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.

Representation of limb kinematics in Purkinje cell simple spike discharge is conserved across multiple tasks

PubMed Central

Hewitt, Angela L.; Popa, Laurentiu S.; Pasalar, Siavash; Hendrix, Claudia M.

2011-01-01

Encoding of movement kinematics in Purkinje cell simple spike discharge has important implications for hypotheses of cerebellar cortical function. Several outstanding questions remain regarding representation of these kinematic signals. It is uncertain whether kinematic encoding occurs in unpredictable, feedback-dependent tasks or kinematic signals are conserved across tasks. Additionally, there is a need to understand the signals encoded in the instantaneous discharge of single cells without averaging across trials or time. To address these questions, this study recorded Purkinje cell firing in monkeys trained to perform a manual random tracking task in addition to circular tracking and center-out reach. Random tracking provides for extensive coverage of kinematic workspaces. Direction and speed errors are significantly greater during random than circular tracking. Cross-correlation analyses comparing hand and target velocity profiles show that hand velocity lags target velocity during random tracking. Correlations between simple spike firing from 120 Purkinje cells and hand position, velocity, and speed were evaluated with linear regression models including a time constant, τ, as a measure of the firing lead/lag relative to the kinematic parameters. Across the population, velocity accounts for the majority of simple spike firing variability (63 ± 30% of Radj2), followed by position (28 ± 24% of Radj2) and speed (11 ± 19% of Radj2). Simple spike firing often leads hand kinematics. Comparison of regression models based on averaged vs. nonaveraged firing and kinematics reveals lower Radj2 values for nonaveraged data; however, regression coefficients and τ values are highly similar. Finally, for most cells, model coefficients generated from random tracking accurately estimate simple spike firing in either circular tracking or center-out reach. These findings imply that the cerebellum controls movement kinematics, consistent with a forward internal model that predicts upcoming limb kinematics. PMID:21795616

A population-based study on the association between rheumatoid arthritis and voice problems.

PubMed

Hah, J Hun; An, Soo-Youn; Sim, Songyong; Kim, So Young; Oh, Dong Jun; Park, Bumjung; Kim, Sung-Gyun; Choi, Hyo Geun

2016-07-01

The objective of this study was to investigate whether rheumatoid arthritis increases the frequency of organic laryngeal lesions and the subjective voice complaint rate in those with no organic laryngeal lesion. We performed a cross-sectional study using the data from 19,368 participants (418 rheumatoid arthritis patients and 18,950 controls) of the 2008-2011 Korea National Health and Nutrition Examination Survey. The associations between rheumatoid arthritis and organic laryngeal lesions/subjective voice complaints were analyzed using simple/multiple logistic regression analysis with complex sample adjusting for confounding factors, including age, sex, smoking status, stress level, and body mass index, which could provoke voice problems. Vocal nodules, vocal polyp, and vocal palsy were not associated with rheumatoid arthritis in a multiple regression analysis, and only laryngitis showed a positive association (adjusted odds ratio, 1.59; 95 % confidence interval, 1.01-2.52; P = 0.047). Rheumatoid arthritis was associated with subjective voice discomfort in a simple regression analysis, but not in a multiple regression analysis. Participants with rheumatoid arthritis were older, more often female, and had higher stress levels than those without rheumatoid arthritis. These factors were associated with subjective voice complaints in both simple and multiple regression analyses. Rheumatoid arthritis was not associated with organic laryngeal diseases except laryngitis. Rheumatoid arthritis did not increase the odds ratio for subjective voice complaints. Voice problems in participants with rheumatoid arthritis originated from the characteristics of the rheumatoid arthritis group (higher mean age, female sex, and stress level) rather than rheumatoid arthritis itself.

Simple to complex modeling of breathing volume using a motion sensor.

PubMed

John, Dinesh; Staudenmayer, John; Freedson, Patty

2013-06-01

To compare simple and complex modeling techniques to estimate categories of low, medium, and high ventilation (VE) from ActiGraph™ activity counts. Vertical axis ActiGraph™ GT1M activity counts, oxygen consumption and VE were measured during treadmill walking and running, sports, household chores and labor-intensive employment activities. Categories of low (<19.3 l/min), medium (19.3 to 35.4 l/min) and high (>35.4 l/min) VEs were derived from activity intensity classifications (light <2.9 METs, moderate 3.0 to 5.9 METs and vigorous >6.0 METs). We examined the accuracy of two simple techniques (multiple regression and activity count cut-point analyses) and one complex (random forest technique) modeling technique in predicting VE from activity counts. Prediction accuracy of the complex random forest technique was marginally better than the simple multiple regression method. Both techniques accurately predicted VE categories almost 80% of the time. The multiple regression and random forest techniques were more accurate (85 to 88%) in predicting medium VE. Both techniques predicted the high VE (70 to 73%) with greater accuracy than low VE (57 to 60%). Actigraph™ cut-points for light, medium and high VEs were <1381, 1381 to 3660 and >3660 cpm. There were minor differences in prediction accuracy between the multiple regression and the random forest technique. This study provides methods to objectively estimate VE categories using activity monitors that can easily be deployed in the field. Objective estimates of VE should provide a better understanding of the dose-response relationship between internal exposure to pollutants and disease. Copyright © 2013 Elsevier B.V. All rights reserved.

A sampling study on rock properties affecting drilling rate index (DRI)

NASA Astrophysics Data System (ADS)

Yenice, Hayati; Özdoğan, Mehmet V.; Özfırat, M. Kemal

2018-05-01

Drilling rate index (DRI) developed in Norway is a very useful index in determining the drillability of rocks and even in performance prediction of hard rock TBMs and it requires special laboratory test equipment. Drillability is one of the most important subjects in rock excavation. However, determining drillability index from physical and mechanical properties of rocks is very important for practicing engineers such as underground excavation, drilling operations in open pit mining, underground mining and natural stone production. That is why many researchers have studied concerned with drillability to find the correlations between drilling rate index (DRI) and penetration rate, influence of geological properties on drillability prediction in tunneling, correlations between rock properties and drillability. In this study, the relationships between drilling rate index (DRI) and some physico-mechanical properties (Density, Shore hardness, uniaxial compressive strength (UCS, σc), Indirect tensile strength (ITS, σt)) of three different rock groups including magmatic, sedimentary and metamorphic were evaluated using both simple and multiple regression analysis. This study reveals the effects of rock properties on DRI according to different types of rocks. In simple regression, quite high correlations were found between DRI and uniaxial compressive strength (UCS) and also between DRI and indirect tensile strength (ITS) values. Multiple regression analyses revealed even higher correlations when compared to simple regression. Especially, UCS, ITS, Shore hardness (SH) and the interactions between them were found to be very effective on DRI values.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

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

PubMed

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

2008-09-01

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

Can the prevalence of high blood drug concentrations in a population be estimated by analysing oral fluid? A study of tetrahydrocannabinol and amphetamine.

PubMed

Gjerde, Hallvard; Verstraete, Alain

2010-02-25

To study several methods for estimating the prevalence of high blood concentrations of tetrahydrocannabinol and amphetamine in a population of drug users by analysing oral fluid (saliva). Five methods were compared, including simple calculation procedures dividing the drug concentrations in oral fluid by average or median oral fluid/blood (OF/B) drug concentration ratios or linear regression coefficients, and more complex Monte Carlo simulations. Populations of 311 cannabis users and 197 amphetamine users from the Rosita-2 Project were studied. The results of a feasibility study suggested that the Monte Carlo simulations might give better accuracies than simple calculations if good data on OF/B ratios is available. If using only 20 randomly selected OF/B ratios, a Monte Carlo simulation gave the best accuracy but not the best precision. Dividing by the OF/B regression coefficient gave acceptable accuracy and precision, and was therefore the best method. None of the methods gave acceptable accuracy if the prevalence of high blood drug concentrations was less than 15%. Dividing the drug concentration in oral fluid by the OF/B regression coefficient gave an acceptable estimation of high blood drug concentrations in a population, and may therefore give valuable additional information on possible drug impairment, e.g. in roadside surveys of drugs and driving. If good data on the distribution of OF/B ratios are available, a Monte Carlo simulation may give better accuracy. 2009 Elsevier Ireland Ltd. All rights reserved.

Advanced statistics: linear regression, part I: simple linear regression.

PubMed

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

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

PubMed

Saunders, Christina T; Blume, Jeffrey D

2017-10-26

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

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

PubMed Central

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

2012-01-01

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

Representation of limb kinematics in Purkinje cell simple spike discharge is conserved across multiple tasks.

PubMed

Hewitt, Angela L; Popa, Laurentiu S; Pasalar, Siavash; Hendrix, Claudia M; Ebner, Timothy J

2011-11-01

Encoding of movement kinematics in Purkinje cell simple spike discharge has important implications for hypotheses of cerebellar cortical function. Several outstanding questions remain regarding representation of these kinematic signals. It is uncertain whether kinematic encoding occurs in unpredictable, feedback-dependent tasks or kinematic signals are conserved across tasks. Additionally, there is a need to understand the signals encoded in the instantaneous discharge of single cells without averaging across trials or time. To address these questions, this study recorded Purkinje cell firing in monkeys trained to perform a manual random tracking task in addition to circular tracking and center-out reach. Random tracking provides for extensive coverage of kinematic workspaces. Direction and speed errors are significantly greater during random than circular tracking. Cross-correlation analyses comparing hand and target velocity profiles show that hand velocity lags target velocity during random tracking. Correlations between simple spike firing from 120 Purkinje cells and hand position, velocity, and speed were evaluated with linear regression models including a time constant, τ, as a measure of the firing lead/lag relative to the kinematic parameters. Across the population, velocity accounts for the majority of simple spike firing variability (63 ± 30% of R(adj)(2)), followed by position (28 ± 24% of R(adj)(2)) and speed (11 ± 19% of R(adj)(2)). Simple spike firing often leads hand kinematics. Comparison of regression models based on averaged vs. nonaveraged firing and kinematics reveals lower R(adj)(2) values for nonaveraged data; however, regression coefficients and τ values are highly similar. Finally, for most cells, model coefficients generated from random tracking accurately estimate simple spike firing in either circular tracking or center-out reach. These findings imply that the cerebellum controls movement kinematics, consistent with a forward internal model that predicts upcoming limb kinematics.

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

USGS Publications Warehouse

Koltun, G.F.

2003-01-01

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

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

PubMed

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

2015-11-01

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

Triglyceride glucose index and common carotid wall shear stress.

PubMed

Tripolino, Cesare; Irace, Concetta; Scavelli, Faustina B; de Franceschi, Maria S; Esposito, Teresa; Carallo, Claudio; Gnasso, Agostino

2014-02-01

Alterations in wall shear stress contribute to both clinical and subclinical atherosclerosis. Several conditions such as hypertension, diabetes, and obesity can impair shear stress, but the role of insulin resistance has never been investigated. The present study was designed to investigate whether insulin resistance assessed by TyG Index associates with wall shear stress in the common carotid artery. One hundred six individuals were enrolled. Blood pressure, lipids, glucose, and cigarette smoking were evaluated. TyG Index was calculated as log[fasting triglycerides × fasting glucose / 2]. Subjects underwent blood viscosity measurement and echo-Doppler evaluation of carotid arteries to calculate wall shear stress. The association between TyG Index and carotid wall shear stress was assessed by simple and multiple regression analyses. TyG Index was significantly and inversely associated with carotid wall shear stress both in simple (r = -0.44, P < 0.001) and multiple regression analyses accounting for age, sex, and major cardiovascular risk factors. The association was further confirmed after exclusion of subjects with diabetes, dyslipidemia, fasting blood glucose greater than 100 mg/dL, and triglycerides greater than 150 mg/dL. The present findings suggest that increasing insulin resistance, as assessed by TyG Index, associates with atherosclerosis-prone shear stress reduction in the common carotid artery.

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.

Intra-individual reaction time variability and all-cause mortality over 17 years: a community-based cohort study.

PubMed

Batterham, Philip J; Bunce, David; Mackinnon, Andrew J; Christensen, Helen

2014-01-01

very few studies have examined the association between intra-individual reaction time variability and subsequent mortality. Furthermore, the ability of simple measures of variability to predict mortality has not been compared with more complex measures. a prospective cohort study of 896 community-based Australian adults aged 70+ were interviewed up to four times from 1990 to 2002, with vital status assessed until June 2007. From this cohort, 770-790 participants were included in Cox proportional hazards regression models of survival. Vital status and time in study were used to conduct survival analyses. The mean reaction time and three measures of intra-individual reaction time variability were calculated separately across 20 trials of simple and choice reaction time tasks. Models were adjusted for a range of demographic, physical health and mental health measures. greater intra-individual simple reaction time variability, as assessed by the raw standard deviation (raw SD), coefficient of variation (CV) or the intra-individual standard deviation (ISD), was strongly associated with an increased hazard of all-cause mortality in adjusted Cox regression models. The mean reaction time had no significant association with mortality. intra-individual variability in simple reaction time appears to have a robust association with mortality over 17 years. Health professionals such as neuropsychologists may benefit in their detection of neuropathology by supplementing neuropsychiatric testing with the straightforward process of testing simple reaction time and calculating raw SD or CV.

Morse Code, Scrabble, and the Alphabet

ERIC Educational Resources Information Center

Richardson, Mary; Gabrosek, John; Reischman, Diann; Curtiss, Phyliss

2004-01-01

In this paper we describe an interactive activity that illustrates simple linear regression. Students collect data and analyze it using simple linear regression techniques taught in an introductory applied statistics course. The activity is extended to illustrate checks for regression assumptions and regression diagnostics taught in an…

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)

Model Estimation Using Ridge Regression with the Variance Normalization Criterion. Interim Report No. 2. The Education and Inequality in Canada Project.

ERIC Educational Resources Information Center

Lee, Wan-Fung; Bulcock, Jeffrey Wilson

The purposes of this study are: (1) to demonstrate the superiority of simple ridge regression over ordinary least squares regression through theoretical argument and empirical example; (2) to modify ridge regression through use of the variance normalization criterion; and (3) to demonstrate the superiority of simple ridge regression based on the…

A Simple and Effective Image Normalization Method to Monitor Boreal Forest Change in a Siberian Burn Chronosequence across Sensors and across Time

NASA Astrophysics Data System (ADS)

Chen, X.; Vierling, L. A.; Deering, D. W.

2004-12-01

Satellite data offer unique perspectives for monitoring and quantifying land cover change, however, the radiometric consistency among co-located multi-temporal images is difficult to maintain due to variations in sensors and atmosphere. To detect accurate landscape change using multi-temporal images, we developed a new relative radiometric normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on 9 June 1990 (Landsat 4), 20 June 2000, and 26 August 2001 (Landsat 7) for analyses over boreal forests near the Siberian city of Krasnoyarsk. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Reduced Simple Ratio (RSR) were investigated in the normalization study. The temporally invariant cluster (TIC) centers were identified through a point density map of the base image and the target image and a normalization regression line was created through all TIC centers. The target image digital data were then converted using the regression function so that the two images could be compared using the resulting common radiometric scale. We found that EVI was very sensitive to vegetation structure and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. NDVI was a very effective vegetation index to reduce the influence of shadow, while EVI was very sensitive to shadowing. After normalization, correlations of NDVI and EVI with field collected total Leaf Area Index (LAI) data in 2000 and 2001 were significantly improved; the r-square values in these regressions increased from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI ¡°cancellation effect¡± where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a post fire chronosequence. These findings indicate that the TIC method provides a simple, effective and repeatable method to create radiometrically comparable data sets for remote detection of landscape change. Compared with some previous relative normalization methods, this new method can avoid subjective selection of a normalization regression line. It does not require high level programming and statistical analyses, yet remains sensitive to landscape changes occurring over seasonal and inter-annual time scales. In addition, the TIC method maintains sensitivity to subtle changes in vegetation phenology and enables normalization even when invariant features are rare.

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

NASA Astrophysics Data System (ADS)

Yilmaz, Işık

2009-06-01

The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat—Turkey). Digital elevation model (DEM) was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index (TWI) and stream power index (SPI) were used in the landslide susceptibility analyses. Landslide susceptibility maps were produced from the frequency ratio, logistic regression and neural networks models, and they were then compared by means of their validations. The higher accuracies of the susceptibility maps for all three models were obtained from the comparison of the landslide susceptibility maps with the known landslide locations. However, respective area under curve (AUC) values of 0.826, 0.842 and 0.852 for frequency ratio, logistic regression and artificial neural networks showed that the map obtained from ANN model is more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results obtained in this study also showed that the frequency ratio model can be used as a simple tool in assessment of landslide susceptibility when a sufficient number of data were obtained. Input process, calculations and output process are very simple and can be readily understood in the frequency ratio model, however logistic regression and neural networks require the conversion of data to ASCII or other formats. Moreover, it is also very hard to process the large amount of data in the statistical package.

Continuous monitoring of fetal scalp temperature in labor: a new technology validated in a fetal lamb model.

PubMed

Lavesson, Tony; Amer-Wåhlin, Isis; Hansson, Stefan; Ley, David; Marsál, Karel; Olofsson, Per

2010-06-01

To evaluate a new technical equipment for continuous recording of human fetal scalp temperature in labor. Experimental animal study. Two temperature sensors were placed subcutaneously and intracranially on the forehead of 10 fetal lambs and connected to a temperature monitoring system. The system records temperatures simultaneously on-line and stores data to be analyzed off-line. Throughout the experiment, the fetus was oxygenated via the umbilical cord circulation. Asphyxia was induced by intermittent cord compression, as assessed by pH in jugular vein blood. The intracranial (ICT) and subcutaneous (SCT) temperatures were compared with simple and polynomial regression analyses. Absolute and delta ICT and SCT changes. ICT and SCT were both successfully recorded in all 10 cases. With increasing acidosis, the temperatures decreased. The correlation coefficient between ICT and SCT had a range of 0.76-0.97 (median 0.88) by simple linear regression and 0.80-0.99 (median 0.89) by second grade polynomial regression. After an initial system stabilization period of 10 minutes, the delta temperature values (ICT minus SCT) were less than 1.5 degrees C throughout the experiment in all but one case. The fetal forehead SCT mirrored the ICT closely, with the ICT being higher.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.

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

PubMed

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

2016-10-28

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

Power and sample size for multivariate logistic modeling of unmatched case-control studies.

PubMed

Gail, Mitchell H; Haneuse, Sebastien

2017-01-01

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.

A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection.

PubMed

Kwan, Johnny S H; Kung, Annie W C; Sham, Pak C

2011-09-01

Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias.

Practical Session: Simple Linear Regression

NASA Astrophysics Data System (ADS)

Clausel, M.; Grégoire, G.

2014-12-01

Two exercises are proposed to illustrate the simple linear regression. The first one is based on the famous Galton's data set on heredity. We use the lm R command and get coefficients estimates, standard error of the error, R2, residuals …In the second example, devoted to data related to the vapor tension of mercury, we fit a simple linear regression, predict values, and anticipate on multiple linear regression. This pratical session is an excerpt from practical exercises proposed by A. Dalalyan at EPNC (see Exercises 1 and 2 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_4.pdf).

Teaching the Concept of Breakdown Point in Simple Linear Regression.

ERIC Educational Resources Information Center

Chan, Wai-Sum

2001-01-01

Most introductory textbooks on simple linear regression analysis mention the fact that extreme data points have a great influence on ordinary least-squares regression estimation; however, not many textbooks provide a rigorous mathematical explanation of this phenomenon. Suggests a way to fill this gap by teaching students the concept of breakdown…

Use of admission serum lactate and sodium levels to predict mortality in necrotizing soft-tissue infections.

PubMed

Yaghoubian, Arezou; de Virgilio, Christian; Dauphine, Christine; Lewis, Roger J; Lin, Matthew

2007-09-01

Simple admission laboratory values can be used to classify patients with necrotizing soft-tissue infection (NSTI) into high and low mortality risk groups. Chart review. Public teaching hospital. All patients with NSTI from 1997 through 2006. Variables analyzed included medical history, admission vital signs, laboratory values, and microbiologic findings. Data analyses included univariate and classification and regression tree analyses. Mortality. One hundred twenty-four patients were identified with NSTI. The overall mortality rate was 21 of 124 (17%). On univariate analysis, factors associated with mortality included a history of cancer (P = .03), intravenous drug abuse (P < .001), low systolic blood pressure on admission (P = .03), base deficit (P = .009), and elevated white blood cell count (P = .06). On exploratory classification and regression tree analysis, admission serum lactate and sodium levels were predictors of mortality, with a sensitivity of 100%, specificity of 28%, positive predictive value of 23%, and negative predictive value of 100%. A serum lactate level greater than or equal to 54.1 mg/dL (6 mmol/L) alone was associated with a 32% mortality, whereas a serum sodium level greater than or equal to 135 mEq/L combined with a lactate level less than 54.1 mg/dL was associated with a mortality of 0%. Mortality for NSTIs remains high. A simple model, using admission serum lactate and serum sodium levels, may help identify patients at greatest risk for death.

Determination of grain-size characteristics from electromagnetic seabed mapping data: A NW Iberian shelf study

NASA Astrophysics Data System (ADS)

Baasch, Benjamin; Müller, Hendrik; von Dobeneck, Tilo; Oberle, Ferdinand K. J.

2017-05-01

The electric conductivity and magnetic susceptibility of sediments are fundamental parameters in environmental geophysics. Both can be derived from marine electromagnetic profiling, a novel, fast and non-invasive seafloor mapping technique. Here we present statistical evidence that electric conductivity and magnetic susceptibility can help to determine physical grain-size characteristics (size, sorting and mud content) of marine surficial sediments. Electromagnetic data acquired with the bottom-towed electromagnetic profiler MARUM NERIDIS III were analysed and compared with grain size data from 33 samples across the NW Iberian continental shelf. A negative correlation between mean grain size and conductivity (R=-0.79) as well as mean grain size and susceptibility (R=-0.78) was found. Simple and multiple linear regression analyses were carried out to predict mean grain size, mud content and the standard deviation of the grain-size distribution from conductivity and susceptibility. The comparison of both methods showed that multiple linear regression models predict the grain-size distribution characteristics better than the simple models. This exemplary study demonstrates that electromagnetic benthic profiling is capable to estimate mean grain size, sorting and mud content of marine surficial sediments at a very high significance level. Transfer functions can be calibrated using grains-size data from a few reference samples and extrapolated along shelf-wide survey lines. This study suggests that electromagnetic benthic profiling should play a larger role for coastal zone management, seafloor contamination and sediment provenance studies in worldwide continental shelf systems.

Family practitioners' diagnostic decision-making processes regarding patients with respiratory tract infections: an observational study.

PubMed

Fischer, Thomas; Fischer, Susanne; Himmel, Wolfgang; Kochen, Michael M; Hummers-Pradier, Eva

2008-01-01

The influence of patient characteristics on family practitioners' (FPs') diagnostic decision making has mainly been investigated using indirect methods such as vignettes or questionnaires. Direct observation-borrowed from social and cultural anthropology-may be an alternative method for describing FPs' real-life behavior and may help in gaining insight into how FPs diagnose respiratory tract infections, which are frequent in primary care. To clarify FPs' diagnostic processes when treating patients suffering from symptoms of respiratory tract infection. This direct observation study was performed in 30 family practices using a checklist for patient complaints, history taking, physical examination, and diagnoses. The influence of patients' symptoms and complaints on the FPs' physical examination and diagnosis was calculated by logistic regression analyses. Dummy variables based on combinations of symptoms and complaints were constructed and tested against saturated (full) and backward regression models. In total, 273 patients (median age 37 years, 51% women) were included. The median number of symptoms described was 4 per patient, and most information was provided at the patients' own initiative. Multiple logistic regression analysis showed a strong association between patients' complaints and the physical examination. Frequent diagnoses were upper respiratory tract infection (URTI)/common cold (43%), bronchitis (26%), sinusitis (12%), and tonsillitis (11%). There were no significant statistical differences between "simple heuristic'' models and saturated regression models in the diagnoses of bronchitis, sinusitis, and tonsillitis, indicating that simple heuristics are probably used by the FPs, whereas "URTI/common cold'' was better explained by the full model. FPs tended to make their diagnosis based on a few patient symptoms and a limited physical examination. Simple heuristic models were almost as powerful in explaining most diagnoses as saturated models. Direct observation allowed for the study of decision making under real conditions, yielding both quantitative data and "qualitative'' information about the FPs' performance. It is important for investigators to be aware of the specific disadvantages of the method (e.g., a possible observer effect).

A simple algorithm for the identification of clinical COPD phenotypes.

PubMed

Burgel, Pierre-Régis; Paillasseur, Jean-Louis; Janssens, Wim; Piquet, Jacques; Ter Riet, Gerben; Garcia-Aymerich, Judith; Cosio, Borja; Bakke, Per; Puhan, Milo A; Langhammer, Arnulf; Alfageme, Inmaculada; Almagro, Pere; Ancochea, Julio; Celli, Bartolome R; Casanova, Ciro; de-Torres, Juan P; Decramer, Marc; Echazarreta, Andrés; Esteban, Cristobal; Gomez Punter, Rosa Mar; Han, MeiLan K; Johannessen, Ane; Kaiser, Bernhard; Lamprecht, Bernd; Lange, Peter; Leivseth, Linda; Marin, Jose M; Martin, Francis; Martinez-Camblor, Pablo; Miravitlles, Marc; Oga, Toru; Sofia Ramírez, Ana; Sin, Don D; Sobradillo, Patricia; Soler-Cataluña, Juan J; Turner, Alice M; Verdu Rivera, Francisco Javier; Soriano, Joan B; Roche, Nicolas

2017-11-01

This study aimed to identify simple rules for allocating chronic obstructive pulmonary disease (COPD) patients to clinical phenotypes identified by cluster analyses.Data from 2409 COPD patients of French/Belgian COPD cohorts were analysed using cluster analysis resulting in the identification of subgroups, for which clinical relevance was determined by comparing 3-year all-cause mortality. Classification and regression trees (CARTs) were used to develop an algorithm for allocating patients to these subgroups. This algorithm was tested in 3651 patients from the COPD Cohorts Collaborative International Assessment (3CIA) initiative.Cluster analysis identified five subgroups of COPD patients with different clinical characteristics (especially regarding severity of respiratory disease and the presence of cardiovascular comorbidities and diabetes). The CART-based algorithm indicated that the variables relevant for patient grouping differed markedly between patients with isolated respiratory disease (FEV 1 , dyspnoea grade) and those with multi-morbidity (dyspnoea grade, age, FEV 1 and body mass index). Application of this algorithm to the 3CIA cohorts confirmed that it identified subgroups of patients with different clinical characteristics, mortality rates (median, from 4% to 27%) and age at death (median, from 68 to 76 years).A simple algorithm, integrating respiratory characteristics and comorbidities, allowed the identification of clinically relevant COPD phenotypes. Copyright ©ERS 2017.

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math.

PubMed

Raizada, Rajeev D S; Tsao, Feng-Ming; Liu, Huei-Mei; Holloway, Ian D; Ansari, Daniel; Kuhl, Patricia K

2010-05-15

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain-behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain-behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain-behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math

PubMed Central

Raizada, Rajeev D.S.; Tsao, Feng-Ming; Liu, Huei-Mei; Holloway, Ian D.; Ansari, Daniel; Kuhl, Patricia K.

2010-01-01

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain–behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain–behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain–behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. PMID:20132896

New diagnostic index for sarcopenia in patients with cardiovascular diseases

PubMed Central

Kai, Hisashi; Shibata, Rei; Niiyama, Hiroshi; Nishiyama, Yasuhiro; Murohara, Toyoaki; Yoshida, Noriko; Katoh, Atsushi; Ikeda, Hisao

2017-01-01

Background Sarcopenia is an aging and disease-related syndrome characterized by progressive and generalized loss of skeletal muscle mass and strength, with the risk of frailty and poor quality of life. Sarcopenia is diagnosed by a decrease in skeletal muscle index (SMI) and reduction of either handgrip strength or gait speed. However, measurement of SMI is difficult for general physicians because it requires special equipment for bioelectrical impedance assay or dual-energy X-ray absorptiometry. The purpose of this study was, therefore, to explore a novel, simple diagnostic method of sarcopenia evaluation in patients with cardiovascular diseases (CVD). Methods We retrospectively investigated 132 inpatients with CVD (age: 72±12 years, age range: 27–93 years, males: 61%) Binomial logistic regression and correlation analyses were used to assess the associations of sarcopenia with simple physical data and biomarkers, including muscle-related inflammation makers and nutritional markers. Results Sarcopenia was present in 29.5% of the study population. Serum concentrations of adiponectin and sialic acid were significantly higher in sarcopenic than non-sarcopenic CVD patients. Stepwise multivariate binomial logistic regression analysis revealed that adiponectin, sialic acid, sex, age, and body mass index were independent factors for sarcopenia detection. Sarcopenia index, obtained from the diagnostic regression formula for sarcopenia detection including the five independent factors, indicated a high accuracy in ROC curve analysis (sensitivity 94.9%, specificity 69.9%) and the cutoff value for sarcopenia detection was -1.6134. Sarcopenia index had a significant correlation with the conventional diagnostic parameters of sarcopenia. Conclusions Our new sarcopenia index using simple parameters would be useful for diagnosing sarcopenia in CVD patients. PMID:28542531

Using Simple Linear Regression to Assess the Success of the Montreal Protocol in Reducing Atmospheric Chlorofluorocarbons

ERIC Educational Resources Information Center

Nelson, Dean

2009-01-01

Following the Guidelines for Assessment and Instruction in Statistics Education (GAISE) recommendation to use real data, an example is presented in which simple linear regression is used to evaluate the effect of the Montreal Protocol on atmospheric concentration of chlorofluorocarbons. This simple set of data, obtained from a public archive, can…

Length bias correction in gene ontology enrichment analysis using logistic regression.

PubMed

Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

2012-01-01

When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

Recent trends in counts of migrant hawks from northeastern North America

USGS Publications Warehouse

Titus, K.; Fuller, M.R.

1990-01-01

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

"Singing in the Tube"--audiovisual assay of plant oil repellent activity against mosquitoes (Culex pipiens).

PubMed

Adams, Temitope F; Wongchai, Chatchawal; Chaidee, Anchalee; Pfeiffer, Wolfgang

2016-01-01

Plant essential oils have been suggested as a promising alternative to the established mosquito repellent DEET (N,N-diethyl-meta-toluamide). Searching for an assay with generally available equipment, we designed a new audiovisual assay of repellent activity against mosquitoes "Singing in the Tube," testing single mosquitoes in Drosophila cultivation tubes. Statistics with regression analysis should compensate for limitations of simple hardware. The assay was established with female Culex pipiens mosquitoes in 60 experiments, 120-h audio recording, and 2580 estimations of the distance between mosquito sitting position and the chemical. Correlations between parameters of sitting position, flight activity pattern, and flight tone spectrum were analyzed. Regression analysis of psycho-acoustic data of audio files (dB[A]) used a squared and modified sinus function determining wing beat frequency WBF ± SD (357 ± 47 Hz). Application of logistic regression defined the repelling velocity constant. The repelling velocity constant showed a decreasing order of efficiency of plant essential oils: rosemary (Rosmarinus officinalis), eucalyptus (Eucalyptus globulus), lavender (Lavandula angustifolia), citronella (Cymbopogon nardus), tea tree (Melaleuca alternifolia), clove (Syzygium aromaticum), lemon (Citrus limon), patchouli (Pogostemon cablin), DEET, cedar wood (Cedrus atlantica). In conclusion, we suggest (1) disease vector control (e.g., impregnation of bed nets) by eight plant essential oils with repelling velocity superior to DEET, (2) simple mosquito repellency testing in Drosophila cultivation tubes, (3) automated approaches and room surveillance by generally available audio equipment (dB[A]: ISO standard 226), and (4) quantification of repellent activity by parameters of the audiovisual assay defined by correlation and regression analyses.

Patterns and correlates of serostatus disclosure to sexual partners by perinatally-infected adolescents and young adults

PubMed Central

Weintraub, Amy; Mellins, Claude; Warne, Patricia; Dolezal, Curtis; Elkington, Katherine; Bucek, Amelia; Leu, Cheng-Shiun; Bamji, Mahrukh; Wiznia, Andrew; Abrams, Elaine J

2017-01-01

Similar to same-age peers, perinatally HIV-infected (PHIV+) youth in the US are engaging in sex, including condomless sex. Understanding decisions about serostatus disclosure to sexual partners is important to domestic and global HIV prevention efforts, since large numbers of PHIV+ children are entering adolescence and becoming sexually active. Using Social Action Theory (SAT) to inform variable selection, we examined correlates of disclosure among 98 PHIV+ adolescents/young adults in New York City. Over half of these youth reported not disclosing to any casual partners (59%) and to any partners when using condoms (55%). In simple regression analyses, increased disclosure was associated with older age; being female; earlier age of learning one’s serostatus; and increased STD knowledge, disclosure intentions, and parent-child communication. Multiple regression analyses revealed a strong fit with the SAT model. As with adults, disclosure to sexual partners is difficult for PHIV+ youth and challenges prevention efforts. Effective interventions that help youth with disclosure decisions are needed to curb the epidemic. PMID:26874846

Bayesian generalized least squares regression with application to log Pearson type 3 regional skew estimation

NASA Astrophysics Data System (ADS)

Reis, D. S.; Stedinger, J. R.; Martins, E. S.

2005-10-01

This paper develops a Bayesian approach to analysis of a generalized least squares (GLS) regression model for regional analyses of hydrologic data. The new approach allows computation of the posterior distributions of the parameters and the model error variance using a quasi-analytic approach. Two regional skew estimation studies illustrate the value of the Bayesian GLS approach for regional statistical analysis of a shape parameter and demonstrate that regional skew models can be relatively precise with effective record lengths in excess of 60 years. With Bayesian GLS the marginal posterior distribution of the model error variance and the corresponding mean and variance of the parameters can be computed directly, thereby providing a simple but important extension of the regional GLS regression procedures popularized by Tasker and Stedinger (1989), which is sensitive to the likely values of the model error variance when it is small relative to the sampling error in the at-site estimator.

The Effect of Education on Old Age Cognitive Abilities: Evidence from a Regression Discontinuity Design*

PubMed Central

Banks, James; Mazzonna, Fabrizio

2011-01-01

In this paper we exploit the 1947 change to the minimum school-leaving age in England from 14 to 15, to evaluate the causal effect of a year of education on cognitive abilities at older ages. We use a regression discontinuity design analysis and find a large and significant effect of the reform on males’ memory and executive functioning at older ages, using simple cognitive tests from the English Longitudinal Survey on Ageing (ELSA) as our outcome measures. This result is particularly remarkable since the reform had a powerful and immediate effect on about half the population of 14-year-olds. We investigate and discuss the potential channels by which this reform may have had its effects, as well as carrying out a full set of sensitivity analyses and robustness checks. PMID:22611283

Satisfaction and adherence in patients with iron overload receiving iron chelation therapy as assessed by a newly developed patient instrument.

PubMed

Rofail, Diana; Abetz, Linda; Viala, Muriel; Gait, Claire; Baladi, Jean-Francois; Payne, Krista

2009-01-01

This study assesses satisfaction with iron chelation therapy (ICT) based on a reliable and valid instrument, and explores the relationship between satisfaction and adherence to ICT. Patients in the USA and UK completed a new "Satisfaction with ICT" (SICT) instrument consisting of 28 items, three pertaining to adherence. Simple and multivariate regression analyses assessed the relationship between satisfaction with different aspects of ICT and adherence. First assessments of the SICT instrument indicate its validity and reliability. Recommended thresholds for internal consistency, convergent validity, discriminant validity, and floor and ceiling effects were met. A number of variables were identified in the simple linear regression analyses as significant predictors of "never thinking about stopping ICT," a proxy for adherence. These significant variables were entered into the multivariate model to assess the combined factor effects, explaining 42% of the total variance of "never thinking about stopping ICT." A significant and positive relationship was demonstrated between "never thinking about stopping ICT" and age (P = 0.04), Perceived Effectiveness of ICT (P = 0.003), low Burden of ICT (P = 0.002), and low Side Effects of ICT (P = 0.01). The SICT is a reliable and valid instrument which will be useful in ICT clinical trials. Furthermore, the administration of ICT by slow subcutaneous infusion negatively impacts on satisfaction with ICT which was shown to be a determinant of adherence. This points to the need for new more convenient and less burdensome oral iron chelators to increase adherence, and ultimately to improve patient outcomes.

Occupational exposure to potentially infectious biological material in a dental teaching environment.

PubMed

Machado-Carvalhais, Helenaura P; Ramos-Jorge, Maria L; Auad, Sheyla M; Martins, Laura H P M; Paiva, Saul M; Pordeus, Isabela A

2008-10-01

The aims of this cross-sectional study were to determine the prevalence of occupational accidents with exposure to biological material among undergraduate students of dentistry and to estimate potential risk factors associated with exposure to blood. Data were collected through a self-administered questionnaire (86.4 percent return rate), which was completed by a sample of 286 undergraduate dental students (mean age 22.4 +/-2.4 years). The students were enrolled in the clinical component of the curriculum, which corresponds to the final six semesters of study. Descriptive, bivariate, simple logistic regression and multiple logistic regression (Forward Stepwise Procedure) analyses were performed. The level of statistical significance was set at 5 percent. Percutaneous and mucous exposures to potentially infectious biological material were reported by 102 individuals (35.6 percent); 26.8 percent reported the occurrence of multiple episodes of exposure. The logistic regression analyses revealed that the incomplete use of individual protection equipment (OR=3.7; 95 percent CI 1.5-9.3), disciplines where surgical procedures are carried out (OR=16.3; 95 percent CI 7.1-37.2), and handling sharp instruments (OR=4.4; 95 percent CI 2.1-9.1), more specifically, hollow-bore needles (OR=6.8; 95 percent CI 2.1-19.0), were independently associated with exposure to blood. Policies of reviewing the procedures during clinical practice are recommended in order to reduce occupational exposure.

Determinants of outcomes in patients with simple gastroschisis.

PubMed

Youssef, Fouad; Laberge, Jean-Martin; Puligandla, Pramod; Emil, Sherif

2017-05-01

We analyzed the determinants of outcomes in simple gastroschisis (GS) not complicated by intestinal atresia, perforation, or necrosis. All simple GS patients enrolled in a national prospective registry from 2005 to 2013 were studied. Patients below the median for total parenteral nutrition (TPN) duration (26days) and hospital stay (34days) were compared to those above. Univariate and multivariate logistic and linear regression analyses were employed using maternal, patient, postnatal, and treatment variables. Of 700 patients with simple GS, representing 76.8% of all GS patients, 690 (98.6%) survived. TPN was used in 352 (51.6%) and 330 (48.4%) patients for ≤26 and >26days, respectively. Hospital stay for 356 (51.9%) and 330 (48.1%) infants was ≤34 and >34days, respectively. Univariate analysis revealed significant differences in several patient, treatment, and postnatal factors. On multivariate analysis, prenatal sonographic bowel dilation, older age at closure, necrotizing enterocolitis, longer mechanical ventilation, and central-line associated blood stream infection (CLABSI) were independently associated with longer TPN duration and hospital stay, with CLABSI being the strongest predictor. Prenatal bowel dilation is associated with increased morbidity in simple GS. CLABSI is the strongest predictor of outcomes. Bowel matting is not an independent risk factor. 2c. Copyright © 2017 Elsevier Inc. All rights reserved.

Estimating linear temporal trends from aggregated environmental monitoring data

USGS Publications Warehouse

Erickson, Richard A.; Gray, Brian R.; Eager, Eric A.

2017-01-01

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.

Participation and Performance Trends in Triple Iron Ultra-triathlon – a Cross-sectional and Longitudinal Data Analysis

PubMed Central

Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Rosemann, Thomas; Lepers, Romuald

2012-01-01

Purpose The aims of the present study were to investigate (i) the changes in participation and performance and (ii) the gender difference in Triple Iron ultra-triathlon (11.4 km swimming, 540 km cycling and 126.6 km running) across years from 1988 to 2011. Methods For the cross-sectional data analysis, the association between with overall race times and split times was investigated using simple linear regression analyses and analysis of variance. For the longitudinal data analysis, the changes in race times for the five men and women with the highest number of participations were analysed using simple linear regression analyses. Results During the studied period, the number of finishers were 824 (71.4%) for men and 80 (78.4%) for women. Participation increased for men (r 2=0.27, P<0.01) while it remained stable for women (8%). Total race times were 2,146 ± 127.3 min for men and 2,615 ± 327.2 min for women (P<0.001). Total race time decreased for men (r 2=0.17; P=0.043), while it increased for women (r 2=0.49; P=0.001) across years. The gender difference in overall race time for winners increased from 10% in 1992 to 42% in 2011 (r 2=0.63; P<0.001). The longitudinal analysis of the five women and five men with the highest number of participations showed that performance decreased in one female (r 2=0.45; P=0.01). The four other women as well as all five men showed no change in overall race times across years. Conclusions Participation increased and performance improved for male Triple Iron ultra-triathletes while participation remained unchanged and performance decreased for females between 1988 and 2011. The reasons for the increase of the gap between female and male Triple Iron ultra-triathletes need further investigations. PMID:23012633

Shape information from glucose curves: Functional data analysis compared with traditional summary measures

PubMed Central

2013-01-01

Background Plasma glucose levels are important measures in medical care and research, and are often obtained from oral glucose tolerance tests (OGTT) with repeated measurements over 2–3 hours. It is common practice to use simple summary measures of OGTT curves. However, different OGTT curves can yield similar summary measures, and information of physiological or clinical interest may be lost. Our mean aim was to extract information inherent in the shape of OGTT glucose curves, compare it with the information from simple summary measures, and explore the clinical usefulness of such information. Methods OGTTs with five glucose measurements over two hours were recorded for 974 healthy pregnant women in their first trimester. For each woman, the five measurements were transformed into smooth OGTT glucose curves by functional data analysis (FDA), a collection of statistical methods developed specifically to analyse curve data. The essential modes of temporal variation between OGTT glucose curves were extracted by functional principal component analysis. The resultant functional principal component (FPC) scores were compared with commonly used simple summary measures: fasting and two-hour (2-h) values, area under the curve (AUC) and simple shape index (2-h minus 90-min values, or 90-min minus 60-min values). Clinical usefulness of FDA was explored by regression analyses of glucose tolerance later in pregnancy. Results Over 99% of the variation between individually fitted curves was expressed in the first three FPCs, interpreted physiologically as “general level” (FPC1), “time to peak” (FPC2) and “oscillations” (FPC3). FPC1 scores correlated strongly with AUC (r=0.999), but less with the other simple summary measures (−0.42≤r≤0.79). FPC2 scores gave shape information not captured by simple summary measures (−0.12≤r≤0.40). FPC2 scores, but not FPC1 nor the simple summary measures, discriminated between women who did and did not develop gestational diabetes later in pregnancy. Conclusions FDA of OGTT glucose curves in early pregnancy extracted shape information that was not identified by commonly used simple summary measures. This information discriminated between women with and without gestational diabetes later in pregnancy. PMID:23327294

Shape information from glucose curves: functional data analysis compared with traditional summary measures.

PubMed

Frøslie, Kathrine Frey; Røislien, Jo; Qvigstad, Elisabeth; Godang, Kristin; Bollerslev, Jens; Voldner, Nanna; Henriksen, Tore; Veierød, Marit B

2013-01-17

Plasma glucose levels are important measures in medical care and research, and are often obtained from oral glucose tolerance tests (OGTT) with repeated measurements over 2-3  hours. It is common practice to use simple summary measures of OGTT curves. However, different OGTT curves can yield similar summary measures, and information of physiological or clinical interest may be lost. Our mean aim was to extract information inherent in the shape of OGTT glucose curves, compare it with the information from simple summary measures, and explore the clinical usefulness of such information. OGTTs with five glucose measurements over two hours were recorded for 974 healthy pregnant women in their first trimester. For each woman, the five measurements were transformed into smooth OGTT glucose curves by functional data analysis (FDA), a collection of statistical methods developed specifically to analyse curve data. The essential modes of temporal variation between OGTT glucose curves were extracted by functional principal component analysis. The resultant functional principal component (FPC) scores were compared with commonly used simple summary measures: fasting and two-hour (2-h) values, area under the curve (AUC) and simple shape index (2-h minus 90-min values, or 90-min minus 60-min values). Clinical usefulness of FDA was explored by regression analyses of glucose tolerance later in pregnancy. Over 99% of the variation between individually fitted curves was expressed in the first three FPCs, interpreted physiologically as "general level" (FPC1), "time to peak" (FPC2) and "oscillations" (FPC3). FPC1 scores correlated strongly with AUC (r=0.999), but less with the other simple summary measures (-0.42≤r≤0.79). FPC2 scores gave shape information not captured by simple summary measures (-0.12≤r≤0.40). FPC2 scores, but not FPC1 nor the simple summary measures, discriminated between women who did and did not develop gestational diabetes later in pregnancy. FDA of OGTT glucose curves in early pregnancy extracted shape information that was not identified by commonly used simple summary measures. This information discriminated between women with and without gestational diabetes later in pregnancy.

Introduction, comparison, and validation of Meta‐Essentials: A free and simple tool for meta‐analysis

PubMed Central

van Rhee, Henk; Hak, Tony

2017-01-01

We present a new tool for meta‐analysis, Meta‐Essentials, which is free of charge and easy to use. In this paper, we introduce the tool and compare its features to other tools for meta‐analysis. We also provide detailed information on the validation of the tool. Although free of charge and simple, Meta‐Essentials automatically calculates effect sizes from a wide range of statistics and can be used for a wide range of meta‐analysis applications, including subgroup analysis, moderator analysis, and publication bias analyses. The confidence interval of the overall effect is automatically based on the Knapp‐Hartung adjustment of the DerSimonian‐Laird estimator. However, more advanced meta‐analysis methods such as meta‐analytical structural equation modelling and meta‐regression with multiple covariates are not available. In summary, Meta‐Essentials may prove a valuable resource for meta‐analysts, including researchers, teachers, and students. PMID:28801932

Weighing Evidence "Steampunk" Style via the Meta-Analyser.

PubMed

Bowden, Jack; Jackson, Chris

2016-10-01

The funnel plot is a graphical visualization of summary data estimates from a meta-analysis, and is a useful tool for detecting departures from the standard modeling assumptions. Although perhaps not widely appreciated, a simple extension of the funnel plot can help to facilitate an intuitive interpretation of the mathematics underlying a meta-analysis at a more fundamental level, by equating it to determining the center of mass of a physical system. We used this analogy to explain the concepts of weighing evidence and of biased evidence to a young audience at the Cambridge Science Festival, without recourse to precise definitions or statistical formulas and with a little help from Sherlock Holmes! Following on from the science fair, we have developed an interactive web-application (named the Meta-Analyser) to bring these ideas to a wider audience. We envisage that our application will be a useful tool for researchers when interpreting their data. First, to facilitate a simple understanding of fixed and random effects modeling approaches; second, to assess the importance of outliers; and third, to show the impact of adjusting for small study bias. This final aim is realized by introducing a novel graphical interpretation of the well-known method of Egger regression.

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

PubMed Central

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

2017-01-01

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

Personality traits associated with intrinsic academic motivation in medical students.

PubMed

Tanaka, Masaaki; Mizuno, Kei; Fukuda, Sanae; Tajima, Seiki; Watanabe, Yasuyoshi

2009-04-01

Motivation is one of the most important psychological concepts in education and is related to academic outcomes in medical students. In this study, the relationships between personality traits and intrinsic academic motivation were examined in medical students. The study group consisted of 119 Year 2 medical students at Osaka City University Graduate School of Medicine. They completed questionnaires dealing with intrinsic academic motivation (the Intrinsic Motivation Scale toward Learning) and personality (the Temperament and Character Inventory [TCI]). On simple regression analyses, the TCI dimensions of persistence, self-directedness, co-operativeness and self-transcendence were positively associated with intrinsic academic motivation. On multiple regression analysis adjusted for age and gender, the TCI dimensions of persistence, self-directedness and self-transcendence were positively associated with intrinsic academic motivation. The temperament dimension of persistence and the character dimensions of self-directedness and self-transcendence are associated with intrinsic academic motivation in medical students.

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

NASA Astrophysics Data System (ADS)

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

2008-06-01

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

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

PubMed Central

Jang, Hye Won; Park, Seung Won

2016-01-01

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

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

PubMed

Jang, Hye Won; Park, Seung Won

2016-12-01

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

Anodic microbial community diversity as a predictor of the power output of microbial fuel cells.

PubMed

Stratford, James P; Beecroft, Nelli J; Slade, Robert C T; Grüning, André; Avignone-Rossa, Claudio

2014-03-01

The relationship between the diversity of mixed-species microbial consortia and their electrogenic potential in the anodes of microbial fuel cells was examined using different diversity measures as predictors. Identical microbial fuel cells were sampled at multiple time-points. Biofilm and suspension communities were analysed by denaturing gradient gel electrophoresis to calculate the number and relative abundance of species. Shannon and Simpson indices and richness were examined for association with power using bivariate and multiple linear regression, with biofilm DNA as an additional variable. In simple bivariate regressions, the correlation of Shannon diversity of the biofilm and power is stronger (r=0.65, p=0.001) than between power and richness (r=0.39, p=0.076), or between power and the Simpson index (r=0.5, p=0.018). Using Shannon diversity and biofilm DNA as predictors of power, a regression model can be constructed (r=0.73, p<0.001). Ecological parameters such as the Shannon index are predictive of the electrogenic potential of microbial communities. Copyright © 2014 Elsevier Ltd. All rights reserved.

The relationship between psychological distress and baseline sports-related concussion testing.

PubMed

Bailey, Christopher M; Samples, Hillary L; Broshek, Donna K; Freeman, Jason R; Barth, Jeffrey T

2010-07-01

This study examined the effect of psychological distress on neurocognitive performance measured during baseline concussion testing. Archival data were utilized to examine correlations between personality testing and computerized baseline concussion testing. Significantly correlated personality measures were entered into linear regression analyses, predicting baseline concussion testing performance. Suicidal ideation was examined categorically. Athletes underwent testing and screening at a university athletic training facility. Participants included 47 collegiate football players 17 to 19 years old, the majority of whom were in their first year of college. Participants were administered the Concussion Resolution Index (CRI), an internet-based neurocognitive test designed to monitor and manage both at-risk and concussed athletes. Participants took the Personality Assessment Inventory (PAI), a self-administered inventory designed to measure clinical syndromes, treatment considerations, and interpersonal style. Scales and subscales from the PAI were utilized to determine the influence psychological distress had on the CRI indices: simple reaction time, complex reaction time, and processing speed. Analyses revealed several significant correlations among aspects of somatic concern, depression, anxiety, substance abuse, and suicidal ideation and CRI performance, each with at least a moderate effect. When entered into a linear regression, the block of combined psychological symptoms accounted for a significant amount of baseline CRI performance, with moderate to large effects (r = 0.23-0.30). When examined categorically, participants with suicidal ideation showed significantly slower simple reaction time and complex reaction time, with a similar trend on processing speed. Given the possibility of obscured concussion deficits after injury, implications for premature return to play, and the need to target psychological distress outright, these findings heighten the clinical importance of screening for psychological distress during baseline and post-injury concussion evaluations.

Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data

USGS Publications Warehouse

Rasmussen, Patrick P.; Gray, John R.; Glysson, G. Douglas; Ziegler, Andrew C.

2009-01-01

In-stream continuous turbidity and streamflow data, calibrated with measured suspended-sediment concentration data, can be used to compute a time series of suspended-sediment concentration and load at a stream site. Development of a simple linear (ordinary least squares) regression model for computing suspended-sediment concentrations from instantaneous turbidity data is the first step in the computation process. If the model standard percentage error (MSPE) of the simple linear regression model meets a minimum criterion, this model should be used to compute a time series of suspended-sediment concentrations. Otherwise, a multiple linear regression model using paired instantaneous turbidity and streamflow data is developed and compared to the simple regression model. If the inclusion of the streamflow variable proves to be statistically significant and the uncertainty associated with the multiple regression model results in an improvement over that for the simple linear model, the turbidity-streamflow multiple linear regression model should be used to compute a suspended-sediment concentration time series. The computed concentration time series is subsequently used with its paired streamflow time series to compute suspended-sediment loads by standard U.S. Geological Survey techniques. Once an acceptable regression model is developed, it can be used to compute suspended-sediment concentration beyond the period of record used in model development with proper ongoing collection and analysis of calibration samples. Regression models to compute suspended-sediment concentrations are generally site specific and should never be considered static, but they represent a set period in a continually dynamic system in which additional data will help verify any change in sediment load, type, and source.

Peering into the "black box" of education interventions and attitude change: Audience characteristics moderate the effectiveness…and then only toward specific targets.

PubMed

Mansoori-Rostam, Sara Michelle; Tate, Charlotte Chucky

2017-01-01

To probe the inconsistent link between education and attitude change toward minority social groups, we conducted a field study that focused on audience characteristics and education about lesbian, gay, and transgender (LGT) targets. Participants enrolled in a sexuality course were compared to those in a neurology course, both taught by the same professor. Multiple regression analyses predicted attitude change toward LGT targets from social dominance orientation (SDO), right-wing authoritarianism (RWA), ratings of professor's characteristics, SDO by course interaction, and RWA by course interaction. Only the SDO by course interaction significantly predicted attitude change. Simple slopes analyses indicated that high-SDO participants in the sexuality course showed the most positive attitude change. These findings suggest that education may reduce prejudice for certain audience characteristics.

Isolating the Effects of Training Using Simple Regression Analysis: An Example of the Procedure.

ERIC Educational Resources Information Center

Waugh, C. Keith

This paper provides a case example of simple regression analysis, a forecasting procedure used to isolate the effects of training from an identified extraneous variable. This case example focuses on results of a three-day sales training program to improve bank loan officers' knowledge, skill-level, and attitude regarding solicitation and sale of…

Determination of cellulose I crystallinity by FT-Raman spectroscopy

Treesearch

Umesh P. Agarwal; Richard S. Reiner; Sally A. Ralph

2009-01-01

Two new methods based on FT-Raman spectroscopy, one simple, based on band intensity ratio, and the other, using a partial least-squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in semicrystalline cellulose I samples was determined based on univariate regression that was first developed using the...

Sample size determination for logistic regression on a logit-normal distribution.

PubMed

Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance

2017-06-01

Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for [Formula: see text] for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.

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

PubMed

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

2009-03-01

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

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…

Neighbourhood social capital as a moderator between individual cognitions and sports behaviour among Dutch adolescents.

PubMed

Prins, R G; Beenackers, M A; Boog, M C; Van Lenthe, F J; Brug, J; Oenema, A

2014-03-01

This study aimed to explore whether individual cognitions and neighbourhood social capital strengthen each other in their relation with engaging in sports at least three times per week. Cross-sectional analyses on data from the last wave of the YouRAction trial (2009-2010, Rotterdam, the Netherlands; baseline response: 98%) were conducted. In total 1129 had data on the last wave questionnaire (93%) and 832 of them had complete data on a self-administered questionnaire on frequency of sports participation, perceived neighbourhood social capital, cognitions (attitude, subjective norm, perceived behavioural control and intention toward sport participation) and demographics. Ecometric methods were used to aggregate perceived neighbourhood social capital to the neighbourhood level. Multilevel logistic regression analyses (neighbourhood and individual as levels) were conducted to examine associations of cognitions, neighbourhood social capital and the social capital by individual cognition interaction with fit norm compliance. If the interaction was significant, simple slopes analyses were conducted to decompose interaction effects. It was found that neighbourhood social capital was significantly associated with fit norm compliance (OR: 5.40; 95% CI: 1.13-25.74). Moreover, neighbourhood social capital moderated the association of attitude, perceived behavioural control and intention with fit norm compliance. The simple slope analyses visualized that the associations of cognitions with fit norm compliance were stronger in case of more neighbourhood social capital. Hence, higher levels of neighbourhood social capital strengthen the associations of attitude, perceived behavioural control and intention in their association with fit norm compliance. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

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

PubMed

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

2016-02-01

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

The Regional Differences of Gpp Estimation by Solar Induced Fluorescence

NASA Astrophysics Data System (ADS)

Wang, X.; Lu, S.

2018-04-01

Estimating gross primary productivity (GPP) at large spatial scales is important for studying the global carbon cycle and global climate change. In this study, the relationship between solar-induced chlorophyll fluorescence (SIF) and GPP is analysed in different levels of annual average temperature and annual total precipitation respectively using simple linear regression analysis. The results showed high correlation between SIF and GPP, when the area satisfied annual average temperature in the range of -5 °C to 15 °C and the annual total precipitation is higher than 200 mm. These results can provide a basis for future estimation of GPP research.

Population dynamics of pond zooplankton, I. Diaptomus pallidus Herrick

USGS Publications Warehouse

Armitage, K.B.; Saxena, B.; Angino, E.E.

1973-01-01

The simultaneous and lag relationships between 27 environmental variables and seven population components of a perennial calanoid copepod were examined by simple and partial correlations and stepwise regression. The analyses consistently explained more than 70% of the variation of a population component. The multiple correlation coefficient (R) usually was highest in no lag or in 3-week or 4-week lag except for clutch size in which R was highest in 1-week lag. Population control, egg-bearing, and clutch size were affected primarily by environmental components categorized as weather; food apparently was relatively minor in affecting population control or reproduction. ?? 1973 Dr. W. Junk B.V. Publishers.

Statistical analysis of water-level, springflow, and streamflow data for the Edwards Aquifer in south-central Texas

USGS Publications Warehouse

Puente, Celso

1976-01-01

Water-level, springflow, and streamflow data were used to develop simple and multiple linear-regression equations for use in estimating water levels in wells and the flow of three major springs in the Edwards aquifer in the eastern San Antonio area. The equations provide daily, monthly, and annual estimates that compare very favorably with observed data. Analyses of geologic and hydrologic data indicate that the water discharged by the major springs is supplied primarily by regional underflow from the west and southwest and by local recharge in the infiltration area in northern Bexar, Comal, and Hays Counties.

Cellulose I crystallinity determination using FT-Raman spectroscopy : univariate and multivariate methods

Treesearch

Umesh P. Agarwal; Richard S. Reiner; Sally A. Ralph

2010-01-01

Two new methods based on FT–Raman spectroscopy, one simple, based on band intensity ratio, and the other using a partial least squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in cellulose I samples was determined based on univariate regression that was first developed using the Raman band...

Reprint of: Relationship between cataract severity and socioeconomic status.

PubMed

Wesolosky, Jason D; Rudnisky, Christopher J

2015-06-01

To determine the relationship between cataract severity and socioeconomic status (SES). Retrospective, observational case series. A total of 1350 eyes underwent phacoemulsification cataract extraction by a single surgeon using an Alcon Infiniti system. Cataract severity was measured using phaco time in seconds. SES was measured using area-level aggregate census data: median income, education, proportion of common-law couples, and employment rate. Preoperative best corrected visual acuity was obtained and converted to logarithm of the minimum angle of resolution values. For patients undergoing bilateral surgery, the generalized estimating equation was used to account for the correlation between eyes. Univariate analyses were performed using simple regression, and multivariate analyses were performed to account for variables with significant relationships (p < 0.05) on univariate testing. Sensitivity analyses were performed to assess the effect of including patient age in the controlled analyses. Multivariate analyses demonstrated that cataracts were more severe when the median income was lower (p = 0.001) and the proportion of common-law couples living in a patient's community (p = 0.012) and the unemployment rate (p = 0.002) were higher. These associations persisted even when controlling for patient age. Patients of lower SES have more severe cataracts. Copyright © 2015. Published by Elsevier Inc.

Using Time-Series Regression to Predict Academic Library Circulations.

ERIC Educational Resources Information Center

Brooks, Terrence A.

1984-01-01

Four methods were used to forecast monthly circulation totals in 15 midwestern academic libraries: dummy time-series regression, lagged time-series regression, simple average (straight-line forecasting), monthly average (naive forecasting). In tests of forecasting accuracy, dummy regression method and monthly mean method exhibited smallest average…

Analyzing industrial energy use through ordinary least squares regression models

NASA Astrophysics Data System (ADS)

Golden, Allyson Katherine

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

No evidence of reaction time slowing in autism spectrum disorder.

PubMed

Ferraro, F Richard

2016-01-01

A total of 32 studies comprising 238 simple reaction time and choice reaction time conditions were examined in individuals with autism spectrum disorder (n = 964) and controls (n = 1032). A Brinley plot/multiple regression analysis was performed on mean reaction times, regressing autism spectrum disorder performance onto the control performance as a way to examine any generalized simple reaction time/choice reaction time slowing exhibited by the autism spectrum disorder group. The resulting regression equation was Y (autism spectrum disorder) = 0.99 × (control) + 87.93, which accounted for 92.3% of the variance. These results suggest that there are little if any simple reaction time/choice reaction time slowing in this sample of individual with autism spectrum disorder, in comparison with controls. While many cognitive and information processing domains are compromised in autism spectrum disorder, it appears that simple reaction time/choice reaction time remain relatively unaffected in autism spectrum disorder. © The Author(s) 2014.

Linear models for calculating digestibile energy for sheep diets.

PubMed

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

1981-05-01

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

Disentangling the Correlates of Drug Use in a Clinic and Community Sample: A Regression Analysis of the Associations between Drug Use, Years-of-School, Impulsivity, IQ, Working Memory, and Psychiatric Symptoms.

PubMed

Heyman, Gene M; Dunn, Brian J; Mignone, Jason

2014-01-01

Years-of-school is negatively correlated with illicit drug use. However, educational attainment is positively correlated with IQ and negatively correlated with impulsivity, two traits that are also correlated with drug use. Thus, the negative correlation between education and drug use may reflect the correlates of schooling, not schooling itself. To help disentangle these relations we obtained measures of working memory, simple memory, IQ, disposition (impulsivity and psychiatric status), years-of-school and frequency of illicit and licit drug use in methadone clinic and community drug users. We found strong zero-order correlations between all measures, including IQ, impulsivity, years-of-school, psychiatric symptoms, and drug use. However, multiple regression analyses revealed a different picture. The significant predictors of illicit drug use were gender, involvement in a methadone clinic, and years-of-school. That is, psychiatric symptoms, impulsivity, cognition, and IQ no longer predicted illicit drug use in the multiple regression analyses. Moreover, high risk subjects (low IQ and/or high impulsivity) who spent 14 or more years in school used stimulants and opiates less than did low risk subjects who had spent <14 years in school. Smoking and drinking had a different correlational structure. IQ and years-of-school predicted whether someone ever became a smoker, whereas impulsivity predicted the frequency of drinking bouts, but years-of-school did not. Many subjects reported no use of one or more drugs, resulting in a large number of "zeroes" in the data sets. Cragg's Double-Hurdle regression method proved the best approach for dealing with this problem. To our knowledge, this is the first report to show that years-of-school predicts lower levels of illicit drug use after controlling for IQ and impulsivity. This paper also highlights the advantages of Double-Hurdle regression methods for analyzing the correlates of drug use in community samples.

Perceived school safety is strongly associated with adolescent mental health problems.

PubMed

Nijs, Miesje M; Bun, Clothilde J E; Tempelaar, Wanda M; de Wit, Niek J; Burger, Huibert; Plevier, Carolien M; Boks, Marco P M

2014-02-01

School environment is an important determinant of psychosocial function and may also be related to mental health. We therefore investigated whether perceived school safety, a simple measure of this environment, is related to mental health problems. In a population-based sample of 11,130 secondary school students, we analysed the relationship of perceived school safety with mental health problems using multiple logistic regression analyses to adjust for potential confounders. Mental health problems were defined using the clinical cut-off of the self-reported Strengths and Difficulties Questionnaire. School safety showed an exposure-response relationship with mental health problems after adjustment for confounders. Odds ratios increased from 2.48 ("sometimes unsafe") to 8.05 ("very often unsafe"). The association was strongest in girls and young and middle-aged adolescents. Irrespective of the causal background of this association, school safety deserves attention either as a risk factor or as an indicator of mental health problems.

A chemometric approach to the characterisation of historical mortars

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

Rampazzi, L.; Pozzi, A.; Sansonetti, A.

2006-06-15

The compositional knowledge of historical mortars is of great concern in case of provenance and dating investigations and of conservation works since the nature of the raw materials suggests the most compatible conservation products. The classic characterisation usually goes through various analytical determinations, while conservation laboratories call for simple and quick analyses able to enlighten the nature of mortars, usually in terms of the binder fraction. A chemometric approach to the matter is here undertaken. Specimens of mortars were prepared with calcitic and dolomitic binders and analysed by Atomic Spectroscopy. Principal Components Analysis (PCA) was used to investigate the featuresmore » of specimens and samples. A Partial Least Square (PLS1) regression was done in order to predict the binder/aggregate ratio. The model was applied to historical mortars from the churches of St. Lorenzo (Milan) and St. Abbondio (Como). The accordance between the predictive model and the real samples is discussed.« less

Reversed inverse regression for the univariate linear calibration and its statistical properties derived using a new methodology

NASA Astrophysics Data System (ADS)

Kang, Pilsang; Koo, Changhoi; Roh, Hokyu

2017-11-01

Since simple linear regression theory was established at the beginning of the 1900s, it has been used in a variety of fields. Unfortunately, it cannot be used directly for calibration. In practical calibrations, the observed measurements (the inputs) are subject to errors, and hence they vary, thus violating the assumption that the inputs are fixed. Therefore, in the case of calibration, the regression line fitted using the method of least squares is not consistent with the statistical properties of simple linear regression as already established based on this assumption. To resolve this problem, "classical regression" and "inverse regression" have been proposed. However, they do not completely resolve the problem. As a fundamental solution, we introduce "reversed inverse regression" along with a new methodology for deriving its statistical properties. In this study, the statistical properties of this regression are derived using the "error propagation rule" and the "method of simultaneous error equations" and are compared with those of the existing regression approaches. The accuracy of the statistical properties thus derived is investigated in a simulation study. We conclude that the newly proposed regression and methodology constitute the complete regression approach for univariate linear calibrations.

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

PubMed

Packard, Gary C

2017-01-01

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

Who Adopts Improved Fuels and Cookstoves? A Systematic Review

PubMed Central

Lewis, Jessica J.

2012-01-01

Background: The global focus on improved cookstoves (ICSs) and clean fuels has increased because of their potential for delivering triple dividends: household health, local environmental quality, and regional climate benefits. However, ICS and clean fuel dissemination programs have met with low rates of adoption. Objectives: We reviewed empirical studies on ICSs and fuel choice to describe the literature, examine determinants of fuel and stove choice, and identify knowledge gaps. Methods: We conducted a systematic review of the literature on the adoption of ICSs or cleaner fuels by households in developing countries. Results are synthesized through a simple vote-counting meta-analysis. Results: We identified 32 research studies that reported 146 separate regression analyses of ICS adoption (11 analyses) or fuel choice (135 analyses) from Asia (60%), Africa (27%), and Latin America (19%). Most studies apply multivariate regression methods to consider 7–13 determinants of choice. Income, education, and urban location were positively associated with adoption in most but not all studies. However, the influence of fuel availability and prices, household size and composition, and sex is unclear. Potentially important drivers such as credit, supply-chain strengthening, and social marketing have been ignored. Conclusions: Adoption studies of ICSs or clean energy are scarce, scattered, and of differential quality, even though global distribution programs are quickly expanding. Future research should examine an expanded set of contextual variables to improve implementation of stove programs that can realize the “win-win-win” of health, local environmental quality, and climate associated with these technologies. PMID:22296719

Weighing Evidence “Steampunk” Style via the Meta-Analyser

PubMed Central

Bowden, Jack; Jackson, Chris

2016-01-01

ABSTRACT The funnel plot is a graphical visualization of summary data estimates from a meta-analysis, and is a useful tool for detecting departures from the standard modeling assumptions. Although perhaps not widely appreciated, a simple extension of the funnel plot can help to facilitate an intuitive interpretation of the mathematics underlying a meta-analysis at a more fundamental level, by equating it to determining the center of mass of a physical system. We used this analogy to explain the concepts of weighing evidence and of biased evidence to a young audience at the Cambridge Science Festival, without recourse to precise definitions or statistical formulas and with a little help from Sherlock Holmes! Following on from the science fair, we have developed an interactive web-application (named the Meta-Analyser) to bring these ideas to a wider audience. We envisage that our application will be a useful tool for researchers when interpreting their data. First, to facilitate a simple understanding of fixed and random effects modeling approaches; second, to assess the importance of outliers; and third, to show the impact of adjusting for small study bias. This final aim is realized by introducing a novel graphical interpretation of the well-known method of Egger regression. PMID:28003684

Exploring visuospatial abilities and their contribution to constructional abilities and nonverbal intelligence.

PubMed

Trojano, Luigi; Siciliano, Mattia; Cristinzio, Chiara; Grossi, Dario

2018-01-01

The present study aimed at exploring relationships among the visuospatial tasks included in the Battery for Visuospatial Abilities (BVA), and at assessing the relative contribution of different facets of visuospatial processing on tests tapping constructional abilities and nonverbal abstract reasoning. One hundred forty-four healthy subjects with a normal score on Mini Mental State Examination completed the BVA plus Raven's Coloured Progressive Matrices and Constructional Apraxia test. We used Principal Axis Factoring and Parallel Analysis to investigate relationships among the BVA visuospatial tasks, and performed regression analyses to assess the visuospatial contribution to constructional abilities and nonverbal abstract reasoning. Principal Axis Factoring and Parallel Analysis revealed two eigenvalues exceeding 1, accounting for about 60% of the variance. A 2-factor model provided the best fit. Factor 1 included sub-tests exploring "complex" visuospatial skills, whereas Factor 2 included two subtests tapping "simple" visuospatial skills. Regression analyses revealed that both Factor 1 and Factor 2 significantly affected performance on Raven's Coloured Progressive Matrices, whereas only the Factor 1 affected performance on Constructional Apraxia test. Our results supported functional segregation proposed by De Renzi, suggesting clinical caution to utilize a single test to assess visuospatial domain, and qualified the visuospatial contribution in drawing and non-verbal intelligence test.

Association between adolescent marriage and marital violence among young adult women in India

PubMed Central

Raj, Anita; Saggurti, Niranjan; Lawrence, Danielle; Balaiah, Donta; Silverman, Jay G.

2010-01-01

Objective To assess whether a history of adolescent marriage (<18 years) places women in young adulthood in India at increased risk of physical or sexual marital violence. Methods Cross-sectional analysis was performed on data from a nationally representative household study of 124 385 Indian women aged 15–49 years collected in 2005–2006. The analyses were restricted to married women aged 20–24 years who participated in the marital violence (MV) survey module (n=10 514). Simple regression models and models adjusted for participant demographics were constructed to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between adolescent marriage and MV. Results Over half (58%) of the participants were married before 18 years of age; 35% of the women had experienced physical or sexual violence in their marriage; and 27% reported such abuse in the last year. Adjusted regression analyses revealed that women married as minors were significantly more likely than those married as adults to report ever experiencing MV (adjusted OR 1.77; 95% CI, 1.61–1.95) and in the last 12 months (adjusted OR 1.51; 95% CI, 1.36–1.67). Conclusions Women who were married as adolescents remain at increased risk of MV into young adulthood. PMID:20347089

Polygenic scores via penalized regression on summary statistics.

PubMed

Mak, Timothy Shin Heng; Porsch, Robert Milan; Choi, Shing Wan; Zhou, Xueya; Sham, Pak Chung

2017-09-01

Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating PGS have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can use LD information available elsewhere to supplement such analyses. To answer this question, we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and P-value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred. © 2017 WILEY PERIODICALS, INC.

Refractive Status at Birth: Its Relation to Newborn Physical Parameters at Birth and Gestational Age

PubMed Central

Varghese, Raji Mathew; Sreenivas, Vishnubhatla; Puliyel, Jacob Mammen; Varughese, Sara

2009-01-01

Background Refractive status at birth is related to gestational age. Preterm babies have myopia which decreases as gestational age increases and term babies are known to be hypermetropic. This study looked at the correlation of refractive status with birth weight in term and preterm babies, and with physical indicators of intra-uterine growth such as the head circumference and length of the baby at birth. Methods All babies delivered at St. Stephens Hospital and admitted in the nursery were eligible for the study. Refraction was performed within the first week of life. 0.8% tropicamide with 0.5% phenylephrine was used to achieve cycloplegia and paralysis of accommodation. 599 newborn babies participated in the study. Data pertaining to the right eye is utilized for all the analyses except that for anisometropia where the two eyes were compared. Growth parameters were measured soon after birth. Simple linear regression analysis was performed to see the association of refractive status, (mean spherical equivalent (MSE), astigmatism and anisometropia) with each of the study variables, namely gestation, length, weight and head circumference. Subsequently, multiple linear regression was carried out to identify the independent predictors for each of the outcome parameters. Results Simple linear regression showed a significant relation between all 4 study variables and refractive error but in multiple regression only gestational age and weight were related to refractive error. The partial correlation of weight with MSE adjusted for gestation was 0.28 and that of gestation with MSE adjusted for weight was 0.10. Birth weight had a higher correlation to MSE than gestational age. Conclusion This is the first study to look at refractive error against all these growth parameters, in preterm and term babies at birth. It would appear from this study that birth weight rather than gestation should be used as criteria for screening for refractive error, especially in developing countries where the incidence of intrauterine malnutrition is higher. PMID:19214228

Correlation and simple linear regression.

PubMed

Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G

2003-06-01

In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

Cervical Vertebral Body's Volume as a New Parameter for Predicting the Skeletal Maturation Stages.

PubMed

Choi, Youn-Kyung; Kim, Jinmi; Yamaguchi, Tetsutaro; Maki, Koutaro; Ko, Ching-Chang; Kim, Yong-Il

2016-01-01

This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies from 102 Japanese patients (54 women and 48 men, 5-18 years of age). We performed Pearson's correlation coefficient analysis and simple regression analysis. All volume parameters derived from the second, third, and fourth cervical vertebrae exhibited statistically significant correlations (P < 0.05). The simple regression model with the greatest R-square indicated the fourth-cervical-vertebra volume as an independent variable with a variance inflation factor less than ten. The explanation power was 81.76%. Volumetric parameters of cervical vertebrae using cone beam computed tomography are useful in regression models. The derived regression model has the potential for clinical application as it enables a simple and quantitative analysis to evaluate skeletal maturation level.

Cervical Vertebral Body's Volume as a New Parameter for Predicting the Skeletal Maturation Stages

PubMed Central

Choi, Youn-Kyung; Kim, Jinmi; Maki, Koutaro; Ko, Ching-Chang

2016-01-01

This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies from 102 Japanese patients (54 women and 48 men, 5–18 years of age). We performed Pearson's correlation coefficient analysis and simple regression analysis. All volume parameters derived from the second, third, and fourth cervical vertebrae exhibited statistically significant correlations (P < 0.05). The simple regression model with the greatest R-square indicated the fourth-cervical-vertebra volume as an independent variable with a variance inflation factor less than ten. The explanation power was 81.76%. Volumetric parameters of cervical vertebrae using cone beam computed tomography are useful in regression models. The derived regression model has the potential for clinical application as it enables a simple and quantitative analysis to evaluate skeletal maturation level. PMID:27340668

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Factors associated with active commuting to work among women.

PubMed

Bopp, Melissa; Child, Stephanie; Campbell, Matthew

2014-01-01

Active commuting (AC), the act of walking or biking to work, has notable health benefits though rates of AC remain low among women. This study used a social-ecological framework to examine the factors associated with AC among women. A convenience sample of employed, working women (n = 709) completed an online survey about their mode of travel to work. Individual, interpersonal, institutional, community, and environmental influences were assessed. Basic descriptive statistics and frequencies described the sample. Simple logistic regression models examined associations with the independent variables with AC participation and multiple logistic regression analysis determined the relative influence of social ecological factors on AC participation. The sample was primarily middle-aged (44.09±11.38 years) and non-Hispanic White (92%). Univariate analyses revealed several individual, interpersonal, institutional, community and environmental factors significantly associated with AC. The multivariable logistic regression analysis results indicated that significant factors associated with AC included number of children, income, perceived behavioral control, coworker AC, coworker AC normative beliefs, employer and community supports for AC, and traffic. The results of this study contribute to the limited body of knowledge on AC participation for women and may help to inform gender-tailored interventions to enhance AC behavior and improve health.

Methods for estimating tributary streamflow in the Chattahoochee River basin between Buford Dam and Franklin, Georgia

USGS Publications Warehouse

Stamey, Timothy C.

1998-01-01

Simple and reliable methods for estimating hourly streamflow are needed for the calibration and verification of a Chattahoochee River basin model between Buford Dam and Franklin, Ga. The river basin model is being developed by Georgia Department of Natural Resources, Environmental Protection Division, as part of their Chattahoochee River Modeling Project. Concurrent streamflow data collected at 19 continuous-record, and 31 partial-record streamflow stations, were used in ordinary least-squares linear regression analyses to define estimating equations, and in verifying drainage-area prorations. The resulting regression or drainage-area ratio estimating equations were used to compute hourly streamflow at the partial-record stations. The coefficients of determination (r-squared values) for the regression estimating equations ranged from 0.90 to 0.99. Observed and estimated hourly and daily streamflow data were computed for May 1, 1995, through October 31, 1995. Comparisons of observed and estimated daily streamflow data for 12 continuous-record tributary stations, that had available streamflow data for all or part of the period from May 1, 1995, to October 31, 1995, indicate that the mean error of estimate for the daily streamflow was about 25 percent.

Pilot study: safety and effectiveness of simple ultrasound-guided high-intensity focused ultrasound ablating uterine leiomyoma with a diameter greater than 10 cm.

PubMed

Hou, Ruijie; Wang, Liwei; Li, Shaoping; Rong, Fengmin; Wang, Yuanyuan; Qin, Xuena; Wang, Shijin

2018-02-01

The study aimed to prospectively investigate whether uterine leiomyoma greater than 10 cm in diameter could be treated with simple ultrasound-guided high-intensity focused ultrasound (USgHIFU) in one-time treatment. A total of 36 patients with 36 symptomatic uterine leiomyoma greater than 10 cm in diameter who underwent simple USgHIFU treatment alone were analysed. Enhanced MRI was performed before and after HIFU treatment, and all patients had follow-up for 6 months after treatment. Symptom severity scores, treatment time, treatment speed, ablation rate, energy effect ratio, uterine leiomyoma regression rate, adverse events, liver and kidney functions, coagulation function and routine blood count were included in the study endpoints. The mean diameter of uterine leiomyoma was 11.2 ± 1.3 cm (10.0-14.3 cm). The median treatment time and treatment speed were 104.0 min (90.0-140.0 min) and 118.8 cm 3  h -1  (86.2-247.1 cm 3  h -1 ), respectively. The ablation rate of uterine leiomyoma was 71.9 ± 20.4% (32.1-100.0%), and the regression rate of uterine leiomyoma was 40.8 ± 7.5% (25.6-59.9%) at 6 months after treatment. The mean symptom severity scores decreased by an average of approximately 8.6 ± 2.3 (5-14) points. There were no significant changes in haemogram and blood chemical indexes of patients, except for the transient elevation of aspartate aminotransferase, total bilirubin and white blood cells after treatment. No serious adverse reactions occurred. According to our preliminary results, simple USgHIFU is a safe and effective single-treatment method of treating uterine leiomyoma greater than 10 cm in diameter and is an almost innocuous alternative therapeutic strategy. Advances in knowledge: The conclusions indicate simple USgHIFU is safe and effective as one-time treatment of uterine leiomyoma greater than 10 cm in diameter, it could be a promising therapeutic strategy.

Introduction, comparison, and validation of Meta-Essentials: A free and simple tool for meta-analysis.

PubMed

Suurmond, Robert; van Rhee, Henk; Hak, Tony

2017-12-01

We present a new tool for meta-analysis, Meta-Essentials, which is free of charge and easy to use. In this paper, we introduce the tool and compare its features to other tools for meta-analysis. We also provide detailed information on the validation of the tool. Although free of charge and simple, Meta-Essentials automatically calculates effect sizes from a wide range of statistics and can be used for a wide range of meta-analysis applications, including subgroup analysis, moderator analysis, and publication bias analyses. The confidence interval of the overall effect is automatically based on the Knapp-Hartung adjustment of the DerSimonian-Laird estimator. However, more advanced meta-analysis methods such as meta-analytical structural equation modelling and meta-regression with multiple covariates are not available. In summary, Meta-Essentials may prove a valuable resource for meta-analysts, including researchers, teachers, and students. © 2017 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.

Prediction of the Main Engine Power of a New Container Ship at the Preliminary Design Stage

NASA Astrophysics Data System (ADS)

Cepowski, Tomasz

2017-06-01

The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

Ready-to-eat cereals improve nutrient, milk and fruit intake at breakfast in European adolescents.

PubMed

Michels, Nathalie; De Henauw, Stefaan; Beghin, Laurent; Cuenca-García, Magdalena; Gonzalez-Gross, Marcela; Hallstrom, Lena; Kafatos, Anthony; Kersting, Mathilde; Manios, Yannis; Marcos, Ascensión; Molnar, Denes; Roccaldo, Romana; Santaliestra-Pasías, Alba M; Sjostrom, Michael; Reye, Béatrice; Thielecke, Frank; Widhalm, Kurt; Claessens, Mandy

2016-03-01

Breakfast consumption has been recommended as part of a healthy diet. Recently, ready-to-eat cereals (RTEC) became more popular as a breakfast item. Our aim was to analyse the dietary characteristics of an RTEC breakfast in European adolescents and to compare them with other breakfast options. From the European multi-centre HELENA study, two 24-h dietary recalls of 3137 adolescents were available. Food items (RTEC or bread, milk/yoghurt, fruit) and macro- and micronutrient intakes at breakfast were calculated. Cross-sectional regression analyses were adjusted for gender, age, socio-economic status and city. Compared to bread breakfasts (39 %) and all other breakfasts (41.5 %), RTEC breakfast (19.5 %) was associated with improved nutrient intake (less fat and less sucrose; more fibre, protein and some micronutrients like vitamin B, calcium, magnesium and phosphorus) at the breakfast occasion. Exceptions were more simple sugars in RTEC breakfast consumers: more lactose and galactose due to increased milk consumption, but also higher glucose and fructose than bread consumers. RTEC consumers had a significantly higher frequency (92.5 vs. 50.4 and 60.2 %) and quantity of milk/yoghurt intake and a slightly higher frequency of fruit intake (13.4 vs. 10.9 and 8.0 %) at breakfast. Among European adolescents, RTEC consumers showed a more favourable nutrient intake than consumers of bread or other breakfasts, except for simple sugars. Therefore, RTEC may be regarded as a good breakfast option as part of a varied and balanced diet. Nevertheless, more research is warranted concerning the role of different RTEC types in nutrient intake, especially for simple sugars.

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

USGS Publications Warehouse

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

2015-01-01

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

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

PubMed

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

2009-11-01

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

Conceptual and statistical problems associated with the use of diversity indices in ecology.

PubMed

Barrantes, Gilbert; Sandoval, Luis

2009-09-01

Diversity indices, particularly the Shannon-Wiener index, have extensively been used in analyzing patterns of diversity at different geographic and ecological scales. These indices have serious conceptual and statistical problems which make comparisons of species richness or species abundances across communities nearly impossible. There is often no a single statistical method that retains all information needed to answer even a simple question. However, multivariate analyses could be used instead of diversity indices, such as cluster analyses or multiple regressions. More complex multivariate analyses, such as Canonical Correspondence Analysis, provide very valuable information on environmental variables associated to the presence and abundance of the species in a community. In addition, particular hypotheses associated to changes in species richness across localities, or change in abundance of one, or a group of species can be tested using univariate, bivariate, and/or rarefaction statistical tests. The rarefaction method has proved to be robust to standardize all samples to a common size. Even the simplest method as reporting the number of species per taxonomic category possibly provides more information than a diversity index value.

Mental chronometry with simple linear regression.

PubMed

Chen, J Y

1997-10-01

Typically, mental chronometry is performed by means of introducing an independent variable postulated to affect selectively some stage of a presumed multistage process. However, the effect could be a global one that spreads proportionally over all stages of the process. Currently, there is no method to test this possibility although simple linear regression might serve the purpose. In the present study, the regression approach was tested with tasks (memory scanning and mental rotation) that involved a selective effect and with a task (word superiority effect) that involved a global effect, by the dominant theories. The results indicate (1) the manipulation of the size of a memory set or of angular disparity affects the intercept of the regression function that relates the times for memory scanning with different set sizes or for mental rotation with different angular disparities and (2) the manipulation of context affects the slope of the regression function that relates the times for detecting a target character under word and nonword conditions. These ratify the regression approach as a useful method for doing mental chronometry.

Strength/Brittleness Classification of Igneous Intact Rocks Based on Basic Physical and Dynamic Properties

NASA Astrophysics Data System (ADS)

Aligholi, Saeed; Lashkaripour, Gholam Reza; Ghafoori, Mohammad

2017-01-01

This paper sheds further light on the fundamental relationships between simple methods, rock strength, and brittleness of igneous rocks. In particular, the relationship between mechanical (point load strength index I s(50) and brittleness value S 20), basic physical (dry density and porosity), and dynamic properties (P-wave velocity and Schmidt rebound values) for a wide range of Iranian igneous rocks is investigated. First, 30 statistical models (including simple and multiple linear regression analyses) were built to identify the relationships between mechanical properties and simple methods. The results imply that rocks with different Schmidt hardness (SH) rebound values have different physicomechanical properties or relations. Second, using these results, it was proved that dry density, P-wave velocity, and SH rebound value provide a fine complement to mechanical properties classification of rock materials. Further, a detailed investigation was conducted on the relationships between mechanical and simple tests, which are established with limited ranges of P-wave velocity and dry density. The results show that strength values decrease with the SH rebound value. In addition, there is a systematic trend between dry density, P-wave velocity, rebound hardness, and brittleness value of the studied rocks, and rocks with medium hardness have a higher brittleness value. Finally, a strength classification chart and a brittleness classification table are presented, providing reliable and low-cost methods for the classification of igneous rocks.

Ozone indices based on simple meteorological parameters: potentials and limitations of regression and neural network models

NASA Astrophysics Data System (ADS)

Soja, G.; Soja, A.-M.

This study tested the usefulness of extremely simple meteorological models for the prediction of ozone indices. The models were developed with the input parameters of daily maximum temperature and sunshine duration and are based on a data collection period of three years. For a rural environment in eastern Austria, the meteorological and ozone data of three summer periods have been used to develop functions to describe three ozone exposure indices (daily maximum, 7 h mean 9.00-16.00 h, accumulated ozone dose AOT40). Data sets for other years or stations not included in the development of the models were used as test data to validate the performance of the models. Generally, optimized regression models performed better than simplest linear models, especially in the case of AOT40. For the description of the summer period from May to September, the mean absolute daily differences between observed and calculated indices were 8±6 ppb for the maximum half hour mean value, 6±5 ppb for the 7 h mean and 41±40 ppb h for the AOT40. When the parameters were further optimized to describe individual months separately, the mean absolute residuals decreased by ⩽10%. Neural network models did not always perform better than the regression models. This is attributed to the low number of inputs in this comparison and to the simple architecture of these models (2-2-1). Further factorial analyses of those days when the residuals were higher than the mean plus one standard deviation should reveal possible reasons why the models did not perform well on certain days. It was observed that overestimations by the models mainly occurred on days with partly overcast, hazy or very windy conditions. Underestimations more frequently occurred on weekdays than on weekends. It is suggested that the application of this kind of meteorological model will be more successful in topographically homogeneous regions and in rural environments with relatively constant rates of emission and long-range transport of ozone precursors. Under conditions too demanding for advanced physico/chemical models, the presented models may offer useful alternatives to derive ecologically relevant ozone indices directly from meteorological parameters.

Relationship between cataract severity and socioeconomic status.

PubMed

Wesolosky, Jason D; Rudnisky, Christopher J

2013-12-01

To determine the relationship between cataract severity and socioeconomic status (SES). Retrospective, observational case series. A total of 1350 eyes underwent phacoemulsification cataract extraction by a single surgeon using an Alcon Infiniti system. Cataract severity was measured using phaco time in seconds. SES was measured using area-level aggregate census data: median income, education, proportion of common-law couples, and employment rate. Preoperative best corrected visual acuity was obtained and converted to logarithm of the minimum angle of resolution values. For patients undergoing bilateral surgery, the generalized estimating equation was used to account for the correlation between eyes. Univariate analyses were performed using simple regression, and multivariate analyses were performed to account for variables with significant relationships (p < 0.05) on univariate testing. Sensitivity analyses were performed to assess the effect of including patient age in the controlled analyses. Multivariate analyses demonstrated that cataracts were more severe when the median income was lower (p = 0.001) and the proportion of common-law couples living in a patient's community (p = 0.012) and the unemployment rate (p = 0.002) were higher. These associations persisted even when controlling for patient age. Patients of lower SES have more severe cataracts. Copyright © 2013 Canadian Ophthalmological Society. Published by Elsevier Inc. All rights reserved.

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.

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

NASA Astrophysics Data System (ADS)

Vance, Leisha Ann

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

Suppression Situations in Multiple Linear Regression

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

Amount of balance necessary for the independence of transfer and stair-climbing in stroke inpatients.

PubMed

Fujita, Takaaki; Sato, Atsushi; Ohashi, Yuji; Nishiyama, Kazutaka; Ohashi, Takuro; Yamane, Kazuhiro; Yamamoto, Yuichi; Tsuchiya, Kenji; Otsuki, Koji; Tozato, Fusae

2018-05-01

The purpose of this study was to clarify the amount of balance necessary for the independence of transfer and stair-climbing in stroke patients. This study included 111 stroke inpatients. Simple and multiple regression analyses were conducted to establish the association between the FIM ® instrument scores for transfer or stair-climbing and Berg Balance Scale. Furthermore, receiver operating characteristic curves were used to elucidate the amount of balance necessary for the independence of transfer and stair-climbing. Simple and multiple regression analyses showed that the FIM ® instrument scores for transfer and stair-climbing were strongly associated with Berg Balance Scale. On comparison of the independent and supervision-dependent groups, Berg Balance Scale cut-off values for transfer and stair-climbing were 41/40 and 54/53 points, respectively. On comparison of the independent-supervision and dependent groups, the cut-off values for transfer and stair-climbing were 30/29 and 41/40 points, respectively. The calculated cut-off values indicated the amount of balance necessary for the independence of transfer and stair-climbing, with and without supervision, in stroke patients. Berg Balance Scale has a good discriminatory ability and cut-off values are clinically useful to determine the appropriate independence levels of transfer and stair-climbing in hospital wards. Implications for rehabilitation The Berg Balance Scale's (BBS) strong association with transfer and stair-climbing independence and performance indicates that establishing cut-off values is vitally important for the established use of the BBS clinically. The cut-off values calculated herein accurately demonstrate the level of balance necessary for transfer and stair-climbing independence, with and without supervision, in stroke patients. These criteria should be employed clinically for determining the level of independence for transfer and stair-climbing as well as for setting balance training goals aimed at improving transfer and stair-climbing.

The Variance Normalization Method of Ridge Regression Analysis.

ERIC Educational Resources Information Center

Bulcock, J. W.; And Others

The testing of contemporary sociological theory often calls for the application of structural-equation models to data which are inherently collinear. It is shown that simple ridge regression, which is commonly used for controlling the instability of ordinary least squares regression estimates in ill-conditioned data sets, is not a legitimate…

Factors associated to quality of life in active elderly.

PubMed

Alexandre, Tiago da Silva; Cordeiro, Renata Cereda; Ramos, Luiz Roberto

2009-08-01

To analyze whether quality of life in active, healthy elderly individuals is influenced by functional status and sociodemographic characteristics, as well as psychological parameters. Study conducted in a sample of 120 active elderly subjects recruited from two open universities of the third age in the cities of São Paulo and São José dos Campos (Southeastern Brazil) between May 2005 and April 2006. Quality of life was measured using the abbreviated Brazilian version of the World Health Organization Quality of Live (WHOQOL-bref) questionnaire. Sociodemographic, clinical and functional variables were measured through crossculturally validated assessments by the Mini Mental State Examination, Geriatric Depression Scale, Functional Reach, One-Leg Balance Test, Timed Up and Go Test, Six-Minute Walk Test, Human Activity Profile and a complementary questionnaire. Simple descriptive analyses, Pearson's correlation coefficient, Student's t-test for non-related samples, analyses of variance, linear regression analyses and variance inflation factor were performed. The significance level for all statistical tests was set at 0.05. Linear regression analysis showed an independent correlation without colinearity between depressive symptoms measured by the Geriatric Depression Scale and four domains of the WHOQOL-bref. Not having a conjugal life implied greater perception in the social domain; developing leisure activities and having an income over five minimum wages implied greater perception in the environment domain. Functional status had no influence on the Quality of Life variable in the analysis models in active elderly. In contrast, psychological factors, as assessed by the Geriatric Depression Scale, and sociodemographic characteristics, such as marital status, income and leisure activities, had an impact on quality of life.

The Dementia Services Mini-Screen: A Simple Method to Identify Patients and Caregivers Needing Enhanced Dementia Care Services

PubMed Central

Borson, Soo; Scanlan, James M.; Sadak, Tatiana; Lessig, Mary; Vitaliano, Peter

2014-01-01

Objective The National Alzheimer’s Plan calls for targeted health system change to improve outcomes for persons with dementia and their family caregivers. We explored whether dementia-specific service needs and gaps could be predicted from simple information that can be readily acquired in routine medical care settings. Method Primary family caregivers for cognitively impaired older adults (n=215) were asked about current stress, challenging patient behaviors, and prior-year needs and gaps in 16 medical and psychosocial services. Demographic data, caregiver stress, and patient clinical features were evaluated in regression analyses to identify unique predictors of service needs and gaps. Results Caregiver stress and patient behavior problems together accounted for an average of 24% of the whole-sample variance in total needs and gaps. Across all analyses, including total, medical, and psychosocial services needs and gaps, all other variables combined (comorbid chronic disease, dementia severity, age, caregiver relationship, and residence) accounted for an accounted for a mean of 3%, with no variable yielding more than 4% in any equation. We combined stress and behavior problem indicators into a simple screen. In early/mild dementia dyads (n=111) typical in primary care settings, the screen identified gaps in total and psychosocial care in 84% and 77%, respectively, of those with high stress/high behavior problems vs. 25% and 23%, respectively, of those with low stress/low behavior problems. Medical care gaps were dramatically higher in high stress/high behavior problem dyads (66%) than all others (12%). Conclusion A simple tool (likely completed in 1–2 minutes) which combines caregiver stress and patient behavior problems, the Dementia Services Mini-Screen, could help clinicians rapidly identify high need, high gap dyads. Health care systems could use it to estimate population needs for targeted dementia services and facilitate their development. PMID:24315560

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

PubMed Central

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

2011-01-01

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

Monitoring the quality consistency of Weibizhi tablets by micellar electrokinetic chromatography fingerprints combined with multivariate statistical analyses, the simple quantified ratio fingerprint method, and the fingerprint-efficacy relationship.

PubMed

Liu, Yingchun; Sun, Guoxiang; Wang, Yan; Yang, Lanping; Yang, Fangliang

2015-06-01

Micellar electrokinetic chromatography fingerprinting combined with quantification was successfully developed and applied to monitor the quality consistency of Weibizhi tablets, which is a classical compound preparation used to treat gastric ulcers. A background electrolyte composed of 57 mmol/L sodium borate, 21 mmol/L sodium dodecylsulfate and 100 mmol/L sodium hydroxide was used to separate compounds. To optimize capillary electrophoresis conditions, multivariate statistical analyses were applied. First, the most important factors influencing sample electrophoretic behavior were identified as background electrolyte concentrations. Then, a Box-Benhnken design response surface strategy using resolution index RF as an integrated response was set up to correlate factors with response. RF reflects the effective signal amount, resolution, and signal homogenization in an electropherogram, thus, it was regarded as an excellent indicator. In fingerprint assessments, simple quantified ratio fingerprint method was established for comprehensive quality discrimination of traditional Chinese medicines/herbal medicines from qualitative and quantitative perspectives, by which the quality of 27 samples from the same manufacturer were well differentiated. In addition, the fingerprint-efficacy relationship between fingerprints and antioxidant activities was established using partial least squares regression, which provided important medicinal efficacy information for quality control. The present study offered an efficient means for monitoring Weibizhi tablet quality consistency. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Sonographic Measurement of Fetal Ear Length in Turkish Women with a Normal Pregnancy

PubMed Central

Özdemir, Mucize Eriç; Uzun, Işıl; Karahasanoğlu, Ayşe; Aygün, Mehmet; Akın, Hale; Yazıcıoğlu, Fehmi

2014-01-01

Background: Abnormal fetal ear length is a feature of chromosomal disorders. Fetal ear length measurement is a simple measurement that can be obtained during ultrasonographic examinations. Aims: To develop a nomogram for fetal ear length measurements in our population and investigate the correlation between fetal ear length, gestational age, and other standard fetal biometric measurements. Study Design: Cohort study. Methods: Ear lengths of the fetuses were measured in normal singleton pregnancies. The relationship between gestational age and fetal ear length in millimetres was analysed by simple linear regression. In addition, the correlation of fetal ear length measurements with biparietal diameter, head circumference, abdominal circumference, and femur length were evaluated.Ear length measurements were obtained from fetuses in 389 normal singleton pregnancies ranging between 16 and 28 weeks of gestation. Results: A nomogram was developed by linear regression analysis of the parameters ear length and gestational age. Fetal ear length (mm) = y = (1.348 X gestational age)−12.265), where gestational ages is in weeks. A high correlation was found between fetal ear length and gestational age, and a significant correlation was also found between fetal ear length and the biparietal diameter (r=0.962; p<0.001). Similar correlations were found between fetal ear length and head circumference, and fetal ear length and femur length. Conclusion: The results of this study provide a nomogram for fetal ear length. The study also demonstrates the relationship between ear length and other biometric measurements. PMID:25667783

Simple linear and multivariate regression models.

PubMed

Rodríguez del Águila, M M; Benítez-Parejo, N

2011-01-01

In biomedical research it is common to find problems in which we wish to relate a response variable to one or more variables capable of describing the behaviour of the former variable by means of mathematical models. Regression techniques are used to this effect, in which an equation is determined relating the two variables. While such equations can have different forms, linear equations are the most widely used form and are easy to interpret. The present article describes simple and multiple linear regression models, how they are calculated, and how their applicability assumptions are checked. Illustrative examples are provided, based on the use of the freely accessible R program. Copyright © 2011 SEICAP. Published by Elsevier Espana. All rights reserved.

Exposure and effects of perfluoroalkyl substances in tree swallows nesting in Minnesota and Wisconsin, USA

USGS Publications Warehouse

Custer, Christine M.; Custer, Thomas W.; Dummer, Paul; Etterson, Matthew A.; Thogmartin, Wayne E.; Wu, Qian; Kannan, Kurunthachalam; Trowbridge, Annette; McKann, Patrick C.

2013-01-01

The exposure and effects of perfluoroalkyl substances (PFASs) were studied at eight locations in Minnesota and Wisconsin between 2007 and 2011 using tree swallows (Tachycineta bicolor). Concentrations of PFASs were quantified as were reproductive success end points. The sample egg method was used wherein an egg sample is collected, and the hatching success of the remaining eggs in the nest is assessed. The association between PFAS exposure and reproductive success was assessed by site comparisons, logistic regression analysis, and multistate modeling, a technique not previously used in this context. There was a negative association between concentrations of perfluorooctane sulfonate (PFOS) in eggs and hatching success. The concentration at which effects became evident (150–200 ng/g wet weight) was far lower than effect levels found in laboratory feeding trials or egg-injection studies of other avian species. This discrepancy was likely because behavioral effects and other extrinsic factors are not accounted for in these laboratory studies and the possibility that tree swallows are unusually sensitive to PFASs. The results from multistate modeling and simple logistic regression analyses were nearly identical. Multistate modeling provides a better method to examine possible effects of additional covariates and assessment of models using Akaike information criteria analyses. There was a credible association between PFOS concentrations in plasma and eggs, so extrapolation between these two commonly sampled tissues can be performed.

Changes in the timing of snowmelt and streamflow in Colorado: A response to recent warming

USGS Publications Warehouse

Clow, David W.

2010-01-01

Trends in the timing of snowmelt and associated runoff in Colorado were evaluated for the 1978-2007 water years using the regional Kendall test (RKT) on daily snow-water equivalent (SWE) data from snowpack telemetry (SNOTEL) sites and daily streamflow data from headwater streams. The RKT is a robust, nonparametric test that provides an increased power of trend detection by grouping data from multiple sites within a given geographic region. The RKT analyses indicated strong, pervasive trends in snowmelt and streamflow timing, which have shifted toward earlier in the year by a median of 2-3 weeks over the 29-yr study period. In contrast, relatively few statistically significant trends were detected using simple linear regression. RKT analyses also indicated that November-May air temperatures increased by a median of 0.9 degrees C decade-1, while 1 April SWE and maximum SWE declined by a median of 4.1 and 3.6 cm decade-1, respectively. Multiple linear regression models were created, using monthly air temperatures, snowfall, latitude, and elevation as explanatory variables to identify major controlling factors on snowmelt timing. The models accounted for 45% of the variance in snowmelt onset, and 78% of the variance in the snowmelt center of mass (when half the snowpack had melted). Variations in springtime air temperature and SWE explained most of the interannual variability in snowmelt timing. Regression coefficients for air temperature were negative, indicating that warm temperatures promote early melt. Regression coefficients for SWE, latitude, and elevation were positive, indicating that abundant snowfall tends to delay snowmelt, and snowmelt tends to occur later at northern latitudes and high elevations. Results from this study indicate that even the mountains of Colorado, with their high elevations and cold snowpacks, are experiencing substantial shifts in the timing of snowmelt and snowmelt runoff toward earlier in the year.

Assessment of the spatial scaling behaviour of floods in the United Kingdom

NASA Astrophysics Data System (ADS)

Formetta, Giuseppe; Stewart, Elizabeth; Bell, Victoria

2017-04-01

Floods are among the most dangerous natural hazards, causing loss of life and significant damage to private and public property. Regional flood-frequency analysis (FFA) methods are essential tools to assess the flood hazard and plan interventions for its mitigation. FFA methods are often based on the well-known index flood method that assumes the invariance of the coefficient of variation of floods with drainage area. This assumption is equivalent to the simple scaling or self-similarity assumption for peak floods, i.e. their spatial structure remains similar in a particular, relatively simple, way to itself over a range of scales. Spatial scaling of floods has been evaluated at national scale for different countries such as Canada, USA, and Australia. According our knowledge. Such a study has not been conducted for the United Kingdom even though the standard FFA method there is based on the index flood assumption. In this work we present an integrated approach to assess of the spatial scaling behaviour of floods in the United Kingdom using three different methods: product moments (PM), probability weighted moments (PWM), and quantile analysis (QA). We analyse both instantaneous and daily annual observed maximum floods and performed our analysis both across the entire country and in its sub-climatic regions as defined in the Flood Studies Report (NERC, 1975). To evaluate the relationship between the k-th moments or quantiles and the drainage area we used both regression with area alone and multiple regression considering other explanatory variables to account for the geomorphology, amount of rainfall, and soil type of the catchments. The latter multiple regression approach was only recently demonstrated being more robust than the traditional regression with area alone that can lead to biased estimates of scaling exponents and misinterpretation of spatial scaling behaviour. We tested our framework on almost 600 rural catchments in UK considered as entire region and split in 11 sub-regions with 50 catchments per region on average. Preliminary results from the three different spatial scaling methods are generally in agreement and indicate that: i) only some of the peak flow variability is explained by area alone (approximately 50% for the entire country and ranging between the 40% and 70% for the sub-regions); ii) this percentage increases to 90% for the entire country and ranges between 80% and 95% for the sub-regions when the multiple regression is used; iii) the simple scaling hypothesis holds in all sub-regions with the exception of weak multi-scaling found in the regions 2 (North), and 5 and 6 (South East). We hypothesize that these deviations can be explained by heterogeneity in large scale precipitation and by the influence of the soil type (predominantly chalk) on the flood formation process in regions 5 and 6.

Purkinje cells signal hand shape and grasp force during reach-to-grasp in the monkey.

PubMed

Mason, Carolyn R; Hendrix, Claudia M; Ebner, Timothy J

2006-01-01

The cerebellar cortex and nuclei play important roles in the learning, planning, and execution of reach-to-grasp and prehensile movements. However, few studies have investigated the signals carried by cerebellar neurons during reach-to-grasp, particularly signals relating to target object properties, hand shape, and grasp force. In this study, the simple spike discharge of 77 Purkinje cells was recorded as two rhesus monkeys reached and grasped 16 objects. The objects varied systematically in volume, shape, and orientation and each was grasped at five different force levels. Linear multiple regression analyses showed the simple spike discharge was significantly modulated in relation to objects and force levels. Object related modulation occurred preferentially during reach or early in the grasp and was linearly related to grasp aperture. The simple spike discharge was positively correlated with grasp force during both the reach and the grasp. There was no significant interaction between object and grasp force modulation, supporting previous kinematic findings that grasp kinematics and force are signaled independently. Singular value decomposition (SVD) was used to quantify the temporal patterns in the simple spike discharge. Most cells had a predominant discharge pattern that remained relatively constant across object grasp dimensions and force levels. A single predominant simple spike discharge pattern that spans reach and grasp and accounts for most of the variation (>60%) is consistent with the concept that the cerebellum is involved with synergies underlying prehension. Therefore Purkinje cells are involved with the signaling of prehension, providing independent signals for hand shaping and grasp force.

Estimating population diversity with CatchAll

PubMed Central

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

2012-01-01

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

The influence of nondisclosure on the mental health of urban African-American adolescents exposed to community violence.

PubMed

Dinizulu, Sonya Mathies; Grant, Kathryn E; McIntosh, Jeanne M

2014-01-01

African-American youth residing in urban poverty have been shown to be at increased risk for exposure to violence and internalizing symptoms, but there has been little investigation of moderating processes that might attenuate or exacerbate this association. The current study examined nondisclosure as a possible moderator of the association between community violence and internalizing symptoms with a sample of 152 low-income urban African-American early adolescents using hierarchical regression analyses. Results revealed that nondisclosure for relationship reasons (e.g., adults could not be trusted to provide needed support) moderated the association between exposure to community violence and internalizing symptoms. Unexpectedly, however, results of simple effects analyses revealed a stronger association between exposure to violence and internalizing symptoms for youth who disclosed more to adults. Although unexpected, this pattern builds upon prior research indicating that adult-child relationships are compromised within the context of urban poverty and that protective factors may lose their power under conditions of extreme stress.

Correction of body-mass index using body-shape perception and socioeconomic status in adolescent self-report surveys.

PubMed

Legleye, Stéphane; Beck, François; Spilka, Stanislas; Chau, Nearkasen

2014-01-01

To propose a simple correction of body-mass index (BMI) based on self-reported weight and height (reported BMI) using gender, body shape perception and socioeconomic status in an adolescent population. 341 boys and girls aged 17-18 years were randomly selected from a representative sample of 2165 French adolescents living in Paris surveyed in 2010. After an anonymous self-administered pen-and-paper questionnaire asking for height, weight, body shape perception (feeling too thin, about the right weight or too fat) and socioeconomic status, subjects were measured and weighed. BMI categories were computed according to Cole's cut-offs. Reported BMIs were corrected using linear regressions and ROC analyses and checked with cross-validation and multiple imputations to handle missing values. Agreement between actual and corrected BMI values was estimated with Kappa indexes and Intraclass correlation coefficients (ICC). On average, BMIs were underreported, especially among girls. Kappa indexes between actual and reported BMI were low, especially for girls: 0.56 95%CI = [0.42-0.70] for boys and 0.45 95%CI = [0.30-0.60] for girls. The regression of reported BMI by gender and body shape perception gave the most balanced results for both genders: the Kappa and ICC obtained were 0.63 95%CI = [0.50-0.76] and 0.67, 95%CI = [0.58-0.74] for boys; 0.65 95%CI = [0.52-0.78] and 0.74, 95%CI = [0.66-0.81] for girls. The regression of reported BMI by gender and socioeconomic status led to similar corrections while the ROC analyses were inaccurate. Using body shape perception, or socioeconomic status and gender is a promising way of correcting BMI in self-administered questionnaires, especially for girls.

Quantification of trace metals in infant formula premixes using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Cama-Moncunill, Raquel; Casado-Gavalda, Maria P.; Cama-Moncunill, Xavier; Markiewicz-Keszycka, Maria; Dixit, Yash; Cullen, Patrick J.; Sullivan, Carl

2017-09-01

Infant formula is a human milk substitute generally based upon fortified cow milk components. In order to mimic the composition of breast milk, trace elements such as copper, iron and zinc are usually added in a single operation using a premix. The correct addition of premixes must be verified to ensure that the target levels in infant formulae are achieved. In this study, a laser-induced breakdown spectroscopy (LIBS) system was assessed as a fast validation tool for trace element premixes. LIBS is a promising emission spectroscopic technique for elemental analysis, which offers real-time analyses, little to no sample preparation and ease of use. LIBS was employed for copper and iron determinations of premix samples ranging approximately from 0 to 120 mg/kg Cu/1640 mg/kg Fe. LIBS spectra are affected by several parameters, hindering subsequent quantitative analyses. This work aimed at testing three matrix-matched calibration approaches (simple-linear regression, multi-linear regression and partial least squares regression (PLS)) as means for precision and accuracy enhancement of LIBS quantitative analysis. All calibration models were first developed using a training set and then validated with an independent test set. PLS yielded the best results. For instance, the PLS model for copper provided a coefficient of determination (R2) of 0.995 and a root mean square error of prediction (RMSEP) of 14 mg/kg. Furthermore, LIBS was employed to penetrate through the samples by repetitively measuring the same spot. Consequently, LIBS spectra can be obtained as a function of sample layers. This information was used to explore whether measuring deeper into the sample could reduce possible surface-contaminant effects and provide better quantifications.

A Primer on Logistic Regression.

ERIC Educational Resources Information Center

Woldbeck, Tanya

This paper introduces logistic regression as a viable alternative when the researcher is faced with variables that are not continuous. If one is to use simple regression, the dependent variable must be measured on a continuous scale. In the behavioral sciences, it may not always be appropriate or possible to have a measured dependent variable on a…

Testing hypotheses for differences between linear regression lines

Treesearch

Stanley J. Zarnoch

2009-01-01

Five hypotheses are identified for testing differences between simple linear regression lines. The distinctions between these hypotheses are based on a priori assumptions and illustrated with full and reduced models. The contrast approach is presented as an easy and complete method for testing for overall differences between the regressions and for making pairwise...

Simple and multiple linear regression: sample size considerations.

PubMed

Hanley, James A

2016-11-01

The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright © 2016 Elsevier Inc. All rights reserved.

Genetic analyses of protein yield in dairy cows applying random regression models with time-dependent and temperature x humidity-dependent covariates.

PubMed

Brügemann, K; Gernand, E; von Borstel, U U; König, S

2011-08-01

Data used in the present study included 1,095,980 first-lactation test-day records for protein yield of 154,880 Holstein cows housed on 196 large-scale dairy farms in Germany. Data were recorded between 2002 and 2009 and merged with meteorological data from public weather stations. The maximum distance between each farm and its corresponding weather station was 50 km. Hourly temperature-humidity indexes (THI) were calculated using the mean of hourly measurements of dry bulb temperature and relative humidity. On the phenotypic scale, an increase in THI was generally associated with a decrease in daily protein yield. For genetic analyses, a random regression model was applied using time-dependent (d in milk, DIM) and THI-dependent covariates. Additive genetic and permanent environmental effects were fitted with this random regression model and Legendre polynomials of order 3 for DIM and THI. In addition, the fixed curve was modeled with Legendre polynomials of order 3. Heterogeneous residuals were fitted by dividing DIM into 5 classes, and by dividing THI into 4 classes, resulting in 20 different classes. Additive genetic variances for daily protein yield decreased with increasing degrees of heat stress and were lowest at the beginning of lactation and at extreme THI. Due to higher additive genetic variances, slightly higher permanent environment variances, and similar residual variances, heritabilities were highest for low THI in combination with DIM at the end of lactation. Genetic correlations among individual values for THI were generally >0.90. These trends from the complex random regression model were verified by applying relatively simple bivariate animal models for protein yield measured in 2 THI environments; that is, defining a THI value of 60 as a threshold. These high correlations indicate the absence of any substantial genotype × environment interaction for protein yield. However, heritabilities and additive genetic variances from the random regression model tended to be slightly higher in the THI range corresponding to cows' comfort zone. Selecting such superior environments for progeny testing can contribute to an accurate genetic differentiation among selection candidates. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Bayesian feature selection for high-dimensional linear regression via the Ising approximation with applications to genomics.

PubMed

Fisher, Charles K; Mehta, Pankaj

2015-06-01

Feature selection, identifying a subset of variables that are relevant for predicting a response, is an important and challenging component of many methods in statistics and machine learning. Feature selection is especially difficult and computationally intensive when the number of variables approaches or exceeds the number of samples, as is often the case for many genomic datasets. Here, we introduce a new approach--the Bayesian Ising Approximation (BIA)-to rapidly calculate posterior probabilities for feature relevance in L2 penalized linear regression. In the regime where the regression problem is strongly regularized by the prior, we show that computing the marginal posterior probabilities for features is equivalent to computing the magnetizations of an Ising model with weak couplings. Using a mean field approximation, we show it is possible to rapidly compute the feature selection path described by the posterior probabilities as a function of the L2 penalty. We present simulations and analytical results illustrating the accuracy of the BIA on some simple regression problems. Finally, we demonstrate the applicability of the BIA to high-dimensional regression by analyzing a gene expression dataset with nearly 30 000 features. These results also highlight the impact of correlations between features on Bayesian feature selection. An implementation of the BIA in C++, along with data for reproducing our gene expression analyses, are freely available at http://physics.bu.edu/∼pankajm/BIACode. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Linking land cover and water quality in New York City's water supply watersheds.

PubMed

Mehaffey, M H; Nash, M S; Wade, T G; Ebert, D W; Jones, K B; Rager, A

2005-08-01

The Catskill/Delaware reservoirs supply 90% of New York City's drinking water. The City has implemented a series of watershed protection measures, including land acquisition, aimed at preserving water quality in the Catskill/Delaware watersheds. The objective of this study was to examine how relationships between landscape and surface water measurements change between years. Thirty-two drainage areas delineated from surface water sample points (total nitrogen, total phosphorus, and fecal coliform bacteria concentrations) were used in step-wise regression analyses to test landscape and surface-water quality relationships. Two measurements of land use, percent agriculture and percent urban development, were positively related to water quality and consistently present in all regression models. Together these two land uses explained 25 to 75% of the regression model variation. However, the contribution of agriculture to water quality condition showed a decreasing trend with time as overall agricultural land cover decreased. Results from this study demonstrate that relationships between land cover and surface water concentrations of total nitrogen, total phosphorus, and fecal coliform bacteria counts over a large area can be evaluated using a relatively simple geographic information system method. Land managers may find this method useful for targeting resources in relation to a particular water quality concern, focusing best management efforts, and maximizing benefits to water quality with minimal costs.

Identification of molecular markers associated with mite resistance in coconut (Cocos nucifera L.).

PubMed

Shalini, K V; Manjunatha, S; Lebrun, P; Berger, A; Baudouin, L; Pirany, N; Ranganath, R M; Prasad, D Theertha

2007-01-01

Coconut mite (Aceria guerreronis 'Keifer') has become a major threat to Indian coconut (Coçcos nucifera L.) cultivators and the processing industry. Chemical and biological control measures have proved to be costly, ineffective, and ecologically undesirable. Planting mite-resistant coconut cultivars is the most effective method of preventing yield loss and should form a major component of any integrated pest management stratagem. Coconut genotypes, and mite-resistant and -susceptible accessions were collected from different parts of South India. Thirty-two simple sequence repeat (SSR) and 7 RAPD primers were used for molecular analyses. In single-marker analysis, 9 SSR and 4 RAPD markers associated with mite resistance were identified. In stepwise multiple regression analysis of SSRs, a combination of 6 markers showed 100% association with mite infestation. Stepwise multiple regression analysis for RAPD data revealed that a combination of 3 markers accounted for 83.86% of mite resistance in the selected materials. Combined stepwise multiple regression analysis of RAPD and SSR data showed that a combination of 5 markers explained 100% of the association with mite resistance in coconut. Markers associated with mite resistance are important in coconut breeding programs and will facilitate the selection of mite-resistant plants at an early stage as well as mother plants for breeding programs.

Advantage of multiple spot urine collections for estimating daily sodium excretion: comparison with two 24-h urine collections as reference.

PubMed

Uechi, Ken; Asakura, Keiko; Ri, Yui; Masayasu, Shizuko; Sasaki, Satoshi

2016-02-01

Several estimation methods for 24-h sodium excretion using spot urine sample have been reported, but accurate estimation at the individual level remains difficult. We aimed to clarify the most accurate method of estimating 24-h sodium excretion with different numbers of available spot urine samples. A total of 370 participants from throughout Japan collected multiple 24-h urine and spot urine samples independently. Participants were allocated randomly into a development and a validation dataset. Two estimation methods were established in the development dataset using the two 24-h sodium excretion samples as reference: the 'simple mean method' estimated by multiplying the sodium-creatinine ratio by predicted 24-h creatinine excretion, whereas the 'regression method' employed linear regression analysis. The accuracy of the two methods was examined by comparing the estimated means and concordance correlation coefficients (CCC) in the validation dataset. Mean sodium excretion by the simple mean method with three spot urine samples was closest to that by 24-h collection (difference: -1.62  mmol/day). CCC with the simple mean method increased with an increased number of spot urine samples at 0.20, 0.31, and 0.42 using one, two, and three samples, respectively. This method with three spot urine samples yielded higher CCC than the regression method (0.40). When only one spot urine sample was available for each study participant, CCC was higher with the regression method (0.36). The simple mean method with three spot urine samples yielded the most accurate estimates of sodium excretion. When only one spot urine sample was available, the regression method was preferable.

Firewood, smoke and respiratory diseases in developing countries—The neglected role of outdoor cooking

PubMed Central

2017-01-01

Smoke from cooking in the kitchen is one of the world’s leading causes of premature child death, claiming the lives of 500,000 children under five annually. This study analyses the role of outdoor cooking and the prevalence of respiratory diseases among children under five years by means of probit regressions using information from 41 surveys conducted in 30 developing countries from Asia, Africa and Latin America. I find that outdoor cooking reduces respiratory diseases among young children aged 0-4 by around 9 percent, an effect that reaches 13 percent among children aged 0-1. The results suggest that simple behavioral interventions, such as promoting outdoor cooking, can have a substantial impact on health hazards. PMID:28658290

The application of dimensional analysis to the problem of solar wind-magnetosphere energy coupling

NASA Technical Reports Server (NTRS)

Bargatze, L. F.; Mcpherron, R. L.; Baker, D. N.; Hones, E. W., Jr.

1984-01-01

The constraints imposed by dimensional analysis are used to find how the solar wind-magnetosphere energy transfer rate depends upon interplanetary parameters. The analyses assume that only magnetohydrodynamic processes are important in controlling the rate of energy transfer. The study utilizes ISEE-3 solar wind observations, the AE index, and UT from three 10-day intervals during the International Magnetospheric Study. Simple linear regression and histogram techniques are used to find the value of the magnetohydrodynamic coupling exponent, alpha, which is consistent with observations of magnetospheric response. Once alpha is estimated, the form of the solar wind energy transfer rate is obtained by substitution into an equation of the interplanetary variables whose exponents depend upon alpha.

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

PubMed

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

2015-12-01

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

Simple vs. Complex Carbohydrate Dietary Patterns and the Global Overweight and Obesity Pandemic.

PubMed

Ferretti, Fabrizio; Mariani, Michele

2017-10-04

Nowadays, obesity and being overweight are among the major global health concerns. Many, diet-related diseases impose high tangible and intangible costs, and threaten the sustainability of health-care systems worldwide. In this study, we model, at the macroeconomic level, the impact of energy intake from different types of carbohydrates on the population's BMI (body mass index). We proceed in three steps. First, we develop a framework to analyse both the consumption choices between simple and complex carbohydrates and the effects of these choices on people health conditions. Second, we collect figures for 185 countries (over the period 2012-2014) regarding the shares of simple (sugar and sweetener) and complex (cereal) carbohydrates in each country's total dietary energy supply. Third, we use regression techniques to: (1) estimate the impact of these shares on the country's prevalence of obesity and being overweight; (2) compute for each country an indicator of dietary pattern based on the ratio between simple and complex carbohydrates, weighted by their estimated effects on the prevalence of obesity and being overweight; and (3) measure the elasticity of the prevalence of obesity and being overweight with respect to changes in both carbohydrate dietary pattern and income per capita. We find that unhealthy eating habits and the associated prevalence of excessive body fat accumulation tend to behave as a 'normal good' in low, medium- and high-HDI (Human Development Index) countries, but as an 'inferior good' in very high-HDI countries.

Deriving the Regression Equation without Using Calculus

ERIC Educational Resources Information Center

Gordon, Sheldon P.; Gordon, Florence S.

2004-01-01

Probably the one "new" mathematical topic that is most responsible for modernizing courses in college algebra and precalculus over the last few years is the idea of fitting a function to a set of data in the sense of a least squares fit. Whether it be simple linear regression or nonlinear regression, this topic opens the door to applying the…

Sampling and sensitivity analyses tools (SaSAT) for computational modelling

PubMed Central

Hoare, Alexander; Regan, David G; Wilson, David P

2008-01-01

SaSAT (Sampling and Sensitivity Analysis Tools) is a user-friendly software package for applying uncertainty and sensitivity analyses to mathematical and computational models of arbitrary complexity and context. The toolbox is built in Matlab®, a numerical mathematical software package, and utilises algorithms contained in the Matlab® Statistics Toolbox. However, Matlab® is not required to use SaSAT as the software package is provided as an executable file with all the necessary supplementary files. The SaSAT package is also designed to work seamlessly with Microsoft Excel but no functionality is forfeited if that software is not available. A comprehensive suite of tools is provided to enable the following tasks to be easily performed: efficient and equitable sampling of parameter space by various methodologies; calculation of correlation coefficients; regression analysis; factor prioritisation; and graphical output of results, including response surfaces, tornado plots, and scatterplots. Use of SaSAT is exemplified by application to a simple epidemic model. To our knowledge, a number of the methods available in SaSAT for performing sensitivity analyses have not previously been used in epidemiological modelling and their usefulness in this context is demonstrated. PMID:18304361

Accuracy of clinical coding from 1210 appendicectomies in a British district general hospital.

PubMed

Bhangu, Aneel; Nepogodiev, Dmitri; Taylor, Caroline; Durkin, Natalie; Patel, Rajan

2012-01-01

The primary aim of this study was to assess the accuracy of clinical coding in identifying negative appendicectomies. The secondary aim was to analyse trends over time in rates of simple, complex (gangrenous or perforated) and negative appendicectomies. Retrospective review of 1210 patients undergoing emergency appendicectomy during a five year period (2006-2010). Histopathology reports were taken as gold standard for diagnosis and compared to clinical coding lists. Clinical coding is the process by which non-medical administrators apply standardised diagnostic codes to patients, based upon clinical notes at discharge. These codes then contribute to national databases. Statistical analysis included correlation studies and regression analyses. Clinical coding had only moderate correlation with histopathology, with an overall kappa of 0.421. Annual kappa values varied between 0.378 and 0.500. Overall 14% of patients were incorrectly coded as having had appendicitis when in fact they had a histopathologically normal appendix (153/1107), whereas 4% were falsely coded as having received a negative appendicectomy when they had appendicitis (48/1107). There was an overall significant fall and then rise in the rate of simple appendicitis (B coefficient -0.239 (95% confidence interval -0.426, -0.051), p = 0.014) but no change in the rate of complex appendicitis (B coefficient 0.008 (-0.015, 0.031), p = 0.476). Clinical coding for negative appendicectomy was unreliable. Negative rates may be higher than suspected. This has implications for the validity of national database analyses. Using this form of data as a quality indictor for appendicitis should be reconsidered until its quality is improved. Copyright © 2012 Surgical Associates Ltd. Published by Elsevier Ltd. 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.

Domestic Violence, Unwanted Pregnancy and Pregnancy Termination among Urban Women of Bangladesh

PubMed Central

2013-01-01

Objective This paper explores the relationship between domestic violence against women inflicted by husbands, unwanted pregnancy and pregnancy termination of Bangladeshi urban women. Materials and methods The study used the nationally representative 2007 Bangladesh Demographic and Health Survey (BDHS) data. The BDHS covered a representative sample of 10,996 ever married women from rural and urban areas. The BDHS used a separate module to collect information from women regarding domestic violence. The survey gathered information of domestic violence from 1,013 urban women which are the basis of the study. Simple cross tabulation, bivariate and multivariate statistical analyses were performed to analyzing data. Results Overall, the lifetime prevalence of domestic violence was 47.5%. Of the most recent pregnancies, 15.6% were unwanted and 16.0% of the women terminated pregnancy in their marital life. The multivariate binary logistic regression analyses yielded quantitatively important and reliable estimate of unwanted pregnancy and pregnancy termination. The regression analyses yielded significantly (p < 0.05) increased risk of unwanted pregnancy only for physical violence (OR = 2.35, 95% CI = 1.28-4.32) and for both physical and sexual violence (OR = 2.27, 95% CI = 1.02-5.28), and higher risk of pregnancy termination for only physical violence (OR = 1.41, 95% CI = 0.95-2.10) and for both physical and sexual violence (OR = 1.81, 95% CI = 1.07-3.04) than women who were never abused. Current age, higher parity and early marriage are also important determinants of unwanted pregnancy and pregnancy termination. Conclusion Violence against women inflicted by husbands is commonplace in Bangladesh. Any strategy to reduce the burden of unwanted pregnancy and induced abortion should include prevention of violence against women and strengthening women's sexual and reproductive health. PMID:24971097

Postoperative liver volume was accurately predicted by a medical image three dimensional visualization system in hepatectomy for liver cancer.

PubMed

Cai, Wei; Fan, Yingfang; Hu, Haoyu; Xiang, Nan; Fang, Chihua; Jia, Fucang

2017-06-01

Liver cancer is the second most common cause of cancer death worldwide. The hepatectomy is the most effective and the only potentially curative treatment for patients with resectable neoplasm. Precisely preoperative assessment of remnant liver volume is essential in preventing postoperative liver failure. The aim of our study is to report our experience of using a medical image three dimensional (3D) visualization system (MI-3DVS), which was developed by our team, in assisting hepatectomy for patients with liver cancer. Between January 2010 and June 2016, 69 patients with liver cancer underwent hepatic resection based on the MI-3DVS were enrolled in this study. All patients underwent CT scan 5 days before the surgery and within 5 days after resection. CT images were reconstructed with the MI-3DVS to assist to perform hepatectomy. Simple linear regression, intra-class correlation coefficient (ICC) and Bland-Altman analysis were used to evaluate the relationship and agreement between actual excisional liver volume (AELV) and predicted excisional liver volume (PELV). Among 69 patients in this study, 62(89.85%) of them were diagnosed with hepatocellular carcinoma by histopathologic examination, and 41(59.42%) underwent major hepatectomy. The average AELV was 330.13 cm 3 and the average PELV was 287.67 cm 3 . The simple regression equation is AELV = 1.016 × PELV+30.39(r = 0.966; p < 0.0003). PELV (ICC = 0.964) achieved an excellent agreement with AELV with statistical significance (p < 0.001). 65 of 69 dots are in the range of 95% confidence interval in Bland-Altman analyses. The MI-3DVS has advantages of simple usage and convenient hold. It is accurate in assessment of postoperative liver volume and improve safety in liver resection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Wastewater-Based Epidemiology of Stimulant Drugs: Functional Data Analysis Compared to Traditional Statistical Methods.

PubMed

Salvatore, Stefania; Bramness, Jørgen Gustav; Reid, Malcolm J; Thomas, Kevin Victor; Harman, Christopher; Røislien, Jo

2015-01-01

Wastewater-based epidemiology (WBE) is a new methodology for estimating the drug load in a population. Simple summary statistics and specification tests have typically been used to analyze WBE data, comparing differences between weekday and weekend loads. Such standard statistical methods may, however, overlook important nuanced information in the data. In this study, we apply functional data analysis (FDA) to WBE data and compare the results to those obtained from more traditional summary measures. We analysed temporal WBE data from 42 European cities, using sewage samples collected daily for one week in March 2013. For each city, the main temporal features of two selected drugs were extracted using functional principal component (FPC) analysis, along with simpler measures such as the area under the curve (AUC). The individual cities' scores on each of the temporal FPCs were then used as outcome variables in multiple linear regression analysis with various city and country characteristics as predictors. The results were compared to those of functional analysis of variance (FANOVA). The three first FPCs explained more than 99% of the temporal variation. The first component (FPC1) represented the level of the drug load, while the second and third temporal components represented the level and the timing of a weekend peak. AUC was highly correlated with FPC1, but other temporal characteristic were not captured by the simple summary measures. FANOVA was less flexible than the FPCA-based regression, and even showed concordance results. Geographical location was the main predictor for the general level of the drug load. FDA of WBE data extracts more detailed information about drug load patterns during the week which are not identified by more traditional statistical methods. Results also suggest that regression based on FPC results is a valuable addition to FANOVA for estimating associations between temporal patterns and covariate information.

Cuttability Assessment of Selected Rocks Through Different Brittleness Values

NASA Astrophysics Data System (ADS)

Dursun, Arif Emre; Gokay, M. Kemal

2016-04-01

Prediction of cuttability is a critical issue for successful execution of tunnel or mining excavation projects. Rock cuttability is also used to determine specific energy, which is defined as the work done by the cutting force to excavate a unit volume of yield. Specific energy is a meaningful inverse measure of cutting efficiency, since it simply states how much energy must be expended to excavate a unit volume of rock. Brittleness is a fundamental rock property and applied in drilling and rock excavation. Brittleness is one of the most crucial rock features for rock excavation. For this reason, determination of relations between cuttability and brittleness will help rock engineers. This study aims to estimate the specific energy from different brittleness values of rocks by means of simple and multiple regression analyses. In this study, rock cutting, rock property, and brittleness index tests were carried out on 24 different rock samples with different strength values, including marble, travertine, and tuff, collected from sites around Konya Province, Turkey. Four previously used brittleness concepts were evaluated in this study, denoted as B 1 (ratio of compressive to tensile strength), B 2 (ratio of the difference between compressive and tensile strength to the sum of compressive and tensile strength), B 3 (area under the stress-strain line in relation to compressive and tensile strength), and B 9 = S 20, the percentage of fines (<11.2 mm) formed in an impact test for the Norwegian University of Science and Technology (NTNU) model as well as B 9p (B 9 as predicted from uniaxial compressive, Brazilian tensile, and point load strengths of rocks using multiple regression analysis). The results suggest that the proposed simple regression-based prediction models including B 3, B 9, and B 9p outperform the other models including B 1 and B 2 and can be used for more accurate and reliable estimation of specific energy.

Statistical Approaches for Spatiotemporal Prediction of Low Flows

NASA Astrophysics Data System (ADS)

Fangmann, A.; Haberlandt, U.

2017-12-01

An adequate assessment of regional climate change impacts on streamflow requires the integration of various sources of information and modeling approaches. This study proposes simple statistical tools for inclusion into model ensembles, which are fast and straightforward in their application, yet able to yield accurate streamflow predictions in time and space. Target variables for all approaches are annual low flow indices derived from a data set of 51 records of average daily discharge for northwestern Germany. The models require input of climatic data in the form of meteorological drought indices, derived from observed daily climatic variables, averaged over the streamflow gauges' catchments areas. Four different modeling approaches are analyzed. Basis for all pose multiple linear regression models that estimate low flows as a function of a set of meteorological indices and/or physiographic and climatic catchment descriptors. For the first method, individual regression models are fitted at each station, predicting annual low flow values from a set of annual meteorological indices, which are subsequently regionalized using a set of catchment characteristics. The second method combines temporal and spatial prediction within a single panel data regression model, allowing estimation of annual low flow values from input of both annual meteorological indices and catchment descriptors. The third and fourth methods represent non-stationary low flow frequency analyses and require fitting of regional distribution functions. Method three is subject to a spatiotemporal prediction of an index value, method four to estimation of L-moments that adapt the regional frequency distribution to the at-site conditions. The results show that method two outperforms successive prediction in time and space. Method three also shows a high performance in the near future period, but since it relies on a stationary distribution, its application for prediction of far future changes may be problematic. Spatiotemporal prediction of L-moments appeared highly uncertain for higher-order moments resulting in unrealistic future low flow values. All in all, the results promote an inclusion of simple statistical methods in climate change impact assessment.

Proposal of a Clinical Decision Tree Algorithm Using Factors Associated with Severe Dengue Infection.

PubMed

Tamibmaniam, Jayashamani; Hussin, Narwani; Cheah, Wee Kooi; Ng, Kee Sing; Muninathan, Prema

2016-01-01

WHO's new classification in 2009: dengue with or without warning signs and severe dengue, has necessitated large numbers of admissions to hospitals of dengue patients which in turn has been imposing a huge economical and physical burden on many hospitals around the globe, particularly South East Asia and Malaysia where the disease has seen a rapid surge in numbers in recent years. Lack of a simple tool to differentiate mild from life threatening infection has led to unnecessary hospitalization of dengue patients. We conducted a single-centre, retrospective study involving serologically confirmed dengue fever patients, admitted in a single ward, in Hospital Kuala Lumpur, Malaysia. Data was collected for 4 months from February to May 2014. Socio demography, co-morbidity, days of illness before admission, symptoms, warning signs, vital signs and laboratory result were all recorded. Descriptive statistics was tabulated and simple and multiple logistic regression analysis was done to determine significant risk factors associated with severe dengue. 657 patients with confirmed dengue were analysed, of which 59 (9.0%) had severe dengue. Overall, the commonest warning sign were vomiting (36.1%) and abdominal pain (32.1%). Previous co-morbid, vomiting, diarrhoea, pleural effusion, low systolic blood pressure, high haematocrit, low albumin and high urea were found as significant risk factors for severe dengue using simple logistic regression. However the significant risk factors for severe dengue with multiple logistic regressions were only vomiting, pleural effusion, and low systolic blood pressure. Using those 3 risk factors, we plotted an algorithm for predicting severe dengue. When compared to the classification of severe dengue based on the WHO criteria, the decision tree algorithm had a sensitivity of 0.81, specificity of 0.54, positive predictive value of 0.16 and negative predictive of 0.96. The decision tree algorithm proposed in this study showed high sensitivity and NPV in predicting patients with severe dengue that may warrant admission. This tool upon further validation study can be used to help clinicians decide on further managing a patient upon first encounter. It also will have a substantial impact on health resources as low risk patients can be managed as outpatients hence reserving the scarce hospital beds and medical resources for other patients in need.

Detection of outliers in the response and explanatory variables of the simple circular regression model

NASA Astrophysics Data System (ADS)

Mahmood, Ehab A.; Rana, Sohel; Hussin, Abdul Ghapor; Midi, Habshah

2016-06-01

The circular regression model may contain one or more data points which appear to be peculiar or inconsistent with the main part of the model. This may be occur due to recording errors, sudden short events, sampling under abnormal conditions etc. The existence of these data points "outliers" in the data set cause lot of problems in the research results and the conclusions. Therefore, we should identify them before applying statistical analysis. In this article, we aim to propose a statistic to identify outliers in the both of the response and explanatory variables of the simple circular regression model. Our proposed statistic is robust circular distance RCDxy and it is justified by the three robust measurements such as proportion of detection outliers, masking and swamping rates.

Circulating CD34-Positive Cells Are Associated with Handgrip Strength in Japanese Older Men: The Nagasaki Islands Study.

PubMed

Yamanashi, H; Shimizu, Y; Koyamatsu, J; Nagayoshi, M; Kadota, K; Tamai, M; Maeda, T

2017-01-01

Handgrip strength is a simple measurement of overall muscular strength and is used to detect sarcopenia. It also predicts adverse events in later life. Many mechanisms of sarcopenia development have been reported. A hypertensive status impairs endothelial dysfunction, which might deteriorate skeletal muscle if vascular angiogenesis is not maintained. This study investigated muscle strength and circulating CD34-positive cells as a marker of vascular angiogenesis. Cross-sectional study. 262 male Japanese community dwellers aged 60 to 69 years. The participants' handgrip strength, medical history, and blood samples were taken. We stratified the participants by hypertensive status to investigate the association between handgrip strength and circulating CD34-positive cells according to hypertensive status. Pearson correlation and linear regression analyses were used. In the Pearson correlation analysis, handgrip strength and the logarithm of circulating CD34-positive cells were significantly associated in hypertensive participants (r=0.22, p=0.021), but not in non-hypertensive participants (r=-0.01, p=0.943). This relationship was only significant in hypertensive participants (ß=1.94, p=0.021) in the simple linear regression analysis, and it remained significant after adjusting for classic cardiovascular risk factors (ß=1.92, p=0.020). The relationship was not significant in non-hypertensive participants (ß=-0.09, p=0.903). We found a positive association between handgrip strength and circulating CD34-positive cells in hypertensive men. Vascular maintenance attributed by circulating CD34-positive cells is thought to be a background mechanism of this association after hypertension-induced vascular injury in skeletal muscle.

Temporal trends in sperm count: a systematic review and meta-regression analysis.

PubMed

Levine, Hagai; Jørgensen, Niels; Martino-Andrade, Anderson; Mendiola, Jaime; Weksler-Derri, Dan; Mindlis, Irina; Pinotti, Rachel; Swan, Shanna H

2017-11-01

Reported declines in sperm counts remain controversial today and recent trends are unknown. A definitive meta-analysis is critical given the predictive value of sperm count for fertility, morbidity and mortality. To provide a systematic review and meta-regression analysis of recent trends in sperm counts as measured by sperm concentration (SC) and total sperm count (TSC), and their modification by fertility and geographic group. PubMed/MEDLINE and EMBASE were searched for English language studies of human SC published in 1981-2013. Following a predefined protocol 7518 abstracts were screened and 2510 full articles reporting primary data on SC were reviewed. A total of 244 estimates of SC and TSC from 185 studies of 42 935 men who provided semen samples in 1973-2011 were extracted for meta-regression analysis, as well as information on years of sample collection and covariates [fertility group ('Unselected by fertility' versus 'Fertile'), geographic group ('Western', including North America, Europe Australia and New Zealand versus 'Other', including South America, Asia and Africa), age, ejaculation abstinence time, semen collection method, method of measuring SC and semen volume, exclusion criteria and indicators of completeness of covariate data]. The slopes of SC and TSC were estimated as functions of sample collection year using both simple linear regression and weighted meta-regression models and the latter were adjusted for pre-determined covariates and modification by fertility and geographic group. Assumptions were examined using multiple sensitivity analyses and nonlinear models. SC declined significantly between 1973 and 2011 (slope in unadjusted simple regression models -0.70 million/ml/year; 95% CI: -0.72 to -0.69; P < 0.001; slope in adjusted meta-regression models = -0.64; -1.06 to -0.22; P = 0.003). The slopes in the meta-regression model were modified by fertility (P for interaction = 0.064) and geographic group (P for interaction = 0.027). There was a significant decline in SC between 1973 and 2011 among Unselected Western (-1.38; -2.02 to -0.74; P < 0.001) and among Fertile Western (-0.68; -1.31 to -0.05; P = 0.033), while no significant trends were seen among Unselected Other and Fertile Other. Among Unselected Western studies, the mean SC declined, on average, 1.4% per year with an overall decline of 52.4% between 1973 and 2011. Trends for TSC and SC were similar, with a steep decline among Unselected Western (-5.33 million/year, -7.56 to -3.11; P < 0.001), corresponding to an average decline in mean TSC of 1.6% per year and overall decline of 59.3%. Results changed minimally in multiple sensitivity analyses, and there was no statistical support for the use of a nonlinear model. In a model restricted to data post-1995, the slope both for SC and TSC among Unselected Western was similar to that for the entire period (-2.06 million/ml, -3.38 to -0.74; P = 0.004 and -8.12 million, -13.73 to -2.51, P = 0.006, respectively). This comprehensive meta-regression analysis reports a significant decline in sperm counts (as measured by SC and TSC) between 1973 and 2011, driven by a 50-60% decline among men unselected by fertility from North America, Europe, Australia and New Zealand. Because of the significant public health implications of these results, research on the causes of this continuing decline is urgently needed. © The Author 2017. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com

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

PubMed

Marill, Keith A

2004-01-01

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

Bootstrap Methods: A Very Leisurely Look.

ERIC Educational Resources Information Center

Hinkle, Dennis E.; Winstead, Wayland H.

The Bootstrap method, a computer-intensive statistical method of estimation, is illustrated using a simple and efficient Statistical Analysis System (SAS) routine. The utility of the method for generating unknown parameters, including standard errors for simple statistics, regression coefficients, discriminant function coefficients, and factor…

Estimating the use of morphometric measurements from museum specimens for sex determination in Mountain Plovers (Charadrius montanus)

USGS Publications Warehouse

Iko, W.M.; Dinsmore, S.J.; Knopf, F.L.

2004-01-01

The Mountain Plover (Charadrius montanus) is a shorebird species endemic to the dry, terrestrial ecosystems of the Great Plains and southwestern United States. Breeding Bird Survey data suggest that Mountain Plover populations have declined by >60% in the last 30 years. A better understanding of the population dynamics of the Mountain Plover is important in determining future management goals for this species. However, this effort is hampered by the inability to determine the sex of Mountain Plovers accurately under field conditions. In an effort to develop a simple method for sexing plovers in the hand, we measured external morphometric characteristics from 190 museum specimens of adult Mountain Plovers in alternate (breeding) plumage. Logistic regression and discriminant function analyses were performed on 10 external morphometric measurements (lengths of unflattened wing chord, 10th primary, central rectrix, outer rectrix, total head length, exposed culmen, culmen, bill depth, bill width, and tarsus). The results of these analyses indicated that Mountain Plover sexes were similar for all measures except culmen length. However, further analysis determined that culmen length accurately predicted sex in less than two-thirds of the specimens, suggesting that this measure is a poor predictor of sex in Mountain Plovers. Structurally, Mountain Plovers appear to be nearly identical between the sexes, and other methods of sexing birds (e.g., plumage characteristics, behavioral observations, or molecular markers) should be further assessed for devising a simple method for sexing Mountain Plovers under field conditions.

The impact of workplace spirituality dimensions on organisational citizenship behaviour among nurses with the mediating effect of affective organisational commitment.

PubMed

Kazemipour, Farahnaz; Mohd Amin, Salmiah

2012-12-01

To investigate the relationship between workplace spirituality dimensions and organisational citizenship behaviour (OCB) among nurses through the mediating effect of affective organisational commitment. Nurses' OCB has been considered recently to improve the quality of services to patients and subsequently, their performance. As an influential attitude, affective organisational commitment has been recognized to influence OCB, and ultimately, organisational performance. Meanwhile, workplace spirituality is introduced as a new organisational behaviour concept to increase affective commitment influencing employees' OCB. The cross-sectional study and the respective data were collected with a questionnaire-based survey. The questionnaires were distributed to 305 nurses employed in four public and general Iranian hospitals. To analyse the data, descriptive statistics, Pearson coefficient, simple regression, multiple regression and path analyses were also conducted. The results indicated that workplace spirituality dimensions including meaningful work, a sense of community and an alignment with organisational values have a significant positive relationship with OCB. Moreover, affective organisational commitment mediated the impact of workplace spirituality on OCB. The concept of workplace spirituality through its dimensions predicts nurses' OCB, and affective organisational commitment partially mediated the relationship between workplace spirituality and OCB. Nurses' managers should consider the potentially positive influence of workplace spirituality on OCB and affective commitment among their nurses. With any plan to increase workplace spirituality, the respective managers can improve nurses' performance and would be of considerable importance in the healthcare system. © 2012 Blackwell Publishing Ltd.

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...

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.

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

PubMed

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

2015-10-14

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

Simple and robust diagnosis of early, small and AFP-negative primary hepatic carcinomas: an integrative approach of serum fluorescence and conventional blood tests.

PubMed

Wang, Ting; Zhang, Kun-He; Hu, Piao-Ping; Huang, Zeng-Yong; Zhang, Pan; Wan, Qin-Si; Huang, De-Qiang; Lv, Nong-Hua

2016-09-27

The diagnosis of early, small and alpha-fetoprotein (AFP)-negative primary hepatic carcinomas (PHCs) remains a significant challenge. We developed a simple and robust approach to noninvasively detect these PHCs. A rapid, high-throughput and single-tube method was firstly developed to measure serum autofluorescence and cell-free DNA (cfDNA)-related fluorescence using a real-time PCR system, and both types of serum fluorescence were measured and routine laboratory data were collected in 1229 subjects, including 353 PHC patients, 331 liver cirrhosis (LC) patients, 213 chronic hepatitis (CH) patients and 332 normal controls (NC). The results showed that fluorescence indicators of PHC differed from those of NC, CH and LC to various extents, and all of them were not associated with age, gender, or AFP level. The logistic regression models established with the fluorescence indicators alone and combined with AFP, hepatic function tests and blood cell analyses were valuable for distinguishing early, small, AFP-negative and all PHC from LC, CH, NC and all non-PHC, with areas under the receiver operating characteristic curves 0.857-0.993 and diagnostic accuracies 80.2-97.7%. Conclusively, serum autofluorescence and cfDNA-related fluorescence are able to be rapidly and simultaneously measured by our simple method and valuable for diagnosing early, small and AFP-negative PHCs, especially integrating with AFP and conventional blood tests.

A simple approach to power and sample size calculations in logistic regression and Cox regression models.

PubMed

Vaeth, Michael; Skovlund, Eva

2004-06-15

For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.

A Simple Introduction to Moving Least Squares and Local Regression Estimation

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

Garimella, Rao Veerabhadra

In this brief note, a highly simpli ed introduction to esimating functions over a set of particles is presented. The note starts from Global Least Squares tting, going on to Moving Least Squares estimation (MLS) and nally, Local Regression Estimation (LRE).

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

Cone-Beam Computed Tomography as a Diagnostic Method for Determination of Gingival Thickness and Distance between Gingival Margin and Bone Crest

PubMed Central

Borges, Germana Jayme; Ruiz, Luis Fernando Naldi; de Alencar, Ana Helena Gonçalves; Porto, Olavo César Lyra; Estrela, Carlos

2015-01-01

The objective of the present study was to assess cone-beam computed tomography (CBCT) as a diagnostic method for determination of gingival thickness (GT) and distance between gingival margin and vestibular (GMBC-V) and interproximal bone crests (GMBC-I). GT and GMBC-V were measured in 348 teeth and GMBC-I was measured in 377 tooth regions of 29 patients with gummy smile. GT was assessed using transgingival probing (TP), ultrasound (US), and CBCT, whereas GMBC-V and GMBC-I were assessed by transsurgical clinical evaluation (TCE) and CBCT. Statistical analyses used independent t-test, Pearson's correlation coefficient, and simple linear regression. Difference was observed for GT: between TP, CBCT, and US considering all teeth; between TP and CBCT and between TP and US in incisors and canines; between TP and US in premolars and first molars. TP presented the highest means for GT. Positive correlation and linear regression were observed between TP and CBCT, TP and US, and CBCT and US. Difference was observed for GMBC-V and GMBC-I using TCE and CBCT, considering all teeth. Correlation and linear regression results were significant for GMBC-V and GMBC-I in incisors, canines, and premolars. CBCT is an effective diagnostic method to visualize and measure GT, GMBC-V, and GMBC-I. PMID:25918737

Global Food Demand Scenarios for the 21st Century

PubMed Central

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

2015-01-01

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

Global Food Demand Scenarios for the 21st Century.

PubMed

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

2015-01-01

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

Finite-sample and asymptotic sign-based tests for parameters of non-linear quantile regression with Markov noise

NASA Astrophysics Data System (ADS)

Sirenko, M. A.; Tarasenko, P. F.; Pushkarev, M. I.

2017-01-01

One of the most noticeable features of sign-based statistical procedures is an opportunity to build an exact test for simple hypothesis testing of parameters in a regression model. In this article, we expanded a sing-based approach to the nonlinear case with dependent noise. The examined model is a multi-quantile regression, which makes it possible to test hypothesis not only of regression parameters, but of noise parameters as well.

A Practical Model for Forecasting New Freshman Enrollment during the Application Period.

ERIC Educational Resources Information Center

Paulsen, Michael B.

1989-01-01

A simple and effective model for forecasting freshman enrollment during the application period is presented step by step. The model requires minimal and readily available information, uses a simple linear regression analysis on a personal computer, and provides updated monthly forecasts. (MSE)

Ridge Regression for Interactive Models.

ERIC Educational Resources Information Center

Tate, Richard L.

1988-01-01

An exploratory study of the value of ridge regression for interactive models is reported. Assuming that the linear terms in a simple interactive model are centered to eliminate non-essential multicollinearity, a variety of common models, representing both ordinal and disordinal interactions, are shown to have "orientations" that are…

Additivity of nonlinear biomass equations

Treesearch

Bernard R. Parresol

2001-01-01

Two procedures that guarantee the property of additivity among the components of tree biomass and total tree biomass utilizing nonlinear functions are developed. Procedure 1 is a simple combination approach, and procedure 2 is based on nonlinear joint-generalized regression (nonlinear seemingly unrelated regressions) with parameter restrictions. Statistical theory is...

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

PubMed

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

2018-02-01

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

Detecting trends in raptor counts: power and type I error rates of various statistical tests

USGS Publications Warehouse

Hatfield, J.S.; Gould, W.R.; Hoover, B.A.; Fuller, M.R.; Lindquist, E.L.

1996-01-01

We conducted simulations that estimated power and type I error rates of statistical tests for detecting trends in raptor population count data collected from a single monitoring site. Results of the simulations were used to help analyze count data of bald eagles (Haliaeetus leucocephalus) from 7 national forests in Michigan, Minnesota, and Wisconsin during 1980-1989. Seven statistical tests were evaluated, including simple linear regression on the log scale and linear regression with a permutation test. Using 1,000 replications each, we simulated n = 10 and n = 50 years of count data and trends ranging from -5 to 5% change/year. We evaluated the tests at 3 critical levels (alpha = 0.01, 0.05, and 0.10) for both upper- and lower-tailed tests. Exponential count data were simulated by adding sampling error with a coefficient of variation of 40% from either a log-normal or autocorrelated log-normal distribution. Not surprisingly, tests performed with 50 years of data were much more powerful than tests with 10 years of data. Positive autocorrelation inflated alpha-levels upward from their nominal levels, making the tests less conservative and more likely to reject the null hypothesis of no trend. Of the tests studied, Cox and Stuart's test and Pollard's test clearly had lower power than the others. Surprisingly, the linear regression t-test, Collins' linear regression permutation test, and the nonparametric Lehmann's and Mann's tests all had similar power in our simulations. Analyses of the count data suggested that bald eagles had increasing trends on at least 2 of the 7 national forests during 1980-1989.

Rapid Measurement of Soil Carbon in Rice Paddy Field of Lombok Island Indonesia Using Near Infrared Technology

NASA Astrophysics Data System (ADS)

Kusumo, B. H.; Sukartono, S.; Bustan, B.

2018-02-01

Measuring soil organic carbon (C) using conventional analysis is tedious procedure, time consuming and expensive. It is needed simple procedure which is cheap and saves time. Near infrared technology offers rapid procedure as it works based on the soil spectral reflectance and without any chemicals. The aim of this research is to test whether this technology able to rapidly measure soil organic C in rice paddy field. Soil samples were collected from rice paddy field of Lombok Island Indonesia, and the coordinates of the samples were recorded. Parts of the samples were analysed using conventional analysis (Walkley and Black) and some other parts were scanned using near infrared spectroscopy (NIRS) for soil spectral collection. Partial Least Square Regression (PLSR) Models were developed using data of soil C analysed using conventional analysis and data from soil spectral reflectance. The models were moderately successful to measure soil C in rice paddy field of Lombok Island. This shows that the NIR technology can be further used to monitor the C change in rice paddy soil.

A novel health indicator for on-line lithium-ion batteries remaining useful life prediction

NASA Astrophysics Data System (ADS)

Zhou, Yapeng; Huang, Miaohua; Chen, Yupu; Tao, Ye

2016-07-01

Prediction of lithium-ion batteries remaining useful life (RUL) plays an important role in an intelligent battery management system. The capacity and internal resistance are often used as the batteries health indicator (HI) for quantifying degradation and predicting RUL. However, on-line measurement of capacity and internal resistance are hardly realizable due to the not fully charged and discharged condition and the extremely expensive cost, respectively. Therefore, there is a great need to find an optional way to deal with this plight. In this work, a novel HI is extracted from the operating parameters of lithium-ion batteries for degradation modeling and RUL prediction. Moreover, Box-Cox transformation is employed to improve HI performance. Then Pearson and Spearman correlation analyses are utilized to evaluate the similarity between real capacity and the estimated capacity derived from the HI. Next, both simple statistical regression technique and optimized relevance vector machine are employed to predict the RUL based on the presented HI. The correlation analyses and prediction results show the efficiency and effectiveness of the proposed HI for battery degradation modeling and RUL prediction.

Ratio manipulating spectrophotometry versus chemometry as stability indicating methods for cefquinome sulfate determination

NASA Astrophysics Data System (ADS)

Yehia, Ali M.; Arafa, Reham M.; Abbas, Samah S.; Amer, Sawsan M.

2016-01-01

Spectral resolution of cefquinome sulfate (CFQ) in the presence of its degradation products was studied. Three selective, accurate and rapid spectrophotometric methods were performed for the determination of CFQ in the presence of either its hydrolytic, oxidative or photo-degradation products. The proposed ratio difference, derivative ratio and mean centering are ratio manipulating spectrophotometric methods that were satisfactorily applied for selective determination of CFQ within linear range of 5.0-40.0 μg mL- 1. Concentration Residuals Augmented Classical Least Squares was applied and evaluated for the determination of the cited drug in the presence of its all degradation products. Traditional Partial Least Squares regression was also applied and benchmarked against the proposed advanced multivariate calibration. Experimentally designed 25 synthetic mixtures of three factors at five levels were used to calibrate and validate the multivariate models. Advanced chemometrics succeeded in quantitative and qualitative analyses of CFQ along with its hydrolytic, oxidative and photo-degradation products. The proposed methods were applied successfully for different pharmaceutical formulations analyses. These developed methods were simple and cost-effective compared with the manufacturer's RP-HPLC method.

Logits and Tigers and Bears, Oh My! A Brief Look at the Simple Math of Logistic Regression and How It Can Improve Dissemination of Results

ERIC Educational Resources Information Center

Osborne, Jason W.

2012-01-01

Logistic regression is slowly gaining acceptance in the social sciences, and fills an important niche in the researcher's toolkit: being able to predict important outcomes that are not continuous in nature. While OLS regression is a valuable tool, it cannot routinely be used to predict outcomes that are binary or categorical in nature. These…

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.

Interrelationships between trait anxiety, situational stress and mental effort predict phonological processing efficiency, but not effectiveness.

PubMed

Edwards, Elizabeth J; Edwards, Mark S; Lyvers, Michael

2016-08-01

Attentional control theory (ACT) describes the mechanisms associated with the relationship between anxiety and cognitive performance. We investigated the relationship between cognitive trait anxiety, situational stress and mental effort on phonological performance using a simple (forward-) and complex (backward-) word span task. Ninety undergraduate students participated in the study. Predictor variables were cognitive trait anxiety, indexed using questionnaire scores; situational stress, manipulated using ego threat instructions; and perceived level of mental effort, measured using a visual analogue scale. Criterion variables (a) performance effectiveness (accuracy) and (b) processing efficiency (accuracy divided by response time) were analyzed in separate multiple moderated-regression analyses. The results revealed (a) no relationship between the predictors and performance effectiveness, and (b) a significant 3-way interaction on processing efficiency for both the simple and complex tasks, such that at higher effort, trait anxiety and situational stress did not predict processing efficiency, whereas at lower effort, higher trait anxiety was associated with lower efficiency at high situational stress, but not at low situational stress. Our results were in full support of the assumptions of ACT and implications for future research are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Adults' strategies for simple addition and multiplication: verbal self-reports and the operand recognition paradigm.

PubMed

Metcalfe, Arron W S; Campbell, Jamie I D

2011-05-01

Accurate measurement of cognitive strategies is important in diverse areas of psychological research. Strategy self-reports are a common measure, but C. Thevenot, M. Fanget, and M. Fayol (2007) proposed a more objective method to distinguish different strategies in the context of mental arithmetic. In their operand recognition paradigm, speed of recognition memory for problem operands after solving a problem indexes strategy (e.g., direct memory retrieval vs. a procedural strategy). Here, in 2 experiments, operand recognition time was the same following simple addition or multiplication, but, consistent with a wide variety of previous research, strategy reports indicated much greater use of procedures (e.g., counting) for addition than multiplication. Operation, problem size (e.g., 2 + 3 vs. 8 + 9), and operand format (digits vs. words) had interactive effects on reported procedure use that were not reflected in recognition performance. Regression analyses suggested that recognition time was influenced at least as much by the relative difficulty of the preceding problem as by the strategy used. The findings indicate that the operand recognition paradigm is not a reliable substitute for strategy reports and highlight the potential impact of difficulty-related carryover effects in sequential cognitive tasks.

Poor anaerobic power/capability and static balance predicted prospective musculoskeletal injuries among Soldiers of the 101st Airborne (Air Assault) Division.

PubMed

Nagai, Takashi; Lovalekar, Mita; Wohleber, Meleesa F; Perlsweig, Katherine A; Wirt, Michael D; Beals, Kim

2017-11-01

Musculoskeletal injuries have negatively impacted tactical readiness. The identification of prospective and modifiable risk factors of preventable musculoskeletal injuries can guide specific injury prevention strategies for Soldiers and health care providers. To analyze physiological and neuromuscular characteristics as predictors of preventable musculoskeletal injuries. Prospective-cohort study. A total of 491 Soldiers were enrolled and participated in the baseline laboratory testing, including body composition, aerobic capacity, anaerobic power/capacity, muscular strength, flexibility, static balance, and landing biomechanics. After reviewing their medical charts, 275 male Soldiers who met the criteria were divided into two groups: with injuries (INJ) and no injuries (NOI). Simple and multiple logistic regression analyses were used to calculate the odds ratio (OR) and significant predictors of musculoskeletal injuries (p<0.05). The final multiple logistic regression model included the static balance with eyes-closed and peak anaerobic power as predictors of future injuries (p<0.001). The current results highlighted the importance of anaerobic power/capacity and static balance. High intensity training and balance exercise should be incorporated in their physical training as countermeasures. Copyright © 2017 Sports Medicine Australia. All rights reserved.

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

ERIC Educational Resources Information Center

Shafiq, M. Najeeb

2011-01-01

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

The impact of energy, agriculture, macroeconomic and human-induced indicators on environmental pollution: evidence from Ghana.

PubMed

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2017-03-01

In this study, the impact of energy, agriculture, macroeconomic and human-induced indicators on environmental pollution from 1971 to 2011 is investigated using the statistically inspired modification of partial least squares (SIMPLS) regression model. There was evidence of a linear relationship between energy, agriculture, macroeconomic and human-induced indicators and carbon dioxide emissions. Evidence from the SIMPLS regression shows that a 1% increase in crop production index will reduce carbon dioxide emissions by 0.71%. Economic growth increased by 1% will reduce carbon dioxide emissions by 0.46%, which means that an increase in Ghana's economic growth may lead to a reduction in environmental pollution. The increase in electricity production from hydroelectric sources by 1% will reduce carbon dioxide emissions by 0.30%; thus, increasing renewable energy sources in Ghana's energy portfolio will help mitigate carbon dioxide emissions. Increasing enteric emissions by 1% will increase carbon dioxide emissions by 4.22%, and a 1% increase in the nitrogen content of manure management will increase carbon dioxide emissions by 6.69%. The SIMPLS regression forecasting exhibited a 5% MAPE from the prediction of carbon dioxide emissions.

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.

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

PubMed

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

2018-01-17

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

Determination of Trace Available Heavy Metals in Soil Using Laser-Induced Breakdown Spectroscopy Assisted with Phase Transformation Method.

PubMed

Yi, Rongxing; Yang, Xinyan; Zhou, Ran; Li, Jiaming; Yu, Huiwu; Hao, Zhongqi; Guo, Lianbo; Li, Xiangyou; Lu, Yongfeng; Zeng, Xiaoyan

2018-05-18

To detect available heavy metals in soil using laser-induced breakdown spectroscopy (LIBS) and improve its poor detection sensitivity, a simple and low cost sample pretreatment method named solid-liquid-solid transformation was proposed. By this method, available heavy metals were extracted from soil through ultrasonic vibration and centrifuging and then deposited on a glass slide. Utilization of this solid-liquid-solid transformation method, available Cd and Pb elements in soil were detected successfully. The results show that the regression coefficients of calibration curves for soil analyses reach to more than 0.98. The limits of detection could reach to 0.067 and 0.94 ppm for available Cd and Pb elements in soil under optimized conditions, respectively, which are much better than those obtained by conventional LIBS.

[Can teenage obesity affect mental health?].

PubMed

Assunção, Maria Cecília Formoso; Muniz, Ludmila Correa; Schäfer, Antônio Augusto; Meller, Fernanda de Oliveira; Carús, Juliana Pires; Quadros, Lenice de Castro Muniz de; Domingues, Lídice Rodrigues; da Silva, Vera Lúcia Schmidt; Gonçalves, Helen; Hallal, Pedro Curi; Menezes, Ana Maria Baptista

2013-09-01

This study evaluated the association between obesity and emotional and behavioral difficulties in adolescents. We studied 4,325 individuals 11 to 15 years of age who were members of the 1993 birth cohort in Pelotas, Rio Grande do Sul State, Brazil. Information on body mass index (BMI), maternal assessment of the adolescents' emotional and behavioral health (Strengths and Difficulties Questionnaire - SDQ), and sociodemographic and behavioral characteristics were used. Gender-stratified analyses were conducted with simple and multivariate linear regression. In the adjusted analysis, obesity only correlated with total SDQ scores in boys. Among the latter, teenage obesity was associated with higher scores on the subscale of relational problems with peers. Given current knowledge on the future implications of obesity and mental health and in dealing with adolescents, studies on gender differences in adolescence may contribute to understanding such associations.

Seat Integrated and Conventional Restraints: A Study of Crash Injury/Fatality Rates in Rollovers

PubMed Central

Padmanaban, Jeya; Burnett, Roger A.

2008-01-01

This study used police-reported motor vehicle crash data from eleven states to determine ejection, fatality, and fatal/serious injury risks for belted drivers in vehicles with conventional seatbelts compared to belted drivers in vehicles with seat integrated restraint systems (SIRS). Risks were compared for 11,159 belted drivers involved in single- or multiple-vehicle rollover crashes. Simple driver ejection (partial and complete), fatality, and injury rates were derived, and logistic regression analyses were used to determine relative contribution of factors (including event calendar year, vehicle age, driver age/gender/alcohol use) that significantly influence the likelihood of fatality and fatal/serious injury to belted drivers in rollovers. Results show no statistically significant difference in driver ejection, fatality, or fatal/serious injury rates between vehicles with conventional belts and vehicles with SIRS. PMID:19026243

An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission

NASA Astrophysics Data System (ADS)

Aye, S. A.; Heyns, P. S.

2017-02-01

This paper proposes an optimal Gaussian process regression (GPR) for the prediction of remaining useful life (RUL) of slow speed bearings based on a novel degradation assessment index obtained from acoustic emission signal. The optimal GPR is obtained from an integration or combination of existing simple mean and covariance functions in order to capture the observed trend of the bearing degradation as well the irregularities in the data. The resulting integrated GPR model provides an excellent fit to the data and improves over the simple GPR models that are based on simple mean and covariance functions. In addition, it achieves a low percentage error prediction of the remaining useful life of slow speed bearings. These findings are robust under varying operating conditions such as loading and speed and can be applied to nonlinear and nonstationary machine response signals useful for effective preventive machine maintenance purposes.

Correction of Body-Mass Index Using Body-Shape Perception and Socioeconomic Status in Adolescent Self-Report Surveys

PubMed Central

Legleye, Stéphane; Beck, François; Spilka, Stanislas; Chau, Nearkasen

2014-01-01

Objectives To propose a simple correction of body-mass index (BMI) based on self-reported weight and height (reported BMI) using gender, body shape perception and socioeconomic status in an adolescent population. Methods 341 boys and girls aged 17–18 years were randomly selected from a representative sample of 2165 French adolescents living in Paris surveyed in 2010. After an anonymous self-administered pen-and-paper questionnaire asking for height, weight, body shape perception (feeling too thin, about the right weight or too fat) and socioeconomic status, subjects were measured and weighed. BMI categories were computed according to Cole’s cut-offs. Reported BMIs were corrected using linear regressions and ROC analyses and checked with cross-validation and multiple imputations to handle missing values. Agreement between actual and corrected BMI values was estimated with Kappa indexes and Intraclass correlation coefficients (ICC). Results On average, BMIs were underreported, especially among girls. Kappa indexes between actual and reported BMI were low, especially for girls: 0.56 95%CI = [0.42–0.70] for boys and 0.45 95%CI = [0.30–0.60] for girls. The regression of reported BMI by gender and body shape perception gave the most balanced results for both genders: the Kappa and ICC obtained were 0.63 95%CI = [0.50–0.76] and 0.67, 95%CI = [0.58–0.74] for boys; 0.65 95%CI = [0.52–0.78] and 0.74, 95%CI = [0.66–0.81] for girls. The regression of reported BMI by gender and socioeconomic status led to similar corrections while the ROC analyses were inaccurate. Conclusions Using body shape perception, or socioeconomic status and gender is a promising way of correcting BMI in self-administered questionnaires, especially for girls. PMID:24844229

[Comparison of simple pooling and bivariate model used in meta-analyses of diagnostic test accuracy published in Chinese journals].

PubMed

Huang, Yuan-sheng; Yang, Zhi-rong; Zhan, Si-yan

2015-06-18

To investigate the use of simple pooling and bivariate model in meta-analyses of diagnostic test accuracy (DTA) published in Chinese journals (January to November, 2014), compare the differences of results from these two models, and explore the impact of between-study variability of sensitivity and specificity on the differences. DTA meta-analyses were searched through Chinese Biomedical Literature Database (January to November, 2014). Details in models and data for fourfold table were extracted. Descriptive analysis was conducted to investigate the prevalence of the use of simple pooling method and bivariate model in the included literature. Data were re-analyzed with the two models respectively. Differences in the results were examined by Wilcoxon signed rank test. How the results differences were affected by between-study variability of sensitivity and specificity, expressed by I2, was explored. The 55 systematic reviews, containing 58 DTA meta-analyses, were included and 25 DTA meta-analyses were eligible for re-analysis. Simple pooling was used in 50 (90.9%) systematic reviews and bivariate model in 1 (1.8%). The remaining 4 (7.3%) articles used other models pooling sensitivity and specificity or pooled neither of them. Of the reviews simply pooling sensitivity and specificity, 41(82.0%) were at the risk of wrongly using Meta-disc software. The differences in medians of sensitivity and specificity between two models were both 0.011 (P<0.001, P=0.031 respectively). Greater differences could be found as I2 of sensitivity or specificity became larger, especially when I2>75%. Most DTA meta-analyses published in Chinese journals(January to November, 2014) combine the sensitivity and specificity by simple pooling. Meta-disc software can pool the sensitivity and specificity only through fixed-effect model, but a high proportion of authors think it can implement random-effect model. Simple pooling tends to underestimate the results compared with bivariate model. The greater the between-study variance is, the more likely the simple pooling has larger deviation. It is necessary to increase the knowledge level of statistical methods and software for meta-analyses of DTA data.

[Correlation between percentage of body fat and simple anthropometric parameters in children aged 6-9 years in Guangzhou].

PubMed

Yan, H C; Hao, Y T; Guo, Y F; Wei, Y H; Zhang, J H; Huang, G P; Mao, L M; Zhang, Z Q

2017-11-10

Objective: To evaluate the accuracy of simple anthropometric parameters in diagnosing obesity in children in Guangzhou. Methods: A cross-sectional study, including 465 children aged 6-9 years, was carried out in Guangzhou. Their body height and weight, waist circumference (WC) and hip circumference were measured according to standard procedure. Body mass index (BMI), waist to hip ratio (WHR) and waist-to-height ratio (WHtR) were calculated. Body fat percentage (BF%) was determined by dual-energy X-ray absorptiometry. Multiple regression analysis was applied to evaluate the correlations between age-adjusted physical indicators and BF%, after the adjustment for age. Obesity was defined by BF%. Receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic accuracy of the indicators for childhood obesity. Area under-ROC curves (AUCs) were calculated and the best cut-off point that maximizing 'sensitivity + specificity-1' was determined. Results: BMI showed the strongest association with BF% through multiple regression analysis. For 'per-standard deviation increase' of BMI, BF% increased by 5.3% ( t =23.1, P <0.01) in boys and 4.6% ( t =17.5, P <0.01) in girls, respectively. The ROC curve analysis indicated that BMI exhibited the largest AUC in both boys (AUC=0.908) and girls (AUC=0.895). The sensitivity was 80.8% in boys and 81.8% in girls, and the specificity was 88.2% in boys and 87.1% in girls. Both the AUCs for WHtR and WC were less than 0.8 in boys and girls. WHR had the smallest AUCs (<0.8) in both boys and girls. Conclusion: BMI appeared to be a good predicator for BF% in children aged 6-9 years in Guangzhou.

Bullying and truancy: Predictors to sexual practices among school-going adolescents in Malaysia - a cross-sectional study.

PubMed

Azmawati, M N; Dalila, R; Idris, I B; Hod, R

2017-10-01

Adolescents' involvement in sexual practices are becoming a major public health concern in Malaysia. This study aims to determine the prevalence of sexual practices among Malaysian school-going adolescents and its predictive factors. A cross-sectional study was carried out from April 2012 till September 2012 among 16-year-old school adolescents from two different schools. They were selected through simple random sampling and these adolescents answered a self-administered questionnaire consisting of three sections i.e. socio-demography, risk-taking behaviours and family-adolescents relationship. Data were analysed using Pearson Chi-Square test while Simple Logistic Regression and Multiple Logistic Regression were applied to determine the predictive factors. The prevalence of sexual practices among the adolescents was 30.1% in which they were either involved in pornography (26.8%), pre-sexual activities (8.5%) or premarital sex (2.9%). Six predictive factors associated with sexual practices among this age group were identified which were male (adjusted Odds Ratio (aOR) 2.7, 95% Confidence Interval (95%CI) 1.4 to 2.5), truancy (aOR 2.3, 95%CI 1.3 to 4.2), bully (aOR 3.5, 95%CI 1.7 to 7.3), hanging out (aOR 2.8, 95% 1.4 to 5.6), staying out late (aOR 3.2, 95%CI 1.5 to 6.8) and conflict with family (aOR 4.1, 95%CI 1.9 to 8.9). Asian background differs from the western countries and findings of this study may suggest suitable intervention programmes that can prevent high-risk sexual practices among Asian school-going adolescents.

Physical function interfering with pain and symptoms in fibromyalgia patients.

PubMed

Assumpção, A; Sauer, J F; Mango, P C; Pascual Marques, A

2010-01-01

The aim of this study was to assess the relationship between variables of physical assessment - muscular strength, flexibility and dynamic balance - with pain, pain threshold, and fibromyalgia symptoms (FM). Our sample consists of 55 women, with age ranging from 30 to 55 years (mean of 46.5, (standard deviation, SD=6.6)), mean body mass index (BMI) of 28.7 (3.8) and diagnosed for FM according to the American College of Rheumatology criteria. Pain intensity was measured using a visual analogue scale (VAS) and pain threshold (PT) using Fisher's dolorimeter. FM symptoms were assessed by the Fibromyalgia Impact Questionnaire (FIQ); flexibility by the third finger to floor test (3FF); the muscular strength index (MSI) by the maximum volunteer isometric contraction at flexion and extension of right knee and elbow using a force transducer, dynamic balance by the time to get up and go (TUG) test and the functional reach test (FRT). Data were analysed using Pearson's correlation, as well as simple and multivariate regression tests, with significance level of 5%. PT and FIQ were weakly but significantly correlated with the TUG, MSI and 3FF as well as VAS with the TUG and MSI (p<0.05). VAS, PT and FIQ was not correlated with FRT. Simple regression suggests that, alone, TUG, FR, MSI and 3FF are low predictors of VAS, PT and FIQ. For the VAS, the best predictive model includes TUG and MSI, explaining 12.6% of pain variability. For TP and total symptoms, as obtained by the FIQ, most predictive model includes 3FF and MSI, which respectively respond by 30% and 21% of the variability. Muscular strength, flexibility and balance are associated with pain, pain threshold, and symptoms in FM patients.

Association of lung function and chronic obstructive pulmonary disease with American Heart Association's Life's Simple 7 cardiovascular health metrics.

PubMed

Fan, Wenjun; Lee, Hwa; Lee, Angela; Kieu, Chi; Wong, Nathan D

2017-10-01

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the U.S. There is a strong association between COPD and cardiovascular (CV) disease; however, the relation between COPD and CV health factors is not well defined. We examined the relation between lung function and CV health factors defined by American Heart Association's (AHA) Life's Simple 7 (LS7). We studied 6352 adults aged ≥20 from the National Health and Nutrition Examination Survey (NHANES) 2009-2012. Analysis of variance was used to compare mean FEV1% of predicted across levels of each LS7 metric and population attributable risk was calculated based on COPD prevalence. We also conducted linear regression and logistic regression analyses to determine the association between lung function, COPD and LS7 score. Overall 19.9% of subjects were defined as having COPD. Subjects in the highest categories of the LS7 metrics had the highest mean values of FEV1% of predicted (p < 0.0001 except for total cholesterol). Current smoking and hypertension had a population attributed risk of 21.8% and 21.1% of COPD, respectively. Compared to subjects with 0 ideal health factors, the gender and ethnicity-adjusted odds (95% CI) for COPD were 0.45 (0.22-0.93), 0.22 (0.11-0.43) for those with 4 and 5-7 factors, but adjustment for age attenuated this relation. LS7 score is associated with lung function as well as the odds of COPD that is largely explained by age. Studies are needed to show if promotion of CV health will preserve healthy lung function. Copyright © 2017 Elsevier Ltd. All rights reserved.

Quantitation & Case-Study-Driven Inquiry to Enhance Yeast Fermentation Studies

ERIC Educational Resources Information Center

Grammer, Robert T.

2012-01-01

We propose a procedure for the assay of fermentation in yeast in microcentrifuge tubes that is simple and rapid, permitting assay replicates, descriptive statistics, and the preparation of line graphs that indicate reproducibility. Using regression and simple derivatives to determine initial velocities, we suggest methods to compare the effects of…

Cloud-Free Satellite Image Mosaics with Regression Trees and Histogram Matching.

Treesearch

E.H. Helmer; B. Ruefenacht

2005-01-01

Cloud-free optical satellite imagery simplifies remote sensing, but land-cover phenology limits existing solutions to persistent cloudiness to compositing temporally resolute, spatially coarser imagery. Here, a new strategy for developing cloud-free imagery at finer resolution permits simple automatic change detection. The strategy uses regression trees to predict...

A Diagrammatic Exposition of Regression and Instrumental Variables for the Beginning Student

ERIC Educational Resources Information Center

Foster, Gigi

2009-01-01

Some beginning students of statistics and econometrics have difficulty with traditional algebraic approaches to explaining regression and related techniques. For these students, a simple and intuitive diagrammatic introduction as advocated by Kennedy (2008) may prove a useful framework to support further study. The author presents a series of…

A Simple and Convenient Method of Multiple Linear Regression to Calculate Iodine Molecular Constants

ERIC Educational Resources Information Center

Cooper, Paul D.

2010-01-01

A new procedure using a student-friendly least-squares multiple linear-regression technique utilizing a function within Microsoft Excel is described that enables students to calculate molecular constants from the vibronic spectrum of iodine. This method is advantageous pedagogically as it calculates molecular constants for ground and excited…

Fitting program for linear regressions according to Mahon (1996)

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

Trappitsch, Reto G.

2018-01-09

This program takes the users' Input data and fits a linear regression to it using the prescription presented by Mahon (1996). Compared to the commonly used York fit, this method has the correct prescription for measurement error propagation. This software should facilitate the proper fitting of measurements with a simple Interface.

Revisiting the Scale-Invariant, Two-Dimensional Linear Regression Method

ERIC Educational Resources Information Center

Patzer, A. Beate C.; Bauer, Hans; Chang, Christian; Bolte, Jan; Su¨lzle, Detlev

2018-01-01

The scale-invariant way to analyze two-dimensional experimental and theoretical data with statistical errors in both the independent and dependent variables is revisited by using what we call the triangular linear regression method. This is compared to the standard least-squares fit approach by applying it to typical simple sets of example data…

No Evidence of Reaction Time Slowing in Autism Spectrum Disorder

ERIC Educational Resources Information Center

Ferraro, F. Richard

2016-01-01

A total of 32 studies comprising 238 simple reaction time and choice reaction time conditions were examined in individuals with autism spectrum disorder (n?=?964) and controls (n?=?1032). A Brinley plot/multiple regression analysis was performed on mean reaction times, regressing autism spectrum disorder performance onto the control performance as…

Assistive Technologies for Second-Year Statistics Students Who Are Blind

ERIC Educational Resources Information Center

Erhardt, Robert J.; Shuman, Michael P.

2015-01-01

At Wake Forest University, a student who is blind enrolled in a second course in statistics. The course covered simple and multiple regression, model diagnostics, model selection, data visualization, and elementary logistic regression. These topics required that the student both interpret and produce three sets of materials: mathematical writing,…

Which factors predict the time spent answering queries to a drug information centre?

PubMed Central

Reppe, Linda A.; Spigset, Olav

2010-01-01

Objective To develop a model based upon factors able to predict the time spent answering drug-related queries to Norwegian drug information centres (DICs). Setting and method Drug-related queries received at 5 DICs in Norway from March to May 2007 were randomly assigned to 20 employees until each of them had answered a minimum of five queries. The employees reported the number of drugs involved, the type of literature search performed, and whether the queries were considered judgmental or not, using a specifically developed scoring system. Main outcome measures The scores of these three factors were added together to define a workload score for each query. Workload and its individual factors were subsequently related to the measured time spent answering the queries by simple or multiple linear regression analyses. Results Ninety-six query/answer pairs were analyzed. Workload significantly predicted the time spent answering the queries (adjusted R2 = 0.22, P < 0.001). Literature search was the individual factor best predicting the time spent answering the queries (adjusted R2 = 0.17, P < 0.001), and this variable also contributed the most in the multiple regression analyses. Conclusion The most important workload factor predicting the time spent handling the queries in this study was the type of literature search that had to be performed. The categorisation of queries as judgmental or not, also affected the time spent answering the queries. The number of drugs involved did not significantly influence the time spent answering drug information queries. PMID:20922480

Dysfunctional attitudes and poor problem solving skills predict hopelessness in major depression.

PubMed

Cannon, B; Mulroy, R; Otto, M W; Rosenbaum, J F; Fava, M; Nierenberg, A A

1999-09-01

Hopelessness is a significant predictor of suicidality, but not all depressed patients feel hopeless. If clinicians can predict hopelessness, they may be able to identify those patients at risk of suicide and focus interventions on factors associated with hopelessness. In this study, we examined potential predictors of hopelessness in a sample of depressed outpatients. In this study, we examined potential demographic, diagnostic, and symptom predictors of hopelessness in a sample of 138 medication-free outpatients (73 women and 65 men) with a primary diagnosis of major depression. The significance of predictors was evaluated in both simple and multiple regression analyses. Consistent with previous studies, we found no significant associations between demographic and diagnostic variables and greater hopelessness. Hopelessness was significantly associated with greater depression severity, poor problem solving abilities as assessed by the Problem Solving Inventory, and each of two measures of dysfunctional cognitions (the Dysfunctional Attitudes Scale and the Cognitions Questionnaire). In a stepwise multiple regression equation, however, only dysfunctional cognitions and poor problem solving offered non-redundant prediction of hopelessness scores, and accounted for 20% of the variance in these scores. This study is based on depressed patients entering into an outpatient treatment protocol. All analyses were correlational in nature, and no causal links can be concluded. Our findings, identifying clinical correlates of hopelessness, provide clinicians with potential additional targets for assessment and treatment of suicidal risk. In particular, clinical attention to dysfunctional attitudes and problem solving skills may be important for further reduction of hopelessness and perhaps suicidal risk.

Cumulative stress and autonomic dysregulation in a community sample.

PubMed

Lampert, Rachel; Tuit, Keri; Hong, Kwang-Ik; Donovan, Theresa; Lee, Forrester; Sinha, Rajita

2016-05-01

Whether cumulative stress, including both chronic stress and adverse life events, is associated with decreased heart rate variability (HRV), a non-invasive measure of autonomic status which predicts poor cardiovascular outcomes, is unknown. Healthy community dwelling volunteers (N = 157, mean age 29 years) participated in the Cumulative Stress/Adversity Interview (CAI), a 140-item event interview measuring cumulative adversity including major life events, life trauma, recent life events and chronic stressors, and underwent 24-h ambulatory ECG monitoring. HRV was analyzed in the frequency domain and standard deviation of NN intervals (SDNN) calculated. Initial simple regression analyses revealed that total cumulative stress score, chronic stressors and cumulative adverse life events (CALE) were all inversely associated with ultra low-frequency (ULF), very low-frequency (VLF) and low-frequency (LF) power and SDNN (all p < 0.05). In hierarchical regression analyses, total cumulative stress and chronic stress each was significantly associated with SDNN and ULF even after the highly significant contributions of age and sex, with no other covariates accounting for additional appreciable variance. For VLF and LF, both total cumulative stress and chronic stress significantly contributed to the variance alone but were not longer significant after adjusting for race and health behaviors. In summary, total cumulative stress, and its components of adverse life events and chronic stress were associated with decreased cardiac autonomic function as measured by HRV. Findings suggest one potential mechanism by which stress may exert adverse effects on mortality in healthy individuals. Primary preventive strategies including stress management may prove beneficial.

Usefulness of the Trabecular Bone Score for assessing the risk of osteoporotic fracture.

PubMed

Redondo, L; Puigoriol, E; Rodríguez, J R; Peris, P; Kanterewicz, E

2018-04-01

The trabecular bone score (TBS) is an imaging technique that assesses the condition of the trabecular microarchitecture. Preliminary results suggest that TBS, along with the bone mineral density assessment, could improve the calculation of the osteoporotic fracture risk. The aim of this study was to analyse TBS values and their relationship with the clinical characteristics, bone mineral density and history of fractures of a cohort of posmenopausal women. We analysed 2,257 posmenopausal women from the FRODOS cohort, which was created to determine the risk factors for osteoporotic fracture through a clinical survey and bone densitometry with vertebral morphometry. TBS was applied to the densitometry images. TBS values ≤1230 were considered indicative of degraded microarchitecture. We performed a simple and multiple linear regression to determine the factors associated with this index. The mean TBS value in L1-L4 was 1.203±0.121. Some 55.3% of the women showed values indicating degraded microarchitecture. In the multiple linear regression analysis, the factors associated with low TBS values were age, weight, height, spinal T-score, glucocorticoid treatment, presence of type 2 diabetes and a history of fractures due to frailty. TBS showed microarchitecture degradation values in the participants of the FRODOS cohort and was associated with anthropometric factors, low bone mineral density values, the presence of fractures, a history of type 2 diabetes mellitus and the use of glucocorticoids. Copyright © 2017 Elsevier España, S.L.U. and Sociedad Española de Medicina Interna (SEMI). All rights reserved.

Cumulative stress and autonomic dysregulation in a community sample

PubMed Central

Lampert, Rachel; Tuit, Keri; Hong, Kwang-ik; Donovan, Theresa; Lee, Forrester; Sinha, Rajita

2016-01-01

Whether cumulative stress, including both chronic stress and adverse life events, is associated with decreased heart rate variability (HRV), a non-invasive measure of autonomic status which predicts poor cardiovascular outcomes, is unknown. Healthy community dwelling volunteers, (N= 157, mean age 29 years) participated in the Cumulative Stress/Adversity Interview, (CAI) a 140-item event interview measuring cumulative adversity including major life events, life trauma, recent life events and chronic stressors, and underwent 24 hour ambulatory ECG monitoring. HRV was analyzed in the frequency domain and standard deviation of NN intervals (SDNN) calculated. Initial simple regression analyses revealed that total cumulative stress score, chronic stressors, and cumulative adverse life events (CALE) were all inversely associated with ultra low frequency (ULF), very low frequency (VLF), and low frequency (LF) power and SDNN (all p<0.05). In hierarchical regression analyses, total cumulative stress and chronic stress each was significantly associated with SDNN and ULF even after the high significant contribution of age and sex, with no other covariates accounting for additional appreciable variance. For VLF and LF, both total cumulative stress and chronic stress significantly contributed to the variance were no longer significant after adjusting for race and health behaviors. (p’s<.05). In summary, total cumulative stress, and its components of adverse life events and chronic stress were associated with decreased cardiac autonomic function as measured by HRV. Findings suggest one potential mechanism by which stress may exert adverse effects on mortality in healthy individuals. Primary preventive strategies including stress management may prove beneficial. PMID:27112063

Socioeconomic Correlates of Contraceptive Use among the Ethnic Tribal Women of Bangladesh: Does Sex Preference Matter?

PubMed

Kamal, S M Mostafa; Hassan, Che Hashim

2013-06-01

To examine the relationship between socioeconomic factors affecting contraceptive use among tribal women of Bangladesh with focusing on son preference over daughter. The study used data gathered through a cross sectional survey on four tribal communities resided in the Rangamati Hill District of the Chittagong Hill Tracts, Bangladesh. A multistage random sampling procedure was applied to collect data from 865 currently married women of whom 806 women were currently married, non-pregnant and had at least one living child, which are the basis of this study. The information was recorded in a pre-structured questionnaire. Simple cross tabulation, chi-square tests and logistic regression analyses were performed to analyzing data. The contraceptive prevalence rate among the study tribal women was 73%. The multivariate analyses yielded quantitatively important and reliable estimates of likelihood of contraceptive use. Findings revealed that after controlling for other variables, the likelihood of contraceptive use was found not to be significant among women with at least one son than those who had only daughters, indicating no preference of son over daughter. Multivariate logistic regression analysis suggests that home visitations by family planning workers, tribal identity, place of residence, husband's education, and type of family, television ownership, electricity connection in the household and number of times married are important determinants of any contraceptive method use among the tribal women. The contraceptive use rate among the disadvantaged tribal women was more than that of the national level. Door-step delivery services of modern methods should be reached and available targeting the poor and remote zones.

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

USGS Publications Warehouse

Brown, C. Erwin

1993-01-01

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

Regression dilution bias: tools for correction methods and sample size calculation.

PubMed

Berglund, Lars

2012-08-01

Random errors in measurement of a risk factor will introduce downward bias of an estimated association to a disease or a disease marker. This phenomenon is called regression dilution bias. A bias correction may be made with data from a validity study or a reliability study. In this article we give a non-technical description of designs of reliability studies with emphasis on selection of individuals for a repeated measurement, assumptions of measurement error models, and correction methods for the slope in a simple linear regression model where the dependent variable is a continuous variable. Also, we describe situations where correction for regression dilution bias is not appropriate. The methods are illustrated with the association between insulin sensitivity measured with the euglycaemic insulin clamp technique and fasting insulin, where measurement of the latter variable carries noticeable random error. We provide software tools for estimation of a corrected slope in a simple linear regression model assuming data for a continuous dependent variable and a continuous risk factor from a main study and an additional measurement of the risk factor in a reliability study. Also, we supply programs for estimation of the number of individuals needed in the reliability study and for choice of its design. Our conclusion is that correction for regression dilution bias is seldom applied in epidemiological studies. This may cause important effects of risk factors with large measurement errors to be neglected.

Impact of Contextual Factors on Prostate Cancer Risk and Outcomes

DTIC Science & Technology

2013-07-01

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

Modelling Nitrogen Oxides in Los Angeles Using a Hybrid Dispersion/Land Use Regression Model

NASA Astrophysics Data System (ADS)

Wilton, Darren C.

The goal of this dissertation is to develop models capable of predicting long term annual average NOx concentrations in urban areas. Predictions from simple meteorological dispersion models and seasonal proxies for NO2 oxidation were included as covariates in a land use regression (LUR) model for NOx in Los Angeles, CA. The NO x measurements were obtained from a comprehensive measurement campaign that is part of the Multi-Ethnic Study of Atherosclerosis Air Pollution Study (MESA Air). Simple land use regression models were initially developed using a suite of GIS-derived land use variables developed from various buffer sizes (R²=0.15). Caline3, a simple steady-state Gaussian line source model, was initially incorporated into the land-use regression framework. The addition of this spatio-temporally varying Caline3 covariate improved the simple LUR model predictions. The extent of improvement was much more pronounced for models based solely on the summer measurements (simple LUR: R²=0.45; Caline3/LUR: R²=0.70), than it was for models based on all seasons (R²=0.20). We then used a Lagrangian dispersion model to convert static land use covariates for population density, commercial/industrial area into spatially and temporally varying covariates. The inclusion of these covariates resulted in significant improvement in model prediction (R²=0.57). In addition to the dispersion model covariates described above, a two-week average value of daily peak-hour ozone was included as a surrogate of the oxidation of NO2 during the different sampling periods. This additional covariate further improved overall model performance for all models. The best model by 10-fold cross validation (R²=0.73) contained the Caline3 prediction, a static covariate for length of A3 roads within 50 meters, the Calpuff-adjusted covariates derived from both population density and industrial/commercial land area, and the ozone covariate. This model was tested against annual average NOx concentrations from an independent data set from the EPA's Air Quality System (AQS) and MESA Air fixed site monitors, and performed very well (R²=0.82).

Chicken barn climate and hazardous volatile compounds control using simple linear regression and PID

NASA Astrophysics Data System (ADS)

Abdullah, A. H.; Bakar, M. A. A.; Shukor, S. A. A.; Saad, F. S. A.; Kamis, M. S.; Mustafa, M. H.; Khalid, N. S.

2016-07-01

The hazardous volatile compounds from chicken manure in chicken barn are potentially to be a health threat to the farm animals and workers. Ammonia (NH3) and hydrogen sulphide (H2S) produced in chicken barn are influenced by climate changes. The Electronic Nose (e-nose) is used for the barn's air, temperature and humidity data sampling. Simple Linear Regression is used to identify the correlation between temperature-humidity, humidity-ammonia and ammonia-hydrogen sulphide. MATLAB Simulink software was used for the sample data analysis using PID controller. Results shows that the performance of PID controller using the Ziegler-Nichols technique can improve the system controller to control climate in chicken barn.

Common y-intercept and single compound regressions of gas-particle partitioning data vs 1/T

NASA Astrophysics Data System (ADS)

Pankow, James F.

Confidence intervals are placed around the log Kp vs 1/ T correlation equations obtained using simple linear regressions (SLR) with the gas-particle partitioning data set of Yamasaki et al. [(1982) Env. Sci. Technol.16, 189-194]. The compounds and groups of compounds studied include the polycylic aromatic hydrocarbons phenanthrene + anthracene, me-phenanthrene + me-anthracene, fluoranthene, pyrene, benzo[ a]fluorene + benzo[ b]fluorene, chrysene + benz[ a]anthracene + triphenylene, benzo[ b]fluoranthene + benzo[ k]fluoranthene, and benzo[ a]pyrene + benzo[ e]pyrene (note: me = methyl). For any given compound, at equilibrium, the partition coefficient Kp equals ( F/ TSP)/ A where F is the particulate-matter associated concentration (ng m -3), A is the gas-phase concentration (ng m -3), and TSP is the concentration of particulate matter (μg m -3). At temperatures more than 10°C from the mean sampling temperature of 17°C, the confidence intervals are quite wide. Since theory predicts that similar compounds sorbing on the same particulate matter should possess very similar y-intercepts, the data set was also fitted using a special common y-intercept regression (CYIR). For most of the compounds, the CYIR equations fell inside of the SLR 95% confidence intervals. The CYIR y-intercept value is -18.48, and is reasonably close to the type of value that can be predicted for PAH compounds. The set of CYIR regression equations is probably more reliable than the set of SLR equations. For example, the CYIR-derived desorption enthalpies are much more highly correlated with vaporization enthalpies than are the SLR-derived desorption enthalpies. It is recommended that the CYIR approach be considered whenever analysing temperature-dependent gas-particle partitioning data.

A Simple Microsoft Excel Method to Predict Antibiotic Outbreaks and Underutilization.

PubMed

Miglis, Cristina; Rhodes, Nathaniel J; Avedissian, Sean N; Zembower, Teresa R; Postelnick, Michael; Wunderink, Richard G; Sutton, Sarah H; Scheetz, Marc H

2017-07-01

Benchmarking strategies are needed to promote the appropriate use of antibiotics. We have adapted a simple regressive method in Microsoft Excel that is easily implementable and creates predictive indices. This method trends consumption over time and can identify periods of over- and underuse at the hospital level. Infect Control Hosp Epidemiol 2017;38:860-862.

Understanding logistic regression analysis.

PubMed

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

Testing the Hypothesis of a Homoscedastic Error Term in Simple, Nonparametric Regression

ERIC Educational Resources Information Center

Wilcox, Rand R.

2006-01-01

Consider the nonparametric regression model Y = m(X)+ [tau](X)[epsilon], where X and [epsilon] are independent random variables, [epsilon] has a median of zero and variance [sigma][squared], [tau] is some unknown function used to model heteroscedasticity, and m(X) is an unknown function reflecting some conditional measure of location associated…

Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

ERIC Educational Resources Information Center

Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

2006-01-01

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

Forecasting Air Force Logistics Command Second Destination Transportation: An Application of Multiple Regression Analysis and Neural Networks

DTIC Science & Technology

1990-09-01

without the help from the DSXR staff. William Lyons, Charles Ramsey , and Martin Meeks went above and beyond to help complete this research. Special...develop a valid forecasting model that is significantly more accurate than the one presently used by DSXR and suggested the development and testing of a...method, Strom tested DSXR’s iterative linear regression forecasting technique by examining P1 in the simple regression equation to determine whether

A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

PubMed

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

2006-08-01

A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

Simple ultrasound rules to distinguish between benign and malignant adnexal masses before surgery: prospective validation by IOTA group

PubMed Central

Ameye, Lieveke; Fischerova, Daniela; Epstein, Elisabeth; Melis, Gian Benedetto; Guerriero, Stefano; Van Holsbeke, Caroline; Savelli, Luca; Fruscio, Robert; Lissoni, Andrea Alberto; Testa, Antonia Carla; Veldman, Joan; Vergote, Ignace; Van Huffel, Sabine; Bourne, Tom; Valentin, Lil

2010-01-01

Objectives To prospectively assess the diagnostic performance of simple ultrasound rules to predict benignity/malignancy in an adnexal mass and to test the performance of the risk of malignancy index, two logistic regression models, and subjective assessment of ultrasonic findings by an experienced ultrasound examiner in adnexal masses for which the simple rules yield an inconclusive result. Design Prospective temporal and external validation of simple ultrasound rules to distinguish benign from malignant adnexal masses. The rules comprised five ultrasonic features (including shape, size, solidity, and results of colour Doppler examination) to predict a malignant tumour (M features) and five to predict a benign tumour (B features). If one or more M features were present in the absence of a B feature, the mass was classified as malignant. If one or more B features were present in the absence of an M feature, it was classified as benign. If both M features and B features were present, or if none of the features was present, the simple rules were inconclusive. Setting 19 ultrasound centres in eight countries. Participants 1938 women with an adnexal mass examined with ultrasound by the principal investigator at each centre with a standardised research protocol. Reference standard Histological classification of the excised adnexal mass as benign or malignant. Main outcome measures Diagnostic sensitivity and specificity. Results Of the 1938 patients with an adnexal mass, 1396 (72%) had benign tumours, 373 (19.2%) had primary invasive tumours, 111 (5.7%) had borderline malignant tumours, and 58 (3%) had metastatic tumours in the ovary. The simple rules yielded a conclusive result in 1501 (77%) masses, for which they resulted in a sensitivity of 92% (95% confidence interval 89% to 94%) and a specificity of 96% (94% to 97%). The corresponding sensitivity and specificity of subjective assessment were 91% (88% to 94%) and 96% (94% to 97%). In the 357 masses for which the simple rules yielded an inconclusive result and with available results of CA-125 measurements, the sensitivities were 89% (83% to 93%) for subjective assessment, 50% (42% to 58%) for the risk of malignancy index, 89% (83% to 93%) for logistic regression model 1, and 82% (75% to 87%) for logistic regression model 2; the corresponding specificities were 78% (72% to 83%), 84% (78% to 88%), 44% (38% to 51%), and 48% (42% to 55%). Use of the simple rules as a triage test and subjective assessment for those masses for which the simple rules yielded an inconclusive result gave a sensitivity of 91% (88% to 93%) and a specificity of 93% (91% to 94%), compared with a sensitivity of 90% (88% to 93%) and a specificity of 93% (91% to 94%) when subjective assessment was used in all masses. Conclusions The use of the simple rules has the potential to improve the management of women with adnexal masses. In adnexal masses for which the rules yielded an inconclusive result, subjective assessment of ultrasonic findings by an experienced ultrasound examiner was the most accurate diagnostic test; the risk of malignancy index and the two regression models were not useful. PMID:21156740

Shrinkage regression-based methods for microarray missing value imputation.

PubMed

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

2013-01-01

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

Application of stepwise multiple regression techniques to inversion of Nimbus 'IRIS' observations.

NASA Technical Reports Server (NTRS)

Ohring, G.

1972-01-01

Exploratory studies with Nimbus-3 infrared interferometer-spectrometer (IRIS) data indicate that, in addition to temperature, such meteorological parameters as geopotential heights of pressure surfaces, tropopause pressure, and tropopause temperature can be inferred from the observed spectra with the use of simple regression equations. The technique of screening the IRIS spectral data by means of stepwise regression to obtain the best radiation predictors of meteorological parameters is validated. The simplicity of application of the technique and the simplicity of the derived linear regression equations - which contain only a few terms - suggest usefulness for this approach. Based upon the results obtained, suggestions are made for further development and exploitation of the stepwise regression analysis technique.

An experimental and analytical investigation of stall effects on flap-lag stability in forward flight

NASA Technical Reports Server (NTRS)

Nagabhushanam, J.; Gaonkar, Gopal H.; Mcnulty, Michael J.

1987-01-01

Experiments have been performed with a 1.62 m diameter hingeless rotor in a wind tunnel to investigate flap-lag stability of isolated rotors in forward flight. The three-bladed rotor model closely approaches the simple theoretical concept of a hingeless rotor as a set of rigid, articulated flap-lag blades with offset and spring restrained flap and lag hinges. Lag regressing mode stability data was obtained for advance ratios as high as 0.55 for various combinations of collective pitch and shaft angle. The prediction includes quasi-steady stall effects on rotor trim and Floquet stability analyses. Correlation between data and prediction is presented and is compared with that of an earlier study based on a linear theory without stall effects. While the results with stall effects show marked differences from the linear theory results, the stall theory still falls short of adequate agreement with the experimental data.

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

PubMed

Okun, Morris A; Kim, Ga Young

2016-01-01

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

Evaluation method of membrane performance in membrane distillation process for seawater desalination.

PubMed

Chung, Seungjoon; Seo, Chang Duck; Choi, Jae-Hoon; Chung, Jinwook

2014-01-01

Membrane distillation (MD) is an emerging desalination technology as an energy-saving alternative to conventional distillation and reverse osmosis method. The selection of appropriate membrane is a prerequisite for the design of an optimized MD process. We proposed a simple approximation method to evaluate the performance of membranes for MD process. Three hollow fibre-type commercial membranes with different thicknesses and pore sizes were tested. Experimental results showed that one membrane was advantageous due to the highest flux, whereas another membrane was due to the lowest feed temperature drop. Regression analyses and multi-stage calculations were used to account for the trade-offeffects of flux and feed temperature drop. The most desirable membrane was selected from tested membranes in terms of the mean flux in a multi-stage process. This method would be useful for the selection of the membranes without complicated simulation techniques.

On the mishandling of probabilities in Lamotte & Wells' commentary on J.P. Michaud, G. Moreau, Predicting the visitation of carcasses by carrion-related insects under different rates of degree-day accumulation.

PubMed

Moreau, Gaétan; Michaud, J-P

2017-01-01

LaMotte and Wells re-analyzed and criticized one of our articles in which we proposed a novel statistical test for predicting postmortem interval from insect succession data. Using simple mathematical examples, we demonstrate that LaMotte and Wells erred because their analyses are based on an erroneous interpretation of the nature of probabilities that disregards more than 300 years of scientific literature on probability combination. We also argue that the methods presented in our article, more specifically the use of degree-day-based logistic regression analysis to model succession, was a positive contribution to the fields of forensic entomology and carrion ecology, which LaMotte and Wells forgot to mention by instead focusing on issues that were either trivial or did not exist. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

PubMed Central

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

2014-01-01

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

An Empirical Study of Eight Nonparametric Tests in Hierarchical Regression.

ERIC Educational Resources Information Center

Harwell, Michael; Serlin, Ronald C.

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

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

ERIC Educational Resources Information Center

Green, Samuel B.

1991-01-01

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

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

USGS Publications Warehouse

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

1974-01-01

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

cp-R, an interface the R programming language for clinical laboratory method comparisons.

PubMed

Holmes, Daniel T

2015-02-01

Clinical scientists frequently need to compare two different bioanalytical methods as part of assay validation/monitoring. As a matter necessity, regression methods for quantitative comparison in clinical chemistry, hematology and other clinical laboratory disciplines must allow for error in both the x and y variables. Traditionally the methods popularized by 1) Deming and 2) Passing and Bablok have been recommended. While commercial tools exist, no simple open source tool is available. The purpose of this work was to develop and entirely open-source GUI-driven program for bioanalytical method comparisons capable of performing these regression methods and able to produce highly customized graphical output. The GUI is written in python and PyQt4 with R scripts performing regression and graphical functions. The program can be run from source code or as a pre-compiled binary executable. The software performs three forms of regression and offers weighting where applicable. Confidence bands of the regression are calculated using bootstrapping for Deming and Passing Bablok methods. Users can customize regression plots according to the tools available in R and can produced output in any of: jpg, png, tiff, bmp at any desired resolution or ps and pdf vector formats. Bland Altman plots and some regression diagnostic plots are also generated. Correctness of regression parameter estimates was confirmed against existing R packages. The program allows for rapid and highly customizable graphical output capable of conforming to the publication requirements of any clinical chemistry journal. Quick method comparisons can also be performed and cut and paste into spreadsheet or word processing applications. We present a simple and intuitive open source tool for quantitative method comparison in a clinical laboratory environment. Copyright © 2014 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Biological Parametric Mapping: A Statistical Toolbox for Multi-Modality Brain Image Analysis

PubMed Central

Casanova, Ramon; Ryali, Srikanth; Baer, Aaron; Laurienti, Paul J.; Burdette, Jonathan H.; Hayasaka, Satoru; Flowers, Lynn; Wood, Frank; Maldjian, Joseph A.

2006-01-01

In recent years multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric mapping (BPM), based on a voxel-wise use of the general linear model. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in MATLAB with a user friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely-used T-field, has been implemented in the correlation analysis for more accurate results. An example with in-vivo data is presented demonstrating the potential of the BPM methodology as a tool for multimodal image analysis. PMID:17070709

The Role of Ethnic and National Identifications in Perceived Discrimination for Asian Americans: Toward a Better Understanding of the Buffering Effect of Group Identifications on Psychological Distress

PubMed Central

Huynh, Que-Lam; Devos, Thierry; Goldberg, Robyn

2013-01-01

A robust relationship between perceived racial discrimination and psychological distress has been established. Yet, mixed evidence exists regarding the extent to which ethnic identification moderates this relationship, and scarce attention has been paid to the moderating role of national identification. We propose that the role of group identifications in the perceived discrimination–psychological distress relationship is best understood by simultaneously and interactively considering ethnic and national identifications. A sample of 259 Asian American students completed measures of perceived discrimination, group identifications (specific ethnic identification stated by respondents and national or “mainstream American” identification), and psychological distress (anxiety and depression symptoms). Regression analyses revealed a significant three-way interaction of perceived discrimination, ethnic identification, and national identification on psychological distress. Simple-slope analyses indicated that dual identification (strong ethnic and national identifications) was linked to a weaker relationship between perceived discrimination and psychological distress compared with other group identification configurations. These findings underscore the need to consider the interconnections between ethnic and national identifications to better understand the circumstances under which group identifications are likely to buffer individuals against the adverse effects of racial discrimination. PMID:25258674

Ratio manipulating spectrophotometry versus chemometry as stability indicating methods for cefquinome sulfate determination.

PubMed

Yehia, Ali M; Arafa, Reham M; Abbas, Samah S; Amer, Sawsan M

2016-01-15

Spectral resolution of cefquinome sulfate (CFQ) in the presence of its degradation products was studied. Three selective, accurate and rapid spectrophotometric methods were performed for the determination of CFQ in the presence of either its hydrolytic, oxidative or photo-degradation products. The proposed ratio difference, derivative ratio and mean centering are ratio manipulating spectrophotometric methods that were satisfactorily applied for selective determination of CFQ within linear range of 5.0-40.0 μg mL(-1). Concentration Residuals Augmented Classical Least Squares was applied and evaluated for the determination of the cited drug in the presence of its all degradation products. Traditional Partial Least Squares regression was also applied and benchmarked against the proposed advanced multivariate calibration. Experimentally designed 25 synthetic mixtures of three factors at five levels were used to calibrate and validate the multivariate models. Advanced chemometrics succeeded in quantitative and qualitative analyses of CFQ along with its hydrolytic, oxidative and photo-degradation products. The proposed methods were applied successfully for different pharmaceutical formulations analyses. These developed methods were simple and cost-effective compared with the manufacturer's RP-HPLC method. Copyright © 2015 Elsevier B.V. All rights reserved.

Multi-scaling allometric analysis for urban and regional development

NASA Astrophysics Data System (ADS)

Chen, Yanguang

2017-01-01

The concept of allometric growth is based on scaling relations, and it has been applied to urban and regional analysis for a long time. However, most allometric analyses were devoted to the single proportional relation between two elements of a geographical system. Few researches focus on the allometric scaling of multielements. In this paper, a process of multiscaling allometric analysis is developed for the studies on spatio-temporal evolution of complex systems. By means of linear algebra, general system theory, and by analogy with the analytical hierarchy process, the concepts of allometric growth can be integrated with the ideas from fractal dimension. Thus a new methodology of geo-spatial analysis and the related theoretical models emerge. Based on the least squares regression and matrix operations, a simple algorithm is proposed to solve the multiscaling allometric equation. Applying the analytical method of multielement allometry to Chinese cities and regions yields satisfying results. A conclusion is reached that the multiscaling allometric analysis can be employed to make a comprehensive evaluation for the relative levels of urban and regional development, and explain spatial heterogeneity. The notion of multiscaling allometry may enrich the current theory and methodology of spatial analyses of urban and regional evolution.

Quantifying prosthetic gait deviation using simple outcome measures

PubMed Central

Kark, Lauren; Odell, Ross; McIntosh, Andrew S; Simmons, Anne

2016-01-01

AIM: To develop a subset of simple outcome measures to quantify prosthetic gait deviation without needing three-dimensional gait analysis (3DGA). METHODS: Eight unilateral, transfemoral amputees and 12 unilateral, transtibial amputees were recruited. Twenty-eight able-bodied controls were recruited. All participants underwent 3DGA, the timed-up-and-go test and the six-minute walk test (6MWT). The lower-limb amputees also completed the Prosthesis Evaluation Questionnaire. Results from 3DGA were summarised using the gait deviation index (GDI), which was subsequently regressed, using stepwise regression, against the other measures. RESULTS: Step-length (SL), self-selected walking speed (SSWS) and the distance walked during the 6MWT (6MWD) were significantly correlated with GDI. The 6MWD was the strongest, single predictor of the GDI, followed by SL and SSWS. The predictive ability of the regression equations were improved following inclusion of self-report data related to mobility and prosthetic utility. CONCLUSION: This study offers a practicable alternative to quantifying kinematic deviation without the need to conduct complete 3DGA. PMID:27335814

Simple estimation procedures for regression analysis of interval-censored failure time data under the proportional hazards model.

PubMed

Sun, Jianguo; Feng, Yanqin; Zhao, Hui

2015-01-01

Interval-censored failure time data occur in many fields including epidemiological and medical studies as well as financial and sociological studies, and many authors have investigated their analysis (Sun, The statistical analysis of interval-censored failure time data, 2006; Zhang, Stat Modeling 9:321-343, 2009). In particular, a number of procedures have been developed for regression analysis of interval-censored data arising from the proportional hazards model (Finkelstein, Biometrics 42:845-854, 1986; Huang, Ann Stat 24:540-568, 1996; Pan, Biometrics 56:199-203, 2000). For most of these procedures, however, one drawback is that they involve estimation of both regression parameters and baseline cumulative hazard function. In this paper, we propose two simple estimation approaches that do not need estimation of the baseline cumulative hazard function. The asymptotic properties of the resulting estimates are given, and an extensive simulation study is conducted and indicates that they work well for practical situations.

Forecasting outbreaks of the Douglas-fir tussock moth from lower crown cocoon samples.

Treesearch

Richard R. Mason; Donald W. Scott; H. Gene Paul

1993-01-01

A predictive technique using a simple linear regression was developed to forecast the midcrown density of small tussock moth larvae from estimates of cocoon density in the previous generation. The regression estimator was derived from field samples of cocoons and larvae taken from a wide range of nonoutbreak tussock moth populations. The accuracy of the predictions was...

Solar energy distribution over Egypt using cloudiness from Meteosat photos

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

Mosalam Shaltout, M.A.; Hassen, A.H.

1990-01-01

In Egypt, there are 10 ground stations for measuring the global solar radiation, and five stations for measuring the diffuse solar radiation. Every day at noon, the Meteorological Authority in Cairo receives three photographs of cloudiness over Egypt from the Meteosat satellite, one in the visible, and two in the infra-red bands (10.5-12.5 {mu}m) and (5.7-7.1 {mu}m). The monthly average cloudiness for 24 sites over Egypt are measured and calculated from Meteosat observations during the period 1985-1986. Correlation analysis between the cloudiness observed by Meteosat and global solar radiation measured from the ground stations is carried out. It is foundmore » that, the correlation coefficients are about 0.90 for the simple linear regression, and increase for the second and third degree regressions. Also, the correlation coefficients for the cloudiness with the diffuse solar radiation are about 0.80 for the simple linear regression, and increase for the second and third degree regression. Models and empirical relations for estimating the global and diffuse solar radiation from Meteosat cloudiness data over Egypt are deduced and tested. Seasonal maps for the global and diffuse radiation over Egypt are carried out.« less

An empirical model for estimating annual consumption by freshwater fish populations

USGS Publications Warehouse

Liao, H.; Pierce, C.L.; Larscheid, J.G.

2005-01-01

Population consumption is an important process linking predator populations to their prey resources. Simple tools are needed to enable fisheries managers to estimate population consumption. We assembled 74 individual estimates of annual consumption by freshwater fish populations and their mean annual population size, 41 of which also included estimates of mean annual biomass. The data set included 14 freshwater fish species from 10 different bodies of water. From this data set we developed two simple linear regression models predicting annual population consumption. Log-transformed population size explained 94% of the variation in log-transformed annual population consumption. Log-transformed biomass explained 98% of the variation in log-transformed annual population consumption. We quantified the accuracy of our regressions and three alternative consumption models as the mean percent difference from observed (bioenergetics-derived) estimates in a test data set. Predictions from our population-size regression matched observed consumption estimates poorly (mean percent difference = 222%). Predictions from our biomass regression matched observed consumption reasonably well (mean percent difference = 24%). The biomass regression was superior to an alternative model, similar in complexity, and comparable to two alternative models that were more complex and difficult to apply. Our biomass regression model, log10(consumption) = 0.5442 + 0.9962??log10(biomass), will be a useful tool for fishery managers, enabling them to make reasonably accurate annual population consumption predictions from mean annual biomass estimates. ?? Copyright by the American Fisheries Society 2005.

On the calibration process of film dosimetry: OLS inverse regression versus WLS inverse prediction.

PubMed

Crop, F; Van Rompaye, B; Paelinck, L; Vakaet, L; Thierens, H; De Wagter, C

2008-07-21

The purpose of this study was both putting forward a statistically correct model for film calibration and the optimization of this process. A reliable calibration is needed in order to perform accurate reference dosimetry with radiographic (Gafchromic) film. Sometimes, an ordinary least squares simple linear (in the parameters) regression is applied to the dose-optical-density (OD) curve with the dose as a function of OD (inverse regression) or sometimes OD as a function of dose (inverse prediction). The application of a simple linear regression fit is an invalid method because heteroscedasticity of the data is not taken into account. This could lead to erroneous results originating from the calibration process itself and thus to a lower accuracy. In this work, we compare the ordinary least squares (OLS) inverse regression method with the correct weighted least squares (WLS) inverse prediction method to create calibration curves. We found that the OLS inverse regression method could lead to a prediction bias of up to 7.3 cGy at 300 cGy and total prediction errors of 3% or more for Gafchromic EBT film. Application of the WLS inverse prediction method resulted in a maximum prediction bias of 1.4 cGy and total prediction errors below 2% in a 0-400 cGy range. We developed a Monte-Carlo-based process to optimize calibrations, depending on the needs of the experiment. This type of thorough analysis can lead to a higher accuracy for film dosimetry.

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

PubMed Central

Zhu, Xiang; Stephens, Matthew

2017-01-01

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

Bayesian Unimodal Density Regression for Causal Inference

ERIC Educational Resources Information Center

Karabatsos, George; Walker, Stephen G.

2011-01-01

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

Estimating population trends with a linear model: Technical comments

USGS Publications Warehouse

Sauer, John R.; Link, William A.; Royle, J. Andrew

2004-01-01

Controversy has sometimes arisen over whether there is a need to accommodate the limitations of survey design in estimating population change from the count data collected in bird surveys. Analyses of surveys such as the North American Breeding Bird Survey (BBS) can be quite complex; it is natural to ask if the complexity is necessary, or whether the statisticians have run amok. Bart et al. (2003) propose a very simple analysis involving nothing more complicated than simple linear regression, and contrast their approach with model-based procedures. We review the assumptions implicit to their proposed method, and document that these assumptions are unlikely to be valid for surveys such as the BBS. One fundamental limitation of a purely design-based approach is the absence of controls for factors that influence detection of birds at survey sites. We show that failure to model observer effects in survey data leads to substantial bias in estimation of population trends from BBS data for the 20 species that Bart et al. (2003) used as the basis of their simulations. Finally, we note that the simulations presented in Bart et al. (2003) do not provide a useful evaluation of their proposed method, nor do they provide a valid comparison to the estimating- equations alternative they consider.

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

NASA Astrophysics Data System (ADS)

Guns, M.; Vanacker, V.

2012-06-01

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

Relationship between mechanical factors and pelvic tilt in adults with and without low back pain.

PubMed

Król, Anita; Polak, Maciej; Szczygieł, Elżbieta; Wójcik, Paweł; Gleb, Klaudia

2017-01-01

The assessment of the lumbo-pelvic complex parameters is the basic procedure during the examination of the patients with low back pain syndrome (LBP). The aim of the study was to define the relationship between pelvic tilt and following factors: age, BMI, ability to activate deep abdominal muscles, iliopsoas and hamstrings muscles length, lumbar lordosis and thoracic kyphosis angle value, in adults with and without low back pain. The study covered a group of 60 female students aged 20-26. Average age was 22 years ± 1.83 (median = 22.5 years). In order to investigate the relationship between the anterior pelvic tilt and the analysed variables, simple linear regression and multiple linear regression were carried out. Individuals with and without pain differed significantly in terms of age, p < 0.001. There was a statistically significant relationship between the anterior pelvic tilt and the LBP (R2 = 0.07, p = 0.049) and the lumbar lordosis (R2 = 0.13, p = 0.02). The position of the pelvis depends on age, angle value of lumbar lordosis and BMI. Individuals with and without pain differed significantly in terms of the anterior pelvic tilt. The risk of LBP incidence increased with age in the study group.

Two-Stage Residual Inclusion Estimation in Health Services Research and Health Economics.

PubMed

Terza, Joseph V

2018-06-01

Empirical analyses in health services research and health economics often require implementation of nonlinear models whose regressors include one or more endogenous variables-regressors that are correlated with the unobserved random component of the model. In such cases, implementation of conventional regression methods that ignore endogeneity will likely produce results that are biased and not causally interpretable. Terza et al. (2008) discuss a relatively simple estimation method that avoids endogeneity bias and is applicable in a wide variety of nonlinear regression contexts. They call this method two-stage residual inclusion (2SRI). In the present paper, I offer a 2SRI how-to guide for practitioners and a step-by-step protocol that can be implemented with any of the popular statistical or econometric software packages. We introduce the protocol and its Stata implementation in the context of a real data example. Implementation of 2SRI for a very broad class of nonlinear models is then discussed. Additional examples are given. We analyze cigarette smoking as a determinant of infant birthweight using data from Mullahy (1997). It is hoped that the discussion will serve as a practical guide to implementation of the 2SRI protocol for applied researchers. © Health Research and Educational Trust.

Can a model of overlapping gestures account for scanning speech patterns?

PubMed

Tjaden, K

1999-06-01

A simple acoustic model of overlapping, sliding gestures was used to evaluate whether coproduction was reduced for neurologic speakers with scanning speech patterns. F2 onset frequency was used as an acoustic measure of coproduction or gesture overlap. The effects of speaking rate (habitual versus fast) and utterance position (initial versus medial) on F2 frequency, and presumably gesture overlap, were examined. Regression analyses also were used to evaluate the extent to which across-repetition temporal variability in F2 trajectories could be explained as variation in coproduction for consonants and vowels. The lower F2 onset frequencies for disordered speakers suggested that gesture overlap was reduced for neurologic individuals with scanning speech. Speaking rate change did not influence F2 onset frequencies, and presumably gesture overlap, for healthy or disordered speakers. F2 onset frequency differences for utterance-initial and -medial repetitions were interpreted to suggest reduced coproduction for the utterance-initial position. The utterance-position effects on F2 onset frequency, however, likely were complicated by position-related differences in articulatory scaling. The results of the regression analysis indicated that gesture sliding accounts, in part, for temporal variability in F2 trajectories. Taken together, the results of this study provide support for the idea that speech production theory for healthy talkers helps to account for disordered speech production.

Bankfull characteristics of Ohio streams and their relation to peak streamflows

USGS Publications Warehouse

Sherwood, James M.; Huitger, Carrie A.

2005-01-01

Regional curves, simple-regression equations, and multiple-regression equations were developed to estimate bankfull width, bankfull mean depth, bankfull cross-sectional area, and bankfull discharge of rural, unregulated streams in Ohio. The methods are based on geomorphic, basin, and flood-frequency data collected at 50 study sites on unregulated natural alluvial streams in Ohio, of which 40 sites are near streamflow-gaging stations. The regional curves and simple-regression equations relate the bankfull characteristics to drainage area. The multiple-regression equations relate the bankfull characteristics to drainage area, main-channel slope, main-channel elevation index, median bed-material particle size, bankfull cross-sectional area, and local-channel slope. Average standard errors of prediction for bankfull width equations range from 20.6 to 24.8 percent; for bankfull mean depth, 18.8 to 20.6 percent; for bankfull cross-sectional area, 25.4 to 30.6 percent; and for bankfull discharge, 27.0 to 78.7 percent. The simple-regression (drainage-area only) equations have the highest average standard errors of prediction. The multiple-regression equations in which the explanatory variables included drainage area, main-channel slope, main-channel elevation index, median bed-material particle size, bankfull cross-sectional area, and local-channel slope have the lowest average standard errors of prediction. Field surveys were done at each of the 50 study sites to collect the geomorphic data. Bankfull indicators were identified and evaluated, cross-section and longitudinal profiles were surveyed, and bed- and bank-material were sampled. Field data were analyzed to determine various geomorphic characteristics such as bankfull width, bankfull mean depth, bankfull cross-sectional area, bankfull discharge, streambed slope, and bed- and bank-material particle-size distribution. The various geomorphic characteristics were analyzed by means of a combination of graphical and statistical techniques. The logarithms of the annual peak discharges for the 40 gaged study sites were fit by a Pearson Type III frequency distribution to develop flood-peak discharges associated with recurrence intervals of 2, 5, 10, 25, 50, and 100 years. The peak-frequency data were related to geomorphic, basin, and climatic variables by multiple-regression analysis. Simple-regression equations were developed to estimate 2-, 5-, 10-, 25-, 50-, and 100-year flood-peak discharges of rural, unregulated streams in Ohio from bankfull channel cross-sectional area. The average standard errors of prediction are 31.6, 32.6, 35.9, 41.5, 46.2, and 51.2 percent, respectively. The study and methods developed are intended to improve understanding of the relations between geomorphic, basin, and flood characteristics of streams in Ohio and to aid in the design of hydraulic structures, such as culverts and bridges, where stability of the stream and structure is an important element of the design criteria. The study was done in cooperation with the Ohio Department of Transportation and the U.S. Department of Transportation, Federal Highway Administration.

Interaction effect of job insecurity and role ambiguity on psychological distress in Japanese employees: a cross-sectional study.

PubMed

Inoue, Akiomi; Kawakami, Norito; Eguchi, Hisashi; Tsutsumi, Akizumi

2018-05-01

We examined the interaction effect of job insecurity (JI) and role ambiguity (RA) on psychological distress in Japanese employees. Overall, 2184 male and 805 female employees from two factories of a manufacturing company in Japan completed a self-administered questionnaire comprising the scales measuring JI (Job Content Questionnaire), RA (National Institute for Occupational Safety and Health Generic Job Stress Questionnaire), psychological distress (K6 scale), and potential confounders (i.e., age, education, family size, occupational class, and work shift). Taking psychological distress as a dependent variable, hierarchical multiple regression analyses were conducted by gender and employment status (i.e., permanent and non-permanent employees). An interaction term of JI × RA was included in the model. After adjusting for potential confounders, the main effects of JI and RA on psychological distress were significant regardless of gender or employment status. Furthermore, the significant interaction effect of JI × RA on psychological distress was observed among permanent male employees (β = 0.053, p = 0.010). Post hoc simple slope analyses showed that the simple slope of JI was greater at higher levels of RA (i.e., one standard deviation [SD] above the mean) (β = 0.300, p < 0.001) compared to lower levels of RA (i.e., one SD below the mean) (β = 0.212, p < 0.001). On the other hand, the interaction effect of JI × RA was not significant among permanent or non-permanent female employees. The present study suggests that higher levels of RA strengthen the association of JI with psychological distress, at least among Japanese permanent male employees.

Effects of aripiprazole and haloperidol on neural activation during a simple motor task in healthy individuals: A functional MRI study.

PubMed

Goozee, Rhianna; O'Daly, Owen; Handley, Rowena; Reis Marques, Tiago; Taylor, Heather; McQueen, Grant; Hubbard, Kathryn; Pariante, Carmine; Mondelli, Valeria; Reinders, Antje A T S; Dazzan, Paola

2017-04-01

The dopaminergic system plays a key role in motor function and motor abnormalities have been shown to be a specific feature of psychosis. Due to their dopaminergic action, antipsychotic drugs may be expected to modulate motor function, but the precise effects of these drugs on motor function remain unclear. We carried out a within-subject, double-blind, randomized study of the effects of aripiprazole, haloperidol and placebo on motor function in 20 healthy men. For each condition, motor performance on an auditory-paced task was investigated. We entered maps of neural activation into a random effects general linear regression model to investigate motor function main effects. Whole-brain imaging revealed a significant treatment effect in a distributed network encompassing posterior orbitofrontal/anterior insula cortices, and the inferior temporal and postcentral gyri. Post-hoc comparison of treatments showed neural activation after aripiprazole did not differ significantly from placebo in either voxel-wise or region of interest analyses, with the results above driven primarily by haloperidol. We also observed a simple main effect of haloperidol compared with placebo, with increased task-related recruitment of posterior cingulate and precentral gyri. Furthermore, region of interest analyses revealed greater activation following haloperidol compared with placebo in the precentral and post-central gyri, and the putamen. These diverse modifications in cortical motor activation may relate to the different pharmacological profiles of haloperidol and aripiprazole, although the specific mechanisms underlying these differences remain unclear. Evaluating healthy individuals can allow investigation of the effects of different antipsychotics on cortical activation, independently of either disease-related pathology or previous treatment. Hum Brain Mapp 38:1833-1845, 2017. © 2017 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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

PubMed

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

2005-05-01

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

Acute otitis media and sociomedical risk factors among unselected children in Greenland.

PubMed

Homøe, P; Christensen, R B; Bretlau, P

1999-06-15

To describe the sociomedical risk factors associated with episodes of acute otitis media (AOM), recurrent AOM (rAOM), and chronic otitis media (COM) in Greenlandic children and especially to point out children at high risk of rAOM (defined as > 5 AOM episodes since birth) and COM which are prevalent among Inuit children all over the Arctic. The study design was cross-sectional and included 740 unselected children, 3, 4, 5, and 8-years-old, living in two major Greenlandic towns, Nuuk and Sisimiut. All children were otologically examined and the parents answered a questionnaire containing sociomedical variables including ethnicity, family history of OM, housing, insulation, crowding, daycare, passive cigarette smoking, breast feeding, type of diet, allergy, and chronic diseases. Historical data were cross-checked in medical records which also formed the basis for the drop-out analyses. Statistical analyses included frequency tests, calculation of odds ratio (OR), and multiple logistic regression. The attendance rate was 86%. Former episode of AOM was reported by 2/3 of the children, rAOM by 20%, and COM by 9%. The following variables were found significantly more often in children with AOM by simple frequency testing: Parental (OR = 1.83), sibling (OR = 1.62), and parental plus sibling (OR = 2.56) history of OM, crowding (OR = 5.55), long period of exclusive breast feeding ( > 4 months) (OR = 2.47), and recent acute disease (P = 0.034). The following variables were found significantly more often in children with rAOM or COM by simple frequency testing: Parental history of OM (OR = 1.60; OR = 2.11, respectively) and no recall of breast feeding (P = 0.005; P = 0.003, respectively). Also, COM was found significantly more often in children with two Greenlandic parents (OR = 3.07). A multiple logistic regression test denoted only parental history of OM (OR = 1.82) and long period of exclusive breast feeding (OR = 1.14) as significant predictors of AOM. Many of the risk factors usually associated with AOM could not be confirmed as risk factors in this survey. Parental history of OM and long period of exclusive breast feeding were the strongest factors associated with AOM in Greenlandic children and ethnicity was associated with COM. However, the study confirms that AOM is a multifactorial disease determined by a number of genetic and environmental factors.

Price, Weather, and `Acreage Abandonment' in Western Great Plains Wheat Culture.

NASA Astrophysics Data System (ADS)

Michaels, Patrick J.

1983-07-01

Multivariate analyses of acreage abandonment patterns in the U.S. Great Plains winter wheat region indicate that the major mode of variation is an in-phase oscillation confined to the western half of the overall area, which is also the area with lowest average yields. This is one of the more agroclimatically marginal environments in the United States, with wide interannual fluctuations in both climate and profitability.We developed a multiple regression model to determine the relative roles of weather and expected price in the decision not to harvest. The overall model explained 77% of the spatial and temporal variation in abandonment. The 36.5% of the non-spatial variation was explained by two simple transformations of climatic data from three monthly aggregates-September-October, November-February and March-April. Price factors, expressed as indexed future delivery quotations,were barely significant, with only between 3 and 5% of the non-spatial variation explained, depending upon the model.The model was based upon weather, climate and price data from 1932 through 1975. It was tested by sequentially withholding three-year blocks of data, and using the respecified regression coefficients, along with observed weather and price, to estimate abandonment in the withheld years. Error analyses indicate no loss of model fidelity in the test mode. Also, prediction errors in the 1970-75 period, characterized by widely fluctuating prices, were not different from those in the rest of the model.The overall results suggest that the perceived quality of the crop, as influenced by weather, is a much more important determinant of the abandonment decision than are expected returns based upon price considerations.

Population-based study of survival for women with serous cancer of the ovary, fallopian tube, peritoneum or undesignated origin - on behalf of the Swedish gynecological cancer group (SweGCG).

PubMed

Dahm-Kähler, Pernilla; Borgfeldt, Christer; Holmberg, Erik; Staf, Christian; Falconer, Henrik; Bjurberg, Maria; Kjölhede, Preben; Rosenberg, Per; Stålberg, Karin; Högberg, Thomas; Åvall-Lundqvist, Elisabeth

2017-01-01

The aim of the study was to determine survival outcome in patients with serous cancer in the ovary, fallopian tube, peritoneum and of undesignated origin. Nation-wide population-based study of women≥18years with histologically verified non-uterine serous cancer, included in the Swedish Quality Registry for primary cancer of the ovary, fallopian tube and peritoneum diagnosed 2009-2013. Relative survival (RS) was estimated using the Ederer II method. Simple and multivariable analyses were estimated by Poisson regression models. Of 5627 women identified, 1246 (22%) had borderline tumors and 4381 had malignant tumors. In total, 2359 women had serous cancer; 71% originated in the ovary (OC), 9% in the fallopian tube (FTC), 9% in the peritoneum (PPC) and 11% at an undesignated primary site (UPS). Estimated RS at 5-years was 37%; for FTC 54%, 40% for OC, 34% for PPC and 13% for UPS. In multivariable regression analyses restricted to women who had undergone primary or interval debulking surgery for OC, FTC and PPC, site of origin was not independently associated with survival. Significant associations with worse survival were found for advanced stages (RR 2.63, P<0.001), moderate (RR 1.90, P<0.047) and poor differentiation (RR 2.20, P<0.009), neoadjuvant chemotherapy (RR1.33, P<0.022), residual tumor (RR 2.65, P<0.001) and platinum single (2.34, P<0.001) compared to platinum combination chemotherapy. Survival was poorer for serous cancer at UPS than for ovarian, fallopian tube and peritoneal cancer. Serous cancer at UPS needs to be addressed when reporting and comparing survival rates of ovarian cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Adherence and health literacy as related to outcome of patients treated for rheumatoid arthritis : Analyses of a large-scale observational study.

PubMed

Kuipers, J G; Koller, M; Zeman, F; Müller, K; Rüffer, J U

2018-04-24

Disabilities in daily living and quality of life are key endpoints for evaluating the treatment outcome for rheumatoid arthritis (RA). Factors possibly contributing to good outcome are adherence and health literacy. The survey included a representative nationwide sample of German rheumatologists and their patients with RA. The physician questionnaire included the disease activity score (DAS28) and medical prescriptions. The patient questionnaire included fatigue (EORTC QLQ-FA13), health assessment questionnaire (HAQ), quality of life (SF-12), health literacy (HELP), and patients' listings of their medications. Adherence was operationalized as follows: patient-reported (CQR5), behavioral (concordance between physicians' and patients' listings of medications), physician-assessed, and a combined measure of physician rating (1 = very adherent, 0 = less adherent) and the match between physicians' prescriptions and patients' accounts of their medications (1 = perfect match, 0 = no perfect match) that yielded three categories of adherence: high, medium, and low. Simple and multiple linear regressions (controlling for age, sex, smoking, drinking alcohol, and sport) were calculated using adherence and health literacy as predictor variables, and disease activity and patient-reported outcomes as dependent variables. 708 pairs of patient and physician questionnaires were analyzed. The mean patient age (73% women) was 60 years (SD = 12). Multiple regression analyses showed that high adherence was significantly associated with 5/7 outcome variables and health literacy with 7/7 outcome variables. Adherence and health literacy had weak but consistent effects on most outcomes. Thus, enhancing adherence and understanding of medical information could improve outcome, which should be investigated in future interventional studies.

Socioeconomic Correlates of Contraceptive Use among the Ethnic Tribal Women of Bangladesh: Does Sex Preference Matter?

PubMed Central

Hassan, Che Hashim

2013-01-01

Objective To examine the relationship between socioeconomic factors affecting contraceptive use among tribal women of Bangladesh with focusing on son preference over daughter. Materials and methods The study used data gathered through a cross sectional survey on four tribal communities resided in the Rangamati Hill District of the Chittagong Hill Tracts, Bangladesh. A multistage random sampling procedure was applied to collect data from 865 currently married women of whom 806 women were currently married, non-pregnant and had at least one living child, which are the basis of this study. The information was recorded in a pre-structured questionnaire. Simple cross tabulation, chi-square tests and logistic regression analyses were performed to analyzing data. Results The contraceptive prevalence rate among the study tribal women was 73%. The multivariate analyses yielded quantitatively important and reliable estimates of likelihood of contraceptive use. Findings revealed that after controlling for other variables, the likelihood of contraceptive use was found not to be significant among women with at least one son than those who had only daughters, indicating no preference of son over daughter. Multivariate logistic regression analysis suggests that home visitations by family planning workers, tribal identity, place of residence, husband's education, and type of family, television ownership, electricity connection in the household and number of times married are important determinants of any contraceptive method use among the tribal women. Conclusion The contraceptive use rate among the disadvantaged tribal women was more than that of the national level. Door-step delivery services of modern methods should be reached and available targeting the poor and remote zones. PMID:24971107

Accumulation of subcutaneous fat, but not visceral fat, is a predictor of adiponectin levels in preterm infants at term-equivalent age.

PubMed

Nakano, Yuya; Itabashi, Kazuo; Sakurai, Motoichiro; Aizawa, Madoka; Dobashi, Kazushige; Mizuno, Katsumi

2014-05-01

Preterm infants have altered fat tissue development, including a higher percentage of fat mass and increased volume of visceral fat. They also have altered adiponectin levels, including a lower ratio of high-molecular-weight adiponectin (HMW-Ad) to total adiponectin (T-Ad) at term-equivalent age, compared with term infants. The objective of this study was to investigate the association between adiponectin levels and fat tissue accumulation or distribution in preterm infants at term-equivalent age. Cross-sectional clinical study. Study subjects were 53 preterm infants born at ≤34weeks gestation with a mean birth weight of 1592g. Serum levels of T-Ad and HMW-Ad were measured and a computed tomography (CT) scan was performed at the level of the umbilicus at term-equivalent age to analyze how fat tissue accumulation or distribution was correlated with adiponectin levels. T-Ad (r=0.315, p=0.022) and HMW-Ad levels (r=0.338, p=0.013) were positively associated with subcutaneous fat area evaluated by performing CT scan at term-equivalent age, but were not associated with visceral fat area in simple regression analyses. In addition, T-Ad (β=0.487, p=0.003) and HMW-Ad levels (β=0.602, p<0.001) were positively associated with subcutaneous fat tissue area, but they were not associated with visceral fat area also in multiple regression analyses. Subcutaneous fat accumulation contributes to increased levels of T-Ad and HMW-Ad, while visceral fat accumulation does not influence adiponectin levels in preterm infants at term-equivalent age. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

PubMed Central

Riley, Richard D.

2017-01-01

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

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

PubMed

Willis, Brian H; Riley, Richard D

2017-09-20

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

Differential effects of anxiety and depressive symptoms on working memory components in children and adolescents with ADHD combined type and ADHD inattentive type.

PubMed

Ferrin, Maite; Vance, Alasdair

2014-12-01

Working memory (WM) deficits have been shown to be associated with core ADHD symptoms, worse academic achievement and peer-relationship problems. Internalizing symptoms, such as anxiety and depression, have also been associated with impaired WM performance. However, the association of anxiety and depression and WM performance remains unclear for children and adolescents with ADHD. Further, it is unknown how these comorbid conditions might affect WM performance in the two main ADHD subtypes. The association of anxiety and depression and the specific components of spatial (SWM) and verbal working memory (VWM) were examined in 303 children and adolescents with ADHD, combined type (ADHD-CT) and 77 ADHD, inattentive type (ADHD-IA) compared to 128 age- and gender-matched typically developing participants. The relationship between anxiety and depression and WM was assessed using multiple linear regression analyses and separate simple regression analyses. Higher levels of anxiety/depression were associated with (1) increased between-search errors in the typically developing participants alone, (2) a better strategy performance in the ADHD-CT group, and (3) a better spatial span performance in the ADHD-IA group. VWM was equally impaired in the ADHD-CT and ADHD-IA groups, independent of the levels of anxiety and depression. The results suggest that the effects of internalizing symptoms on WM differ in typically developing children and adolescents compared to those with ADHD. Further, high levels of anxiety and depression modified WM performance differently according to the specific ADHD subtypes. This might help explain contradictory findings observed in previous studies of mixed samples of participants with ADHD-CT and ADHD-IA.

Development and validation of the Surgical Outcome Risk Tool (SORT)

PubMed Central

Protopapa, K L; Simpson, J C; Smith, N C E; Moonesinghe, S R

2014-01-01

Background Existing risk stratification tools have limitations and clinical experience suggests they are not used routinely. The aim of this study was to develop and validate a preoperative risk stratification tool to predict 30-day mortality after non-cardiac surgery in adults by analysis of data from the observational National Confidential Enquiry into Patient Outcome and Death (NCEPOD) Knowing the Risk study. Methods The data set was split into derivation and validation cohorts. Logistic regression was used to construct a model in the derivation cohort to create the Surgical Outcome Risk Tool (SORT), which was tested in the validation cohort. Results Prospective data for 19 097 cases in 326 hospitals were obtained from the NCEPOD study. Following exclusion of 2309, details of 16 788 patients were analysed (derivation cohort 11 219, validation cohort 5569). A model of 45 risk factors was refined on repeated regression analyses to develop a model comprising six variables: American Society of Anesthesiologists Physical Status (ASA-PS) grade, urgency of surgery (expedited, urgent, immediate), high-risk surgical specialty (gastrointestinal, thoracic, vascular), surgical severity (from minor to complex major), cancer and age 65 years or over. In the validation cohort, the SORT was well calibrated and demonstrated better discrimination than the ASA-PS and Surgical Risk Scale; areas under the receiver operating characteristic (ROC) curve were 0·91 (95 per cent c.i. 0·88 to 0·94), 0·87 (0·84 to 0·91) and 0·88 (0·84 to 0·92) respectively (P < 0·001). Conclusion The SORT allows rapid and simple data entry of six preoperative variables, and provides a percentage mortality risk for individuals undergoing surgery. PMID:25388883

Estimating regression coefficients from clustered samples: Sampling errors and optimum sample allocation

NASA Technical Reports Server (NTRS)

Kalton, G.

1983-01-01

A number of surveys were conducted to study the relationship between the level of aircraft or traffic noise exposure experienced by people living in a particular area and their annoyance with it. These surveys generally employ a clustered sample design which affects the precision of the survey estimates. Regression analysis of annoyance on noise measures and other variables is often an important component of the survey analysis. Formulae are presented for estimating the standard errors of regression coefficients and ratio of regression coefficients that are applicable with a two- or three-stage clustered sample design. Using a simple cost function, they also determine the optimum allocation of the sample across the stages of the sample design for the estimation of a regression coefficient.

Changes in Clavicle Length and Maturation in Americans: 1840-1980.

PubMed

Langley, Natalie R; Cridlin, Sandra

2016-01-01

Secular changes refer to short-term biological changes ostensibly due to environmental factors. Two well-documented secular trends in many populations are earlier age of menarche and increasing stature. This study synthesizes data on maximum clavicle length and fusion of the medial epiphysis in 1840-1980 American birth cohorts to provide a comprehensive assessment of developmental and morphological change in the clavicle. Clavicles from the Hamann-Todd Human Osteological Collection (n = 354), McKern and Stewart Korean War males (n = 341), Forensic Anthropology Data Bank (n = 1,239), and the McCormick Clavicle Collection (n = 1,137) were used in the analysis. Transition analysis was used to evaluate fusion of the medial epiphysis (scored as unfused, fusing, or fused). Several statistical treatments were used to assess fluctuations in maximum clavicle length. First, Durbin-Watson tests were used to evaluate autocorrelation, and a local regression (LOESS) was used to identify visual shifts in the regression slope. Next, piecewise regression was used to fit linear regression models before and after the estimated breakpoints. Multiple starting parameters were tested in the range determined to contain the breakpoint, and the model with the smallest mean squared error was chosen as the best fit. The parameters from the best-fit models were then used to derive the piecewise models, which were compared with the initial simple linear regression models to determine which model provided the best fit for the secular change data. The epiphyseal union data indicate a decline in the age at onset of fusion since the early twentieth century. Fusion commences approximately four years earlier in mid- to late twentieth-century birth cohorts than in late nineteenth- and early twentieth-century birth cohorts. However, fusion is completed at roughly the same age across cohorts. The most significant decline in age at onset of epiphyseal union appears to have occurred since the mid-twentieth century. LOESS plots show a breakpoint in the clavicle length data around the mid-twentieth century in both sexes, and piecewise regression models indicate a significant decrease in clavicle length in the American population after 1940. The piecewise model provides a slightly better fit than the simple linear model. Since the model standard error is not substantially different from the piecewise model, an argument could be made to select the less complex linear model. However, we chose the piecewise model to detect changes in clavicle length that are overfitted with a linear model. The decrease in maximum clavicle length is in line with a documented narrowing of the American skeletal form, as shown by analyses of cranial and facial breadth and bi-iliac breadth of the pelvis. Environmental influences on skeletal form include increases in body mass index, health improvements, improved socioeconomic status, and elimination of infectious diseases. Secular changes in bony dimensions and skeletal maturation stipulate that medical and forensic standards used to deduce information about growth, health, and biological traits must be derived from modern populations.

Birefringence of Cellotape: Jones Representation and Experimental Analysis

ERIC Educational Resources Information Center

Belendez, Augusto; Fernandez, Elena; Frances, Jorge; Neipp, Cristian

2010-01-01

In this paper, we analyse a simple experiment to study the effects of polarized light. A simple optical system composed of a polarizer, a retarder (cellotape) and an analyser is used to study the effect on the polarization state of the light which impinges on the setup. The optical system is characterized by means of a Jones matrix, and a simple…

Forecasting USAF JP-8 Fuel Needs

DTIC Science & Technology

2009-03-01

versus complex ones. When we consider long -term forecasts, 5-years in this case, multiple regression outperforms ANN modeling within the specified...with more simple and easy-to-implement methods, versus complex ones. When we consider long -term 5-year forecasts, our multiple regression model...effort. The insight and experience was certainly appreciated. Special thanks to my Turkish peers for their continuous support and help during this long

Extension of the Peters–Belson method to estimate health disparities among multiple groups using logistic regression with survey data

PubMed Central

Li, Y.; Graubard, B. I.; Huang, P.; Gastwirth, J. L.

2015-01-01

Determining the extent of a disparity, if any, between groups of people, for example, race or gender, is of interest in many fields, including public health for medical treatment and prevention of disease. An observed difference in the mean outcome between an advantaged group (AG) and disadvantaged group (DG) can be due to differences in the distribution of relevant covariates. The Peters–Belson (PB) method fits a regression model with covariates to the AG to predict, for each DG member, their outcome measure as if they had been from the AG. The difference between the mean predicted and the mean observed outcomes of DG members is the (unexplained) disparity of interest. We focus on applying the PB method to estimate the disparity based on binary/multinomial/proportional odds logistic regression models using data collected from complex surveys with more than one DG. Estimators of the unexplained disparity, an analytic variance–covariance estimator that is based on the Taylor linearization variance–covariance estimation method, as well as a Wald test for testing a joint null hypothesis of zero for unexplained disparities between two or more minority groups and a majority group, are provided. Simulation studies with data selected from simple random sampling and cluster sampling, as well as the analyses of disparity in body mass index in the National Health and Nutrition Examination Survey 1999–2004, are conducted. Empirical results indicate that the Taylor linearization variance–covariance estimation is accurate and that the proposed Wald test maintains the nominal level. PMID:25382235

Identification of International Classification of Functioning, Disability and Health categories for patients with peripheral arterial disease.

PubMed

Vyskocil, Erich; Gruther, Wolfgang; Steiner, Irene; Schuhfried, Othmar

2014-07-01

Disease-specific categories of the International Classification of Functioning, Disability and Health have not yet been described for patients with chronic peripheral arterial obstructive disease (PAD). The authors examined the relationship between the categories of the Brief Core Sets for ischemic heart diseases with the Peripheral Artery Questionnaire and the ankle-brachial index to determine which International Classification of Functioning, Disability and Health categories are most relevant for patients with PAD. This is a retrospective cohort study including 77 patients with verified PAD. Statistical analyses of the relationship between International Classification of Functioning, Disability and Health categories as independent variables and the endpoints Peripheral Artery Questionnaire or ankle-brachial index were carried out by simple and stepwise linear regression models adjusting for age, sex, and leg (left vs. right). The stepwise linear regression model with the ankle-brachial index as dependent variable revealed a significant effect of the variables blood vessel functions and muscle endurance functions. Calculating a stepwise linear regression model with the Peripheral Artery Questionnaire as dependent variable, a significant effect of age, emotional functions, energy and drive functions, carrying out daily routine, as well as walking could be observed. This study identifies International Classification of Functioning, Disability and Health categories in the Brief Core Sets for ischemic heart diseases that show a significant effect on the ankle-brachial index and the Peripheral Artery Questionnaire score in patients with PAD. These categories provide fundamental information on functioning of patients with PAD and patient-centered outcomes for rehabilitation interventions.

Using Linear Equating to Map PROMIS(®) Global Health Items and the PROMIS-29 V2.0 Profile Measure to the Health Utilities Index Mark 3.

PubMed

Hays, Ron D; Revicki, Dennis A; Feeny, David; Fayers, Peter; Spritzer, Karen L; Cella, David

2016-10-01

Preference-based health-related quality of life (HR-QOL) scores are useful as outcome measures in clinical studies, for monitoring the health of populations, and for estimating quality-adjusted life-years. This was a secondary analysis of data collected in an internet survey as part of the Patient-Reported Outcomes Measurement Information System (PROMIS(®)) project. To estimate Health Utilities Index Mark 3 (HUI-3) preference scores, we used the ten PROMIS(®) global health items, the PROMIS-29 V2.0 single pain intensity item and seven multi-item scales (physical functioning, fatigue, pain interference, depressive symptoms, anxiety, ability to participate in social roles and activities, sleep disturbance), and the PROMIS-29 V2.0 items. Linear regression analyses were used to identify significant predictors, followed by simple linear equating to avoid regression to the mean. The regression models explained 48 % (global health items), 61 % (PROMIS-29 V2.0 scales), and 64 % (PROMIS-29 V2.0 items) of the variance in the HUI-3 preference score. Linear equated scores were similar to observed scores, although differences tended to be larger for older study participants. HUI-3 preference scores can be estimated from the PROMIS(®) global health items or PROMIS-29 V2.0. The estimated HUI-3 scores from the PROMIS(®) health measures can be used for economic applications and as a measure of overall HR-QOL in research.

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

ERIC Educational Resources Information Center

Shafiq, M. Najeeb

2013-01-01

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

Seismic Safety Of Simple Masonry Buildings

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

Guadagnuolo, Mariateresa; Faella, Giuseppe

2008-07-08

Several masonry buildings comply with the rules for simple buildings provided by seismic codes. For these buildings explicit safety verifications are not compulsory if specific code rules are fulfilled. In fact it is assumed that their fulfilment ensures a suitable seismic behaviour of buildings and thus adequate safety under earthquakes. Italian and European seismic codes differ in the requirements for simple masonry buildings, mostly concerning the building typology, the building geometry and the acceleration at site. Obviously, a wide percentage of buildings assumed simple by codes should satisfy the numerical safety verification, so that no confusion and uncertainty have tomore » be given rise to designers who must use the codes. This paper aims at evaluating the seismic response of some simple unreinforced masonry buildings that comply with the provisions of the new Italian seismic code. Two-story buildings, having different geometry, are analysed and results from nonlinear static analyses performed by varying the acceleration at site are presented and discussed. Indications on the congruence between code rules and results of numerical analyses performed according to the code itself are supplied and, in this context, the obtained result can provide a contribution for improving the seismic code requirements.« less

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

PubMed Central

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

2009-01-01

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

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

PubMed

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

2009-02-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Associations between Verbal Learning Slope and Neuroimaging Markers across the Cognitive Aging Spectrum.

PubMed

Gifford, Katherine A; Phillips, Jeffrey S; Samuels, Lauren R; Lane, Elizabeth M; Bell, Susan P; Liu, Dandan; Hohman, Timothy J; Romano, Raymond R; Fritzsche, Laura R; Lu, Zengqi; Jefferson, Angela L

2015-07-01

A symptom of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is a flat learning profile. Learning slope calculation methods vary, and the optimal method for capturing neuroanatomical changes associated with MCI and early AD pathology is unclear. This study cross-sectionally compared four different learning slope measures from the Rey Auditory Verbal Learning Test (simple slope, regression-based slope, two-slope method, peak slope) to structural neuroimaging markers of early AD neurodegeneration (hippocampal volume, cortical thickness in parahippocampal gyrus, precuneus, and lateral prefrontal cortex) across the cognitive aging spectrum [normal control (NC); (n=198; age=76±5), MCI (n=370; age=75±7), and AD (n=171; age=76±7)] in ADNI. Within diagnostic group, general linear models related slope methods individually to neuroimaging variables, adjusting for age, sex, education, and APOE4 status. Among MCI, better learning performance on simple slope, regression-based slope, and late slope (Trial 2-5) from the two-slope method related to larger parahippocampal thickness (all p-values<.01) and hippocampal volume (p<.01). Better regression-based slope (p<.01) and late slope (p<.01) were related to larger ventrolateral prefrontal cortex in MCI. No significant associations emerged between any slope and neuroimaging variables for NC (p-values ≥.05) or AD (p-values ≥.02). Better learning performances related to larger medial temporal lobe (i.e., hippocampal volume, parahippocampal gyrus thickness) and ventrolateral prefrontal cortex in MCI only. Regression-based and late slope were most highly correlated with neuroimaging markers and explained more variance above and beyond other common memory indices, such as total learning. Simple slope may offer an acceptable alternative given its ease of calculation.

A simple code for use in shielding and radiation dosage analyses

NASA Technical Reports Server (NTRS)

Wan, C. C.

1972-01-01

A simple code for use in analyses of gamma radiation effects in laminated materials is described. Simple and good geometry is assumed so that all multiple collision and scattering events are excluded from consideration. The code is capable of handling laminates up to six layers. However, for laminates of more than six layers, the same code may be used to incorporate two additional layers at a time, making use of punch-tape outputs from previous computation on all preceding layers. Spectrum of attenuated radiation are obtained as both printed output and punch tape output as desired.

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…

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

PubMed Central

Meyer, Karin

2005-01-01

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

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

PubMed

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

2013-04-01

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

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

PubMed

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

2017-06-01

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

Regression and multivariate models for predicting particulate matter concentration level.

PubMed

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

2018-01-01

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

Accuracy of simple biochemical tests in identifying liver fibrosis in patients co-infected with human immunodeficiency virus and hepatitis C virus.

PubMed

Tural, Cristina; Tor, Jordi; Sanvisens, Arantza; Pérez-Alvarez, Núria; Martínez, Elisenda; Ojanguren, Isabel; García-Samaniego, Javier; Rockstroh, Juergen; Barluenga, Eva; Muga, Robert; Planas, Ramon; Sirera, Guillem; Rey-Joly, Celestino; Clotet, Bonaventura

2009-03-01

We assessed the ability of 3 simple biochemical tests to stage liver fibrosis in patients co-infected with human immunodeficiency virus (HIV) and hepatitis C virus (HCV). We analyzed liver biopsy samples from 324 consecutive HIV/HCV-positive patients (72% men; mean age, 38 y; mean CD4+ T-cell counts, 548 cells/mm(3)). Scheuer fibrosis scores were as follows: 30% had F0, 22% had F1, 19% had F2, 23% had F3, and 6% had F4. Logistic regression analyses were used to predict the probability of significant (>or=F2) or advanced (>or=F3) fibrosis, based on numeric scores from the APRI, FORNS, or FIB-4 tests (alone and in combination). Area under the receiver operating characteristic curves were analyzed to assess diagnostic performance. Area under the receiver operating characteristic curves analyses indicated that the 3 tests had similar abilities to identify F2 and F3; the ability of APRI, FORNS, and FIB-4 were as follows: F2 or greater: 0.72, 0.67, and 0.72, respectively; F3 or greater: 0.75, 0.73, and 0.78, respectively. The accuracy of each test in predicting which samples were F3 or greater was significantly higher than for F2 or greater (APRI, FORNS, and FIB-4: >or=F3: 75%, 76%, and 76%, respectively; >or=F2: 66%, 62%, and 68%, respectively). By using the lowest cut-off values for all 3 tests, F3 or greater was ruled out with sensitivity and negative predictive values of 79% to 94% and 87% to 91%, respectively, and 47% to 70% accuracy. Advanced liver fibrosis (>or=F3) was identified using the highest cut-off value, with specificity and positive predictive values of 90% to 96% and 63% to 73%, respectively, and 75% to 77% accuracy. Simple biochemical tests accurately predicted liver fibrosis in more than half the HIV/HCV co-infected patients. The absence and presence of liver fibrosis are predicted fairly using the lowest and highest cut-off levels, respectively.

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

Association of daily asthma emergency department visits and hospital admissions with ambient air pollutants among the pediatric Medicaid population in Detroit: time-series and time-stratified case-crossover analyses with threshold effects.

PubMed

Li, Shi; Batterman, Stuart; Wasilevich, Elizabeth; Wahl, Robert; Wirth, Julie; Su, Feng-Chiao; Mukherjee, Bhramar

2011-11-01

Asthma morbidity has been associated with ambient air pollutants in time-series and case-crossover studies. In such study designs, threshold effects of air pollutants on asthma outcomes have been relatively unexplored, which are of potential interest for exploring concentration-response relationships. This study analyzes daily data on the asthma morbidity experienced by the pediatric Medicaid population (ages 2-18 years) of Detroit, Michigan and concentrations of pollutants fine particles (PM2.5), CO, NO2 and SO2 for the 2004-2006 period, using both time-series and case-crossover designs. We use a simple, testable and readily implementable profile likelihood-based approach to estimate threshold parameters in both designs. Evidence of significant increases in daily acute asthma events was found for SO2 and PM2.5, and a significant threshold effect was estimated for PM2.5 at 13 and 11 μg m(-3) using generalized additive models and conditional logistic regression models, respectively. Stronger effect sizes above the threshold were typically noted compared to standard linear relationship, e.g., in the time series analysis, an interquartile range increase (9.2 μg m(-3)) in PM2.5 (5-day-moving average) had a risk ratio of 1.030 (95% CI: 1.001, 1.061) in the generalized additive models, and 1.066 (95% CI: 1.031, 1.102) in the threshold generalized additive models. The corresponding estimates for the case-crossover design were 1.039 (95% CI: 1.013, 1.066) in the conditional logistic regression, and 1.054 (95% CI: 1.023, 1.086) in the threshold conditional logistic regression. This study indicates that the associations of SO2 and PM2.5 concentrations with asthma emergency department visits and hospitalizations, as well as the estimated PM2.5 threshold were fairly consistent across time-series and case-crossover analyses, and suggests that effect estimates based on linear models (without thresholds) may underestimate the true risk. Copyright © 2011 Elsevier Inc. All rights reserved.

Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling.

PubMed

Chatzis, Sotirios P; Andreou, Andreas S

2015-11-01

Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.

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

EPA Science Inventory

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

Optimal Reflectance, Transmittance, and Absorptance Wavebands and Band Ratios for the Estimation of Leaf Chlorophyll Concentration

NASA Technical Reports Server (NTRS)

Carter, Gregory A.; Spiering, Bruce A.

2000-01-01

The present study utilized regression analysis to identify: wavebands and band ratios within the 400-850 nm range that could be used to estimate total chlorophyll concentration with minimal error; and simple regression models that were most effective in estimating chlorophyll concentrations were measured for two broadleaved species, a broadleaved vine, a needle-leaved conifer, and a representative of the grass family.Overall, reflectance, transmittance, and absorptance corresponded most precisely with chlorophyll concentration at wavelengths near 700 nm, although regressions were strong as well in the 550-625 nm range.

A Simulation Investigation of Principal Component Regression.

ERIC Educational Resources Information Center

Allen, David E.

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

A phylogenetic Kalman filter for ancestral trait reconstruction using molecular data.

PubMed

Lartillot, Nicolas

2014-02-15

Correlation between life history or ecological traits and genomic features such as nucleotide or amino acid composition can be used for reconstructing the evolutionary history of the traits of interest along phylogenies. Thus far, however, such ancestral reconstructions have been done using simple linear regression approaches that do not account for phylogenetic inertia. These reconstructions could instead be seen as a genuine comparative regression problem, such as formalized by classical generalized least-square comparative methods, in which the trait of interest and the molecular predictor are represented as correlated Brownian characters coevolving along the phylogeny. Here, a Bayesian sampler is introduced, representing an alternative and more efficient algorithmic solution to this comparative regression problem, compared with currently existing generalized least-square approaches. Technically, ancestral trait reconstruction based on a molecular predictor is shown to be formally equivalent to a phylogenetic Kalman filter problem, for which backward and forward recursions are developed and implemented in the context of a Markov chain Monte Carlo sampler. The comparative regression method results in more accurate reconstructions and a more faithful representation of uncertainty, compared with simple linear regression. Application to the reconstruction of the evolution of optimal growth temperature in Archaea, using GC composition in ribosomal RNA stems and amino acid composition of a sample of protein-coding genes, confirms previous findings, in particular, pointing to a hyperthermophilic ancestor for the kingdom. The program is freely available at www.phylobayes.org.

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

PubMed

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

2018-02-04

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

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

PubMed

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

2017-06-01

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

Forecasting paratransit services demand : review and recommendations.

DOT National Transportation Integrated Search

2013-06-01

Travel demand forecasting tools for Floridas paratransit services are outdated, utilizing old national trip : generation rate generalities and simple linear regression models. In its guidance for the development of : mandated Transportation Disadv...

A rapid assessment scorecard to identify informal settlements at higher maternal and child health risk in Mumbai.

PubMed

Osrin, David; Das, Sushmita; Bapat, Ujwala; Alcock, Glyn A; Joshi, Wasundhara; More, Neena Shah

2011-10-01

The communities who live in urban informal settlements are diverse, as are their environmental conditions. Characteristics include inadequate access to safe water and sanitation, poor quality of housing, overcrowding, and insecure residential status. Interventions to improve health should be equity-driven and target those at higher risk, but it is not clear how to prioritise informal settlements for health action. In implementing a maternal and child health programme in Mumbai, India, we had conducted a detailed vulnerability assessment which, though important, was time-consuming and may have included collection of redundant information. Subsequent data collection allowed us to examine three issues: whether community environmental characteristics were associated with maternal and newborn healthcare and outcomes; whether it was possible to develop a triage scorecard to rank the health vulnerability of informal settlements based on a few rapidly observable characteristics; and whether the scorecard might be useful for future prioritisation. The City Initiative for Newborn Health documented births in 48 urban slum areas over 2 years. Information was collected on maternal and newborn care and mortality, and also on household and community environment. We selected three outcomes-less than three antenatal care visits, home delivery, and neonatal mortality-and used logistic regression and classification and regression tree analysis to test their association with rapidly observable environmental characteristics. We developed a simple triage scorecard and tested its utility as a means of assessing maternal and newborn health risk. In analyses on a sample of 10,754 births, we found associations of health vulnerability with inadequate access to water, toilets, and electricity; non-durable housing; hazardous location; and rental tenancy. A simple scorecard based on these had limited sensitivity and positive predictive value, but relatively high specificity and negative predictive value. The scorecard needs further testing in a range of urban contexts, but we intend to use it to identify informal settlements in particular need of family health interventions in a subsequent program.

Cognitive models of physicians' legal standard and personal judgments of competency in patients with Alzheimer's disease.

PubMed

Earnst, K S; Marson, D C; Harrell, L E

2000-08-01

To investigate measures of patient cognitive abilities as predictors of physician judgments of medical treatment consent capacity (competency) in patients with Alzheimer's disease (AD). Predictor models of legal standards (LS) and personal competency judgments were developed for each study physician using independent neuropsychological test measures and logistic regression analyses. A university medical center. Five physicians with experience assessing the competency of AD patients were recruited to make competency judgments of videotaped vignettes from 10 older controls and 21 patients with AD (10 with mild and 11 with moderate dementia). The 31 patient and control videotapes of performance on a measure of treatment consent capacity (Capacity to Consent to Treatment Instrument) (CCTI) were rated by the five physicians. The CCTI consists of two clinical vignettes (A-neoplasm and B-cardiac) that test competency under five LS. Each study physician viewed each vignette videotape individually, made judgments of competent or incompetent under each of the LS, and then made his/her own personal competency judgment. Physicians were blinded to participant diagnosis and neuropsychological test performance. Stepwise logistic regression was conducted to identify cognitive predictors of each physician's LS and personal competency judgments for Vignette A using the full sample (n = 31). Classification logistic regression analysis was used to determine how well these cognitive predictor models classified each physician's competency judgments for Vignette A. These classification models were then cross-validated using physician's Vignette B judgments. Cognitive predictor models for Vignette A competency judgments differed across individual physicians, and were related to difficulty of LS and to incompetency outcome rates across LS for AD patients. Measures of semantic knowledge and receptive language predicted judgments under less difficult LS of evidencing a treatment choice (LS1) and making the reasonable treatment choice (LS2). Measures of semantic knowledge, short-term verbal recall, and simple reasoning ability predicted judgments under more difficult and clinically relevant LS of appreciating consequences of a treatment choice (LS3), providing rational reasons for a treatment choice (LS4), and understanding the treatment situation and choices (LSS). Cognitive models for physicians' personal competency judgments were virtually identical to their respective models for LS5 judgments. For AD patients, shortterm memory predictors were associated with high incompetency outcome rates (over 70%), a simple reasoning measure was associated with moderately high incompetency outcome rates (60-70%), and a semantic knowledge measure was associated with lower incompetency outcome rates (30-60%). Overall, single predictor models were relatively robust, correctly classifying an average of 83% of physician judgments for Vignette A and 80% of judgments for Vignette B. Multiple cognitive functions predicted physicians' LS and personal competency judgments. Declines in semantic knowledge, short-term verbal recall, and simple reasoning ability predicted physicians' judgments on the three most difficult and clinically most relevant LS (LS3-LS5), as well as their personal competency judgments. Our findings suggest that clinical assessment of competency should include evaluation of semantic knowledge, verbal recall, and simple reasoning abilities.

Predicting Word Reading Ability: A Quantile Regression Study

ERIC Educational Resources Information Center

McIlraith, Autumn L.

2018-01-01

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

The Digital Shoreline Analysis System (DSAS) Version 4.0 - An ArcGIS extension for calculating shoreline change

USGS Publications Warehouse

Thieler, E. Robert; Himmelstoss, Emily A.; Zichichi, Jessica L.; Ergul, Ayhan

2009-01-01

The Digital Shoreline Analysis System (DSAS) version 4.0 is a software extension to ESRI ArcGIS v.9.2 and above that enables a user to calculate shoreline rate-of-change statistics from multiple historic shoreline positions. A user-friendly interface of simple buttons and menus guides the user through the major steps of shoreline change analysis. Components of the extension and user guide include (1) instruction on the proper way to define a reference baseline for measurements, (2) automated and manual generation of measurement transects and metadata based on user-specified parameters, and (3) output of calculated rates of shoreline change and other statistical information. DSAS computes shoreline rates of change using four different methods: (1) endpoint rate, (2) simple linear regression, (3) weighted linear regression, and (4) least median of squares. The standard error, correlation coefficient, and confidence interval are also computed for the simple and weighted linear-regression methods. The results of all rate calculations are output to a table that can be linked to the transect file by a common attribute field. DSAS is intended to facilitate the shoreline change-calculation process and to provide rate-of-change information and the statistical data necessary to establish the reliability of the calculated results. The software is also suitable for any generic application that calculates positional change over time, such as assessing rates of change of glacier limits in sequential aerial photos, river edge boundaries, land-cover changes, and so on.

Methodological and Reporting Quality of Systematic Reviews and Meta-analyses in Endodontics.

PubMed

Nagendrababu, Venkateshbabu; Pulikkotil, Shaju Jacob; Sultan, Omer Sheriff; Jayaraman, Jayakumar; Peters, Ove A

2018-06-01

The aim of this systematic review (SR) was to evaluate the quality of SRs and meta-analyses (MAs) in endodontics. A comprehensive literature search was conducted to identify relevant articles in the electronic databases from January 2000 to June 2017. Two reviewers independently assessed the articles for eligibility and data extraction. SRs and MAs on interventional studies with a minimum of 2 therapeutic strategies in endodontics were included in this SR. Methodologic and reporting quality were assessed using A Measurement Tool to Assess Systematic Reviews (AMSTAR) and Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA), respectively. The interobserver reliability was calculated using the Cohen kappa statistic. Statistical analysis with the level of significance at P < .05 was performed using Kruskal-Wallis tests and simple linear regression analysis. A total of 30 articles were selected for the current SR. Using AMSTAR, the item related to the scientific quality of studies used in conclusion was adhered by less than 40% of studies. Using PRISMA, 3 items were reported by less than 40% of studies, which were on objectives, protocol registration, and funding. No association was evident comparing the number of authors and country with quality. Statistical significance was observed when quality was compared among journals, with studies published as Cochrane reviews superior to those published in other journals. AMSTAR and PRISMA scores were significantly related. SRs in endodontics showed variability in both methodologic and reporting quality. Copyright © 2018 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Can group-based reassuring information alter low back pain behavior? A cluster-randomized controlled trial

PubMed Central

Indahl, Aage; Andersen, Lars L.; Burton, Kim; Hertzum-Larsen, Rasmus

2017-01-01

Background Low back pain (LBP) is common in the population and multifactorial in nature, often involving negative consequences. Reassuring information to improve coping is recommended for reducing the negative consequences of LBP. Adding a simple non-threatening explanation for the pain (temporary muscular dysfunction) has been successful at altering beliefs and behavior when delivered with other intervention elements. This study investigates the isolated effect of this specific information on future occupational behavior outcomes when delivered to the workforce. Design A cluster-randomized controlled trial. Methods Publically employed workers (n = 505) from 11 Danish municipality centers were randomized at center-level (cluster) to either intervention (two 1-hour group-based talks at the workplace) or control. The talks provided reassuring information together with a simple non-threatening explanation for LBP—the ‘functional-disturbance’-model. Data collections took place monthly over a 1-year period using text message tracking (SMS). Primary outcomes were self-reported days of cutting down usual activities and work participation. Secondary outcomes were self-reported back beliefs, work ability, number of healthcare visits, bothersomeness, restricted activity, use of pain medication, and sadness/depression. Results There was no between-group difference in the development of LBP during follow-up. Cumulative logistic regression analyses showed no between-group difference on days of cutting down activities, but increased odds for more days of work participation in the intervention group (OR = 1.83 95% CI: 1.08–3.12). Furthermore, the intervention group was more likely to report: higher work ability, reduced visits to healthcare professionals, lower bothersomeness, lower levels of sadness/depression, and positive back beliefs. Conclusion Reassuring information involving a simple non-threatening explanation for LBP significantly increased the odds for days of work participation and higher work ability among workers who went on to experience LBP during the 12-month follow-up. Our results confirm the potential for public-health education for LBP, and add to the discussion of simple versus multidisciplinary interventions. PMID:28346472

The Prediction Properties of Inverse and Reverse Regression for the Simple Linear Calibration Problem

NASA Technical Reports Server (NTRS)

Parker, Peter A.; Geoffrey, Vining G.; Wilson, Sara R.; Szarka, John L., III; Johnson, Nels G.

2010-01-01

The calibration of measurement systems is a fundamental but under-studied problem within industrial statistics. The origins of this problem go back to basic chemical analysis based on NIST standards. In today's world these issues extend to mechanical, electrical, and materials engineering. Often, these new scenarios do not provide "gold standards" such as the standard weights provided by NIST. This paper considers the classic "forward regression followed by inverse regression" approach. In this approach the initial experiment treats the "standards" as the regressor and the observed values as the response to calibrate the instrument. The analyst then must invert the resulting regression model in order to use the instrument to make actual measurements in practice. This paper compares this classical approach to "reverse regression," which treats the standards as the response and the observed measurements as the regressor in the calibration experiment. Such an approach is intuitively appealing because it avoids the need for the inverse regression. However, it also violates some of the basic regression assumptions.

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

PubMed

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

2011-01-01

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

Bayesian isotonic density regression

PubMed Central

Wang, Lianming; Dunson, David B.

2011-01-01

Density regression models allow the conditional distribution of the response given predictors to change flexibly over the predictor space. Such models are much more flexible than nonparametric mean regression models with nonparametric residual distributions, and are well supported in many applications. A rich variety of Bayesian methods have been proposed for density regression, but it is not clear whether such priors have full support so that any true data-generating model can be accurately approximated. This article develops a new class of density regression models that incorporate stochastic-ordering constraints which are natural when a response tends to increase or decrease monotonely with a predictor. Theory is developed showing large support. Methods are developed for hypothesis testing, with posterior computation relying on a simple Gibbs sampler. Frequentist properties are illustrated in a simulation study, and an epidemiology application is considered. PMID:22822259

The influence of physical and cognitive factors on reactive agility performance in men basketball players.

PubMed

Scanlan, Aaron; Humphries, Brendan; Tucker, Patrick S; Dalbo, Vincent

2014-01-01

This study explored the influence of physical and cognitive measures on reactive agility performance in basketball players. Twelve men basketball players performed multiple sprint, Change of Direction Speed Test, and Reactive Agility Test trials. Pearson's correlation analyses were used to determine relationships between the predictor variables (stature, mass, body composition, 5-m, 10-m and 20-m sprint times, peak speed, closed-skill agility time, response time and decision-making time) and reactive agility time (response variable). Simple and stepwise regression analyses determined the individual influence of each predictor variable and the best predictor model for reactive agility time. Morphological (r = -0.45 to 0.19), sprint (r = -0.40 to 0.41) and change-of-direction speed measures (r = 0.43) had small to moderate correlations with reactive agility time. Response time (r = 0.76, P = 0.004) and decision-making time (r = 0.58, P = 0.049) had large to very large relationships with reactive agility time. Response time was identified as the sole predictor variable for reactive agility time in the stepwise model (R(2) = 0.58, P = 0.004). In conclusion, cognitive measures had the greatest influence on reactive agility performance in men basketball players. These findings suggest reaction and decision-making drills should be incorporated in basketball training programmes.

Rising air and stream-water temperatures in Chesapeake Bay region, USA

USGS Publications Warehouse

Rice, Karen C.; Jastram, John D.

2015-01-01

Monthly mean air temperature (AT) at 85 sites and instantaneous stream-water temperature (WT) at 129 sites for 1960–2010 are examined for the mid-Atlantic region, USA. Temperature anomalies for two periods, 1961–1985 and 1985–2010, relative to the climate normal period of 1971–2000, indicate that the latter period was statistically significantly warmer than the former for both mean AT and WT. Statistically significant temporal trends across the region of 0.023 °C per year for AT and 0.028 °C per year for WT are detected using simple linear regression. Sensitivity analyses show that the irregularly sampled WT data are appropriate for trend analyses, resulting in conservative estimates of trend magnitude. Relations between 190 landscape factors and significant trends in AT-WT relations are examined using principal components analysis. Measures of major dams and deciduous forest are correlated with WT increasing slower than AT, whereas agriculture in the absence of major dams is correlated with WT increasing faster than AT. Increasing WT trends are detected despite increasing trends in streamflow in the northern part of the study area. Continued warming of contributing streams to Chesapeake Bay likely will result in shifts in distributions of aquatic biota and contribute to worsened eutrophic conditions in the bay and its estuaries.

What contributes to perceived stress in later life? A recursive partitioning approach.

PubMed

Scott, Stacey B; Jackson, Brenda R; Bergeman, C S

2011-12-01

One possible explanation for the individual differences in outcomes of stress is the diversity of inputs that produce perceptions of being stressed. The current study examines how combinations of contextual features (e.g., social isolation, neighborhood quality, health problems, age discrimination, financial concerns, and recent life events) of later life contribute to overall feelings of stress. Recursive partitioning techniques (regression trees and random forests) were used to examine unique interrelations between predictors of perceived stress in a sample of 282 community-dwelling adults. Trees provided possible examples of equifinality (i.e., subsets of people with similar levels of perceived stress but different predictors) as well as identification both of contextual combinations that separated participants with very high and very low perceived stress. Random forest analyses aggregated across many trees based on permuted versions of the data and predictors; loneliness, financial strain, neighborhood strain, ageism, and to some extent life events emerged as important predictors. Interviews with a subsample of participants provided both thick description of the complex relationships identified in the trees, as well as additional risks not appearing in the survey results. Together, the analyses highlight what may be missed when stress is used as a simple unidimensional construct and can guide differential intervention efforts.

What contributes to perceived stress in later life? A recursive partitioning approach

PubMed Central

Scott, Stacey B.; Jackson, Brenda R.; Bergeman, C. S.

2011-01-01

One possible explanation for the individual differences in outcomes of stress is the diversity of inputs that produce perceptions of being stressed. The current study examines how combinations of contextual features (e.g., social isolation, neighborhood quality, health problems, age discrimination, financial concerns, and recent life events) of later life contribute to overall feelings of stress. Recursive partitioning techniques (regression trees and random forests) were used to examine unique interrelations between predictors of perceived stress in a sample of 282 community-dwelling adults. Trees provided possible examples of equifinality (i.e., subsets of people with similar levels of perceived stress but different predictors) as well as for the identification both of contextual combinations that separated participants with very high and very low perceived stress. Random forest analyses aggregated across many trees based on permuted versions of the data and predictors; loneliness, financial strain, neighborhood strain, ageism, and to some extent life events emerged as important predictors. Interviews with a subsample of participants provided both thick description of the complex relationships identified in the trees, as well as additional risks not appearing in the survey results. Together, the analyses highlight what may be missed when stress is used as a simple unidimensional construct and can guide differential intervention efforts. PMID:21604885

Pictorial Superiority Effects in Oldest-Old People

PubMed Central

Cherry, Katie E.; Hawley, Karri S.; Jackson, Erin M.; Volaufova, Julia; Su, L. Joseph; Jazwinski, S. Michal

2008-01-01

In this article, we examined memory for pictures and words in middle-age (45-59 years), young-old (60-74 years), old-old (75-89 years) and the oldest-old adults (90-97 years) in the Louisiana Healthy Aging Study. Stimulus items were presented and retention was tested in a blocked order where half of the participants studied 16 simple line drawings and the other half studied matching words during acquisition. Free recall and recognition followed. In the next acquisition/test block, a new set of items was used where the stimulus format was changed relative to the first block. Results yielded pictorial superiority effects in both retention measures for all age groups. Follow-up analyses of clustering in free recall revealed a greater number of categories were accessed (which reflects participants' retrieval plan) and more items were recalled per category (which reflects participants' encoding strategy) when pictures served as stimuli compared to words. Cognitive status and working memory span were correlated with picture and word recall. Regression analyses confirmed that these individual difference variables accounted for significant age-related variance in recall. These data strongly suggest that the oldest-old can utilize nonverbal memory codes to support long-term retention as effectively as do younger adults. PMID:18651263

Parent Health Literacy and Communication With Diabetes Educators in a Pediatric Diabetes Clinic: A Mixed Methods Approach.

PubMed

Howe, Carol J; Cipher, Daisha J; LeFlore, Judy; Lipman, Terri H

2015-01-01

Low health literacy is associated with poor communication between adults and providers, but little is known about how parents' health literacy influences communication in pediatric encounters. We examined how parent health literacy affected communication between parents and diabetes educators in a pediatric diabetes clinic. A mixed methods study was conducted including a cross-sectional survey of 162 parents and semi-structured interviews with a subsample of 24 parents of a child with Type 1 diabetes. Parent and child characteristics, parents' report of quality of communication, and parent health literacy were assessed. Logistic regression was performed to determine associations between health literacy and 4 subscales of the Interpersonal Processes of Care (IPC) survey; directed content analyses of interview data were completed. Although health literacy was not significantly associated with the IPC subscales, results from directed content analyses revealed different communication experiences for parents by health literacy classification. Low health literate parents were confused by diabetes jargon, preferred hands-on teaching, and wished for information to be communicated in simple language, broken down into key points, and repeated. Parents with adequate health literacy wanted comprehensive information communicated through ongoing dialogue. Findings indicate that learner-driven curricula may be most appropriate for diabetes education.

Intention stability assessed using residual change scores moderates the intention-behaviour association: a prospective cohort study.

PubMed

Rowan, Alicia A; McDermott, Máirtín S; Allen, Mark S

2017-12-01

Intention stability is considered to be one of the key pre-requisites for a strong association between intention and behaviour. It has been claimed, however, that studies examining the moderating impact of intention stability may be invalid, as they have relied on statistically inferior methods. Residual change scores have been suggested as a more appropriate method of measuring change (or lack thereof) in constructs. The aim of the current study, therefore, is to test whether intention stability, calculated using residual change scores, moderates the intention-physical activity behaviour association. A total of 163 participants (124 women, 39 men) completed questionnaires online at three time points separated by 14 day intervals. The moderating impact of intention stability was assessed using multiple linear regression followed up using simple slope analyses to identify the direction of any effect. The interaction of intention and intention stability was found to significantly improve the overall model fit. Intentions had a stronger positive association with behaviour when intentions were more stable than when they were more unstable. However, sensitivity analyses revealed that the association was not robust and reduced to non-significant with the removal of potential multivariate outliers. Future research should use residual change scores as the preferred method of assessing intention stability.

Possible health-protecting effects of feeling useful to others on symptoms of depression and sleep disturbance in the workplace.

PubMed

Takaki, Jiro; Tsutsumi, Akizumi; Irimajiri, Hirohiko; Hayama, Asako; Hibino, Yuri; Kanbara, Sakiko; Sakano, Noriko; Ogino, Keiki

2010-01-01

The aim of this study was to examine the health-protecting effects of feeling useful to others on symptoms of depression and sleep disturbance in the workplace, as well as its buffering effects on associations between stressful work environments and symptoms of depression and sleep disturbance. The subjects of this cross-sectional survey were 773 Japanese workers (response rate: 64.8%) of five organizations. Feelings of being useful to others were assessed with one simple question used in a previous study. Psychosocial work environment, sleep disturbance, and depressive symptoms were assessed using the Japanese versions of the Effort-Reward Imbalance Questionnaire, the Pittsburgh Sleep Quality Index, and the 28-item General Health Questionnaire, respectively. We tested for linear and interactive effects with hierarchical regression analyses. Feeling useful to others was significantly (p<0.05) and negatively associated with scores of depression and sleep disturbance both in the univariate analyses and after adjusting for age in both genders. Significant (p<0.05) interactions showed that, in both genders, as the effort-reward balance worsened, symptoms of depression increased, but feeling useful to others buffered the associations. The results support the notion that feeling useful to others in both genders in the workplace has possible health-protecting effects.

Deriving Vegetation Dynamics of Natural Terrestrial Ecosystems from MODIS NDVI/EVI Data over Turkey.

PubMed

Evrendilek, Fatih; Gulbeyaz, Onder

2008-09-01

The 16-day composite MODIS vegetation indices (VIs) at 500-m resolution for the period between 2000 to 2007 were seasonally averaged on the basis of the estimated distribution of 16 potential natural terrestrial ecosystems (NTEs) across Turkey. Graphical and statistical analyses of the time-series VIs for the NTEs spatially disaggregated in terms of biogeoclimate zones and land cover types included descriptive statistics, correlations, discrete Fourier transform (DFT), time-series decomposition, and simple linear regression (SLR) models. Our spatio-temporal analyses revealed that both MODIS VIs, on average, depicted similar seasonal variations for the NTEs, with the NDVI values having higher mean and SD values. The seasonal VIs were most correlated in decreasing order for: barren/sparsely vegetated land > grassland > shrubland/woodland > forest; (sub)nival > warm temperate > alpine > cool temperate > boreal = Mediterranean; and summer > spring > autumn > winter. Most pronounced differences between the MODIS VI responses over Turkey occurred in boreal and Mediterranean climate zones and forests, and in winter (the senescence phase of the growing season). Our results showed the potential of the time-series MODIS VI datasets in the estimation and monitoring of seasonal and interannual ecosystem dynamics over Turkey that needs to be further improved and refined through systematic and extensive field measurements and validations across various biomes.

I spy with my little eye - the detection of intentional contingency in early psychosis.

PubMed

Fett, Anne-Kathrin J; González Berdugo, Clara Isabel; Hanssen, Esther; Lemmers-Jansen, Imke; Shergill, Sukhi S; Krabbendam, Lydia

2015-01-01

Paranoid delusions have been associated with a tendency to over-attribute intentionality and contingency to others' actions and incidental events in individuals with chronic psychosis. However, this hyper-associative perception bias has not been investigated in the early illness stages of psychosis, during which it may play a particularly crucial role in the formation of symptoms. We used an experimental paradigm with 20 short film clips of simple animate and inanimate shapes that either moved in a contingent or non-contingent manner to investigate the perception of contingency in 38 adolescents with early psychosis and 93 healthy control adolescents. Participants rated the contingency between the shapes' movements on a scale from 0 to 10. The data were analysed with multilevel regression analyses to account for repeated measures within subjects. There were no significant differences between patients and controls; both perceived the contingency of the shapes' movements similarly across all conditions and patients' contingency perception was unrelated to their levels of paranoid delusions. Contingency perception was unimpaired in patients with early psychosis, suggesting that it might still be intact in the early illness stages. Future studies should set out to determine whether the early illness stages could offer a window for interventions that counteract the development of hyper-associative perceptions of contingency.

10 CFR 436.23 - Estimated simple payback time.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Methodology and Procedures for Life Cycle Cost Analyses § 436.23 Estimated simple payback time. The estimated simple payback time is the number of years required for the cumulative value of energy or water cost savings less future non-fuel or non-water costs to equal the investment costs of the building energy or...

10 CFR 436.23 - Estimated simple payback time.

Code of Federal Regulations, 2014 CFR

2014-01-01

... Methodology and Procedures for Life Cycle Cost Analyses § 436.23 Estimated simple payback time. The estimated simple payback time is the number of years required for the cumulative value of energy or water cost savings less future non-fuel or non-water costs to equal the investment costs of the building energy or...

10 CFR 436.23 - Estimated simple payback time.

Code of Federal Regulations, 2011 CFR

2011-01-01

... Methodology and Procedures for Life Cycle Cost Analyses § 436.23 Estimated simple payback time. The estimated simple payback time is the number of years required for the cumulative value of energy or water cost savings less future non-fuel or non-water costs to equal the investment costs of the building energy or...

10 CFR 436.23 - Estimated simple payback time.

Code of Federal Regulations, 2013 CFR

2013-01-01

... Methodology and Procedures for Life Cycle Cost Analyses § 436.23 Estimated simple payback time. The estimated simple payback time is the number of years required for the cumulative value of energy or water cost savings less future non-fuel or non-water costs to equal the investment costs of the building energy or...

10 CFR 436.23 - Estimated simple payback time.

Code of Federal Regulations, 2012 CFR

2012-01-01

... Methodology and Procedures for Life Cycle Cost Analyses § 436.23 Estimated simple payback time. The estimated simple payback time is the number of years required for the cumulative value of energy or water cost savings less future non-fuel or non-water costs to equal the investment costs of the building energy or...

Betel-quid and alcohol use were associated with lipid accumulation product among male factory workers in Taiwan.

PubMed

Huang, Chih-Fang; Chen, Chao-Tung; Wang, Pei-Ming; Koo, Malcolm

2015-05-01

In this study, cardiometabolic risk associated with betel-quid, alcohol and cigarette use, based on a simple index-lipid accumulation product (LAP), was investigated in Taiwanese male factory workers. Male factory workers were recruited during their annual routine health examination at a hospital in south Taiwan. The risk of cardiometabolic disorders was estimated by the use of LAP, calculated as (waist circumference [cm]-65)×(triglyceride concentration [mmol/l]). Multiple linear regression analyses were conducted to assess the risk factors of natural logarithm-transformed LAP. Of the 815 participants, 40% (325/815) were current alcohol users, 30% (248/815) were current smokers and 7% (53/815) were current betel-quid users. Current betel-quid use, alcohol use, older age, lack of exercise and higher body mass index were found to be significant and independent factors associated with natural logarithm-transformed LAP. Betel-quid and alcohol, but not cigarette use, were independent risk factors of logarithm-transformed LAP, adjusting for age, exercise and body mass index in male Taiwanese factory workers. LAP can be considered as a simple and useful method for screening of cardiometabolic risk. © The Author 2014. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Ratio of mean platelet volume to platelet count is a potential surrogate marker predicting liver cirrhosis.

PubMed

Iida, Hiroya; Kaibori, Masaki; Matsui, Kosuke; Ishizaki, Morihiko; Kon, Masanori

2018-01-27

To provide a simple surrogate marker predictive of liver cirrhosis (LC). Specimens from 302 patients who underwent resection for hepatocellular carcinoma between January 2006 and December 2012 were retrospectively analyzed. Based on pathologic findings, patients were divided into groups based on whether or not they had LC. Parameters associated with hepatic functional reserve were compared in these two groups using Mann-Whitney U -test for univariate analysis. Factors differing significantly in univariate analyses were entered into multivariate logistic regression analysis. There were significant differences between the LC group ( n = 100) and non-LC group ( n = 202) in prothrombin activity, concentrations of alanine aminotransferase, aspartate aminotransferase, total bilirubin, albumin, cholinesterase, type IV collagen, hyaluronic acid, indocyanine green retention rate at 15 min, maximal removal rate of technitium-99m diethylene triamine penta-acetic acid-galactosyl human serum albumin and ratio of mean platelet volume to platelet count (MPV/PLT). Multivariate analysis showed that prothrombin activity, concentrations of alanine aminotransferase, aspartate aminotransferase, total bilirubin and hyaluronic acid, and MPV/PLT ratio were factors independently predictive of LC. The area under the curve value for MPV/PLT was 0.78, with a 0.8 cutoff value having a sensitivity of 65% and a specificity of 78%. The MPV/PLT ratio, which can be determined simply from the complete blood count, may be a simple surrogate marker predicting LC.

Demonstrating the robustness of population surveillance data: implications of error rates on demographic and mortality estimates.

PubMed

Fottrell, Edward; Byass, Peter; Berhane, Yemane

2008-03-25

As in any measurement process, a certain amount of error may be expected in routine population surveillance operations such as those in demographic surveillance sites (DSSs). Vital events are likely to be missed and errors made no matter what method of data capture is used or what quality control procedures are in place. The extent to which random errors in large, longitudinal datasets affect overall health and demographic profiles has important implications for the role of DSSs as platforms for public health research and clinical trials. Such knowledge is also of particular importance if the outputs of DSSs are to be extrapolated and aggregated with realistic margins of error and validity. This study uses the first 10-year dataset from the Butajira Rural Health Project (BRHP) DSS, Ethiopia, covering approximately 336,000 person-years of data. Simple programmes were written to introduce random errors and omissions into new versions of the definitive 10-year Butajira dataset. Key parameters of sex, age, death, literacy and roof material (an indicator of poverty) were selected for the introduction of errors based on their obvious importance in demographic and health surveillance and their established significant associations with mortality. Defining the original 10-year dataset as the 'gold standard' for the purposes of this investigation, population, age and sex compositions and Poisson regression models of mortality rate ratios were compared between each of the intentionally erroneous datasets and the original 'gold standard' 10-year data. The composition of the Butajira population was well represented despite introducing random errors, and differences between population pyramids based on the derived datasets were subtle. Regression analyses of well-established mortality risk factors were largely unaffected even by relatively high levels of random errors in the data. The low sensitivity of parameter estimates and regression analyses to significant amounts of randomly introduced errors indicates a high level of robustness of the dataset. This apparent inertia of population parameter estimates to simulated errors is largely due to the size of the dataset. Tolerable margins of random error in DSS data may exceed 20%. While this is not an argument in favour of poor quality data, reducing the time and valuable resources spent on detecting and correcting random errors in routine DSS operations may be justifiable as the returns from such procedures diminish with increasing overall accuracy. The money and effort currently spent on endlessly correcting DSS datasets would perhaps be better spent on increasing the surveillance population size and geographic spread of DSSs and analysing and disseminating research findings.

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

PubMed

Henrard, S; Speybroeck, N; Hermans, C

2015-11-01

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

The Relation between the Fear-Avoidance Model and Constructs from the Social Cognitive Theory in Acute WAD.

PubMed

Sandborgh, Maria; Johansson, Ann-Christin; Söderlund, Anne

2016-01-01

In the fear-avoidance (FA) model social cognitive constructs could add to explaining the disabling process in whiplash associated disorder (WAD). The aim was to exemplify the possible input from Social Cognitive Theory on the FA model. Specifically the role of functional self-efficacy and perceived responses from a spouse/intimate partner was studied. A cross-sectional and correlational design was used. Data from 64 patients with acute WAD were used. Measures were pain intensity measured with a numerical rating scale, the Pain Disability Index, support, punishing responses, solicitous responses, and distracting responses subscales from the Multidimensional Pain Inventory, the Catastrophizing subscale from the Coping Strategies Questionnaire, the Tampa Scale of Kinesiophobia, and the Self-Efficacy Scale. Bivariate correlational, simple linear regression, and multiple regression analyses were used. In the statistical prediction models high pain intensity indicated high punishing responses, which indicated high catastrophizing. High catastrophizing indicated high fear of movement, which indicated low self-efficacy. Low self-efficacy indicated high disability, which indicated high pain intensity. All independent variables together explained 66.4% of the variance in pain disability, p < 0.001. Results suggest a possible link between one aspect of the social environment, perceived punishing responses from a spouse/intimate partner, pain intensity, and catastrophizing. Further, results support a mediating role of self-efficacy between fear of movement and disability in WAD.

Relationship between tinnitus and suicidal behaviour in Korean men and women: a cross-sectional study.

PubMed

Seo, J H; Kang, J M; Hwang, S H; Han, K D; Joo, Y H

2016-06-01

This study investigated the prevalence of suicidal ideation and behaviour in a representative sample of South Koreans with or without tinnitus. A cross-sectional study. Based on data from the 2010 to 2012 Korean National Health and Nutrition Examination Survey (KNHANES). The study included 17 446 Korean individuals. Participants provided demographic, socio-economic and behavioural information, as well as responses to questionnaires assessing the presence and severity of tinnitus, mental health status regarding stress, depression, and suicidal ideation and attempts. In the univariate analysis, the Rao-Scott chi-square test and logistic regression analysis were used to test the association between tinnitus and risk factors. Simple and multiple linear regression analyses were used to examine the association between tinnitus and mental status. A total of 20.9% and 1.2% of participants with tinnitus, and 12.2% and 0.6% of those without, reported suicidal ideation and attempts, respectively (P < 0.0001 and P = 0.001). Participants reporting suicide attempts showed a higher proportion of severe annoying (6.0%) and irritating (11.8%) tinnitus than those with suicidal ideation (1.4% and 10.2%, respectively). Risks for experiencing tinnitus were significantly associated with suicidal ideation and attempts after adjusting for confounding variables. This study has important implications for enhanced screening and evaluation of mental health status and suicidal ideation/behaviour among tinnitus patients. © 2015 John Wiley & Sons Ltd.

Exploring spatial patterns and drivers of forest fires in Portugal (1980-2014).

PubMed

Nunes, A N; Lourenço, L; Meira, A C Castro

2016-12-15

Information on the spatial incidence of fire ignition density and burnt area, trends and drivers of wildfires is vitally important in providing support for environmental and civil protection policies, designing appropriate prevention measures and allocating firefighting resources. The key objectives of this study were to analyse the geographical incidence and temporal trends for wildfires, as well as the main drivers of fire ignition and burnt area in Portugal on a municipal level. The results show that fires are not distributed uniformly throughout Portuguese territory, both in terms of ignition density and burnt area. One spot in the north-western area is well defined, covering 10% of the municipalities where more than one third of the total fire ignitions are concentrated. In >80% of Portuguese municipalities, ignition density has registered a positive trend since the 1980s. With regard to burnt area, 60% of the municipalities had a nil annual trend, 35% showed a positive trend and 5%, located mainly in the central region, revealed negative trends. Geographically weighted regression proved more efficient in identifying the most relevant physical and anthropogenic drivers of municipal wildfires in comparison with simple linear regression models. Topography, density of population, land cover and livestock were found to be significant in both ignition density and burnt area, although considerable variations were observed in municipal explanatory power. Copyright © 2016 Elsevier B.V. All rights reserved.

Validation of a prediction model that allows direct comparison of the Oxford Knee Score and American Knee Society clinical rating system.

PubMed

Maempel, J F; Clement, N D; Brenkel, I J; Walmsley, P J

2015-04-01

This study demonstrates a significant correlation between the American Knee Society (AKS) Clinical Rating System and the Oxford Knee Score (OKS) and provides a validated prediction tool to estimate score conversion. A total of 1022 patients were prospectively clinically assessed five years after TKR and completed AKS assessments and an OKS questionnaire. Multivariate regression analysis demonstrated significant correlations between OKS and the AKS knee and function scores but a stronger correlation (r = 0.68, p < 0.001) when using the sum of the AKS knee and function scores. Addition of body mass index and age (other statistically significant predictors of OKS) to the algorithm did not significantly increase the predictive value. The simple regression model was used to predict the OKS in a group of 236 patients who were clinically assessed nine to ten years after TKR using the AKS system. The predicted OKS was compared with actual OKS in the second group. Intra-class correlation demonstrated excellent reliability (r = 0.81, 95% confidence intervals 0.75 to 0.85) for the combined knee and function score when used to predict OKS. Our findings will facilitate comparison of outcome data from studies and registries using either the OKS or the AKS scores and may also be of value for those undertaking meta-analyses and systematic reviews. ©2015 The British Editorial Society of Bone & Joint Surgery.

Ultrasonographic measurement of the optic nerve sheath diameter and its association with eyeball transverse diameter in 585 healthy volunteers.

PubMed

Kim, Dong Hwan; Jun, Jin-Sun; Kim, Ryul

2017-11-21

The optic nerve sheath diameter (ONSD) is considered as an indirect marker for intracranial pressure (ICP). However, the optimal cut-off value for an abnormal ONSD indicating elevated ICP and its associated factors have been unclear. Thus, we investigated normative values for the ONSD using ultrasonography and investigate the potential factors affecting it. We prospectively recruited healthy volunteers between September 2016 and March 2017. A total of 585 individuals were included, in which the mean ONSD was 4.11 mm [95% confidence interval (CI), 4.09-4.14 mm]. Although ONSD was correlated with sex (p = 0.015), height (p = 0.003), and eyeball transverse diameter (ETD) (p < 0.001) in simple linear regression analyses, multiple linear regression analysis revealed that only ETD was independently associated with ONSD (p < 0.001). Accordingly, we further established a normative value for the ONSD/ETD ratio and its associated factors. The mean ONSD/ETD ratio was 0.18 (95% CI, 0.18-0.18), but the ONSD/ETD ratio was not correlated with sex, height, weight, body mass index, and head circumference. Our findings suggest that the ONSD had a strong correlation with ETD, and ONSD/ETD ratio might provide more reliable data than ONSD itself as a marker of ICP.

Predictors of transformational leadership of nurse managers.

PubMed

Echevarria, Ilia M; Patterson, Barbara J; Krouse, Anne

2017-04-01

The aim of this study was to examine the relationships among education, leadership experience, emotional intelligence and transformational leadership of nurse managers. Nursing leadership research provides limited evidence of predictors of transformational leadership style in nurse managers. A predictive correlational design was used with a sample of nurse managers (n = 148) working in varied health care settings. Data were collected using the Genos Emotional Intelligence Inventory, the Multi-factor Leadership Questionnaire and a demographic questionnaire. Simple linear and multiple regression analyses were used to examine relationships. A statistically significant relationship was found between emotional intelligence and transformational leadership (r = 0.59, P < 0.001) explaining 34% variance in transformational leadership. Nurse managers should be well informed of the predictors of transformational leadership in order to pursue continuing education and development opportunities related to those predictors. The results of this study emphasise the need for emotional intelligence continuing education, leadership development and leader assessment programmes. © 2016 John Wiley & Sons Ltd.

Everything should be as simple as possible, but no simpler: towards a protocol for accumulating evidence regarding the active content of health behaviour change interventions.

PubMed

Peters, Gjalt-Jorn Ygram; de Bruin, Marijn; Crutzen, Rik

2015-01-01

There is a need to consolidate the evidence base underlying our toolbox of methods of behaviour change. Recent efforts to this effect have conducted meta-regressions on evaluations of behaviour change interventions, deriving each method's effectiveness from its association to intervention effect size. However, there are a range of issues that raise concern about whether this approach is actually furthering or instead obstructing the advancement of health psychology theories and the quality of health behaviour change interventions. Using examples from theory, the literature and data from previous meta-analyses, these concerns and their implications are explained and illustrated. An iterative protocol for evidence base accumulation is proposed that integrates evidence derived from both experimental and applied behaviour change research, and combines theory development in experimental settings with theory testing in applied real-life settings. As evidence gathered in this manner accumulates, a cumulative science of behaviour change can develop.

Everything should be as simple as possible, but no simpler: towards a protocol for accumulating evidence regarding the active content of health behaviour change interventions

PubMed Central

Peters, Gjalt-Jorn Ygram; de Bruin, Marijn; Crutzen, Rik

2015-01-01

There is a need to consolidate the evidence base underlying our toolbox of methods of behaviour change. Recent efforts to this effect have conducted meta-regressions on evaluations of behaviour change interventions, deriving each method's effectiveness from its association to intervention effect size. However, there are a range of issues that raise concern about whether this approach is actually furthering or instead obstructing the advancement of health psychology theories and the quality of health behaviour change interventions. Using examples from theory, the literature and data from previous meta-analyses, these concerns and their implications are explained and illustrated. An iterative protocol for evidence base accumulation is proposed that integrates evidence derived from both experimental and applied behaviour change research, and combines theory development in experimental settings with theory testing in applied real-life settings. As evidence gathered in this manner accumulates, a cumulative science of behaviour change can develop. PMID:25793484

What Physical Fitness Component Is Most Closely Associated With Adolescents' Blood Pressure?

PubMed

Nunes, Heloyse E G; Alves, Carlos A S; Gonçalves, Eliane C A; Silva, Diego A S

2017-12-01

This study aimed to determine which of four selected physical fitness variables, would be most associated with blood pressure changes (systolic and diastolic) in a large sample of adolescents. This was a descriptive and cross-sectional, epidemiological study of 1,117 adolescents aged 14-19 years from southern Brazil. Systolic and diastolic blood pressure were measured by a digital pressure device, and the selected physical fitness variables were body composition (body mass index), flexibility (sit-and-reach test), muscle strength/resistance (manual dynamometer), and aerobic fitness (Modified Canadian Aerobic Fitness Test). Simple and multiple linear regression analyses revealed that aerobic fitness and muscle strength/resistance best explained variations in systolic blood pressure for boys (17.3% and 7.4% of variance) and girls (7.4% of variance). Aerobic fitness, body composition, and muscle strength/resistance are all important indicators of blood pressure control, but aerobic fitness was a stronger predictor of systolic blood pressure in boys and of diastolic blood pressure in both sexes.

Using Survey Data to Determine a Numeric Criterion for Nutrient Pollution

NASA Astrophysics Data System (ADS)

Jakus, Paul M.; Nelson, Nanette; Ostermiller, Jeffrey

2017-12-01

We present a scientific replication of a benthic algae nuisance threshold study originally conducted in Montana, but we do so using a different sampling methodology in a different state. Respondents are asked to rate eight photographs that depict varying algae conditions. Our initial results show that Utah resident preferences for benthic algae levels are quite similar to those of Montana residents, thus replicating the Montana study. For the full Utah sample, though, Cronbach's α indicated poor internal consistency in rating the photographs, so a "monotonicity rule" was used to identify respondents providing monotonic preferences with respect to chlorophyll a densities. Simple graphical analyses are combined with ordered probit analysis to determine the maximum desirable density of chlorophyll a (Chl a). Our analysis indicates that Chl a levels in excess of 150 mg Chl a/m2 are undesirable, but the regression model suggests that those with strictly monotonic preferences were far more likely favor a more stringent standard.

The Use of Infrared Thermography for Porosity Assessment of Intact Rock

NASA Astrophysics Data System (ADS)

Mineo, S.; Pappalardo, G.

2016-08-01

Preliminary results on a new test for the indirect assessment of porosity through infrared thermography are presented. The study of the cooling behavior of rock samples in laboratory, through the analysis of thermograms, proved an innovative tool for the estimation of such an important property, which is one of the main features affecting the mechanical behavior of rocks. A detailed experimentation was performed on artificially heated volcanic rock samples characterized by different porosity values. The cooling trend was described both graphically and numerically, with the help of cooling curves and Cooling Rate Index. The latter, which proved strictly linked to porosity, was employed to find reliable equations for its indirect estimation. Simple and multiple regression analyses returned satisfactory outcomes, highlighting the great match between predicted and measured porosity values, thus confirming the goodness of the proposed model. This study brings a novelty in rock mechanics, laying the foundation for future researches aimed at refining achieved results for the validation of the model in a larger scale.

Relational conflict and outcomes from an online divorce education program.

PubMed

Cronin, Sarah; Becher, Emily H; McCann, Ellie; McGuire, Jenifer; Powell, Sharon

2017-06-01

The impact of conflict on co-parenting outcomes of divorce education programs is not widely explored in the literature despite the prevalence of conflict in divorce. This study used outcome data from a sample of participants (N=272) who took the online Parents Forever™ course between 2012 and 2014. Participants were asked questions about positive and negative co-parenting behaviors as well their levels of conflict before and after the divorce or separation. There was on average a slight increase in conflict from post to follow-up (M=-0.397, SD=1.54). Simple linear regression analyses indicated that change in conflict explained a significant proportion of the variance in positive co-parenting scores, R 2 =0.07, F(1, 270)=19.98, p<0.001 and negative co-parenting scores, R 2 =0.08, F(1, 270)=23.78, p<0.001. Results suggest that conflict significantly impacts co-parenting behaviors targeted in the Parents Forever ™ course. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparison of CEAS and Williams-type models for spring wheat yields in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Barnett, T. L. (Principal Investigator)

1982-01-01

The CEAS and Williams-type yield models are both based on multiple regression analysis of historical time series data at CRD level. The CEAS model develops a separate relation for each CRD; the Williams-type model pools CRD data to regional level (groups of similar CRDs). Basic variables considered in the analyses are USDA yield, monthly mean temperature, monthly precipitation, and variables derived from these. The Williams-type model also used soil texture and topographic information. Technological trend is represented in both by piecewise linear functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test of each model (1970-1979) demonstrate that the models are very similar in performance in all respects. Both models are about equally objective, adequate, timely, simple, and inexpensive. Both consider scientific knowledge on a broad scale but not in detail. Neither provides a good current measure of modeled yield reliability. The CEAS model is considered very slightly preferable for AgRISTARS applications.

The relative contributions of speechreading and vocabulary to deaf and hearing children's reading ability.

PubMed

Kyle, Fiona Elizabeth; Campbell, Ruth; MacSweeney, Mairéad

2016-01-01

Vocabulary knowledge and speechreading are important for deaf children's reading development but it is unknown whether they are independent predictors of reading ability. This study investigated the relationships between reading, speechreading and vocabulary in a large cohort of deaf and hearing children aged 5 to 14 years. 86 severely and profoundly deaf children and 91 hearing children participated in this study. All children completed assessments of reading comprehension, word reading accuracy, speechreading and vocabulary. Regression analyses showed that vocabulary and speechreading accounted for unique variance in both reading accuracy and comprehension for deaf children. For hearing children, vocabulary was an independent predictor of both reading accuracy and comprehension skills but speechreading only accounted for unique variance in reading accuracy. Speechreading and vocabulary are important for reading development in deaf children. The results are interpreted within the Simple View of Reading framework and the theoretical implications for deaf children's reading are discussed. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predictive modeling studies for the ecotoxicity of ionic liquids towards the green algae Scenedesmus vacuolatus.

PubMed

Das, Rudra Narayan; Roy, Kunal

2014-06-01

Hazardous potential of ionic liquids is becoming an issue of high concern with increasing application of these compounds in various industrial processes. Predictive toxicological modeling on ionic liquids provides a rational assessment strategy and aids in developing suitable guidance for designing novel analogues. The present study attempts to explore the chemical features of ionic liquids responsible for their ecotoxicity towards the green algae Scenedesmus vacuolatus by developing mathematical models using extended topochemical atom (ETA) indices along with other categories of chemical descriptors. The entire study has been conducted with reference to the OECD guidelines for QSAR model development using predictive classification and regression modeling strategies. The best models from both the analyses showed that ecotoxicity of ionic liquids can be decreased by reducing chain length of cationic substituents and increasing hydrogen bond donor feature in cations, and replacing bulky unsaturated anions with simple saturated moiety having less lipophilic heteroatoms. Copyright © 2013 Elsevier Ltd. All rights reserved.

Cyclic voltammetry to evaluate the antioxidant potential in winemaking by-products.

PubMed

José Jara-Palacios, M; Luisa Escudero-Gilete, M; Miguel Hernández-Hierro, J; Heredia, Francisco J; Hernanz, Dolores

2017-04-01

Grape pomace is composed of seeds, skins and stems that are an important source of phenolic substances, which have antioxidant properties and potential benefits to human health. Cyclic voltammetry (CV) has been used to measure the total antioxidant potential of different winemaking by-products. The electrochemical behavior of pomace, seeds, skins and stems was measured by CV and lipid peroxidation inhibition by thiobarbituric acid reactive substances (TBARS) method. Differences for the electrochemical parameter were found between the by-products, pomace and seeds, which presented the greatest voltammetric peak area. Furthermore, the by-products induced inhibition of lipid peroxidation in rat liver homogenates. Pomace and seeds showed higher capacity to inhibit lipid peroxidation than stems and skins, which could be because these by-products are richer in flavanols. Simple regression analyses showed that voltammetric parameters are highly correlated to the values obtained for lipid peroxidation inhibition. CV is a promising technique to estimate the total antioxidant potential of phenolic extract from winemaking by-products. Copyright © 2016 Elsevier B.V. All rights reserved.

Competition among Turkish hospitals and its effect on hospital efficiency and service quality.

PubMed

Torun, Nazan; Celik, Yusuf; Younis, Mustafa Z

2013-01-01

The level of competition among hospitals in Turkey was analyzed for the years 1990 through 2006 using the Herfindahl-Hirschman Index (HHI). Multiple and simple regression analyses were run to observe the development of competition among hospitals over this period of time, to examine likely determinants of competition, and to calculate the effects of competition on efficiency and quality in individual hospitals. This study found that the level of competition among hospitals in Turkey has increased throughout the years. Also, competition has had a positive effect on the efficiency of hospitals; however, it did not have a significant positive effect on their quality. Moreover, there are important differences in the level of competition among hospitals that vary according to the geographical region, the type of ownership, and the type of hospital. This study is one of the first to evaluate the effects of health policies on competition as well as the effects of increasing competition on hospital quality and efficiency in Turkey.

Visual but not motor processes predict simple visuomotor reaction time of badminton players.

PubMed

Hülsdünker, Thorben; Strüder, Heiko K; Mierau, Andreas

2018-03-01

The athlete's brain exhibits significant functional adaptations that facilitate visuomotor reaction performance. However, it is currently unclear if the same neurophysiological processes that differentiate athletes from non-athletes also determine performance within a homogeneous group of athletes. This information can provide valuable help for athletes and coaches aiming to optimize existing training regimes. Therefore, this study aimed to identify the neurophysiological correlates of visuomotor reaction performance in a group of skilled athletes. In 36 skilled badminton athletes, electroencephalography (EEG) was used to investigate pattern reversal and motion onset visual-evoked potentials (VEPs) as well as visuomotor reaction time (VMRT) during a simple reaction task. Stimulus-locked and response-locked event-related potentials (ERPs) in visual and motor regions as well as the onset of muscle activation (EMG onset) were determined. Correlation and multiple regression analyses identified the neurophysiological parameters predicting EMG onset and VMRT. For pattern reversal stimuli, the P100 latency and age best predicted EMG onset (r = 0.43; p = .003) and VMRT (r = 0.62; p = .001). In the motion onset experiment, EMG onset (r = 0.80; p < .001) and VMRT (r = 0.78; p < .001) were predicted by N2 latency and age. In both conditions, cortical potentials in motor regions were not correlated with EMG onset or VMRT. It is concluded that previously identified neurophysiological parameters differentiating athletes from non-athletes do not necessarily determine performance within a homogeneous group of athletes. Specifically, the speed of visual perception/processing predicts EMG onset and VMRT in skilled badminton players while motor-related processes, although differentiating athletes from non-athletes, are not associated simple with visuomotor reaction performance.

A simple, sensitive and non-destructive technique for characterizing bovine dental enamel erosion: attenuated total reflection Fourier transform infrared spectroscopy

PubMed Central

Kim, In-Hye; Son, Jun Sik; Min, Bong Ki; Kim, Young Kyoung; Kim, Kyo-Han; Kwon, Tae-Yub

2016-01-01

Although many techniques are available to assess enamel erosion in vitro, a simple, non-destructive method with sufficient sensitivity for quantifying dental erosion is required. This study characterized the bovine dental enamel erosion induced by various acidic beverages in vitro using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. Deionized water (control) and 10 acidic beverages were selected to study erosion, and the pH and neutralizable acidity were measured. Bovine anterior teeth (110) were polished with up to 1 200-grit silicon carbide paper to produce flat enamel surfaces, which were then immersed in 20 mL of the beverages for 30 min at 37 °C. The degree of erosion was evaluated using ATR-FTIR spectroscopy and Vickers' microhardness measurements. The spectra obtained were interpreted in two ways that focused on the ν1, ν3 phosphate contour: the ratio of the height amplitude of ν3 PO4 to that of ν1 PO4 (Method 1) and the shift of the ν3 PO4 peak to a higher wavenumber (Method 2). The percentage changes in microhardness after the erosion treatments were primarily affected by the pH of the immersion media. Regression analyses revealed highly significant correlations between the surface hardness change and the degree of erosion, as detected by ATR-FTIR spectroscopy (P<0.001). Method 1 was the most sensitive to these changes, followed by surface hardness change measurements and Method 2. This study suggests that ATR-FTIR spectroscopy is potentially advantageous over the microhardness test as a simple, non-destructive, sensitive technique for the quantification of enamel erosion. PMID:27025266

A simple, sensitive and non-destructive technique for characterizing bovine dental enamel erosion: attenuated total reflection Fourier transform infrared spectroscopy.

PubMed

Kim, In-Hye; Son, Jun Sik; Min, Bong Ki; Kim, Young Kyoung; Kim, Kyo-Han; Kwon, Tae-Yub

2016-03-30

Although many techniques are available to assess enamel erosion in vitro, a simple, non-destructive method with sufficient sensitivity for quantifying dental erosion is required. This study characterized the bovine dental enamel erosion induced by various acidic beverages in vitro using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. Deionized water (control) and 10 acidic beverages were selected to study erosion, and the pH and neutralizable acidity were measured. Bovine anterior teeth (110) were polished with up to 1 200-grit silicon carbide paper to produce flat enamel surfaces, which were then immersed in 20 mL of the beverages for 30 min at 37 °C. The degree of erosion was evaluated using ATR-FTIR spectroscopy and Vickers' microhardness measurements. The spectra obtained were interpreted in two ways that focused on the ν1, ν3 phosphate contour: the ratio of the height amplitude of ν3 PO4 to that of ν1 PO4 (Method 1) and the shift of the ν3 PO4 peak to a higher wavenumber (Method 2). The percentage changes in microhardness after the erosion treatments were primarily affected by the pH of the immersion media. Regression analyses revealed highly significant correlations between the surface hardness change and the degree of erosion, as detected by ATR-FTIR spectroscopy (P<0.001). Method 1 was the most sensitive to these changes, followed by surface hardness change measurements and Method 2. This study suggests that ATR-FTIR spectroscopy is potentially advantageous over the microhardness test as a simple, non-destructive, sensitive technique for the quantification of enamel erosion.

Feedback on oral presentations during pediatric clerkships: a randomized controlled trial.

PubMed

Sox, Colin M; Dell, Michael; Phillipi, Carrie A; Cabral, Howard J; Vargas, Gabriela; Lewin, Linda O

2014-11-01

To measure the effects of participating in structured oral presentation evaluation sessions early in pediatric clerkships on students' subsequent presentations. We conducted a single-blind, 3-arm, cluster randomized controlled trial during pediatric clerkships at Boston University School of Medicine, University of Maryland School of Medicine, Oregon Health & Science University, and Case Western Reserve University School of Medicine. Blocks of students at each school were randomly assigned to experience either (1) no formal presentation feedback (control) or a small-group presentation feedback session early in pediatric clerkships in which students gave live presentations and received feedback from faculty who rated their presentations by using a (2) single-item (simple) or (3) 18-item (detailed) evaluation form. At the clerkship end, overall quality of subjects' presentations was rated by faculty blinded to randomization status, and subjects reported whether their presentations had improved. Analyses included multivariable linear and logistic regressions clustered on clerkship block that controlled for medical school. A total of 476 participants were evenly divided into the 3 arms, which had similar characteristics. Compared with controls, presentation quality was significantly associated with participating in detailed (coefficient: 0.38; 95% confidence interval [CI]: 0.07-0.69) but not simple (coefficient: 0.16; 95% CI: -0.12-0.43) feedback sessions. Similarly, student self-report of presentation improvement was significantly associated with participating in detailed (odds ratio: 2.16; 95% CI: 1.11-4.18] but not simple (odds ratio: 1.89; 95% CI: 0.91-3.93) feedback sessions. Small-group presentation feedback sessions led by faculty using a detailed evaluation form resulted in clerkship students delivering oral presentations of higher quality compared with controls. Copyright © 2014 by the American Academy of Pediatrics.

Physical activity, health status and risk of hospitalization in patients with severe chronic obstructive pulmonary disease.

PubMed

Benzo, Roberto P; Chang, Chung-Chou H; Farrell, Max H; Kaplan, Robert; Ries, Andrew; Martinez, Fernando J; Wise, Robert; Make, Barry; Sciurba, Frank

2010-01-01

Chronic obstructive pulmonary disease (COPD) is a leading cause of death and 70% of the cost of COPD is due to hospitalizations. Self-reported daily physical activity and health status have been reported as predictors of a hospitalization in COPD but are not routinely assessed. We tested the hypothesis that self-reported daily physical activity and health status assessed by a simple question were predictors of a hospitalization in a well-characterized cohort of patients with severe emphysema. Investigators gathered daily physical activity and health status data assessed by a simple question in 597 patients with severe emphysema and tested the association of those patient-reported outcomes to the occurrence of a hospitalization in the following year. Multiple logistic regression analyses were used to determine predictors of hospitalization during the first 12 months after randomization. The two variables tested in the hypothesis were significant predictors of a hospitalization after adjusting for all univariable significant predictors: >2 h of physical activity per week had a protective effect [odds ratio (OR) 0.60; 95% confidence interval (95% CI) 0.41-0.88] and self-reported health status as fair or poor had a deleterious effect (OR 1.57; 95% CI 1.10-2.23). In addition, two other variables became significant in the multivariate model: total lung capacity (every 10% increase) had a protective effect (OR 0.88; 95% CI 0.78-0.99) and self-reported anxiety had a deleterious effect (OR 1.75; 95% CI 1.13-2.70). Self-reported daily physical activity and health status are independently associated with COPD hospitalizations. Our findings, assessed by simple questions, suggest the value of patient-reported outcomes in developing risk assessment tools that are easy to use.

Copeptin is associated with kidney length, renal function, and prevalence of simple cysts in a population-based study.

PubMed

Ponte, Belen; Pruijm, Menno; Ackermann, Daniel; Vuistiner, Philippe; Guessous, Idris; Ehret, Georg; Alwan, Heba; Youhanna, Sonia; Paccaud, Fred; Mohaupt, Markus; Péchère-Bertschi, Antoinette; Vogt, Bruno; Burnier, Michel; Martin, Pierre-Yves; Devuyst, Olivier; Bochud, Murielle

2015-06-01

Arginine vasopressin (AVP) has a key role in osmoregulation by facilitating water transport in the collecting duct. Recent evidence suggests that AVP may have additional effects on renal function and favor cyst growth in polycystic kidney disease. Whether AVP also affects kidney structure in the general population is unknown. We analyzed the association of copeptin, an established surrogate for AVP, with parameters of renal function and morphology in a multicentric population-based cohort. Participants from families of European ancestry were randomly selected in three Swiss cities. We used linear multilevel regression analysis to explore the association of copeptin with renal function parameters as well as kidney length and the presence of simple renal cysts assessed by ultrasound examination. Copeptin levels were log-transformed. The 529 women and 481 men had median copeptin levels of 3.0 and 5.2 pmol/L, respectively (P<0.001). In multivariable analyses, the copeptin level was associated inversely with eGFR (β=-2.1; 95% confidence interval [95% CI], -3.3 to -0.8; P=0.002) and kidney length (β=-1.2; 95% CI, -1.9 to -0.4; P=0.003) but positively with 24-hour urinary albumin excretion (β=0.11; 95% CI, 0.01 to 0.20; P=0.03) and urine osmolality (β=0.08; 95% CI, 0.05 to 0.10; P<0.001). A positive association was found between the copeptin level and the presence of renal cysts (odds ratio, 1.6; 95% CI, 1.1 to 2.4; P=0.02). These results suggest that AVP has a pleiotropic role in renal function and may favor the development of simple renal cysts. Copyright © 2015 by the American Society of Nephrology.

Copeptin Is Associated with Kidney Length, Renal Function, and Prevalence of Simple Cysts in a Population-Based Study

PubMed Central

Ponte, Belen; Pruijm, Menno; Ackermann, Daniel; Vuistiner, Philippe; Guessous, Idris; Ehret, Georg; Alwan, Heba; Youhanna, Sonia; Paccaud, Fred; Mohaupt, Markus; Péchère-Bertschi, Antoinette; Vogt, Bruno; Burnier, Michel; Martin, Pierre-Yves; Devuyst, Olivier

2015-01-01

Arginine vasopressin (AVP) has a key role in osmoregulation by facilitating water transport in the collecting duct. Recent evidence suggests that AVP may have additional effects on renal function and favor cyst growth in polycystic kidney disease. Whether AVP also affects kidney structure in the general population is unknown. We analyzed the association of copeptin, an established surrogate for AVP, with parameters of renal function and morphology in a multicentric population-based cohort. Participants from families of European ancestry were randomly selected in three Swiss cities. We used linear multilevel regression analysis to explore the association of copeptin with renal function parameters as well as kidney length and the presence of simple renal cysts assessed by ultrasound examination. Copeptin levels were log-transformed. The 529 women and 481 men had median copeptin levels of 3.0 and 5.2 pmol/L, respectively (P<0.001). In multivariable analyses, the copeptin level was associated inversely with eGFR (β=−2.1; 95% confidence interval [95% CI], −3.3 to −0.8; P=0.002) and kidney length (β=−1.2; 95% CI, −1.9 to −0.4; P=0.003) but positively with 24-hour urinary albumin excretion (β=0.11; 95% CI, 0.01 to 0.20; P=0.03) and urine osmolality (β=0.08; 95% CI, 0.05 to 0.10; P<0.001). A positive association was found between the copeptin level and the presence of renal cysts (odds ratio, 1.6; 95% CI, 1.1 to 2.4; P=0.02). These results suggest that AVP has a pleiotropic role in renal function and may favor the development of simple renal cysts. PMID:25270071

Placebo and Nocebo Effects: The Advantage of Measuring Expectations and Psychological Factors

PubMed Central

Corsi, Nicole; Colloca, Luana

2017-01-01

Several studies have explored the predictability of placebo and nocebo individual responses by investigating personality factors and expectations of pain decreases and increases. Psychological factors such as optimism, suggestibility, empathy and neuroticism have been linked to placebo effects, while pessimism, anxiety and catastrophizing have been associated to nocebo effects. We aimed to investigate the interplay between psychological factors, expectations of low and high pain and placebo hypoalgesia and nocebo hyperalgesia. We studied 46 healthy participants using a well-validated conditioning paradigm with contact heat thermal stimulations. Visual cues were presented to alert participants about the level of intensity of an upcoming thermal pain. We delivered high, medium and low levels of pain associated with red, yellow and green cues, respectively, during the conditioning phase. During the testing phase, the level of painful stimulations was surreptitiously set at the medium control level with all the three cues to measure placebo and nocebo effects. We found both robust placebo hypolagesic and nocebo hyperalgesic responses that were highly correlated with expectancy of low and high pain. Simple linear regression analyses showed that placebo responses were negatively correlated with anxiety severity and different aspects of fear of pain (e.g., medical pain, severe pain). Nocebo responses were positively correlated with anxiety sensitivity and physiological suggestibility with a trend toward catastrophizing. Step-wise regression analyses indicated that an aggregate score of motivation (value/utility and pressure/tense subscales) and suggestibility (physiological reactivity and persuadability subscales), accounted for the 51% of the variance in the placebo responsiveness. When considered together, anxiety severity, NEO openness-extraversion and depression accounted for the 49.1% of the variance of the nocebo responses. Psychological factors per se did not influence expectations. In fact, mediation analyses including expectations, personality factors and placebo and nocebo responses, revealed that expectations were not influenced by personality factors. These findings highlight the potential advantage of considering batteries of personality factors and measurements of expectation in predicting placebo and nocebo effects related to experimental acute pain. PMID:28321201

Regression Analysis: Legal Applications in Institutional Research

ERIC Educational Resources Information Center

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

2008-01-01

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

Regression Effects in Angoff Ratings: Examples from Credentialing Exams

ERIC Educational Resources Information Center

Wyse, Adam E.

2018-01-01

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

Automatic identification of variables in epidemiological datasets using logic regression.

PubMed

Lorenz, Matthias W; Abdi, Negin Ashtiani; Scheckenbach, Frank; Pflug, Anja; Bülbül, Alpaslan; Catapano, Alberico L; Agewall, Stefan; Ezhov, Marat; Bots, Michiel L; Kiechl, Stefan; Orth, Andreas

2017-04-13

For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies.

Influence of anthropometric parameters on ultrasound measurements of Os calcis.

PubMed

Hans, D; Schott, A M; Arlot, M E; Sornay, E; Delmas, P D; Meunier, P J

1995-01-01

Few data have been published concerning the influence of height, weight and body mass index (BMI) on broadband ultrasound attenuation (BUA), speed of sound (SOS) and Lunar "stiffness" index, and always in small population samples. The first ain of the present cross-sectional study was to determine whether anthropometric factors have a significant influence on ultrasound measurements. The second objective was to establish whether these parameters have real effect on whether their influence is due only to measurement errors. We measured, in 271 healthy French women (mean age 77 +/- 11 years; range 31-97 years), the following parameters: age, height, weight, lean and fat body mass, heel width, foot length, knee height and external malleolus (HEM). Simple linear regression analyses between ultrasound and anthropometric parameters were performed. Age, height, and heel width were significant predictors of SOS; age, height, weight, foot length, heel width, HEM, fat mass and lean mass were significant predictors of BUA; age, height, weight, heel width, HEM, fat mass and lean mass were significant predictors of stiffness. In the multiple regression analysis, once the analysis had been adjusted for age, only heel width was a significant predictor for SOS (p = 0.0007), weight for BUA (p = 0.0001), and weight (p = 0.0001) and heel width (p = 0.004) for the stiffness index. Besides their statistical meaning, the regression coefficients have a more clinically relevant interpretation which is developed in the text. These results confirm the influence of anthropometric factors on the ultrasonic parameter values, because BUA and SOS were in part dependent on heel width and weight. The influence of the position of the transducer on the calcaneus should be taken into account to optimize the methods of measurement using ultrasound.

Comparative assessment of GIS-based methods and metrics for estimating long-term exposures to air pollution

NASA Astrophysics Data System (ADS)

Gulliver, John; de Hoogh, Kees; Fecht, Daniela; Vienneau, Danielle; Briggs, David

2011-12-01

The development of geographical information system techniques has opened up a wide array of methods for air pollution exposure assessment. The extent to which these provide reliable estimates of air pollution concentrations is nevertheless not clearly established. Nor is it clear which methods or metrics should be preferred in epidemiological studies. This paper compares the performance of ten different methods and metrics in terms of their ability to predict mean annual PM 10 concentrations across 52 monitoring sites in London, UK. Metrics analysed include indicators (distance to nearest road, traffic volume on nearest road, heavy duty vehicle (HDV) volume on nearest road, road density within 150 m, traffic volume within 150 m and HDV volume within 150 m) and four modelling approaches: based on the nearest monitoring site, kriging, dispersion modelling and land use regression (LUR). Measures were computed in a GIS, and resulting metrics calibrated and validated against monitoring data using a form of grouped jack-knife analysis. The results show that PM 10 concentrations across London show little spatial variation. As a consequence, most methods can predict the average without serious bias. Few of the approaches, however, show good correlations with monitored PM 10 concentrations, and most predict no better than a simple classification based on site type. Only land use regression reaches acceptable levels of correlation ( R2 = 0.47), though this can be improved by also including information on site type. This might therefore be taken as a recommended approach in many studies, though care is needed in developing meaningful land use regression models, and like any method they need to be validated against local data before their application as part of epidemiological studies.

Suspicion of respiratory tract infection with multidrug-resistant Enterobacteriaceae: epidemiology and risk factors from a Paediatric Intensive Care Unit.

PubMed

Renk, Hanna; Stoll, Lenja; Neunhoeffer, Felix; Hölzl, Florian; Kumpf, Matthias; Hofbeck, Michael; Hartl, Dominik

2017-02-21

Multidrug-resistant (MDR) infections are a serious concern for children admitted to the Paediatric Intensive Care Unit (PICU). Tracheal colonization with MDR Enterobacteriaceae predisposes to respiratory infection, but underlying risk factors are poorly understood. This study aims to determine the incidence of children with suspected infection during mechanical ventilation and analyses risk factors for the finding of MDR Enterobacteriaceae in tracheal aspirates. A retrospective single-centre analysis of Enterobacteriaceae isolates from the lower respiratory tract of ventilated PICU patients from 2005 to 2014 was performed. Resistance status was determined and clinical records were reviewed for potential risk factors. A classification and regression tree (CRT) to predict risk factors for infection with MDR Enterobacteriaceae was employed. The model was validated by simple and multivariable logistic regression. One hundred sixty-seven Enterobacteriaceae isolates in 123 children were identified. The most frequent isolates were Enterobacter spp., Klebsiella spp. and E.coli. Among these, 116 (69%) isolates were susceptible and 51 (31%) were MDR. In the CRT analysis, antibiotic exposure for ≥ 7 days and presence of gastrointestinal comorbidity were the most relevant predictors for an MDR isolate. Antibiotic exposure for ≥ 7 days was confirmed as a significant risk factor for infection with MDR Enterobacteriaceae by a multivariable logistic regression model. This study shows that critically-ill children with tracheal Enterobacteriaceae infection are at risk of carrying MDR isolates. Prior use of antibiotics for ≥ 7 days significantly increased the risk of finding MDR organisms in ventilated PICU patients with suspected infection. Our results imply that early identification of patients at risk, rapid microbiological diagnostics and tailored antibiotic therapy are essential to improve management of critically ill children infected with Enterobacteriaceae.

Black Clouds vs Random Variation in Hospital Admissions.

PubMed

Ong, Luei Wern; Dawson, Jeffrey D; Ely, John W

2018-06-01

Physicians often accuse their peers of being "black clouds" if they repeatedly have more than the average number of hospital admissions while on call. Our purpose was to determine whether the black-cloud phenomenon is real or explainable by random variation. We analyzed hospital admissions to the University of Iowa family medicine service from July 1, 2010 to June 30, 2015. Analyses were stratified by peer group (eg, night shift attending physicians, day shift senior residents). We analyzed admission numbers to find evidence of black-cloud physicians (those with significantly more admissions than their peers) and white-cloud physicians (those with significantly fewer admissions). The statistical significance of whether there were actual differences across physicians was tested with mixed-effects negative binomial regression. The 5-year study included 96 physicians and 6,194 admissions. The number of daytime admissions ranged from 0 to 10 (mean 2.17, SD 1.63). Night admissions ranged from 0 to 11 (mean 1.23, SD 1.22). Admissions increased from 1,016 in the first year to 1,523 in the fifth year. We found 18 white-cloud and 16 black-cloud physicians in simple regression models that did not control for this upward trend. After including study year and other potential confounding variables in the regression models, there were no significant associations between physicians and admission numbers and therefore no true black or white clouds. In this study, apparent black-cloud and white-cloud physicians could be explained by random variation in hospital admissions. However, this randomness incorporated a wide range in workload among physicians, with potential impact on resident education at the low end and patient safety at the high end.

Surgery for left ventricular aneurysm: early and late survival after simple linear repair and endoventricular patch plasty.

PubMed

Lundblad, Runar; Abdelnoor, Michel; Svennevig, Jan Ludvig

2004-09-01

Simple linear resection and endoventricular patch plasty are alternative techniques to repair postinfarction left ventricular aneurysm. The aim of the study was to compare these 2 methods with regard to early mortality and long-term survival. We retrospectively reviewed 159 patients undergoing operations between 1989 and 2003. The epidemiologic design was of an exposed (simple linear repair, n = 74) versus nonexposed (endoventricular patch plasty, n = 85) cohort with 2 endpoints: early mortality and long-term survival. The crude effect of aneurysm repair technique versus endpoint was estimated by odds ratio, rate ratio, or relative risk and their 95% confidence intervals. Stratification analysis by using the Mantel-Haenszel method was done to quantify confounders and pinpoint effect modifiers. Adjustment for multiconfounders was performed by using logistic regression and Cox regression analysis. Survival curves were analyzed with the Breslow test and the log-rank test. Early mortality was 8.2% for all patients, 13.5% after linear repair and 3.5% after endoventricular patch plasty. When adjusted for multiconfounders, the risk of early mortality was significantly higher after simple linear repair than after endoventricular patch plasty (odds ratio, 4.4; 95% confidence interval, 1.1-17.8). Mean follow-up was 5.8 +/- 3.8 years (range, 0-14.0 years). Overall 5-year cumulative survival was 78%, 70.1% after linear repair and 91.4% after endoventricular patch plasty. The risk of total mortality was significantly higher after linear repair than after endoventricular patch plasty when controlled for multiconfounders (relative risk, 4.5; 95% confidence interval, 2.0-9.7). Linear repair dominated early in the series and patch plasty dominated later, giving a possible learning-curve bias in favor of patch plasty that could not be adjusted for in the regression analysis. Postinfarction left ventricular aneurysm can be repaired with satisfactory early and late results. Surgical risk was lower and long-term survival was higher after endoventricular patch plasty than simple linear repair. Differences in outcome should be interpreted with care because of the retrospective study design and the chronology of the 2 repair methods.

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.

Partial F-tests with multiply imputed data in the linear regression framework via coefficient of determination.

PubMed

Chaurasia, Ashok; Harel, Ofer

2015-02-10

Tests for regression coefficients such as global, local, and partial F-tests are common in applied research. In the framework of multiple imputation, there are several papers addressing tests for regression coefficients. However, for simultaneous hypothesis testing, the existing methods are computationally intensive because they involve calculation with vectors and (inversion of) matrices. In this paper, we propose a simple method based on the scalar entity, coefficient of determination, to perform (global, local, and partial) F-tests with multiply imputed data. The proposed method is evaluated using simulated data and applied to suicide prevention data. Copyright © 2014 John Wiley & Sons, Ltd.

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

PubMed Central

2012-01-01

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

Publication bias in obesity treatment trials?

PubMed

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

1996-10-01

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

Vascular plant and vertebrate species richness in national parks of the eastern United States

USGS Publications Warehouse

Hatfield, Jeffrey S.; Myrick, Kaci E.; Huston, Michael A.; Weckerly, Floyd W.; Green, M. Clay

2013-01-01

Given the estimates that species diversity is diminishing at 50-100 times the normal rate, it is critical that we be able to evaluate changes in species richness in order to make informed decisions for conserving species diversity. In this study, we examined the potential of vascular plant species richness to be used as a surrogate for vertebrate species richness in the classes of amphibians, reptiles, birds, and mammals. Vascular plants, as primary producers, represent the biotic starting point for ecological community structure and are the logical place to start for understanding vertebrate species associations. We used data collected by the United States (US) National Park Service (NPS) on species presence within parks in the eastern US to estimate simple linear regressions between plant species richness and vertebrate richness. Because environmental factors may also influence species diversity, we performed simple linear regressions of species richness versus natural logarithm of park area, park latitude, mean annual precipitation, mean annual temperature, and human population density surrounding the parks. We then combined plant species richness and environmental variables in multiple regressions to determine the variables that remained as significant predictors of vertebrate species richness. As expected, we detected significant relationships between plant species richness and amphibian, bird, and mammal species richness. In some cases, plant species richness was predicted by park area alone. Species richness of mammals was only related to plant species richness. Reptile species richness, on the other hand, was related to plant species richness, park latitude and annual precipitation, while amphibian species richness was related to park latitude, park area, and plant species richness. Thus, plant species richness predicted species richness of different vertebrate groups to varying degrees and should not be used exclusively as a surrogate for vertebrate species richness. Plant species richness should be included with other variables such as area and climate when considering strategies to manage and conserve species in US National Parks. It is not always appropriate to draw conclusions about analyses of taxonomic surrogates from one area to another. Two patterns evident from the linear regressions were the increase in species richness with the increase of park area and with increase of vascular plant species richness. To test whether there were differences in these patterns among networks, we used analysis of covariance (ANCOVA). Differences among networks were detected only in bird species richness versus plant species richness and for all taxa except mammals for vertebrate species richness versus park area. Some of these results may be due to small sample size among networks, and therefore, low statistical power. Other factors that could have contributed to these results were differences in average park area and habitat heterogeneity among networks, latitudinal gradients, low variation in mean annual precipitation, and different use of vegetation by migratory species. Based on these results we recommend that management of biodiversity be approached from local and site specific criteria rather than applying management directives derived from other regions of the US. It is also recommended that analyses similar to those presented here be conducted for all national parks, once data become available for all networks in the US, to gain a better understanding of how vascular plant species richness, area, and vertebrate species richness are related in the US.

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.

The Effectiveness of a Peer Coaching Education on Control and Management of Type 2 Diabetes in Women: A Protocol for a Randomized Controlled Trial.

PubMed

Aalaa, Maryam; Sanjari, Mahnaz; Aghaei Meybodi, Hamid Reza; Amini, Mohammad Reza; Qorbani, Mostafa; Adibi, Hossien; Mehrdad, Neda

2017-04-01

Diabetes Education by Peer Coaching is a strategy which helps the patients with diabetes in the field of behavioral and emotional problems. However, the results of studies in this field in other countries could not be generalized in our context. So, the current study aimed to examine the effectiveness of Diabetes Education by Peer Coaching on Diabetes Management. Outcome variables for patients and peer coaches are measured at baseline and in3,6 and 12 months. The primary outcome consisted of Fasting Blood Sugar (FBS) and HbA1c. Secondary outcomes included Blood Pressure (BP), Body Mass Index (BMI,) Waist-Hip Ratio (WHR), Lipid Profile, diabetes self-care activities, diabetes-related quality of life, depression, and Social Capital levels.Initial analyses compared the frequency of baseline levels of outcome and other variables using a simple Chi-square test, t-test and the Mann-Whitney- U test. Sequential measurements in each group were evaluated by two-way analysis of variance. If significant differences in baseline characteristics were found, analyses were repeated adjusting for these differences using ANOVA and logistic regression for multivariate analyses. Additional analyses were conducted to look for the evidence of effect modification by pre-specified subgroups. The fact is that self-control and self-efficacy in diabetes management and treatment of diabetes could be important components. It seems that this research in this special setting with cultural differences would provide more evidence about peer-coaching model. It seems that if the peer-coaching model improves learning situations between patients with diabetes by offering one-on-one Diabetes Self Management Education, it could be an interactive approach to diabetic education. Trial Registration Number: IRCT201501128175N3.

Predicting Diameter at Breast Height from Stump Diameters for Northeastern Tree Species

Treesearch

Eric H. Wharton; Eric H. Wharton

1984-01-01

Presents equations to predict diameter at breast height from stump diameter measurements for 17 northeastern tree species. Simple linear regression was used to develop the equations. Application of the equations is discussed.

Meteorological adjustment of yearly mean values for air pollutant concentration comparison

NASA Technical Reports Server (NTRS)

Sidik, S. M.; Neustadter, H. E.

1976-01-01

Using multiple linear regression analysis, models which estimate mean concentrations of Total Suspended Particulate (TSP), sulfur dioxide, and nitrogen dioxide as a function of several meteorologic variables, two rough economic indicators, and a simple trend in time are studied. Meteorologic data were obtained and do not include inversion heights. The goodness of fit of the estimated models is partially reflected by the squared coefficient of multiple correlation which indicates that, at the various sampling stations, the models accounted for about 23 to 47 percent of the total variance of the observed TSP concentrations. If the resulting model equations are used in place of simple overall means of the observed concentrations, there is about a 20 percent improvement in either: (1) predicting mean concentrations for specified meteorological conditions; or (2) adjusting successive yearly averages to allow for comparisons devoid of meteorological effects. An application to source identification is presented using regression coefficients of wind velocity predictor variables.

Estimation of sex and stature using anthropometry of the upper extremity in an Australian population.

PubMed

Howley, Donna; Howley, Peter; Oxenham, Marc F

2018-06-01

Stature and a further 8 anthropometric dimensions were recorded from the arms and hands of a sample of 96 staff and students from the Australian National University and The University of Newcastle, Australia. These dimensions were used to create simple and multiple logistic regression models for sex estimation and simple and multiple linear regression equations for stature estimation of a contemporary Australian population. Overall sex classification accuracies using the models created were comparable to similar studies. The stature estimation models achieved standard errors of estimates (SEE) which were comparable to and in many cases lower than those achieved in similar research. Generic, non sex-specific models achieved similar SEEs and R 2 values to the sex-specific models indicating stature may be accurately estimated when sex is unknown. Copyright © 2018 Elsevier B.V. All rights reserved.

Simple method for quick estimation of aquifer hydrogeological parameters

NASA Astrophysics Data System (ADS)

Ma, C.; Li, Y. Y.

2017-08-01

Development of simple and accurate methods to determine the aquifer hydrogeological parameters was of importance for groundwater resources assessment and management. Aiming at the present issue of estimating aquifer parameters based on some data of the unsteady pumping test, a fitting function of Theis well function was proposed using fitting optimization method and then a unitary linear regression equation was established. The aquifer parameters could be obtained by solving coefficients of the regression equation. The application of the proposed method was illustrated, using two published data sets. By the error statistics and analysis on the pumping drawdown, it showed that the method proposed in this paper yielded quick and accurate estimates of the aquifer parameters. The proposed method could reliably identify the aquifer parameters from long distance observed drawdowns and early drawdowns. It was hoped that the proposed method in this paper would be helpful for practicing hydrogeologists and hydrologists.

A Powerful Test for Comparing Multiple Regression Functions.

PubMed

Maity, Arnab

2012-09-01

In this article, we address the important problem of comparison of two or more population regression functions. Recently, Pardo-Fernández, Van Keilegom and González-Manteiga (2007) developed test statistics for simple nonparametric regression models: Y(ij) = θ(j)(Z(ij)) + σ(j)(Z(ij))∊(ij), based on empirical distributions of the errors in each population j = 1, … , J. In this paper, we propose a test for equality of the θ(j)(·) based on the concept of generalized likelihood ratio type statistics. We also generalize our test for other nonparametric regression setups, e.g, nonparametric logistic regression, where the loglikelihood for population j is any general smooth function [Formula: see text]. We describe a resampling procedure to obtain the critical values of the test. In addition, we present a simulation study to evaluate the performance of the proposed test and compare our results to those in Pardo-Fernández et al. (2007).

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.

[Analysing the defect of control design of acupuncture: taking RCTs of treating simple obesity with acupuncture for example].

PubMed

Zeng, Yi; Qi, Shulan; Meng, Xing; Chen, Yinyin

2018-03-12

By analysing the defect of control design in randomized controlled trials (RCTs) of simple obesity treated with acupuncture and using acupuncture as the contrast, presenting the essential factors which should be taken into account as designing the control of clinical trial to further improve the clinical research. Setting RCTs of acupuncture treating simple obesity as a example, we searched RCTs of acupuncture treating simple obesity with acupuncture control. According to the characteristics of acupuncture therapy, this research sorted and analysed the control approach of intervention from aspects of acupoint selection, the penetration of needle, the depth of insertion, etc, then calculated the amount of difference factor between the two groups and analyzed the rationality. In 15 RCTs meeting the inclusion criterias, 7 published in English, 8 in Chinese, the amount of difference factors between two groups greater than 1 was 6 (40%), 4 published in English abroad, 2 in Chinese, while only 1 was 9 (60%), 3 published in English, 6 in Chinese. Control design of acupuncture in some clinical RCTs is unreasonable for not considering the amount of difference factors between the two groups.

DIY soundcard based temperature logging system. Part II: applications

NASA Astrophysics Data System (ADS)

Nunn, John

2016-11-01

This paper demonstrates some simple applications of how temperature logging systems may be used to monitor simple heat experiments, and how the data obtained can be analysed to get some additional insight into the physical processes.

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

ERIC Educational Resources Information Center

Brown, Diane Peacock

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

Hidden Connections between Regression Models of Strain-Gage Balance Calibration Data

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert

2013-01-01

Hidden connections between regression models of wind tunnel strain-gage balance calibration data are investigated. These connections become visible whenever balance calibration data is supplied in its design format and both the Iterative and Non-Iterative Method are used to process the data. First, it is shown how the regression coefficients of the fitted balance loads of a force balance can be approximated by using the corresponding regression coefficients of the fitted strain-gage outputs. Then, data from the manual calibration of the Ames MK40 six-component force balance is chosen to illustrate how estimates of the regression coefficients of the fitted balance loads can be obtained from the regression coefficients of the fitted strain-gage outputs. The study illustrates that load predictions obtained by applying the Iterative or the Non-Iterative Method originate from two related regression solutions of the balance calibration data as long as balance loads are given in the design format of the balance, gage outputs behave highly linear, strict statistical quality metrics are used to assess regression models of the data, and regression model term combinations of the fitted loads and gage outputs can be obtained by a simple variable exchange.

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 extraction of simple relationships in growth factor-specific multiple-input and multiple-output systems in cell-fate decisions by backward elimination PLS regression.

PubMed

Akimoto, Yuki; Yugi, Katsuyuki; Uda, Shinsuke; Kudo, Takamasa; Komori, Yasunori; Kubota, Hiroyuki; Kuroda, Shinya

2013-01-01

Cells use common signaling molecules for the selective control of downstream gene expression and cell-fate decisions. The relationship between signaling molecules and downstream gene expression and cellular phenotypes is a multiple-input and multiple-output (MIMO) system and is difficult to understand due to its complexity. For example, it has been reported that, in PC12 cells, different types of growth factors activate MAP kinases (MAPKs) including ERK, JNK, and p38, and CREB, for selective protein expression of immediate early genes (IEGs) such as c-FOS, c-JUN, EGR1, JUNB, and FOSB, leading to cell differentiation, proliferation and cell death; however, how multiple-inputs such as MAPKs and CREB regulate multiple-outputs such as expression of the IEGs and cellular phenotypes remains unclear. To address this issue, we employed a statistical method called partial least squares (PLS) regression, which involves a reduction of the dimensionality of the inputs and outputs into latent variables and a linear regression between these latent variables. We measured 1,200 data points for MAPKs and CREB as the inputs and 1,900 data points for IEGs and cellular phenotypes as the outputs, and we constructed the PLS model from these data. The PLS model highlighted the complexity of the MIMO system and growth factor-specific input-output relationships of cell-fate decisions in PC12 cells. Furthermore, to reduce the complexity, we applied a backward elimination method to the PLS regression, in which 60 input variables were reduced to 5 variables, including the phosphorylation of ERK at 10 min, CREB at 5 min and 60 min, AKT at 5 min and JNK at 30 min. The simple PLS model with only 5 input variables demonstrated a predictive ability comparable to that of the full PLS model. The 5 input variables effectively extracted the growth factor-specific simple relationships within the MIMO system in cell-fate decisions in PC12 cells.

Age estimation standards for a Western Australian population using the coronal pulp cavity index.

PubMed

Karkhanis, Shalmira; Mack, Peter; Franklin, Daniel

2013-09-10

Age estimation is a vital aspect in creating a biological profile and aids investigators by narrowing down potentially matching identities from the available pool. In addition to routine casework, in the present global political scenario, age estimation in living individuals is required in cases of refugees, asylum seekers, human trafficking and to ascertain age of criminal responsibility. Thus robust methods that are simple, non-invasive and ethically viable are required. The aim of the present study is, therefore, to test the reliability and applicability of the coronal pulp cavity index method, for the purpose of developing age estimation standards for an adult Western Australian population. A total of 450 orthopantomograms (220 females and 230 males) of Australian individuals were analyzed. Crown and coronal pulp chamber heights were measured in the mandibular left and right premolars, and the first and second molars. These measurements were then used to calculate the tooth coronal index. Data was analyzed using paired sample t-tests to assess bilateral asymmetry followed by simple linear and multiple regressions to develop age estimation models. The most accurate age estimation based on simple linear regression model was with mandibular right first molar (SEE ±8.271 years). Multiple regression models improved age prediction accuracy considerably and the most accurate model was with bilateral first and second molars (SEE ±6.692 years). This study represents the first investigation of this method in a Western Australian population and our results indicate that the method is suitable for forensic application. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Approximate Expressions for the Period of a Simple Pendulum Using a Taylor Series Expansion

ERIC Educational Resources Information Center

Belendez, Augusto; Arribas, Enrique; Marquez, Andres; Ortuno, Manuel; Gallego, Sergi

2011-01-01

An approximate scheme for obtaining the period of a simple pendulum for large-amplitude oscillations is analysed and discussed. When students express the exact frequency or the period of a simple pendulum as a function of the oscillation amplitude, and they are told to expand this function in a Taylor series, they always do so using the…

Relative Motion of the WDS 05110+3203 STF 648 System, With a Protocol for Calculating Relative Motion

NASA Astrophysics Data System (ADS)

Wiley, E. O.

2010-07-01

Relative motion studies of visual double stars can be investigated using least squares regression techniques and readily accessible programs such as Microsoft Excel and a calculator. Optical pairs differ from physical pairs under most geometries in both their simple scatter plots and their regression models. A step-by-step protocol for estimating the rectilinear elements of an optical pair is presented. The characteristics of physical pairs using these techniques are discussed.

An introduction to g methods.

PubMed

Naimi, Ashley I; Cole, Stephen R; Kennedy, Edward H

2017-04-01

Robins' generalized methods (g methods) provide consistent estimates of contrasts (e.g. differences, ratios) of potential outcomes under a less restrictive set of identification conditions than do standard regression methods (e.g. linear, logistic, Cox regression). Uptake of g methods by epidemiologists has been hampered by limitations in understanding both conceptual and technical details. We present a simple worked example that illustrates basic concepts, while minimizing technical complications. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

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

PubMed

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

2015-04-01

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

Variable selection and model choice in geoadditive regression models.

PubMed

Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

2009-06-01

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

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

PubMed

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

2015-01-01

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

Robust mislabel logistic regression without modeling mislabel probabilities.

PubMed

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

Supercomputer use in orthopaedic biomechanics research: focus on functional adaptation of bone.

PubMed

Hart, R T; Thongpreda, N; Van Buskirk, W C

1988-01-01

The authors describe two biomechanical analyses carried out using numerical methods. One is an analysis of the stress and strain in a human mandible, and the other analysis involves modeling the adaptive response of a sheep bone to mechanical loading. The computing environment required for the two types of analyses is discussed. It is shown that a simple stress analysis of a geometrically complex mandible can be accomplished using a minicomputer. However, more sophisticated analyses of the same model with dynamic loading or nonlinear materials would require supercomputer capabilities. A supercomputer is also required for modeling the adaptive response of living bone, even when simple geometric and material models are use.

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

ERIC Educational Resources Information Center

Tong, Fuhui

2006-01-01

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

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

Treesearch

Sandra E. Ryan; Laurie S. Porth

2007-01-01

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

Bioreactivity: Studies on a Simple Brain Stem Reflex in Behaving Animals

DTIC Science & Technology

1988-07-22

neuromodulation , or complex behavioral processes, such as arousal, is finding a simple system that will permit such analyses. The brain stem...systems important in neuromodulation and arousal. Initial pharmacologic studies showed that locally applied norepinephrine facilitated the reflex

Multiple long-term trends and trend reversals dominate environmental conditions in a man-made freshwater reservoir.

PubMed

Znachor, Petr; Nedoma, Jiří; Hejzlar, Josef; Seďa, Jaromír; Kopáček, Jiří; Boukal, David; Mrkvička, Tomáš

2018-05-15

Man-made reservoirs are common across the world and provide a wide range of ecological services. Environmental conditions in riverine reservoirs are affected by the changing climate, catchment-wide processes and manipulations with the water level, and water abstraction from the reservoir. Long-term trends of environmental conditions in reservoirs thus reflect a wider range of drivers in comparison to lakes, which makes the understanding of reservoir dynamics more challenging. We analysed a 32-year time series of 36 environmental variables characterising weather, land use in the catchment, reservoir hydrochemistry, hydrology and light availability in the small, canyon-shaped Římov Reservoir in the Czech Republic to detect underlying trends, trend reversals and regime shifts. To do so, we fitted linear and piecewise linear regression and a regime shift model to the time series of mean annual values of each variable and to principal components produced by Principal Component Analysis. Models were weighted and ranked using Akaike information criterion and the model selection approach. Most environmental variables exhibited temporal changes that included time-varying trends and trend reversals. For instance, dissolved organic carbon showed a linear increasing trend while nitrate concentration or conductivity exemplified trend reversal. All trend reversals and cessations of temporal trends in reservoir hydrochemistry (except total phosphorus concentrations) occurred in the late 1980s and during 1990s as a consequence of dramatic socioeconomic changes. After a series of heavy rains in the late 1990s, an administrative decision to increase the flood-retention volume of the reservoir resulted in a significant regime shift in reservoir hydraulic conditions in 1999. Our analyses also highlight the utility of the model selection framework, based on relatively simple extensions of linear regression, to describe temporal trends in reservoir characteristics. This approach can provide a solid basis for a better understanding of processes in freshwater reservoirs. Copyright © 2017 Elsevier B.V. All rights reserved.

Association factor analysis between osteoporosis with cerebral artery disease: The STROBE study.

PubMed

Jin, Eun-Sun; Jeong, Je Hoon; Lee, Bora; Im, Soo Bin

2017-03-01

The purpose of this study was to determine the clinical association factors between osteoporosis and cerebral artery disease in Korean population. Two hundred nineteen postmenopausal women and men undergoing cerebral computed tomography angiography were enrolled in this study to evaluate the cerebral artery disease by cross-sectional study. Cerebral artery disease was diagnosed if there was narrowing of 50% higher diameter in one or more cerebral vessel artery or presence of vascular calcification. History of osteoporotic fracture was assessed using medical record, and radiographic data such as simple radiography, MRI, and bone scan. Bone mineral density was checked by dual-energy x-ray absorptiometry. We reviewed clinical characteristics in all patients and also performed subgroup analysis for total or extracranial/ intracranial cerebral artery disease group retrospectively. We performed statistical analysis by means of chi-square test or Fisher's exact test for categorical variables and Student's t-test or Wilcoxon's rank sum test for continuous variables. We also used univariate and multivariate logistic regression analyses were conducted to assess the factors associated with the prevalence of cerebral artery disease. A two-tailed p-value of less than 0.05 was considered as statistically significant. All statistical analyses were performed using R (version 3.1.3; The R Foundation for Statistical Computing, Vienna, Austria) and SPSS (version 14.0; SPSS, Inc, Chicago, Ill, USA). Of the 219 patients, 142 had cerebral artery disease. All vertebral fracture was observed in 29 (13.24%) patients. There was significant difference in hip fracture according to the presence or absence of cerebral artery disease. In logistic regression analysis, osteoporotic hip fracture was significantly associated with extracranial cerebral artery disease after adjusting for multiple risk factors. Females with osteoporotic hip fracture were associated with total calcified cerebral artery disease. Some clinical factors such as age, hypertension, and osteoporotic hip fracture, smoking history and anti-osteoporosis drug use were associated with cerebral artery disease.

Classification of Large-Scale Remote Sensing Images for Automatic Identification of Health Hazards: Smoke Detection Using an Autologistic Regression Classifier.

PubMed

Wolters, Mark A; Dean, C B

2017-01-01

Remote sensing images from Earth-orbiting satellites are a potentially rich data source for monitoring and cataloguing atmospheric health hazards that cover large geographic regions. A method is proposed for classifying such images into hazard and nonhazard regions using the autologistic regression model, which may be viewed as a spatial extension of logistic regression. The method includes a novel and simple approach to parameter estimation that makes it well suited to handling the large and high-dimensional datasets arising from satellite-borne instruments. The methodology is demonstrated on both simulated images and a real application to the identification of forest fire smoke.

Research on the effects of urbanization on small stream flow quantity

DOT National Transportation Integrated Search

1978-12-01

This study is a preliminary investigation into the feasibility of using simple techniques to evaluate the effects of urbanization on flood flows in small streams. A number of regression techniques and computer simulation techniques were evaluated, an...

A Two-Step Approach for Analysis of Nonignorable Missing Outcomes in Longitudinal Regression: an Application to Upstate KIDS Study.

PubMed

Liu, Danping; Yeung, Edwina H; McLain, Alexander C; Xie, Yunlong; Buck Louis, Germaine M; Sundaram, Rajeshwari

2017-09-01

Imperfect follow-up in longitudinal studies commonly leads to missing outcome data that can potentially bias the inference when the missingness is nonignorable; that is, the propensity of missingness depends on missing values in the data. In the Upstate KIDS Study, we seek to determine if the missingness of child development outcomes is nonignorable, and how a simple model assuming ignorable missingness would compare with more complicated models for a nonignorable mechanism. To correct for nonignorable missingness, the shared random effects model (SREM) jointly models the outcome and the missing mechanism. However, the computational complexity and lack of software packages has limited its practical applications. This paper proposes a novel two-step approach to handle nonignorable missing outcomes in generalized linear mixed models. We first analyse the missing mechanism with a generalized linear mixed model and predict values of the random effects; then, the outcome model is fitted adjusting for the predicted random effects to account for heterogeneity in the missingness propensity. Extensive simulation studies suggest that the proposed method is a reliable approximation to SREM, with a much faster computation. The nonignorability of missing data in the Upstate KIDS Study is estimated to be mild to moderate, and the analyses using the two-step approach or SREM are similar to the model assuming ignorable missingness. The two-step approach is a computationally straightforward method that can be conducted as sensitivity analyses in longitudinal studies to examine violations to the ignorable missingness assumption and the implications relative to health outcomes. © 2017 John Wiley & Sons Ltd.

Penalized regression procedures for variable selection in the potential outcomes framework

PubMed Central

Ghosh, Debashis; Zhu, Yeying; Coffman, Donna L.

2015-01-01

A recent topic of much interest in causal inference is model selection. In this article, we describe a framework in which to consider penalized regression approaches to variable selection for causal effects. The framework leads to a simple ‘impute, then select’ class of procedures that is agnostic to the type of imputation algorithm as well as penalized regression used. It also clarifies how model selection involves a multivariate regression model for causal inference problems, and that these methods can be applied for identifying subgroups in which treatment effects are homogeneous. Analogies and links with the literature on machine learning methods, missing data and imputation are drawn. A difference LASSO algorithm is defined, along with its multiple imputation analogues. The procedures are illustrated using a well-known right heart catheterization dataset. PMID:25628185

A methodology for the design of experiments in computational intelligence with multiple regression models.

PubMed

Fernandez-Lozano, Carlos; Gestal, Marcos; Munteanu, Cristian R; Dorado, Julian; Pazos, Alejandro

2016-01-01

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

A methodology for the design of experiments in computational intelligence with multiple regression models

PubMed Central

Gestal, Marcos; Munteanu, Cristian R.; Dorado, Julian; Pazos, Alejandro

2016-01-01

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable. PMID:27920952

ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.

PubMed

Choi, Soo Beom; Choi, Joon Yul; Park, Jee Soo; Kim, Deok Won

2016-07-01

In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.

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

PubMed Central

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

1984-01-01

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

Association of Urogenital Symptoms with History of Water Contact in Young Women in Areas Endemic for S. haematobium. A Cross-Sectional Study in Rural South Africa.

PubMed

Galappaththi-Arachchige, Hashini Nilushika; Amlie Hegertun, Ingrid Elise; Holmen, Sigve; Qvigstad, Erik; Kleppa, Elisabeth; Sebitloane, Motshedisi; Ndhlovu, Patricia Doris; Vennervald, Birgitte Jyding; Gundersen, Svein Gunnar; Taylor, Myra; Kjetland, Eyrun Floerecke

2016-11-14

Female genital schistosomiasis is a neglected tropical disease caused by Schistosoma haematobium . Infected females may suffer from symptoms mimicking sexually transmitted infections. We explored if self-reported history of unsafe water contact could be used as a simple predictor of genital schistosomiasis. In a cross-sectional study in rural South Africa, 883 sexually active women aged 16-22 years were included. Questions were asked about urogenital symptoms and water contact history. Urine samples were tested for S. haematobium ova. A score based on self-reported water contact was calculated and the association with symptoms was explored while adjusting for other genital infections using multivariable logistic regression analyses. S. haematobium ova were detected in the urine of 30.5% of subjects. Having ova in the urine was associated with the water contact score ( p < 0.001). Symptoms that were associated with water contact included burning sensation in the genitals ( p = 0.005), spot bleeding ( p = 0.012), abnormal discharge smell ( p = 0.018), bloody discharge ( p = 0.020), genital ulcer ( p = 0.038), red urine ( p < 0.001), stress incontinence ( p = 0.001) and lower abdominal pain ( p = 0.028). In S. haematobium endemic areas, self-reported water contact was strongly associated with urogenital symptoms. In low-resource settings, a simple history including risk of water contact behaviour can serve as an indicator of urogenital schistosomiasis.

Sensitivity of the Gravity Recovery and Climate Experiment (GRACE) to the complexity of aquifer systems for monitoring of groundwater

NASA Astrophysics Data System (ADS)

Katpatal, Yashwant B.; Rishma, C.; Singh, Chandan K.

2018-05-01

The Gravity Recovery and Climate Experiment (GRACE) satellite mission is aimed at assessment of groundwater storage under different terrestrial conditions. The main objective of the presented study is to highlight the significance of aquifer complexity to improve the performance of GRACE in monitoring groundwater. Vidarbha region of Maharashtra, central India, was selected as the study area for analysis, since the region comprises a simple aquifer system in the western region and a complex aquifer system in the eastern region. Groundwater-level-trend analyses of the different aquifer systems and spatial and temporal variation of the terrestrial water storage anomaly were studied to understand the groundwater scenario. GRACE and its field application involve selecting four pixels from the GRACE output with different aquifer systems, where each GRACE pixel encompasses 50-90 monitoring wells. Groundwater storage anomalies (GWSA) are derived for each pixel for the period 2002 to 2015 using the Release 05 (RL05) monthly GRACE gravity models and the Global Land Data Assimilation System (GLDAS) land-surface models (GWSAGRACE) as well as the actual field data (GWSAActual). Correlation analysis between GWSAGRACE and GWSAActual was performed using linear regression. The Pearson and Spearman methods show that the performance of GRACE is good in the region with simple aquifers; however, performance is poorer in the region with multiple aquifer systems. The study highlights the importance of incorporating the sensitivity of GRACE in estimation of groundwater storage in complex aquifer systems in future studies.

Does my step look big in this? A visual illusion leads to safer stepping behaviour.

PubMed

Elliott, David B; Vale, Anna; Whitaker, David; Buckley, John G

2009-01-01

Tripping is a common factor in falls and a typical safety strategy to avoid tripping on steps or stairs is to increase foot clearance over the step edge. In the present study we asked whether the perceived height of a step could be increased using a visual illusion and whether this would lead to the adoption of a safer stepping strategy, in terms of greater foot clearance over the step edge. The study also addressed the controversial question of whether motor actions are dissociated from visual perception. 21 young, healthy subjects perceived the step to be higher in a configuration of the horizontal-vertical illusion compared to a reverse configuration (p = 0.01). During a simple stepping task, maximum toe elevation changed by an amount corresponding to the size of the visual illusion (p<0.001). Linear regression analyses showed highly significant associations between perceived step height and maximum toe elevation for all conditions. The perceived height of a step can be manipulated using a simple visual illusion, leading to the adoption of a safer stepping strategy in terms of greater foot clearance over a step edge. In addition, the strong link found between perception of a visual illusion and visuomotor action provides additional support to the view that the original, controversial proposal by Goodale and Milner (1992) of two separate and distinct visual streams for perception and visuomotor action should be re-evaluated.

The Global Precipitation Climatology Project: First Algorithm Intercomparison Project

NASA Technical Reports Server (NTRS)

Arkin, Phillip A.; Xie, Pingping

1994-01-01

The Global Precipitation Climatology Project (GPCP) was established by the World Climate Research Program to produce global analyses of the area- and time-averaged precipitation for use in climate research. To achieve the required spatial coverage, the GPCP uses simple rainfall estimates derived from IR and microwave satellite observations. In this paper, we describe the GPCP and its first Algorithm Intercomparison Project (AIP/1), which compared a variety of rainfall estimates derived from Geostationary Meteorological Satellite visible and IR observations and Special Sensor Microwave/Imager (SSM/I) microwave observations with rainfall derived from a combination of radar and raingage data over the Japanese islands and the adjacent ocean regions during the June and mid-July through mid-August periods of 1989. To investigate potential improvements in the use of satellite IR data for the estimation of large-scale rainfall for the GPCP, the relationship between rainfall and the fractional coverage of cold clouds in the AIP/1 dataset is examined. Linear regressions between fractional coverage and rainfall are analyzed for a number of latitude-longitude areas and for a range of averaging times. The results show distinct differences in the character of the relationship for different portions of the area. These results suggest that the simple IR-based estimation technique currently used in the GPCP can be used to estimate rainfall for global tropical and subtropical areas, provided that a method for adjusting the proportional coefficient for varying areas and seasons can be determined.

[Approach to the Development of Mind and Persona].

PubMed

Sawaguchi, Toshiko

2018-01-01

To access medical specialists by health specialists working in the regional health field, the possibility of utilizing the voice approach for dissociative identity disorder (DID) patients as a health assessment for medical access (HAMA) was investigated. The first step is to investigate whether the plural personae in a single DID patient can be discriminated by voice analysis. Voices of DID patients including these with different personae were extracted from YouTube and were analysed using the software PRAAT with basic frequency, oral factors, chin factors and tongue factors. In addition, RAKUGO story teller voices made artificially and dramatically were analysed in the same manner. Quantitive and qualitative analysis method were carried out and nested logistic regression and a nested generalized linear model was developed. The voice from different personae in one DID patient could be visually and easily distinquished using basic frequency curve, cluster analysis and factor analysis. In the canonical analysis, only Roy's maximum root was <0.01. In the nested generalized linear model, the model using a standard deviation (SD) indicator fit best and some other possibilities are shown here. In DID patients, the short transition time among plural personae could guide to the risky situation such as suicide. So if the voice approach can show the time threshold of changes between the different personae, it would be useful as an Access Assessment in the form of a simple HAMA.

Stroop performance in major depression: selective attention impairment or psychomotor slowness?

PubMed

Kertzman, Semion; Reznik, Ilya; Hornik-Lurie, Tzipi; Weizman, Abraham; Kotler, Moshe; Amital, Daniela

2010-04-01

Numerous neuropsychological studies reported impaired Stroop performance in major depressive disorder (MDD) patients. The present study attempted to identify possible neuropsychological mechanisms involved in this impairment in untreated MDD outpatients (n=75) as compared to healthy subjects (n=83). Inspection Time, Finger Tapping, Simple and Choice Reaction Time were considered as measures of perceptual, motor, psychomotor speed, and response selection, respectively. MDD patients performed significantly slower than healthy controls in the neutral and the congruent conditions, but not in the incongruent ones. In order to identify predictors of Stroop performance, linear hierarchical regressions analyses were performed. Age, motor and psychomotor speed were predictors of response time and accuracy on Stroop performance. Significant correlations between response time and the number of errors in all three Stroop conditions were found in MDD patients, while such a correlation was obtained in the healthy controls only in the incongruent condition. Although education was included as a covariate in our analyses, suggesting that the observed effects could not be ascribed to education differences, further testing with education-matched samples is warranted. Our study shows that the Stroop task performance is affected by both aging and MDD. Impairment in the Stroop performance can be predicted by psychomotor slowness and by vigilance level in MDD outpatients, but not by impairment of selective attention per se. Copyright 2009 Elsevier B.V. All rights reserved.

[Factors associated with incidence of dengue in Costa Rica].

PubMed

Mena, Nelson; Troyo, Adriana; Bonilla-Carrión, Roger; Calderón-Arguedas, Olger

2011-04-01

Determine the extent to which socioeconomic, demographic, geographic, and climate variables affected the incidence of dengue and dengue hemorrhagic fever (D/DH) in Costa Rica during the period 1999-2007. A correlational epidemiologic study was conducted that analyzed the cumulative incidence of D/DH from 1999 to 2007 and its association with different variables in the country's 81 cantons. Information was obtained from secondary sources, and the independent variables used for the analysis were selected on the basis of their representativeness in terms of sociodemographic, environmental, and health coverage factors that affect the epidemiology of D/DH. These variables were divided into four groups of indicators: demographic, socioeconomic, housing, and climate and geographical. The data were analyzed by means of simple and multiple Poisson regressions. The Costa Rican cantons with a higher incidence of D/DH were located primarily near the coast, coinciding with some of the variables studied. Temperature, altitude, and the human poverty index were the most relevant variables in explaining the incidence of D/DH, while temperature was the most significant variable in the multiple analyses. The analyses made it possible to correlate a higher incidence of D/DH with lower-altitude cantons, higher temperature, and a high human poverty index ranking. This information is relevant as a first step toward prioritizing and optimizing actions for the prevention and control of this disease.

Simultaneous determination of some ultraviolet-absorbing chemicals in sunscreen cosmetics using a high-performance liquid chromatography method.

PubMed

Liu, T; Wu, D

2011-10-01

A method of gradient elution high-performance liquid chromatography (HPLC) for simultaneous determination of 11 different ultraviolet-absorbing chemicals of phenylbenzlmldazole sulphonic acid, 4-aminobenzoic acid, benzophenone-4, benzophenone-3, isoamyl p-methoxycinnamate, 4-methylbenzylidene camphor, octocrylene, ethylhexyl methoxycinnamate, homosalate, ethylhexyl salicylate, methylene bis-benzotriazolyl tetramethylbutyl phenol was developed for the application to sunscreen cosmetic products. In this study, an Agilent SB-C18 analytical column (250 × 4.6 mm, 5 μm) was utilized and methanol, tetrahydrofuran and perchloric acid aqueous solution (0.2 mL HClO(4) + 300 mL H(2)O) were used for gradient elution at a total flow rate of 1.0 mL min(-1). The optimum conditions for 11 different ultraviolet-absorbing chemicals analyses were investigated. All calibration curves showed good linear regression with UV detection (311 nm) within test ranges. The correlation coefficients were better than 0.999 in all cases. The assay was simple, selective, convenient and reproducible and is suitable for the determination of ultraviolet-absorbing chemicals in commercial sunscreen cosmetic products. The use frequency of 11 different ultraviolet absorbents in 100 sunscreen cosmetics was investigated and statistically analysed. The ultraviolet absorbent of maximum use frequency was ethylhexyl methoxycinnamate. © 2011 The Authors. ICS © 2011 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Contribution of parenting to complex syntax development in preschool children with developmental delays or typical development.

PubMed

Moody, C T; Baker, B L; Blacher, J

2018-05-10

Despite studies of how parent-child interactions relate to early child language development, few have examined the continued contribution of parenting to more complex language skills through the preschool years. The current study explored how positive and negative parenting behaviours relate to growth in complex syntax learning from child age 3 to age 4 years, for children with typical development or developmental delays (DDs). Participants were children with or without DD (N = 60) participating in a longitudinal study of development. Parent-child interactions were transcribed and coded for parenting domains and child language. Multiple regression analyses were used to identify the contribution of parenting to complex syntax growth in children with typical development or DD. Analyses supported a final model, F(9,50) = 11.90, P < .001, including a significant three-way interaction between positive parenting behaviours, negative parenting behaviours and child delay status. This model explained 68.16% of the variance in children's complex syntax at age 4. Simple two-way interactions indicated differing effects of parenting variables for children with or without DD. Results have implications for understanding of complex syntax acquisition in young children, as well as implications for interventions. © 2018 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

Childhood adversity in association with personality disorder dimensions: new findings in an old debate.

PubMed

Hengartner, M P; Ajdacic-Gross, V; Rodgers, S; Müller, M; Rössler, W

2013-10-01

Various studies have reported a positive relationship between child maltreatment and personality disorders (PDs). However, few studies included all DSM-IV PDs and even fewer adjusted for other forms of childhood adversity, e.g. bullying or family problems. We analyzed questionnaires completed by 512 participants of the ZInEP epidemiology survey, a comprehensive psychiatric survey of the general population in Zurich, Switzerland. Associations between childhood adversity and PDs were analyzed bivariately via simple regression analyses and multivariately via multiple path analysis. The bivariate analyses revealed that all PD dimensions were significantly related to various forms of family and school problems as well as child abuse. In contrast, according to the multivariate analysis only school problems and emotional abuse were associated with various PDs. Poverty was uniquely associated with schizotypal PD, conflicts with parents with obsessive-compulsive PD, physical abuse with antisocial PD, and physical neglect with narcissistic PD. Sexual abuse was statistically significantly associated with schizotypal and borderline PD, but corresponding effect sizes were small. Childhood adversity has a serious impact on PDs. Bullying and violence in schools and emotional abuse appear to be more salient markers of general personality pathology than other forms of childhood adversity. Associations with sexual abuse were negligible when adjusted for other forms of adversity. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Ethical media competence as a protective factor against cyberbullying and cybervictimization among german school students.

PubMed

Müller, Christin R; Pfetsch, Jan; Ittel, Angela

2014-10-01

The use of digital information and communication technologies is an integral part of adolescents' everyday life. Besides various opportunities for information, entertainment, and communication, media use is associated with risks such as cyberbullying. Cyberbullying refers to aggressive behavior in the context of computer-mediated communication, characterized by repetition, an intention to harm, and power imbalance. Previous studies have shown that increased media use is a major risk factor for cyberbullying and cybervictimization. Given that restricting media use is not a practical way to reduce the negative effects inherent in media use, the present study examines the relevance of ethical media competence. We expected ethical media competence to buffer the effect of increased media use on cyberbullying and cybervictimization. A survey was conducted with 934 students (53% female) aged 10-17 years (M=13.26, SD=1.63). As expected, hierarchical regression analyses showed a positive main effect of media use, a negative main effect of ethical media competence, and a negative interaction effect of media use and media competence on cyberbullying and cybervictimization. Simple slope analyses revealed that at high levels of ethical media competence, media use has almost no effect on cybervictimization and a significant negative effect on cyberbullying. Consequently, promoting ethical media competence constitutes a potential measure to prevent the risks of increased media use for cyberbullying and cybervictimization.

Genome-environment association study suggests local adaptation to climate at the regional scale in Fagus sylvatica.

PubMed

Pluess, Andrea R; Frank, Aline; Heiri, Caroline; Lalagüe, Hadrien; Vendramin, Giovanni G; Oddou-Muratorio, Sylvie

2016-04-01

The evolutionary potential of long-lived species, such as forest trees, is fundamental for their local persistence under climate change (CC). Genome-environment association (GEA) analyses reveal if species in heterogeneous environments at the regional scale are under differential selection resulting in populations with potential preadaptation to CC within this area. In 79 natural Fagus sylvatica populations, neutral genetic patterns were characterized using 12 simple sequence repeat (SSR) markers, and genomic variation (144 single nucleotide polymorphisms (SNPs) out of 52 candidate genes) was related to 87 environmental predictors in the latent factor mixed model, logistic regressions and isolation by distance/environmental (IBD/IBE) tests. SSR diversity revealed relatedness at up to 150 m intertree distance but an absence of large-scale spatial genetic structure and IBE. In the GEA analyses, 16 SNPs in 10 genes responded to one or several environmental predictors and IBE, corrected for IBD, was confirmed. The GEA often reflected the proposed gene functions, including indications for adaptation to water availability and temperature. Genomic divergence and the lack of large-scale neutral genetic patterns suggest that gene flow allows the spread of advantageous alleles in adaptive genes. Thereby, adaptation processes are likely to take place in species occurring in heterogeneous environments, which might reduce their regional extinction risk under CC. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Do drug treatment variables predict cognitive performance in multidrug-treated opioid-dependent patients? A regression analysis study

PubMed Central

2012-01-01

Background Cognitive deficits and multiple psychoactive drug regimens are both common in patients treated for opioid-dependence. Therefore, we examined whether the cognitive performance of patients in opioid-substitution treatment (OST) is associated with their drug treatment variables. Methods Opioid-dependent patients (N = 104) who were treated either with buprenorphine or methadone (n = 52 in both groups) were given attention, working memory, verbal, and visual memory tests after they had been a minimum of six months in treatment. Group-wise results were analysed by analysis of variance. Predictors of cognitive performance were examined by hierarchical regression analysis. Results Buprenorphine-treated patients performed statistically significantly better in a simple reaction time test than methadone-treated ones. No other significant differences between groups in cognitive performance were found. In each OST drug group, approximately 10% of the attention performance could be predicted by drug treatment variables. Use of benzodiazepine medication predicted about 10% of performance variance in working memory. Treatment with more than one other psychoactive drug (than opioid or BZD) and frequent substance abuse during the past month predicted about 20% of verbal memory performance. Conclusions Although this study does not prove a causal relationship between multiple prescription drug use and poor cognitive functioning, the results are relevant for psychosocial recovery, vocational rehabilitation, and psychological treatment of OST patients. Especially for patients with BZD treatment, other treatment options should be actively sought. PMID:23121989

Heuristics as Bayesian inference under extreme priors.

PubMed

Parpart, Paula; Jones, Matt; Love, Bradley C

2018-05-01

Simple heuristics are often regarded as tractable decision strategies because they ignore a great deal of information in the input data. One puzzle is why heuristics can outperform full-information models, such as linear regression, which make full use of the available information. These "less-is-more" effects, in which a relatively simpler model outperforms a more complex model, are prevalent throughout cognitive science, and are frequently argued to demonstrate an inherent advantage of simplifying computation or ignoring information. In contrast, we show at the computational level (where algorithmic restrictions are set aside) that it is never optimal to discard information. Through a formal Bayesian analysis, we prove that popular heuristics, such as tallying and take-the-best, are formally equivalent to Bayesian inference under the limit of infinitely strong priors. Varying the strength of the prior yields a continuum of Bayesian models with the heuristics at one end and ordinary regression at the other. Critically, intermediate models perform better across all our simulations, suggesting that down-weighting information with the appropriate prior is preferable to entirely ignoring it. Rather than because of their simplicity, our analyses suggest heuristics perform well because they implement strong priors that approximate the actual structure of the environment. We end by considering how new heuristics could be derived by infinitely strengthening the priors of other Bayesian models. These formal results have implications for work in psychology, machine learning and economics. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

SkyFACT: high-dimensional modeling of gamma-ray emission with adaptive templates and penalized likelihoods

NASA Astrophysics Data System (ADS)

Storm, Emma; Weniger, Christoph; Calore, Francesca

2017-08-01

We present SkyFACT (Sky Factorization with Adaptive Constrained Templates), a new approach for studying, modeling and decomposing diffuse gamma-ray emission. Like most previous analyses, the approach relies on predictions from cosmic-ray propagation codes like GALPROP and DRAGON. However, in contrast to previous approaches, we account for the fact that models are not perfect and allow for a very large number (gtrsim 105) of nuisance parameters to parameterize these imperfections. We combine methods of image reconstruction and adaptive spatio-spectral template regression in one coherent hybrid approach. To this end, we use penalized Poisson likelihood regression, with regularization functions that are motivated by the maximum entropy method. We introduce methods to efficiently handle the high dimensionality of the convex optimization problem as well as the associated semi-sparse covariance matrix, using the L-BFGS-B algorithm and Cholesky factorization. We test the method both on synthetic data as well as on gamma-ray emission from the inner Galaxy, |l|<90o and |b|<20o, as observed by the Fermi Large Area Telescope. We finally define a simple reference model that removes most of the residual emission from the inner Galaxy, based on conventional diffuse emission components as well as components for the Fermi bubbles, the Fermi Galactic center excess, and extended sources along the Galactic disk. Variants of this reference model can serve as basis for future studies of diffuse emission in and outside the Galactic disk.

Pushing the boundaries of viability: the economic impact of extreme preterm birth.

PubMed

Petrou, Stavros; Henderson, Jane; Bracewell, Melanie; Hockley, Christine; Wolke, Dieter; Marlow, Neil

2006-02-01

Previous assessments of the economic impact of preterm birth focussed on short term health service costs across the broad spectrum of prematurity. To estimate the societal costs of extreme preterm birth during the sixth year after birth. Unit costs were applied to estimates of health, social and broader resource use made by 241 children born at 20 through 25 completed weeks of gestation in the United Kingdom and Republic of Ireland and a comparison group of 160 children born at full term. Societal costs per child during the sixth year after birth were estimated and subjected to a rigorous sensitivity analysis. The effects of gestational age at birth on annual societal costs were analysed, first in a simple linear regression and then in a multiple linear regression. Mean societal costs over the 12 month period were 9541 pounds sterling (standard deviation 11,678 pounds sterling) for the extreme preterm group and 3883 pounds sterling (1098 pounds sterling) for the term group, generating a mean cost difference of 5658 pounds sterling (bootstrap 95% confidence interval: 4203 pounds sterling, 7256 pounds sterling) that was statistically significant (P<0.001). After adjustment for clinical and sociodemographic covariates, sex-specific extreme preterm birth was a strong predictor of high societal costs. The results of this study should facilitate the effective planning of services and may be used to inform the development of future economic evaluations of interventions aimed at preventing extreme preterm birth or alleviating its effects.

Application of reflectance colorimeter measurements and infrared spectroscopy methods to rapid and nondestructive evaluation of carotenoids content in apricot (Prunus armeniaca L.).

PubMed

Ruiz, David; Reich, Maryse; Bureau, Sylvie; Renard, Catherine M G C; Audergon, Jean-Marc

2008-07-09

The importance of carotenoid content in apricot (Prunus armeniaca L.) is recognized not only because of the color that they impart but also because of their protective activity against human diseases. Current methods to assess carotenoid content are time-consuming, expensive, and destructive. In this work, the application of rapid and nondestructive methods such as colorimeter measurements and infrared spectroscopy has been evaluated for carotenoid determination in apricot. Forty apricot genotypes covering a wide range of peel and flesh colors have been analyzed. Color measurements on the skin and flesh ( L*, a*, b*, hue, chroma, and a*/ b* ratio) as well as Fourier transform near-infrared spectroscopy (FT-NIR) on intact fruits and Fourier transform mid-infrared spectroscopy (FT-MIR) on ground flesh were correlated with the carotenoid content measured by high-performance liquid chromatography. A high variability in color values and carotenoid content was observed. Partial least squares regression analyses between beta-carotene content and provitamin A activity and color measurements showed a high fit in peel, flesh, and edible apricot portion (R(2) ranged from 0.81 to 0.91) and low prediction error. Regression equations were developed for predicting carotenoid content by using color values, which appeared as a simple, rapid, reliable, and nondestructive method. However, FT-NIR and FT-MIR models showed very low R(2) values and very high prediction errors for carotenoid content.

Factors affecting dental service quality.

PubMed

Bahadori, Mohammadkarim; Raadabadi, Mehdi; Ravangard, Ramin; Baldacchino, Donia

2015-01-01

Measuring dental clinic service quality is the first and most important factor in improving care. The quality provided plays an important role in patient satisfaction. The purpose of this paper is to identify factors affecting dental service quality from the patients' viewpoint. This cross-sectional, descriptive-analytical study was conducted in a dental clinic in Tehran between January and June 2014. A sample of 385 patients was selected from two work shifts using stratified sampling proportional to size and simple random sampling methods. The data were collected, a self-administered questionnaire designed for the purpose of the study, based on the Parasuraman and Zeithaml's model of service quality which consisted of two parts: the patients' demographic characteristics and a 30-item questionnaire to measure the five dimensions of the service quality. The collected data were analysed using SPSS 21.0 and Amos 18.0 through some descriptive statistics such as mean, standard deviation, as well as analytical methods, including confirmatory factor. Results showed that the correlation coefficients for all dimensions were higher than 0.5. In this model, assurance (regression weight=0.99) and tangibility (regression weight=0.86) had, respectively, the highest and lowest effects on dental service quality. The Parasuraman and Zeithaml's model is suitable to measure quality in dental services. The variables related to dental services quality have been made according to the model. This is a pioneering study that uses Parasuraman and Zeithaml's model and CFA in a dental setting. This study provides useful insights and guidance for dental service quality assurance.

Higher glucose levels associated with lower memory and reduced hippocampal microstructure.

PubMed

Kerti, Lucia; Witte, A Veronica; Winkler, Angela; Grittner, Ulrike; Rujescu, Dan; Flöel, Agnes

2013-11-12

For this cross-sectional study, we aimed to elucidate whether higher glycosylated hemoglobin (HbA1c) and glucose levels exert a negative impact on memory performance and hippocampal volume and microstructure in a cohort of healthy, older, nondiabetic individuals without dementia. In 141 individuals (72 women, mean age 63.1 years ± 6.9 SD), memory was tested using the Rey Auditory Verbal Learning Test. Peripheral levels of fasting HbA1c, glucose, and insulin and 3-tesla MRI scans were acquired to assess hippocampal volume and microstructure, as indicated by gray matter barrier density. Linear regression and simple mediation models were calculated to examine associations among memory, glucose metabolism, and hippocampal parameters. Lower HbA1c and glucose levels were significantly associated with better scores in delayed recall, learning ability, and memory consolidation. In multiple regression models, HbA1c remained strongly associated with memory performance. Moreover, mediation analyses indicated that beneficial effects of lower HbA1c on memory are in part mediated by hippocampal volume and microstructure. Our results indicate that even in the absence of manifest type 2 diabetes mellitus or impaired glucose tolerance, chronically higher blood glucose levels exert a negative influence on cognition, possibly mediated by structural changes in learning-relevant brain areas. Therefore, strategies aimed at lowering glucose levels even in the normal range may beneficially influence cognition in the older population, a hypothesis to be examined in future interventional trials.

Reduced work/academic performance and quality of life in patients with allergic rhinitis and impact of allergen immunotherapy.

PubMed

Roger, A; Arcalá Campillo, E; Torres, M C; Millan, C; Jáuregui, I; Mohedano, E; Liñan, S; Verdu, P; Rubira, N; Santaolalla, M; González, P; Orovitg, A; Villarrubia, E

2016-01-01

Allergic rhinitis (AR) is characterised by burdensome nasal and/or ocular symptoms. This inflammatory disease can be debilitating and thus result in considerable health-related and economic consequences. In a cross-sectional study, adult subjects with AR (N = 683) completed three allergy-specific questionnaires that assessed the impact of AR on the work/academic performance, daily activities, health-related quality of life (HRQOL), and satisfaction with allergen immunotherapy (AIT). Regression analyses were used to examine the associations between several clinical variables and the patient-reported outcomes. Total loss of productivity was 21.0 and 21.2 % for employed and student patients, respectively, whereas the impairment of daily activities was 22.0 %. The mean overall HRQOL score was 1.94 ± 1.29 (on the scale of 0-6 points). Global score for satisfaction with AIT was 65.5 ± 24.8 (on a 0-100 scale). Simple regression analysis found statistically significant associations between loss of work and academic productivity, impairment of daily activities and the type and severity of AR. AIT was a protective factor. The persistent and more severe types of AR and lack of AIT contributed to the worsening of HRQOL. AR (the persistent and more severe form of the disease) has an impact on functional characteristics of adult patients in Spain. AIT might reduce the effect of this disease on the work/academic performance and HRQOL. Trial registration Retrospectively registered.

Psychosocial and cognitive factors associated with adherence to dietary and fluid restriction regimens by people on chronic haemodialysis.

PubMed

Sensky, T; Leger, C; Gilmour, S

1996-01-01

Failure by people on chronic haemodialysis to adhere adequately to dietary and fluid restrictions can have serious medical consequences. Numerous psychosocial factors possibly associated with adherence have been investigated in previous research. However, most previous studies have examined one or a few variables in isolation, and have tended to focus on sociodemographic variables not easily amenable to intervention. Much previous work has tended to ignore potential differences in adherence between male and female dialysands. Sociodemographic and psychosocial factors associated with adherence to dietary and fluid restrictions were investigated in 45 people on haemodialysis attending one renal unit, excluding those with a residual urine volume > 500 ml/day. Multiple regression analyses were used to estimate the contribution to adherence of a range of variables, including gender, age, duration of dialysis, affective disturbance, past psychiatric history, health locus of control, social adjustment and social supports. Adherence to diet (measured by predialysis serum potassium) and to fluid restriction (interdialysis weight gain) were not linked, and had different psychosocial correlates. Regression models of four different aspects of adherence revealed very distinct psychosocial correlates, with contributions to adherence from complex interactions between psychosocial and cognitive variables, notably gender, age, social adjustment, health locus of control, and depression. The findings cast doubt on the results of many previous studies which have used simple models of adherence. Adherence is likely to be influenced in a complex manner by multiple factors including age, gender, locus of control, social adjustment, and past psychiatric history.

Computer Simulation of Human Service Program Evaluations.

ERIC Educational Resources Information Center

Trochim, William M. K.; Davis, James E.

1985-01-01

Describes uses of computer simulations for the context of human service program evaluation. Presents simple mathematical models for most commonly used human service outcome evaluation designs (pretest-posttest randomized experiment, pretest-posttest nonequivalent groups design, and regression-discontinuity design). Translates models into single…

Use of simple models to determine wake vortex categories for new aircraft.

DOT National Transportation Integrated Search

2015-06-22

The paper describes how to use simple models and, if needed, sensitivity analyses to determine the wake vortex categories for new aircraft. The methodology provides a tool for the regulators to assess the relative risk of introducing new aircraft int...

Preparation and Characterization of a Small Library of Thermally-Labile End-Caps for Variable-Temperature Triggering of Self-Immolative Polymers.

PubMed

Taimoory, S Maryamdokht; Sadraei, S Iraj; Fayoumi, Rose Anne; Nasri, Sarah; Revington, Matthew; Trant, John F

2018-04-20

The reaction between furans and maleimides has increasingly become a method of interest as its reversibility makes it a useful tool for applications ranging from self-healing materials, to self-immolative polymers, to hydrogels for cell culture and for the preparation of bone repair. However, most of these applications have relied on simple monosubstituted furans and simple maleimides and have not extensively evaluated the potential thermal variability inherent in the process that is achievable through simple substrate modification. A small library of cycloadducts suitable for the above applications was prepared, and the temperature dependence of the retro-Diels-Alder processes was determined through in situ 1 H NMR analyses complemented by computational calculations. The practical range of the reported systems ranges from 40 to >110 °C. The cycloreversion reactions are more complex than would be expected based on simple trends expected based on frontier molecular orbital analyses of the materials.

Measurement and classification of heart and lung sounds by using LabView for educational use.

PubMed

Altrabsheh, B

2010-01-01

This study presents the design, development and implementation of a simple low-cost method of phonocardiography signal detection. Human heart and lung signals are detected by using a simple microphone through a personal computer; the signals are recorded and analysed using LabView software. Amplitude and frequency analyses are carried out for various phonocardiography pathological cases. Methods for automatic classification of normal and abnormal heart sounds, murmurs and lung sounds are presented. Various cases of heart and lung sound measurement are recorded and analysed. The measurements can be saved for further analysis. The method in this study can be used by doctors as a detection tool aid and may be useful for teaching purposes at medical and nursing schools.

Simple, validated vaginal birth after cesarean delivery prediction model for use at the time of admission.

PubMed

Metz, Torri D; Stoddard, Gregory J; Henry, Erick; Jackson, Marc; Holmgren, Calla; Esplin, Sean

2013-09-01

To create a simple tool for predicting the likelihood of successful trial of labor after cesarean delivery (TOLAC) during the pregnancy after a primary cesarean delivery using variables available at the time of admission. Data for all deliveries at 14 regional hospitals over an 8-year period were reviewed. Women with one cesarean delivery and one subsequent delivery were included. Variables associated with successful VBAC were identified using multivariable logistic regression. Points were assigned to these characteristics, with weighting based on the coefficients in the regression model to calculate an integer VBAC score. The VBAC score was correlated with TOLAC success rate and was externally validated in an independent cohort using a logistic regression model. A total of 5,445 women met inclusion criteria. Of those women, 1,170 (21.5%) underwent TOLAC. Of the women who underwent trial of labor, 938 (80%) had a successful VBAC. A VBAC score was generated based on the Bishop score (cervical examination) at the time of admission, with points added for history of vaginal birth, age younger than 35 years, absence of recurrent indication, and body mass index less than 30. Women with a VBAC score less than 10 had a likelihood of TOLAC success less than 50%. Women with a VBAC score more than 16 had a TOLAC success rate more than 85%. The model performed well in an independent cohort with an area under the curve of 0.80 (95% confidence interval 0.76-0.84). Prediction of TOLAC success at the time of admission is highly dependent on the initial cervical examination. This simple VBAC score can be utilized when counseling women considering TOLAC. II.

Effective Surfactants Blend Concentration Determination for O/W Emulsion Stabilization by Two Nonionic Surfactants by Simple Linear Regression.

PubMed

Hassan, A K

2015-01-01

In this work, O/W emulsion sets were prepared by using different concentrations of two nonionic surfactants. The two surfactants, tween 80(HLB=15.0) and span 80(HLB=4.3) were used in a fixed proportions equal to 0.55:0.45 respectively. HLB value of the surfactants blends were fixed at 10.185. The surfactants blend concentration is starting from 3% up to 19%. For each O/W emulsion set the conductivity was measured at room temperature (25±2°), 40, 50, 60, 70 and 80°. Applying the simple linear regression least squares method statistical analysis to the temperature-conductivity obtained data determines the effective surfactants blend concentration required for preparing the most stable O/W emulsion. These results were confirmed by applying the physical stability centrifugation testing and the phase inversion temperature range measurements. The results indicated that, the relation which represents the most stable O/W emulsion has the strongest direct linear relationship between temperature and conductivity. This relationship is linear up to 80°. This work proves that, the most stable O/W emulsion is determined via the determination of the maximum R² value by applying of the simple linear regression least squares method to the temperature-conductivity obtained data up to 80°, in addition to, the true maximum slope is represented by the equation which has the maximum R² value. Because the conditions would be changed in a more complex formulation, the method of the determination of the effective surfactants blend concentration was verified by applying it for more complex formulations of 2% O/W miconazole nitrate cream and the results indicate its reproducibility.

Predicting acute pain after cesarean delivery using three simple questions.

PubMed

Pan, Peter H; Tonidandel, Ashley M; Aschenbrenner, Carol A; Houle, Timothy T; Harris, Lynne C; Eisenach, James C

2013-05-01

Interindividual variability in postoperative pain presents a clinical challenge. Preoperative quantitative sensory testing is useful but time consuming in predicting postoperative pain intensity. The current study was conducted to develop and validate a predictive model of acute postcesarean pain using a simple three-item preoperative questionnaire. A total of 200 women scheduled for elective cesarean delivery under subarachnoid anesthesia were enrolled (192 subjects analyzed). Patients were asked to rate the intensity of loudness of audio tones, their level of anxiety and anticipated pain, and analgesic need from surgery. Postoperatively, patients reported the intensity of evoked pain. Regression analysis was performed to generate a predictive model for pain from these measures. A validation cohort of 151 women was enrolled to test the reliability of the model (131 subjects analyzed). Responses from each of the three preoperative questions correlated moderately with 24-h evoked pain intensity (r = 0.24-0.33, P < 0.001). Audio tone rating added uniquely, but minimally, to the model and was not included in the predictive model. The multiple regression analysis yielded a statistically significant model (R = 0.20, P < 0.001), whereas the validation cohort showed reliably a very similar regression line (R = 0.18). In predicting the upper 20th percentile of evoked pain scores, the optimal cut point was 46.9 (z =0.24) such that sensitivity of 0.68 and specificity of 0.67 were as balanced as possible. This simple three-item questionnaire is useful to help predict postcesarean evoked pain intensity, and could be applied to further research and clinical application to tailor analgesic therapy to those who need it most.

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

NASA Astrophysics Data System (ADS)

Öktem, H.

2012-01-01

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

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

PubMed

Krishnan, Eswar; Bennett, Mihoko; Chen, Linjun

2014-11-01

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

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

PubMed

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

2010-03-05

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

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

PubMed

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

2012-08-01

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

Space shuttle propulsion parameter estimation using optional estimation techniques

NASA Technical Reports Server (NTRS)

1983-01-01

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

Simple, fast, and low-cost camera-based water content measurement with colorimetric fluorescent indicator

NASA Astrophysics Data System (ADS)

Song, Seok-Jeong; Kim, Tae-Il; Kim, Youngmi; Nam, Hyoungsik

2018-05-01

Recently, a simple, sensitive, and low-cost fluorescent indicator has been proposed to determine water contents in organic solvents, drugs, and foodstuffs. The change of water content leads to the change of the indicator's fluorescence color under the ultra-violet (UV) light. Whereas the water content values could be estimated from the spectrum obtained by a bulky and expensive spectrometer in the previous research, this paper demonstrates a simple and low-cost camera-based water content measurement scheme with the same fluorescent water indicator. Water content is calculated over the range of 0-30% by quadratic polynomial regression models with color information extracted from the captured images of samples. Especially, several color spaces such as RGB, xyY, L∗a∗b∗, u‧v‧, HSV, and YCBCR have been investigated to establish the optimal color information features over both linear and nonlinear RGB data given by a camera before and after gamma correction. In the end, a 2nd order polynomial regression model along with HSV in a linear domain achieves the minimum mean square error of 1.06% for a 3-fold cross validation method. Additionally, the resultant water content estimation model is implemented and evaluated in an off-the-shelf Android-based smartphone.

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

PubMed Central

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

2015-01-01

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

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

PubMed

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

2015-01-01

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

Adjusted variable plots for Cox's proportional hazards regression model.

PubMed

Hall, C B; Zeger, S L; Bandeen-Roche, K J

1996-01-01

Adjusted variable plots are useful in linear regression for outlier detection and for qualitative evaluation of the fit of a model. In this paper, we extend adjusted variable plots to Cox's proportional hazards model for possibly censored survival data. We propose three different plots: a risk level adjusted variable (RLAV) plot in which each observation in each risk set appears, a subject level adjusted variable (SLAV) plot in which each subject is represented by one point, and an event level adjusted variable (ELAV) plot in which the entire risk set at each failure event is represented by a single point. The latter two plots are derived from the RLAV by combining multiple points. In each point, the regression coefficient and standard error from a Cox proportional hazards regression is obtained by a simple linear regression through the origin fit to the coordinates of the pictured points. The plots are illustrated with a reanalysis of a dataset of 65 patients with multiple myeloma.

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

Regression-based model of skin diffuse reflectance for skin color analysis

NASA Astrophysics Data System (ADS)

Tsumura, Norimichi; Kawazoe, Daisuke; Nakaguchi, Toshiya; Ojima, Nobutoshi; Miyake, Yoichi

2008-11-01

A simple regression-based model of skin diffuse reflectance is developed based on reflectance samples calculated by Monte Carlo simulation of light transport in a two-layered skin model. This reflectance model includes the values of spectral reflectance in the visible spectra for Japanese women. The modified Lambert Beer law holds in the proposed model with a modified mean free path length in non-linear density space. The averaged RMS and maximum errors of the proposed model were 1.1 and 3.1%, respectively, in the above range.

Determination of water pH using absorption-based optical sensors: evaluation of different calculation methods

NASA Astrophysics Data System (ADS)

Wang, Hongliang; Liu, Baohua; Ding, Zhongjun; Wang, Xiangxin

2017-02-01

Absorption-based optical sensors have been developed for the determination of water pH. In this paper, based on the preparation of a transparent sol-gel thin film with a phenol red (PR) indicator, several calculation methods, including simple linear regression analysis, quadratic regression analysis and dual-wavelength absorbance ratio analysis, were used to calculate water pH. Results of MSSRR show that dual-wavelength absorbance ratio analysis can improve the calculation accuracy of water pH in long-term measurement.

Analytics For Distracted Driver Behavior Modeling in Dilemma Zone

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

Li, Jan-Mou; Malikopoulos, Andreas; Thakur, Gautam

2014-01-01

In this paper, we present the results obtained and insights gained through the analysis of TRB contest data. We used exploratory analysis, regression, and clustering models for gaining insights into the driver behavior in a dilemma zone while driving under distraction. While simple exploratory analysis showed the distinguishing driver behavior patterns among different popu- lation groups in the dilemma zone, regression analysis showed statically signification relationships between groups of variables. In addition to analyzing the contest data, we have also looked into the possible impact of distracted driving on the fuel economy.

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

EPA Science Inventory

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

Simple models for estimating local removals of timber in the northeast

Treesearch

David N. Larsen; David A. Gansner

1975-01-01

Provides a practical method of estimating subregional removals of timber and demonstrates its application to a typical problem. Stepwise multiple regression analysis is used to develop equations for estimating removals of softwood, hardwood, and all timber from selected characteristics of socioeconomic structure.

Simple, simultaneous gravimetric determination of calcite and dolomite in calcareous soils

USDA-ARS?s Scientific Manuscript database

Literature pertaining to determination of calcite and dolomite is not modern and describes slow methods that require expensive specialized apparatus. The objective of this paper was to describe a new method that requires no specialized equipment. Linear regressions and correlation coefficients for...

Recursive least squares method of regression coefficients estimation as a special case of Kalman filter

NASA Astrophysics Data System (ADS)

Borodachev, S. M.

2016-06-01

The simple derivation of recursive least squares (RLS) method equations is given as special case of Kalman filter estimation of a constant system state under changing observation conditions. A numerical example illustrates application of RLS to multicollinearity problem.

A step-by-step guide to non-linear regression analysis of experimental data using a Microsoft Excel spreadsheet.

PubMed

Brown, A M

2001-06-01

The objective of this present study was to introduce a simple, easily understood method for carrying out non-linear regression analysis based on user input functions. While it is relatively straightforward to fit data with simple functions such as linear or logarithmic functions, fitting data with more complicated non-linear functions is more difficult. Commercial specialist programmes are available that will carry out this analysis, but these programmes are expensive and are not intuitive to learn. An alternative method described here is to use the SOLVER function of the ubiquitous spreadsheet programme Microsoft Excel, which employs an iterative least squares fitting routine to produce the optimal goodness of fit between data and function. The intent of this paper is to lead the reader through an easily understood step-by-step guide to implementing this method, which can be applied to any function in the form y=f(x), and is well suited to fast, reliable analysis of data in all fields of biology.

A modified approach to estimating sample size for simple logistic regression with one continuous covariate.

PubMed

Novikov, I; Fund, N; Freedman, L S

2010-01-15

Different methods for the calculation of sample size for simple logistic regression (LR) with one normally distributed continuous covariate give different results. Sometimes the difference can be large. Furthermore, some methods require the user to specify the prevalence of cases when the covariate equals its population mean, rather than the more natural population prevalence. We focus on two commonly used methods and show through simulations that the power for a given sample size may differ substantially from the nominal value for one method, especially when the covariate effect is large, while the other method performs poorly if the user provides the population prevalence instead of the required parameter. We propose a modification of the method of Hsieh et al. that requires specification of the population prevalence and that employs Schouten's sample size formula for a t-test with unequal variances and group sizes. This approach appears to increase the accuracy of the sample size estimates for LR with one continuous covariate.

Effects of practice on the Wechsler Adult Intelligence Scale-IV across 3- and 6-month intervals.

PubMed

Estevis, Eduardo; Basso, Michael R; Combs, Dennis

2012-01-01

A total of 54 participants (age M = 20.9; education M = 14.9; initial Full Scale IQ M = 111.6) were administered the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) at baseline and again either 3 or 6 months later. Scores on the Full Scale IQ, Verbal Comprehension, Working Memory, Perceptual Reasoning, Processing Speed, and General Ability Indices improved approximately 7, 5, 4, 5, 9, and 6 points, respectively, and increases were similar regardless of whether the re-examination occurred over 3- or 6-month intervals. Reliable change indices (RCI) were computed using the simple difference and bivariate regression methods, providing estimated base rates of change across time. The regression method provided more accurate estimates of reliable change than did the simple difference between baseline and follow-up scores. These findings suggest that prior exposure to the WAIS-IV results in significant score increments. These gains reflect practice effects instead of genuine intellectual changes, which may lead to errors in clinical judgment.

Simple, Efficient Estimators of Treatment Effects in Randomized Trials Using Generalized Linear Models to Leverage Baseline Variables

PubMed Central

Rosenblum, Michael; van der Laan, Mark J.

2010-01-01

Models, such as logistic regression and Poisson regression models, are often used to estimate treatment effects in randomized trials. These models leverage information in variables collected before randomization, in order to obtain more precise estimates of treatment effects. However, there is the danger that model misspecification will lead to bias. We show that certain easy to compute, model-based estimators are asymptotically unbiased even when the working model used is arbitrarily misspecified. Furthermore, these estimators are locally efficient. As a special case of our main result, we consider a simple Poisson working model containing only main terms; in this case, we prove the maximum likelihood estimate of the coefficient corresponding to the treatment variable is an asymptotically unbiased estimator of the marginal log rate ratio, even when the working model is arbitrarily misspecified. This is the log-linear analog of ANCOVA for linear models. Our results demonstrate one application of targeted maximum likelihood estimation. PMID:20628636

Transmission of linear regression patterns between time series: From relationship in time series to complex networks

NASA Astrophysics Data System (ADS)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

PubMed

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

A consistent framework for Horton regression statistics that leads to a modified Hack's law

USGS Publications Warehouse

Furey, P.R.; Troutman, B.M.

2008-01-01

A statistical framework is introduced that resolves important problems with the interpretation and use of traditional Horton regression statistics. The framework is based on a univariate regression model that leads to an alternative expression for Horton ratio, connects Horton regression statistics to distributional simple scaling, and improves the accuracy in estimating Horton plot parameters. The model is used to examine data for drainage area A and mainstream length L from two groups of basins located in different physiographic settings. Results show that confidence intervals for the Horton plot regression statistics are quite wide. Nonetheless, an analysis of covariance shows that regression intercepts, but not regression slopes, can be used to distinguish between basin groups. The univariate model is generalized to include n > 1 dependent variables. For the case where the dependent variables represent ln A and ln L, the generalized model performs somewhat better at distinguishing between basin groups than two separate univariate models. The generalized model leads to a modification of Hack's law where L depends on both A and Strahler order ??. Data show that ?? plays a statistically significant role in the modified Hack's law expression. ?? 2008 Elsevier B.V.

Quantum State Tomography via Linear Regression Estimation

PubMed Central

Qi, Bo; Hou, Zhibo; Li, Li; Dong, Daoyi; Xiang, Guoyong; Guo, Guangcan

2013-01-01

A simple yet efficient state reconstruction algorithm of linear regression estimation (LRE) is presented for quantum state tomography. In this method, quantum state reconstruction is converted into a parameter estimation problem of a linear regression model and the least-squares method is employed to estimate the unknown parameters. An asymptotic mean squared error (MSE) upper bound for all possible states to be estimated is given analytically, which depends explicitly upon the involved measurement bases. This analytical MSE upper bound can guide one to choose optimal measurement sets. The computational complexity of LRE is O(d4) where d is the dimension of the quantum state. Numerical examples show that LRE is much faster than maximum-likelihood estimation for quantum state tomography. PMID:24336519

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

PubMed

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

2010-12-01

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

An analysis of input errors in precipitation-runoff models using regression with errors in the independent variables

USGS Publications Warehouse

Troutman, Brent M.

1982-01-01

Errors in runoff prediction caused by input data errors are analyzed by treating precipitation-runoff models as regression (conditional expectation) models. Independent variables of the regression consist of precipitation and other input measurements; the dependent variable is runoff. In models using erroneous input data, prediction errors are inflated and estimates of expected storm runoff for given observed input variables are biased. This bias in expected runoff estimation results in biased parameter estimates if these parameter estimates are obtained by a least squares fit of predicted to observed runoff values. The problems of error inflation and bias are examined in detail for a simple linear regression of runoff on rainfall and for a nonlinear U.S. Geological Survey precipitation-runoff model. Some implications for flood frequency analysis are considered. A case study using a set of data from Turtle Creek near Dallas, Texas illustrates the problems of model input errors.

Cognitive-Based Interventions to Improve Mobility: A Systematic Review and Meta-analysis.

PubMed

Marusic, Uros; Verghese, Joe; Mahoney, Jeannette R

2018-06-01

A strong relation between cognition and mobility has been identified in aging, supporting a role for enhancement mobility through cognitive-based interventions. However, a critical evaluation of the consistency of treatment effects of cognitive-based interventions is currently lacking. The objective of this study was 2-fold: (1) to review the existing literature on cognitive-based interventions aimed at improving mobility in older adults and (2) to assess the clinical effectiveness of cognitive interventions on gait performance. A systematic review of randomized controlled trials (RCT) of cognitive training interventions for improving simple (normal walking) and complex (dual task walking) gait was conducted in February 2018. Older adults without major cognitive, psychiatric, neurologic, and/or sensory impairments were included. Random effect meta-analyses and a subsequent meta-regression were performed to generate overall cognitive intervention effects on single- and dual-task walking conditions. Ten RCTs met inclusion criteria, with a total of 351 participants included in this meta-analysis. Cognitive training interventions revealed a small effect of intervention on complex gait [effect size (ES) = 0.47, 95% confidence interval (CI) 0.13 to 0.81, P = .007, I 2  = 15.85%], but not simple gait (ES = 0.35, 95% CI -0.01 to 0.71, P = .057, I 2  = 57.32%). Moreover, a meta-regression analysis revealed that intervention duration, training frequency, total number of sessions, and total minutes spent in intervention were not significant predictors of improvement in dual-task walking speed, though there was a suggestive trend toward a negative association between dual-task walking speed improvements and individual training session duration (P = .067). This meta-analysis provides support for the fact that cognitive training interventions can improve mobility-related outcomes, especially during challenging walking conditions requiring higher-order executive functions. Additional evidence from well-designed large-scale randomized clinical trials is warranted to confirm the observed effects. Copyright © 2018 AMDA – The Society for Post-Acute and Long-Term Care Medicine. All rights reserved.

[The keys to success in French Medical National Ranking Examination: Integrated training activities in teaching hospital and medical school].

PubMed

Gillois, Pierre; Fourcot, Marie; Genty, Céline; Morand, Patrice; Bosson, Jean-Luc

2015-12-01

The National Ranking Examination (NRE) is the key to the choice of career and specialty for future physicians; it lets them choose their place of employment in a specialty and an hospital for their internship. It seems interesting to model the success factors to this exam for the medical students from Grenoble University. For each of the medical students at Grenoble University who did apply to the NRE in 2012, data have been collected about their academic background and personal details from the administration of the University. A simple logistic regression with success set as being ranked in the first 2000 students, then a polytomous logistic regression, have been performed. The 191 students in the models are 59% female, 25 years old in average (SD 1.8). The factors associated to a ranking in the first 2000 are: not repeating the PCEM1 class (odds ratio [OR] 2.63, CI95: [1.26; 5.56]), performing nurse practice during internships (OR=1.27 [1.00; 1.62]), being ranked in the first half of the class for S3 pole (OR=6.04 [1.21; 30.20] for the first quarter, OR=5.65 [1.15; 27.74] for the second quarter) and being in the first quarter at T5 pole (OR=3.42 [1.08; 10.82]). Our study finds four factors independently contributing to the success at NRE: not repeating PCEM1, performing nurse practice and being ranked in the top of the class at certain academic fields. The AUC is 0.76 and student accuracy is more than 80%. However, some items, for example repeating DCEM4 or participating in NRE mock exams, have no influence on success. A different motivation should be a part of the explanation… As these analysed data are mainly institutional, they are accurate and reliable. The polytomic logistic model, sharing 3 factors with the simple logistic model, replace a performing nurse practice factor's by a grant recipient factor. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

RRegrs: an R package for computer-aided model selection with multiple regression models.

PubMed

Tsiliki, Georgia; Munteanu, Cristian R; Seoane, Jose A; Fernandez-Lozano, Carlos; Sarimveis, Haralambos; Willighagen, Egon L

2015-01-01

Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others. We propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package. The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR modelling is shown with three use cases: proteomics data for surface-modified gold nanoparticles, nano-metal oxides descriptor data, and molecular descriptors for acute aquatic toxicity data. The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications. Its good performance as well as its adaptability in terms of parameter optimization could make RRegrs a popular framework to assist the initial exploration of predictive models, and with that, the design of more comprehensive in silico screening applications.Graphical abstractRRegrs is a computer-aided model selection framework for R multiple regression models; this is a fully validated procedure with application to QSAR modelling.

Association of Vagotomy and Decreased Risk of Subsequent Ischemic Stroke in Complicated Peptic Ulcer Patients: an Asian Population Study.

PubMed

Fang, Chu-Wen; Tseng, Chun-Hung; Wu, Shih-Chi; Chen, William Tzu-Liang; Muo, Chih-Hsin

2017-12-01

The primary management of peptic ulcers is medical treatment. Persistent exacerbation of a peptic ulcer may lead to complications (perforation and/or bleeding). There has been a trend toward the use of a less invasive surgical simple suture, simple local suture or non-operative (endoscopic/angiography) hemostasis rather than acid-reducing vagotomy (i.e., vagus nerve severance) for treating complicated peptic ulcers. Other studies have shown the relationship between high vagus nerve activity and survival in cancer patients via reduced levels of inflammation, indicating the essential role of the vagus nerve. We were interested in the role of the vagus nerve and attempted to assess the long-term systemic effects after vagus nerve severance. Complicated peptic ulcer patients who underwent truncal vagotomy may represent an appropriate study population for investigating the association between vagus nerve severance and long-term effects. Therefore, we assessed the risks of subsequent ischemic stroke using different treatment methods in complicated peptic ulcer patients who underwent simple suture/hemostasis or truncal vagotomy/pyloroplasty. We selected 299,742 peptic ulcer patients without a history of stroke and Helicobacter pylori infection and an additional 299,742 matched controls without ulcer, stroke, and Helicobacter pylori infection from the National Health Insurance database. The controls were frequency matched for age, gender, Charlson comorbidity index (CCI) score, hypertension, hyperlipidemia history, and index year. Then, we measured the incidence of overall ischemic stroke in the two cohorts. The hazard ratio (HR) and the 95% confidence intervals (CIs) were estimated by Cox proportional hazard regression. Compared to the controls, peptic ulcer patients had a 1.86-fold higher risk of ischemic stroke. There were similar results in gender, age, CCI, hypertension, and hyperlipidemia stratified analyses. In complicated peptic ulcer patients, those who received truncal vagotomy and pyloroplasty had a lower risk of ischemic stroke than patients who received simple suture/hemostasis (HR = 0.70, 95% CI = 0.60-0.81). Our findings suggest that patients with peptic ulcers have an elevated risk of subsequent ischemic stroke. Moreover, there were associations between vagotomy and a decreased risk of subsequent ischemic stroke in complicated peptic ulcer patients.

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

USGS Publications Warehouse

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

2004-01-01

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

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

USGS Publications Warehouse

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

2000-01-01

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

Motor vehicle and fall related deaths among older Americans 1990-98: sex, race, and ethnic disparities.

PubMed

Stevens, J A; Dellinger, A M

2002-12-01

To examine differences in motor vehicle and fall related death rates among older adults by sex, race, and ethnicity. Annual mortality tapes for 1990-98 provided demographic data including race and ethnicity, date, and cause of death. Trend analyses were conducted using Poisson regression. From 1990-98, overall motor vehicle related death rates remained stable while death rates from unintentional falls increased. Motor vehicle and fall related death rates were higher among men. Motor vehicle related death rates were higher among people of color while fall related death rates were higher among whites. Among whites, fall death rates increased significantly during the study period, with an annual relative increase of 3.6% for men and 3.2% for women. The risk of death from motor vehicle and fall related injuries among older adults differed by sex, race and ethnicity, results obscured by simple age and sex specific death rates. This study found important patterns and disparities in these death rates by race and ethnicity useful for identifying high risk groups and guiding prevention strategies.

Influence of nicotine on positive affect in anhedonic smokers.

PubMed

Cook, Jessica Werth; Spring, Bonnie; McChargue, Dennis

2007-05-01

The possibility that individuals administer nicotine to self-regulate persistent negative affect has received interest as a possible explanation for the high prevalence of affectively vulnerable smokers. Relatively overlooked, however, is the possibility that smokers might also self-administer nicotine to elevate low positive affect. This study examined whether nicotine administration augmented anhedonic smokers' positive affective response to a positive mood induction. Fifty regular smokers (50% female) underwent two positive mood inductions during which they smoked either a nicotinized or denicotinized cigarette in counterbalanced order. Positive affect was assessed before and at two time points after smoking. Random effects regression showed a significant anhedonia by condition-by-time interaction [t(181)=-2.01, p = 0.04], supporting the hypothesis that anhedonia moderated nicotine's effect on changes in positive affect. Simple effect analyses showed a significant condition-by-time interaction among high anhedonic smokers [t(91)= 2.47, p = 0.01] but not among less anhedonic smokers [t(91)= 0.34, p = 0.73]. Smoking nicotine vs placebo heightened anhedonic smokers' ability to be induced into a positive mood, whereas nicotine had no effect on more hedonic smokers' positive mood.

Prediction of Performance of Diamond Wire Saw with Respect to Texture Characteristics of Rock / Prognozowanie Wydajności Pracy Strunowej Piły Diamentowej W Odniesieniu Do Charakterystyki Tekstury Skał

NASA Astrophysics Data System (ADS)

Ghaysari, N.; Ataei, M.; Sereshki, F.; Mikaiel, R.

2012-12-01

In this study, prediction of production rate in diamond wire saw has been investigated. Performance measurements of diamond wire saw carried out in 7 different quarries of carbonate rocks in Iran. For determination textural properties, rock samples were collected from these quarries. At first, a thin section was prepared for each rock and then 5 digital photographs were taken from each section. After this, all images were digitized using AutoCAD software. Then, area, perimeter, longest diameter and shortest diameter were assigned. According to these parameters, all of the other textural characteristics and texture coefficient were determined too. The correlation between sawing rate and textural characteristics were evaluated using multiple and simple regression analyses. Then developed model was validated by P-value test. It was concluded that area, perimeter, diameter equivalent and index of grain size homogeneity are very effective on production rate. Production rate using diamond wire saw can reliably be predicted using developed model.

Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

PubMed Central

Power, Jonathan D; Barnes, Kelly A; Snyder, Abraham Z; Schlaggar, Bradley L; Petersen, Steven E

2011-01-01

Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements. PMID:22019881

[Dental and gingival pain and associated factors among Brazilian adolescents: an analysis of the Brazilian Oral Health Survey 2002-2003].

PubMed

Borges, Carolina Marques; Cascaes, Andreia Morales; Fischer, Tatiana Konrad; Boing, Antonio Fernando; Peres, Marco Aurélio; Peres, Karen Glazer

2008-08-01

The aim of this study was to estimate the prevalence of dental and gingival pain and associated factors among Brazilian adolescents (15-19 years of age). Data from 16,126 adolescents who participated in the Brazilian Oral Health Survey SB-Brazil 2002-2003 were used. The outcome measured was dental and gingival pain in the last six months. Independent variables were per capita income, schooling, school enrollment, gender, skin color, age, area of residence, time since last dental appointment, type of dental service, DMFT index and its components, dental calculus, and Dental Aesthetic Index. Simple and multiple Poisson regression analyses were performed. Prevalence of dental and gingival pain was 35.6% (95%CI: 34.8-36.4). Increased prevalence of pain was associated with: female gender, low income, non-students, students enrolled in public schools, and grade-for-age lag. In addition, adolescents with high levels of dental caries and dental calculus also reported higher prevalence of dental pain. Dental and gingival pain can be considered a relevant public health problem, suggesting the need for preventive measures.

Software for Storage and Management of Microclimatic Data for Preventive Conservation of Cultural Heritage

PubMed Central

Fernández-Navajas, Ángel; Merello, Paloma; Beltrán, Pedro; García-Diego, Fernando-Juan

2013-01-01

Cultural Heritage preventive conservation requires the monitoring of the parameters involved in the process of deterioration of artworks. Thus, both long-term monitoring of the environmental parameters as well as further analysis of the recorded data are necessary. The long-term monitoring at frequencies higher than 1 data point/day generates large volumes of data that are difficult to store, manage and analyze. This paper presents software which uses a free open source database engine that allows managing and interacting with huge amounts of data from environmental monitoring of cultural heritage sites. It is of simple operation and offers multiple capabilities, such as detection of anomalous data, inquiries, graph plotting and mean trajectories. It is also possible to export the data to a spreadsheet for analyses with more advanced statistical methods (principal component analysis, ANOVA, linear regression, etc.). This paper also deals with a practical application developed for the Renaissance frescoes of the Cathedral of Valencia. The results suggest infiltration of rainwater in the vault and weekly relative humidity changes related with the religious service schedules. PMID:23447005

Differential Effects of Social Networks on Mammography Use by Poverty Status.

PubMed

Yeo, Younsook

2016-01-01

This study examines whether social networks have differential effects on mammography use depending on poverty status. Data were analyzed on US women (40+), employing logistic regression and simple slope analyses for a post hoc probing of moderating effects. Among women not in poverty, living with a spouse/partner and attending church, regardless of frequency, were positively associated with mammography use; family size was negatively associated. Among women living in poverty, mammography showed a positive association only with weekly church attendance. Mammography was negatively associated with health-related social interactions occurring through the Internet. Post hoc probing showed significant moderating effects of poverty on the relationship between online health-related interactions and mammography use. To make the Internet a meaningful health empowerment tool for women in poverty, future research should identify how health-related interactions that occur online affect women in poverty's psychological and behavioral reactions that will contribute to our understanding of why they are discouraged from having mammograms. The mechanisms behind the differential effects of church attendance and poverty status on mammography also need further clarification.

Clinical evaluation of seven anticalculus dentifrice formulations.

PubMed

Scruggs, R R; Stewart, P W; Samuels, M S; Stamm, J W

1991-01-01

One hundred ninety-two subjects completed a clinical trial to determine the effects of seven dentifrice formulations on calculus inhibition. The double-blind study involved a ten-day control phase and a ten-day experimental phase. For the control phase, subjects were evaluated for calculus present, received a prophylaxis and had pre-weighed mylar strips attached to the lingual surfaces of the mandibular incisors to harvest mineral deposits. Subjects were then assigned the placebo dentifrice for unsupervised twice-daily use and were required to report once a day for a supervised mouthrinse using a 1:3 dilution of the dentrifice. The experimental phase was identical except that subjects were allocated the experimental dentifices using a stratified random assignment based on age, gender and the initial presence of calculus. Simple linear regression analyses of the dry and ash log weights obtained from the strips were performed. The results showed no statistically significant differences among the test products; however, two formulations containing zinc citrate showed some calculus inhibition-potential suggesting that further research and development of such products may be warranted.

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

EPA Science Inventory

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

DIY Soundcard Based Temperature Logging System. Part II: Applications

ERIC Educational Resources Information Center

Nunn, John

2016-01-01

This paper demonstrates some simple applications of how temperature logging systems may be used to monitor simple heat experiments, and how the data obtained can be analysed to get some additional insight into the physical processes. [For "DIY Soundcard Based Temperature Logging System. Part I: Design," see EJ1114124.

Modeling of tropospheric NO2 column over different climatic zones and land use/land cover types in South Asia

NASA Astrophysics Data System (ADS)

ul-Haq, Zia; Rana, Asim Daud; Tariq, Salman; Mahmood, Khalid; Ali, Muhammad; Bashir, Iqra

2018-03-01

We have applied regression analyses for the modeling of tropospheric NO2 (tropo-NO2) as the function of anthropogenic nitrogen oxides (NOx) emissions, aerosol optical depth (AOD), and some important meteorological parameters such as temperature (Temp), precipitation (Preci), relative humidity (RH), wind speed (WS), cloud fraction (CLF) and outgoing long-wave radiation (OLR) over different climatic zones and land use/land cover types in South Asia during October 2004-December 2015. Simple linear regression shows that, over South Asia, tropo-NO2 variability is significantly linked to AOD, WS, NOx, Preci and CLF. Also zone-5, consisting of tropical monsoon areas of eastern India and Myanmar, is the only study zone over which all the selected parameters show their influence on tropo-NO2 at statistical significance levels. In stepwise multiple linear modeling, tropo-NO2 column over landmass of South Asia, is significantly predicted by the combination of RH (standardized regression coefficient, β = - 49), AOD (β = 0.42) and NOx (β = 0.25). The leading predictors of tropo-NO2 columns over zones 1-5 are OLR, AOD, Temp, OLR, and RH respectively. Overall, as revealed by the higher correlation coefficients (r), the multiple regressions provide reasonable models for tropo-NO2 over South Asia (r = 0.82), zone-4 (r = 0.90) and zone-5 (r = 0.93). The lowest r (of 0.66) has been found for hot semi-arid region in northwestern Indus-Ganges Basin (zone-2). The highest value of β for urban area AOD (of 0.42) is observed for megacity Lahore, located in warm semi-arid zone-2 with large scale crop-residue burning, indicating strong influence of aerosols on the modeled tropo-NO2 column. A statistical significant correlation (r = 0.22) at the 0.05 level is found between tropo-NO2 and AOD over Lahore. Also NOx emissions appear as the highest contributor (β = 0.59) for modeled tropo-NO2 column over megacity Dhaka.

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.

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

PubMed

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

2012-04-01

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

Browning of the landscape of interior Alaska based on 1986-2009 Landsat sensor NDVI

Treesearch

Rebecca A. Baird; David Verbyla; Teresa N. Hollingsworth

2012-01-01

We used a time series of 1986-2009 Landsat sensor data to compute the Normalized Difference Vegetation Index (NDVI) for 30 m pixels within the Bonanza Creek Experimental Forest of interior Alaska. Based on simple linear regression, we found significant (p

Naive vs. Sophisticated Methods of Forecasting Public Library Circulations.

ERIC Educational Resources Information Center

Brooks, Terrence A.

1984-01-01

Two sophisticated--autoregressive integrated moving average (ARIMA), straight-line regression--and two naive--simple average, monthly average--forecasting techniques were used to forecast monthly circulation totals of 34 public libraries. Comparisons of forecasts and actual totals revealed that ARIMA and monthly average methods had smallest mean…

Effect of Ankle Range of Motion (ROM) and Lower-Extremity Muscle Strength on Static Balance Control Ability in Young Adults: A Regression Analysis

PubMed Central

Kim, Seong-Gil

2018-01-01

Background The purpose of this study was to investigate the effect of ankle ROM and lower-extremity muscle strength on static balance control ability in young adults. Material/Methods This study was conducted with 65 young adults, but 10 young adults dropped out during the measurement, so 55 young adults (male: 19, female: 36) completed the study. Postural sway (length and velocity) was measured with eyes open and closed, and ankle ROM (AROM and PROM of dorsiflexion and plantarflexion) and lower-extremity muscle strength (flexor and extensor of hip, knee, and ankle joint) were measured. Pearson correlation coefficient was used to examine the correlation between variables and static balance ability. Simple linear regression analysis and multiple linear regression analysis were used to examine the effect of variables on static balance ability. Results In correlation analysis, plantarflexion ROM (AROM and PROM) and lower-extremity muscle strength (except hip extensor) were significantly correlated with postural sway (p<0.05). In simple correlation analysis, all variables that passed the correlation analysis procedure had significant influence (p<0.05). In multiple linear regression analysis, plantar flexion PROM with eyes open significantly influenced sway length (B=0.681) and sway velocity (B=0.011). Conclusions Lower-extremity muscle strength and ankle plantarflexion ROM influenced static balance control ability, with ankle plantarflexion PROM showing the greatest influence. Therefore, both contractile structures and non-contractile structures should be of interest when considering static balance control ability improvement. PMID:29760375

Probabilistic regional climate projection in Japan using a regression model with CMIP5 multi-model ensemble experiments

NASA Astrophysics Data System (ADS)

Ishizaki, N. N.; Dairaku, K.; Ueno, G.

2016-12-01

We have developed a statistical downscaling method for estimating probabilistic climate projection using CMIP5 multi general circulation models (GCMs). A regression model was established so that the combination of weights of GCMs reflects the characteristics of the variation of observations at each grid point. Cross validations were conducted to select GCMs and to evaluate the regression model to avoid multicollinearity. By using spatially high resolution observation system, we conducted statistically downscaled probabilistic climate projections with 20-km horizontal grid spacing. Root mean squared errors for monthly mean air surface temperature and precipitation estimated by the regression method were the smallest compared with the results derived from a simple ensemble mean of GCMs and a cumulative distribution function based bias correction method. Projected changes in the mean temperature and precipitation were basically similar to those of the simple ensemble mean of GCMs. Mean precipitation was generally projected to increase associated with increased temperature and consequent increased moisture content in the air. Weakening of the winter monsoon may affect precipitation decrease in some areas. Temperature increase in excess of 4 K was expected in most areas of Japan in the end of 21st century under RCP8.5 scenario. The estimated probability of monthly precipitation exceeding 300 mm would increase around the Pacific side during the summer and the Japan Sea side during the winter season. This probabilistic climate projection based on the statistical method can be expected to bring useful information to the impact studies and risk assessments.

Effect of Ankle Range of Motion (ROM) and Lower-Extremity Muscle Strength on Static Balance Control Ability in Young Adults: A Regression Analysis.

PubMed

Kim, Seong-Gil; Kim, Wan-Soo

2018-05-15

BACKGROUND The purpose of this study was to investigate the effect of ankle ROM and lower-extremity muscle strength on static balance control ability in young adults. MATERIAL AND METHODS This study was conducted with 65 young adults, but 10 young adults dropped out during the measurement, so 55 young adults (male: 19, female: 36) completed the study. Postural sway (length and velocity) was measured with eyes open and closed, and ankle ROM (AROM and PROM of dorsiflexion and plantarflexion) and lower-extremity muscle strength (flexor and extensor of hip, knee, and ankle joint) were measured. Pearson correlation coefficient was used to examine the correlation between variables and static balance ability. Simple linear regression analysis and multiple linear regression analysis were used to examine the effect of variables on static balance ability. RESULTS In correlation analysis, plantarflexion ROM (AROM and PROM) and lower-extremity muscle strength (except hip extensor) were significantly correlated with postural sway (p<0.05). In simple correlation analysis, all variables that passed the correlation analysis procedure had significant influence (p<0.05). In multiple linear regression analysis, plantar flexion PROM with eyes open significantly influenced sway length (B=0.681) and sway velocity (B=0.011). CONCLUSIONS Lower-extremity muscle strength and ankle plantarflexion ROM influenced static balance control ability, with ankle plantarflexion PROM showing the greatest influence. Therefore, both contractile structures and non-contractile structures should be of interest when considering static balance control ability improvement.

A computer tool for a minimax criterion in binary response and heteroscedastic simple linear regression models.

PubMed

Casero-Alonso, V; López-Fidalgo, J; Torsney, B

2017-01-01

Binary response models are used in many real applications. For these models the Fisher information matrix (FIM) is proportional to the FIM of a weighted simple linear regression model. The same is also true when the weight function has a finite integral. Thus, optimal designs for one binary model are also optimal for the corresponding weighted linear regression model. The main objective of this paper is to provide a tool for the construction of MV-optimal designs, minimizing the maximum of the variances of the estimates, for a general design space. MV-optimality is a potentially difficult criterion because of its nondifferentiability at equal variance designs. A methodology for obtaining MV-optimal designs where the design space is a compact interval [a, b] will be given for several standard weight functions. The methodology will allow us to build a user-friendly computer tool based on Mathematica to compute MV-optimal designs. Some illustrative examples will show a representation of MV-optimal designs in the Euclidean plane, taking a and b as the axes. The applet will be explained using two relevant models. In the first one the case of a weighted linear regression model is considered, where the weight function is directly chosen from a typical family. In the second example a binary response model is assumed, where the probability of the outcome is given by a typical probability distribution. Practitioners can use the provided applet to identify the solution and to know the exact support points and design weights. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

PubMed

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

2018-02-01

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

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

EPA Science Inventory

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

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

PubMed

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

2014-01-01

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

Relationships Among Peripheral and Central Electrophysiological Measures of Spatial and Spectral Selectivity and Speech Perception in Cochlear Implant Users.

PubMed

Scheperle, Rachel A; Abbas, Paul J

2015-01-01

The ability to perceive speech is related to the listener's ability to differentiate among frequencies (i.e., spectral resolution). Cochlear implant (CI) users exhibit variable speech-perception and spectral-resolution abilities, which can be attributed in part to the extent of electrode interactions at the periphery (i.e., spatial selectivity). However, electrophysiological measures of peripheral spatial selectivity have not been found to correlate with speech perception. The purpose of this study was to evaluate auditory processing at the periphery and cortex using both simple and spectrally complex stimuli to better understand the stages of neural processing underlying speech perception. The hypotheses were that (1) by more completely characterizing peripheral excitation patterns than in previous studies, significant correlations with measures of spectral selectivity and speech perception would be observed, (2) adding information about processing at a level central to the auditory nerve would account for additional variability in speech perception, and (3) responses elicited with spectrally complex stimuli would be more strongly correlated with speech perception than responses elicited with spectrally simple stimuli. Eleven adult CI users participated. Three experimental processor programs (MAPs) were created to vary the likelihood of electrode interactions within each participant. For each MAP, a subset of 7 of 22 intracochlear electrodes was activated: adjacent (MAP 1), every other (MAP 2), or every third (MAP 3). Peripheral spatial selectivity was assessed using the electrically evoked compound action potential (ECAP) to obtain channel-interaction functions for all activated electrodes (13 functions total). Central processing was assessed by eliciting the auditory change complex with both spatial (electrode pairs) and spectral (rippled noise) stimulus changes. Speech-perception measures included vowel discrimination and the Bamford-Kowal-Bench Speech-in-Noise test. Spatial and spectral selectivity and speech perception were expected to be poorest with MAP 1 (closest electrode spacing) and best with MAP 3 (widest electrode spacing). Relationships among the electrophysiological and speech-perception measures were evaluated using mixed-model and simple linear regression analyses. All electrophysiological measures were significantly correlated with each other and with speech scores for the mixed-model analysis, which takes into account multiple measures per person (i.e., experimental MAPs). The ECAP measures were the best predictor. In the simple linear regression analysis on MAP 3 data, only the cortical measures were significantly correlated with speech scores; spectral auditory change complex amplitude was the strongest predictor. The results suggest that both peripheral and central electrophysiological measures of spatial and spectral selectivity provide valuable information about speech perception. Clinically, it is often desirable to optimize performance for individual CI users. These results suggest that ECAP measures may be most useful for within-subject applications when multiple measures are performed to make decisions about processor options. They also suggest that if the goal is to compare performance across individuals based on a single measure, then processing central to the auditory nerve (specifically, cortical measures of discriminability) should be considered.

Washington State's alcohol ignition interlock law: effects on recidivism among first-time DUI offenders.

PubMed

McCartt, Anne T; Leaf, William A; Farmer, Charles M; Eichelberger, Angela H

2013-01-01

To examine the effects of changes to Washington State's ignition interlock laws: moving issuance of interlock orders from courts to the driver licensing department in July 2003 and extending the interlock order requirement to first-time offenders with blood alcohol concentrations (BACs) below 0.15 percent ("first simple driving under the influence [DUI]") in June 2004. Trends in conviction types, interlock installation rates, and 2-year cumulative recidivism rates were examined for first-time convictions (simple, high-BAC, test refusal DUI; deferred prosecution; alcohol-related negligent driving) stemming from DUI arrests between January 1999 and June 2006. Regression analyses examined recidivism effects of the law changes and interlock installation rates. To examine general deterrent effects, trends in single-vehicle late-night crashes in Washington were compared with trends in California and Oregon. After the 2004 law change, the proportion of simple DUIs declined somewhat, though the proportion of negligent driving convictions (no interlock order requirement) continued an upward trend. Interlock installation rates for first simple DUIs were 3 to 6 percent in the year before the law change and one third after. Recidivism declined by an estimated 12 percent (e.g., expected 10.6% without law change vs. 9.3% among offenders arrested between April and June 2006, the last study quarter) among first simple DUI offenders and an estimated 11 percent (expected 10.2% vs. 9.1%) among all first-time offenders. There was an estimated 0.06 percentage point decrease in the recidivism rate for each percentage point increase in the proportion of first simple DUI offenders with interlocks. If installation rates had been 100 vs. 34 percent for first simple DUI offenders arrested between April and June 2006, and if the linear relationship between rates of recidivism and installations continued, recidivism could have been reduced from 9.3 to 5.3 percent. With installation rates of 100 vs. 24 percent for all first offenders, their recidivism rate could have fallen from 9.1 to 3.2 percent. Although installation rates increased somewhat after the 2003 law change, recidivism rates were not significantly affected, perhaps due to the short follow-up period before the 2004 law change. The 2004 law change was associated with an 8.3 percent reduction in single-vehicle late-night crash risk. Mandating interlock orders for all first DUI convictions was associated with reductions in recidivism, even with low interlock use rates, and reductions in crashes. Additional gains are likely achievable with higher rates. Jurisdictions should seek to increase use rates and reconsider permitting reductions in DUI charges to other traffic offenses without interlock order requirements.

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

PubMed

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

2012-07-01

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

A New Technique for Personality Scale Construction. Preliminary Findings.

ERIC Educational Resources Information Center

Schaffner, Paul E.; Darlington, Richard B.

Most methods of personality scale construction have clear statistical disadvantages. A hybrid method (Darlington and Bishop, 1966) was found to increase scale validity more than any other method, with large item pools. A simple modification of the Darlington-Bishop method (algebraically and conceptually similar to ridge regression, but…