77 for model calibration (training), and a low root mean square error (RMSE), indicating a high capacity to simulate the dynamics of N2O flux. According to a sensitivity analysis of the GRNN, nonlinear relationships between input variables and N2O flux were well explained. Our results suggest that the GRNN developed in this study has a greater performance in simulating variations in N2O flux than nonlinear regressions.

Using instant messaging to enhance the interpersonal relationships of Taiwanese adolescents: evidence from quantile regression analysis.

PubMed

Lee, Yueh-Chiang; Sun, Ya Chung

2009-01-01

Even though use of the internet by adolescents has grown exponentially, little is known about the correlation between their interaction via Instant Messaging (IM) and the evolution of their interpersonal relationships in real life. In the present study, 369 junior high school students in Taiwan responded to questions regarding their IM usage and their dispositional measures of real-life interpersonal relationships. Descriptive statistics, factor analysis, and quantile regression methods were used to analyze the data. Results indicate that (1) IM helps define adolescents' self-identity (forming and maintaining individual friendships) and social-identity (belonging to a peer group), and (2) how development of an interpersonal relationship is impacted by the use of IM since it appears that adolescents use IM to improve their interpersonal relationships in real life.

Comparing nouns and verbs in a lexical task.

PubMed

Cordier, Françoise; Croizet, Jean-Claude; Rigalleau, François

2013-02-01

We analyzed the differential processing of nouns and verbs in a lexical decision task. Moderate and high-frequency nouns and verbs were compared. The characteristics of our material were specified at the formal level (number of letters and syllables, number of homographs, orthographic neighbors, frequency and age of acquisition), and at the semantic level (imagery, number and strength of associations, number of meanings, context dependency). A regression analysis indicated a classical frequency effect and a word-type effect, with latencies for verbs being slower than for nouns. The regression analysis did not permit the conclusion that semantic effects were involved (particularly imageability). Nevertheless, the semantic opposition between nouns as prototypical representations of objects, and verbs as prototypical representation of actions was not tested in this experiment and remains a good candidate explanation of the response time discrepancies between verbs and nouns.

Prediction models for CO2 emission in Malaysia using best subsets regression and multi-linear regression

NASA Astrophysics Data System (ADS)

Tan, C. H.; Matjafri, M. Z.; Lim, H. S.

2015-10-01

This paper presents the prediction models which analyze and compute the CO2 emission in Malaysia. Each prediction model for CO2 emission will be analyzed based on three main groups which is transportation, electricity and heat production as well as residential buildings and commercial and public services. The prediction models were generated using data obtained from World Bank Open Data. Best subset method will be used to remove irrelevant data and followed by multi linear regression to produce the prediction models. From the results, high R-square (prediction) value was obtained and this implies that the models are reliable to predict the CO2 emission by using specific data. In addition, the CO2 emissions from these three groups are forecasted using trend analysis plots for observation purpose.

Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

PubMed Central

Huang, Jian; Zhang, Cun-Hui

2013-01-01

The ℓ1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted ℓ1-penalized estimators for convex loss functions of a general form, including the generalized linear models. We study the estimation, prediction, selection and sparsity properties of the weighted ℓ1-penalized estimator in sparse, high-dimensional settings where the number of predictors p can be much larger than the sample size n. Adaptive Lasso is considered as a special case. A multistage method is developed to approximate concave regularized estimation by applying an adaptive Lasso recursively. We provide prediction and estimation oracle inequalities for single- and multi-stage estimators, a general selection consistency theorem, and an upper bound for the dimension of the Lasso estimator. Important models including the linear regression, logistic regression and log-linear models are used throughout to illustrate the applications of the general results. PMID:24348100

Geographically weighted poisson regression semiparametric on modeling of the number of tuberculosis cases (Case study: Bandung city)

NASA Astrophysics Data System (ADS)

Octavianty, Toharudin, Toni; Jaya, I. G. N. Mindra

2017-03-01

Tuberculosis (TB) is a disease caused by a bacterium, called Mycobacterium tuberculosis, which typically attacks the lungs but can also affect the kidney, spine, and brain (Centers for Disease Control and Prevention). Indonesia had the largest number of TB cases after India (Global Tuberculosis Report 2015 by WHO). The distribution of Mycobacterium tuberculosis genotypes in Indonesia showed the high genetic diversity and tended to vary by geographic regions. For instance, in Bandung city, the prevalence rate of TB morbidity is quite high. A number of TB patients belong to the counted data. To determine the factors that significantly influence the number of tuberculosis patients in each location of the observations can be used statistical analysis tool that is Geographically Weighted Poisson Regression Semiparametric (GWPRS). GWPRS is an extension of the Poisson regression and GWPR that is influenced by geographical factors, and there is also variables that influence globally and locally. Using the TB Data in Bandung city (in 2015), the results show that the global and local variables that influence the number of tuberculosis patients in every sub-district.

Regression Model Optimization for the Analysis of Experimental Data

NASA Technical Reports Server (NTRS)

Ulbrich, N.

2009-01-01

A candidate math model search algorithm was developed at Ames Research Center that determines a recommended math model for the multivariate regression analysis of experimental data. The search algorithm is applicable to classical regression analysis problems as well as wind tunnel strain gage balance calibration analysis applications. The algorithm compares the predictive capability of different regression models using the standard deviation of the PRESS residuals of the responses as a search metric. This search metric is minimized during the search. Singular value decomposition is used during the search to reject math models that lead to a singular solution of the regression analysis problem. Two threshold dependent constraints are also applied. The first constraint rejects math models with insignificant terms. The second constraint rejects math models with near-linear dependencies between terms. The math term hierarchy rule may also be applied as an optional constraint during or after the candidate math model search. The final term selection of the recommended math model depends on the regressor and response values of the data set, the user s function class combination choice, the user s constraint selections, and the result of the search metric minimization. A frequently used regression analysis example from the literature is used to illustrate the application of the search algorithm to experimental data.

A guide to understanding meta-analysis.

PubMed

Israel, Heidi; Richter, Randy R

2011-07-01

With the focus on evidence-based practice in healthcare, a well-conducted systematic review that includes a meta-analysis where indicated represents a high level of evidence for treatment effectiveness. The purpose of this commentary is to assist clinicians in understanding meta-analysis as a statistical tool using both published articles and explanations of components of the technique. We describe what meta-analysis is, what heterogeneity is, and how it affects meta-analysis, effect size, the modeling techniques of meta-analysis, and strengths and weaknesses of meta-analysis. Common components like forest plot interpretation, software that may be used, special cases for meta-analysis, such as subgroup analysis, individual patient data, and meta-regression, and a discussion of criticisms, are included.

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.

Quantile Regression for Analyzing Heterogeneity in Ultra-high Dimension

PubMed Central

Wang, Lan; Wu, Yichao

2012-01-01

Ultra-high dimensional data often display heterogeneity due to either heteroscedastic variance or other forms of non-location-scale covariate effects. To accommodate heterogeneity, we advocate a more general interpretation of sparsity which assumes that only a small number of covariates influence the conditional distribution of the response variable given all candidate covariates; however, the sets of relevant covariates may differ when we consider different segments of the conditional distribution. In this framework, we investigate the methodology and theory of nonconvex penalized quantile regression in ultra-high dimension. The proposed approach has two distinctive features: (1) it enables us to explore the entire conditional distribution of the response variable given the ultra-high dimensional covariates and provides a more realistic picture of the sparsity pattern; (2) it requires substantially weaker conditions compared with alternative methods in the literature; thus, it greatly alleviates the difficulty of model checking in the ultra-high dimension. In theoretic development, it is challenging to deal with both the nonsmooth loss function and the nonconvex penalty function in ultra-high dimensional parameter space. We introduce a novel sufficient optimality condition which relies on a convex differencing representation of the penalized loss function and the subdifferential calculus. Exploring this optimality condition enables us to establish the oracle property for sparse quantile regression in the ultra-high dimension under relaxed conditions. The proposed method greatly enhances existing tools for ultra-high dimensional data analysis. Monte Carlo simulations demonstrate the usefulness of the proposed procedure. The real data example we analyzed demonstrates that the new approach reveals substantially more information compared with alternative methods. PMID:23082036

Cytologic regression in women with atypical squamous cells of unknown significance and negative human papillomavirus test.

PubMed

Wang, Shu; Lang, Jing He; Cheng, Xue Mei

2009-12-01

The aim of this study was to investigate the cytologic regression in women with atypical squamous cells of unknown significance and negative high-risk human papillomavirus test. The 45 women with atypical squamous cells of unknown significance and negative high-risk human papillomavirus at baseline were analyzed about the cytologic regression during 2 years of follow-up. The cumulative rate of cytologic regression was calculated by Kaplan-Meier curves. Of 45 women, the cumulative rates were as follows: 55.6% obtained cytologic regression before 6 months, 84.4% by 1 year, and 95.6% at 2 years. Cytologic regression was not influenced by age, menopausal status, and baseline human papillomavirus load. However, the 1-year cumulative regression rate in women with previous cervical lesions was significantly lower than those without (P=.02), even much lower in women with high-grade intraepithelial neoplasia or worse (P=.008). Most women with atypical squamous cells of unknown significance and negative high-risk human papillomavirus could obtain cytologic regression within 2 years. Women with antecedent cervical lesions need longer time to reach this regression.

Understanding the Acculturation Experience of Chinese Adolescent Students: Sociocultural Adaptation Strategies and a Positive Bicultural and Bilingual Identity

ERIC Educational Resources Information Center

Tong, Virginia M.

2014-01-01

The acculturation of Chinese immigrant high school students was examined as it relates to students' level of interaction with teachers and peers and participation in American school activities. Findings from a regression analysis revealed five variables (sociocultural adaptation strategies) that facilitate students' adjustment process:…

Predicting Higher Education Outcomes and Implications for a Postsecondary Institution Ratings System

ERIC Educational Resources Information Center

Walker, Eddie G., II

2016-01-01

The accountability of colleges and universities is a high priority for those making policy decisions. The purpose of this study was to determine institutional characteristics predicting retention rates, graduation rates and transfer-out rates using publicly available data from the US Department of Education. Using regression analysis, it was…

Determinants of Anabolic-Androgenic Steroid Risk Perceptions in Youth Populations: A Multivariate Analysis

ERIC Educational Resources Information Center

Denham, Bryan E.

2009-01-01

Grounded conceptually in social cognitive theory, this research examines how personal, behavioral, and environmental factors are associated with risk perceptions of anabolic-androgenic steroids. Ordinal logistic regression and logit log-linear models applied to data gathered from high-school seniors (N = 2,160) in the 2005 Monitoring the Future…

Predictors of College Readiness: An Analysis of the Student Readiness Inventory

ERIC Educational Resources Information Center

Wilson, James K., III

2012-01-01

The purpose of this study was to better predict how a first semester college freshman becomes prepared for college. The theoretical framework guiding this study is Vrooms' expectancy theory, motivation plays a key role in success. This study used a hierarchical multiple regression model. The independent variables of interest included high school…

Inferring a Child's Level of Self-esteem from a Knowledge of Other Personality Factors.

ERIC Educational Resources Information Center

Kawash, George F.; Clewes, Janet L.

1986-01-01

Correlation and regression analysis confirmed that there is a high degree of shared variance between Coopersmith's Self-Esteem Inventory (SEI) and the Children's Personality Questionnaire (CPQ), suggesting that self-esteem may be more integrated within an individual's total personality functioning than has been discussed in the literature.…

Toward a Better Understanding of Student Perceptions of Writing Feedback: A Mixed Methods Study

ERIC Educational Resources Information Center

Zumbrunn, Sharon; Marrs, Sarah; Mewborn, Caitlin

2016-01-01

This explanatory sequential mixed methods study investigated the writing feedback perceptions of middle and high school students (N = 598). The predictive and mediational roles of writing self-efficacy and perceptions of writing feedback on student writing self-regulation aptitude were examined using mediation regression analysis. To augment the…

Dysfunctional Career Thoughts and Attitudes as Predictors of Vocational Identity among Young Adults with Attention Deficit Hyperactivity Disorder

ERIC Educational Resources Information Center

Dipeolu, Abiola; Sniatecki, Jessica L.; Storlie, Cassandra A.; Hargrave, Stephanie

2013-01-01

This study examined dysfunctional career thoughts and attitudes as predictors of vocational identity among high school students with Attention Deficit Hyperactivity Disorder (ADHD). Regression analysis results indicated that dysfunctional career thoughts and attitudes were significant predictors of vocational identity, accounting for 42% of the…

Student Characteristics and Collegiate Environments That Contribute to the Overall Satisfaction with Academic Advising among College Students

ERIC Educational Resources Information Center

DeLaRosby, Hal R.

2017-01-01

Academic advising satisfaction is highly correlated with retention in higher education. Thriving Quotient survey responses were collected from undergraduate students at a private, liberal arts college in the Pacific Northwest. Using a multiple regression analysis, this study examined what "student characteristics" and "collegiate…

The Influence of Loneliness and Interpersonal Relations on Latina/o Middle School Students' Wellbeing

ERIC Educational Resources Information Center

Heredia, Dagoberto, Jr.; Sanchéz Gonzalez, Mayra L.; Rosner, Christine M.; He, Xiao; Castillo, Linda G.; Ojeda, Lizette

2017-01-01

This study examined associations of loneliness and relationships (e.g., teacher/student relationships, peer support, and family support) with wellbeing among Latina/o middle school students. A hierarchical regression analysis demonstrated that age and loneliness predicted wellbeing; older students and students with high levels of loneliness…

Uncertainty Analysis on Heat Transfer Correlations for RP-1 Fuel in Copper Tubing

NASA Technical Reports Server (NTRS)

Driscoll, E. A.; Landrum, D. B.

2004-01-01

NASA is studying kerosene (RP-1) for application in Next Generation Launch Technology (NGLT). Accurate heat transfer correlations in narrow passages at high temperatures and pressures are needed. Hydrocarbon fuels, such as RP-1, produce carbon deposition (coke) along the inside of tube walls when heated to high temperatures. A series of tests to measure the heat transfer using RP-1 fuel and examine the coking were performed in NASA Glenn Research Center's Heated Tube Facility. The facility models regenerative cooling by flowing room temperature RP-1 through resistively heated copper tubing. A Regression analysis is performed on the data to determine the heat transfer correlation for Nusselt number as a function of Reynolds and Prandtl numbers. Each measurement and calculation is analyzed to identify sources of uncertainty, including RP-1 property variations. Monte Carlo simulation is used to determine how each uncertainty source propagates through the regression and an overall uncertainty in predicted heat transfer coefficient. The implications of these uncertainties on engine design and ways to minimize existing uncertainties are discussed.

The role of depression and anxiety in impulsive and obsessive-compulsive behaviors among anorexic and bulimic patients.

PubMed

Finzi-Dottan, Ricky; Zubery, Eynat

2009-01-01

Eating disorders are believed to range across a spectrum of varying degrees of obsessive-compulsive and impulsive behavior. Sixty anorexic (mean age = 19.8; sd = 5.9) and 109 bulimic (mean age = 26.9; sd = 11.3) female patients completed self-report questionnaires assessing obsessive-compulsiveness, impulsivity, depression and anxiety, as well as two eating disorder scales. Results yielded significantly higher levels of impulsivity and negative body image in the bulimic compared to the anorexic group. Regression analysis predicting impulsivity showed that bulimia and negative body image were the main contributors. Regression analysis for predicting obsessive-compulsive behavior suggested that depression and anxiety obscure the link between anorexia and obsessive-compulsive behavior, and a high BMI intensifies the association between anxiety and obsessive-compulsive behavior. The high rates of both impulsivity and obsessive-compulsiveness found in both groups, and their association with the severity of the eating disorder, may suggest that impulsivity and obsessive-compulsiveness are not mutually exclusive and can both be found among anorexic and bulimic patients.

Myxomatosis in wild rabbit: design of control programs in Mediterranean ecosystems.

PubMed

García-Bocanegra, Ignacio; Astorga, Rafael Jesús; Napp, Sebastián; Casal, Jordi; Huerta, Belén; Borge, Carmen; Arenas, Antonio

2010-01-01

A cross-sectional study was carried out in natural wild rabbit (Oryctolagus cuniculus) populations from southern Spain to identify risk factors associated to myxoma virus infection. Blood samples from 619 wild rabbits were collected, and questionnaires which included variables related to host, disease, game management and environment were completed. A logistic regression analysis was conducted to investigate the associations between myxomatosis seropositivity (dependent variable) across 7 hunting estates and an extensive set of explanatory variables obtained from the questionnaires. The prevalence of antibodies against myxomatosis virus was 56.4% (95% CI: 52.5-60.3) and ranged between 21.4% (95% CI: 9.0-33.8) and 70.2% (95% CI: 58.3-82.1) among the different sampling areas. The logistic regression analysis showed that autumn (OR 9.0), high abundance of mosquitoes (OR 8.2), reproductive activity (OR 4.1), warren's insecticide treatment (OR 3.7), rabbit haemorrhagic disease (RHD) seropositivity (OR 2.6), high hunting pressure (OR 6.3) and sheep presence (OR 6.4) were associated with seropositivity to myxomatosis. Based on the results, diverse management measures for myxomatosis control are proposed.

Energy absorption as a predictor of leg impedance in highly trained females.

PubMed

Kulas, Anthony S; Schmitz, Randy J; Schultz, Sandra J; Watson, Mary Allen; Perrin, David H

2006-08-01

Although leg spring stiffness represents active muscular recruitment of the lower extremity during dynamic tasks such as hopping and running, the joint-specific characteristics comprising the damping portion of this measure, leg impedance, are uncertain. The purpose of this investigation was to assess the relationship between leg impedance and energy absorption at the ankle, knee, and hip during early (impact) and late (stabilization) phases of landing. Twenty highly trained female dancers (age = 20.3 +/- 1.4 years, height = 163.7 +/- 6.0 cm, mass = 62.1 +/- 8.1 kg) were instrumented for biomechanical analysis. Subjects performed three sets of double-leg landings from under preferred, stiff, and soft landing conditions. A stepwise linear regression analysis revealed that ankle and knee energy absorption at impact, and knee and hip energy absorption during the stabilization phases of landing explained 75.5% of the variance in leg impedance. The primary predictor of leg impedance was knee energy absorption during the stabilization phase, independently accounting for 55% of the variance. Future validation studies applying this regression model to other groups of individuals are warranted.

Cross-cultural relationships between self-concept and body image in high school-age boys.

PubMed

Austin, J K; Champion, V L; Tzeng, O C

1989-08-01

The relationship between self-concept and body image was investigated through a secondary analysis of data from a sample of 1,200 high school male students from 30 language/culture communities (Osgood, May, & Myron, 1975). Subjects rated adjectives pertaining to self-concept and body image using 7-step semantic differential bipolar scales. Adjectives were related to the dimensions of Evaluation, Potency, and Activity. Correlation, factor analysis, and multiple regression were utilized to examine multivariate relationships among self-concept dimensions and body-image dimensions. Significant positive correlations were found between self-concept and body image. In addition, significant positive relationships were found when self-concept factors were regressed on the body-image factor (R2 = .49 to .57, p less than or equal to .001) for Activity and Potency. Results support the existence of a strong positive relationship between self-concept and body image across the 30 cultures involved. Findings have important implications for nursing in assessment and interventions with clients who have deficits in either self-concept or body image.

[Prevalence of vitamin D deficiency and associated factors in women and newborns in the immediate postpartum period].

PubMed

do Prado, Mara Rúbia Maciel Cardoso; Oliveira, Fabiana de Cássia Carvalho; Assis, Karine Franklin; Ribeiro, Sarah Aparecida Vieira; do Prado Junior, Pedro Paulo; Sant'Ana, Luciana Ferreira da Rocha; Priore, Silvia Eloiza; Franceschini, Sylvia do Carmo Castro

2015-01-01

To assess the prevalence of vitamin D deficiency and its associated factors in women and their newborns in the postpartum period. This cross-sectional study evaluated vitamin D deficiency/insufficiency in 226 women and their newborns in Viçosa (Minas Gerais, BR) between December 2011 and November 2012. Cord blood and venous maternal blood were collected to evaluate the following biochemical parameters: vitamin D, alkaline phosphatase, calcium, phosphorus and parathyroid hormone. Poisson regression analysis, with a confidence interval of 95% was applied to assess vitamin D deficiency and its associated factors. Multiple linear regression analysis was performed to identify factors associated with 25(OH)D deficiency in the newborns and women from the study. The criteria for variable inclusion in the multiple linear regression model was the association with the dependent variable in the simple linear regression analysis, considering p<0.20. Significance level was α<5%. From 226 women included, 200 (88.5%) were 20 to 44 years old; the median age was 28 years. Deficient/insufficient levels of vitamin D were found in 192 (85%) women and in 182 (80.5%) neonates. The maternal 25(OH)D and alkaline phosphatase levels were independently associated with vitamin D deficiency in infants. This study identified a high prevalence of vitamin D deficiency and insufficiency in women and newborns and the association between maternal nutritional status of vitamin D and their infants' vitamin D status. Copyright © 2015 Sociedade de Pediatria de São Paulo. Publicado por Elsevier Editora Ltda. All rights reserved.

High-risk sexual behavior among people living with HIV/AIDS attending tertiary care hospitals in district of Northern India

PubMed Central

Shukla, Mukesh; Agarwal, Monica; Singh, Jai Vir; Tripathi, Anil Kumar; Srivastava, Anand Kumar; Singh, Vijay Kumar

2016-01-01

Context: Prevention with a positive approach has been advocated as one of the main strategies to diminish the new instances of HIV and the target are those who are engaged in high-risk sexual behavior. Therefore, understanding the risky behaviors of the HIV-infected individual is important. Aims: This study aimed to assess the prevalence and the predictors of high-risk sexual behavior among people living with HIV/AIDS (PLHA). Settings and Design: A hospital-based cross-sectional study was conducted at antiretroviral therapy centers of two tertiary care hospitals in Lucknow. Materials and Methods: A total of 322 HIV-positive patients were interviewed about their sexual behaviors during last 3 months using a pretested questionnaire. Statistical Analysis Used: Probability (p) was calculated to test for statistical significance at 5% level of significance. Association between risk factors and high-risk sexual behavior was determined using bivariate analysis followed by multivariate logistic regression. Results: Prevalence of high-risk sexual behavior was 24.5%. Of these patients, multiple sexual partners were reported by 67.3% whereas about 46.9% were engaged in unprotected sex. Multivariate logistic regression analysis revealed that high-risk sexual behavior was significantly associated with nonsupporting attitude of spouse (odds ratio [OR]: 18; 95% confidence interval [CI]: 1.4–225.5; P = 0.02) and alcohol consumption (OR: 9.3; 95% CI: 2.4–35.4; P = 0.001). Conclusions: Specific intervention addressing alcohol consumption and encouragement of spouse and family support should be integrated in the routine HIV/AIDS care and treatment apart from HIV transmission and prevention knowledge. PMID:27190412

Decoy receptor 3 is a prognostic factor in renal cell cancer.

PubMed

Macher-Goeppinger, Stephan; Aulmann, Sebastian; Wagener, Nina; Funke, Benjamin; Tagscherer, Katrin E; Haferkamp, Axel; Hohenfellner, Markus; Kim, Sunghee; Autschbach, Frank; Schirmacher, Peter; Roth, Wilfried

2008-10-01

Decoy receptor 3 (DcR3) is a soluble protein that binds to and inactivates the death ligand CD95L. Here, we studied a possible association between DcR3 expression and prognosis in patients with renal cell carcinomas (RCCs). A tissue microarray containing RCC tumor tissue samples and corresponding normal tissue samples was generated. Decoy receptor 3 expression in tumors of 560 patients was examined by immunohistochemistry. The effect of DcR3 expression on disease-specific survival and progression-free survival was assessed using univariate analysis and multivariate Cox regression analysis. Decoy receptor 3 serum levels were determined by ELISA. High DcR3 expression was associated with high-grade (P = .005) and high-stage (P = .048) RCCs. The incidence of distant metastasis (P = .03) and lymph node metastasis (P = .002) was significantly higher in the group with high DcR3 expression. Decoy receptor 3 expression correlated negatively with disease-specific survival (P < .001) and progression-free survival (P < .001) in univariate analyses. A multivariate Cox regression analysis retained DcR3 expression as an independent prognostic factor that outperformed the Karnofsky performance status. In patients with high-stage RCCs expressing DcR3, the 2-year survival probability was 25%, whereas in patients with DcR3-negative tumors, the survival probability was 65% (P < .001). Moreover, DcR3 serum levels were significantly higher in patients with high-stage localized disease (P = .007) and metastatic disease (P = .001). DcR3 expression is an independent prognostic factor of RCC progression and mortality. Therefore, the assessment of DcR3 expression levels offers valuable prognostic information that could be used to select patients for adjuvant therapy studies.

Ethical climate as a moderator between organizational trust and whistle-blowing among nurses and secretaries

PubMed Central

Aydan, Seda; Kaya, Sidika

2018-01-01

Objectives: To reveal the effect of perception of ethical climate by nurses and secretaries and their level of organizational trust on their whistleblowing intention. Methods: Nurses and secretaries working in a University Hospital in Ankara, Turkey, were enrolled in the study conducted in 2016. Responses were received from 369 nurses and secretaries working at Clinics and Polyclinics. Path analysis, investigation of structural equation models used while multi-regression analysis was also applied. Results: According to the regression model, ethical climate dimensions, profession, gender, and work place had significant impact on the whistleblowing intention. According to Path analysis, ethical climate had direct impact of 69% on whistleblowing intention. It was seen that organizational trust had an indirect impact of 27% on the whistleblowing score when ethical climate had a moderator role. Conclusion: In order to promote whistleblowing in organizations, it is important to keep the ethical climate perception of employees and the level of their organizational trust at high levels. PMID:29805421

Ethical climate as a moderator between organizational trust and whistle-blowing among nurses and secretaries.

PubMed

Aydan, Seda; Kaya, Sidika

2018-01-01

To reveal the effect of perception of ethical climate by nurses and secretaries and their level of organizational trust on their whistleblowing intention. Nurses and secretaries working in a University Hospital in Ankara, Turkey, were enrolled in the study conducted in 2016. Responses were received from 369 nurses and secretaries working at Clinics and Polyclinics. Path analysis, investigation of structural equation models used while multi-regression analysis was also applied. According to the regression model, ethical climate dimensions, profession, gender, and work place had significant impact on the whistleblowing intention. According to Path analysis, ethical climate had direct impact of 69% on whistleblowing intention. It was seen that organizational trust had an indirect impact of 27% on the whistleblowing score when ethical climate had a moderator role. In order to promote whistleblowing in organizations, it is important to keep the ethical climate perception of employees and the level of their organizational trust at high levels.

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M. (Principal Investigator)

1979-01-01

The spatial characteristics of the data were evaluated. A program was developed to reduce the spatial distortions resulting from variable viewing distance, and geometrically adjusted data sets were generated. The potential need for some level of radiometric adjustment was evidenced by an along track band of high reflectance across different cover types in the Varian imagery. A multiple regression analysis was employed to explore the viewing angle effect on measured reflectance. Areas in the data set which appeared to have no across track stratification of cover type were identified. A program was developed which computed the average reflectance by column for each channel, over all of the scan lines in the designated areas. A regression analysis was then run using the first, second, and third degree polynomials, for each channel. An atmospheric effect as a component of the viewing angle source of variance is discussed. Cover type maps were completed and training and test field selection was initiated.

A comparison between Bayes discriminant analysis and logistic regression for prediction of debris flow in southwest Sichuan, China

NASA Astrophysics Data System (ADS)

Xu, Wenbo; Jing, Shaocai; Yu, Wenjuan; Wang, Zhaoxian; Zhang, Guoping; Huang, Jianxi

2013-11-01

In this study, the high risk areas of Sichuan Province with debris flow, Panzhihua and Liangshan Yi Autonomous Prefecture, were taken as the studied areas. By using rainfall and environmental factors as the predictors and based on the different prior probability combinations of debris flows, the prediction of debris flows was compared in the areas with statistical methods: logistic regression (LR) and Bayes discriminant analysis (BDA). The results through the comprehensive analysis show that (a) with the mid-range scale prior probability, the overall predicting accuracy of BDA is higher than those of LR; (b) with equal and extreme prior probabilities, the overall predicting accuracy of LR is higher than those of BDA; (c) the regional predicting models of debris flows with rainfall factors only have worse performance than those introduced environmental factors, and the predicting accuracies of occurrence and nonoccurrence of debris flows have been changed in the opposite direction as the supplemented information.

Short wavelength Raman spectroscopy applied to the discrimination and characterization of three cultivars of extra virgin olive oils in different maturation stages.

PubMed

Gouvinhas, Irene; Machado, Nelson; Carvalho, Teresa; de Almeida, José M M M; Barros, Ana I R N A

2015-01-01

Extra virgin olive oils produced from three cultivars on different maturation stages were characterized using Raman spectroscopy. Chemometric methods (principal component analysis, discriminant analysis, principal component regression and partial least squares regression) applied to Raman spectral data were utilized to evaluate and quantify the statistical differences between cultivars and their ripening process. The models for predicting the peroxide value and free acidity of olive oils showed good calibration and prediction values and presented high coefficients of determination (>0.933). Both the R(2), and the correlation equations between the measured chemical parameters, and the values predicted by each approach are presented; these comprehend both PCR and PLS, used to assess SNV normalized Raman data, as well as first and second derivative of the spectra. This study demonstrates that a combination of Raman spectroscopy with multivariate analysis methods can be useful to predict rapidly olive oil chemical characteristics during the maturation process. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

Examining the Impact of a Highly Targeted State Administered Merit Aid Program on Brain Drain: Evidence from a Regression Discontinuity Analysis of Missouri's Bright Flight Program

ERIC Educational Resources Information Center

Harrington, James R.; Muñoz, José; Curs, Bradley R.; Ehlert, Mark

2016-01-01

The adoption of state-funded merit-based aid programs has become increasingly popular among policy-makers, particularly in the southeastern part of the United States. One of the primary rationales of state-funded merit-based aid is to provide scholarships to the best and brightest students as a means to retain high quality human capital in the…

Serum Irisin Predicts Mortality Risk in Acute Heart Failure Patients.

PubMed

Shen, Shutong; Gao, Rongrong; Bei, Yihua; Li, Jin; Zhang, Haifeng; Zhou, Yanli; Yao, Wenming; Xu, Dongjie; Zhou, Fang; Jin, Mengchao; Wei, Siqi; Wang, Kai; Xu, Xuejuan; Li, Yongqin; Xiao, Junjie; Li, Xinli

2017-01-01

Irisin is a peptide hormone cleaved from a plasma membrane protein fibronectin type III domain containing protein 5 (FNDC5). Emerging studies have indicated association between serum irisin and many major chronic diseases including cardiovascular diseases. However, the role of serum irisin as a predictor for mortality risk in acute heart failure (AHF) patients is not clear. AHF patients were enrolled and serum was collected at the admission and all patients were followed up for 1 year. Enzyme-linked immunosorbent assay was used to measure serum irisin levels. To explore predictors for AHF mortality, the univariate and multivariate logistic regression analysis, and receiver-operator characteristic (ROC) curve analysis were used. To determine the role of serum irisin levels in predicting survival, Kaplan-Meier survival analysis was used. In this study, 161 AHF patients were enrolled and serum irisin level was found to be significantly higher in patients deceased in 1-year follow-up. The univariate logistic regression analysis identified 18 variables associated with all-cause mortality in AHF patients, while the multivariate logistic regression analysis identified 2 variables namely blood urea nitrogen and serum irisin. ROC curve analysis indicated that blood urea nitrogen and the most commonly used biomarker, NT-pro-BNP, displayed poor prognostic value for AHF (AUCs ≤ 0.700) compared to serum irisin (AUC = 0.753). Kaplan-Meier survival analysis demonstrated that AHF patients with higher serum irisin had significantly higher mortality (P<0.001). Collectively, our study identified serum irisin as a predictive biomarker for 1-year all-cause mortality in AHF patients though large multicenter studies are highly needed. © 2017 The Author(s). Published by S. Karger AG, Basel.

Cement Leakage in Percutaneous Vertebral Augmentation for Osteoporotic Vertebral Compression Fractures: Analysis of Risk Factors.

PubMed

Xie, Weixing; Jin, Daxiang; Ma, Hui; Ding, Jinyong; Xu, Jixi; Zhang, Shuncong; Liang, De

2016-05-01

The risk factors for cement leakage were retrospectively reviewed in 192 patients who underwent percutaneous vertebral augmentation (PVA). To discuss the factors related to the cement leakage in PVA procedure for the treatment of osteoporotic vertebral compression fractures. PVA is widely applied for the treatment of osteoporotic vertebral fractures. Cement leakage is a major complication of this procedure. The risk factors for cement leakage were controversial. A retrospective review of 192 patients who underwent PVA was conducted. The following data were recorded: age, sex, bone density, number of fractured vertebrae before surgery, number of treated vertebrae, severity of the treated vertebrae, operative approach, volume of injected bone cement, preoperative vertebral compression ratio, preoperative local kyphosis angle, intraosseous clefts, preoperative vertebral cortical bone defect, and ratio and type of cement leakage. To study the correlation between each factor and cement leakage ratio, bivariate regression analysis was employed to perform univariate analysis, whereas multivariate linear regression analysis was employed to perform multivariate analysis. The study included 192 patients (282 treated vertebrae), and cement leakage occurred in 100 vertebrae (35.46%). The vertebrae with preoperative cortical bone defects generally exhibited higher cement leakage ratio, and the leakage is typically type C. Vertebrae with intact cortical bones before the procedure tend to experience type S leakage. Univariate analysis showed that patient age, bone density, number of fractured vertebrae before surgery, and vertebral cortical bone were associated with cement leakage ratio (P<0.05). Multivariate analysis showed that the main factors influencing bone cement leakage are bone density and vertebral cortical bone defect, with standardized partial regression coefficients of -0.085 and 0.144, respectively. High bone density and vertebral cortical bone defect are independent risk factors associated with bone cement leakage.

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…

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

Interleukin-6 Level among Shift and Night Workers in Japan: Cross-Sectional Analysis of the J-HOPE Study.

PubMed

Amano, Hoichi; Fukuda, Yoshiharu; Yokoo, Takashi; Yamaoka, Kazue

2018-03-27

Shift workers have a high risk of cardiovascular disease (CVD). Systemic inflammation measured has been associated with the risk of CVD onset, in addition to classical risk factors. However, the association between work schedule and inflammatory cytokine levels remains unclear. The purpose of this study was to examine the association between work schedule and interleukin-6 (IL-6)/high-sensitivity C-reactive protein (hs-CRP) levels among Japanese workers. The present cross-sectional study was a part of the Japanese Study of Health, Occupation and Psychosocial Factors Related Equity (J-HOPE). A total of 5259 persons who measured inflammatory cytokine were analyzed in this study. One-way analysis of variance was used to test log-transformed IL-6/hs-CRP differences by work schedule. Multiple regression analysis was used to examine the difference adjusted for other possible CVD risk factors. There were 3660 participants who had a regular work schedule; the remaining schedules were shift work without night work for 181 participants, shift work with night work for 1276 participants, and only night work for 142 participants. The unadjusted model showed that only night workers were significantly related to high levels of IL-6 compared with regular workers. Even in the multiple regression analysis, the higher level of IL-6 among only night workers remained significant (β=0.058, P=0.01). On the contrary, hs-CRP was not. The present study revealed that only night shift work is significantly associated with high levels of IL-6 in Japanese workers. These observations help us understand the mechanism for the association between work schedule and CVD onset.

A Selective Review of Group Selection in High-Dimensional Models

PubMed Central

Huang, Jian; Breheny, Patrick; Ma, Shuangge

2013-01-01

Grouping structures arise naturally in many statistical modeling problems. Several methods have been proposed for variable selection that respect grouping structure in variables. Examples include the group LASSO and several concave group selection methods. In this article, we give a selective review of group selection concerning methodological developments, theoretical properties and computational algorithms. We pay particular attention to group selection methods involving concave penalties. We address both group selection and bi-level selection methods. We describe several applications of these methods in nonparametric additive models, semiparametric regression, seemingly unrelated regressions, genomic data analysis and genome wide association studies. We also highlight some issues that require further study. PMID:24174707

Face Hallucination with Linear Regression Model in Semi-Orthogonal Multilinear PCA Method

NASA Astrophysics Data System (ADS)

Asavaskulkiet, Krissada

2018-04-01

In this paper, we propose a new face hallucination technique, face images reconstruction in HSV color space with a semi-orthogonal multilinear principal component analysis method. This novel hallucination technique can perform directly from tensors via tensor-to-vector projection by imposing the orthogonality constraint in only one mode. In our experiments, we use facial images from FERET database to test our hallucination approach which is demonstrated by extensive experiments with high-quality hallucinated color faces. The experimental results assure clearly demonstrated that we can generate photorealistic color face images by using the SO-MPCA subspace with a linear regression model.

Evaluation of in-line Raman data for end-point determination of a coating process: Comparison of Science-Based Calibration, PLS-regression and univariate data analysis.

PubMed

Barimani, Shirin; Kleinebudde, Peter

2017-10-01

A multivariate analysis method, Science-Based Calibration (SBC), was used for the first time for endpoint determination of a tablet coating process using Raman data. Two types of tablet cores, placebo and caffeine cores, received a coating suspension comprising a polyvinyl alcohol-polyethylene glycol graft-copolymer and titanium dioxide to a maximum coating thickness of 80µm. Raman spectroscopy was used as in-line PAT tool. The spectra were acquired every minute and correlated to the amount of applied aqueous coating suspension. SBC was compared to another well-known multivariate analysis method, Partial Least Squares-regression (PLS) and a simpler approach, Univariate Data Analysis (UVDA). All developed calibration models had coefficient of determination values (R 2 ) higher than 0.99. The coating endpoints could be predicted with root mean square errors (RMSEP) less than 3.1% of the applied coating suspensions. Compared to PLS and UVDA, SBC proved to be an alternative multivariate calibration method with high predictive power. Copyright © 2017 Elsevier B.V. All rights reserved.

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

Diagnostic accuracy of atypical p-ANCA in autoimmune hepatitis using ROC- and multivariate regression analysis.

PubMed

Terjung, B; Bogsch, F; Klein, R; Söhne, J; Reichel, C; Wasmuth, J-C; Beuers, U; Sauerbruch, T; Spengler, U

2004-09-29

Antineutrophil cytoplasmic antibodies (atypical p-ANCA) are detected at high prevalence in sera from patients with autoimmune hepatitis (AIH), but their diagnostic relevance for AIH has not been systematically evaluated so far. Here, we studied sera from 357 patients with autoimmune (autoimmune hepatitis n=175, primary sclerosing cholangitis (PSC) n=35, primary biliary cirrhosis n=45), non-autoimmune chronic liver disease (alcoholic liver cirrhosis n=62; chronic hepatitis C virus infection (HCV) n=21) or healthy controls (n=19) for the presence of various non-organ specific autoantibodies. Atypical p-ANCA, antinuclear antibodies (ANA), antibodies against smooth muscles (SMA), antibodies against liver/kidney microsomes (anti-Lkm1) and antimitochondrial antibodies (AMA) were detected by indirect immunofluorescence microscopy, antibodies against the M2 antigen (anti-M2), antibodies against soluble liver antigen (anti-SLA/LP) and anti-Lkm1 by using enzyme linked immunosorbent assays. To define the diagnostic precision of the autoantibodies, results of autoantibody testing were analyzed by receiver operating characteristics (ROC) and forward conditional logistic regression analysis. Atypical p-ANCA were detected at high prevalence in sera from patients with AIH (81%) and PSC (94%). ROC- and logistic regression analysis revealed atypical p-ANCA and SMA, but not ANA as significant diagnostic seromarkers for AIH (atypical p-ANCA: AUC 0.754+/-0.026, odds ratio [OR] 3.4; SMA: 0.652+/-0.028, OR 4.1). Atypical p-ANCA also emerged as the only diagnostically relevant seromarker for PSC (AUC 0.690+/-0.04, OR 3.4). None of the tested antibodies yielded a significant diagnostic accuracy for patients with alcoholic liver cirrhosis, HCV or healthy controls. Atypical p-ANCA along with SMA represent a seromarker with high diagnostic accuracy for AIH and should be explicitly considered in a revised version of the diagnostic score for AIH.

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

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

PubMed

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

2016-03-01

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

Analysis of a database to predict the result of allergy testing in vivo in patients with chronic nasal symptoms.

PubMed

Lacagnina, Valerio; Leto-Barone, Maria S; La Piana, Simona; Seidita, Aurelio; Pingitore, Giuseppe; Di Lorenzo, Gabriele

2014-01-01

This article uses the logistic regression model for diagnostic decision making in patients with chronic nasal symptoms. We studied the ability of the logistic regression model, obtained by the evaluation of a database, to detect patients with positive allergy skin-prick test (SPT) and patients with negative SPT. The model developed was validated using the data set obtained from another medical institution. The analysis was performed using a database obtained from a questionnaire administered to the patients with nasal symptoms containing personal data, clinical data, and results of allergy testing (SPT). All variables found to be significantly different between patients with positive and negative SPT (p < 0.05) were selected for the logistic regression models and were analyzed with backward stepwise logistic regression, evaluated with area under the curve of the receiver operating characteristic curve. A second set of patients from another institution was used to prove the model. The accuracy of the model in identifying, over the second set, both patients whose SPT will be positive and negative was high. The model detected 96% of patients with nasal symptoms and positive SPT and classified 94% of those with negative SPT. This study is preliminary to the creation of a software that could help the primary care doctors in a diagnostic decision making process (need of allergy testing) in patients complaining of chronic nasal symptoms.

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

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.

Factors Associated With Work Ability in Patients Undergoing Surgery for Cervical Radiculopathy.

PubMed

Ng, Eunice; Johnston, Venerina; Wibault, Johanna; Löfgren, Håkan; Dedering, Åsa; Öberg, Birgitta; Zsigmond, Peter; Peolsson, Anneli

2015-08-15

Cross-sectional study. To investigate the factors associated with work ability in patients undergoing surgery for cervical radiculopathy. Surgery is a common treatment of cervical radiculopathy in people of working age. However, few studies have investigated the impact on the work ability of these patients. Patients undergoing surgery for cervical radiculopathy (n = 201) were recruited from spine centers in Sweden to complete a battery of questionnaires and physical measures the day before surgery. The associations between various individual, psychological, and work-related factors and self-reported work ability were investigated by Spearman rank correlation coefficient, multivariate linear regression, and forward stepwise regression analyses. Factors that were significant (P < 0.05) in each statistical analysis were entered into the successive analysis to reveal the factors most related to work ability. Work ability was assessed using the Work Ability Index. The mean Work Ability Index score was 28 (SD, 9.0). The forward stepwise regression analysis revealed 6 factors significantly associated with work ability, which explained 62% of the variance in the Work Ability Index. Factors highly correlated with greater work ability included greater self-efficacy in performing self-cares, lower physical load on the neck at work, greater self-reported chance of being able to work in 6 months' time, greater use of active coping strategies, lower frequency of hand weakness, and higher health-related quality of life. Psychological, work-related and individual factors were significantly associated with work ability in patients undergoing surgery for cervical radiculopathy. High self-efficacy was most associated with greater work ability. Consideration of these factors by surgeons preoperatively may provide optimal return to work outcomes after surgery. 3.

Cell cycle-related genes as modifiers of age of onset of colorectal cancer in Lynch syndrome: a large-scale study in non-Hispanic white patients.

PubMed

Chen, Jinyun; Pande, Mala; Huang, Yu-Jing; Wei, Chongjuan; Amos, Christopher I; Talseth-Palmer, Bente A; Meldrum, Cliff J; Chen, Wei V; Gorlov, Ivan P; Lynch, Patrick M; Scott, Rodney J; Frazier, Marsha L

2013-02-01

Heterogeneity in age of onset of colorectal cancer in individuals with mutations in DNA mismatch repair genes (Lynch syndrome) suggests the influence of other lifestyle and genetic modifiers. We hypothesized that genes regulating the cell cycle influence the observed heterogeneity as cell cycle-related genes respond to DNA damage by arresting the cell cycle to provide time for repair and induce transcription of genes that facilitate repair. We examined the association of 1456 single nucleotide polymorphisms (SNPs) in 128 cell cycle-related genes and 31 DNA repair-related genes in 485 non-Hispanic white participants with Lynch syndrome to determine whether there are SNPs associated with age of onset of colorectal cancer. Genotyping was performed on an Illumina GoldenGate platform, and data were analyzed using Kaplan-Meier survival analysis, Cox regression analysis and classification and regression tree (CART) methods. Ten SNPs were independently significant in a multivariable Cox proportional hazards regression model after correcting for multiple comparisons (P < 5 × 10(-4)). Furthermore, risk modeling using CART analysis defined combinations of genotypes for these SNPs with which subjects could be classified into low-risk, moderate-risk and high-risk groups that had median ages of colorectal cancer onset of 63, 50 and 42 years, respectively. The age-associated risk of colorectal cancer in the high-risk group was more than four times the risk in the low-risk group (hazard ratio = 4.67, 95% CI = 3.16-6.92). The additional genetic markers identified may help in refining risk groups for more tailored screening and follow-up of non-Hispanic white patients with Lynch syndrome.

Cell cycle–related genes as modifiers of age of onset of colorectal cancer in Lynch syndrome: a large-scale study in non-Hispanic white patients

PubMed Central

Chen, Jinyun; Pande, Mala

2013-01-01

Heterogeneity in age of onset of colorectal cancer in individuals with mutations in DNA mismatch repair genes (Lynch syndrome) suggests the influence of other lifestyle and genetic modifiers. We hypothesized that genes regulating the cell cycle influence the observed heterogeneity as cell cycle–related genes respond to DNA damage by arresting the cell cycle to provide time for repair and induce transcription of genes that facilitate repair. We examined the association of 1456 single nucleotide polymorphisms (SNPs) in 128 cell cycle–related genes and 31 DNA repair–related genes in 485 non-Hispanic white participants with Lynch syndrome to determine whether there are SNPs associated with age of onset of colorectal cancer. Genotyping was performed on an Illumina GoldenGate platform, and data were analyzed using Kaplan–Meier survival analysis, Cox regression analysis and classification and regression tree (CART) methods. Ten SNPs were independently significant in a multivariable Cox proportional hazards regression model after correcting for multiple comparisons (P < 5×10–4). Furthermore, risk modeling using CART analysis defined combinations of genotypes for these SNPs with which subjects could be classified into low-risk, moderate-risk and high-risk groups that had median ages of colorectal cancer onset of 63, 50 and 42 years, respectively. The age-associated risk of colorectal cancer in the high-risk group was more than four times the risk in the low-risk group (hazard ratio = 4.67, 95% CI = 3.16–6.92). The additional genetic markers identified may help in refining risk groups for more tailored screening and follow-up of non-Hispanic white patients with Lynch syndrome. PMID:23125224

Discrepancies Between Perceptions of the Parent-Adolescent Relationship and Early Adolescent Depressive Symptoms: An Illustration of Polynomial Regression Analysis.

PubMed

Nelemans, S A; Branje, S J T; Hale, W W; Goossens, L; Koot, H M; Oldehinkel, A J; Meeus, W H J

2016-10-01

Adolescence is a critical period for the development of depressive symptoms. Lower quality of the parent-adolescent relationship has been consistently associated with higher adolescent depressive symptoms, but discrepancies in perceptions of parents and adolescents regarding the quality of their relationship may be particularly important to consider. In the present study, we therefore examined how discrepancies in parents' and adolescents' perceptions of the parent-adolescent relationship were associated with early adolescent depressive symptoms, both concurrently and longitudinally over a 1-year period. Our sample consisted of 497 Dutch adolescents (57 % boys, M age = 13.03 years), residing in the western and central regions of the Netherlands, and their mothers and fathers, who all completed several questionnaires on two occasions with a 1-year interval. Adolescents reported on depressive symptoms and all informants reported on levels of negative interaction in the parent-adolescent relationship. Results from polynomial regression analyses including interaction terms between informants' perceptions, which have recently been proposed as more valid tests of hypotheses involving informant discrepancies than difference scores, suggested the highest adolescent depressive symptoms when both the mother and the adolescent reported high negative interaction, and when the adolescent reported high but the father reported low negative interaction. This pattern of findings underscores the need for a more sophisticated methodology such as polynomial regression analysis including tests of moderation, rather than the use of difference scores, which can adequately address both congruence and discrepancies in perceptions of adolescents and mothers/fathers of the parent-adolescent relationship in detail. Such an analysis can contribute to a more comprehensive understanding of risk factors for early adolescent depressive symptoms.

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...

Comparison of 3 Methods for Identifying Dietary Patterns Associated With Risk of Disease

PubMed Central

DiBello, Julia R.; Kraft, Peter; McGarvey, Stephen T.; Goldberg, Robert; Campos, Hannia

2008-01-01

Reduced rank regression and partial least-squares regression (PLS) are proposed alternatives to principal component analysis (PCA). Using all 3 methods, the authors derived dietary patterns in Costa Rican data collected on 3,574 cases and controls in 1994–2004 and related the resulting patterns to risk of first incident myocardial infarction. Four dietary patterns associated with myocardial infarction were identified. Factor 1, characterized by high intakes of lean chicken, vegetables, fruit, and polyunsaturated oil, was generated by all 3 dietary pattern methods and was associated with a significantly decreased adjusted risk of myocardial infarction (28%–46%, depending on the method used). PCA and PLS also each yielded a pattern associated with a significantly decreased risk of myocardial infarction (31% and 23%, respectively); this pattern was characterized by moderate intake of alcohol and polyunsaturated oil and low intake of high-fat dairy products. The fourth factor derived from PCA was significantly associated with a 38% increased risk of myocardial infarction and was characterized by high intakes of coffee and palm oil. Contrary to previous studies, the authors found PCA and PLS to produce more patterns associated with cardiovascular disease than reduced rank regression. The most effective method for deriving dietary patterns related to disease may vary depending on the study goals. PMID:18945692

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

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

Treesearch

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

1994-01-01

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

High Prevalence of Leptotrichia amnionii, Atopobium vaginae, Sneathia sanguinegens, and Factor 1 Microbes and Association of Spontaneous Abortion among Korean Women.

PubMed

Seo, Sang Soo; Arokiyaraj, Selvaraj; Kim, Mi Kyung; Oh, Hea Young; Kwon, Minji; Kong, Ji Sook; Shin, Moon Kyung; Yu, Ye Lee; Lee, Jae Kwan

2017-01-01

Objective. The purpose of this study was to (i) determine the cervical microbial composition in different abortion samples and to (ii) investigate the correlation between spontaneous abortion and cervical microbes in Korean women. Methods. We collected cervical swabs from women who had never undergone abortion ( N = 36), had spontaneous abortion ( N = 23), and had undergone induced abortion ( N = 88) and subjected those samples to 16S rRNA pyrosequencing. Further, factor analysis and correlation between cervical microbiota and spontaneous abortion were evaluated by logistic regression analysis. Results. In spontaneous abortion women, 16 S rRNA gene sequences showed significant increases in Atopobium vaginae , Megasphaera spp., Gardnerella vaginalis , Leptotrichia amnionii , and Sneathia sanguinegens compared to women in nonabortion group. In multivariate logistic regression analysis, A. vaginae (OD = 11.27; 95% = 1.57-81) , L. amnionii (OD = 11.47; 95% = 1.22-107.94), S. sanguinegens (OD = 6.89; 95% = 1.07-44.33), and factor 1 microbes (OD = 16.4; 95% = 1.88-42.5) were strongly associated with spontaneous abortion. Conclusions. This study showed a high prevalence of L. amnionii, A. vaginae, S. sanguinegens , and factor 1 microbes in spontaneous abortion and association with spontaneous abortion in Korean women.

High Prevalence of Leptotrichia amnionii, Atopobium vaginae, Sneathia sanguinegens, and Factor 1 Microbes and Association of Spontaneous Abortion among Korean Women

PubMed Central

Seo, Sang Soo; Arokiyaraj, Selvaraj; Oh, Hea Young; Kwon, Minji; Kong, Ji Sook; Shin, Moon Kyung; Yu, Ye Lee; Lee, Jae Kwan

2017-01-01

Objective. The purpose of this study was to (i) determine the cervical microbial composition in different abortion samples and to (ii) investigate the correlation between spontaneous abortion and cervical microbes in Korean women. Methods. We collected cervical swabs from women who had never undergone abortion (N = 36), had spontaneous abortion (N = 23), and had undergone induced abortion (N = 88) and subjected those samples to 16S rRNA pyrosequencing. Further, factor analysis and correlation between cervical microbiota and spontaneous abortion were evaluated by logistic regression analysis. Results. In spontaneous abortion women, 16 S rRNA gene sequences showed significant increases in Atopobium vaginae, Megasphaera spp., Gardnerella vaginalis, Leptotrichia amnionii, and Sneathia sanguinegens compared to women in nonabortion group. In multivariate logistic regression analysis, A. vaginae (OD = 11.27; 95% = 1.57–81), L. amnionii (OD = 11.47; 95% = 1.22–107.94), S. sanguinegens (OD = 6.89; 95% = 1.07–44.33), and factor 1 microbes (OD = 16.4; 95% = 1.88–42.5) were strongly associated with spontaneous abortion. Conclusions. This study showed a high prevalence of L. amnionii, A. vaginae, S. sanguinegens, and factor 1 microbes in spontaneous abortion and association with spontaneous abortion in Korean women. PMID:29479540

Development and evaluation of an electromagnetic hypersensitivity questionnaire for Japanese people

PubMed Central

Tokiya, Mikiko; Mizuki, Masami; Miyata, Mikio; Kanatani, Kumiko T.; Takagi, Airi; Tsurikisawa, Naomi; Kame, Setsuko; Katoh, Takahiko; Tsujiuchi, Takuya; Kumano, Hiroaki

2016-01-01

The purpose of the present study was to evaluate the validity and reliability of a Japanese version of an electromagnetic hypersensitivity (EHS) questionnaire, originally developed by Eltiti et al. in the United Kingdom. Using this Japanese EHS questionnaire, surveys were conducted on 1306 controls and 127 self‐selected EHS subjects in Japan. Principal component analysis of controls revealed eight principal symptom groups, namely, nervous, skin‐related, head‐related, auditory and vestibular, musculoskeletal, allergy‐related, sensory, and heart/chest‐related. The reliability of the Japanese EHS questionnaire was confirmed by high to moderate intraclass correlation coefficients in a test–retest analysis, and high Cronbach's α coefficients (0.853–0.953) from each subscale. A comparison of scores of each subscale between self‐selected EHS subjects and age‐ and sex‐matched controls using bivariate logistic regression analysis, Mann–Whitney U‐ and χ 2 tests, verified the validity of the questionnaire. This study demonstrated that the Japanese EHS questionnaire is reliable and valid, and can be used for surveillance of EHS individuals in Japan. Furthermore, based on multiple logistic regression and receiver operating characteristic analyses, we propose specific preliminary criteria for screening EHS individuals in Japan. Bioelectromagnetics. 37:353–372, 2016. © 2016 The Authors. Bioelectromagnetics Published by Wiley Periodicals, Inc. PMID:27324106

Sensory impairments of the lower limb after stroke: a pooled analysis of individual patient data.

PubMed

Tyson, Sarah F; Crow, J Lesley; Connell, Louise; Winward, Charlotte; Hillier, Susan

2013-01-01

To obtain more generalizable information on the frequency and factors influencing sensory impairment after stroke and their relationship to mobility and function. A pooled analysis of individual data of stroke survivors (N = 459); mean (SD) age = 67.2 (14.8) years, 54% male, mean (SD) time since stroke = 22.33 (63.1) days, 50% left-sided weakness. Where different measurement tools were used, data were recorded. Descriptive statistics described frequency of sensory impairments, kappa coefficients investigated relationships between sensory modalities, binary logistic regression explored the factors influencing sensory impairments, and linear regression assessed the impact of sensory impairments on activity limitations. Most patients' sensation was intact (55%), and individual sensory modalities were highly associated (κ = 0.60, P < .001). Weakness and neglect influenced sensory impairment (P < .001), but demographics, stroke pathology, and spasticity did not. Sensation influenced independence in activities of daily living, mobility, and balance but less strongly than weakness. Pooled individual data analysis showed sensation of the lower limb is grossly preserved in most stroke survivors but, when present, it affects function. Sensory modalities are highly interrelated; interventions that treat the motor system during functional tasks may be as effective at treating the sensory system as sensory retraining alone.

A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

PubMed

Anderson, Carl A; McRae, Allan F; Visscher, Peter M

2006-07-01

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

Support vector regression and artificial neural network models for stability indicating analysis of mebeverine hydrochloride and sulpiride mixtures in pharmaceutical preparation: A comparative study

NASA Astrophysics Data System (ADS)

Naguib, Ibrahim A.; Darwish, Hany W.

2012-02-01

A comparison between support vector regression (SVR) and Artificial Neural Networks (ANNs) multivariate regression methods is established showing the underlying algorithm for each and making a comparison between them to indicate the inherent advantages and limitations. In this paper we compare SVR to ANN with and without variable selection procedure (genetic algorithm (GA)). To project the comparison in a sensible way, the methods are used for the stability indicating quantitative analysis of mixtures of mebeverine hydrochloride and sulpiride in binary mixtures as a case study in presence of their reported impurities and degradation products (summing up to 6 components) in raw materials and pharmaceutical dosage form via handling the UV spectral data. For proper analysis, a 6 factor 5 level experimental design was established resulting in a training set of 25 mixtures containing different ratios of the interfering species. An independent test set consisting of 5 mixtures was used to validate the prediction ability of the suggested models. The proposed methods (linear SVR (without GA) and linear GA-ANN) were successfully applied to the analysis of pharmaceutical tablets containing mebeverine hydrochloride and sulpiride mixtures. The results manifest the problem of nonlinearity and how models like the SVR and ANN can handle it. The methods indicate the ability of the mentioned multivariate calibration models to deconvolute the highly overlapped UV spectra of the 6 components' mixtures, yet using cheap and easy to handle instruments like the UV spectrophotometer.

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

PubMed

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

2017-08-01

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

Quantifying female bodily attractiveness by a statistical analysis of body measurements.

PubMed

Gründl, Martin; Eisenmann-Klein, Marita; Prantl, Lukas

2009-03-01

To investigate what makes a female figure attractive, an extensive experiment was conducted using high-quality photographic stimulus material and several systematically varied figure parameters. The objective was to predict female bodily attractiveness by using figure measurements. For generating stimulus material, a frontal-view photograph of a woman with normal body proportions was taken. Using morphing software, 243 variations of this photograph were produced by systematically manipulating the following features: weight, hip width, waist width, bust size, and leg length. More than 34,000 people participated in the web-based experiment and judged the attractiveness of the figures. All of the altered figures were measured (e.g., bust width, underbust width, waist width, hip width, and so on). Based on these measurements, ratios were calculated (e.g., waist-to-hip ratio). A multiple regression analysis was designed to predict the attractiveness rank of a figure by using figure measurements. The results show that the attractiveness of a woman's figure may be predicted by using her body measurements. The regression analysis explains a variance of 80 percent. Important predictors are bust-to-underbust ratio, bust-to-waist ratio, waist-to-hip ratio, and an androgyny index (an indicator of a typical female body). The study shows that the attractiveness of a female figure is the result of complex interactions of numerous factors. It affirms the importance of viewing the appearance of a bodily feature in the context of other bodily features when performing preoperative analysis. Based on the standardized beta-weights of the regression model, the relative importance of figure parameters in context of preoperative analysis is discussed.

Knowledge, Attitude, and Practices Regarding Vector-borne Diseases in Western Jamaica.

PubMed

Alobuia, Wilson M; Missikpode, Celestin; Aung, Maung; Jolly, Pauline E

2015-01-01

Outbreaks of vector-borne diseases (VBDs) such as dengue and malaria can overwhelm health systems in resource-poor countries. Environmental management strategies that reduce or eliminate vector breeding sites combined with improved personal prevention strategies can help to significantly reduce transmission of these infections. The aim of this study was to assess the knowledge, attitudes, and practices (KAPs) of residents in western Jamaica regarding control of mosquito vectors and protection from mosquito bites. A cross-sectional study was conducted between May and August 2010 among patients or family members of patients waiting to be seen at hospitals in western Jamaica. Participants completed an interviewer-administered questionnaire on sociodemographic factors and KAPs regarding VBDs. KAP scores were calculated and categorized as high or low based on the number of correct or positive responses. Logistic regression analyses were conducted to identify predictors of KAP and linear regression analysis conducted to determine if knowledge and attitude scores predicted practice scores. In all, 361 (85 men and 276 women) people participated in the study. Most participants (87%) scored low on knowledge and practice items (78%). Conversely, 78% scored high on attitude items. By multivariate logistic regression, housewives were 82% less likely than laborers to have high attitude scores; homeowners were 65% less likely than renters to have high attitude scores. Participants from households with 1 to 2 children were 3.4 times more likely to have high attitude scores compared with those from households with no children. Participants from households with at least 5 people were 65% less likely than those from households with fewer than 5 people to have high practice scores. By multivariable linear regression knowledge and attitude scores were significant predictors of practice score. The study revealed poor knowledge of VBDs and poor prevention practices among participants. It identified specific groups that can be targeted with vector control and personal protection interventions to decrease transmission of the infections. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

A Highly Efficient Design Strategy for Regression with Outcome Pooling

PubMed Central

Mitchell, Emily M.; Lyles, Robert H.; Manatunga, Amita K.; Perkins, Neil J.; Schisterman, Enrique F.

2014-01-01

The potential for research involving biospecimens can be hindered by the prohibitive cost of performing laboratory assays on individual samples. To mitigate this cost, strategies such as randomly selecting a portion of specimens for analysis or randomly pooling specimens prior to performing laboratory assays may be employed. These techniques, while effective in reducing cost, are often accompanied by a considerable loss of statistical efficiency. We propose a novel pooling strategy based on the k-means clustering algorithm to reduce laboratory costs while maintaining a high level of statistical efficiency when predictor variables are measured on all subjects, but the outcome of interest is assessed in pools. We perform simulations motivated by the BioCycle study to compare this k-means pooling strategy with current pooling and selection techniques under simple and multiple linear regression models. While all of the methods considered produce unbiased estimates and confidence intervals with appropriate coverage, pooling under k-means clustering provides the most precise estimates, closely approximating results from the full data and losing minimal precision as the total number of pools decreases. The benefits of k-means clustering evident in the simulation study are then applied to an analysis of the BioCycle dataset. In conclusion, when the number of lab tests is limited by budget, pooling specimens based on k-means clustering prior to performing lab assays can be an effective way to save money with minimal information loss in a regression setting. PMID:25220822

A highly efficient design strategy for regression with outcome pooling.

PubMed

Mitchell, Emily M; Lyles, Robert H; Manatunga, Amita K; Perkins, Neil J; Schisterman, Enrique F

2014-12-10

The potential for research involving biospecimens can be hindered by the prohibitive cost of performing laboratory assays on individual samples. To mitigate this cost, strategies such as randomly selecting a portion of specimens for analysis or randomly pooling specimens prior to performing laboratory assays may be employed. These techniques, while effective in reducing cost, are often accompanied by a considerable loss of statistical efficiency. We propose a novel pooling strategy based on the k-means clustering algorithm to reduce laboratory costs while maintaining a high level of statistical efficiency when predictor variables are measured on all subjects, but the outcome of interest is assessed in pools. We perform simulations motivated by the BioCycle study to compare this k-means pooling strategy with current pooling and selection techniques under simple and multiple linear regression models. While all of the methods considered produce unbiased estimates and confidence intervals with appropriate coverage, pooling under k-means clustering provides the most precise estimates, closely approximating results from the full data and losing minimal precision as the total number of pools decreases. The benefits of k-means clustering evident in the simulation study are then applied to an analysis of the BioCycle dataset. In conclusion, when the number of lab tests is limited by budget, pooling specimens based on k-means clustering prior to performing lab assays can be an effective way to save money with minimal information loss in a regression setting. Copyright © 2014 John Wiley & Sons, Ltd.

Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data.

PubMed

Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D; Nichols, Thomas E

2018-03-01

Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterized as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. © 2017, The International Biometric Society.

Quantification of brain lipids by FTIR spectroscopy and partial least squares regression

NASA Astrophysics Data System (ADS)

Dreissig, Isabell; Machill, Susanne; Salzer, Reiner; Krafft, Christoph

2009-01-01

Brain tissue is characterized by high lipid content. Its content decreases and the lipid composition changes during transformation from normal brain tissue to tumors. Therefore, the analysis of brain lipids might complement the existing diagnostic tools to determine the tumor type and tumor grade. Objective of this work is to extract lipids from gray matter and white matter of porcine brain tissue, record infrared (IR) spectra of these extracts and develop a quantification model for the main lipids based on partial least squares (PLS) regression. IR spectra of the pure lipids cholesterol, cholesterol ester, phosphatidic acid, phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, phosphatidylinositol, sphingomyelin, galactocerebroside and sulfatide were used as references. Two lipid mixtures were prepared for training and validation of the quantification model. The composition of lipid extracts that were predicted by the PLS regression of IR spectra was compared with lipid quantification by thin layer chromatography.

Father and adolescent son variables related to son's HIV prevention.

PubMed

Glenn, Betty L; Demi, Alice; Kimble, Laura P

2008-02-01

The purpose of this study was to examine the relationship between fathers' influences and African American male adolescents' perceptions of self-efficacy to reduce high-risk sexual behavior. A convenience sample of 70 fathers was recruited from churches in a large metropolitan area in the South. Hierarchical multiple linear regression analysis indicated father-related factors and son-related factors were associated with 26.1% of the variance in son's self-efficacy to be abstinent. In the regression model greater son's perception of the communication of sexual standards and greater father's perception of his son's self-efficacy were significantly related to greater son's self-efficacy for abstinence. The second regression model with son's self-efficacy for safer sex as the criterion was not statistically significant. Data support the need for fathers to express confidence in their sons' ability to be abstinent or practice safer sex and to communicate with their sons regarding sexual issues and standards.

The relationship between perceived social capital and the health promotion willingness of companies: a systematic telephone survey with chief executive officers in the information and communication technology sector.

PubMed

Jung, Julia; Nitzsche, Anika; Ernstmann, Nicole; Driller, Elke; Wasem, Jürgen; Stieler-Lorenz, Brigitte; Pfaff, Holger

2011-03-01

This study examines the association between perceived social capital and health promotion willingness (HPW) of companies from a chief executive officer's perspective. Data for the cross-sectional study were collected through telephone interviews with one chief executive officer from randomly selected companies within the German information and communication technology sector. A hierarchical multivariate logistic regression analysis was performed. Results of the logistic regression analysis of data from a total of n = 522 interviews suggest that higher values of perceived social capital are associated with pronounced HPW in companies (odds ratio = 3.78; 95% confidence intervals, 2.24 to 6.37). Our findings suggest that characteristics of high social capital, such as an established environment of trust as well as a feeling of common values and convictions could help promote HPW.

Correlates of Protective Motivation Theory (PMT) to Adolescents’ Drug Use Intention

PubMed Central

Wu, Cynthia Sau Ting; Wong, Ho Ting; Chou, Lai Yan; To, Bobby Pak Wai; Lee, Wai Lok; Loke, Alice Yuen

2014-01-01

Early onset and increasing proliferation of illicit adolescent drug-use poses a global health concern. This study aimed to examine the correlation between Protective Motivation Theory (PMT) measures and the intention to use drugs among adolescents. An exploratory quantitative correlation design and convenience sampling were adopted. A total of 318 students completed a self-reported questionnaire that solicited information related to their demographics and activities, measures of threat appraisal and coping appraisal, and the intention to use drugs. Logistic regression analysis showed that intrinsic and extrinsic rewards were significant predictors of intention. The odds ratios were equal to 2.90 (p < 0.05) and 8.04 (p < 0.001), respectively. The logistic regression model analysis resulted in a high Nagelkerke R2 of 0.49, which suggests that PMT related measures could be used in predicting drug use intention among adolescents. Further research should be conducted with non-school adolescents to confirm the application. PMID:24394215

Impact of job characteristics on psychological health of Chinese single working women.

PubMed

Yeung, D Y; Tang, C S

2001-01-01

This study aims at investigating the impact of individual and contextual job characteristics of control, psychological and physical demand, and security on psychological distress of 193 Chinese single working women in Hong Kong. The mediating role of job satisfaction in the job characteristics-distress relation is also assessed. Multiple regression analysis results show that job satisfaction mediates the effects of job control and security in predicting psychological distress; whereas psychological job demand has an independent effect on mental distress after considering the effect of job satisfaction. This main effect model indicates that psychological distress is best predicted by small company size, high psychological job demand, and low job satisfaction. Results from a separate regression analysis fails to support the overall combined effect of job demand-control on psychological distress. However, a significant physical job demand-control interaction effect on mental distress is noted, which reduces slightly after controlling the effect of job satisfaction.

Individual and community risk factors and sexually transmitted diseases among arrested youths: a two level analysis.

PubMed

Dembo, Richard; Belenko, Steven; Childs, Kristina; Wareham, Jennifer; Schmeidler, James

2009-08-01

High rates of infection for chlamydia and gonorrhea have been noted among youths involved in the juvenile justice system. Although both individual and community-level factors have been found to be associated with sexually transmitted disease (STD) risk, their relative importance has not been tested in this population. A two-level logistic regression analysis was completed to assess the influence of individual-level and community-level predictors on STD test results among arrested youths processed at a centralized intake facility. Results from weighted two level logistic regression analyses (n = 1,368) indicated individual-level factors of gender (being female), age, race (being African American), and criminal history predicted the youths' positive STD status. For the community-level predictors, concentrated disadvantage significantly and positively predicted the youths' STD status. Implications of these findings for future research and public health policy are discussed.

Cancer prevalence and education by cancer site: logistic regression analysis.

PubMed

Johnson, Stephanie; Corsten, Martin J; McDonald, James T; Gupta, Michael

2010-10-01

Previously, using the American National Health Interview Survey (NHIS) and a logistic regression analysis, we found that upper aerodigestive tract (UADT) cancer is correlated with low socioeconomic status (SES). The objective of this study was to determine if this correlation between low SES and cancer prevalence exists for other cancers. We again used the NHIS and employed education level as our main measure of SES. We controlled for potentially confounding factors, including smoking status and alcohol consumption. We found that only two cancer subsites shared the pattern of increased prevalence with low education level and decreased prevalence with high education level: UADT cancer and cervical cancer. UADT cancer and cervical cancer were the only two cancers identified that had a link between prevalence and lower education level. This raises the possibility that an associated risk factor for the two cancers is causing the relationship between lower education level and prevalence.

Correlates of Protective Motivation Theory (PMT) to adolescents' drug use intention.

PubMed

Wu, Cynthia Sau Ting; Wong, Ho Ting; Chou, Lai Yan; To, Bobby Pak Wai; Lee, Wai Lok; Loke, Alice Yuen

2014-01-03

Early onset and increasing proliferation of illicit adolescent drug-use poses a global health concern. This study aimed to examine the correlation between Protective Motivation Theory (PMT) measures and the intention to use drugs among adolescents. An exploratory quantitative correlation design and convenience sampling were adopted. A total of 318 students completed a self-reported questionnaire that solicited information related to their demographics and activities, measures of threat appraisal and coping appraisal, and the intention to use drugs. Logistic regression analysis showed that intrinsic and extrinsic rewards were significant predictors of intention. The odds ratios were equal to 2.90 (p < 0.05) and 8.04 (p < 0.001), respectively. The logistic regression model analysis resulted in a high Nagelkerke R2 of 0.49, which suggests that PMT related measures could be used in predicting drug use intention among adolescents. Further research should be conducted with non-school adolescents to confirm the application.

Estimation of the stand ages of tropical secondary forests after shifting cultivation based on the combination of WorldView-2 and time-series Landsat images

NASA Astrophysics Data System (ADS)

Fujiki, Shogoro; Okada, Kei-ichi; Nishio, Shogo; Kitayama, Kanehiro

2016-09-01

We developed a new method to estimate stand ages of secondary vegetation in the Bornean montane zone, where local people conduct traditional shifting cultivation and protected areas are surrounded by patches of recovering secondary vegetation of various ages. Identifying stand ages at the landscape level is critical to improve conservation policies. We combined a high-resolution satellite image (WorldView-2) with time-series Landsat images. We extracted stand ages (the time elapsed since the most recent slash and burn) from a change-detection analysis with Landsat time-series images and superimposed the derived stand ages on the segments classified by object-based image analysis using WorldView-2. We regarded stand ages as a response variable, and object-based metrics as independent variables, to develop regression models that explain stand ages. Subsequently, we classified the vegetation of the target area into six age units and one rubber plantation unit (1-3 yr, 3-5 yr, 5-7 yr, 7-30 yr, 30-50 yr, >50 yr and 'rubber plantation') using regression models and linear discriminant analyses. Validation demonstrated an accuracy of 84.3%. Our approach is particularly effective in classifying highly dynamic pioneer vegetation younger than 7 years into 2-yr intervals, suggesting that rapid changes in vegetation canopies can be detected with high accuracy. The combination of a spectral time-series analysis and object-based metrics based on high-resolution imagery enabled the classification of dynamic vegetation under intensive shifting cultivation and yielded an informative land cover map based on stand ages.

Correlates of cognitive function scores in elderly outpatients.

PubMed

Mangione, C M; Seddon, J M; Cook, E F; Krug, J H; Sahagian, C R; Campion, E W; Glynn, R J

1993-05-01

To determine medical, ophthalmologic, and demographic predictors of cognitive function scores as measured by the Telephone Interview for Cognitive Status (TICS), an adaptation of the Folstein Mini-Mental Status Exam. A secondary objective was to perform an item-by-item analysis of the TICS scores to determine which items correlated most highly with the overall scores. Cross-sectional cohort study. The Glaucoma Consultation Service of the Massachusetts Eye and Ear Infirmary. 472 of 565 consecutive patients age 65 and older who were seen at the Glaucoma Consultation Service between November 1, 1987 and October 31, 1988. Each subject had a standard visual examination and review of medical history at entry, followed by a telephone interview that collected information on demographic characteristics, cognitive status, health status, accidents, falls, symptoms of depression, and alcohol intake. A multivariate linear regression model of correlates of TICS score found the strongest correlates to be education, age, occupation, and the presence of depressive symptoms. The only significant ocular condition that correlated with lower TICS score was the presence of surgical aphakia (model R2 = .46). Forty-six percent (216/472) of patients fell below the established definition of normal on the mental status scale. In a logistic regression analysis, the strongest correlates of an abnormal cognitive function score were age, diabetes, educational status, and occupational status. An item analysis using step-wise linear regression showed that 85 percent of the variance in the TICS score was explained by the ability to perform serial sevens and to repeat 10 items immediately after hearing them. Educational status correlated most highly with both of these items (Kendall Tau R = .43 and Kendall Tau R = .30, respectively). Education, occupation, depression, and age were the strongest correlates of the score on this new screening test for assessing cognitive status. These factors were stronger correlates of the TICS score than chronic medical conditions, visual loss, or medications. The Telephone Interview for Cognitive Status is a useful instrument, but it may overestimate the prevalence of dementia in studies with a high prevalence of persons with less than a high school education.

Simultaneous Quantification of Syringic Acid and Kaempferol in Extracts of Bergenia Species Using Validated High-Performance Thin-Layer Chromatographic-Densitometric Method.

PubMed

Srivastava, Nishi; Srivastava, Amit; Srivastava, Sharad; Rawat, Ajay Kumar Singh; Khan, Abdul Rahman

2016-03-01

A rapid, sensitive, selective and robust quantitative densitometric high-performance thin-layer chromatographic method was developed and validated for separation and quantification of syringic acid (SYA) and kaempferol (KML) in the hydrolyzed extracts of Bergenia ciliata and Bergenia stracheyi. The separation was performed on silica gel 60F254 high-performance thin-layer chromatography plates using toluene : ethyl acetate : formic acid (5 : 4: 1, v/v/v) as the mobile phase. The quantification of SYA and KML was carried out using a densitometric reflection/absorption mode at 290 nm. A dense spot of SYA and KML appeared on the developed plate at a retention factor value of 0.61 ± 0.02 and 0.70 ± 0.01. A precise and accurate quantification was performed using linear regression analysis by plotting the peak area vs concentration 100-600 ng/band (correlation coefficient: r = 0.997, regression coefficient: R(2) = 0.996) for SYA and 100-600 ng/band (correlation coefficient: r = 0.995, regression coefficient: R(2) = 0.991) for KML. The developed method was validated in terms of accuracy, recovery and inter- and intraday study as per International Conference on Harmonisation guidelines. The limit of detection and limit of quantification of SYA and KML were determined, respectively, as 91.63, 142.26 and 277.67, 431.09 ng. The statistical data analysis showed that the method is reproducible and selective for the estimation of SYA and KML in extracts of B. ciliata and B. stracheyi. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Landslide susceptibility mapping for a part of North Anatolian Fault Zone (Northeast Turkey) using logistic regression model

NASA Astrophysics Data System (ADS)

Demir, Gökhan; aytekin, mustafa; banu ikizler, sabriye; angın, zekai

2013-04-01

The North Anatolian Fault is know as one of the most active and destructive fault zone which produced many earthquakes with high magnitudes. Along this fault zone, the morphology and the lithological features are prone to landsliding. However, many earthquake induced landslides were recorded by several studies along this fault zone, and these landslides caused both injuiries and live losts. Therefore, a detailed landslide susceptibility assessment for this area is indispancable. In this context, a landslide susceptibility assessment for the 1445 km2 area in the Kelkit River valley a part of North Anatolian Fault zone (Eastern Black Sea region of Turkey) was intended with this study, and the results of this study are summarized here. For this purpose, geographical information system (GIS) and a bivariate statistical model were used. Initially, Landslide inventory maps are prepared by using landslide data determined by field surveys and landslide data taken from General Directorate of Mineral Research and Exploration. The landslide conditioning factors are considered to be lithology, slope gradient, slope aspect, topographical elevation, distance to streams, distance to roads and distance to faults, drainage density and fault density. ArcGIS package was used to manipulate and analyze all the collected data Logistic regression method was applied to create a landslide susceptibility map. Landslide susceptibility maps were divided into five susceptibility regions such as very low, low, moderate, high and very high. The result of the analysis was verified using the inventoried landslide locations and compared with the produced probability model. For this purpose, Area Under Curvature (AUC) approach was applied, and a AUC value was obtained. Based on this AUC value, the obtained landslide susceptibility map was concluded as satisfactory. Keywords: North Anatolian Fault Zone, Landslide susceptibility map, Geographical Information Systems, Logistic Regression Analysis.

Test anxiety and academic performance in chiropractic students.

PubMed

Zhang, Niu; Henderson, Charles N R

2014-01-01

Objective : We assessed the level of students' test anxiety, and the relationship between test anxiety and academic performance. Methods : We recruited 166 third-quarter students. The Test Anxiety Inventory (TAI) was administered to all participants. Total scores from written examinations and objective structured clinical examinations (OSCEs) were used as response variables. Results : Multiple regression analysis shows that there was a modest, but statistically significant negative correlation between TAI scores and written exam scores, but not OSCE scores. Worry and emotionality were the best predictive models for written exam scores. Mean total anxiety and emotionality scores for females were significantly higher than those for males, but not worry scores. Conclusion : Moderate-to-high test anxiety was observed in 85% of the chiropractic students examined. However, total test anxiety, as measured by the TAI score, was a very weak predictive model for written exam performance. Multiple regression analysis demonstrated that replacing total anxiety (TAI) with worry and emotionality (TAI subscales) produces a much more effective predictive model of written exam performance. Sex, age, highest current academic degree, and ethnicity contributed little additional predictive power in either regression model. Moreover, TAI scores were not found to be statistically significant predictors of physical exam skill performance, as measured by OSCEs.

A PDE approach for quantifying and visualizing tumor progression and regression

NASA Astrophysics Data System (ADS)

Sintay, Benjamin J.; Bourland, J. Daniel

2009-02-01

Quantification of changes in tumor shape and size allows physicians the ability to determine the effectiveness of various treatment options, adapt treatment, predict outcome, and map potential problem sites. Conventional methods are often based on metrics such as volume, diameter, or maximum cross sectional area. This work seeks to improve the visualization and analysis of tumor changes by simultaneously analyzing changes in the entire tumor volume. This method utilizes an elliptic partial differential equation (PDE) to provide a roadmap of boundary displacement that does not suffer from the discontinuities associated with other measures such as Euclidean distance. Streamline pathways defined by Laplace's equation (a commonly used PDE) are used to track tumor progression and regression at the tumor boundary. Laplace's equation is particularly useful because it provides a smooth, continuous solution that can be evaluated with sub-pixel precision on variable grid sizes. Several metrics are demonstrated including maximum, average, and total regression and progression. This method provides many advantages over conventional means of quantifying change in tumor shape because it is observer independent, stable for highly unusual geometries, and provides an analysis of the entire three-dimensional tumor volume.

Systematic Review and Meta-Analysis: Dose-Response Relationship of Selective Serotonin Reuptake Inhibitors in Major Depressive Disorder.

PubMed

Jakubovski, Ewgeni; Varigonda, Anjali L; Freemantle, Nicholas; Taylor, Matthew J; Bloch, Michael H

2016-02-01

Previous studies suggested that the treatment response to selective serotonin reuptake inhibitors (SSRIs) in major depressive disorder follows a flat response curve within the therapeutic dose range. The present study was designed to clarify the relationship between dosage and treatment response in major depressive disorder. The authors searched PubMed for randomized placebo-controlled trials examining the efficacy of SSRIs for treating adults with major depressive disorder. Trials were also required to assess improvement in depression severity at multiple time points. Additional data were collected on treatment response and all-cause and side effect-related discontinuation. All medication doses were transformed into imipramine-equivalent doses. The longitudinal data were analyzed with a mixed-regression model. Endpoint and tolerability analyses were analyzed using meta-regression and stratified subgroup analysis by predefined SSRI dose categories in order to assess the effect of SSRI dosing on the efficacy and tolerability of SSRIs for major depressive disorder. Forty studies involving 10,039 participants were included. Longitudinal modeling (dose-by-time interaction=0.0007, 95% CI=0.0001-0.0013) and endpoint analysis (meta-regression: β=0.00053, 95% CI=0.00018-0.00088, z=2.98) demonstrated a small but statistically significant positive association between SSRI dose and efficacy. Higher doses of SSRIs were associated with an increased likelihood of dropouts due to side effects (meta-regression: β=0.00207, 95% CI=0.00071-0.00342, z=2.98) and decreased likelihood of all-cause dropout (meta-regression: β=-0.00093, 95% CI=-0.00165 to -0.00021, z=-2.54). Higher doses of SSRIs appear slightly more effective in major depressive disorder. This benefit appears to plateau at around 250 mg of imipramine equivalents (50 mg of fluoxetine). The slightly increased benefits of SSRIs at higher doses are somewhat offset by decreased tolerability at high doses.

Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

PubMed

Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro

2017-09-01

To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Association Between Dietary Intake and Function in Amyotrophic Lateral Sclerosis.

PubMed

Nieves, Jeri W; Gennings, Chris; Factor-Litvak, Pam; Hupf, Jonathan; Singleton, Jessica; Sharf, Valerie; Oskarsson, Björn; Fernandes Filho, J Americo M; Sorenson, Eric J; D'Amico, Emanuele; Goetz, Ray; Mitsumoto, Hiroshi

2016-12-01

There is growing interest in the role of nutrition in the pathogenesis and progression of amyotrophic lateral sclerosis (ALS). To evaluate the associations between nutrients, individually and in groups, and ALS function and respiratory function at diagnosis. A cross-sectional baseline analysis of the Amyotrophic Lateral Sclerosis Multicenter Cohort Study of Oxidative Stress study was conducted from March 14, 2008, to February 27, 2013, at 16 ALS clinics throughout the United States among 302 patients with ALS symptom duration of 18 months or less. Nutrient intake, measured using a modified Block Food Frequency Questionnaire (FFQ). Amyotrophic lateral sclerosis function, measured using the ALS Functional Rating Scale-Revised (ALSFRS-R), and respiratory function, measured using percentage of predicted forced vital capacity (FVC). Baseline data were available on 302 patients with ALS (median age, 63.2 years [interquartile range, 55.5-68.0 years]; 178 men and 124 women). Regression analysis of nutrients found that higher intakes of antioxidants and carotenes from vegetables were associated with higher ALSFRS-R scores or percentage FVC. Empirically weighted indices using the weighted quantile sum regression method of "good" micronutrients and "good" food groups were positively associated with ALSFRS-R scores (β [SE], 2.7 [0.69] and 2.9 [0.9], respectively) and percentage FVC (β [SE], 12.1 [2.8] and 11.5 [3.4], respectively) (all P < .001). Positive and significant associations with ALSFRS-R scores (β [SE], 1.5 [0.61]; P = .02) and percentage FVC (β [SE], 5.2 [2.2]; P = .02) for selected vitamins were found in exploratory analyses. Antioxidants, carotenes, fruits, and vegetables were associated with higher ALS function at baseline by regression of nutrient indices and weighted quantile sum regression analysis. We also demonstrated the usefulness of the weighted quantile sum regression method in the evaluation of diet. Those responsible for nutritional care of the patient with ALS should consider promoting fruit and vegetable intake since they are high in antioxidants and carotenes.

[Application of SAS macro to evaluated multiplicative and additive interaction in logistic and Cox regression in clinical practices].

PubMed

Nie, Z Q; Ou, Y Q; Zhuang, J; Qu, Y J; Mai, J Z; Chen, J M; Liu, X Q

2016-05-01

Conditional logistic regression analysis and unconditional logistic regression analysis are commonly used in case control study, but Cox proportional hazard model is often used in survival data analysis. Most literature only refer to main effect model, however, generalized linear model differs from general linear model, and the interaction was composed of multiplicative interaction and additive interaction. The former is only statistical significant, but the latter has biological significance. In this paper, macros was written by using SAS 9.4 and the contrast ratio, attributable proportion due to interaction and synergy index were calculated while calculating the items of logistic and Cox regression interactions, and the confidence intervals of Wald, delta and profile likelihood were used to evaluate additive interaction for the reference in big data analysis in clinical epidemiology and in analysis of genetic multiplicative and additive interactions.

Prediction by regression and intrarange data scatter in surface-process studies

USGS Publications Warehouse

Toy, T.J.; Osterkamp, W.R.; Renard, K.G.

1993-01-01

Modeling is a major component of contemporary earth science, and regression analysis occupies a central position in the parameterization, calibration, and validation of geomorphic and hydrologic models. Although this methodology can be used in many ways, we are primarily concerned with the prediction of values for one variable from another variable. Examination of the literature reveals considerable inconsistency in the presentation of the results of regression analysis and the occurrence of patterns in the scatter of data points about the regression line. Both circumstances confound utilization and evaluation of the models. Statisticians are well aware of various problems associated with the use of regression analysis and offer improved practices; often, however, their guidelines are not followed. After a review of the aforementioned circumstances and until standard criteria for model evaluation become established, we recommend, as a minimum, inclusion of scatter diagrams, the standard error of the estimate, and sample size in reporting the results of regression analyses for most surface-process studies. ?? 1993 Springer-Verlag.

Noninvasive spectral imaging of skin chromophores based on multiple regression analysis aided by Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Wiswadarma, Aditya; Hase, Yota; Tanaka, Noriyuki; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa

2011-08-01

In order to visualize melanin and blood concentrations and oxygen saturation in human skin tissue, a simple imaging technique based on multispectral diffuse reflectance images acquired at six wavelengths (500, 520, 540, 560, 580 and 600nm) was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.

The Association between Electronic Bullying and School Absenteeism among High School Students in the United States

ERIC Educational Resources Information Center

Grinshteyn, Erin; Yang, Y. T.

2017-01-01

Background: We examined the relationship between exposure to electronic bullying and absenteeism as a result of being afraid. Methods: This multivariate, multinomial regression analysis of the 2013 Youth Risk Behavior Survey data assessed the association between experiencing electronic bullying in the past year and how often students were absent…

The Relationship between Science Achievement and Self-Concept among Gifted Students from the Third International Earth Science Olympiad

ERIC Educational Resources Information Center

Chang, Chun-Yen; Lin, Pei-Ling

2017-01-01

This study investigated the relationship between gifted students' academic self-concept (ASC) and academic achievement (AC) in earth science with internationally representative high-school students from the third International Earth Science Olympiad (IESO) held in Taiwan in 2009. The results of regression analysis indicated that IESO students' ASC…

Poverty and Algebra Performance: A Comparative Spatial Analysis of a Border South State

ERIC Educational Resources Information Center

Tate, William F.; Hogrebe, Mark C.

2015-01-01

This research uses two measures of poverty, as well as mobility and selected education variables to study how their relationships vary across 543 Missouri high school districts. Using Missouri and U.S. Census American Community Survey (ACS) data, local R[superscript 2]'s from geographically weighted regressions are spatially mapped to demonstrate…

How Do Schools Affect Ethnic Saliency Levels of Students in Bosnia and Herzegovina?

ERIC Educational Resources Information Center

Becker, Matthew Thomas

2017-01-01

This article measures the role of schools in the ethnic socialization and identity formation processes of high school seniors in Bosnia and Herzegovina (BiH) via ordinary least squares regression analysis and attempts to contribute to a better understanding of educational transitions in the postsocialist space and youth identity formation in a…

Short-term, high-dose glucocorticoid treatment does not contribute to reduced bone mineral density in patients with multiple sclerosis.

PubMed

Olsson, A; Oturai, D B; Sørensen, P S; Oturai, P S; Oturai, A B

2015-10-01

Patients with multiple sclerosis (MS) are at increased risk of reduced bone mineral density (BMD). A contributing factor might be treatment with high-dose glucocorticoids (GCs). The objective of this paper is to assess bone mass in patients with MS and evaluate the importance of short-term, high-dose GC treatment and other risk factors that affect BMD in patients with MS. A total of 260 patients with MS received short-term high-dose GC treatment and had their BMD measured by dual x-ray absorptiometry. BMD was compared to a healthy age-matched reference population (Z-scores). Data regarding GCs, age, body mass index (BMI), serum 25(OH)D, disease duration and severity were collected retrospectively and analysed in a multiple linear regression analysis to evaluate the association between each risk factor and BMD. Osteopenia was present in 38% and osteoporosis in 7% of the study population. Mean Z-score was significantly below zero, indicating a decreased BMD in our MS patients. Multiple linear regression analysis showed no significant association between GCs and BMD. In contrast, age, BMI and disease severity were independently associated with both lumbar and femoral BMD. Reduced BMD was prevalent in patients with MS. GC treatment appears not to be the primary underlying cause of secondary osteoporosis in MS patients. © The Author(s), 2015.

CADDIS Volume 4. Data Analysis: PECBO Appendix - R Scripts for Non-Parametric Regressions

EPA Pesticide Factsheets

Script for computing nonparametric regression analysis. Overview of using scripts to infer environmental conditions from biological observations, statistically estimating species-environment relationships, statistical scripts.

Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

2013-06-01

This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

Chromium (VI) induced oxidative damage to DNA: increase of urinary 8-hydroxydeoxyguanosine concentrations (8-OHdG) among electroplating workers

PubMed Central

Kuo, H; Chang, S; Wu, K; Wu, F

2003-01-01

Aims: To investigate the concentration of urinary 8-hydroxydeoxyguanosine (8-OHdG) among electroplating workers in Taiwan. Methods: Fifty workers were selected from five chromium (Cr) electroplating plants in central Taiwan. The 20 control subjects were office workers with no previous exposure to Cr. Urinary 8-OHdG concentrations were determined using high performance liquid chromatography with electrochemical detection. Results: Urinary 8-OHdG concentrations among Cr workers (1149.5 pmol/kg/day) were higher than those in the control group (730.2 pmol/kg/day). There was a positive correlation between urinary 8-OHdG concentrations and urinary Cr concentration (r = 0.447, p < 0.01), and urinary 8-OHdG correlated positively with airborne Cr concentration (r = 0.285). Using multiple regression analysis, the factors that affected urinary 8-OHdG concentrations were alcohol, the common cold, and high urinary Cr concentration. There was a high correlation of urinary 8-OHdG with both smoking and drinking, but multiple regression analysis showed that smoking was not a significant factor. Age and gender were also non-significant factors. Conclusion: 8-OHdG, which is an indicator of oxidative DNA damage, was a sensitive biomarker for Cr exposure. PMID:12883020

High Serum Adiponectin Level Is a Risk Factor for Anemia in Japanese Men: A Prospective Observational Study of 1,029 Japanese Subjects

PubMed Central

Kohno, Kei; Narimatsu, Hiroto; Shiono, Yosuke; Suzuki, Ikuko; Kato, Yuichi; Sho, Ri; Otani, Katsumi; Ishizawa, Kenichi; Yamashita, Hidetoshi; Kubota, Isao; Ueno, Yoshiyuki; Kato, Takeo; Fukao, Akira; Kayama, Takamasa

2016-01-01

Erythroid abnormalities including anemia and polycythemia are often observed in the general clinical setting. Because recent studies reported that adiponectin negatively affects hematopoiesis, we performed a prospective observational study to assess the relationship between anemia and adiponectin, as well as other parameters, in 1029 Japanese subjects (477 men and 552 women) 40 years of age and older. Body measurements, blood tests, and nutrition intake studies were performed at baseline, and 5 to 7 years later (follow-up). Hemoglobin (Hb) and hematocrit (Hct) levels in men with high serum adiponectin levels were lower at follow-up than at baseline. Multiple regression analysis showed that age, body mass index, adiponectin, and glutamic-pyruvic transaminase were significantly associated with erythroid-related variables (red blood cells, Hb, and Hct) in both men and women (P <0.05). In a logistic regression analysis, adiponectin, fasting blood glucose, and β-natriuretic peptide were significant risk factors for anemia in men, and blood urea nitrogen and amylase were significant risk factors in women. Physical features and nutrient intake were not risk factors for anemia. Our study demonstrates, both clinically and epidemiologically, that a high serum adiponectin level decreases the amounts of erythroid-related variables and is a risk factor for anemia in Japanese men. PMID:27918575

Vietnamese validation of the short version of Internet Addiction Test.

PubMed

Tran, Bach Xuan; Mai, Hue Thi; Nguyen, Long Hoang; Nguyen, Cuong Tat; Latkin, Carl A; Zhang, Melvyn W B; Ho, Roger C M

2017-12-01

The main goal of the present study was to examine the psychometric properties of a Vietnamese version of the short-version of Internet Addiction Test (s-IAT) and to assess the relationship between s-IAT scores and demographics, health related qualify of life and perceived stress scores in young Vietnamese. The Vietnamese version of s-IAT was administered to a sample of 589 participants. Exploratory factor and reliability analyses were performed. Regression analysis was used to identify the associated factors. The two-factor model of Vietnamese version of s-IAT demonstrated good psychometric properties. The internal consistency of Factor 1 (loss of control/time management) was high (Cronbach's alpha = 0.82) and Factor 2 (craving/social problems) was satisfactory (Cronbach's alpha = 0.75). Findings indicated that 20.9% youths were addicted to the Internet. Regression analysis revealed significant associations between Internet addiction and having problems in self-care, lower quality of life and high perceived stress scores. The Vietnamese version of s-IAT is a valid and reliable instrument to assess IA in Vietnamese population. Due to the high prevalence of IA among Vietnamese youths, IA should be paid attention in future intervention programs. s-IAT can be a useful screening tool for IA to promptly inform and treat the IA among Vietnamese youths.

Suicidality and its relationships with individual, family, peer, and psychopathology factors among adolescents with attention-deficit/hyperactivity disorder.

PubMed

Chou, Wen-Jiun; Liu, Tai-Ling; Hu, Huei-Fan; Yen, Cheng-Fang

2016-01-01

The aim of this study was to examine the prevalence rates of suicidal intent and its correlates among adolescents diagnosed with ADHD in Taiwan. A total of 287 adolescents aged 11-18 years and diagnosed with ADHD participated in this study. Their suicidal ideation and suicide attempts were assessed. Logistic regression analysis was used to examine the associations of suicide with individual, family, peer, ADHD, and psychopathology factors. A total of 12.2% of the participants reported suicidal ideation or a suicide attempt. A logistic regression analysis model showed that adolescents who were older, were bullying perpetrators, and reported high depression level were more likely to have suicidal intent. These three factors were also significantly correlated with suicidal ideation; however, only having high depression level was significantly correlated with suicidal attempts. The results of this study showed that a high proportion of adolescents with ADHD reported suicidal ideation or a suicide attempt. Multiple factors were significantly associated with suicidal intent among adolescents with ADHD. Clinicians, educational professionals, and parents of adolescents with ADHD should monitor the possibility of suicide in adolescents with ADHD who exhibit the correlates of suicidal intent identified in this study. Copyright © 2016 Elsevier Ltd. All rights reserved.

Burnout and Associated Factors among Iranian Emergency Medicine Practitioners

PubMed Central

JALILI, Mohammad; SADEGHIPOUR ROODSARI, Gholamreza; BASSIR NIA, Anahita

2013-01-01

Abstract Background Emergency physicians are at risk of burnout, which can affect their mental health, as well as patient care. We assessed burnout level among Iranian emergency physicians and investigated demographic, work-related factors and stressors associated with higher burnout. Methods In a cross-sectional study, we surveyed all 188 emergency medicine residents and practitioners in Iran. We measured burnout using 22-item Maslach Burnout Inventory assessing emotional exhaustion, depersonalization and personal accomplishment, also demographic factors, work related factors and sources of stress in emergency department using anonymous self-administered questionnaire. Descriptive analysis, univariate analysis to evaluate association with higher score of burnout, and multivariate logistic regression analysis to predict high burnout in 3 subscales was performed. Results Totally, 165 questionnaires were filled (response rate: 88%; mean age: 33.6 years, 91% male). Mean burnout scores were 22.94 for emotional exhaustion (95% CI=20.78-25.01; moderate), 9.3 for depersonalization (95% CI=8.24-10.36; moderate to high), and 31.47 for personal accomplishment (95% CI=29.87-33.07; moderate to high). Frequent reported sources of stress were shortage of equipment, problem with work physical environment, and relationship with other services. All 19 sources of stress were associated with higher score of emotional exhaustion and depersonalization; while twelve out of 19 were significantly associated with lower level of personal accomplishment. In logistic regression model, the significant predictors for high emotional exhaustion were work overload, feeling of insecurity for future career and difficulties to balance professional and private life. Conclusion Burnout is high among Iranian emergency medicine practitioners and some interventions can be proposed to reduce stress. PMID:26060665

Isolated and synergistic effects of PM10 and average temperature on cardiovascular and respiratory mortality.

PubMed

Pinheiro, Samya de Lara Lins de Araujo; Saldiva, Paulo Hilário Nascimento; Schwartz, Joel; Zanobetti, Antonella

2014-12-01

OBJECTIVE To analyze the effect of air pollution and temperature on mortality due to cardiovascular and respiratory diseases. METHODS We evaluated the isolated and synergistic effects of temperature and particulate matter with aerodynamic diameter < 10 µm (PM10) on the mortality of individuals > 40 years old due to cardiovascular disease and that of individuals > 60 years old due to respiratory diseases in Sao Paulo, SP, Southeastern Brazil, between 1998 and 2008. Three methodologies were used to evaluate the isolated association: time-series analysis using Poisson regression model, bidirectional case-crossover analysis matched by period, and case-crossover analysis matched by the confounding factor, i.e., average temperature or pollutant concentration. The graphical representation of the response surface, generated by the interaction term between these factors added to the Poisson regression model, was interpreted to evaluate the synergistic effect of the risk factors. RESULTS No differences were observed between the results of the case-crossover and time-series analyses. The percentage change in the relative risk of cardiovascular and respiratory mortality was 0.85% (0.45;1.25) and 1.60% (0.74;2.46), respectively, due to an increase of 10 μg/m3 in the PM10 concentration. The pattern of correlation of the temperature with cardiovascular mortality was U-shaped and that with respiratory mortality was J-shaped, indicating an increased relative risk at high temperatures. The values for the interaction term indicated a higher relative risk for cardiovascular and respiratory mortalities at low temperatures and high temperatures, respectively, when the pollution levels reached approximately 60 μg/m3. CONCLUSIONS The positive association standardized in the Poisson regression model for pollutant concentration is not confounded by temperature, and the effect of temperature is not confounded by the pollutant levels in the time-series analysis. The simultaneous exposure to different levels of environmental factors can create synergistic effects that are as disturbing as those caused by extreme concentrations.

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.

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

PubMed

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

2011-04-01

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

Using "Excel" for White's Test--An Important Technique for Evaluating the Equality of Variance Assumption and Model Specification in a Regression Analysis

ERIC Educational Resources Information Center

Berenson, Mark L.

2013-01-01

There is consensus in the statistical literature that severe departures from its assumptions invalidate the use of regression modeling for purposes of inference. The assumptions of regression modeling are usually evaluated subjectively through visual, graphic displays in a residual analysis but such an approach, taken alone, may be insufficient…

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

Treesearch

L.R. Grosenbaugh

1967-01-01

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

Understanding software faults and their role in software reliability modeling

NASA Technical Reports Server (NTRS)

Munson, John C.

1994-01-01

This study is a direct result of an on-going project to model the reliability of a large real-time control avionics system. In previous modeling efforts with this system, hardware reliability models were applied in modeling the reliability behavior of this system. In an attempt to enhance the performance of the adapted reliability models, certain software attributes were introduced in these models to control for differences between programs and also sequential executions of the same program. As the basic nature of the software attributes that affect software reliability become better understood in the modeling process, this information begins to have important implications on the software development process. A significant problem arises when raw attribute measures are to be used in statistical models as predictors, for example, of measures of software quality. This is because many of the metrics are highly correlated. Consider the two attributes: lines of code, LOC, and number of program statements, Stmts. In this case, it is quite obvious that a program with a high value of LOC probably will also have a relatively high value of Stmts. In the case of low level languages, such as assembly language programs, there might be a one-to-one relationship between the statement count and the lines of code. When there is a complete absence of linear relationship among the metrics, they are said to be orthogonal or uncorrelated. Usually the lack of orthogonality is not serious enough to affect a statistical analysis. However, for the purposes of some statistical analysis such as multiple regression, the software metrics are so strongly interrelated that the regression results may be ambiguous and possibly even misleading. Typically, it is difficult to estimate the unique effects of individual software metrics in the regression equation. The estimated values of the coefficients are very sensitive to slight changes in the data and to the addition or deletion of variables in the regression equation. Since most of the existing metrics have common elements and are linear combinations of these common elements, it seems reasonable to investigate the structure of the underlying common factors or components that make up the raw metrics. The technique we have chosen to use to explore this structure is a procedure called principal components analysis. Principal components analysis is a decomposition technique that may be used to detect and analyze collinearity in software metrics. When confronted with a large number of metrics measuring a single construct, it may be desirable to represent the set by some smaller number of variables that convey all, or most, of the information in the original set. Principal components are linear transformations of a set of random variables that summarize the information contained in the variables. The transformations are chosen so that the first component accounts for the maximal amount of variation of the measures of any possible linear transform; the second component accounts for the maximal amount of residual variation; and so on. The principal components are constructed so that they represent transformed scores on dimensions that are orthogonal. Through the use of principal components analysis, it is possible to have a set of highly related software attributes mapped into a small number of uncorrelated attribute domains. This definitively solves the problem of multi-collinearity in subsequent regression analysis. There are many software metrics in the literature, but principal component analysis reveals that there are few distinct sources of variation, i.e. dimensions, in this set of metrics. It would appear perfectly reasonable to characterize the measurable attributes of a program with a simple function of a small number of orthogonal metrics each of which represents a distinct software attribute domain.

HbA1c is significantly associated with arterial stiffness but not with carotid atherosclerosis in a community-based population without type 2 diabetes: The Dong-gu study.

PubMed

Lee, Young-Hoon; Shin, Min-Ho; Choi, Jin-Su; Rhee, Jung-Ae; Nam, Hae-Sung; Jeong, Seul-Ki; Park, Kyeong-Soo; Ryu, So-Yeon; Choi, Seong-Woo; Kim, Bok-Hee; Oh, Gyung-Jae; Kweon, Sun-Seog

2016-04-01

We examined the associations between HbA1c levels and various atherosclerotic vascular parameters among adults without diabetes from the general population. A total of 6500 community-dwelling adults, who were free of type 2 diabetes and ≥50 years of age, were included. High-resolution B-mode ultrasound was used to evaluate carotid artery structure, including intima-media thickness (IMT), plaque, and luminal diameter. Brachial-ankle pulse wave velocity (baPWV), which is a useful indicator of systemic arterial stiffness, was determined using an automatic waveform analysis device. No significant associations were observed between HbA1c, carotid IMT, plaque, or luminal diameter in a fully adjusted model. However, the odds ratio (95% confidence interval) for high baPWV (defined as the highest quartile) increased by 1.43 (1.19-1.71) per 1% HbA1c increase after adjusting for conventional risk factors in a multivariate logistic regression analysis. In addition, HbA1c was independently associated with baPWV in a multivariate linear regression analysis. High-normal HbA1c level was independently associated with arterial stiffness, but not with carotid atherosclerotic parameters, in the general population without diabetes. Our results suggest that the functional atherosclerotic process may already be accelerated according to HbA1c level, even at a level below the diagnostic threshold for diabetes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Risk factors for repetitive strain injuries among school teachers in Thailand.

PubMed

Chaiklieng, Sunisa; Suggaravetsiri, Pornnapa

2012-01-01

Prolonged posture, static works and repetition are previously reported as the cause of repetitive strain injuries (RSIs) among workers including teachers. This cross-sectional analytic study aimed to investigate the prevalence and risk factors of RSIs among school teachers. Participants were 452 full-time school teachers in Thailand. Data were collected by the structural questionnaires, illuminance measurements and the physical fitness tests. Descriptive statistics and inferential statistics which were Chi-square test and multiple logistic regression analysis were used. Most teachers in this study were females (57.3%), the mean years of work experience was 22.6 ± 10.4 years. The six-month prevalence of RSIs was 73.7%. The univariate analysis identified the related risk factors to RSIs which were chronic disease (OR=1.8; 95% CI = 1.16-2.73), history of trauma (OR=2.0; 95% CI = 1.02-4.01), member of family had RSIs (OR=2.0; 95% CI = 1.02- 4.01), stretch to write on board (OR=1.7; 95% CI = 1.06-1.70) and high heel shoe >2 inch (OR=1.6; 95% CI = 1.03-2.51). Multiple logistic regression analysis showed that chronic diseases and high heel shoe >2 inch significantly related to developing of RSIs. The poor grip strength and back muscle flexibility significantly affected RSIs of teachers. In conclusions, RSIs were highly prevalent in school teachers that they should be aware of health promotion to prevent RSIs.

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.

Comparison of pulmonary and extrapulmonary tuberculosis in Nepal- a hospital-based retrospective study.

PubMed

Sreeramareddy, Chandrashekhar T; Panduru, Kishore V; Verma, Sharat C; Joshi, Hari S; Bates, Michael N

2008-01-24

Studies from developed countries have reported on host-related risk factors for extra-pulmonary tuberculosis (EPTB). However, similar studies from high-burden countries like Nepal are lacking. Therefore, we carried out this study to compare demographic, life-style and clinical characteristics between EPTB and PTB patients. A retrospective analysis was carried out on 474 Tuberculosis (TB) patients diagnosed in a tertiary care hospital in western Nepal. Characteristics of demography, life-style and clinical features were obtained from medical case records. Risk factors for being an EPTB patient relative to a PTB patient were identified using logistic regression analysis. The age distribution of the TB patients had a bimodal distribution. The male to female ratio for PTB was 2.29. EPTB was more common at younger ages (< 25 years) and in females. Common sites for EPTB were lymph nodes (42.6%) and peritoneum and/or intestines (14.8%). By logistic regression analysis, age less than 25 years (OR 2.11 95% CI 1.12-3.68) and female gender (OR 1.69, 95% CI 1.12-2.56) were associated with EPTB. Smoking, use of immunosuppressive drugs/steroids, diabetes and past history of TB were more likely to be associated with PTB. Results suggest that younger age and female gender may be independent risk factors for EPTB in a high-burden country like Nepal. TB control programmes may target young and female populations for EPTB case-finding. Further studies are necessary in other high-burden countries to confirm our findings.

Investigation of marital satisfaction and its relationship with job stress and general health of nurses in Qazvin, Iran

PubMed Central

Azimian, Jalil; Piran, Pegah; Jahanihashemi, Hassan; Dehghankar, Leila

2017-01-01

Background Pressures in nursing can affect family life and marital problems, disrupt common social problems, increase work-family conflicts and endanger people’s general health. Aim To determine marital satisfaction and its relationship with job stress and general health of nurses. Methods This descriptive and cross-sectional study was done in 2015 in medical educational centers of Qazvin by using an ENRICH marital satisfaction scale and General Health and Job Stress questionnaires completed by 123 nurses. Analysis was done by SPSS version 19 using descriptive and analytical statistics (Pearson correlation, t-test, ANOVA, Chi-square, regression line, multiple regression analysis). Results The findings showed that 64.4% of nurses had marital satisfaction. There was significant relationship between age (p=0.03), job experience (p=0.01), age of spouse (p=0.01) and marital satisfaction. The results showed that there was a significant relationship between marital satisfaction and general health (p<0.0001). Multiple regression analysis showed that there was a significant relationship between depression (p=0.012) and anxiety (p=0.001) with marital satisfaction. Conclusions Due to high levels of job stress and disorder in general health of nurses and low marital satisfaction by running health promotion programs and paying attention to its dimensions can help work and family health of nurses. PMID:28607660

High-risk regions and outbreak modelling of tularemia in humans.

PubMed

Desvars-Larrive, A; Liu, X; Hjertqvist, M; Sjöstedt, A; Johansson, A; Rydén, P

2017-02-01

Sweden reports large and variable numbers of human tularemia cases, but the high-risk regions are anecdotally defined and factors explaining annual variations are poorly understood. Here, high-risk regions were identified by spatial cluster analysis on disease surveillance data for 1984-2012. Negative binomial regression with five previously validated predictors (including predicted mosquito abundance and predictors based on local weather data) was used to model the annual number of tularemia cases within the high-risk regions. Seven high-risk regions were identified with annual incidences of 3·8-44 cases/100 000 inhabitants, accounting for 56·4% of the tularemia cases but only 9·3% of Sweden's population. For all high-risk regions, most cases occurred between July and September. The regression models explained the annual variation of tularemia cases within most high-risk regions and discriminated between years with and without outbreaks. In conclusion, tularemia in Sweden is concentrated in a few high-risk regions and shows high annual and seasonal variations. We present reproducible methods for identifying tularemia high-risk regions and modelling tularemia cases within these regions. The results may help health authorities to target populations at risk and lay the foundation for developing an early warning system for outbreaks.

Deep ensemble learning of sparse regression models for brain disease diagnosis.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2017-04-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

Deep ensemble learning of sparse regression models for brain disease diagnosis

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2018-01-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer’s disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call ‘ Deep Ensemble Sparse Regression Network.’ To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. PMID:28167394

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

PubMed

Karabatsos, George

2017-02-01

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

Applications of statistics to medical science, III. Correlation and regression.

PubMed

Watanabe, Hiroshi

2012-01-01

In this third part of a series surveying medical statistics, the concepts of correlation and regression are reviewed. In particular, methods of linear regression and logistic regression are discussed. Arguments related to survival analysis will be made in a subsequent paper.

Interferon alpha adjuvant therapy in patients with high-risk melanoma: a systematic review and meta-analysis.

PubMed

Mocellin, Simone; Pasquali, Sandro; Rossi, Carlo R; Nitti, Donato

2010-04-07

Based on previous meta-analyses of randomized controlled trials (RCTs), the use of interferon alpha (IFN-alpha) in the adjuvant setting improves disease-free survival (DFS) in patients with high-risk cutaneous melanoma. However, RCTs have yielded conflicting data on the effect of IFN-alpha on overall survival (OS). We conducted a systematic review and meta-analysis to examine the effect of IFN-alpha on DFS and OS in patients with high-risk cutaneous melanoma. The systematic review was performed by searching MEDLINE, EMBASE, Cancerlit, Cochrane, ISI Web of Science, and ASCO databases. The meta-analysis was performed using time-to-event data from which hazard ratios (HRs) and 95% confidence intervals (CIs) of DFS and OS were estimated. Subgroup and meta-regression analyses to investigate the effect of dose and treatment duration were also performed. Statistical tests were two-sided. The meta-analysis included 14 RCTs, published between 1990 and 2008, and involved 8122 patients, of which 4362 patients were allocated to the IFN-alpha arm. IFN-alpha alone was compared with observation in 12 of the 14 trials, and 17 comparisons (IFN-alpha vs comparator) were generated in total. IFN-alpha treatment was associated with a statistically significant improvement in DFS in 10 of the 17 comparisons (HR for disease recurrence = 0.82, 95% CI = 0.77 to 0.87; P < .001) and improved OS in four of the 14 comparisons (HR for death = 0.89, 95% CI = 0.83 to 0.96; P = .002). No between-study heterogeneity in either DFS or OS was observed. No optimal IFN-alpha dose and/or treatment duration or a subset of patients more responsive to adjuvant therapy was identified using subgroup analysis and meta-regression. In patients with high-risk cutaneous melanoma, IFN-alpha adjuvant treatment showed statistically significant improvement in both DFS and OS.

Optimizing methods for linking cinematic features to fMRI data.

PubMed

Kauttonen, Janne; Hlushchuk, Yevhen; Tikka, Pia

2015-04-15

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

Prognostic significance of the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in patients with stage III and IV colorectal cancer

PubMed Central

Kim, Jae Hyun; Lee, Jun Yeop; Kim, Hae Koo; Lee, Jin Wook; Jung, Sung Gyu; Jung, Kyoungwon; Kim, Sung Eun; Moon, Won; Park, Moo In; Park, Seun Ja

2017-01-01

AIM To evaluate the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in patients with colorectal cancer (CRC). METHODS Between April 1996 and December 2010, medical records from a total of 1868 patients with CRC were retrospectively reviewed. The values of simple inflammatory markers including NLR and PLR in predicting the long-term outcomes of these patients were evaluated using Kaplan-Meier curves and Cox regression models. RESULTS The median follow-up duration was 46 mo (interquartile range, 22-73). The estimation of NLR and PLR was based on the time of diagnosis. In multivariate Cox regression analysis, high NLR (≥ 3.0) and high PLR (≥ 160) were independent risk factors predicting poor long-term outcomes in patients with stage III and IV CRC. However, high NLR and high PLR were not prognostic factors in patients with stage I and II CRC. CONCLUSION In this study, we identified that high NLR (≥ 3.0) and high PLR (≥ 160) are useful prognostic factors to predict long-term outcomes in patients with stage III and IV CRC. PMID:28210087

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

USGS Publications Warehouse

Ahearn, Elizabeth A.

2010-01-01

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

Age, Body Mass Index, and Frequency of Sexual Activity are Independent Predictors of Testosterone Deficiency in Men With Erectile Dysfunction.

PubMed

Pagano, Matthew J; De Fazio, Adam; Levy, Alison; RoyChoudhury, Arindam; Stahl, Peter J

2016-04-01

To identify clinical predictors of testosterone deficiency (TD) in men with erectile dysfunction (ED), thereby identifying subgroups that are most likely to benefit from targeted testosterone screening. Retrospective review was conducted on 498 men evaluated for ED between January 2013 and July 2014. Testing for TD by early morning serum measurement was offered to all eligible men. Patients with history of prostate cancer or testosterone replacement were excluded. Univariable linear regression was conducted to analyze 19 clinical variables for associations with serum total testosterone (TT), calculated free testosterone (cFT), and TD (T <300 ng/dL or cFT <6.5 ng/dL). Variables significant on univariable analysis were included in multiple regression models. A total of 225 men met inclusion criteria. Lower TT levels were associated with greater body mass index (BMI), less frequent sexual activity, and absence of clinical depression on multiple regression analysis. TT decreased by 49.5 ng/dL for each 5-point increase in BMI. BMI and age were the only independent predictors of cFT levels on multivariable analysis. Overall, 62 subjects (27.6%) met criteria for TD. Older age, greater BMI, and less frequent sexual activity were the only independent predictors of TD on multiple regression. We observed a 2.2-fold increase in the odds of TD for every 5-point increase in BMI, and a 1.8-fold increase for every 10 year increase in age. Men with ED and elevated BMI, advanced age, or infrequent sexual activity appear to be at high risk of TD, and such patients represent excellent potential candidates for targeted testosterone screening. Copyright © 2016 Elsevier Inc. All rights reserved.

Gene set analysis using variance component tests.

PubMed

Huang, Yen-Tsung; Lin, Xihong

2013-06-28

Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

Investigative clinical study on prostate cancer part III: exploring total PSA and free testosterone distributions and linear correlations in groups and subgroups of operated prostate cancer patients according to the total PSA/FT ratio.

PubMed

Porcaro, Antonio B; Petrozziello, Aldo; Romano, Mario; Sava, Teodoro; Ghimenton, Claudio; Caruso, Beatrice; Migliorini, Filippo; Zecchini Antoniolli, Stefano; Rubilotta, Emanuele; Lacola, Vincenzo; Monaco, Carmelo; Comunale, Luigi

2010-01-01

Prostate cancer is an interesting tumor for endocrine investigation. The prostate-specific antigen/free testosterone (PSA/FT) ratio has been shown to be effective in clustering patients in prognostic groups as follows: low risk (PSA/FT ≤0.20), intermediate risk (PSA/FT >0.20 and ≤0.40) and high risk (PSA/FT >0.40 and ≤1.5). In the present study we explored the total PSA and FT distributions, and linear regression of FT predicting PSA in the different groups (PSA/FT, pT and pG) and subgroups (pT and pG) of patients according to the prognostic PSA/FT ratio. The study included 128 operated prostate cancer patients. Pretreatment simultaneous serum samples were obtained for measuring free testosterone (FT) and total PSA levels. Patients were grouped according to the total PSA/FT ratio prognostic clusters (≤0.20, >0.20 and ≤0.40, >0.40), pT (2, 3a and 3b+4) and pathological Gleason score (pG) (≤6, = 7 >3 + 4, ≥7 >4 + 3). The pT and pG sets were subgrouped according to the prognostic PSA/FT ratio. Linear regression analysis of FT predicting total PSA was computed according to the different PSA/FT prognostic clusters for the: (1) total sample population, (2) pT and pG groups, (3) intraprostatic (pT2) and extraprostatic disease (pT3a/3b/4), and (4) low-intermediate grade (pG ≤6) and high-grade (pG ≥7) prostate cancer. Analysis of variance always showed highly significant different PSA distributions for (1) the different PSA/FT, pT and pG groups; and (2) the pT and pG prognostic subgroups. Significant FT distributions were detected for the (1) PSA/FT and pT groups; and (2) the pT2, pT3a and pG ≤6 prognostic PSA/FT subgroups. Correlation, variance and linear regression analysis of FT predicting total PSA was significant for (1) the PSA/FT prognostic clusters, (2) all the pT2 and pT3a subgroups, and (3) the pT3b/4 subgroup with PSA/FT >0.20 and ≤0.40, and (4) all the pG subsets. Linear regression analysis showed that the slopes of the predicting variable (FT) were always highly significant for patients with (1) intraprostate and extraprostate disease, and (2) low-grade and high-grade prostate cancer. According to the prognostic PSA/FT ratio, significantly lower levels of FT are detected in prostate cancer patients with extensive and high-grade disease. Also, significant linear correlations of FT predicting PSA are assessed in the different groups and subgroups of patients clustered according to the prognostic PSA/FT ratio. Confirmatory studies are needed. Copyright © 2010 S. Karger AG, Basel.

Perineural invasion is associated with increased relapse after external beam radiotherapy for men with low-risk prostate cancer and may be a marker for occult, high-grade cancer.

PubMed

Beard, C J; Chen, M H; Cote, K; Loffredo, M; Renshaw, A A; Hurwitz, M; D'Amico, A V

2004-01-01

To investigate the risk of postradiotherapy prostate-specific antigen (PSA) failure on the basis of pretreatment risk factors in prostate cancer patients with and without perineural invasion (PNI) in prostate biopsy specimens and to explain the observation that otherwise low-risk patients with PNI experience decreased freedom from PSA failure after external beam radiotherapy (RT). The study cohort consisted of 381 patients who underwent RT between 1989 and 2000 for clinically localized prostate cancer. A single genitourinary pathologist scored the absence or presence of PNI on all prostate biopsy specimens. Patients were divided into low-, intermediate- and high-risk subgroups on the basis of their 1992 American Joint Committee on Cancer T-stage, pretreatment PSA level, and Gleason score. Cox regression uni- and multivariate analyses were performed to evaluate whether the presence or absence of PNI in the biopsy specimen was a predictor of the time to post-RT PSA failure for patients in each pretreatment risk group. PSA failure was defined using the American Society for Therapeutic Radiology and Oncology consensus definition. Actuarial PSA failure-free survival was estimated using the Kaplan-Meier method, and comparisons were performed using the log-rank test. Cox regression univariate analysis revealed that PNI was a significant predictor of the time to PSA failure in the low-risk (p = 0.04) and high-risk (p = 0.03) cohorts. The 5-year PSA failure-free survival rate was 50% vs. 80% (p = 0.04) in low-risk patients, 70% vs. 75% (p = 0.72) in intermediate-risk patients, and 29% vs. 53% (p = 0.03) in high-risk patients with and without PNI, respectively. Cox regression multivariate analysis within the high-risk group revealed that a PSA level > or =20 ng/mL (p = 0.01) and Gleason score > or =8 (p = 0.02), but not PNI, were the only significant predictors of the time to PSA failure after RT. However, an association was found between the presence of PNI in the needle biopsy specimen and a biopsy Gleason score of 8-10 (p = 0.06). The association was stronger between the presence of PNI in the needle biopsy specimen and a biopsy Gleason score of 7-10 (p = 0. 033). A decrement in PSA outcome after RT for low-risk patients with PNI-positive biopsy specimens was found. The association between PNI and high Gleason score provides a possible explanation for the loss of statistical significance of PNI in the Cox regression multivariate analysis of the high-risk cohort. The data suggest that PNI found in the biopsy specimen of an otherwise low-risk patient predicts for occult high-grade disease that is missed owing to the sampling error associated with prostate biopsy. The association between PNI and a high Gleason score argues for the use of more aggressive therapy, such as hormonal therapy with RT and/or dose escalation, in these select patients.

Effects of export concentration on CO2 emissions in developed countries: an empirical analysis.

PubMed

Apergis, Nicholas; Can, Muhlis; Gozgor, Giray; Lau, Chi Keung Marco

2018-03-08

This paper provides the evidence on the short- and the long-run effects of the export product concentration on the level of CO 2 emissions in 19 developed (high-income) economies, spanning the period 1962-2010. To this end, the paper makes use of the nonlinear panel unit root and cointegration tests with multiple endogenous structural breaks. It also considers the mean group estimations, the autoregressive distributed lag model, and the panel quantile regression estimations. The findings illustrate that the environmental Kuznets curve (EKC) hypothesis is valid in the panel dataset of 19 developed economies. In addition, it documents that a higher level of the product concentration of exports leads to lower CO 2 emissions. The results from the panel quantile regressions also indicate that the effect of the export product concentration upon the per capita CO 2 emissions is relatively high at the higher quantiles.

Use of ocean color scanner data in water quality mapping

NASA Technical Reports Server (NTRS)

Khorram, S.

1981-01-01

Remotely sensed data, in combination with in situ data, are used in assessing water quality parameters within the San Francisco Bay-Delta. The parameters include suspended solids, chlorophyll, and turbidity. Regression models are developed between each of the water quality parameter measurements and the Ocean Color Scanner (OCS) data. The models are then extended to the entire study area for mapping water quality parameters. The results include a series of color-coded maps, each pertaining to one of the water quality parameters, and the statistical analysis of the OCS data and regression models. It is found that concurrently collected OCS data and surface truth measurements are highly useful in mapping the selected water quality parameters and locating areas having relatively high biological activity. In addition, it is found to be virtually impossible, at least within this test site, to locate such areas on U-2 color and color-infrared photography.

Vitamin D supplementation as a potential cause of U-shaped associations between vitamin D levels and negative health outcomes: a decision tree analysis for risk of frailty.

PubMed

Kojima, Gotaro; Iliffe, Steve; Tanabe, Marianne

2017-10-16

A recent controversy in vitamin D research is a "U-shaped association", with elevated disease risks at both high and low 25-hydroxyvitamin D (25 (OH) D) levels. This is a cross-sectional study of 238 male nursing home veterans in Hawaii. Classification and regression tree (CART) analysis identified groups based on 25 (OH) D and vitamin D supplementation for frailty risk. Characteristics were examined and compared across the groups using logistic regression and receiver operating characteristic (ROC) curve analyses. CART analysis identified three distinct groups: vitamin D supplement users (n = 86), non-users with low vitamin D (n = 55), and non-users with high vitamin D (n = 97). Supplement users were the most frail, but had high mean 25 (OH) D of 26.6 ng/mL, which was compatible with 27.1 ng/mL in non-users with high vitamin D, while mean 25 (OH) D of non-users with low vitamin D was 11.7 ng/mL. Supplement users and non-users with low vitamin D were significantly more likely to be frail (odds ratio (OR) = 9.90, 95% CI = 2.18-44.86, p = 0.003; OR = 4.28, 95% CI = 1.44-12.68, p = 0.009, respectively), compared with non-users with low vitamin D. ROC curve analysis showed the three groups significantly predicted frailty (area under the curve = 0.73), with sensitivity of 64.4% and specificity of 76.7%, while 25 (OH) D did not predict frailty. In these nursing home veterans, vitamin D supplement users were the most frail but with high 25 (OH) D. This can potentially be a cause of U-shaped associations between vitamin D levels and negative health outcomes.

Extramural vascular invasion and response to neoadjuvant chemoradiotherapy in rectal cancer: Influence of the CpG island methylator phenotype

PubMed Central

Williamson, Jeremy Stuart; Jones, Huw Geraint; Williams, Namor; Griffiths, Anthony Paul; Jenkins, Gareth; Beynon, John; Harris, Dean Anthony

2017-01-01

AIM To identify whether CpG island methylator phenotype (CIMP) is predictive of response to neoadjuvant chemoradiotherapy (NACRT) and outcomes in rectal cancer. METHODS Patients undergoing NACRT and surgical resection for rectal cancer in a tertiary referral centre between 2002-2011 were identified. Pre-treatment tumour biopsies were analysed for CIMP status (high, intermediate or low) using methylation specific PCR. KRAS and BRAF status were also determined using pyrosequencing analysis. Clinical information was extracted from case records and cancer services databases. Response to radiotherapy was measured by tumour regression scores determined upon histological examination of the resected specimen. The relationship between these molecular features, response to NACRT and oncological outcomes were analysed. RESULTS There were 160 patients analysed with a median follow-up time of 46.4 mo. Twenty-one (13%) patients demonstrated high levels of CIMP methylation (CIMP-H) and this was significantly associated with increased risk of extramural vascular invasion (EMVI) compared with CIMP-L [8/21 (38%) vs 15/99 (15%), P = 0.028]. CIMP status was not related to tumour regression after radiotherapy or survival, however EMVI was significantly associated with adverse survival (P < 0.001). Intermediate CIMP status was significantly associated with KRAS mutation (P = 0.01). There were 14 (9%) patients with a pathological complete response (pCR) compared to 116 (73%) patients having no or minimal regression after neoadjuvant chemoradiotherapy. Those patients with pCR had median survival of 106 mo compared to 65.8 mo with minimal regression, although this was not statistically significant (P = 0.26). Binary logistic regression analysis of the relationship between EMVI and other prognostic features revealed, EMVI positivity was associated with poor overall survival, advanced “T” stage and CIMP-H but not nodal status, age, sex, KRAS mutation status and presence of local or systemic recurrence. CONCLUSION We report a novel association of pre-treatment characterisation of CIMP-H with EMVI status which has prognostic implications and is not readily detectable on pre-treatment histological examination. PMID:28567185

Extramural vascular invasion and response to neoadjuvant chemoradiotherapy in rectal cancer: Influence of the CpG island methylator phenotype.

PubMed

Williamson, Jeremy Stuart; Jones, Huw Geraint; Williams, Namor; Griffiths, Anthony Paul; Jenkins, Gareth; Beynon, John; Harris, Dean Anthony

2017-05-15

To identify whether CpG island methylator phenotype (CIMP) is predictive of response to neoadjuvant chemoradiotherapy (NACRT) and outcomes in rectal cancer. Patients undergoing NACRT and surgical resection for rectal cancer in a tertiary referral centre between 2002-2011 were identified. Pre-treatment tumour biopsies were analysed for CIMP status (high, intermediate or low) using methylation specific PCR. KRAS and BRAF status were also determined using pyrosequencing analysis. Clinical information was extracted from case records and cancer services databases. Response to radiotherapy was measured by tumour regression scores determined upon histological examination of the resected specimen. The relationship between these molecular features, response to NACRT and oncological outcomes were analysed. There were 160 patients analysed with a median follow-up time of 46.4 mo. Twenty-one (13%) patients demonstrated high levels of CIMP methylation (CIMP-H) and this was significantly associated with increased risk of extramural vascular invasion (EMVI) compared with CIMP-L [8/21 (38%) vs 15/99 (15%), P = 0.028]. CIMP status was not related to tumour regression after radiotherapy or survival, however EMVI was significantly associated with adverse survival ( P < 0.001). Intermediate CIMP status was significantly associated with KRAS mutation ( P = 0.01). There were 14 (9%) patients with a pathological complete response (pCR) compared to 116 (73%) patients having no or minimal regression after neoadjuvant chemoradiotherapy. Those patients with pCR had median survival of 106 mo compared to 65.8 mo with minimal regression, although this was not statistically significant ( P = 0.26). Binary logistic regression analysis of the relationship between EMVI and other prognostic features revealed, EMVI positivity was associated with poor overall survival, advanced "T" stage and CIMP-H but not nodal status, age, sex, KRAS mutation status and presence of local or systemic recurrence. We report a novel association of pre-treatment characterisation of CIMP-H with EMVI status which has prognostic implications and is not readily detectable on pre-treatment histological examination.

Use of magnetic resonance imaging to predict the body composition of pigs in vivo.

PubMed

Kremer, P V; Förster, M; Scholz, A M

2013-06-01

The objective of the study was to evaluate whether magnetic resonance imaging (MRI) offers the opportunity to reliably analyze body composition of pigs in vivo. Therefore, the relation between areas of loin eye muscle and its back fat based on MRI images were used to predict body composition values measured by dual energy X-ray absorptiometry (DXA). During the study, a total of 77 pigs were studied by MRI and DXA, with a BW ranging between 42 and 102 kg. The pigs originated from different extensive or conventional breeds or crossbreds such as Cerdo Iberico, Duroc, German Landrace, German Large White, Hampshire and Pietrain. A Siemens Magnetom Open was used for MRI in the thorax region between 13th and 14th vertebrae in order to measure the loin eye area (MRI-LA) and the above back fat area (MRI-FA) of both body sides, whereas a whole body scan was performed by DXA with a GE Lunar DPX-IQ in order to measure the amount and percentage of fat tissue (DXA-FM; DXA-%FM) and lean tissue mass (DXA-LM; DXA-%LM). A linear single regression analysis was performed to quantify the linear relationships between MRI- and DXA-derived traits. In addition, a stepwise regression procedure was carried out to calculate (multiple) regression equations between MRI and DXA variables (including BW). Single regression analyses showed high relationships between DXA-%FM and MRI-FA (R 2 = 0.89, √MSE = 2.39%), DXA-FM and MRI-FA (R 2 = 0.82, √MSE = 2757 g) and DXA-LM and MRI-LA (R 2 = 0.82, √MSE = 4018 g). Only DXA-%LM and MRI-LA did not show any relationship (R 2 = 0). As a result of the multiple regression analysis, DXA-LM and DXA-FM were both highly related to MRI-LA, MRI-FA and BW (R 2 = 0.96; √MSE = 1784 g, and R 2 = 0.95, √MSE = 1496 g). Therefore, it can be concluded that the use of MRI-derived images provides exact information about important 'carcass-traits' in pigs and may be used to reliably predict the body composition in vivo.

Analysis of the Magnitude and Frequency of Peak Discharges for the Navajo Nation in Arizona, Utah, Colorado, and New Mexico

USGS Publications Warehouse

Waltemeyer, Scott D.

2006-01-01

Estimates of the magnitude and frequency of peak discharges are necessary for the reliable flood-hazard mapping in the Navajo Nation in Arizona, Utah, Colorado, and New Mexico. The Bureau of Indian Affairs, U.S. Army Corps of Engineers, and Navajo Nation requested that the U.S. Geological Survey update estimates of peak discharge magnitude for gaging stations in the region and update regional equations for estimation of peak discharge and frequency at ungaged sites. Equations were developed for estimating the magnitude of peak discharges for recurrence intervals of 2, 5, 10, 25, 50, 100, and 500 years at ungaged sites using data collected through 1999 at 146 gaging stations, an additional 13 years of peak-discharge data since a 1997 investigation, which used gaging-station data through 1986. The equations for estimation of peak discharges at ungaged sites were developed for flood regions 8, 11, high elevation, and 6 and are delineated on the basis of the hydrologic codes from the 1997 investigation. Peak discharges for selected recurrence intervals were determined at gaging stations by fitting observed data to a log-Pearson Type III distribution with adjustments for a low-discharge threshold and a zero skew coefficient. A low-discharge threshold was applied to frequency analysis of 82 of the 146 gaging stations. This application provides an improved fit of the log-Pearson Type III frequency distribution. Use of the low-discharge threshold generally eliminated the peak discharge having a recurrence interval of less than 1.4 years in the probability-density function. Within each region, logarithms of the peak discharges for selected recurrence intervals were related to logarithms of basin and climatic characteristics using stepwise ordinary least-squares regression techniques for exploratory data analysis. Generalized least-squares regression techniques, an improved regression procedure that accounts for time and spatial sampling errors, then was applied to the same data used in the ordinary least-squares regression analyses. The average standard error of prediction for a peak discharge have a recurrence interval of 100-years for region 8 was 53 percent (average) for the 100-year flood. The average standard of prediction, which includes average sampling error and average standard error of regression, ranged from 45 to 83 percent for the 100-year flood. Estimated standard error of prediction for a hybrid method for region 11 was large in the 1997 investigation. No distinction of floods produced from a high-elevation region was presented in the 1997 investigation. Overall, the equations based on generalized least-squares regression techniques are considered to be more reliable than those in the 1997 report because of the increased length of record and improved GIS method. Techniques for transferring flood-frequency relations to ungaged sites on the same stream can be estimated at an ungaged site by a direct application of the regional regression equation or at an ungaged site on a stream that has a gaging station upstream or downstream by using the drainage-area ratio and the drainage-area exponent from the regional regression equation of the respective region.

Leader-follower value congruence in social responsibility and ethical satisfaction: a polynomial regression analysis.

PubMed

Kang, Seung-Wan; Byun, Gukdo; Park, Hun-Joon

2014-12-01

This paper presents empirical research into the relationship between leader-follower value congruence in social responsibility and the level of ethical satisfaction for employees in the workplace. 163 dyads were analyzed, each consisting of a team leader and an employee working at a large manufacturing company in South Korea. Following current methodological recommendations for congruence research, polynomial regression and response surface modeling methodologies were used to determine the effects of value congruence. Results indicate that leader-follower value congruence in social responsibility was positively related to the ethical satisfaction of employees. Furthermore, employees' ethical satisfaction was stronger when aligned with a leader with high social responsibility. The theoretical and practical implications are discussed.

Risk factors for highly pathogenic avian influenza in commercial layer chicken farms in bangladesh during 2011.

PubMed

Osmani, M G; Thornton, R N; Dhand, N K; Hoque, M A; Milon, Sk M A; Kalam, M A; Hossain, M; Yamage, M

2014-12-01

A case-control study conducted during 2011 involved 90 randomly selected commercial layer farms infected with highly pathogenic avian influenza type A subtype H5N1 (HPAI) and 175 control farms randomly selected from within 5 km of infected farms. A questionnaire was designed to obtain information about potential risk factors for contracting HPAI and was administered to farm owners or managers. Logistic regression analyses were conducted to identify significant risk factors. A total of 20 of 43 risk factors for contracting HPAI were identified after univariable logistic regression analysis. A multivariable logistic regression model was derived by forward stepwise selection. Both unmatched and matched analyses were performed. The key risk factors identified were numbers of staff, frequency of veterinary visits, presence of village chickens roaming on the farm and staff trading birds. Aggregating these findings with those from other studies resulted in a list of 16 key risk factors identified in Bangladesh. Most of these related to biosecurity. It is considered feasible for Bangladesh to achieve a very low incidence of HPAI. Using the cumulative list of risk factors to enhance biosecurity pertaining to commercial farms would facilitate this objective. © 2013 Blackwell Verlag GmbH.

The Economic Value of Mangroves: A Meta-Analysis

Treesearch

Marwa Salem; D. Evan Mercer

2012-01-01

This paper presents a synthesis of the mangrove ecosystem valuation literature through a meta-regression analysis. The main contribution of this study is that it is the first meta-analysis focusing solely on mangrove forests, whereas previous studies have included different types of wetlands. The number of studies included in the regression analysis is 44 for a total...

Introduction to methodology of dose-response meta-analysis for binary outcome: With application on software.

PubMed

Zhang, Chao; Jia, Pengli; Yu, Liu; Xu, Chang

2018-05-01

Dose-response meta-analysis (DRMA) is widely applied to investigate the dose-specific relationship between independent and dependent variables. Such methods have been in use for over 30 years and are increasingly employed in healthcare and clinical decision-making. In this article, we give an overview of the methodology used in DRMA. We summarize the commonly used regression model and the pooled method in DRMA. We also use an example to illustrate how to employ a DRMA by these methods. Five regression models, linear regression, piecewise regression, natural polynomial regression, fractional polynomial regression, and restricted cubic spline regression, were illustrated in this article to fit the dose-response relationship. And two types of pooling approaches, that is, one-stage approach and two-stage approach are illustrated to pool the dose-response relationship across studies. The example showed similar results among these models. Several dose-response meta-analysis methods can be used for investigating the relationship between exposure level and the risk of an outcome. However the methodology of DRMA still needs to be improved. © 2018 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.

Predictors of postoperative outcomes of cubital tunnel syndrome treatments using multiple logistic regression analysis.

PubMed

Suzuki, Taku; Iwamoto, Takuji; Shizu, Kanae; Suzuki, Katsuji; Yamada, Harumoto; Sato, Kazuki

2017-05-01

This retrospective study was designed to investigate prognostic factors for postoperative outcomes for cubital tunnel syndrome (CubTS) using multiple logistic regression analysis with a large number of patients. Eighty-three patients with CubTS who underwent surgeries were enrolled. The following potential prognostic factors for disease severity were selected according to previous reports: sex, age, type of surgery, disease duration, body mass index, cervical lesion, presence of diabetes mellitus, Workers' Compensation status, preoperative severity, and preoperative electrodiagnostic testing. Postoperative severity of disease was assessed 2 years after surgery by Messina's criteria which is an outcome measure specifically for CubTS. Bivariate analysis was performed to select candidate prognostic factors for multiple linear regression analyses. Multiple logistic regression analysis was conducted to identify the association between postoperative severity and selected prognostic factors. Both bivariate and multiple linear regression analysis revealed only preoperative severity as an independent risk factor for poor prognosis, while other factors did not show any significant association. Although conflicting results exist regarding prognosis of CubTS, this study supports evidence from previous studies and concludes early surgical intervention portends the most favorable prognosis. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Drug treatment rates with beta-blockers and ACE-inhibitors/angiotensin receptor blockers and recurrences in takotsubo cardiomyopathy: A meta-regression analysis.

PubMed

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

2016-07-01

In a recent paper Singh et al. analyzed the effect of drug treatment on recurrence of takotsubo cardiomyopathy (TTC) in a comprehensive meta-analysis. The study found that recurrence rates were independent of clinic utilization of BB prescription, but inversely correlated with ACEi/ARB prescription: authors therefore conclude that ACEi/ARB rather than BB may reduce risk of recurrence. We aimed to re-analyze data reported in the study, now weighted for populations' size, in a meta-regression analysis. After multiple meta-regression analysis, we found a significant regression between rates of prescription of ACEi and rates of recurrence of TTC; regression was not statistically significant for BBs. On the bases of our re-analysis, we confirm that rates of recurrence of TTC are lower in populations of patients with higher rates of treatment with ACEi/ARB. That could not necessarily imply that ACEi may prevent recurrence of TTC, but barely that, for example, rates of recurrence are lower in cohorts more compliant with therapy or more prescribed with ACEi because more carefully followed. Randomized prospective studies are surely warranted. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

PubMed

Simmonds, Mark C; Higgins, Julian Pt

2016-12-01

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

Examination of influential observations in penalized spline regression

NASA Astrophysics Data System (ADS)

Türkan, Semra

2013-10-01

In parametric or nonparametric regression models, the results of regression analysis are affected by some anomalous observations in the data set. Thus, detection of these observations is one of the major steps in regression analysis. These observations are precisely detected by well-known influence measures. Pena's statistic is one of them. In this study, Pena's approach is formulated for penalized spline regression in terms of ordinary residuals and leverages. The real data and artificial data are used to see illustrate the effectiveness of Pena's statistic as to Cook's distance on detecting influential observations. The results of the study clearly reveal that the proposed measure is superior to Cook's Distance to detect these observations in large data set.

Robust analysis of trends in noisy tokamak confinement data using geodesic least squares regression

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

Verdoolaege, G., E-mail: geert.verdoolaege@ugent.be; Laboratory for Plasma Physics, Royal Military Academy, B-1000 Brussels; Shabbir, A.

Regression analysis is a very common activity in fusion science for unveiling trends and parametric dependencies, but it can be a difficult matter. We have recently developed the method of geodesic least squares (GLS) regression that is able to handle errors in all variables, is robust against data outliers and uncertainty in the regression model, and can be used with arbitrary distribution models and regression functions. We here report on first results of application of GLS to estimation of the multi-machine scaling law for the energy confinement time in tokamaks, demonstrating improved consistency of the GLS results compared to standardmore » least squares.« less

Can Functional Cardiac Age be Predicted from ECG in a Normal Healthy Population

NASA Technical Reports Server (NTRS)

Schlegel, Todd; Starc, Vito; Leban, Manja; Sinigoj, Petra; Vrhovec, Milos

2011-01-01

In a normal healthy population, we desired to determine the most age-dependent conventional and advanced ECG parameters. We hypothesized that changes in several ECG parameters might correlate with age and together reliably characterize the functional age of the heart. Methods: An initial study population of 313 apparently healthy subjects was ultimately reduced to 148 subjects (74 men, 84 women, in the range from 10 to 75 years of age) after exclusion criteria. In all subjects, ECG recordings (resting 5-minute 12-lead high frequency ECG) were evaluated via custom software programs to calculate up to 85 different conventional and advanced ECG parameters including beat-to-beat QT and RR variability, waveform complexity, and signal-averaged, high-frequency and spatial/spatiotemporal ECG parameters. The prediction of functional age was evaluated by multiple linear regression analysis using the best 5 univariate predictors. Results: Ignoring what were ultimately small differences between males and females, the functional age was found to be predicted (R2= 0.69, P < 0.001) from a linear combination of 5 independent variables: QRS elevation in the frontal plane (p<0.001), a new repolarization parameter QTcorr (p<0.001), mean high frequency QRS amplitude (p=0.009), the variability parameter % VLF of RRV (p=0.021) and the P-wave width (p=0.10). Here, QTcorr represents the correlation between the calculated QT and the measured QT signal. Conclusions: In apparently healthy subjects with normal conventional ECGs, functional cardiac age can be estimated by multiple linear regression analysis of mostly advanced ECG results. Because some parameters in the regression formula, such as QTcorr, high frequency QRS amplitude and P-wave width also change with disease in the same direction as with increased age, increased functional age of the heart may reflect subtle age-related pathologies in cardiac electrical function that are usually hidden on conventional ECG.

Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool.

PubMed

Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi

2007-10-01

Traditionally, the multiple linear regression technique has been one of the most widely used models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, neuro-fuzzy systems have gained much popularity for calibrating the nonlinear relationships. This study evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. The effectiveness of the proposed identification technique was demonstrated through a simulation study of the river flow time series of the Citarum River in Indonesia. Furthermore, in order to provide the uncertainty associated with the estimation of river flow, a Monte Carlo simulation was performed. As a comparison, a multiple linear regression analysis that was being used by the Citarum River Authority was also examined using various statistical indices. The simulation results using 95% confidence intervals indicated that the neuro-fuzzy model consistently underestimated the magnitude of high flow while the low and medium flow magnitudes were estimated closer to the observed data. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13.52% and 10.73%, respectively. Considering its simplicity and efficiency, the neuro-fuzzy model is recommended as an alternative tool for modeling of flow dynamics in the study area.

Geographic information systems and logistic regression for high-resolution malaria risk mapping in a rural settlement of the southern Brazilian Amazon.

PubMed

de Oliveira, Elaine Cristina; dos Santos, Emerson Soares; Zeilhofer, Peter; Souza-Santos, Reinaldo; Atanaka-Santos, Marina

2013-11-15

In Brazil, 99% of the cases of malaria are concentrated in the Amazon region, with high level of transmission. The objectives of the study were to use geographic information systems (GIS) analysis and logistic regression as a tool to identify and analyse the relative likelihood and its socio-environmental determinants of malaria infection in the Vale do Amanhecer rural settlement, Brazil. A GIS database of georeferenced malaria cases, recorded in 2005, and multiple explanatory data layers was built, based on a multispectral Landsat 5 TM image, digital map of the settlement blocks and a SRTM digital elevation model. Satellite imagery was used to map the spatial patterns of land use and cover (LUC) and to derive spectral indices of vegetation density (NDVI) and soil/vegetation humidity (VSHI). An Euclidian distance operator was applied to measure proximity of domiciles to potential mosquito breeding habitats and gold mining areas. The malaria risk model was generated by multiple logistic regression, in which environmental factors were considered as independent variables and the number of cases, binarized by a threshold value was the dependent variable. Out of a total of 336 cases of malaria, 133 positive slides were from inhabitants at Road 08, which corresponds to 37.60% of the notifications. The southern region of the settlement presented 276 cases and a greater number of domiciles in which more than ten cases/home were notified. From these, 102 (30.36%) cases were caused by Plasmodium falciparum and 174 (51.79%) cases by Plasmodium vivax. Malaria risk is the highest in the south of the settlement, associated with proximity to gold mining sites, intense land use, high levels of soil/vegetation humidity and low vegetation density. Mid-resolution, remote sensing data and GIS-derived distance measures can be successfully combined with digital maps of the housing location of (non-) infected inhabitants to predict relative likelihood of disease infection through the analysis by logistic regression. Obtained findings on the relation between malaria cases and environmental factors should be applied in the future for land use planning in rural settlements in the Southern Amazon to minimize risks of disease transmission.

Analysis of flight data from a High-Incidence Research Model by system identification methods

NASA Technical Reports Server (NTRS)

Batterson, James G.; Klein, Vladislav

1989-01-01

Data partitioning and modified stepwise regression were applied to recorded flight data from a Royal Aerospace Establishment high incidence research model. An aerodynamic model structure and corresponding stability and control derivatives were determined for angles of attack between 18 and 30 deg. Several nonlinearities in angles of attack and sideslip as well as a unique roll-dominated set of lateral modes were found. All flight estimated values were compared to available wind tunnel measurements.

Authoritative school climate and high school dropout rates.

PubMed

Jia, Yuane; Konold, Timothy R; Cornell, Dewey

2016-06-01

This study tested the association between school-wide measures of an authoritative school climate and high school dropout rates in a statewide sample of 315 high schools. Regression models at the school level of analysis used teacher and student measures of disciplinary structure, student support, and academic expectations to predict overall high school dropout rates. Analyses controlled for school demographics of school enrollment size, percentage of low-income students, percentage of minority students, and urbanicity. Consistent with authoritative school climate theory, moderation analyses found that when students perceive their teachers as supportive, high academic expectations are associated with lower dropout rates. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

A secure distributed logistic regression protocol for the detection of rare adverse drug events

PubMed Central

El Emam, Khaled; Samet, Saeed; Arbuckle, Luk; Tamblyn, Robyn; Earle, Craig; Kantarcioglu, Murat

2013-01-01

Background There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. Objective To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. Methods We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. Results The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. Conclusion The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through generalized estimating equations, and to accommodate other link functions by extending it to generalized linear models. PMID:22871397

A secure distributed logistic regression protocol for the detection of rare adverse drug events.

PubMed

El Emam, Khaled; Samet, Saeed; Arbuckle, Luk; Tamblyn, Robyn; Earle, Craig; Kantarcioglu, Murat

2013-05-01

There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through generalized estimating equations, and to accommodate other link functions by extending it to generalized linear models.

Noninvasive detection of hepatic lipidosis in dairy cows with calibrated ultrasonographic image analysis.

PubMed

Starke, A; Haudum, A; Weijers, G; Herzog, K; Wohlsein, P; Beyerbach, M; de Korte, C L; Thijssen, J M; Rehage, J

2010-07-01

The aim was to test the accuracy of calibrated digital analysis of ultrasonographic hepatic images for diagnosing fatty liver in dairy cows. Digital analysis was performed by means of a novel method, computer-aided ultrasound diagnosis (CAUS), previously published by the authors. This method implies a set of pre- and postprocessing steps to normalize and correct the transcutaneous ultrasonographic images. Transcutaneous hepatic ultrasonography was performed before surgical correction on 151 German Holstein dairy cows (mean +/- standard error of the means; body weight: 571+/-7 kg; age: 4.9+/-0.2 yr; DIM: 35+/-5) with left-sided abomasal displacement. Concentration of triacylglycerol (TAG) was biochemically determined in liver samples collected via biopsy and values were considered the gold standard to which ultrasound estimates were compared. According to histopathologic examination of biopsies, none of the cows suffered from hepatic disorders other than hepatic lipidosis. Hepatic TAG concentrations ranged from 4.6 to 292.4 mg/g of liver fresh weight (FW). High correlations were found between the hepatic TAG and mean echo level (r=0.59) and residual attenuation (ResAtt; r=0.80) obtained in ultrasonographic imaging. High correlation existed between ResAtt and mean echo level (r=0.76). The 151 studied cows were split randomly into a training set of 76 cows and a test set of 75 cows. Based on the data from the training set, ResAtt was statistically selected by means of stepwise multiple regression analysis for hepatic TAG prediction (R(2)=0.69). Then, using the predicted TAG data of the test set, receiver operating characteristic analysis was performed to summarize the accuracy and predictive potential of the differentiation between various measured hepatic TAG values, based on TAG predicted from the regression formula. The area under the curve values of the receiver operating characteristic based on the regression equation were 0.94 (<50 vs. >or=50mg of TAG/g of FW), 0.83 (<100 vs. >or=100mg of TAG/g of FW), and 0.97 (<50 vs. >or=100mg of TAG/g of FW). The CAUS methodology and software for digitally analyzing liver ultrasonographic images is considered feasible for noninvasive screening of fatty liver in dairy herd health programs. Using the single parameter linear regression equation might be ideal for practical applications. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Placement Model for First-Time Freshmen in Calculus I (Math 131): University of Northern Colorado

ERIC Educational Resources Information Center

Heiny, Robert L.; Heiny, Erik L.; Raymond, Karen

2017-01-01

Two approaches, Linear Discriminant Analysis, and Logistic Regression are used and compared to predict success or failure for first-time freshmen in the first calculus course at a medium-sized public, 4-year institution prior to Fall registration. The predictor variables are high school GPA, the number, and GPA's of college prep mathematics…

Experiences That Predict Early Career Teacher Commitment to and Retention in High-Poverty Urban Schools

ERIC Educational Resources Information Center

Whipp, Joan L.; Geronime, Lara

2017-01-01

Correlation analysis was used to analyze what experiences before and during teacher preparation for 72 graduates of an urban teacher education program were associated with urban commitment, first job location, and retention in urban schools for 3 or more years. Binary logistic regression was then used to analyze whether urban K-12 schooling,…

Bootstrap evaluation of a young Douglas-fir height growth model for the Pacific Northwest

Treesearch

Nicholas R. Vaughn; Eric C. Turnblom; Martin W. Ritchie

2010-01-01

We evaluated the stability of a complex regression model developed to predict the annual height growth of young Douglas-fir. This model is highly nonlinear and is fit in an iterative manner for annual growth coefficients from data with multiple periodic remeasurement intervals. The traditional methods for such a sensitivity analysis either involve laborious math or...

An assessment of the geographical risks of wild and vaccine-derived poliomyelitis outbreaks in Africa and Asia.

PubMed

O'Reilly, Kathleen M; Lamoureux, Christine; Molodecky, Natalie A; Lyons, Hil; Grassly, Nicholas C; Tallis, Graham

2017-05-26

The international spread of wild poliomyelitis outbreaks continues to threaten eradication of poliomyelitis and in 2014 a public health emergency of international concern was declared. Here we describe a risk scoring system that has been used to assess country-level risks of wild poliomyelitis outbreaks, to inform prioritisation of mass vaccination planning, and describe the change in risk from 2014 to 2016. The methods were also used to assess the risk of emergence of vaccine-derived poliomyelitis outbreaks. Potential explanatory variables were tested against the reported outbreaks of wild poliomyelitis since 2003 using multivariable regression analysis. The regression analysis was translated to a risk score and used to classify countries as Low, Medium, Medium High and High risk, based on the predictive ability of the score. Indicators of population immunity, population displacement and diarrhoeal disease were associated with an increased risk of both wild and vaccine-derived outbreaks. High migration from countries with wild cases was associated with wild outbreaks. High birth numbers were associated with an increased risk of vaccine-derived outbreaks. Use of the scoring system is a transparent and rapid approach to assess country risk of wild and vaccine-derived poliomyelitis outbreaks. Since 2008 there has been a steep reduction in the number of wild poliomyelitis outbreaks and the reduction in countries classified as High and Medium High risk has reflected this. The risk of vaccine-derived poliomyelitis outbreaks has varied geographically. These findings highlight that many countries remain susceptible to poliomyelitis outbreaks and maintenance or improvement in routine immunisation is vital.

[RS estimation of inventory parameters and carbon storage of moso bamboo forest based on synergistic use of object-based image analysis and decision tree].

PubMed

Du, Hua Qiang; Sun, Xiao Yan; Han, Ning; Mao, Fang Jie

2017-10-01

By synergistically using the object-based image analysis (OBIA) and the classification and regression tree (CART) methods, the distribution information, the indexes (including diameter at breast, tree height, and crown closure), and the aboveground carbon storage (AGC) of moso bamboo forest in Shanchuan Town, Anji County, Zhejiang Province were investigated. The results showed that the moso bamboo forest could be accurately delineated by integrating the multi-scale ima ge segmentation in OBIA technique and CART, which connected the image objects at various scales, with a pretty good producer's accuracy of 89.1%. The investigation of indexes estimated by regression tree model that was constructed based on the features extracted from the image objects reached normal or better accuracy, in which the crown closure model archived the best estimating accuracy of 67.9%. The estimating accuracy of diameter at breast and tree height was relatively low, which was consistent with conclusion that estimating diameter at breast and tree height using optical remote sensing could not achieve satisfactory results. Estimation of AGC reached relatively high accuracy, and accuracy of the region of high value achieved above 80%.

Evaluation of the prediction precision capability of partial least squares regression approach for analysis of high alloy steel by laser induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Sarkar, Arnab; Karki, Vijay; Aggarwal, Suresh K.; Maurya, Gulab S.; Kumar, Rohit; Rai, Awadhesh K.; Mao, Xianglei; Russo, Richard E.

2015-06-01

Laser induced breakdown spectroscopy (LIBS) was applied for elemental characterization of high alloy steel using partial least squares regression (PLSR) with an objective to evaluate the analytical performance of this multivariate approach. The optimization of the number of principle components for minimizing error in PLSR algorithm was investigated. The effect of different pre-treatment procedures on the raw spectral data before PLSR analysis was evaluated based on several statistical (standard error of prediction, percentage relative error of prediction etc.) parameters. The pre-treatment with "NORM" parameter gave the optimum statistical results. The analytical performance of PLSR model improved by increasing the number of laser pulses accumulated per spectrum as well as by truncating the spectrum to appropriate wavelength region. It was found that the statistical benefit of truncating the spectrum can also be accomplished by increasing the number of laser pulses per accumulation without spectral truncation. The constituents (Co and Mo) present in hundreds of ppm were determined with relative precision of 4-9% (2σ), whereas the major constituents Cr and Ni (present at a few percent levels) were determined with a relative precision of ~ 2%(2σ).

Accurate palm vein recognition based on wavelet scattering and spectral regression kernel discriminant analysis

NASA Astrophysics Data System (ADS)

Elnasir, Selma; Shamsuddin, Siti Mariyam; Farokhi, Sajad

2015-01-01

Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases-PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint-show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.

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.

Retro-regression--another important multivariate regression improvement.

PubMed

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

Stability indicating high performance thin-layer chromatographic method for simultaneous estimation of pantoprazole sodium and itopride hydrochloride in combined dosage form

PubMed Central

Bageshwar, Deepak; Khanvilkar, Vineeta; Kadam, Vilasrao

2011-01-01

A specific, precise and stability indicating high-performance thin-layer chromatographic method for simultaneous estimation of pantoprazole sodium and itopride hydrochloride in pharmaceutical formulations was developed and validated. The method employed TLC aluminium plates precoated with silica gel 60F254 as the stationary phase. The solvent system consisted of methanol:water:ammonium acetate; 4.0:1.0:0.5 (v/v/v). This system was found to give compact and dense spots for both itopride hydrochloride (Rf value of 0.55±0.02) and pantoprazole sodium (Rf value of 0.85±0.04). Densitometric analysis of both drugs was carried out in the reflectance–absorbance mode at 289 nm. The linear regression analysis data for the calibration plots showed a good linear relationship with R2=0.9988±0.0012 in the concentration range of 100–400 ng for pantoprazole sodium. Also, the linear regression analysis data for the calibration plots showed a good linear relationship with R2=0.9990±0.0008 in the concentration range of 200–1200 ng for itopride hydrochloride. The method was validated for specificity, precision, robustness and recovery. Statistical analysis proves that the method is repeatable and selective for the estimation of both the said drugs. As the method could effectively separate the drug from its degradation products, it can be employed as a stability indicating method. PMID:29403710

Stability indicating high performance thin-layer chromatographic method for simultaneous estimation of pantoprazole sodium and itopride hydrochloride in combined dosage form.

PubMed

Bageshwar, Deepak; Khanvilkar, Vineeta; Kadam, Vilasrao

2011-11-01

A specific, precise and stability indicating high-performance thin-layer chromatographic method for simultaneous estimation of pantoprazole sodium and itopride hydrochloride in pharmaceutical formulations was developed and validated. The method employed TLC aluminium plates precoated with silica gel 60F 254 as the stationary phase. The solvent system consisted of methanol:water:ammonium acetate; 4.0:1.0:0.5 (v/v/v). This system was found to give compact and dense spots for both itopride hydrochloride ( R f value of 0.55±0.02) and pantoprazole sodium ( R f value of 0.85±0.04). Densitometric analysis of both drugs was carried out in the reflectance-absorbance mode at 289 nm. The linear regression analysis data for the calibration plots showed a good linear relationship with R 2 =0.9988±0.0012 in the concentration range of 100-400 ng for pantoprazole sodium. Also, the linear regression analysis data for the calibration plots showed a good linear relationship with R 2 =0.9990±0.0008 in the concentration range of 200-1200 ng for itopride hydrochloride. The method was validated for specificity, precision, robustness and recovery. Statistical analysis proves that the method is repeatable and selective for the estimation of both the said drugs. As the method could effectively separate the drug from its degradation products, it can be employed as a stability indicating method.

Effect of obstructive sleep apnea hypopnea syndrome on lipid profile: a meta-regression analysis.

PubMed

Nadeem, Rashid; Singh, Mukesh; Nida, Mahwish; Waheed, Irfan; Khan, Adnan; Ahmed, Saeed; Naseem, Jawed; Champeau, Daniel

2014-05-15

Obstructive sleep apnea (OSA) is associated with obesity, metabolic syndrome, and dyslipidemia, which may be related to decrease androgen levels found in OSA patients. Dyslipidemia may contribute to atherosclerosis leading to increasing risk of heart disease. Systematic review was conducted using PubMed and Cochrane library by utilizing different combinations of key words; sleep apnea, obstructive sleep apnea, serum lipids, dyslipidemia, cholesterol, total cholesterol, low density lipoprotein (LDL), high density lipoprotein (HDL), and triglyceride (TG). Inclusion criteria were: English articles, and studies with adult population in 2 groups of patients (patients with OSA and without OSA). A total 96 studies were reviewed for inclusion, with 25 studies pooled for analysis. Sixty-four studies were pooled for analysis; since some studies have more than one dataset, there were 107 datasets with 18,116 patients pooled for meta-analysis. All studies measured serum lipids. Total cholesterol pooled standardized difference in means was 0.267 (p = 0.001). LDL cholesterol pooled standardized difference in means was 0.296 (p = 0.001). HDL cholesterol pooled standardized difference in means was -0.433 (p = 0.001). Triglyceride pooled standardized difference in means was 0.603 (p = 0.001). Meta-regression for age, BMI, and AHI showed that age has significant effect for TC, LDL, and HDL. BMI had significant effect for LDL and HDL, while AHI had significant effect for LDL and TG. Patients with OSA appear to have increased dyslipidemia (high total cholesterol, LDL, TG, and low HDL).

Organizational Justice and Physiological Coronary Heart Disease Risk Factors in Japanese Employees: a Cross-Sectional Study.

PubMed

Inoue, Akiomi; Kawakami, Norito; Eguchi, Hisashi; Miyaki, Koichi; Tsutsumi, Akizumi

2015-12-01

Growing evidence has shown that lack of organizational justice (i.e., procedural justice and interactional justice) is associated with coronary heart disease (CHD) while biological mechanisms underlying this association have not yet been fully clarified. The purpose of the present study was to investigate the cross-sectional association of organizational justice with physiological CHD risk factors (i.e., blood pressure, high-density lipoprotein [HDL] cholesterol, low-density lipoprotein [LDL] cholesterol, and triglyceride) in Japanese employees. Overall, 3598 male and 901 female employees from two manufacturing companies in Japan completed self-administered questionnaires measuring organizational justice, demographic characteristics, and lifestyle factors. They completed health checkup, which included blood pressure and serum lipid measurements. Multiple logistic regression analyses and trend tests were conducted. Among male employees, multiple logistic regression analyses and trend tests showed significant associations of low procedural justice and low interactional justice with high triglyceride (defined as 150 mg/dL or greater) after adjusting for demographic characteristics and lifestyle factors. Among female employees, trend tests showed significant dose-response relationship between low interactional justice and high LDL cholesterol (defined as 140 mg/dL or greater) while multiple logistic regression analysis showed only marginally significant or insignificant odds ratio of high LDL cholesterol among the low interactional justice group. Neither procedural justice nor interactional justice was associated with blood pressure or HDL cholesterol. Organizational justice may be an important psychosocial factor associated with increased triglyceride at least among Japanese male employees.

Progression and regression of cervical pap test lesions in an urban AIDS clinic in the combined antiretroviral therapy era: a longitudinal, retrospective study.

PubMed

Lofgren, Sarah M; Tadros, Talaat; Herring-Bailey, Gina; Birdsong, George; Mosunjac, Marina; Flowers, Lisa; Nguyen, Minh Ly

2015-05-01

Our objective was to evaluate the progression and regression of cervical dysplasia in human immunodeficiency virus (HIV)-positive women during the late antiretroviral era. Risk factors as well as outcomes after treatment of cancerous or precancerous lesions were examined. This is a longitudinal retrospective review of cervical Pap tests performed on HIV-infected women with an intact cervix between 2004 and 2011. Subjects needed over two Pap tests for at least 2 years of follow-up. Progression was defined as those who developed a squamous intraepithelial lesion (SIL), atypical glandular cells (AGC), had low-grade SIL (LSIL) followed by atypical squamous cells-cannot exclude high-grade SIL (ASC-H) or high-grade SIL (HSIL), or cancer. Regression was defined as an initial SIL with two or more subsequent normal Pap tests. Persistence was defined as having an SIL without progression or regression. High-risk human papillomavirus (HPV) testing started in 2006 on atypical squamous cells of undetermined significance (ASCUS) Pap tests. AGC at enrollment were excluded from progression analysis. Of 1,445 screened, 383 patients had over two Pap tests for a 2-year period. Of those, 309 had an intact cervix. The median age was 40 years and CD4+ cell count was 277 cells/mL. Four had AGC at enrollment. A quarter had persistently normal Pap tests, 64 (31%) regressed, and 50 (24%) progressed. Four developed cancer. The only risk factor associated with progression was CD4 count. In those with treated lesions, 24 (59%) had negative Pap tests at the end of follow-up. More studies are needed to evaluate follow-up strategies of LSIL patients, potentially combined with HPV testing. Guidelines for HIV-seropositive women who are in care, have improved CD4, and have persistently negative Pap tests could likely lengthen the follow-up interval.

Progression and Regression of Cervical Pap Test Lesions in an Urban AIDS Clinic in the Combined Antiretroviral Therapy Era: A Longitudinal, Retrospective Study

PubMed Central

Tadros, Talaat; Herring-Bailey, Gina; Birdsong, George; Mosunjac, Marina; Flowers, Lisa; Nguyen, Minh Ly

2015-01-01

Abstract Our objective was to evaluate the progression and regression of cervical dysplasia in human immunodeficiency virus (HIV)-positive women during the late antiretroviral era. Risk factors as well as outcomes after treatment of cancerous or precancerous lesions were examined. This is a longitudinal retrospective review of cervical Pap tests performed on HIV-infected women with an intact cervix between 2004 and 2011. Subjects needed over two Pap tests for at least 2 years of follow-up. Progression was defined as those who developed a squamous intraepithelial lesion (SIL), atypical glandular cells (AGC), had low-grade SIL (LSIL) followed by atypical squamous cells-cannot exclude high-grade SIL (ASC-H) or high-grade SIL (HSIL), or cancer. Regression was defined as an initial SIL with two or more subsequent normal Pap tests. Persistence was defined as having an SIL without progression or regression. High-risk human papillomavirus (HPV) testing started in 2006 on atypical squamous cells of undetermined significance (ASCUS) Pap tests. AGC at enrollment were excluded from progression analysis. Of 1,445 screened, 383 patients had over two Pap tests for a 2-year period. Of those, 309 had an intact cervix. The median age was 40 years and CD4+ cell count was 277 cells/mL. Four had AGC at enrollment. A quarter had persistently normal Pap tests, 64 (31%) regressed, and 50 (24%) progressed. Four developed cancer. The only risk factor associated with progression was CD4 count. In those with treated lesions, 24 (59%) had negative Pap tests at the end of follow-up. More studies are needed to evaluate follow-up strategies of LSIL patients, potentially combined with HPV testing. Guidelines for HIV-seropositive women who are in care, have improved CD4, and have persistently negative Pap tests could likely lengthen the follow-up interval. PMID:25693769

Tolerance of ciliated protozoan Paramecium bursaria (Protozoa, Ciliophora) to ammonia and nitrites

NASA Astrophysics Data System (ADS)

Xu, Henglong; Song, Weibo; Lu, Lu; Alan, Warren

2005-09-01

The tolerance to ammonia and nitrites in freshwater ciliate Paramecium bursaria was measured in a conventional open system. The ciliate was exposed to different concentrations of ammonia and nitrites for 2h and 12h in order to determine the lethal concentrations. Linear regression analysis revealed that the 2h-LC50 value for ammonia was 95.94 mg/L and for nitrite 27.35 mg/L using probit scale method (with 95% confidence intervals). There was a linear correlation between the mortality probit scale and logarithmic concentration of ammonia which fit by a regression equation y=7.32 x 9.51 ( R 2=0.98; y, mortality probit scale; x, logarithmic concentration of ammonia), by which 2 h-LC50 value for ammonia was found to be 95.50 mg/L. A linear correlation between mortality probit scales and logarithmic concentration of nitrite is also followed the regression equation y=2.86 x+0.89 ( R 2=0.95; y, mortality probit scale; x, logarithmic concentration of nitrite). The regression analysis of toxicity curves showed that the linear correlation between exposed time of ammonia-N LC50 value and ammonia-N LC50 value followed the regression equation y=2 862.85 e -0.08 x ( R 2=0.95; y, duration of exposure to LC50 value; x, LC50 value), and that between exposed time of nitrite-N LC50 value and nitrite-N LC50 value followed the regression equation y=127.15 e -0.13 x ( R 2=0.91; y, exposed time of LC50 value; x, LC50 value). The results demonstrate that the tolerance to ammonia in P. bursaria is considerably higher than that of the larvae or juveniles of some metozoa, e.g. cultured prawns and oysters. In addition, ciliates, as bacterial predators, are likely to play a positive role in maintaining and improving water quality in aquatic environments with high-level ammonium, such as sewage treatment systems.

Regression analysis using dependent Polya trees.

PubMed

Schörgendorfer, Angela; Branscum, Adam J

2013-11-30

Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.

[Cross-sectional study on high-normal blood pressure and chronic kidney disease in occupational physical examination population in Changsha].

PubMed

Cao, Xia; Xie, Xiumei; Xu, Guo; Yuan, Hong; Chen, Zhiheng

2014-06-01

To investigate the relationship between high-normal blood pressure and chronic kidney disease (CKD) in occupational physical examination population in Changsha. With a convenient sampling method, a cross-sectional survey of representative sample of 11 274 white collar workers was conducted in Changsha between March 2011 and May 2011 in a large comprehensive hospital. All subjects were assigned into 4 groups: a normal blood pressure group, a high-normal blood pressure group, an undiagnosed hypertension group, and a diagnosed hypertension group. Anthropometry, blood pressure, blood sample and urine sample were measured with standard instruments and methodology for all the subjects. Multiple logistic regression analysis was used to identify risk factors for CKD. The prevalence of CKD in the normal blood pressure, high-normal blood pressure, undiagnosed hypertension, and diagnosed hypertension were 3.31%, 6.60%, 11.78%, and 17.35%, respectively. The prevalence of CKD in males was significantly higher than that in females (P<0.01). For males with high-normal blood pressure, the CKD risk was significantly greater (OR, 1.30; 95% CI:1.03 - 1.63) than those with optimal blood pressure. The logistic regression analysis showed that there was an additive effect of hyperuricemia on CKD risk in men with high-normal blood pressure compared with men with optimal blood pressure (OR, 2.25; 95% CI, 1.59 - 3.19; P<0.05). The prevalence of CKD in people with the high-normal blood pressure is 6.60% in occupational physical examination population in Changsha. CKD is a high risk for men with highnormal blood pressure and hyperuricemia is an independent risk factor.

A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

PubMed

Ferrari, Alberto; Comelli, Mario

2016-12-01

In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

Fast shoreline erosion induced by ship wakes in a coastal lagoon: Field evidence and remote sensing analysis.

PubMed

Zaggia, Luca; Lorenzetti, Giuliano; Manfé, Giorgia; Scarpa, Gian Marco; Molinaroli, Emanuela; Parnell, Kevin Ellis; Rapaglia, John Paul; Gionta, Maria; Soomere, Tarmo

2017-01-01

An investigation based on in-situ surveys combined with remote sensing and GIS analysis revealed fast shoreline retreat on the side of a major waterway, the Malamocco Marghera Channel, in the Lagoon of Venice, Italy. Monthly and long-term regression rates caused by ship wakes in a reclaimed industrial area were considered. The short-term analysis, based on field surveys carried out between April 2014 and January 2015, revealed that the speed of shoreline regression was insignificantly dependent on the distance from the navigation channel, but was not constant through time. Periods of high water levels due to tidal forcing or storm surges, more common in the winter season, are characterized by faster regression rates. The retreat is a discontinuous process in time and space depending on the morpho-stratigraphy and the vegetation cover of the artificial deposits. A GIS analysis performed with the available imagery shows an average retreat of 3-4 m/yr in the period between 1974 and 2015. Digitization of historical maps and bathymetric surveys made in April 2015 enabled the construction of two digital terrain models for both past and present situations. The two models have been used to calculate the total volume of sediment lost during the period 1968-2015 (1.19×106 m3). The results show that in the presence of heavy ship traffic, ship-channel interactions can dominate the morphodynamics of a waterway and its margins. The analysis enables a better understanding of how shallow-water systems react to the human activities in the post-industrial period. An adequate evaluation of the temporal and spatial variation of shoreline position is also crucial for the development of future scenarios and for the sustainable management port traffic worldwide.

Fast shoreline erosion induced by ship wakes in a coastal lagoon: Field evidence and remote sensing analysis

PubMed Central

Lorenzetti, Giuliano; Manfé, Giorgia; Scarpa, Gian Marco; Molinaroli, Emanuela; Parnell, Kevin Ellis; Rapaglia, John Paul; Gionta, Maria; Soomere, Tarmo

2017-01-01

An investigation based on in-situ surveys combined with remote sensing and GIS analysis revealed fast shoreline retreat on the side of a major waterway, the Malamocco Marghera Channel, in the Lagoon of Venice, Italy. Monthly and long-term regression rates caused by ship wakes in a reclaimed industrial area were considered. The short-term analysis, based on field surveys carried out between April 2014 and January 2015, revealed that the speed of shoreline regression was insignificantly dependent on the distance from the navigation channel, but was not constant through time. Periods of high water levels due to tidal forcing or storm surges, more common in the winter season, are characterized by faster regression rates. The retreat is a discontinuous process in time and space depending on the morpho-stratigraphy and the vegetation cover of the artificial deposits. A GIS analysis performed with the available imagery shows an average retreat of 3˗4 m/yr in the period between 1974 and 2015. Digitization of historical maps and bathymetric surveys made in April 2015 enabled the construction of two digital terrain models for both past and present situations. The two models have been used to calculate the total volume of sediment lost during the period 1968˗2015 (1.19×106 m3). The results show that in the presence of heavy ship traffic, ship-channel interactions can dominate the morphodynamics of a waterway and its margins. The analysis enables a better understanding of how shallow-water systems react to the human activities in the post-industrial period. An adequate evaluation of the temporal and spatial variation of shoreline position is also crucial for the development of future scenarios and for the sustainable management port traffic worldwide. PMID:29088244

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.

Regression analysis of informative current status data with the additive hazards model.

PubMed

Zhao, Shishun; Hu, Tao; Ma, Ling; Wang, Peijie; Sun, Jianguo

2015-04-01

This paper discusses regression analysis of current status failure time data arising from the additive hazards model in the presence of informative censoring. Many methods have been developed for regression analysis of current status data under various regression models if the censoring is noninformative, and also there exists a large literature on parametric analysis of informative current status data in the context of tumorgenicity experiments. In this paper, a semiparametric maximum likelihood estimation procedure is presented and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring time. Furthermore, I-splines are used to approximate the nonparametric functions involved and the asymptotic consistency and normality of the proposed estimators are established. A simulation study is conducted and indicates that the proposed approach works well for practical situations. An illustrative example is also provided.

Who pays for healthcare in Bangladesh? An analysis of progressivity in health systems financing.

PubMed

Molla, Azaher Ali; Chi, Chunhuei

2017-09-06

The relationship between payments towards healthcare and ability to pay is a measure of financial fairness. Analysis of progressivity is important from an equity perspective as well as for macroeconomic and political analysis of healthcare systems. Bangladesh health systems financing is characterized by high out-of-pocket payments (63.3%), which is increasing. Hence, we aimed to see who pays what part of this high out-of-pocket expenditure. To our knowledge, this was the first progressivity analysis of health systems financing in Bangladesh. We used data from Bangladesh Household Income and Expenditure Survey, 2010. This was a cross sectional and nationally representative sample of 12,240 households consisting of 55,580 individuals. For quantification of progressivity, we adopted the 'ability-to-pay' principle developed by O'Donnell, van Doorslaer, Wagstaff, and Lindelow (2008). We used the Kakwani index to measure the magnitude of progressivity. Health systems financing in Bangladesh is regressive. Inequality increases due to healthcare payments. The differences between the Gini coefficient and the Kakwani index for all sources of finance are negative, which indicates regressivity, and that financing is more concentrated among the poor. Income inequality increases due to high out-of-pocket payments. The increase in income inequality caused by out-of-pocket payments is 89% due to negative vertical effect and 11% due to horizontal inequity. Our findings add substantial evidence of health systems financing impact on inequitable financial burden of healthcare and income. The heavy reliance on out-of-pocket payments may affect household living standards. If the government and people of Bangladesh are concerned about equitable financing burden, our study suggests that Bangladesh needs to reform the health systems financing scheme.

Comparison of cranial sex determination by discriminant analysis and logistic regression.

PubMed

Amores-Ampuero, Anabel; Alemán, Inmaculada

2016-04-05

Various methods have been proposed for estimating dimorphism. The objective of this study was to compare sex determination results from cranial measurements using discriminant analysis or logistic regression. The study sample comprised 130 individuals (70 males) of known sex, age, and cause of death from San José cemetery in Granada (Spain). Measurements of 19 neurocranial dimensions and 11 splanchnocranial dimensions were subjected to discriminant analysis and logistic regression, and the percentages of correct classification were compared between the sex functions obtained with each method. The discriminant capacity of the selected variables was evaluated with a cross-validation procedure. The percentage accuracy with discriminant analysis was 78.2% for the neurocranium (82.4% in females and 74.6% in males) and 73.7% for the splanchnocranium (79.6% in females and 68.8% in males). These percentages were higher with logistic regression analysis: 85.7% for the neurocranium (in both sexes) and 94.1% for the splanchnocranium (100% in females and 91.7% in males).

Building Regression Models: The Importance of Graphics.

ERIC Educational Resources Information Center

Dunn, Richard

1989-01-01

Points out reasons for using graphical methods to teach simple and multiple regression analysis. Argues that a graphically oriented approach has considerable pedagogic advantages in the exposition of simple and multiple regression. Shows that graphical methods may play a central role in the process of building regression models. (Author/LS)

Testing Different Model Building Procedures Using Multiple Regression.

ERIC Educational Resources Information Center

Thayer, Jerome D.

The stepwise regression method of selecting predictors for computer assisted multiple regression analysis was compared with forward, backward, and best subsets regression, using 16 data sets. The results indicated the stepwise method was preferred because of its practical nature, when the models chosen by different selection methods were similar…

Pattern variation of fish fingerling abundance in the Na Thap Tidal river of Southern Thailand: 2005-2015

NASA Astrophysics Data System (ADS)

Donroman, T.; Chesoh, S.; Lim, A.

2018-04-01

This study aimed to investigate the variation patterns of fish fingerling abundance based on month, year and sampling site. Monthly collecting data set of the Na Thap tidal river of southern Thailand, were obtained from June 2005 to October 2015. The square root transformation was employed for maintaining the fingerling data normality. Factor analysis was applied for clustering number of fingerling species and multiple linear regression was used to examine the association between fingerling density and year, month and site. Results from factor analysis classified fingerling into 3 factors based on saline preference; saline water, freshwater and ubiquitous species. The results showed a statistically high significant relation between fingerling density, month, year and site. Abundance of saline water and ubiquitous fingerling density showed similar pattern. Downstream site presented highest fingerling density whereas almost of freshwater fingerling occurred in upstream. This finding confirmed that factor analysis and the general linear regression method can be used as an effective tool for predicting and monitoring wild fingerling density in order to sustain fish stock management.

On statistical analysis of factors affecting anthocyanin extraction from Ixora siamensis

NASA Astrophysics Data System (ADS)

Mat Nor, N. A.; Arof, A. K.

2016-10-01

This study focused on designing an experimental model in order to evaluate the influence of operative extraction parameters employed for anthocyanin extraction from Ixora siamensis on CIE color measurements (a*, b* and color saturation). Extractions were conducted at temperatures of 30, 55 and 80°C, soaking time of 60, 120 and 180 min using acidified methanol solvent with different trifluoroacetic acid (TFA) contents of 0.5, 1.75 and 3% (v/v). The statistical evaluation was performed by running analysis of variance (ANOVA) and regression calculation to investigate the significance of the generated model. Results show that the generated regression models adequately explain the data variation and significantly represented the actual relationship between the independent variables and the responses. Analysis of variance (ANOVA) showed high coefficient determination values (R2) of 0.9687 for a*, 0.9621 for b* and 0.9758 for color saturation, thus ensuring a satisfactory fit of the developed models with the experimental data. Interaction between TFA content and extraction temperature exhibited to the highest significant influence on CIE color parameter.

Structural Analysis of Women’s Heptathlon

PubMed Central

Gassmann, Freya; Fröhlich, Michael; Emrich, Eike

2016-01-01

The heptathlon comprises the results of seven single disciplines, assuming an equal influence from each discipline, depending on the measured performance. Data analysis was based on the data recorded for the individual performances of the 10 winning heptathletes in the World Athletics Championships from 1987 to 2013 and the Olympic Games from 1988 to 2012. In addition to descriptive analysis methods, correlations, bivariate and multivariate linear regressions, and panel data regressions were used. The transformation of the performances from seconds, centimeters, and meters into points showed that the individual disciplines do not equally affect the overall competition result. The currently valid conversion formula for the run, jump, and throw disciplines prefers the sprint and jump disciplines but penalizes the athletes performing in the 800 m run, javelin throw, and shotput disciplines. Furthermore, 21% to 48% of the variance of the sum of points can be attributed to the performances in the disciplines of long jump, 200 m sprint, 100 m hurdles, and high jump. To balance the effects of the single disciplines in the heptathlon, the formula to calculate points should be reevaluated. PMID:29910260

Mixed kernel function support vector regression for global sensitivity analysis

NASA Astrophysics Data System (ADS)

Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng

2017-11-01

Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.

Real-Time Analysis of Isoprene in Breath by Using Ultraviolet-Absorption Spectroscopy with a Hollow Optical Fiber Gas Cell

PubMed Central

Iwata, Takuro; Katagiri, Takashi; Matsuura, Yuji

2016-01-01

A breath analysis system based on ultraviolet-absorption spectroscopy was developed by using a hollow optical fiber as a gas cell for real-time monitoring of isoprene in breath. The hollow optical fiber functions as an ultra-small-volume gas cell with a long path. The measurement sensitivity of the system was evaluated by using nitric-oxide gas as a gas sample. The evaluation result showed that the developed system, using a laser-driven, high-intensity light source and a 3-m-long, aluminum-coated hollow optical fiber, could successfully measure nitric-oxide gas with a 50 ppb concentration. An absorption spectrum of a breath sample in the wavelength region of around 200–300 nm was measured, and the measured spectrum revealed the main absorbing components in breath as water vapor, isoprene, and ozone converted from oxygen by radiation of ultraviolet light. The concentration of isoprene in breath was estimated by multiple linear regression. The regression analysis results showed that the proposed analysis system enables real-time monitoring of isoprene during the exhaling of breath. Accordingly, it is suitable for measuring the circadian variation of isoprene. PMID:27929387

A multivariate analysis of clinical and morphological prognostic factors in squamous cell carcinoma of the vulva.

PubMed

Smyczek-Gargya, B; Volz, B; Geppert, M; Dietl, J

1997-01-01

Clinical and histological data of 168 patients with squamous cell carcinoma of the vulva were analyzed with respect to survival. 151 patients underwent surgery, 12 patients were treated with primary radiation and in 5 patients no treatment was performed. Follow-up lasted from at least 2 up to 22 years' posttreatment. In univariate analysis, the following factors were highly significant: presurgery lymph node status, tumor infiltration beyond the vulva, tumor grading, histological inguinal lymph node status, pre- and postsurgery tumor stage, depth of invasion and tumor diameter. In the multivariate analysis (Cox regression), the most powerful factors were shown to be histological inguinal lymph node status, tumor diameter and tumor grading. The multivariate logistic regression analysis worked out as main prognostic factors for metastases of inguinal lymph nodes: presurgery inguinal lymph node status, tumor size, depth of invasion and tumor grading. Based on these results, tumor biology seems to be the decisive factor concerning recurrence and survival. Therefore, we suggest a more conservative treatment of vulvar carcinoma. Patients with confined carcinoma to the vulva, with a tumor diameter up to 3 cm and without clinical suspected lymph nodes, should be treated by wide excision/partial vulvectomy with ipsilateral lymphadenectomy.

Combining fibre optic Raman spectroscopy and tactile resonance measurement for tissue characterization

NASA Astrophysics Data System (ADS)

Candefjord, Stefan; Nyberg, Morgan; Jalkanen, Ville; Ramser, Kerstin; Lindahl, Olof A.

2010-12-01

Tissue characterization is fundamental for identification of pathological conditions. Raman spectroscopy (RS) and tactile resonance measurement (TRM) are two promising techniques that measure biochemical content and stiffness, respectively. They have potential to complement the golden standard--histological analysis. By combining RS and TRM, complementary information about tissue content can be obtained and specific drawbacks can be avoided. The aim of this study was to develop a multivariate approach to compare RS and TRM information. The approach was evaluated on measurements at the same points on porcine abdominal tissue. The measurement points were divided into five groups by multivariate analysis of the RS data. A regression analysis was performed and receiver operating characteristic (ROC) curves were used to compare the RS and TRM data. TRM identified one group efficiently (area under ROC curve 0.99). The RS data showed that the proportion of saturated fat was high in this group. The regression analysis showed that stiffness was mainly determined by the amount of fat and its composition. We concluded that RS provided additional, important information for tissue identification that was not provided by TRM alone. The results are promising for development of a method combining RS and TRM for intraoperative tissue characterization.